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What is a Literature Review? How to Write It (with Examples)

A literature review is a critical analysis and synthesis of existing research on a particular topic. It provides an overview of the current state of knowledge, identifies gaps, and highlights key findings in the literature. 1 The purpose of a literature review is to situate your own research within the context of existing scholarship, demonstrating your understanding of the topic and showing how your work contributes to the ongoing conversation in the field. Learning how to write a literature review is a critical tool for successful research. Your ability to summarize and synthesize prior research pertaining to a certain topic demonstrates your grasp on the topic of study, and assists in the learning process. 

Table of Contents

• What is the purpose of literature review? 
• a. Habitat Loss and Species Extinction: 
• b. Range Shifts and Phenological Changes: 
• c. Ocean Acidification and Coral Reefs: 
• d. Adaptive Strategies and Conservation Efforts: 

How to write a good literature review 

• Choose a Topic and Define the Research Question: 
• Decide on the Scope of Your Review: 
• Select Databases for Searches: 
• Conduct Searches and Keep Track: 
• Review the Literature: 
• Organize and Write Your Literature Review: 
• How to write a literature review faster with Paperpal? 
• Frequently asked questions 

What is a literature review?

A well-conducted literature review demonstrates the researcher’s familiarity with the existing literature, establishes the context for their own research, and contributes to scholarly conversations on the topic. One of the purposes of a literature review is also to help researchers avoid duplicating previous work and ensure that their research is informed by and builds upon the existing body of knowledge.

What is the purpose of literature review?

A literature review serves several important purposes within academic and research contexts. Here are some key objectives and functions of a literature review: 2  

1. Contextualizing the Research Problem: The literature review provides a background and context for the research problem under investigation. It helps to situate the study within the existing body of knowledge. 

2. Identifying Gaps in Knowledge: By identifying gaps, contradictions, or areas requiring further research, the researcher can shape the research question and justify the significance of the study. This is crucial for ensuring that the new research contributes something novel to the field. 

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3. Understanding Theoretical and Conceptual Frameworks: Literature reviews help researchers gain an understanding of the theoretical and conceptual frameworks used in previous studies. This aids in the development of a theoretical framework for the current research. 

4. Providing Methodological Insights: Another purpose of literature reviews is that it allows researchers to learn about the methodologies employed in previous studies. This can help in choosing appropriate research methods for the current study and avoiding pitfalls that others may have encountered. 

5. Establishing Credibility: A well-conducted literature review demonstrates the researcher’s familiarity with existing scholarship, establishing their credibility and expertise in the field. It also helps in building a solid foundation for the new research. 

6. Informing Hypotheses or Research Questions: The literature review guides the formulation of hypotheses or research questions by highlighting relevant findings and areas of uncertainty in existing literature. 

Literature review example

Let’s delve deeper with a literature review example: Let’s say your literature review is about the impact of climate change on biodiversity. You might format your literature review into sections such as the effects of climate change on habitat loss and species extinction, phenological changes, and marine biodiversity. Each section would then summarize and analyze relevant studies in those areas, highlighting key findings and identifying gaps in the research. The review would conclude by emphasizing the need for further research on specific aspects of the relationship between climate change and biodiversity. The following literature review template provides a glimpse into the recommended literature review structure and content, demonstrating how research findings are organized around specific themes within a broader topic. 

Literature Review on Climate Change Impacts on Biodiversity:

Climate change is a global phenomenon with far-reaching consequences, including significant impacts on biodiversity. This literature review synthesizes key findings from various studies: 

a. Habitat Loss and Species Extinction:

Climate change-induced alterations in temperature and precipitation patterns contribute to habitat loss, affecting numerous species (Thomas et al., 2004). The review discusses how these changes increase the risk of extinction, particularly for species with specific habitat requirements. 

b. Range Shifts and Phenological Changes:

Observations of range shifts and changes in the timing of biological events (phenology) are documented in response to changing climatic conditions (Parmesan & Yohe, 2003). These shifts affect ecosystems and may lead to mismatches between species and their resources. 

c. Ocean Acidification and Coral Reefs:

The review explores the impact of climate change on marine biodiversity, emphasizing ocean acidification’s threat to coral reefs (Hoegh-Guldberg et al., 2007). Changes in pH levels negatively affect coral calcification, disrupting the delicate balance of marine ecosystems. 

d. Adaptive Strategies and Conservation Efforts:

Recognizing the urgency of the situation, the literature review discusses various adaptive strategies adopted by species and conservation efforts aimed at mitigating the impacts of climate change on biodiversity (Hannah et al., 2007). It emphasizes the importance of interdisciplinary approaches for effective conservation planning. 

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Writing a literature review involves summarizing and synthesizing existing research on a particular topic. A good literature review format should include the following elements. 

Introduction: The introduction sets the stage for your literature review, providing context and introducing the main focus of your review. 

• Opening Statement: Begin with a general statement about the broader topic and its significance in the field. 
• Scope and Purpose: Clearly define the scope of your literature review. Explain the specific research question or objective you aim to address. 
• Organizational Framework: Briefly outline the structure of your literature review, indicating how you will categorize and discuss the existing research. 
• Significance of the Study: Highlight why your literature review is important and how it contributes to the understanding of the chosen topic. 
• Thesis Statement: Conclude the introduction with a concise thesis statement that outlines the main argument or perspective you will develop in the body of the literature review. 

Body: The body of the literature review is where you provide a comprehensive analysis of existing literature, grouping studies based on themes, methodologies, or other relevant criteria. 

• Organize by Theme or Concept: Group studies that share common themes, concepts, or methodologies. Discuss each theme or concept in detail, summarizing key findings and identifying gaps or areas of disagreement. 
• Critical Analysis: Evaluate the strengths and weaknesses of each study. Discuss the methodologies used, the quality of evidence, and the overall contribution of each work to the understanding of the topic. 
• Synthesis of Findings: Synthesize the information from different studies to highlight trends, patterns, or areas of consensus in the literature. 
• Identification of Gaps: Discuss any gaps or limitations in the existing research and explain how your review contributes to filling these gaps. 
• Transition between Sections: Provide smooth transitions between different themes or concepts to maintain the flow of your literature review. 

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Conclusion: The conclusion of your literature review should summarize the main findings, highlight the contributions of the review, and suggest avenues for future research. 

• Summary of Key Findings: Recap the main findings from the literature and restate how they contribute to your research question or objective. 
• Contributions to the Field: Discuss the overall contribution of your literature review to the existing knowledge in the field. 
• Implications and Applications: Explore the practical implications of the findings and suggest how they might impact future research or practice. 
• Recommendations for Future Research: Identify areas that require further investigation and propose potential directions for future research in the field. 
• Final Thoughts: Conclude with a final reflection on the importance of your literature review and its relevance to the broader academic community. 

Conducting a literature review

Conducting a literature review is an essential step in research that involves reviewing and analyzing existing literature on a specific topic. It’s important to know how to do a literature review effectively, so here are the steps to follow: 1  

Choose a Topic and Define the Research Question:

• Select a topic that is relevant to your field of study. 
• Clearly define your research question or objective. Determine what specific aspect of the topic do you want to explore? 

Decide on the Scope of Your Review:

• Determine the timeframe for your literature review. Are you focusing on recent developments, or do you want a historical overview? 
• Consider the geographical scope. Is your review global, or are you focusing on a specific region? 
• Define the inclusion and exclusion criteria. What types of sources will you include? Are there specific types of studies or publications you will exclude? 

Select Databases for Searches:

• Identify relevant databases for your field. Examples include PubMed, IEEE Xplore, Scopus, Web of Science, and Google Scholar. 
• Consider searching in library catalogs, institutional repositories, and specialized databases related to your topic. 

Conduct Searches and Keep Track:

• Develop a systematic search strategy using keywords, Boolean operators (AND, OR, NOT), and other search techniques. 
• Record and document your search strategy for transparency and replicability. 
• Keep track of the articles, including publication details, abstracts, and links. Use citation management tools like EndNote, Zotero, or Mendeley to organize your references. 

Review the Literature:

• Evaluate the relevance and quality of each source. Consider the methodology, sample size, and results of studies. 
• Organize the literature by themes or key concepts. Identify patterns, trends, and gaps in the existing research. 
• Summarize key findings and arguments from each source. Compare and contrast different perspectives. 
• Identify areas where there is a consensus in the literature and where there are conflicting opinions. 
• Provide critical analysis and synthesis of the literature. What are the strengths and weaknesses of existing research? 

Organize and Write Your Literature Review:

• Literature review outline should be based on themes, chronological order, or methodological approaches. 
• Write a clear and coherent narrative that synthesizes the information gathered. 
• Use proper citations for each source and ensure consistency in your citation style (APA, MLA, Chicago, etc.). 
• Conclude your literature review by summarizing key findings, identifying gaps, and suggesting areas for future research. 

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How to write a literature review faster with Paperpal?

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• Ask a question: Get started with a new document on paperpal.com. Click on the “Research” feature and type your question in plain English. Paperpal will scour over 250 million research articles, including conference papers and preprints, to provide you with accurate insights and citations. 
• Review and Save: Paperpal summarizes the information, while citing sources and listing relevant reads. You can quickly scan the results to identify relevant references and save these directly to your built-in citations library for later access. 
• Cite with Confidence: Paperpal makes it easy to incorporate relevant citations and references into your writing, ensuring your arguments are well-supported by credible sources. This translates to a polished, well-researched literature review. 

The literature review sample and detailed advice on writing and conducting a review will help you produce a well-structured report. But remember that a good literature review is an ongoing process, and it may be necessary to revisit and update it as your research progresses. By combining effortless research with an easy citation process, Paperpal Research streamlines the literature review process and empowers you to write faster and with more confidence. Try Paperpal Research now and see for yourself.  

Frequently asked questions

A literature review is a critical and comprehensive analysis of existing literature (published and unpublished works) on a specific topic or research question and provides a synthesis of the current state of knowledge in a particular field. A well-conducted literature review is crucial for researchers to build upon existing knowledge, avoid duplication of efforts, and contribute to the advancement of their field. It also helps researchers situate their work within a broader context and facilitates the development of a sound theoretical and conceptual framework for their studies.

Literature review is a crucial component of research writing, providing a solid background for a research paper’s investigation. The aim is to keep professionals up to date by providing an understanding of ongoing developments within a specific field, including research methods, and experimental techniques used in that field, and present that knowledge in the form of a written report. Also, the depth and breadth of the literature review emphasizes the credibility of the scholar in his or her field.  

Before writing a literature review, it’s essential to undertake several preparatory steps to ensure that your review is well-researched, organized, and focused. This includes choosing a topic of general interest to you and doing exploratory research on that topic, writing an annotated bibliography, and noting major points, especially those that relate to the position you have taken on the topic. 

Literature reviews and academic research papers are essential components of scholarly work but serve different purposes within the academic realm. 3 A literature review aims to provide a foundation for understanding the current state of research on a particular topic, identify gaps or controversies, and lay the groundwork for future research. Therefore, it draws heavily from existing academic sources, including books, journal articles, and other scholarly publications. In contrast, an academic research paper aims to present new knowledge, contribute to the academic discourse, and advance the understanding of a specific research question. Therefore, it involves a mix of existing literature (in the introduction and literature review sections) and original data or findings obtained through research methods. 

Literature reviews are essential components of academic and research papers, and various strategies can be employed to conduct them effectively. If you want to know how to write a literature review for a research paper, here are four common approaches that are often used by researchers.  Chronological Review: This strategy involves organizing the literature based on the chronological order of publication. It helps to trace the development of a topic over time, showing how ideas, theories, and research have evolved.  Thematic Review: Thematic reviews focus on identifying and analyzing themes or topics that cut across different studies. Instead of organizing the literature chronologically, it is grouped by key themes or concepts, allowing for a comprehensive exploration of various aspects of the topic.  Methodological Review: This strategy involves organizing the literature based on the research methods employed in different studies. It helps to highlight the strengths and weaknesses of various methodologies and allows the reader to evaluate the reliability and validity of the research findings.  Theoretical Review: A theoretical review examines the literature based on the theoretical frameworks used in different studies. This approach helps to identify the key theories that have been applied to the topic and assess their contributions to the understanding of the subject.  It’s important to note that these strategies are not mutually exclusive, and a literature review may combine elements of more than one approach. The choice of strategy depends on the research question, the nature of the literature available, and the goals of the review. Additionally, other strategies, such as integrative reviews or systematic reviews, may be employed depending on the specific requirements of the research.

The literature review format can vary depending on the specific publication guidelines. However, there are some common elements and structures that are often followed. Here is a general guideline for the format of a literature review:  Introduction:   Provide an overview of the topic.  Define the scope and purpose of the literature review.  State the research question or objective.  Body:   Organize the literature by themes, concepts, or chronology.  Critically analyze and evaluate each source.  Discuss the strengths and weaknesses of the studies.  Highlight any methodological limitations or biases.  Identify patterns, connections, or contradictions in the existing research.  Conclusion:   Summarize the key points discussed in the literature review.  Highlight the research gap.  Address the research question or objective stated in the introduction.  Highlight the contributions of the review and suggest directions for future research.

Both annotated bibliographies and literature reviews involve the examination of scholarly sources. While annotated bibliographies focus on individual sources with brief annotations, literature reviews provide a more in-depth, integrated, and comprehensive analysis of existing literature on a specific topic. The key differences are as follows: 

References 

• Denney, A. S., & Tewksbury, R. (2013). How to write a literature review.  Journal of criminal justice education ,  24 (2), 218-234. 
• Pan, M. L. (2016).  Preparing literature reviews: Qualitative and quantitative approaches . Taylor & Francis. 
• Cantero, C. (2019). How to write a literature review.  San José State University Writing Center . 

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Literature Review: Conducting & Writing

• Sample Literature Reviews
• Steps for Conducting a Lit Review
• Finding "The Literature"
• Organizing/Writing
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Sample Lit Reviews from Communication Arts

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• Literature Review Sample 1
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Literature Review Guide: Examples of Literature Reviews

• What is a Literature Review?
• How to start?
• Search strategies and Databases
• Examples of Literature Reviews
• How to organise the review
• Library summary
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All good quality journal articles will include a small Literature Review after the Introduction paragraph.  It may not be called a Literature Review but gives you an idea of how one is created in miniature.

Sample Literature Reviews as part of a articles or Theses

• Sample Literature Review on Critical Thinking (Gwendolyn Reece, American University Library)
• Hackett, G and Melia, D . The hotel as the holiday/stay destination:trends and innovations. Presented at TRIC Conference, Belfast, Ireland- June 2012 and EuroCHRIE Conference

Links to sample Literature Reviews from other libraries

• Sample literature reviews from University of West Florida

Standalone Literature Reviews

• Attitudes towards the Disability in Ireland
• Martin, A., O'Connor-Fenelon, M. and Lyons, R. (2010). Non-verbal communication between nurses and people with an intellectual disability: A review of the literature. Journal of Intellectual Diabilities, 14(4), 303-314.

Irish Theses

• Phillips, Martin (2015) European airline performance: a data envelopment analysis with extrapolations based on model outputs. Master of Business Studies thesis, Dublin City University.
• The customers’ perception of servicescape’s influence on their behaviours, in the food retail industry : Dublin Business School 2015
• Coughlan, Ray (2015) What was the role of leadership in the transformation of a failing Irish Insurance business. Masters thesis, Dublin, National College of Ireland.
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Writing a Literature Review

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A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels and plays). When we say “literature review” or refer to “the literature,” we are talking about the research ( scholarship ) in a given field. You will often see the terms “the research,” “the scholarship,” and “the literature” used mostly interchangeably.

Where, when, and why would I write a lit review?

There are a number of different situations where you might write a literature review, each with slightly different expectations; different disciplines, too, have field-specific expectations for what a literature review is and does. For instance, in the humanities, authors might include more overt argumentation and interpretation of source material in their literature reviews, whereas in the sciences, authors are more likely to report study designs and results in their literature reviews; these differences reflect these disciplines’ purposes and conventions in scholarship. You should always look at examples from your own discipline and talk to professors or mentors in your field to be sure you understand your discipline’s conventions, for literature reviews as well as for any other genre.

A literature review can be a part of a research paper or scholarly article, usually falling after the introduction and before the research methods sections. In these cases, the lit review just needs to cover scholarship that is important to the issue you are writing about; sometimes it will also cover key sources that informed your research methodology.

Lit reviews can also be standalone pieces, either as assignments in a class or as publications. In a class, a lit review may be assigned to help students familiarize themselves with a topic and with scholarship in their field, get an idea of the other researchers working on the topic they’re interested in, find gaps in existing research in order to propose new projects, and/or develop a theoretical framework and methodology for later research. As a publication, a lit review usually is meant to help make other scholars’ lives easier by collecting and summarizing, synthesizing, and analyzing existing research on a topic. This can be especially helpful for students or scholars getting into a new research area, or for directing an entire community of scholars toward questions that have not yet been answered.

What are the parts of a lit review?

Most lit reviews use a basic introduction-body-conclusion structure; if your lit review is part of a larger paper, the introduction and conclusion pieces may be just a few sentences while you focus most of your attention on the body. If your lit review is a standalone piece, the introduction and conclusion take up more space and give you a place to discuss your goals, research methods, and conclusions separately from where you discuss the literature itself.

Introduction:

• An introductory paragraph that explains what your working topic and thesis is
• A forecast of key topics or texts that will appear in the review
• Potentially, a description of how you found sources and how you analyzed them for inclusion and discussion in the review (more often found in published, standalone literature reviews than in lit review sections in an article or research paper)
• Summarize and synthesize: Give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: Don’t just paraphrase other researchers – add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically Evaluate: Mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: Use transition words and topic sentence to draw connections, comparisons, and contrasts.

Conclusion:

• Summarize the key findings you have taken from the literature and emphasize their significance
• Connect it back to your primary research question

How should I organize my lit review?

Lit reviews can take many different organizational patterns depending on what you are trying to accomplish with the review. Here are some examples:

• Chronological : The simplest approach is to trace the development of the topic over time, which helps familiarize the audience with the topic (for instance if you are introducing something that is not commonly known in your field). If you choose this strategy, be careful to avoid simply listing and summarizing sources in order. Try to analyze the patterns, turning points, and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred (as mentioned previously, this may not be appropriate in your discipline — check with a teacher or mentor if you’re unsure).
• Thematic : If you have found some recurring central themes that you will continue working with throughout your piece, you can organize your literature review into subsections that address different aspects of the topic. For example, if you are reviewing literature about women and religion, key themes can include the role of women in churches and the religious attitude towards women.
• Qualitative versus quantitative research
• Empirical versus theoretical scholarship
• Divide the research by sociological, historical, or cultural sources
• Theoretical : In many humanities articles, the literature review is the foundation for the theoretical framework. You can use it to discuss various theories, models, and definitions of key concepts. You can argue for the relevance of a specific theoretical approach or combine various theorical concepts to create a framework for your research.

What are some strategies or tips I can use while writing my lit review?

Any lit review is only as good as the research it discusses; make sure your sources are well-chosen and your research is thorough. Don’t be afraid to do more research if you discover a new thread as you’re writing. More info on the research process is available in our "Conducting Research" resources .

As you’re doing your research, create an annotated bibliography ( see our page on the this type of document ). Much of the information used in an annotated bibliography can be used also in a literature review, so you’ll be not only partially drafting your lit review as you research, but also developing your sense of the larger conversation going on among scholars, professionals, and any other stakeholders in your topic.

Usually you will need to synthesize research rather than just summarizing it. This means drawing connections between sources to create a picture of the scholarly conversation on a topic over time. Many student writers struggle to synthesize because they feel they don’t have anything to add to the scholars they are citing; here are some strategies to help you:

• It often helps to remember that the point of these kinds of syntheses is to show your readers how you understand your research, to help them read the rest of your paper.
• Writing teachers often say synthesis is like hosting a dinner party: imagine all your sources are together in a room, discussing your topic. What are they saying to each other?
• Look at the in-text citations in each paragraph. Are you citing just one source for each paragraph? This usually indicates summary only. When you have multiple sources cited in a paragraph, you are more likely to be synthesizing them (not always, but often
• Read more about synthesis here.

The most interesting literature reviews are often written as arguments (again, as mentioned at the beginning of the page, this is discipline-specific and doesn’t work for all situations). Often, the literature review is where you can establish your research as filling a particular gap or as relevant in a particular way. You have some chance to do this in your introduction in an article, but the literature review section gives a more extended opportunity to establish the conversation in the way you would like your readers to see it. You can choose the intellectual lineage you would like to be part of and whose definitions matter most to your thinking (mostly humanities-specific, but this goes for sciences as well). In addressing these points, you argue for your place in the conversation, which tends to make the lit review more compelling than a simple reporting of other sources.

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• What is a Literature Review? | Guide, Template, & Examples

What is a Literature Review? | Guide, Template, & Examples

Published on 22 February 2022 by Shona McCombes . Revised on 7 June 2022.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research.

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarise sources – it analyses, synthesises, and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

Why write a literature review, examples of literature reviews, step 1: search for relevant literature, step 2: evaluate and select sources, step 3: identify themes, debates and gaps, step 4: outline your literature review’s structure, step 5: write your literature review, frequently asked questions about literature reviews, introduction.

• Quick Run-through
• Step 1 & 2

When you write a dissertation or thesis, you will have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and scholarly context
• Develop a theoretical framework and methodology for your research
• Position yourself in relation to other researchers and theorists
• Show how your dissertation addresses a gap or contributes to a debate

You might also have to write a literature review as a stand-alone assignment. In this case, the purpose is to evaluate the current state of research and demonstrate your knowledge of scholarly debates around a topic.

The content will look slightly different in each case, but the process of conducting a literature review follows the same steps. We’ve written a step-by-step guide that you can follow below.

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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research objectives and questions .

If you are writing a literature review as a stand-alone assignment, you will have to choose a focus and develop a central question to direct your search. Unlike a dissertation research question, this question has to be answerable without collecting original data. You should be able to answer it based only on a review of existing publications.

Make a list of keywords

Start by creating a list of keywords related to your research topic. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list if you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can use boolean operators to help narrow down your search:

Read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

To identify the most important publications on your topic, take note of recurring citations. If the same authors, books or articles keep appearing in your reading, make sure to seek them out.

You probably won’t be able to read absolutely everything that has been written on the topic – you’ll have to evaluate which sources are most relevant to your questions.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models and methods? Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• How does the publication contribute to your understanding of the topic? What are its key insights and arguments?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible, and make sure you read any landmark studies and major theories in your field of research.

You can find out how many times an article has been cited on Google Scholar – a high citation count means the article has been influential in the field, and should certainly be included in your literature review.

The scope of your review will depend on your topic and discipline: in the sciences you usually only review recent literature, but in the humanities you might take a long historical perspective (for example, to trace how a concept has changed in meaning over time).

Remember that you can use our template to summarise and evaluate sources you’re thinking about using!

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It’s important to keep track of your sources with references to avoid plagiarism . It can be helpful to make an annotated bibliography, where you compile full reference information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

You can use our free APA Reference Generator for quick, correct, consistent citations.

To begin organising your literature review’s argument and structure, you need to understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly-visual platforms like Instagram and Snapchat – this is a gap that you could address in your own research.

There are various approaches to organising the body of a literature review. You should have a rough idea of your strategy before you start writing.

Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarising sources in order.

Try to analyse patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organise your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text, your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

If you are writing the literature review as part of your dissertation or thesis, reiterate your central problem or research question and give a brief summary of the scholarly context. You can emphasise the timeliness of the topic (“many recent studies have focused on the problem of x”) or highlight a gap in the literature (“while there has been much research on x, few researchers have taken y into consideration”).

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, make sure to follow these tips:

• Summarise and synthesise: give an overview of the main points of each source and combine them into a coherent whole.
• Analyse and interpret: don’t just paraphrase other researchers – add your own interpretations, discussing the significance of findings in relation to the literature as a whole.
• Critically evaluate: mention the strengths and weaknesses of your sources.
• Write in well-structured paragraphs: use transitions and topic sentences to draw connections, comparisons and contrasts.

In the conclusion, you should summarise the key findings you have taken from the literature and emphasise their significance.

If the literature review is part of your dissertation or thesis, reiterate how your research addresses gaps and contributes new knowledge, or discuss how you have drawn on existing theories and methods to build a framework for your research. This can lead directly into your methodology section.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a dissertation , thesis, research paper , or proposal .

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarise yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your  dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, June 07). What is a Literature Review? | Guide, Template, & Examples. Scribbr. Retrieved 14 May 2024, from https://www.scribbr.co.uk/thesis-dissertation/literature-review/

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• 04 December 2020
• Correction 09 December 2020

How to write a superb literature review

Andy Tay is a freelance writer based in Singapore.

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Literature reviews are important resources for scientists. They provide historical context for a field while offering opinions on its future trajectory. Creating them can provide inspiration for one’s own research, as well as some practice in writing. But few scientists are trained in how to write a review — or in what constitutes an excellent one. Even picking the appropriate software to use can be an involved decision (see ‘Tools and techniques’). So Nature asked editors and working scientists with well-cited reviews for their tips.

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Correction 09 December 2020 : An earlier version of the tables in this article included some incorrect details about the programs Zotero, Endnote and Manubot. These have now been corrected.

Hsing, I.-M., Xu, Y. & Zhao, W. Electroanalysis 19 , 755–768 (2007).

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Ledesma, H. A. et al. Nature Nanotechnol. 14 , 645–657 (2019).

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Brahlek, M., Koirala, N., Bansal, N. & Oh, S. Solid State Commun. 215–216 , 54–62 (2015).

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How to Write a Literature Review

What is a literature review.

• What Is the Literature
• Writing the Review

A literature review is much more than an annotated bibliography or a list of separate reviews of articles and books. It is a critical, analytical summary and synthesis of the current knowledge of a topic. Thus it should compare and relate different theories, findings, etc, rather than just summarize them individually. In addition, it should have a particular focus or theme to organize the review. It does not have to be an exhaustive account of everything published on the topic, but it should discuss all the significant academic literature and other relevant sources important for that focus.

This is meant to be a general guide to writing a literature review: ways to structure one, what to include, how it supplements other research. For more specific help on writing a review, and especially for help on finding the literature to review, sign up for a Personal Research Session .

The specific organization of a literature review depends on the type and purpose of the review, as well as on the specific field or topic being reviewed. But in general, it is a relatively brief but thorough exploration of past and current work on a topic. Rather than a chronological listing of previous work, though, literature reviews are usually organized thematically, such as different theoretical approaches, methodologies, or specific issues or concepts involved in the topic. A thematic organization makes it much easier to examine contrasting perspectives, theoretical approaches, methodologies, findings, etc, and to analyze the strengths and weaknesses of, and point out any gaps in, previous research. And this is the heart of what a literature review is about. A literature review may offer new interpretations, theoretical approaches, or other ideas; if it is part of a research proposal or report it should demonstrate the relationship of the proposed or reported research to others' work; but whatever else it does, it must provide a critical overview of the current state of research efforts. 

Literature reviews are common and very important in the sciences and social sciences. They are less common and have a less important role in the humanities, but they do have a place, especially stand-alone reviews.

Types of Literature Reviews

There are different types of literature reviews, and different purposes for writing a review, but the most common are:

• Stand-alone literature review articles . These provide an overview and analysis of the current state of research on a topic or question. The goal is to evaluate and compare previous research on a topic to provide an analysis of what is currently known, and also to reveal controversies, weaknesses, and gaps in current work, thus pointing to directions for future research. You can find examples published in any number of academic journals, but there is a series of Annual Reviews of *Subject* which are specifically devoted to literature review articles. Writing a stand-alone review is often an effective way to get a good handle on a topic and to develop ideas for your own research program. For example, contrasting theoretical approaches or conflicting interpretations of findings can be the basis of your research project: can you find evidence supporting one interpretation against another, or can you propose an alternative interpretation that overcomes their limitations?
• Part of a research proposal . This could be a proposal for a PhD dissertation, a senior thesis, or a class project. It could also be a submission for a grant. The literature review, by pointing out the current issues and questions concerning a topic, is a crucial part of demonstrating how your proposed research will contribute to the field, and thus of convincing your thesis committee to allow you to pursue the topic of your interest or a funding agency to pay for your research efforts.
• Part of a research report . When you finish your research and write your thesis or paper to present your findings, it should include a literature review to provide the context to which your work is a contribution. Your report, in addition to detailing the methods, results, etc. of your research, should show how your work relates to others' work.

A literature review for a research report is often a revision of the review for a research proposal, which can be a revision of a stand-alone review. Each revision should be a fairly extensive revision. With the increased knowledge of and experience in the topic as you proceed, your understanding of the topic will increase. Thus, you will be in a better position to analyze and critique the literature. In addition, your focus will change as you proceed in your research. Some areas of the literature you initially reviewed will be marginal or irrelevant for your eventual research, and you will need to explore other areas more thoroughly. 

Examples of Literature Reviews

See the series of Annual Reviews of *Subject* which are specifically devoted to literature review articles to find many examples of stand-alone literature reviews in the biomedical, physical, and social sciences. 

Research report articles vary in how they are organized, but a common general structure is to have sections such as:

• Abstract - Brief summary of the contents of the article
• Introduction - A explanation of the purpose of the study, a statement of the research question(s) the study intends to address
• Literature review - A critical assessment of the work done so far on this topic, to show how the current study relates to what has already been done
• Methods - How the study was carried out (e.g. instruments or equipment, procedures, methods to gather and analyze data)
• Results - What was found in the course of the study
• Discussion - What do the results mean
• Conclusion - State the conclusions and implications of the results, and discuss how it relates to the work reviewed in the literature review; also, point to directions for further work in the area

Here are some articles that illustrate variations on this theme. There is no need to read the entire articles (unless the contents interest you); just quickly browse through to see the sections, and see how each section is introduced and what is contained in them.

The Determinants of Undergraduate Grade Point Average: The Relative Importance of Family Background, High School Resources, and Peer Group Effects , in The Journal of Human Resources , v. 34 no. 2 (Spring 1999), p. 268-293.

This article has a standard breakdown of sections:

• Introduction
• Literature Review
• Some discussion sections

First Encounters of the Bureaucratic Kind: Early Freshman Experiences with a Campus Bureaucracy , in The Journal of Higher Education , v. 67 no. 6 (Nov-Dec 1996), p. 660-691.

This one does not have a section specifically labeled as a "literature review" or "review of the literature," but the first few sections cite a long list of other sources discussing previous research in the area before the authors present their own study they are reporting.

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• Last Updated: Jan 11, 2024 9:48 AM
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Organizing Your Social Sciences Research Paper

• 5. The Literature Review
• Purpose of Guide
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A literature review surveys prior research published in books, scholarly articles, and any other sources relevant to a particular issue, area of research, or theory, and by so doing, provides a description, summary, and critical evaluation of these works in relation to the research problem being investigated. Literature reviews are designed to provide an overview of sources you have used in researching a particular topic and to demonstrate to your readers how your research fits within existing scholarship about the topic.

Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . Fourth edition. Thousand Oaks, CA: SAGE, 2014.

Importance of a Good Literature Review

A literature review may consist of simply a summary of key sources, but in the social sciences, a literature review usually has an organizational pattern and combines both summary and synthesis, often within specific conceptual categories . A summary is a recap of the important information of the source, but a synthesis is a re-organization, or a reshuffling, of that information in a way that informs how you are planning to investigate a research problem. The analytical features of a literature review might:

• Give a new interpretation of old material or combine new with old interpretations,
• Trace the intellectual progression of the field, including major debates,
• Depending on the situation, evaluate the sources and advise the reader on the most pertinent or relevant research, or
• Usually in the conclusion of a literature review, identify where gaps exist in how a problem has been researched to date.

Given this, the purpose of a literature review is to:

• Place each work in the context of its contribution to understanding the research problem being studied.
• Describe the relationship of each work to the others under consideration.
• Identify new ways to interpret prior research.
• Reveal any gaps that exist in the literature.
• Resolve conflicts amongst seemingly contradictory previous studies.
• Identify areas of prior scholarship to prevent duplication of effort.
• Point the way in fulfilling a need for additional research.
• Locate your own research within the context of existing literature [very important].

Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper. 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . Los Angeles, CA: SAGE, 2011; Knopf, Jeffrey W. "Doing a Literature Review." PS: Political Science and Politics 39 (January 2006): 127-132; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012.

Types of Literature Reviews

It is important to think of knowledge in a given field as consisting of three layers. First, there are the primary studies that researchers conduct and publish. Second are the reviews of those studies that summarize and offer new interpretations built from and often extending beyond the primary studies. Third, there are the perceptions, conclusions, opinion, and interpretations that are shared informally among scholars that become part of the body of epistemological traditions within the field.

In composing a literature review, it is important to note that it is often this third layer of knowledge that is cited as "true" even though it often has only a loose relationship to the primary studies and secondary literature reviews. Given this, while literature reviews are designed to provide an overview and synthesis of pertinent sources you have explored, there are a number of approaches you could adopt depending upon the type of analysis underpinning your study.

Argumentative Review This form examines literature selectively in order to support or refute an argument, deeply embedded assumption, or philosophical problem already established in the literature. The purpose is to develop a body of literature that establishes a contrarian viewpoint. Given the value-laden nature of some social science research [e.g., educational reform; immigration control], argumentative approaches to analyzing the literature can be a legitimate and important form of discourse. However, note that they can also introduce problems of bias when they are used to make summary claims of the sort found in systematic reviews [see below].

Integrative Review Considered a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives on the topic are generated. The body of literature includes all studies that address related or identical hypotheses or research problems. A well-done integrative review meets the same standards as primary research in regard to clarity, rigor, and replication. This is the most common form of review in the social sciences.

Historical Review Few things rest in isolation from historical precedent. Historical literature reviews focus on examining research throughout a period of time, often starting with the first time an issue, concept, theory, phenomena emerged in the literature, then tracing its evolution within the scholarship of a discipline. The purpose is to place research in a historical context to show familiarity with state-of-the-art developments and to identify the likely directions for future research.

Methodological Review A review does not always focus on what someone said [findings], but how they came about saying what they say [method of analysis]. Reviewing methods of analysis provides a framework of understanding at different levels [i.e. those of theory, substantive fields, research approaches, and data collection and analysis techniques], how researchers draw upon a wide variety of knowledge ranging from the conceptual level to practical documents for use in fieldwork in the areas of ontological and epistemological consideration, quantitative and qualitative integration, sampling, interviewing, data collection, and data analysis. This approach helps highlight ethical issues which you should be aware of and consider as you go through your own study.

Systematic Review This form consists of an overview of existing evidence pertinent to a clearly formulated research question, which uses pre-specified and standardized methods to identify and critically appraise relevant research, and to collect, report, and analyze data from the studies that are included in the review. The goal is to deliberately document, critically evaluate, and summarize scientifically all of the research about a clearly defined research problem . Typically it focuses on a very specific empirical question, often posed in a cause-and-effect form, such as "To what extent does A contribute to B?" This type of literature review is primarily applied to examining prior research studies in clinical medicine and allied health fields, but it is increasingly being used in the social sciences.

Theoretical Review The purpose of this form is to examine the corpus of theory that has accumulated in regard to an issue, concept, theory, phenomena. The theoretical literature review helps to establish what theories already exist, the relationships between them, to what degree the existing theories have been investigated, and to develop new hypotheses to be tested. Often this form is used to help establish a lack of appropriate theories or reveal that current theories are inadequate for explaining new or emerging research problems. The unit of analysis can focus on a theoretical concept or a whole theory or framework.

NOTE : Most often the literature review will incorporate some combination of types. For example, a review that examines literature supporting or refuting an argument, assumption, or philosophical problem related to the research problem will also need to include writing supported by sources that establish the history of these arguments in the literature.

Baumeister, Roy F. and Mark R. Leary. "Writing Narrative Literature Reviews."  Review of General Psychology 1 (September 1997): 311-320; Mark R. Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Kennedy, Mary M. "Defining a Literature." Educational Researcher 36 (April 2007): 139-147; Petticrew, Mark and Helen Roberts. Systematic Reviews in the Social Sciences: A Practical Guide . Malden, MA: Blackwell Publishers, 2006; Torracro, Richard. "Writing Integrative Literature Reviews: Guidelines and Examples." Human Resource Development Review 4 (September 2005): 356-367; Rocco, Tonette S. and Maria S. Plakhotnik. "Literature Reviews, Conceptual Frameworks, and Theoretical Frameworks: Terms, Functions, and Distinctions." Human Ressource Development Review 8 (March 2008): 120-130; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.

Structure and Writing Style

I.  Thinking About Your Literature Review

The structure of a literature review should include the following in support of understanding the research problem :

• An overview of the subject, issue, or theory under consideration, along with the objectives of the literature review,
• Division of works under review into themes or categories [e.g. works that support a particular position, those against, and those offering alternative approaches entirely],
• An explanation of how each work is similar to and how it varies from the others,
• Conclusions as to which pieces are best considered in their argument, are most convincing of their opinions, and make the greatest contribution to the understanding and development of their area of research.

The critical evaluation of each work should consider :

• Provenance -- what are the author's credentials? Are the author's arguments supported by evidence [e.g. primary historical material, case studies, narratives, statistics, recent scientific findings]?
• Methodology -- were the techniques used to identify, gather, and analyze the data appropriate to addressing the research problem? Was the sample size appropriate? Were the results effectively interpreted and reported?
• Objectivity -- is the author's perspective even-handed or prejudicial? Is contrary data considered or is certain pertinent information ignored to prove the author's point?
• Persuasiveness -- which of the author's theses are most convincing or least convincing?
• Validity -- are the author's arguments and conclusions convincing? Does the work ultimately contribute in any significant way to an understanding of the subject?

II.  Development of the Literature Review

Four Basic Stages of Writing 1.  Problem formulation -- which topic or field is being examined and what are its component issues? 2.  Literature search -- finding materials relevant to the subject being explored. 3.  Data evaluation -- determining which literature makes a significant contribution to the understanding of the topic. 4.  Analysis and interpretation -- discussing the findings and conclusions of pertinent literature.

Consider the following issues before writing the literature review: Clarify If your assignment is not specific about what form your literature review should take, seek clarification from your professor by asking these questions: 1.  Roughly how many sources would be appropriate to include? 2.  What types of sources should I review (books, journal articles, websites; scholarly versus popular sources)? 3.  Should I summarize, synthesize, or critique sources by discussing a common theme or issue? 4.  Should I evaluate the sources in any way beyond evaluating how they relate to understanding the research problem? 5.  Should I provide subheadings and other background information, such as definitions and/or a history? Find Models Use the exercise of reviewing the literature to examine how authors in your discipline or area of interest have composed their literature review sections. Read them to get a sense of the types of themes you might want to look for in your own research or to identify ways to organize your final review. The bibliography or reference section of sources you've already read, such as required readings in the course syllabus, are also excellent entry points into your own research. Narrow the Topic The narrower your topic, the easier it will be to limit the number of sources you need to read in order to obtain a good survey of relevant resources. Your professor will probably not expect you to read everything that's available about the topic, but you'll make the act of reviewing easier if you first limit scope of the research problem. A good strategy is to begin by searching the USC Libraries Catalog for recent books about the topic and review the table of contents for chapters that focuses on specific issues. You can also review the indexes of books to find references to specific issues that can serve as the focus of your research. For example, a book surveying the history of the Israeli-Palestinian conflict may include a chapter on the role Egypt has played in mediating the conflict, or look in the index for the pages where Egypt is mentioned in the text. Consider Whether Your Sources are Current Some disciplines require that you use information that is as current as possible. This is particularly true in disciplines in medicine and the sciences where research conducted becomes obsolete very quickly as new discoveries are made. However, when writing a review in the social sciences, a survey of the history of the literature may be required. In other words, a complete understanding the research problem requires you to deliberately examine how knowledge and perspectives have changed over time. Sort through other current bibliographies or literature reviews in the field to get a sense of what your discipline expects. You can also use this method to explore what is considered by scholars to be a "hot topic" and what is not.

III.  Ways to Organize Your Literature Review

Chronology of Events If your review follows the chronological method, you could write about the materials according to when they were published. This approach should only be followed if a clear path of research building on previous research can be identified and that these trends follow a clear chronological order of development. For example, a literature review that focuses on continuing research about the emergence of German economic power after the fall of the Soviet Union. By Publication Order your sources by publication chronology, then, only if the order demonstrates a more important trend. For instance, you could order a review of literature on environmental studies of brown fields if the progression revealed, for example, a change in the soil collection practices of the researchers who wrote and/or conducted the studies. Thematic [“conceptual categories”] A thematic literature review is the most common approach to summarizing prior research in the social and behavioral sciences. Thematic reviews are organized around a topic or issue, rather than the progression of time, although the progression of time may still be incorporated into a thematic review. For example, a review of the Internet’s impact on American presidential politics could focus on the development of online political satire. While the study focuses on one topic, the Internet’s impact on American presidential politics, it would still be organized chronologically reflecting technological developments in media. The difference in this example between a "chronological" and a "thematic" approach is what is emphasized the most: themes related to the role of the Internet in presidential politics. Note that more authentic thematic reviews tend to break away from chronological order. A review organized in this manner would shift between time periods within each section according to the point being made. Methodological A methodological approach focuses on the methods utilized by the researcher. For the Internet in American presidential politics project, one methodological approach would be to look at cultural differences between the portrayal of American presidents on American, British, and French websites. Or the review might focus on the fundraising impact of the Internet on a particular political party. A methodological scope will influence either the types of documents in the review or the way in which these documents are discussed.

Other Sections of Your Literature Review Once you've decided on the organizational method for your literature review, the sections you need to include in the paper should be easy to figure out because they arise from your organizational strategy. In other words, a chronological review would have subsections for each vital time period; a thematic review would have subtopics based upon factors that relate to the theme or issue. However, sometimes you may need to add additional sections that are necessary for your study, but do not fit in the organizational strategy of the body. What other sections you include in the body is up to you. However, only include what is necessary for the reader to locate your study within the larger scholarship about the research problem.

Here are examples of other sections, usually in the form of a single paragraph, you may need to include depending on the type of review you write:

• Current Situation : Information necessary to understand the current topic or focus of the literature review.
• Sources Used : Describes the methods and resources [e.g., databases] you used to identify the literature you reviewed.
• History : The chronological progression of the field, the research literature, or an idea that is necessary to understand the literature review, if the body of the literature review is not already a chronology.
• Selection Methods : Criteria you used to select (and perhaps exclude) sources in your literature review. For instance, you might explain that your review includes only peer-reviewed [i.e., scholarly] sources.
• Standards : Description of the way in which you present your information.
• Questions for Further Research : What questions about the field has the review sparked? How will you further your research as a result of the review?

IV.  Writing Your Literature Review

Once you've settled on how to organize your literature review, you're ready to write each section. When writing your review, keep in mind these issues.

Use Evidence A literature review section is, in this sense, just like any other academic research paper. Your interpretation of the available sources must be backed up with evidence [citations] that demonstrates that what you are saying is valid. Be Selective Select only the most important points in each source to highlight in the review. The type of information you choose to mention should relate directly to the research problem, whether it is thematic, methodological, or chronological. Related items that provide additional information, but that are not key to understanding the research problem, can be included in a list of further readings . Use Quotes Sparingly Some short quotes are appropriate if you want to emphasize a point, or if what an author stated cannot be easily paraphrased. Sometimes you may need to quote certain terminology that was coined by the author, is not common knowledge, or taken directly from the study. Do not use extensive quotes as a substitute for using your own words in reviewing the literature. Summarize and Synthesize Remember to summarize and synthesize your sources within each thematic paragraph as well as throughout the review. Recapitulate important features of a research study, but then synthesize it by rephrasing the study's significance and relating it to your own work and the work of others. Keep Your Own Voice While the literature review presents others' ideas, your voice [the writer's] should remain front and center. For example, weave references to other sources into what you are writing but maintain your own voice by starting and ending the paragraph with your own ideas and wording. Use Caution When Paraphrasing When paraphrasing a source that is not your own, be sure to represent the author's information or opinions accurately and in your own words. Even when paraphrasing an author’s work, you still must provide a citation to that work.

V.  Common Mistakes to Avoid

These are the most common mistakes made in reviewing social science research literature.

• Sources in your literature review do not clearly relate to the research problem;
• You do not take sufficient time to define and identify the most relevant sources to use in the literature review related to the research problem;
• Relies exclusively on secondary analytical sources rather than including relevant primary research studies or data;
• Uncritically accepts another researcher's findings and interpretations as valid, rather than examining critically all aspects of the research design and analysis;
• Does not describe the search procedures that were used in identifying the literature to review;
• Reports isolated statistical results rather than synthesizing them in chi-squared or meta-analytic methods; and,
• Only includes research that validates assumptions and does not consider contrary findings and alternative interpretations found in the literature.

Cook, Kathleen E. and Elise Murowchick. “Do Literature Review Skills Transfer from One Course to Another?” Psychology Learning and Teaching 13 (March 2014): 3-11; Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . London: SAGE, 2011; Literature Review Handout. Online Writing Center. Liberty University; Literature Reviews. The Writing Center. University of North Carolina; Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: SAGE, 2016; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012; Randolph, Justus J. “A Guide to Writing the Dissertation Literature Review." Practical Assessment, Research, and Evaluation. vol. 14, June 2009; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016; Taylor, Dena. The Literature Review: A Few Tips On Conducting It. University College Writing Centre. University of Toronto; Writing a Literature Review. Academic Skills Centre. University of Canberra.

Writing Tip

Break Out of Your Disciplinary Box!

Thinking interdisciplinarily about a research problem can be a rewarding exercise in applying new ideas, theories, or concepts to an old problem. For example, what might cultural anthropologists say about the continuing conflict in the Middle East? In what ways might geographers view the need for better distribution of social service agencies in large cities than how social workers might study the issue? You don’t want to substitute a thorough review of core research literature in your discipline for studies conducted in other fields of study. However, particularly in the social sciences, thinking about research problems from multiple vectors is a key strategy for finding new solutions to a problem or gaining a new perspective. Consult with a librarian about identifying research databases in other disciplines; almost every field of study has at least one comprehensive database devoted to indexing its research literature.

Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.

Another Writing Tip

Don't Just Review for Content!

While conducting a review of the literature, maximize the time you devote to writing this part of your paper by thinking broadly about what you should be looking for and evaluating. Review not just what scholars are saying, but how are they saying it. Some questions to ask:

• How are they organizing their ideas?
• What methods have they used to study the problem?
• What theories have been used to explain, predict, or understand their research problem?
• What sources have they cited to support their conclusions?
• How have they used non-textual elements [e.g., charts, graphs, figures, etc.] to illustrate key points?

When you begin to write your literature review section, you'll be glad you dug deeper into how the research was designed and constructed because it establishes a means for developing more substantial analysis and interpretation of the research problem.

Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1 998.

Yet Another Writing Tip

When Do I Know I Can Stop Looking and Move On?

Here are several strategies you can utilize to assess whether you've thoroughly reviewed the literature:

• Look for repeating patterns in the research findings . If the same thing is being said, just by different people, then this likely demonstrates that the research problem has hit a conceptual dead end. At this point consider: Does your study extend current research?  Does it forge a new path? Or, does is merely add more of the same thing being said?
• Look at sources the authors cite to in their work . If you begin to see the same researchers cited again and again, then this is often an indication that no new ideas have been generated to address the research problem.
• Search Google Scholar to identify who has subsequently cited leading scholars already identified in your literature review [see next sub-tab]. This is called citation tracking and there are a number of sources that can help you identify who has cited whom, particularly scholars from outside of your discipline. Here again, if the same authors are being cited again and again, this may indicate no new literature has been written on the topic.

Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: Sage, 2016; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.

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7 Writing a Literature Review

Hundreds of original investigation research articles on health science topics are published each year. It is becoming harder and harder to keep on top of all new findings in a topic area and – more importantly – to work out how they all fit together to determine our current understanding of a topic. This is where literature reviews come in.

In this chapter, we explain what a literature review is and outline the stages involved in writing one. We also provide practical tips on how to communicate the results of a review of current literature on a topic in the format of a literature review.

7.1 What is a literature review?

Literature reviews provide a synthesis and evaluation  of the existing literature on a particular topic with the aim of gaining a new, deeper understanding of the topic.

Published literature reviews are typically written by scientists who are experts in that particular area of science. Usually, they will be widely published as authors of their own original work, making them highly qualified to author a literature review.

However, literature reviews are still subject to peer review before being published. Literature reviews provide an important bridge between the expert scientific community and many other communities, such as science journalists, teachers, and medical and allied health professionals. When the most up-to-date knowledge reaches such audiences, it is more likely that this information will find its way to the general public. When this happens, – the ultimate good of science can be realised.

A literature review is structured differently from an original research article. It is developed based on themes, rather than stages of the scientific method.

In the article Ten simple rules for writing a literature review , Marco Pautasso explains the importance of literature reviews:

Literature reviews are in great demand in most scientific fields. Their need stems from the ever-increasing output of scientific publications. For example, compared to 1991, in 2008 three, eight, and forty times more papers were indexed in Web of Science on malaria, obesity, and biodiversity, respectively. Given such mountains of papers, scientists cannot be expected to examine in detail every single new paper relevant to their interests. Thus, it is both advantageous and necessary to rely on regular summaries of the recent literature. Although recognition for scientists mainly comes from primary research, timely literature reviews can lead to new synthetic insights and are often widely read. For such summaries to be useful, however, they need to be compiled in a professional way (Pautasso, 2013, para. 1).

An example of a literature review is shown in Figure 7.1.

Video 7.1: What is a literature review? [2 mins, 11 secs]

Watch this video created by Steely Library at Northern Kentucky Library called ‘ What is a literature review? Note: Closed captions are available by clicking on the CC button below.

Examples of published literature reviews

• Strength training alone, exercise therapy alone, and exercise therapy with passive manual mobilisation each reduce pain and disability in people with knee osteoarthritis: a systematic review
• Traveler’s diarrhea: a clinical review
• Cultural concepts of distress and psychiatric disorders: literature review and research recommendations for global mental health epidemiology

7.2 Steps of writing a literature review

Writing a literature review is a very challenging task. Figure 7.2 summarises the steps of writing a literature review. Depending on why you are writing your literature review, you may be given a topic area, or may choose a topic that particularly interests you or is related to a research project that you wish to undertake.

Chapter 6 provides instructions on finding scientific literature that would form the basis for your literature review.

Once you have your topic and have accessed the literature, the next stages (analysis, synthesis and evaluation) are challenging. Next, we look at these important cognitive skills student scientists will need to develop and employ to successfully write a literature review, and provide some guidance for navigating these stages.

Analysis, synthesis and evaluation

Analysis, synthesis and evaluation are three essential skills required by scientists  and you will need to develop these skills if you are to write a good literature review ( Figure 7.3 ). These important cognitive skills are discussed in more detail in Chapter 9.

The first step in writing a literature review is to analyse the original investigation research papers that you have gathered related to your topic.

Analysis requires examining the papers methodically and in detail, so you can understand and interpret aspects of the study described in each research article.

An analysis grid is a simple tool you can use to help with the careful examination and breakdown of each paper. This tool will allow you to create a concise summary of each research paper; see Table 7.1 for an example of  an analysis grid. When filling in the grid, the aim is to draw out key aspects of each research paper. Use a different row for each paper, and a different column for each aspect of the paper ( Tables 7.2 and 7.3 show how completed analysis grid may look).

Before completing your own grid, look at these examples and note the types of information that have been included, as well as the level of detail. Completing an analysis grid with a sufficient level of detail will help you to complete the synthesis and evaluation stages effectively. This grid will allow you to more easily observe similarities and differences across the findings of the research papers and to identify possible explanations (e.g., differences in methodologies employed) for observed differences between the findings of different research papers.

Table 7.1: Example of an analysis grid

Table 7.3: Sample filled-in analysis grid for research article by Ping and colleagues

Source: Ping, WC, Keong, CC & Bandyopadhyay, A 2010, ‘Effects of acute supplementation of caffeine on cardiorespiratory responses during endurance running in a hot and humid climate’, Indian Journal of Medical Research, vol. 132, pp. 36–41. Used under a CC-BY-NC-SA licence.

Step two of writing a literature review is synthesis.

Synthesis describes combining separate components or elements to form a connected whole.

You will use the results of your analysis to find themes to build your literature review around. Each of the themes identified will become a subheading within the body of your literature review.

A good place to start when identifying themes is with the dependent variables (results/findings) that were investigated in the research studies.

Because all of the research articles you are incorporating into your literature review are related to your topic, it is likely that they have similar study designs and have measured similar dependent variables. Review the ‘Results’ column of your analysis grid. You may like to collate the common themes in a synthesis grid (see, for example Table 7.4 ).

Step three of writing a literature review is evaluation, which can only be done after carefully analysing your research papers and synthesising the common themes (findings).

During the evaluation stage, you are making judgements on the themes presented in the research articles that you have read. This includes providing physiological explanations for the findings. It may be useful to refer to the discussion section of published original investigation research papers, or another literature review, where the authors may mention tested or hypothetical physiological mechanisms that may explain their findings.

When the findings of the investigations related to a particular theme are inconsistent (e.g., one study shows that caffeine effects performance and another study shows that caffeine had no effect on performance) you should attempt to provide explanations of why the results differ, including physiological explanations. A good place to start is by comparing the methodologies to determine if there are any differences that may explain the differences in the findings (see the ‘Experimental design’ column of your analysis grid). An example of evaluation is shown in the examples that follow in this section, under ‘Running performance’ and ‘RPE ratings’.

When the findings of the papers related to a particular theme are consistent (e.g., caffeine had no effect on oxygen uptake in both studies) an evaluation should include an explanation of why the results are similar. Once again, include physiological explanations. It is still a good idea to compare methodologies as a background to the evaluation. An example of evaluation is shown in the following under ‘Oxygen consumption’.

7.3 Writing your literature review

Once you have completed the analysis, and synthesis grids and written your evaluation of the research papers , you can combine synthesis and evaluation information to create a paragraph for a literature review ( Figure 7.4 ).

The following paragraphs are an example of combining the outcome of the synthesis and evaluation stages to produce a paragraph for a literature review.

Note that this is an example using only two papers – most literature reviews would be presenting information on many more papers than this ( (e.g., 106 papers in the review article by Bain and colleagues discussed later in this chapter). However, the same principle applies regardless of the number of papers reviewed.

The next part of this chapter looks at the each section of a literature review and explains how to write them by referring to a review article that was published in Frontiers in Physiology and shown in Figure 7.1. Each section from the published article is annotated to highlight important features of the format of the review article, and identifies the synthesis and evaluation information.

In the examination of each review article section we will point out examples of how the authors have presented certain information and where they display application of important cognitive processes; we will use the colour code shown below:

This should be one paragraph that accurately reflects the contents of the review article.

Introduction

The introduction should establish the context and importance of the review

Body of literature review

The reference section provides a list of the references that you cited in the body of your review article. The format will depend on the journal of publication as each journal has their own specific referencing format.

It is important to accurately cite references in research papers to acknowledge your sources and ensure credit is appropriately given to authors of work you have referred to. An accurate and comprehensive reference list also shows your readers that you are well-read in your topic area and are aware of the key papers that provide the context to your research.

It is important to keep track of your resources and to reference them consistently in the format required by the publication in which your work will appear. Most scientists will use reference management software to store details of all of the journal articles (and other sources) they use while writing their review article. This software also automates the process of adding in-text references and creating a reference list. In the review article by Bain et al. (2014) used as an example in this chapter, the reference list contains 106 items, so you can imagine how much help referencing software would be. Chapter 5 shows you how to use EndNote, one example of reference management software.

Click the drop down below to review the terms learned from this chapter.

Copyright note:

• The quotation from Pautasso, M 2013, ‘Ten simple rules for writing a literature review’, PLoS Computational Biology is use under a CC-BY licence. 
• Content from the annotated article and tables are based on Schubert, MM, Astorino, TA & Azevedo, JJL 2013, ‘The effects of caffeinated ‘energy shots’ on time trial performance’, Nutrients, vol. 5, no. 6, pp. 2062–2075 (used under a CC-BY 3.0 licence ) and P ing, WC, Keong , CC & Bandyopadhyay, A 2010, ‘Effects of acute supplementation of caffeine on cardiorespiratory responses during endurance running in a hot and humid climate’, Indian Journal of Medical Research, vol. 132, pp. 36–41 (used under a CC-BY-NC-SA 4.0 licence ). 

Bain, A.R., Morrison, S.A., & Ainslie, P.N. (2014). Cerebral oxygenation and hyperthermia. Frontiers in Physiology, 5 , 92.

Pautasso, M. (2013). Ten simple rules for writing a literature review. PLoS Computational Biology, 9 (7), e1003149.

How To Do Science Copyright © 2022 by University of Southern Queensland is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• UConn Library
• Literature Review: The What, Why and How-to Guide
• Introduction

Literature Review: The What, Why and How-to Guide — Introduction

• Getting Started
• How to Pick a Topic
• Strategies to Find Sources
• Evaluating Sources & Lit. Reviews
• Tips for Writing Literature Reviews
• Writing Literature Review: Useful Sites
• Citation Resources
• Other Academic Writings

What are Literature Reviews?

So, what is a literature review? "A literature review is an account of what has been published on a topic by accredited scholars and researchers. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available, or a set of summaries." Taylor, D.  The literature review: A few tips on conducting it . University of Toronto Health Sciences Writing Centre.

Goals of Literature Reviews

What are the goals of creating a Literature Review?  A literature could be written to accomplish different aims:

• To develop a theory or evaluate an existing theory
• To summarize the historical or existing state of a research topic
• Identify a problem in a field of research 

Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews .  Review of General Psychology , 1 (3), 311-320.

What kinds of sources require a Literature Review?

• A research paper assigned in a course
• A thesis or dissertation
• A grant proposal
• An article intended for publication in a journal

All these instances require you to collect what has been written about your research topic so that you can demonstrate how your own research sheds new light on the topic.

Types of Literature Reviews

What kinds of literature reviews are written?

Narrative review: The purpose of this type of review is to describe the current state of the research on a specific topic/research and to offer a critical analysis of the literature reviewed. Studies are grouped by research/theoretical categories, and themes and trends, strengths and weakness, and gaps are identified. The review ends with a conclusion section which summarizes the findings regarding the state of the research of the specific study, the gaps identify and if applicable, explains how the author's research will address gaps identify in the review and expand the knowledge on the topic reviewed.

• Example : Predictors and Outcomes of U.S. Quality Maternity Leave: A Review and Conceptual Framework:  10.1177/08948453211037398  

Systematic review : "The authors of a systematic review use a specific procedure to search the research literature, select the studies to include in their review, and critically evaluate the studies they find." (p. 139). Nelson, L. K. (2013). Research in Communication Sciences and Disorders . Plural Publishing.

• Example : The effect of leave policies on increasing fertility: a systematic review:  10.1057/s41599-022-01270-w

Meta-analysis : "Meta-analysis is a method of reviewing research findings in a quantitative fashion by transforming the data from individual studies into what is called an effect size and then pooling and analyzing this information. The basic goal in meta-analysis is to explain why different outcomes have occurred in different studies." (p. 197). Roberts, M. C., & Ilardi, S. S. (2003). Handbook of Research Methods in Clinical Psychology . Blackwell Publishing.

• Example : Employment Instability and Fertility in Europe: A Meta-Analysis:  10.1215/00703370-9164737

Meta-synthesis : "Qualitative meta-synthesis is a type of qualitative study that uses as data the findings from other qualitative studies linked by the same or related topic." (p.312). Zimmer, L. (2006). Qualitative meta-synthesis: A question of dialoguing with texts .  Journal of Advanced Nursing , 53 (3), 311-318.

• Example : Women’s perspectives on career successes and barriers: A qualitative meta-synthesis:  10.1177/05390184221113735

Literature Reviews in the Health Sciences

• UConn Health subject guide on systematic reviews Explanation of the different review types used in health sciences literature as well as tools to help you find the right review type
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How To Structure Your Literature Review

3 options to help structure your chapter.

By: Amy Rommelspacher (PhD) | Reviewer: Dr Eunice Rautenbach | November 2020 (Updated May 2023)

Writing the literature review chapter can seem pretty daunting when you’re piecing together your dissertation or thesis. As  we’ve discussed before , a good literature review needs to achieve a few very important objectives – it should:

• Demonstrate your knowledge of the research topic
• Identify the gaps in the literature and show how your research links to these
• Provide the foundation for your conceptual framework (if you have one)
• Inform your own  methodology and research design

To achieve this, your literature review needs a well-thought-out structure . Get the structure of your literature review chapter wrong and you’ll struggle to achieve these objectives. Don’t worry though – in this post, we’ll look at how to structure your literature review for maximum impact (and marks!).

But wait – is this the right time?

Deciding on the structure of your literature review should come towards the end of the literature review process – after you have collected and digested the literature, but before you start writing the chapter. 

In other words, you need to first develop a rich understanding of the literature before you even attempt to map out a structure. There’s no use trying to develop a structure before you’ve fully wrapped your head around the existing research.

Equally importantly, you need to have a structure in place before you start writing , or your literature review will most likely end up a rambling, disjointed mess. 

Importantly, don’t feel that once you’ve defined a structure you can’t iterate on it. It’s perfectly natural to adjust as you engage in the writing process. As we’ve discussed before , writing is a way of developing your thinking, so it’s quite common for your thinking to change – and therefore, for your chapter structure to change – as you write. 

Need a helping hand?

Like any other chapter in your thesis or dissertation, your literature review needs to have a clear, logical structure. At a minimum, it should have three essential components – an  introduction , a  body   and a  conclusion . 

Let’s take a closer look at each of these.

1: The Introduction Section

Just like any good introduction, the introduction section of your literature review should introduce the purpose and layout (organisation) of the chapter. In other words, your introduction needs to give the reader a taste of what’s to come, and how you’re going to lay that out. Essentially, you should provide the reader with a high-level roadmap of your chapter to give them a taste of the journey that lies ahead.

Here’s an example of the layout visualised in a literature review introduction:

Your introduction should also outline your topic (including any tricky terminology or jargon) and provide an explanation of the scope of your literature review – in other words, what you  will   and  won’t   be covering (the delimitations ). This helps ringfence your review and achieve a clear focus . The clearer and narrower your focus, the deeper you can dive into the topic (which is typically where the magic lies). 

Depending on the nature of your project, you could also present your stance or point of view at this stage. In other words, after grappling with the literature you’ll have an opinion about what the trends and concerns are in the field as well as what’s lacking. The introduction section can then present these ideas so that it is clear to examiners that you’re aware of how your research connects with existing knowledge .

2: The Body Section

The body of your literature review is the centre of your work. This is where you’ll present, analyse, evaluate and synthesise the existing research. In other words, this is where you’re going to earn (or lose) the most marks. Therefore, it’s important to carefully think about how you will organise your discussion to present it in a clear way. 

The body of your literature review should do just as the description of this chapter suggests. It should “review” the literature – in other words, identify, analyse, and synthesise it. So, when thinking about structuring your literature review, you need to think about which structural approach will provide the best “review” for your specific type of research and objectives (we’ll get to this shortly).

There are (broadly speaking)  three options  for organising your literature review.

Option 1: Chronological (according to date)

Organising the literature chronologically is one of the simplest ways to structure your literature review. You start with what was published first and work your way through the literature until you reach the work published most recently. Pretty straightforward.

The benefit of this option is that it makes it easy to discuss the developments and debates in the field as they emerged over time. Organising your literature chronologically also allows you to highlight how specific articles or pieces of work might have changed the course of the field – in other words, which research has had the most impact . Therefore, this approach is very useful when your research is aimed at understanding how the topic has unfolded over time and is often used by scholars in the field of history. That said, this approach can be utilised by anyone that wants to explore change over time .

For example , if a student of politics is investigating how the understanding of democracy has evolved over time, they could use the chronological approach to provide a narrative that demonstrates how this understanding has changed through the ages.

Here are some questions you can ask yourself to help you structure your literature review chronologically.

• What is the earliest literature published relating to this topic?
• How has the field changed over time? Why?
• What are the most recent discoveries/theories?

In some ways, chronology plays a part whichever way you decide to structure your literature review, because you will always, to a certain extent, be analysing how the literature has developed. However, with the chronological approach, the emphasis is very firmly on how the discussion has evolved over time , as opposed to how all the literature links together (which we’ll discuss next ).

Option 2: Thematic (grouped by theme)

The thematic approach to structuring a literature review means organising your literature by theme or category – for example, by independent variables (i.e. factors that have an impact on a specific outcome).

As you’ve been collecting and synthesising literature , you’ll likely have started seeing some themes or patterns emerging. You can then use these themes or patterns as a structure for your body discussion. The thematic approach is the most common approach and is useful for structuring literature reviews in most fields.

For example, if you were researching which factors contributed towards people trusting an organisation, you might find themes such as consumers’ perceptions of an organisation’s competence, benevolence and integrity. Structuring your literature review thematically would mean structuring your literature review’s body section to discuss each of these themes, one section at a time.

Here are some questions to ask yourself when structuring your literature review by themes:

• Are there any patterns that have come to light in the literature?
• What are the central themes and categories used by the researchers?
• Do I have enough evidence of these themes?

PS – you can see an example of a thematically structured literature review in our literature review sample walkthrough video here.

Option 3: Methodological

The methodological option is a way of structuring your literature review by the research methodologies used . In other words, organising your discussion based on the angle from which each piece of research was approached – for example, qualitative , quantitative or mixed  methodologies.

Structuring your literature review by methodology can be useful if you are drawing research from a variety of disciplines and are critiquing different methodologies. The point of this approach is to question  how  existing research has been conducted, as opposed to  what  the conclusions and/or findings the research were.

For example, a sociologist might centre their research around critiquing specific fieldwork practices. Their literature review will then be a summary of the fieldwork methodologies used by different studies.

Here are some questions you can ask yourself when structuring your literature review according to methodology:

• Which methodologies have been utilised in this field?
• Which methodology is the most popular (and why)?
• What are the strengths and weaknesses of the various methodologies?
• How can the existing methodologies inform my own methodology?

3: The Conclusion Section

Once you’ve completed the body section of your literature review using one of the structural approaches we discussed above, you’ll need to “wrap up” your literature review and pull all the pieces together to set the direction for the rest of your dissertation or thesis.

The conclusion is where you’ll present the key findings of your literature review. In this section, you should emphasise the research that is especially important to your research questions and highlight the gaps that exist in the literature. Based on this, you need to make it clear what you will add to the literature – in other words, justify your own research by showing how it will help fill one or more of the gaps you just identified.

Last but not least, if it’s your intention to develop a conceptual framework for your dissertation or thesis, the conclusion section is a good place to present this.

Example: Thematically Structured Review

In the video below, we unpack a literature review chapter so that you can see an example of a thematically structure review in practice.

Let’s Recap

In this article, we’ve  discussed how to structure your literature review for maximum impact. Here’s a quick recap of what  you need to keep in mind when deciding on your literature review structure:

• Just like other chapters, your literature review needs a clear introduction , body and conclusion .
• The introduction section should provide an overview of what you will discuss in your literature review.
• The body section of your literature review can be organised by chronology , theme or methodology . The right structural approach depends on what you’re trying to achieve with your research.
• The conclusion section should draw together the key findings of your literature review and link them to your research questions.

If you’re ready to get started, be sure to download our free literature review template to fast-track your chapter outline.

Psst… there’s more!

This post is an extract from our bestselling short course, Literature Review Bootcamp . If you want to work smart, you don't want to miss this .

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27 Comments

Great work. This is exactly what I was looking for and helps a lot together with your previous post on literature review. One last thing is missing: a link to a great literature chapter of an journal article (maybe with comments of the different sections in this review chapter). Do you know any great literature review chapters?

I agree with you Marin… A great piece

I agree with Marin. This would be quite helpful if you annotate a nicely structured literature from previously published research articles.

Awesome article for my research.

I thank you immensely for this wonderful guide

It is indeed thought and supportive work for the futurist researcher and students

Very educative and good time to get guide. Thank you

Great work, very insightful. Thank you.

Thanks for this wonderful presentation. My question is that do I put all the variables into a single conceptual framework or each hypothesis will have it own conceptual framework?

Thank you very much, very helpful

This is very educative and precise . Thank you very much for dropping this kind of write up .

Pheeww, so damn helpful, thank you for this informative piece.

I’m doing a research project topic ; stool analysis for parasitic worm (enteric) worm, how do I structure it, thanks.

comprehensive explanation. Help us by pasting the URL of some good “literature review” for better understanding.

great piece. thanks for the awesome explanation. it is really worth sharing. I have a little question, if anyone can help me out, which of the options in the body of literature can be best fit if you are writing an architectural thesis that deals with design?

I am doing a research on nanofluids how can l structure it?

Beautifully clear.nThank you!

Lucid! Thankyou!

Brilliant work, well understood, many thanks

I like how this was so clear with simple language 😊😊 thank you so much 😊 for these information 😊

Insightful. I was struggling to come up with a sensible literature review but this has been really helpful. Thank you!

You have given thought-provoking information about the review of the literature.

Thank you. It has made my own research better and to impart your work to students I teach

I learnt a lot from this teaching. It’s a great piece.

I am doing research on EFL teacher motivation for his/her job. How Can I structure it? Is there any detailed template, additional to this?

You are so cool! I do not think I’ve read through something like this before. So nice to find somebody with some genuine thoughts on this issue. Seriously.. thank you for starting this up. This site is one thing that is required on the internet, someone with a little originality!

I’m asked to do conceptual, theoretical and empirical literature, and i just don’t know how to structure it

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Writing a Scientific Review Article: Comprehensive Insights for Beginners

Ayodeji amobonye.

1 Department of Biotechnology and Food Science, Faculty of Applied Sciences, Durban University of Technology, P.O. Box 1334, KwaZulu-Natal, Durban 4000, South Africa

2 Writing Centre, Durban University of Technology, P.O. Box 1334 KwaZulu-Natal, Durban 4000, South Africa

Japareng Lalung

3 School of Industrial Technology, Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia

Santhosh Pillai

Associated data.

The data and materials that support the findings of this study are available from the corresponding author upon reasonable request.

Review articles present comprehensive overview of relevant literature on specific themes and synthesise the studies related to these themes, with the aim of strengthening the foundation of knowledge and facilitating theory development. The significance of review articles in science is immeasurable as both students and researchers rely on these articles as the starting point for their research. Interestingly, many postgraduate students are expected to write review articles for journal publications as a way of demonstrating their ability to contribute to new knowledge in their respective fields. However, there is no comprehensive instructional framework to guide them on how to analyse and synthesise the literature in their niches into publishable review articles. The dearth of ample guidance or explicit training results in students having to learn all by themselves, usually by trial and error, which often leads to high rejection rates from publishing houses. Therefore, this article seeks to identify these challenges from a beginner's perspective and strives to plug the identified gaps and discrepancies. Thus, the purpose of this paper is to serve as a systematic guide for emerging scientists and to summarise the most important information on how to write and structure a publishable review article.

1. Introduction

Early scientists, spanning from the Ancient Egyptian civilization to the Scientific Revolution of the 16 th /17 th century, based their research on intuitions, personal observations, and personal insights. Thus, less time was spent on background reading as there was not much literature to refer to. This is well illustrated in the case of Sir Isaac Newton's apple tree and the theory of gravity, as well as Gregor Mendel's pea plants and the theory of inheritance. However, with the astronomical expansion in scientific knowledge and the emergence of the information age in the last century, new ideas are now being built on previously published works, thus the periodic need to appraise the huge amount of already published literature [ 1 ]. According to Birkle et al. [ 2 ], the Web of Science—an authoritative database of research publications and citations—covered more than 80 million scholarly materials. Hence, a critical review of prior and relevant literature is indispensable for any research endeavour as it provides the necessary framework needed for synthesising new knowledge and for highlighting new insights and perspectives [ 3 ].

Review papers are generally considered secondary research publications that sum up already existing works on a particular research topic or question and relate them to the current status of the topic. This makes review articles distinctly different from scientific research papers. While the primary aim of the latter is to develop new arguments by reporting original research, the former is focused on summarising and synthesising previous ideas, studies, and arguments, without adding new experimental contributions. Review articles basically describe the content and quality of knowledge that are currently available, with a special focus on the significance of the previous works. To this end, a review article cannot simply reiterate a subject matter, but it must contribute to the field of knowledge by synthesising available materials and offering a scholarly critique of theory [ 4 ]. Typically, these articles critically analyse both quantitative and qualitative studies by scrutinising experimental results, the discussion of the experimental data, and in some instances, previous review articles to propose new working theories. Thus, a review article is more than a mere exhaustive compilation of all that has been published on a topic; it must be a balanced, informative, perspective, and unbiased compendium of previous studies which may also include contrasting findings, inconsistencies, and conventional and current views on the subject [ 5 ].

Hence, the essence of a review article is measured by what is achieved, what is discovered, and how information is communicated to the reader [ 6 ]. According to Steward [ 7 ], a good literature review should be analytical, critical, comprehensive, selective, relevant, synthetic, and fully referenced. On the other hand, a review article is considered to be inadequate if it is lacking in focus or outcome, overgeneralised, opinionated, unbalanced, and uncritical [ 7 ]. Most review papers fail to meet these standards and thus can be viewed as mere summaries of previous works in a particular field of study. In one of the few studies that assessed the quality of review articles, none of the 50 papers that were analysed met the predefined criteria for a good review [ 8 ]. However, beginners must also realise that there is no bad writing in the true sense; there is only writing in evolution and under refinement. Literally, every piece of writing can be improved upon, right from the first draft until the final published manuscript. Hence, a paper can only be referred to as bad and unfixable when the author is not open to corrections or when the writer gives up on it.

According to Peat et al. [ 9 ], “everything is easy when you know how,” a maxim which applies to scientific writing in general and review writing in particular. In this regard, the authors emphasized that the writer should be open to learning and should also follow established rules instead of following a blind trial-and-error approach. In contrast to the popular belief that review articles should only be written by experienced scientists and researchers, recent trends have shown that many early-career scientists, especially postgraduate students, are currently expected to write review articles during the course of their studies. However, these scholars have little or no access to formal training on how to analyse and synthesise the research literature in their respective fields [ 10 ]. Consequently, students seeking guidance on how to write or improve their literature reviews are less likely to find published works on the subject, particularly in the science fields. Although various publications have dealt with the challenges of searching for literature, or writing literature reviews for dissertation/thesis purposes, there is little or no information on how to write a comprehensive review article for publication. In addition to the paucity of published information to guide the potential author, the lack of understanding of what constitutes a review paper compounds their challenges. Thus, the purpose of this paper is to serve as a guide for writing review papers for journal publishing. This work draws on the experience of the authors to assist early-career scientists/researchers in the “hard skill” of authoring review articles. Even though there is no single path to writing scientifically, or to writing reviews in particular, this paper attempts to simplify the process by looking at this subject from a beginner's perspective. Hence, this paper highlights the differences between the types of review articles in the sciences while also explaining the needs and purpose of writing review articles. Furthermore, it presents details on how to search for the literature as well as how to structure the manuscript to produce logical and coherent outputs. It is hoped that this work will ease prospective scientific writers into the challenging but rewarding art of writing review articles.

2. Benefits of Review Articles to the Author

Analysing literature gives an overview of the “WHs”: WHat has been reported in a particular field or topic, WHo the key writers are, WHat are the prevailing theories and hypotheses, WHat questions are being asked (and answered), and WHat methods and methodologies are appropriate and useful [ 11 ]. For new or aspiring researchers in a particular field, it can be quite challenging to get a comprehensive overview of their respective fields, especially the historical trends and what has been studied previously. As such, the importance of review articles to knowledge appraisal and contribution cannot be overemphasised, which is reflected in the constant demand for such articles in the research community. However, it is also important for the author, especially the first-time author, to recognise the importance of his/her investing time and effort into writing a quality review article.

Generally, literature reviews are undertaken for many reasons, mainly for publication and for dissertation purposes. The major purpose of literature reviews is to provide direction and information for the improvement of scientific knowledge. They also form a significant component in the research process and in academic assessment [ 12 ]. There may be, however, a thin line between a dissertation literature review and a published review article, given that with some modifications, a literature review can be transformed into a legitimate and publishable scholarly document. According to Gülpınar and Güçlü [ 6 ], the basic motivation for writing a review article is to make a comprehensive synthesis of the most appropriate literature on a specific research inquiry or topic. Thus, conducting a literature review assists in demonstrating the author's knowledge about a particular field of study, which may include but not be limited to its history, theories, key variables, vocabulary, phenomena, and methodologies [ 10 ]. Furthermore, publishing reviews is beneficial as it permits the researchers to examine different questions and, as a result, enhances the depth and diversity of their scientific reasoning [ 1 ]. In addition, writing review articles allows researchers to share insights with the scientific community while identifying knowledge gaps to be addressed in future research. The review writing process can also be a useful tool in training early-career scientists in leadership, coordination, project management, and other important soft skills necessary for success in the research world [ 13 ]. Another important reason for authoring reviews is that such publications have been observed to be remarkably influential, extending the reach of an author in multiple folds of what can be achieved by primary research papers [ 1 ]. The trend in science is for authors to receive more citations from their review articles than from their original research articles. According to Miranda and Garcia-Carpintero [ 14 ], review articles are, on average, three times more frequently cited than original research articles; they also asserted that a 20% increase in review authorship could result in a 40–80% increase in citations of the author. As a result, writing reviews can significantly impact a researcher's citation output and serve as a valuable channel to reach a wider scientific audience. In addition, the references cited in a review article also provide the reader with an opportunity to dig deeper into the topic of interest. Thus, review articles can serve as a valuable repository for consultation, increasing the visibility of the authors and resulting in more citations.

3. Types of Review Articles

The first step in writing a good literature review is to decide on the particular type of review to be written; hence, it is important to distinguish and understand the various types of review articles. Although scientific review articles have been classified according to various schemes, however, they are broadly categorised into narrative reviews, systematic reviews, and meta-analyses [ 15 ]. It was observed that more authors—as well as publishers—were leaning towards systematic reviews and meta-analysis while downplaying narrative reviews; however, the three serve different aims and should all be considered equally important in science [ 1 ]. Bibliometric reviews and patent reviews, which are closely related to meta-analysis, have also gained significant attention recently. However, from another angle, a review could also be of two types. In the first class, authors could deal with a widely studied topic where there is already an accumulated body of knowledge that requires analysis and synthesis [ 3 ]. At the other end of the spectrum, the authors may have to address an emerging issue that would benefit from exposure to potential theoretical foundations; hence, their contribution would arise from the fresh theoretical foundations proposed in developing a conceptual model [ 3 ].

3.1. Narrative Reviews

Narrative reviewers are mainly focused on providing clarification and critical analysis on a particular topic or body of literature through interpretative synthesis, creativity, and expert judgement. According to Green et al. [ 16 ], a narrative review can be in the form of editorials, commentaries, and narrative overviews. However, editorials and commentaries are usually expert opinions; hence, a beginner is more likely to write a narrative overview, which is more general and is also referred to as an unsystematic narrative review. Similarly, the literature review section of most dissertations and empirical papers is typically narrative in nature. Typically, narrative reviews combine results from studies that may have different methodologies to address different questions or to formulate a broad theoretical formulation [ 1 ]. They are largely integrative as strong focus is placed on the assimilation and synthesis of various aspects in the review, which may involve comparing and contrasting research findings or deriving structured implications [ 17 ]. In addition, they are also qualitative studies because they do not follow strict selection processes; hence, choosing publications is relatively more subjective and unsystematic [ 18 ]. However, despite their popularity, there are concerns about their inherent subjectivity. In many instances, when the supporting data for narrative reviews are examined more closely, the evaluations provided by the author(s) become quite questionable [ 19 ]. Nevertheless, if the goal of the author is to formulate a new theory that connects diverse strands of research, a narrative method is most appropriate.

3.2. Systematic Reviews

In contrast to narrative reviews, which are generally descriptive, systematic reviews employ a systematic approach to summarise evidence on research questions. Hence, systematic reviews make use of precise and rigorous criteria to identify, evaluate, and subsequently synthesise all relevant literature on a particular topic [ 12 , 20 ]. As a result, systematic reviews are more likely to inspire research ideas by identifying knowledge gaps or inconsistencies, thus helping the researcher to clearly define the research hypotheses or questions [ 21 ]. Furthermore, systematic reviews may serve as independent research projects in their own right, as they follow a defined methodology to search and combine reliable results to synthesise a new database that can be used for a variety of purposes [ 22 ]. Typically, the peculiarities of the individual reviewer, different search engines, and information databases used all ensure that no two searches will yield the same systematic results even if the searches are conducted simultaneously and under identical criteria [ 11 ]. Hence, attempts are made at standardising the exercise via specific methods that would limit bias and chance effects, prevent duplications, and provide more accurate results upon which conclusions and decisions can be made.

The most established of these methods is the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines which objectively defined statements, guidelines, reporting checklists, and flowcharts for undertaking systematic reviews as well as meta-analysis [ 23 ]. Though mainly designed for research in medical sciences, the PRISMA approach has gained wide acceptance in other fields of science and is based on eight fundamental propositions. These include the explicit definition of the review question, an unambiguous outline of the study protocol, an objective and exhaustive systematic review of reputable literature, and an unambiguous identification of included literature based on defined selection criteria [ 24 ]. Other considerations include an unbiased appraisal of the quality of the selected studies (literature), organic synthesis of the evidence of the study, preparation of the manuscript based on the reporting guidelines, and periodic update of the review as new data emerge [ 24 ]. Other methods such as PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols), MOOSE (Meta-analysis Of Observational Studies in Epidemiology), and ROSES (Reporting Standards for Systematic Evidence Syntheses) have since been developed for systematic reviews (and meta-analysis), with most of them being derived from PRISMA.

Consequently, systematic reviews—unlike narrative reviews—must contain a methodology section which in addition to all that was highlighted above must fully describe the precise criteria used in formulating the research question and setting the inclusion or exclusion criteria used in selecting/accessing the literature. Similarly, the criteria for evaluating the quality of the literature included in the review as well as for analysing, synthesising, and disseminating the findings must be fully described in the methodology section.

3.3. Meta-Analysis

Meta-analyses are considered as more specialised forms of systematic reviews. Generally, they combine the results of many studies that use similar or closely related methods to address the same question or share a common quantitative evaluation method [ 25 ]. However, meta-analyses are also a step higher than other systematic reviews as they are focused on numerical data and involve the use of statistics in evaluating different studies and synthesising new knowledge. The major advantage of this type of review is the increased statistical power leading to more reliable results for inferring modest associations and a more comprehensive understanding of the true impact of a research study [ 26 ]. Unlike in traditional systematic reviews, research topics covered in meta-analyses must be mature enough to allow the inclusion of sufficient homogeneous empirical research in terms of subjects, interventions, and outcomes [ 27 , 28 ].

Being an advanced form of systematic review, meta-analyses must also have a distinct methodology section; hence, the standard procedures involved in the traditional systematic review (especially PRISMA) also apply in meta-analyses [ 23 ]. In addition to the common steps in formulating systematic reviews, meta-analyses are required to describe how nested and missing data are handled, the effect observed in each study, the confidence interval associated with each synthesised effect, and any potential for bias presented within the sample(s) [ 17 ]. According to Paul and Barari [ 28 ], a meta-analysis must also detail the final sample, the meta-analytic model, and the overall analysis, moderator analysis, and software employed. While the overall analysis involves the statistical characterization of the relationships between variables in the meta-analytic framework and their significance, the moderator analysis defines the different variables that may affect variations in the original studies [ 28 , 29 ]. It must also be noted that the accuracy and reliability of meta-analyses have both been significantly enhanced by the incorporation of statistical approaches such as Bayesian analysis [ 30 ], network analysis [ 31 ], and more recently, machine learning [ 32 ].

3.4. Bibliometric Review

A bibliometric review, commonly referred to as bibliometric analysis, is a systematic evaluation of published works within a specific field or discipline [ 33 ]. This bibliometric methodology involves the use of quantitative methods to analyse bibliometric data such as the characteristics and numbers of publications, units of citations, authorship, co-authorship, and journal impact factors [ 34 ]. Academics use bibliometric analysis with different objectives in mind, which includes uncovering emerging trends in article and journal performance, elaborating collaboration patterns and research constituents, evaluating the impact and influence of particular authors, publications, or research groups, and highlighting the intellectual framework of a certain field [ 35 ]. It is also used to inform policy and decision-making. Similarly to meta-analysis, bibliometric reviews rely upon quantitative techniques, thus avoiding the interpretation bias that could arise from the qualitative techniques of other types of reviews [ 36 ]. However, while bibliometric analysis synthesises the bibliometric and intellectual structure of a field by examining the social and structural linkages between various research parts, meta-analysis focuses on summarising empirical evidence by probing the direction and strength of effects and relationships among variables, especially in open research questions [ 37 , 38 ]. However, similarly to systematic review and meta-analysis, a bibliometric review also requires a well-detailed methodology section. The amount of data to be analysed in bibliometric analysis is quite massive, running to hundreds and tens of thousands in some cases. Although the data are objective in nature (e.g., number of citations and publications and occurrences of keywords and topics), the interpretation is usually carried out through both objective (e.g., performance analysis) and subjective (e.g., thematic analysis) evaluations [ 35 ]. However, the invention and availability of bibliometric software such as BibExcel, Gephi, Leximancer, and VOSviewer and scientific databases such as Dimensions, Web of Science, and Scopus have made this type of analysis more feasible.

3.5. Patent Review

Patent reviews provide a comprehensive analysis and critique of a specific patent or a group of related patents, thus presenting a concise understanding of the technology or innovation that is covered by the patent [ 39 ]. This type of article is useful for researchers as it also enhances their understanding of the legal, technical, and commercial aspects of an intellectual property/innovation; in addition, it is also important for stakeholders outside the research community including IP (intellectual property) specialists, legal professionals, and technology-transfer officers [ 40 ]. Typically, patent reviews encompass the scope, background, claims, legal implications, technical specifications, and potential commercial applications of the patent(s). The article may also include a discussion of the patent's strengths and weaknesses, as well as its potential impact on the industry or field in which it operates. Most times, reviews are time specified, they may be regionalised, and the data are usually retrieved via patent searches on databases such as that of the European Patent Office ( https://www.epo.org/searching.html ), United States Patent and Trademark Office ( https://patft.uspto.gov/ ), the World Intellectual Property Organization's PATENTSCOPE ( https://patentscope.wipo.int/search/en/structuredSearch.jsf ), Google Patent ( https://www.google.com/?tbm=pts ), and China National Intellectual Property Administration ( https://pss-system.cponline.cnipa.gov.cn/conventionalSearch ). According to Cerimi et al. [ 41 ], the retrieved data and analysed may include the patent number, patent status, filing date, application date, grant dates, inventor, assignee, and pending applications. While data analysis is usually carried out by general data software such as Microsoft Excel, an intelligence software solely dedicated to patent research and analysis, Orbit Intelligence has been found to be more efficient [ 39 ]. It is also mandatory to include a methodology section in a patent review, and this should be explicit, thorough, and precise to allow a clear understanding of how the analysis was carried out and how the conclusions were arrived at.

4. Searching Literature

One of the most challenging tasks in writing a review article on a subject is the search for relevant literature to populate the manuscript as the author is required to garner information from an endless number of sources. This is even more challenging as research outputs have been increasing astronomically, especially in the last decade, with thousands of new articles published annually in various fields. It is therefore imperative that the author must not only be aware of the overall trajectory in a field of investigation but must also be cognizant of recent studies so as not to publish outdated research or review articles. Basically, the search for the literature involves a coherent conceptual structuring of the topic itself and a thorough collation of evidence under the common themes which might reflect the histories, conflicts, standoffs, revolutions, and/or evolutions in the field [ 7 ]. To start the search process, the author must carefully identify and select broad keywords relevant to the subject; subsequently, the keywords should be developed to refine the search into specific subheadings that would facilitate the structure of the review.

Two main tactics have been identified for searching the literature, namely, systematic and snowballing [ 42 ]. The systematic approach involves searching literature with specific keywords (for example, cancer, antioxidant, and nanoparticles), which leads to an almost unmanageable and overwhelming list of possible sources [ 43 ]. The snowballing approach, however, involves the identification of a particular publication, followed by the compilation of a bibliography of articles based on the reference list of the identified publication [ 44 ]. Many times, it might be necessary to combine both approaches, but irrespective, the author must keep an accurate track and record of papers cited in the search. A simple and efficient strategy for populating the bibliography of review articles is to go through the abstract (and sometimes the conclusion) of a paper; if the abstract is related to the topic of discourse, the author might go ahead and read the entire article; otherwise, he/she is advised to move on [ 45 ]. Winchester and Salji [ 5 ] noted that to learn the background of the subject/topic to be reviewed, starting literature searches with academic textbooks or published review articles is imperative, especially for beginners. Furthermore, it would also assist in compiling the list of keywords, identifying areas of further exploration, and providing a glimpse of the current state of the research. However, past reviews ideally are not to serve as the foundation of a new review as they are written from someone else's viewpoint, which might have been tainted with some bias. Fortunately, the accessibility and search for the literature have been made relatively easier than they were a few decades ago as the current information age has placed an enormous volume of knowledge right at our fingertips [ 46 ]. Nevertheless, when gathering the literature from the Internet, authors should exercise utmost caution as much of the information may not be verified or peer-reviewed and thus may be unregulated and unreliable. For instance, Wikipedia, despite being a large repository of information with more than 6.7 million articles in the English language alone, is considered unreliable for scientific literature reviews, due to its openness to public editing [ 47 ]. However, in addition to peer-reviewed journal publications—which are most ideal—reviews can also be drawn from a wide range of other sources such as technical documents, in-house reports, conference abstracts, and conference proceedings. Similarly, “Google Scholar”—as against “Google” and other general search engines—is more appropriate as its searches are restricted to only academic articles produced by scholarly societies or/and publishers [ 48 ]. Furthermore, the various electronic databases, such as ScienceDirect, Web of Science, PubMed, and MEDLINE, many of which focus on specific fields of research, are also ideal options [ 49 ]. Advancement in computer indexing has remarkably expanded the ease and ability to search large databases for every potentially relevant article. In addition to searching by topic, literature search can be modified by time; however, there must be a balance between old papers and recent ones. The general consensus in science is that publications less than five years old are considered recent.

It is important, especially in systematic reviews and meta-analyses, that the specific method of running the computer searches be properly documented as there is the need to include this in the method (methodology) section of such papers. Typically, the method details the keywords, databases explored, search terms used, and the inclusion/exclusion criteria applied in the selection of data and any other specific decision/criteria. All of these will ensure the reproducibility and thoroughness of the search and the selection procedure. However, Randolph [ 10 ] noted that Internet searches might not give the exhaustive list of articles needed for a review article; hence, it is advised that authors search through the reference lists of articles that were obtained initially from the Internet search. After determining the relevant articles from the list, the author should read through the references of these articles and repeat the cycle until saturation is reached [ 10 ]. After populating the articles needed for the literature review, the next step is to analyse them individually and in their whole entirety. A systematic approach to this is to identify the key information within the papers, examine them in depth, and synthesise original perspectives by integrating the information and making inferences based on the findings. In this regard, it is imperative to link one source to the other in a logical manner, for instance, taking note of studies with similar methodologies, papers that agree, or results that are contradictory [ 42 ].

5. Structuring the Review Article

The title and abstract are the main selling points of a review article, as most readers will only peruse these two elements and usually go on to read the full paper if they are drawn in by either or both of the two. Tullu [ 50 ] recommends that the title of a scientific paper “should be descriptive, direct, accurate, appropriate, interesting, concise, precise, unique, and not be misleading.” In addition to providing “just enough details” to entice the reader, words in the titles are also used by electronic databases, journal websites, and search engines to index and retrieve a particular paper during a search [ 51 ]. Titles are of different types and must be chosen according to the topic under review. They are generally classified as descriptive, declarative, or interrogative and can also be grouped into compound, nominal, or full-sentence titles [ 50 ]. The subject of these categorisations has been extensively discussed in many articles; however, the reader must also be aware of the compound titles, which usually contain a main title and a subtitle. Typically, subtitles provide additional context—to the main title—and they may specify the geographic scope of the research, research methodology, or sample size [ 52 ].

Just like primary research articles, there are many debates about the optimum length of a review article's title. However, the general consensus is to keep the title as brief as possible while not being too general. A title length between 10 and 15 words is recommended, since longer titles can be more challenging to comprehend. Paiva et al. [ 53 ] observed that articles which contain 95 characters or less get more views and citations. However, emphasis must be placed on conciseness as the audience will be more satisfied if they can understand what exactly the review has contributed to the field, rather than just a hint about the general topic area. Authors should also endeavour to stick to the journal's specific requirements, especially regarding the length of the title and what they should or should not contain [ 9 ]. Thus, avoidance of filler words such as “a review on/of,” “an observation of,” or “a study of” is a very simple way to limit title length. In addition, abbreviations or acronyms should be avoided in the title, except the standard or commonly interpreted ones such as AIDS, DNA, HIV, and RNA. In summary, to write an effective title, the authors should consider the following points. What is the paper about? What was the methodology used? What were the highlights and major conclusions? Subsequently, the author should list all the keywords from these answers, construct a sentence from these keywords, and finally delete all redundant words from the sentence title. It is also possible to gain some ideas by scanning indices and article titles in major journals in the field. It is important to emphasise that a title is not chosen and set in stone, and the title is most likely to be continually revised and adjusted until the end of the writing process.

5.2. Abstract

The abstract, also referred to as the synopsis, is a summary of the full research paper; it is typically independent and can stand alone. For most readers, a publication does not exist beyond the abstract, partly because abstracts are often the only section of a paper that is made available to the readers at no cost, whereas the full paper may attract a payment or subscription [ 54 ]. Thus, the abstract is supposed to set the tone for the few readers who wish to read the rest of the paper. It has also been noted that the abstract gives the first impression of a research work to journal editors, conference scientific committees, or referees, who might outright reject the paper if the abstract is poorly written or inadequate [ 50 ]. Hence, it is imperative that the abstract succinctly represents the entire paper and projects it positively. Just like the title, abstracts have to be balanced, comprehensive, concise, functional, independent, precise, scholarly, and unbiased and not be misleading [ 55 ]. Basically, the abstract should be formulated using keywords from all the sections of the main manuscript. Thus, it is pertinent that the abstract conveys the focus, key message, rationale, and novelty of the paper without any compromise or exaggeration. Furthermore, the abstract must be consistent with the rest of the paper; as basic as this instruction might sound, it is not to be taken for granted. For example, a study by Vrijhoef and Steuten [ 56 ] revealed that 18–68% of 264 abstracts from some scientific journals contained information that was inconsistent with the main body of the publications.

Abstracts can either be structured or unstructured; in addition, they can further be classified as either descriptive or informative. Unstructured abstracts, which are used by many scientific journals, are free flowing with no predefined subheadings, while structured abstracts have specific subheadings/subsections under which the abstract needs to be composed. Structured abstracts have been noted to be more informative and are usually divided into subsections which include the study background/introduction, objectives, methodology design, results, and conclusions [ 57 ]. No matter the style chosen, the author must carefully conform to the instructions provided by the potential journal of submission, which may include but are not limited to the format, font size/style, word limit, and subheadings [ 58 ]. The word limit for abstracts in most scientific journals is typically between 150 and 300 words. It is also a general rule that abstracts do not contain any references whatsoever.

Typically, an abstract should be written in the active voice, and there is no such thing as a perfect abstract as it could always be improved on. It is advised that the author first makes an initial draft which would contain all the essential parts of the paper, which could then be polished subsequently. The draft should begin with a brief background which would lead to the research questions. It might also include a general overview of the methodology used (if applicable) and importantly, the major results/observations/highlights of the review paper. The abstract should end with one or few sentences about any implications, perspectives, or future research that may be developed from the review exercise. Finally, the authors should eliminate redundant words and edit the abstract to the correct word count permitted by the journal [ 59 ]. It is always beneficial to read previous abstracts published in the intended journal, related topics/subjects from other journals, and other reputable sources. Furthermore, the author should endeavour to get feedback on the abstract especially from peers and co-authors. As the abstract is the face of the whole paper, it is best that it is the last section to be finalised, as by this time, the author would have developed a clearer understanding of the findings and conclusions of the entire paper.

5.3. Graphical Abstracts

Since the mid-2000s, an increasing number of journals now require authors to provide a graphical abstract (GA) in addition to the traditional written abstract, to increase the accessibility of scientific publications to readers [ 60 ]. A study showed that publications with GA performed better than those without it, when the abstract views, total citations, and downloads were compared [ 61 ]. However, the GA should provide “a single, concise pictorial, and visual summary of the main findings of an article” [ 62 ]. Although they are meant to be a stand-alone summary of the whole paper, it has been noted that they are not so easily comprehensible without having read through the traditionally written abstract [ 63 ]. It is important to note that, like traditional abstracts, many reputable journals require GAs to adhere to certain specifications such as colour, dimension, quality, file size, and file format (usually JPEG/JPG, PDF, PNG, or TIFF). In addition, it is imperative to use engaging and accurate figures, all of which must be synthesised in order to accurately reflect the key message of the paper. Currently, there are various online or downloadable graphical tools that can be used for creating GAs, such as Microsoft Paint or PowerPoint, Mindthegraph, ChemDraw, CorelDraw, and BioRender.

5.4. Keywords

As a standard practice, journals require authors to select 4–8 keywords (or phrases), which are typically listed below the abstract. A good set of keywords will enable indexers and search engines to find relevant papers more easily and can be considered as a very concise abstract [ 64 ]. According to Dewan and Gupta [ 51 ], the selection of appropriate keywords will significantly enhance the retrieval, accession, and consequently, the citation of the review paper. Ideally, keywords can be variants of the terms/phrases used in the title, the abstract, and the main text, but they should ideally not be the exact words in the main title. Choosing the most appropriate keywords for a review article involves listing down the key terms and phrases in the article, including abbreviations. Subsequently, a quick review of the glossary/vocabulary/term list or indexing standard in the specific discipline will assist in selecting the best and most precise keywords that match those used in the databases from the list drawn. In addition, the keywords should not be broad or general terms (e.g., DNA, biology, and enzymes) but must be specific to the field or subfield of study as well as to the particular paper [ 65 ].

5.5. Introduction

The introduction of an article is the first major section of the manuscript, and it presents basic information to the reader without compelling them to study past publications. In addition, the introduction directs the reader to the main arguments and points developed in the main body of the article while clarifying the current state of knowledge in that particular area of research [ 12 ]. The introduction part of a review article is usually sectionalised into background information, a description of the main topic and finally a statement of the main purpose of the review [ 66 ]. Authors may begin the introduction with brief general statements—which provide background knowledge on the subject matter—that lead to more specific ones [ 67 ]. It is at this point that the reader's attention must be caught as the background knowledge must highlight the importance and justification for the subject being discussed, while also identifying the major problem to be addressed [ 68 ]. In addition, the background should be broad enough to attract even nonspecialists in the field to maximise the impact and widen the reach of the article. All of these should be done in the light of current literature; however, old references may also be used for historical purposes. A very important aspect of the introduction is clearly stating and establishing the research problem(s) and how a review of the particular topic contributes to those problem(s). Thus, the research gap which the paper intends to fill, the limitations of previous works and past reviews, if available, and the new knowledge to be contributed must all be highlighted. Inadequate information and the inability to clarify the problem will keep readers (who have the desire to obtain new information) from reading beyond the introduction [ 69 ]. It is also pertinent that the author establishes the purpose of reviewing the literature and defines the scope as well as the major synthesised point of view. Furthermore, a brief insight into the criteria used to select, evaluate, and analyse the literature, as well as the outline or sequence of the review, should be provided in the introduction. Subsequently, the specific objectives of the review article must be presented. The last part of the “introduction” section should focus on the solution, the way forward, the recommendations, and the further areas of research as deduced from the whole review process. According to DeMaria [ 70 ], clearly expressed or recommended solutions to an explicitly revealed problem are very important for the wholesomeness of the “introduction” section. It is believed that following these steps will give readers the opportunity to track the problems and the corresponding solution from their own perspective in the light of current literature. As against some suggestions that the introduction should be written only in present tenses, it is also believed that it could be done with other tenses in addition to the present tense. In this regard, general facts should be written in the present tense, specific research/work should be in the past tense, while the concluding statement should be in the past perfect or simple past. Furthermore, many of the abbreviations to be used in the rest of the manuscript and their explanations should be defined in this section.

5.6. Methodology

Writing a review article is equivalent to conducting a research study, with the information gathered by the author (reviewer) representing the data. Like all major studies, it involves conceptualisation, planning, implementation, and dissemination [ 71 ], all of which may be detailed in a methodology section, if necessary. Hence, the methodological section of a review paper (which can also be referred to as the review protocol) details how the relevant literature was selected and how it was analysed as well as summarised. The selection details may include, but are not limited to, the database consulted and the specific search terms used together with the inclusion/exclusion criteria. As earlier highlighted in Section 3 , a description of the methodology is required for all types of reviews except for narrative reviews. This is partly because unlike narrative reviews, all other review articles follow systematic approaches which must ensure significant reproducibility [ 72 ]. Therefore, where necessary, the methods of data extraction from the literature and data synthesis must also be highlighted as well. In some cases, it is important to show how data were combined by highlighting the statistical methods used, measures of effect, and tests performed, as well as demonstrating heterogeneity and publication bias [ 73 ].

The methodology should also detail the major databases consulted during the literature search, e.g., Dimensions, ScienceDirect, Web of Science, MEDLINE, and PubMed. For meta-analysis, it is imperative to highlight the software and/or package used, which could include Comprehensive Meta-Analysis, OpenMEE, Review Manager (RevMan), Stata, SAS, and R Studio. It is also necessary to state the mathematical methods used for the analysis; examples of these include the Bayesian analysis, the Mantel–Haenszel method, and the inverse variance method. The methodology should also state the number of authors that carried out the initial review stage of the study, as it has been recommended that at least two reviews should be done blindly and in parallel, especially when it comes to the acquisition and synthesis of data [ 74 ]. Finally, the quality and validity assessment of the publication used in the review must be stated and well clarified [ 73 ].

5.7. Main Body of the Review

Ideally, the main body of a publishable review should answer these questions: What is new (contribution)? Why so (logic)? So what (impact)? How well it is done (thoroughness)? The flow of the main body of a review article must be well organised to adequately maintain the attention of the readers as well as guide them through the section. It is recommended that the author should consider drawing a conceptual scheme of the main body first, using methods such as mind-mapping. This will help create a logical flow of thought and presentation, while also linking the various sections of the manuscript together. According to Moreira [ 75 ], “reports do not simply yield their findings, rather reviewers make them yield,” and thus, it is the author's responsibility to transform “resistant” texts into “docile” texts. Hence, after the search for the literature, the essential themes and key concepts of the review paper must be identified and synthesised together. This synthesis primarily involves creating hypotheses about the relationships between the concepts with the aim of increasing the understanding of the topic being reviewed. The important information from the various sources should not only be summarised, but the significance of studies must be related back to the initial question(s) posed by the review article. Furthermore, MacLure [ 76 ] stated that data are not just to be plainly “extracted intact” and “used exactly as extracted,” but must be modified, reconfigured, transformed, transposed, converted, tabulated, graphed, or manipulated to enable synthesis, combination, and comparison. Therefore, different pieces of information must be extracted from the reports in which they were previously deposited and then refined into the body of the new article [ 75 ]. To this end, adequate comparison and combination might require that “qualitative data be quantified” or/and “quantitative data may be qualitized” [ 77 ]. In order to accomplish all of these goals, the author may have to transform, paraphrase, generalize, specify, and reorder the text [ 78 ]. For comprehensiveness, the body paragraphs should be arranged in a similar order as it was initially stated in the abstract or/and introduction. Thus, the main body could be divided into thematic areas, each of which could be independently comprehensive and treated as a mini review. Similarly, the sections can also be arranged chronologically depending on the focus of the review. Furthermore, the abstractions should proceed from a wider general view of the literature being reviewed and then be narrowed down to the specifics. In the process, deep insights should also be provided between the topic of the review and the wider subject area, e.g., fungal enzymes and enzymes in general. The abstractions must also be discussed in more detail by presenting more specific information from the identified sources (with proper citations of course!). For example, it is important to identify and highlight contrary findings and rival interpretations as well as to point out areas of agreement or debate among different bodies of literature. Often, there are previous reviews on the same topic/concept; however, this does not prevent a new author from writing one on the same topic, especially if the previous reviews were written many years ago. However, it is important that the body of the new manuscript be written from a new angle that was not adequately covered in the past reviews and should also incorporate new studies that have accumulated since the last review(s). In addition, the new review might also highlight the approaches, limitations, and conclusions of the past studies. But the authors must not be excessively critical of the past reviews as this is regarded by many authors as a sign of poor professionalism [ 3 , 79 ]. Daft [ 79 ] emphasized that it is more important for a reviewer to state how their research builds on previous work instead of outright claiming that previous works are incompetent and inadequate. However, if a series of related papers on one topic have a common error or research flaw that needs rectification, the reviewer must point this out with the aim of moving the field forward [ 3 ]. Like every other scientific paper, the main body of a review article also needs to be consistent in style, for example, in the choice of passive vs. active voice and present vs. past tense. It is also important to note that tables and figures can serve as a powerful tool for highlighting key points in the body of the review, and they are now considered core elements of reviews. For more guidance and insights into what should make up the contents of a good review article, readers are also advised to get familiarised with the Boote and Beile [ 80 ] literature review scoring rubric as well as the review article checklist of Short [ 81 ].

5.8. Tables and Figures

An ideal review article should be logically structured and efficiently utilise illustrations, in the form of tables and figures, to convey the key findings and relationships in the study. According to Tay [ 13 ], illustrations often take a secondary role in review papers when compared to primary research papers which are focused on illustrations. However, illustrations are very important in review articles as they can serve as succinct means of communicating major findings and insights. Franzblau and Chung [ 82 ] pointed out that illustrations serve three major purposes in a scientific article: they simplify complex data and relationships for better understanding, they minimise reading time by summarising and bringing to focus on the key findings (or trends), and last, they help to reduce the overall word count. Hence, inserting and constructing illustrations in a review article is as meticulous as it is important. However, important decisions should be made on whether the charts, figures, or tables to be potentially inserted in the manuscript are indeed needed and how best to design them [ 83 ]. Illustrations should enhance the text while providing necessary information; thus, the information described in illustrations should not contradict that in the main text and should also not be a repetition of texts [ 84 ]. Furthermore, illustrations must be autonomous, meaning they ought to be intelligible without having to read the text portion of the manuscript; thus, the reader does not have to flip back and forth between the illustration and the main text in order to understand it [ 85 ]. It should be noted that tables or figures that directly reiterate the main text or contain extraneous information will only make a mess of the manuscript and discourage readers [ 86 ].

Kotz and Cals [ 87 ] recommend that the layout of tables and figures should be carefully designed in a clear manner with suitable layouts, which will allow them to be referred to logically and chronologically in the text. In addition, illustrations should only contain simple text, as lengthy details would contradict their initial objective, which was to provide simple examples or an overview. Furthermore, the use of abbreviations in illustrations, especially tables, should be avoided if possible. If not, the abbreviations should be defined explicitly in the footnotes or legends of the illustration [ 88 ]. Similarly, numerical values in tables and graphs should also be correctly approximated [ 84 ]. It is recommended that the number of tables and figures in the manuscript should not exceed the target journal's specification. According to Saver [ 89 ], they ideally should not account for more than one-third of the manuscript. Finally, the author(s) must seek permission and give credits for using an already published illustration when necessary. However, none of these are needed if the graphic is originally created by the author, but if it is a reproduced or an adapted illustration, the author must obtain permission from the copyright owner and include the necessary credit. One of the very important tools for designing illustrations is Creative Commons, a platform that provides a wide range of creative works which are available to the public for use and modification.

5.9. Conclusion/Future Perspectives

It has been observed that many reviews end abruptly with a short conclusion; however, a lot more can be included in this section in addition to what has been said in the major sections of the paper. Basically, the conclusion section of a review article should provide a summary of key findings from the main body of the manuscript. In this section, the author needs to revisit the critical points of the paper as well as highlight the accuracy, validity, and relevance of the inferences drawn in the article review. A good conclusion should highlight the relationship between the major points and the author's hypothesis as well as the relationship between the hypothesis and the broader discussion to demonstrate the significance of the review article in a larger context. In addition to giving a concise summary of the important findings that describe current knowledge, the conclusion must also offer a rationale for conducting future research [ 12 ]. Knowledge gaps should be identified, and themes should be logically developed in order to construct conceptual frameworks as well as present a way forward for future research in the field of study [ 11 ].

Furthermore, the author may have to justify the propositions made earlier in the manuscript, demonstrate how the paper extends past research works, and also suggest ways that the expounded theories can be empirically examined [ 3 ]. Unlike experimental studies which can only draw either a positive conclusion or ambiguous failure to reject the null hypothesis, four possible conclusions can be drawn from review articles [ 1 ]. First, the theory/hypothesis propounded may be correct after being proven from current evidence; second, the hypothesis may not be explicitly proven but is most probably the best guess. The third conclusion is that the currently available evidence does not permit a confident conclusion or a best guess, while the last conclusion is that the theory or hypothesis is false [ 1 ]. It is important not to present new information in the conclusion section which has link whatsoever with the rest of the manuscript. According to Harris et al. [ 90 ], the conclusions should, in essence, answer the question: if a reader were to remember one thing about the review, what would it be?

5.10. References

As it has been noted in different parts of this paper, authors must give the required credit to any work or source(s) of information that was included in the review article. This must include the in-text citations in the main body of the paper and the corresponding entries in the reference list. Ideally, this full bibliographical list is the last part of the review article, and it should contain all the books, book chapters, journal articles, reports, and other media, which were utilised in the manuscript. It has been noted that most journals and publishers have their own specific referencing styles which are all derived from the more popular styles such as the American Psychological Association (APA), Chicago, Harvard, Modern Language Association (MLA), and Vancouver styles. However, all these styles may be categorised into either the parenthetical or numerical referencing style. Although a few journals do not have strict referencing rules, it is the responsibility of the author to reference according to the style and instructions of the journal. Omissions and errors must be avoided at all costs, and this can be easily achieved by going over the references many times for due diligence [ 11 ]. According to Cronin et al. [ 12 ], a separate file for references can be created, and any work used in the manuscript can be added to this list immediately after being cited in the text [ 12 ]. In recent times, the emergence of various referencing management software applications such as Endnote, RefWorks, Mendeley, and Zotero has even made referencing easier. The majority of these software applications require little technical expertise, and many of them are free to use, while others may require a subscription. It is imperative, however, that even after using these software packages, the author must manually curate the references during the final draft, in order to avoid any errors, since these programs are not impervious to errors, particularly formatting errors.

6. Concluding Remarks

Writing a review article is a skill that needs to be learned; it is a rigorous but rewarding endeavour as it can provide a useful platform to project the emerging researcher or postgraduate student into the gratifying world of publishing. Thus, the reviewer must develop the ability to think critically, spot patterns in a large volume of information, and must be invested in writing without tiring. The prospective author must also be inspired and dedicated to the successful completion of the article while also ensuring that the review article is not just a mere list or summary of previous research. It is also important that the review process must be focused on the literature and not on the authors; thus, overt criticism of existing research and personal aspersions must be avoided at all costs. All ideas, sentences, words, and illustrations should be constructed in a way to avoid plagiarism; basically, this can be achieved by paraphrasing, summarising, and giving the necessary acknowledgments. Currently, there are many tools to track and detect plagiarism in manuscripts, ensuring that they fall within a reasonable similarity index (which is typically 15% or lower for most journals). Although the more popular of these tools, such as Turnitin and iThenticate, are subscription-based, there are many freely available web-based options as well. An ideal review article is supposed to motivate the research topic and describe its key concepts while delineating the boundaries of research. In this regard, experience-based information on how to methodologically develop acceptable and impactful review articles has been detailed in this paper. Furthermore, for a beginner, this guide has detailed “the why” and “the how” of authoring a good scientific review article. However, the information in this paper may as a whole or in parts be also applicable to other fields of research and to other writing endeavours such as writing literature review in theses, dissertations, and primary research articles. Finally, the intending authors must put all the basic rules of scientific writing and writing in general into cognizance. A comprehensive study of the articles cited within this paper and other related articles focused on scientific writing will further enhance the ability of the motivated beginner to deliver a good review article.

Acknowledgments

This work was supported by the National Research Foundation of South Africa under grant number UID 138097. The authors would like to thank the Durban University of Technology for funding the postdoctoral fellowship of the first author, Dr. Ayodeji Amobonye.

Data Availability

Conflicts of interest.

The authors declare that they have no conflicts of interest.

• Systematic Review
• Open access
• Published: 12 May 2024

Association between problematic social networking use and anxiety symptoms: a systematic review and meta-analysis

• Mingxuan Du 1 ,
• Chengjia Zhao 2 ,
• Haiyan Hu 1 ,
• Ningning Ding 1 ,
• Jiankang He 1 ,
• Wenwen Tian 1 ,
• Wenqian Zhao 1 ,
• Xiujian Lin 1 ,
• Gaoyang Liu 1 ,
• Wendan Chen 1 ,
• ShuangLiu Wang 1 ,
• Pengcheng Wang 3 ,
• Dongwu Xu 1 ,
• Xinhua Shen 4 &
• Guohua Zhang 1  

BMC Psychology volume  12 , Article number:  263 ( 2024 ) Cite this article

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Metrics details

A growing number of studies have reported that problematic social networking use (PSNU) is strongly associated with anxiety symptoms. However, due to the presence of multiple anxiety subtypes, existing research findings on the extent of this association vary widely, leading to a lack of consensus. The current meta-analysis aimed to summarize studies exploring the relationship between PSNU levels and anxiety symptoms, including generalized anxiety, social anxiety, attachment anxiety, and fear of missing out. 209 studies with a total of 172 articles were included in the meta-analysis, involving 252,337 participants from 28 countries. The results showed a moderately positive association between PSNU and generalized anxiety (GA), social anxiety (SA), attachment anxiety (AA), and fear of missing out (FoMO) respectively (GA: r  = 0.388, 95% CI [0.362, 0.413]; SA: r  = 0.437, 95% CI [0.395, 0.478]; AA: r  = 0.345, 95% CI [0.286, 0.402]; FoMO: r  = 0.496, 95% CI [0.461, 0.529]), and there were different regulatory factors between PSNU and different anxiety subtypes. This study provides the first comprehensive estimate of the association of PSNU with multiple anxiety subtypes, which vary by time of measurement, region, gender, and measurement tool.

Peer Review reports

Introduction

Social network refers to online platforms that allow users to create, share, and exchange information, encompassing text, images, audio, and video [ 1 ]. The use of social network, a term encompassing various activities on these platforms, has been measured from angles such as frequency, duration, intensity, and addictive behavior, all indicative of the extent of social networking usage [ 2 ]. As of April 2023, there are 4.8 billion social network users globally, representing 59.9% of the world’s population [ 3 ]. The usage of social network is considered a normal behavior and a part of everyday life [ 4 , 5 ]. Although social network offers convenience in daily life, excessive use can lead to PSNU [ 6 , 7 ], posing potential threats to mental health, particularly anxiety symptoms (Rasmussen et al., 2020). Empirical research has shown that anxiety symptoms, including generalized anxiety (GA), social anxiety (SA), attachment anxiety (AA), and fear of missing out (FoMO), are closely related to PSNU [ 8 , 9 , 10 , 11 , 12 ]. While some empirical studies have explored the relationship between PSNU and anxiety symptoms, their conclusions are not consistent. Some studies have found a significant positive correlation [ 13 , 14 , 15 ], while others have found no significant correlation [ 16 , 17 , 18 , 19 ]. Furthermore, the degree of correlation varies widely in existing research, with reported r-values ranging from 0.12 to 0.80 [ 20 , 21 ]. Therefore, a systematic meta-analysis is necessary to clarify the impact of PSNU on individual anxiety symptoms.

Previous research lacks a unified concept of PSNU, primarily due to differing theoretical interpretations by various authors, and the use of varied standards and diagnostic tools. Currently, this phenomenon is referred to by several terms, including compulsive social networking use, problematic social networking use, excessive social networking use, social networking dependency, and social networking addiction [ 22 , 23 , 24 , 25 , 26 ]. These conceptual differences hinder the development of a cohesive and systematic research framework, as it remains unclear whether these definitions and tools capture the same underlying construct [ 27 ]. To address this lack of uniformity, this paper will use the term “problematic use” to encompass all the aforementioned nomenclatures (i.e., compulsive, excessive, dependent, and addictive use).

Regarding the relationship between PSNU and anxiety symptoms, two main perspectives exist: the first suggests a positive correlation, while the second proposes a U-shaped relationship. The former perspective, advocating a positive correlation, aligns with the social cognitive theory of mass communication. It posits that PSNU can reinforce certain cognitions, emotions, attitudes, and behaviors [ 28 , 29 ], potentially elevating individuals’ anxiety levels [ 30 ]. Additionally, the cognitive-behavioral model of pathological use, a primary framework for explaining factors related to internet-based addictions, indicates that psychiatric symptoms like depression or anxiety may precede internet addiction, implying that individuals experiencing anxiety may turn to social networking platforms as a coping mechanism [ 31 ]. Empirical research also suggests that highly anxious individuals prefer computer-mediated communication due to the control and social liberation it offers and are more likely to have maladaptive emotional regulation, potentially leading to problematic social network service use [ 32 ]. Turning to the alternate perspective, it proposes a U-shaped relationship as per the digital Goldilocks hypothesis. In this view, moderate social networking usage is considered beneficial for psychosocial adaptation, providing individuals with opportunities for social connection and support. Conversely, both excessive use and abstinence can negatively impact psychosocial adaptation [ 33 ]. In summary, both perspectives offer plausible explanations.

Incorporating findings from previous meta-analyses, we identified seven systematic reviews and two meta-analyses that investigated the association between PSNU and anxiety. The results of these meta-analyses indicated a significant positive correlation between PSNU and anxiety (ranging from 0.33 to 0.38). However, it is evident that these previous meta-analyses had certain limitations. Firstly, they focused only on specific subtypes of anxiety; secondly, they were limited to adolescents and emerging adults in terms of age. In summary, this systematic review aims to ascertain which theoretical perspective more effectively explains the relationship between PSNU and anxiety, addressing the gaps in previous meta-analyses. Additionally, the association between PSNU and anxiety could be moderated by various factors. Drawing from a broad research perspective, any individual study is influenced by researcher-specific designs and associated sample estimates. These may lead to bias compared to the broader population. Considering the selection criteria for moderating variables in empirical studies and meta-analyses [ 34 , 35 ], the heterogeneity of findings on problematic social network usage and anxiety symptoms could be driven by divergence in sample characteristics (e.g., gender, age, region) and research characteristics (measurement instrument of study variables). Since the 2019 coronavirus pandemic, heightened public anxiety may be attributed to the fear of the virus or heightened real life stress. The increased use of electronic devices, particularly smartphones during the pandemic, also instigates the prevalence of problematic social networking. Thus, our analysis focuses on three moderators: sample characteristics (participants’ gender, age, region), measurement tools (for PSNU and anxiety symptoms) and the time of measurement (before COVID-19 vs. during COVID-19).

The present study was conducted in accordance with the 2020 statement on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 36 ]. To facilitate transparency and to avoid unnecessary duplication of research, this study was registered on PROSPERO, and the number is CRD42022350902.

Literature search

Studies on the relationship between the PSNU and anxiety symptoms from 2000 to 2023 were retrieved from seven databases. These databases included China National Knowledge Infrastructure (CNKI), Wanfang Data, Chongqing VIP Information Co. Ltd. (VIP), Web of Science, ScienceDirect, PubMed, and PsycARTICLES. The search strings consisted of (a) anxiety symptoms, (b) social network, and (c) Problematic use. As shown in Table  1 , the keywords for anxiety are as follows: anxiety, generalized anxiety, social anxiety, attachment anxiety, fear of missing out, and FoMO. The keywords for social network are as follows: social network, social media, social networking site, Instagram, and Facebook. The keywords for addiction are as follows: addiction, dependence, problem/problematic use, excessive use. The search deadline was March 19, 2023. A total of 2078 studies were initially retrieved and all were identified ultimately.

Inclusion and exclusion criteria

Retrieved studies were eligible for the present meta-analysis if they met the following inclusion criteria: (a) the study provided Pearson correlation coefficients used to measure the relationship between PSNU and anxiety symptoms; (b) the study reported the sample size and the measurement instruments for the variables; (c) the study was written in English and Chinese; (d) the study provided sufficient statistics to calculate the effect sizes; (e) effect sizes were extracted from independent samples. If multiple independent samples were investigated in the same study, they were coded separately; if the study was a longitudinal study, they were coded by the first measurement. In addition, studies were excluded if they: (a) examined non-problematic social network use; (b) had an abnormal sample population; (c) the results of the same sample were included in another study and (d) were case reports or review articles. Two evaluators with master’s degrees independently assessed the eligibility of the articles. A third evaluator with a PhD examined the results and resolved dissenting views.

Data extraction and quality assessment

Two evaluators independently coded the selected articles according to the following characteristics: literature information, time of measurement (before the COVID-19 vs. during the COVID-19), sample source (developed country vs. developing country), sample size, proportion of males, mean age, type of anxiety, and measurement instruments for PSNU and anxiety symptoms. The following principles needed to be adhered to in the coding process: (a) effect sizes were extracted from independent samples. If multiple independent samples were investigated in the same study, they were coded separately; if the study was a longitudinal study, it was coded by the first measurement; (b) if multiple studies used the same data, the one with the most complete information was selected; (c) If studies reported t or F values rather than r , the following formula \( r=\sqrt{\frac{{t}^{2}}{{t}^{2}+df}}\) ; \( r=\sqrt{\frac{F}{F+d{f}_{e}}}\) was used to convert them into r values [ 37 , 38 ]. Additionally, if some studies only reported the correlation matrix between each dimension of PSNU and anxiety symptoms, the following formula \( {r}_{xy}=\frac{\sum {r}_{xi}{r}_{yj}}{\sqrt{n+n(n-1){r}_{xixj}}\sqrt{m+m(m-1){r}_{yiyj}}}\) was used to synthesize the r values [ 39 ], where n or m is the number of dimensions of variable x or variable y, respectively, and \( {r}_{xixj} \) or \( {r}_{yiyj}\) represents the mean of the correlation coefficients between the dimensions of variable x or variable y, respectively.

Literature quality was determined according to the meta-analysis quality evaluation scale developed [ 40 ]. The quality of the post-screening studies was assessed by five dimensions: sampling method, efficiency of sample collection, level of publication, and reliability of PSNU and anxiety symptom measurement instruments. The total score of the scale ranged from 0 to 10; higher scores indicated better quality of the literature.

Data analysis

All data were performed using Comprehensive Meta Analysis 3.3 (CMA 3.3). Pearson’s product-moment coefficient r was selected as the effect size index in this meta-analysis. Firstly, \( {\text{F}\text{i}\text{s}\text{h}\text{e}\text{r}}^{{\prime }}\text{s} Z=\frac{1}{2}\times \text{ln}\left(\frac{1+r}{1-r}\right)\) was used to convert the correlation coefficient to Fisher Z . Then the formula \( SE=\sqrt{\frac{1}{n-3}}\) was used to calculate the standard error ( SE ). Finally, the summary of r was obtained from the formula \( r=\frac{{e}^{2z}-1}{{e}^{2z}+1}\) for a comprehensive measure of the relationship between PSNU and anxiety symptoms [ 37 , 41 ].

Although the effect sizes estimated by the included studies may be similar, considering the actual differences between studies (e.g., region and gender), the random effects model was a better choice for data analysis for the current meta-analysis. The heterogeneity of the included study effect sizes was measured for significance by Cochran’s Q test and estimated quantitatively by the I 2 statistic [ 42 ]. If the results indicate there is a significant heterogeneity (the Q test: p -value < 0.05, I 2  > 75) and the results of different studies are significantly different from the overall effect size. Conversely, it indicates there are no differences between the studies and the overall effect size. And significant heterogeneity tends to indicate the possible presence of potential moderating variables. Subgroup analysis and meta-regression analysis were used to examine the moderating effect of categorical and continuous variables, respectively.

Funnel plots, fail-safe number (Nfs) and Egger linear regression were utilized to evaluate the publication bias [ 43 , 44 , 45 ]. The likelihood of publication bias was considered low if the intercept obtained from Egger linear regression was not significant. A larger Nfs indicated a lower risk of publication bias, and if Nfs < 5k + 10 (k representing the original number of studies), publication bias should be a concern [ 46 ]. When Egger’s linear regression was significant, the Duval and Tweedie’s trim-and-fill was performed to correct the effect size. If there was no significant change in the effect size, it was assumed that there was no serious publication bias [ 47 ].

A significance level of P  < 0.05 was deemed applicable in this study.

Sample characteristics

The PRISMA search process is depicted in Fig.  1 . The database search yielded 2078 records. After removing duplicate records and screening the title and abstract, the full text was subject to further evaluation. Ultimately, 172 records fit the inclusion criteria, including 209 independent effect sizes. The present meta-analysis included 68 studies on generalized anxiety, 44 on social anxiety, 22 on attachment anxiety, and 75 on fear of missing out. The characteristics of the selected studies are summarized in Table  2 . The majority of the sample group were adults. Quality scores for selected studies ranged from 0 to 10, with only 34 effect sizes below the theoretical mean, indicating high quality for the included studies. The literature included utilized BSMAS as the primary tool to measure PSNU, DASS-21-A to measure GA, IAS to measure SA, ECR to measure AA, and FoMOS to measure FoMO.

Flow chart of the search and selection strategy

Overall analysis, homogeneity tests and publication bias

As shown in Table  3 , there was significant heterogeneity between PSNU and all four anxiety symptoms (GA: Q  = 1623.090, I 2  = 95.872%; SA: Q  = 1396.828, I 2  = 96.922%; AA: Q  = 264.899, I 2  = 92.072%; FoMO: Q  = 1847.110, I 2  = 95.994%), so a random effects model was chosen. The results of the random effects model indicate a moderate positive correlation between PSNU and anxiety symptoms (GA: r  = 0.350, 95% CI [0.323, 0.378]; SA: r  = 0.390, 95% CI [0.347, 0.431]; AA: r  = 0.345, 95% CI [0.286, 0.402]; FoMO: r  = 0.496, 95% CI [0.461, 0.529]).

Figure  2 shows the funnel plot of the relationship between PSNU and anxiety symptoms. No significant symmetry was seen in the funnel plot of the relationship between PSNU and GA and between PSNU and SA. And the Egger’s regression results also indicated that there might be publication bias ( t  = 3.775, p  < 0.001; t  = 2.309, p  < 0.05). Therefore, it was necessary to use fail-safe number (Nfs) and the trim and fill method for further examination and correction. The Nfs for PSNU and GA as well as PSNU and SA are 4591 and 7568, respectively. Both Nfs were much larger than the standard 5 k  + 10. After performing the trim and fill method, 14 effect sizes were added to the right side of the funnel plat (Fig.  2 .a), the correlation coefficient between PSNU and GA changed to ( r  = 0.388, 95% CI [0.362, 0.413]); 10 effect sizes were added to the right side of the funnel plat (Fig.  2 .b), the correlation coefficient between PSNU and SA changed to ( r  = 0.437, 95% CI [0.395, 0.478]). The correlation coefficients did not change significantly, indicating that there was no significant publication bias associated with the relationship between PSNU and these two anxiety symptoms (GA and SA).

Funnel plot of the relationship between PSNU and anxiety symptoms. Note: Black dots indicated additional studies after using trim and fill method; ( a ) = Funnel plot of the PSNU and GA; ( b ) = Funnel plot of the PSNU and SA; ( c ) = Funnel plot of the PSNU and AA; ( d ) = Funnel plot of the PSNU and FoMO

Sensitivity analyses

Initially, the findings obtained through the one-study-removed approach indicated that the heterogeneities in the relationship between PSNU and anxiety symptoms were not attributed to any individual study. Nevertheless, it is important to note that sensitivity analysis should be performed based on literature quality [ 223 ] since low-quality literature could potentially impact result stability. In the relationship between PSNU and GA, the 10 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.402, 95% CI [0.375, 0.428]); In the relationship between PSNU and SA, the 8 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.431, 95% CI [0.387, 0.472]); In the relationship between PSNU and AA, the 5 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.367, 95% CI [0.298, 0.433]); In the relationship between PSNU and FoMO, the 11 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.508, 95% CI [0.470, 0.544]). The revised estimates indicate that meta-analysis results were stable.

Moderator analysis

The impact of moderator variables on the relation between psnu and ga.

The results of subgroup analysis and meta-regression are shown in Table  4 , the time of measurement significantly moderated the correlation between PSNU and GA ( Q between = 19.268, df  = 2, p  < 0.001). The relation between the two variables was significantly higher during the COVID-19 ( r  = 0.392, 95% CI [0.357, 0.425]) than before the COVID-19 ( r  = 0.270, 95% CI [0.227, 0.313]) or measurement time uncertain ( r  = 0.352, 95% CI [0.285, 0.415]).

The moderating effect of the PSNU measurement was significant ( Q between = 6.852, df  = 1, p  = 0.009). The relation was significantly higher when PSNU was measured with the BSMAS ( r  = 0.373, 95% CI [0.341, 0.404]) compared to others ( r  = 0.301, 95% CI [0.256, 0.344]).

The moderating effect of the GA measurement was significant ( Q between = 60.061, df  = 5, p  < 0.001). Specifically, when GA measured by the GAD ( r  = 0.398, 95% CI [0.356, 0.438]) and the DASS-21-A ( r  = 0.433, 95% CI [0.389, 0.475]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the STAI ( r  = 0.232, 95% CI [0.187, 0.276]).

For the relation between PSNU and GA, the moderating effect of region, gender and age were not significant.

The impact of moderator variables on the relation between PSNU and SA

The effects of the moderating variables in the relation between PSNU and SA were shown in Table  5 . The results revealed a gender-moderated variances between the two variables (b = 0.601, 95% CI [ 0.041, 1.161], Q model (1, k = 41) = 4.705, p  = 0.036).

For the relation between PSNU and SA, the moderating effects of time of measurement, region, measurement of PSNU and SA, and age were not significant.

The impact of moderator variables on the relation between PSNU and AA

The effects of the moderating variables in the relation between PSNU and AA were shown in Table  6 , region significantly moderated the correlation between PSNU and AA ( Q between = 6.410, df  = 2, p  = 0.041). The correlation between the two variables was significantly higher in developing country ( r  = 0.378, 95% CI [0.304, 0.448]) than in developed country ( r  = 0.242, 95% CI [0.162, 0.319]).

The moderating effect of the PSNU measurement was significant ( Q between = 6.852, df  = 1, p  = 0.009). Specifically, when AA was measured by the GPIUS-2 ( r  = 0.484, 95% CI [0.200, 0.692]) and the PMSMUAQ ( r  = 0.443, 95% CI [0.381, 0.501]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the BSMAS ( r  = 0.248, 95% CI [0.161, 0.331]) and others ( r  = 0.313, 95% CI [0.250, 0.372]).

The moderating effect of the AA measurement was significant ( Q between = 17.283, df  = 2, p  < 0.001). The correlation was significantly higher when measured using the ECR ( r  = 0.386, 95% CI [0.338, 0.432]) compared to the RQ ( r  = 0.200, 95% CI [0.123, 0.275]).

For the relation between PSNU and AA, the moderating effects of time of measurement, region, gender, and age were not significant.

The impact of moderator variables on the relation between PSNU and FoMO

The effects of the moderating variables in the relation between PSNU and FoMO were shown in Table  7 , the moderating effect of the PSNU measurement was significant ( Q between = 8.170, df  = 2, p  = 0.017). Among the sub-dimensions, the others was excluded because there was only one sample. Specifically, when measured using the FoMOS-MSME ( r  = 0.630, 95% CI [0.513, 0.725]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the FoMOS ( r  = 0.472, 95% CI [0.432, 0.509]) and the T-S FoMOS ( r  = 0.557, 95% CI [0.463, 0.639]).

For the relationship between PSNU and FoMO, the moderating effects of time of measurement, region, measurement of PSNU, gender and age were not significant.

Through systematic review and meta-analysis, this study established a positive correlation between PSNU and anxiety symptoms (i.e., generalized anxiety, social anxiety, attachment anxiety, and fear of missing out), confirming a linear relationship and partially supporting the Social Cognitive Theory of Mass Communication [ 28 ] and the Cognitive Behavioral Model of Pathological Use [ 31 ]. Specifically, a significant positive correlation between PSNU and GA was observed, implying that GA sufferers might resort to social network for validation or as an escape from reality, potentially alleviating their anxiety. Similarly, the meta-analysis demonstrated a strong positive correlation between PSNU and SA, suggesting a preference for computer-mediated communication among those with high social anxiety due to perceived control and liberation offered by social network. This preference is often accompanied by maladaptive emotional regulation, predisposing them to problematic use. In AA, a robust positive correlation was found with PSNU, indicating a higher propensity for such use among individuals with attachment anxiety. Notably, the study identified the strongest correlation in the context of FoMO. FoMO’s significant association with PSNU is multifaceted, stemming from the real-time nature of social networks that engenders a continuous concern about missing crucial updates or events. This drives frequent engagement with social network, thereby establishing a direct link to problematic usage patterns. Additionally, social network’s feedback loops amplify this effect, intensifying FoMO. The culture of social comparison on these platforms further exacerbates FoMO, as users frequently compare their lives with others’ selectively curated portrayals, enhancing both their social networking usage frequency and the pursuit for social validation. Furthermore, the integral role of social network in modern life broadens FoMO’s scope, encompassing anxieties about staying informed and connected.

The notable correlation between FoMO and PSNU can be comprehensively understood through various perspectives. FoMO is inherently linked to the real-time nature of social networks, which cultivates an ongoing concern about missing significant updates or events in one’s social circle [ 221 ]. This anxiety prompts frequent engagement with social network, leading to patterns of problematic use. Moreover, the feedback loops in social network algorithms, designed to enhance user engagement, further intensify this fear [ 224 ]. Additionally, social comparison, a common phenomenon on these platforms, exacerbates FoMO as users continuously compare their lives with the idealized representations of others, amplifying feelings of missing out on key social experiences [ 225 ]. This behavior not only increases social networking usage but also is closely linked to the quest for social validation and identity construction on these platforms. The extensive role of social network in modern life further amplifies FoMO, as these platforms are crucial for information exchange and maintaining social ties. FoMO thus encompasses more than social concerns, extending to anxieties about staying informed with trends and dynamics within social networks [ 226 ]. The multifaceted nature of FoMO in relation to social network underscores its pronounced correlation with problematic social networking usage. In essence, the combination of social network’s intrinsic characteristics, psychological drivers of user behavior, the culture of social comparison, and the pervasiveness of social network in everyday life collectively make FoMO the most pronouncedly correlated anxiety type with PSNU.

Additionally, we conducted subgroup analyses on the timing of measurement (before COVID-19 vs. during COVID-19), measurement tools (for PSNU and anxiety symptoms), sample characteristics (participants’ region), and performed a meta-regression analysis on gender and age in the context of PSNU and anxiety symptoms. It was found that the timing of measurement, tools used for assessing PSNU and anxiety, region, and gender had a moderating effect, whereas age did not show a significant moderating impact.

Firstly, the relationship between PSNU and anxiety symptoms was significantly higher during the COVID-19 period than before, especially between PSNU and GA. However, the moderating effect of measurement timing was not significant in the relationship between PSNU and other types of anxiety. This could be attributed to the increased uncertainty and stress during the pandemic, leading to heightened levels of general anxiety [ 227 ]. The overuse of social network for information seeking and anxiety alleviation might have paradoxically exacerbated anxiety symptoms, particularly among individuals with broad future-related worries [ 228 ]. While the COVID-19 pandemic altered the relationship between PSNU and GA, its impact on other types of anxiety (such as SA and AA) may not have been significant, likely due to these anxiety types being more influenced by other factors like social skills and attachment styles, which were minimally impacted by the epidemic.

Secondly, the observed variance in the relationship between PSNU and AA across different economic contexts, notably between developing and developed countries, underscores the multifaceted influence of socio-economic, cultural, and technological factors on this dynamic. The amplified connection in developing countries may be attributed to greater socio-economic challenges, distinct cultural norms regarding social support and interaction, rising social network penetration, especially among younger demographics, and technological disparities influencing accessibility and user experience [ 229 , 230 ]. Moreover, the role of social network as a coping mechanism for emotional distress, potentially fostering insecure attachment patterns, is more pronounced in these settings [ 231 ]. These findings highlight the necessity of considering contextual variations in assessing the psychological impacts of social network, advocating for a nuanced understanding of how socio-economic and cultural backgrounds mediate the relationship between PSNU and mental health outcomes [ 232 ]. Additionally, the relationship between PSNU and other types of anxiety (such as GA and SA) presents uniform characteristics across different economic contexts.

Thirdly, the significant moderating effects of measurement tools in the context of PSNU and its correlation with various forms of anxiety, including GA, and AA, are crucial in interpreting the research findings. Specifically, the study reveals that the Bergen Social Media Addiction Scale (BSMAS) demonstrates a stronger correlation between PSNU and GA, compared to other tools. Similarly, for AA, the Griffiths’ Problematic Internet Use Scale 2 (GPIUS2) and the Problematic Media Social Media Use Assessment Questionnaire (PMSMUAQ) show a more pronounced correlation with AA than the BSMAS or other instruments, but for SA and FoMO, the PSNU instrument doesn’t significantly moderate the correlation. The PSNU measurement tool typically contains an emotional change dimension. SA and FoMO, due to their specific conditional stimuli triggers and correlation with social networks [ 233 , 234 ], are likely to yield more consistent scores in this dimension, while GA and AA may be less reliable due to their lesser sensitivity to specific conditional stimuli. Consequently, the adjustment effects of PSNU measurements vary across anxiety symptoms. Regarding the measurement tools for anxiety, different scales exhibit varying degrees of sensitivity in detecting the relationship with PSNU. The Generalized Anxiety Disorder Scale (GAD) and the Depression Anxiety Stress Scales 21 (DASS-21) are more effective in illustrating a strong relationship between GA and PSNU than the State-Trait Anxiety Inventory (STAI). In the case of AA, the Experiences in Close Relationships-21 (ECR-21) provides a more substantial correlation than the Relationship Questionnaire (RQ). Furthermore, for FoMO, the Fear of Missing Out Scale - Multi-Social Media Environment (FoMOS-MSME) is more indicative of a strong relationship with PSNU compared to the standard FoMOS or the T-S FoMOS. These findings underscore the importance of the selection of appropriate measurement tools in research. Different tools, due to their unique design, focus, and sensitivity, can reveal varying degrees of correlation between PSNU and anxiety disorders. This highlights the need for careful consideration of tool characteristics and their potential impact on research outcomes. It also cautions against drawing direct comparisons between studies without acknowledging the possible variances introduced by the use of different measurement instruments.

Fourthly, the significant moderating role of gender in the relationship between PSNU and SA, particularly pronounced in samples with a higher proportion of females. Women tend to engage more actively and emotionally with social network, potentially leading to an increased dependency on these platforms when confronting social anxiety [ 235 ]. This intensified use might amplify the association between PSNU and SA. Societal and cultural pressures, especially those related to appearance and social status, are known to disproportionately affect women, possibly exacerbating their experience of social anxiety and prompting a greater reliance on social network for validation and support [ 236 ]. Furthermore, women’s propensity to seek emotional support and express themselves on social network platforms [ 237 ] could strengthen this link, particularly in the context of managing social anxiety. Consequently, the observed gender differences in the relationship between PSNU and SA underscore the importance of considering gender-specific dynamics and cultural influences in psychological research related to social network use. In addition, gender consistency was observed in the association between PSNU and other types of anxiety, indicating no significant gender disparities.

Fifthly, the absence of a significant moderating effect of age on the relationship between PSNU and various forms of anxiety suggests a pervasive influence of social network across different age groups. This finding indicates that the impact of PSNU on anxiety is relatively consistent, irrespective of age, highlighting the universal nature of social network’s psychological implications [ 238 ]. Furthermore, this uniformity suggests that other factors, such as individual psychological traits or socio-cultural influences, might play a more crucial role in the development of anxiety related to social networking usage than age [ 239 ]. The non-significant role of age also points towards a potential generational overlap in social networking usage patterns and their psychological effects, challenging the notion that younger individuals are uniquely susceptible to the adverse effects of social network on mental health [ 240 ]. Therefore, this insight necessitates a broader perspective in understanding the dynamics of social network and mental health, one that transcends age-based assumptions.

Limitations

There are some limitations in this research. First, most of the studies were cross-sectional surveys, resulting in difficulties in inferring causality of variables, longitudinal study data will be needed to evaluate causal interactions in the future. Second, considerable heterogeneity was found in the estimated results, although heterogeneity can be partially explained by differences in study design (e.g., Time of measurement, region, gender, and measurement tools), but this can introduce some uncertainty in the aggregation and generalization of the estimated results. Third, most studies were based on Asian samples, which limits the generality of the results. Fourth, to minimize potential sources of heterogeneity, some less frequently used measurement tools were not included in the classification of measurement tools, which may have some impact on the results of heterogeneity interpretation. Finally, since most of the included studies used self-reported scales, it is possible to get results that deviate from the actual situation to some extent.

This meta-analysis aims to quantifies the correlations between PSNU and four specific types of anxiety symptoms (i.e., generalized anxiety, social anxiety, attachment anxiety, and fear of missing out). The results revealed a significant moderate positive association between PSNU and each of these anxiety symptoms. Furthermore, Subgroup analysis and meta-regression analysis indicated that gender, region, time of measurement, and instrument of measurement significantly influenced the relationship between PSNU and specific anxiety symptoms. Specifically, the measurement time and GA measurement tools significantly influenced the relationship between PSNU and GA. Gender significantly influenced the relationship between PSNU and SA. Region, PSNU measurement tools, and AA measurement tools all significantly influenced the relationship between PSNU and AA. The FoMO measurement tool significantly influenced the relationship between PSNU and FoMO. Regarding these findings, prevention interventions for PSNU and anxiety symptoms are important.

Data availability

The datasets are available from the corresponding author on reasonable request.

Abbreviations

• Problematic social networking use
• Generalized anxiety
• Social anxiety
• Attachment anxiety

Fear of miss out

Bergen Social Media Addiction Scale

Facebook Addiction Scale

Facebook Intrusion Questionnaire

Generalized Problematic Internet Use Scale 2

Problematic Mobile Social Media Usage Assessment Questionnaire

Social Network Addiction Tendency Scale

Brief Symptom Inventory

The anxiety subscale of the Depression Anxiety Stress Scales

Generalized Anxiety Disorder

The anxiety subscale of the Hospital Anxiety and Depression Scale

State-Trait Anxiety Inventory

Interaction Anxiousness Scale

Liebowitz Social Anxiety Scale

Social Anxiety Scale for Social Media Users

Social Anxiety for Adolescents

Social Anxiety Subscale of the Self-Consciousness Scale

Social Interaction Anxiety Scale

Experiences in Close Relationship Scale

Relationship questionnaire

Fear of Missing Out Scale

FoMO Measurement Scale in the Mobile Social Media Environment

Trait-State Fear of missing Out Scale

Rozgonjuk D, Sindermann C, Elhai JD, Montag C. Fear of missing out (FoMO) and social media’s impact on daily-life and productivity at work: do WhatsApp, Facebook, Instagram, and Snapchat Use disorders mediate that association? Addict Behav. 2020;110:106487.

Article   PubMed   Google Scholar  

Mieczkowski H, Lee AY, Hancock JT. Priming effects of social media use scales on well-being outcomes: the influence of intensity and addiction scales on self-reported depression. Social Media + Soc. 2020;6(4):2056305120961784.

Article   Google Scholar  

Global digital population as of April. 2023 [ https://www.statista.com/statistics/617136/digital-population-worldwide/ ].

Marengo D, Settanni M, Fabris MA, Longobardi C. Alone, together: fear of missing out mediates the link between peer exclusion in WhatsApp classmate groups and psychological adjustment in early-adolescent teens. J Social Personal Relationships. 2021;38(4):1371–9.

Marengo D, Fabris MA, Longobardi C, Settanni M. Smartphone and social media use contributed to individual tendencies towards social media addiction in Italian adolescents during the COVID-19 pandemic. Addict Behav. 2022;126:107204.

Müller SM, Wegmann E, Stolze D, Brand M. Maximizing social outcomes? Social zapping and fear of missing out mediate the effects of maximization and procrastination on problematic social networks use. Comput Hum Behav. 2020;107:106296.

Sun Y, Zhang Y. A review of theories and models applied in studies of social media addiction and implications for future research. Addict Behav. 2021;114:106699.

Boustead R, Flack M. Moderated-mediation analysis of problematic social networking use: the role of anxious attachment orientation, fear of missing out and satisfaction with life. Addict Behav 2021, 119.

Hussain Z, Griffiths MD. The associations between problematic social networking Site Use and Sleep Quality, attention-deficit hyperactivity disorder, Depression, anxiety and stress. Int J Mental Health Addict. 2021;19(3):686–700.

Gori A, Topino E, Griffiths MD. The associations between attachment, self-esteem, fear of missing out, daily time expenditure, and problematic social media use: a path analysis model. Addict Behav. 2023;141:107633.

Marino C, Manari T, Vieno A, Imperato C, Spada MM, Franceschini C, Musetti A. Problematic social networking sites use and online social anxiety: the role of attachment, emotion dysregulation and motives. Addict Behav. 2023;138:107572.

Tobin SJ, Graham S. Feedback sensitivity as a mediator of the relationship between attachment anxiety and problematic Facebook Use. Cyberpsychology Behav Social Netw. 2020;23(8):562–6.

Brailovskaia J, Rohmann E, Bierhoff H-W, Margraf J. The anxious addictive narcissist: the relationship between grandiose and vulnerable narcissism, anxiety symptoms and Facebook Addiction. PLoS ONE 2020, 15(11).

Kim S-S, Bae S-M. Social Anxiety and Social Networking Service Addiction Proneness in University students: the Mediating effects of Experiential Avoidance and interpersonal problems. Psychiatry Invest. 2022;19(8):702–702.

Zhao J, Ye B, Yu L, Xia F. Effects of Stressors of COVID-19 on Chinese College Students’ Problematic Social Media Use: A Mediated Moderation Model. Front Psychiatry 2022, 13.

Astolfi Cury GS, Takannune DM, Prates Herrerias GS, Rivera-Sequeiros A, de Barros JR, Baima JP, Saad-Hossne R, Sassaki LY. Clinical and Psychological Factors Associated with Addiction and Compensatory Use of Facebook among patients with inflammatory bowel disease: a cross-sectional study. Int J Gen Med. 2022;15:1447–57.

Balta S, Emirtekin E, Kircaburun K, Griffiths MD. Neuroticism, trait fear of missing out, and Phubbing: the mediating role of state fear of missing out and problematic Instagram Use. Int J Mental Health Addict. 2020;18(3):628–39.

Boursier V, Gioia F, Griffiths MD. Do selfie-expectancies and social appearance anxiety predict adolescents’ problematic social media use? Comput Hum Behav. 2020;110:106395.

Worsley JD, McIntyre JC, Bentall RP, Corcoran R. Childhood maltreatment and problematic social media use: the role of attachment and depression. Psychiatry Res. 2018;267:88–93.

de Bérail P, Guillon M, Bungener C. The relations between YouTube addiction, social anxiety and parasocial relationships with YouTubers: a moderated-mediation model based on a cognitive-behavioral framework. Comput Hum Behav. 2019;99:190–204.

Naidu S, Chand A, Pandaram A, Patel A. Problematic internet and social network site use in young adults: the role of emotional intelligence and fear of negative evaluation. Pers Indiv Differ. 2023;200:111915.

Apaolaza V, Hartmann P, D’Souza C, Gilsanz A. Mindfulness, compulsive Mobile Social Media Use, and derived stress: the mediating roles of self-esteem and social anxiety. Cyberpsychology Behav Social Netw. 2019;22(6):388–96.

Demircioglu ZI, Goncu-Kose A. Antecedents of problematic social media use and cyberbullying among adolescents: attachment, the dark triad and rejection sensitivity. Curr Psychol (New Brunsw NJ) 2022:1–19.

Gao Q, Li Y, Zhu Z, Fu E, Bu X, Peng S, Xiang Y. What links to psychological needs satisfaction and excessive WeChat use? The mediating role of anxiety, depression and WeChat use intensity. BMC Psychol. 2021;9(1):105–105.

Article   PubMed   PubMed Central   Google Scholar  

Malak MZ, Shuhaiber AH, Al-amer RM, Abuadas MH, Aburoomi RJ. Correlation between psychological factors, academic performance and social media addiction: model-based testing. Behav Inform Technol. 2022;41(8):1583–95.

Song C. The effect of the need to belong on mobile phone social media dependence of middle school students: Chain mediating roles of fear of missing out and maladaptive cognition. Sichuan Normal University; 2022.

Tokunaga RS, Rains SA. A review and meta-analysis examining conceptual and operational definitions of problematic internet use. Hum Commun Res. 2016;42(2):165–99.

Bandura A. Social cognitive theory of mass communication. Media effects. edn.: Routledge; 2009. pp. 110–40.

Valkenburg PM, Peter J, Walther JB. Media effects: theory and research. Ann Rev Psychol. 2016;67:315–38.

Slater MD. Reinforcing spirals: the mutual influence of media selectivity and media effects and their impact on individual behavior and social identity. Communication Theory. 2007;17(3):281–303.

Ahmed E, Vaghefi I. Social media addiction: A systematic review through cognitive-behavior model of pathological use. 2021.

She R, han Mo PK, Li J, Liu X, Jiang H, Chen Y, Ma L, fai Lau JT. The double-edged sword effect of social networking use intensity on problematic social networking use among college students: the role of social skills and social anxiety. Comput Hum Behav. 2023;140:107555.

Przybylski AK, Weinstein N. A large-scale test of the goldilocks hypothesis: quantifying the relations between digital-screen use and the mental well-being of adolescents. Psychol Sci. 2017;28(2):204–15.

Ran G, Li J, Zhang Q, Niu X. The association between social anxiety and mobile phone addiction: a three-level meta-analysis. Comput Hum Behav. 2022;130:107198.

Fioravanti G, Casale S, Benucci SB, Prostamo A, Falone A, Ricca V, Rotella F. Fear of missing out and social networking sites use and abuse: a meta-analysis. Comput Hum Behav. 2021;122:106839.

Moher D, Liberati A, Tetzlaff J, Altman DG, Group* P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–9.

Card NA. Applied meta-analysis for social science research. Guilford; 2015.

Peterson RA, Brown SP. On the use of beta coefficients in meta-analysis. J Appl Psychol. 2005;90(1):175.

Hunter JE, Schmidt FL. Methods of meta-analysis: correcting error and bias in research findings. Sage; 2004.

Zhang Y, Li S, Yu G. The relationship between self-esteem and social anxiety: a meta-analysis with Chinese students. Adv Psychol Sci. 2019;27(6):1005–18.

Borenstein M, Hedges LV, Higgins JP, Rothstein HR. Introduction to meta-analysis. Wiley; 2021.

Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.

Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34.

Light RJ, Pillemer DB. Summing up: the science of reviewing research. Harvard University Press; 1984.

Rosenthal R. Meta-Analytic Procedures for Social Science Research Sage Publications: Beverly Hills, 1984, 148 pp. Educational Researcher 1986;15(8):18–20.

Rothstein HR, Sutton AJ, Borenstein M. Publication bias in meta-analysis. Publication bias meta‐analysis: Prev Assess Adjustments 2005:1–7.

Duval S, Tweedie R. Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta‐analysis. Biometrics. 2000;56(2):455–63.

Al-Mamun F, Hosen I, Griffiths MD, Mamun MA. Facebook use and its predictive factors among students: evidence from a lower- and middle-income country, Bangladesh. Front Psychiatry 2022, 13.

Schou Andreassen C, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, Pallesen S. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behaviors: J Soc Psychologists Addict Behav. 2016;30(2):252–62.

Arikan G, Acar IH, Ustundag-Budak AM. A two-generation study: The transmission of attachment and young adults’ depression, anxiety, and social media addiction. Addict Behav 2022, 124.

Arpaci I, Karatas K, Kiran F, Kusci I, Topcu A. Mediating role of positivity in the relationship between state anxiety and problematic social media use during the COVID-19 pandemic. Death Stud. 2022;46(10):2287–97.

Brailovskaia J, Margraf J. Facebook Addiction Disorder (FAD) among German students-A longitudinal approach. PLoS ONE 2017, 12(12).

Brailovskaia J, Margraf J. The relationship between burden caused by coronavirus (Covid-19), addictive social media use, sense of control and anxiety. Comput Hum Behav. 2021;119:106720–106720.

Brailovskaia J, Margraf J. Addictive social media use during Covid-19 outbreak: validation of the Bergen Social Media Addiction Scale (BSMAS) and investigation of protective factors in nine countries. Curr Psychol (New Brunsw NJ) 2022:1–19.

Brailovskaia J, Krasavtseva Y, Kochetkov Y, Tour P, Margraf J. Social media use, mental health, and suicide-related outcomes in Russian women: a cross-sectional comparison between two age groups. Women’s Health (London England). 2022;18:17455057221141292–17455057221141292.

PubMed   Google Scholar  

Chang C-W, Huang R-Y, Strong C, Lin Y-C, Tsai M-C, Chen IH, Lin C-Y, Pakpour AHH, Griffiths MDD. Reciprocal relationships between Problematic Social Media Use, problematic gaming, and psychological distress among University students: a 9-Month Longitudinal Study. Front Public Health 2022, 10.

Charzynska E, Sussman S, Atroszko PA. Profiles of potential behavioral addictions’ severity and their associations with gender, personality, and well-being: A person-centered approach. Addict Behav 2021, 119.

Chen C-Y, Chen IH, Pakpour AH, Lin C-Y, Griffiths MD. Internet-related behaviors and psychological distress among Schoolchildren during the COVID-19 School Hiatus. Cyberpsychology Behav Social Netw. 2021;24(10):654–63.

Da Veiga GF, Sotero L, Pontes HM, Cunha D, Portugal A, Relvas AP. Emerging adults and Facebook Use: the validation of the Bergen Facebook Addiction Scale (BFAS). Int J Mental Health Addict. 2019;17(2):279–94.

Dadiotis A, Bacopoulou F, Kokka I, Vlachakis D, Chrousos GP, Darviri C, Roussos P. Validation of the Greek version of the Bergen Social Media Addiction Scale in Undergraduate Students. EMBnetjournal 2021, 26.

Fekih-Romdhane F, Jahrami H, Away R, Trabelsi K, Pandi-Perumal SR, Seeman MV, Hallit S, Cheour M. The relationship between technology addictions and schizotypal traits: mediating roles of depression, anxiety, and stress. BMC Psychiatry 2023, 23(1).

Flynn S, Noone C, Sarma KM. An exploration of the link between adult attachment and problematic Facebook use. BMC Psychol. 2018;6(1):34–34.

Fung XCC, Siu AMH, Potenza MN, O’Brien KS, Latner JD, Chen C-Y, Chen IH, Lin C-Y. Problematic use of internet-related activities and Perceived Weight Stigma in Schoolchildren: a longitudinal study across different epidemic periods of COVID-19 in China. Front Psychiatry 2021, 12.

Gonzalez-Nuevo C, Cuesta M, Muniz J, Postigo A, Menendez-Aller A, Kuss DJ. Problematic Use of Social Networks during the First Lockdown: User Profiles and the Protective Effect of Resilience and Optimism. Journal of Clinical Medicine 2022, 11(24).

Hou X-L, Wang H-Z, Hu T-Q, Gentile DA, Gaskin J, Wang J-L. The relationship between perceived stress and problematic social networking site use among Chinese college students. J Behav Addictions. 2019;8(2):306–17.

Hussain Z, Wegmann E. Problematic social networking site use and associations with anxiety, attention deficit hyperactivity disorder, and resilience. Computers Hum Behav Rep. 2021;4:100125.

Imani V, Ahorsu DK, Taghizadeh N, Parsapour Z, Nejati B, Chen H-P, Pakpour AH. The mediating roles of anxiety, Depression, Sleepiness, Insomnia, and Sleep Quality in the Association between Problematic Social Media Use and Quality of Life among patients with Cancer. Healthcare 2022, 10(9).

Islam MS, Sujan MSH, Tasnim R, Mohona RA, Ferdous MZ, Kamruzzaman S, Toma TY, Sakib MN, Pinky KN, Islam MR et al. Problematic smartphone and Social Media Use among Bangladeshi College and University students amid COVID-19: the role of Psychological Well-Being and Pandemic related factors. Front Psychiatry 2021, 12.

Islam MS, Jahan I, Dewan MAA, Pontes HM, Koly KN, Sikder MT, Rahman M. Psychometric properties of three online-related addictive behavior instruments among Bangladeshi school-going adolescents. PLoS ONE 2022, 17(12).

Jahan I, Hosen I, Al Mamun F, Kaggwa MM, Griffiths MD, Mamun MA. How has the COVID-19 pandemic impacted Internet Use behaviors and facilitated problematic internet use? A Bangladeshi study. Psychol Res Behav Manage. 2021;14:1127–38.

Jiang Y. Problematic social media usage and anxiety among University Students during the COVID-19 pandemic: the mediating role of Psychological Capital and the moderating role of academic burnout. Front Psychol. 2021;12:612007–612007.

Kim M-R, Oh J-W, Huh B-Y. Analysis of factors related to Social Network Service Addiction among Korean High School Students. J Addictions Nurs. 2020;31(3):203–12.

Koc M, Gulyagci S. Facebook addiction among Turkish college students: the role of psychological health, demographic, and usage characteristics. Cyberpsychology Behav Social Netw. 2013;16(4):279–84.

Lin C-Y, Namdar P, Griffiths MD, Pakpour AH. Mediated roles of generalized trust and perceived social support in the effects of problematic social media use on mental health: a cross-sectional study. Health Expect. 2021;24(1):165–73.

Lin C-Y, Imani V, Griffiths MD, Brostrom A, Nygardh A, Demetrovics Z, Pakpour AH. Temporal associations between morningness/eveningness, problematic social media use, psychological distress and daytime sleepiness: mediated roles of sleep quality and insomnia among young adults. J Sleep Res 2021, 30(1).

Lozano Blasco R, Latorre Cosculluela C, Quilez Robres A. Social Network Addiction and its impact on anxiety level among University students. Sustainability 2020, 12(13).

Marino C, Musetti A, Vieno A, Manari T, Franceschini C. Is psychological distress the key factor in the association between problematic social networking sites and poor sleep quality? Addict Behav 2022, 133.

Meshi D, Ellithorpe ME. Problematic social media use and social support received in real-life versus on social media: associations with depression, anxiety and social isolation. Addict Behav 2021, 119.

Mitropoulou EM, Karagianni M, Thomadakis C. Social Media Addiction, Self-Compassion, and Psychological Well-Being: a structural equation Model. Alpha Psychiatry. 2022;23(6):298–304.

Ozimek P, Brailovskaia J, Bierhoff H-W. Active and passive behavior in social media: validating the Social Media Activity Questionnaire (SMAQ). Telematics Inf Rep. 2023;10:100048.

Phillips WJ, Wisniewski AT. Self-compassion moderates the predictive effects of social media use profiles on depression and anxiety. Computers Hum Behav Rep. 2021;4:100128.

Reer F, Festl R, Quandt T. Investigating problematic social media and game use in a nationally representative sample of adolescents and younger adults. Behav Inform Technol. 2021;40(8):776–89.

Satici B, Kayis AR, Griffiths MD. Exploring the Association between Social Media Addiction and relationship satisfaction: psychological distress as a Mediator. Int J Mental Health Addict 2021.

Sediri S, Zgueb Y, Ouanes S, Ouali U, Bourgou S, Jomli R, Nacef F. Women’s mental health: acute impact of COVID-19 pandemic on domestic violence. Archives Womens Mental Health. 2020;23(6):749–56.

Shabahang R, Shim H, Aruguete MS, Zsila A. Oversharing on Social Media: anxiety, Attention-Seeking, and Social Media Addiction Predict the breadth and depth of sharing. Psychol Rep 2022:332941221122861–332941221122861.

Sotero L, Ferreira Da Veiga G, Carreira D, Portugal A, Relvas AP. Facebook Addiction and emerging adults: the influence of sociodemographic variables, family communication, and differentiation of self. Escritos De Psicología - Psychol Writings. 2019;12(2):81–92.

Stockdale LA, Coyne SM. Bored and online: reasons for using social media, problematic social networking site use, and behavioral outcomes across the transition from adolescence to emerging adulthood. J Adolesc. 2020;79:173–83.

Wang Z, Yang H, Elhai JD. Are there gender differences in comorbidity symptoms networks of problematic social media use, anxiety and depression symptoms? Evidence from network analysis. Pers Indiv Differ. 2022;195:111705.

White-Gosselin C-E, Poulin F. Associations Between Young Adults’ Social Media Addiction, Relationship Quality With Parents, and Internalizing Problems: A Path Analysis Model. 2022.

Wong HY, Mo HY, Potenza MN, Chan MNM, Lau WM, Chui TK, Pakpour AH, Lin C-Y. Relationships between Severity of Internet Gaming Disorder, Severity of Problematic Social Media Use, Sleep Quality and Psychological Distress. Int J Environ Res Public Health 2020, 17(6).

Yam C-W, Pakpour AH, Griffiths MD, Yau W-Y, Lo C-LM, Ng JMT, Lin C-Y, Leung H. Psychometric testing of three Chinese online-related addictive Behavior instruments among Hong Kong University students. Psychiatr Q. 2019;90(1):117–28.

Yuan Y, Zhong Y. A survey on the use of social networks and mental health of college students during the COVID-19 pandemic. J Campus Life Mental Health\. 2021;19(3):209–12.

Google Scholar  

Yurdagul C, Kircaburun K, Emirtekin E, Wang P, Griffiths MD. Psychopathological consequences related to problematic Instagram Use among adolescents: the mediating role of body image dissatisfaction and moderating role of gender. Int J Mental Health Addict. 2021;19(5):1385–97.

Zhang W, Pu J, He R, Yu M, Xu L, He X, Chen Z, Gan Z, Liu K, Tan Y, et al. Demographic characteristics, family environment and psychosocial factors affecting internet addiction in Chinese adolescents. J Affect Disord. 2022;315:130–8.

Zhang L, Wu Y, Jin T, Jia Y. Revision and validation of the Chinese short version of social media disorder. Mod Prev Med. 2021;48(8):1350–3.

Zhang X, Fan L. The influence of anxiety on colleges’ life satisfaction. Chin J Health Educ. 2021;37(5):469–72.

Zhao M, Wang H, Dong Y, Niu Y, Fang Y. The relationship between self-esteem and wechat addiction among undergraduate students: the multiple mediating roles of state anxiety and online interpersonal trust. J Psychol Sci. 2021;44(1):104–10.

Zhao J, Zhou Z, Sun B, Zhang X, Zhang L, Fu S. Attentional Bias is Associated with negative emotions in problematic users of Social Media as measured by a dot-probe Task. Int J Environ Res Public Health 2022, 19(24).

Atroszko PA, Balcerowska JM, Bereznowski P, Biernatowska A, Pallesen S, Schou Andreassen C. Facebook addiction among Polish undergraduate students: validity of measurement and relationship with personality and well-being. Comput Hum Behav. 2018;85:329–38.

Chen Y, Li R, Zhang P, Liu X. The moderating role of state attachment anxiety and avoidance between social anxiety and social networking sites Addiction. Psychol Rep. 2020;123(3):633–47.

Chen B, Zheng X, Sun X. The relationship between problematic social media use and online social anxiety: the roles of social media cognitive overload and dispositional mindfulness. Psychol Dev Educ. 2023;39(5):743–51.

Chentsova VO, Bravo AJ, Mezquita L, Pilatti A, Hogarth L, Cross-Cultural AS. Internalizing symptoms, rumination, and problematic social networking site use: a cross national examination among young adults in seven countries. Addict Behav 2023, 136.

Chu X, Ji S, Wang X, Yu J, Chen Y, Lei L. Peer phubbing and social networking site addiction: the mediating role of social anxiety and the moderating role of Family Financial Difficulty. Front Psychol. 2021;12:670065–670065.

Dempsey AE, O’Brien KD, Tiamiyu MF, Elhai JD. Fear of missing out (FoMO) and rumination mediate relations between social anxiety and problematic Facebook use. Addict Behav Rep. 2019;9:100150–100150.

PubMed   PubMed Central   Google Scholar  

Yildiz Durak H, Seferoglu SS. Modeling of variables related to problematic social media usage: Social desirability tendency example. Scand J Psychol. 2019;60(3):277–88.

Ekinci N, Akat M. The relationship between anxious-ambivalent attachment and social appearance anxiety in adolescents: the serial mediation of positive Youth Development and Instagram Addiction. Psychol Rep 2023:332941231159600–332941231159600.

Foroughi B, Griffiths MD, Iranmanesh M, Salamzadeh Y. Associations between Instagram Addiction, academic performance, social anxiety, Depression, and life satisfaction among University students. Int J Mental Health Addict. 2022;20(4):2221–42.

He L. Influence mechanism and intervention suggestions on addiction of social network addiction. Gannan Normal University; 2021.

Hu Y. The influencing mechanism of type D personality on problematic social networking sites use among adolescents and intervention research. Central China Normal University; 2020.

Jia L. A study of the relationship between neuroticism, perceived social support, social anxiety and problematic social network use in high school students. Harbin Normal University; 2022.

Lee-Won RJ, Herzog L, Park SG. Hooked on Facebook: the role of social anxiety and need for Social Assurance in Problematic Use of Facebook. Cyberpsychology Behav Social Netw. 2015;18(10):567–74.

Li H. Social anxiety and internet interpersonal addiction in adolescents and countermeasures. Central China Normal University; 2022.

Lin W-S, Chen H-R, Lee TS-H, Feng JY. Role of social anxiety on high engagement and addictive behavior in the context of social networking sites. Data Technol Appl. 2019;53(2):156–70.

Liu Y. The influence of family function on social media addiction in adolescents: the chain mediation effect of social anxiety and resilience. Hunan Normal University; 2021.

Lyvers M, Salviani A, Costan S, Thorberg FA. Alexithymia, narcissism and social anxiety in relation to social media and internet addiction symptoms. Int J Psychology: J Int De Psychologie. 2022;57(5):606–12.

Majid A, Yasir M, Javed A, Ali P. From envy to social anxiety and rumination: how social media site addiction triggers task distraction amongst nurses. J Nurs Adm Manag. 2020;28(3):504–13.

Mou Q, Zhuang J, Gao Y, Zhong Y, Lu Q, Gao F, Zhao M. The relationship between social anxiety and academic engagement among Chinese college students: a serial mediation model. J Affect Disord. 2022;311:247–53.

Ruggieri S, Santoro G, Pace U, Passanisi A, Schimmenti A. Problematic Facebook use and anxiety concerning use of social media in mothers and their offspring: an actor-partner interdependence model. Addict Behav Rep. 2020;11:100256–100256.

Ruiz MJ, Saez G, Villanueva-Moya L, Exposito F. Adolescent sexting: the role of body shame, Social Physique anxiety, and social networking site addiction. Cyberpsychology Behav Social Netw. 2021;24(12):799–805.

She R, Kit Han Mo P, Li J, Liu X, Jiang H, Chen Y, Ma L, Tak Fai Lau J. The double-edged sword effect of social networking use intensity on problematic social networking use among college students: the role of social skills and social anxiety. Comput Hum Behav. 2023;140:107555.

Stănculescu E. The Bergen Social Media Addiction Scale Validity in a Romanian sample using item response theory and network analysis. Int J Mental Health Addict 2022.

Teng X, Lei H, Li J, Wen Z. The influence of social anxiety on social network site addiction of college students: the moderator of intentional self-regulation. Chin J Clin Psychol. 2021;29(3):514–7.

Tong W. Influence of boredom on the problematic mobile social networks usage in adolescents: multiple chain mediator. Chin J Clin Psychol. 2019;27(5):932–6.

Tu W, Jiang H, Liu Q. Peer victimization and adolescent Mobile Social Addiction: mediation of social anxiety and gender differences. Int J Environ Res Public Health 2022, 19(17).

Wang S. The influence of college students self-esteem, social anxiety and fear of missing out on the problematic mobile social networks usage. Huaibei Normal University; 2021.

Wang X. The impact of peer relationship and social anxiety on secondary vocational school students’ problematic social network use and intervention study. Huaibei Normal University; 2022.

Wegmann E, Stodt B, Brand M. Addictive use of social networking sites can be explained by the interaction of internet use expectancies, internet literacy, and psychopathological symptoms. J Behav Addictions. 2015;4(3):155–62.

Yang W. The relationship between the type of internet addiction and the personality traits in college students. Huazhong University of Science and Technology; 2004.

Yang Z. The relationship between social variables and social networking usage among shanghai working population. East China Normal University; 2013.

Zhang C. The relationship between perceived social support and problematic social network use among junior high school students: a chain mediation model and an intervention study. Hebei University; 2022.

Zhang J, Chang F, Huang D, Wen X. The relationship between neuroticism and the problematic mobile social networks use in adolescents: the mediating role of anxiety and positive self-presentation. Chin J Clin Psychol. 2021;29(3):598–602.

Zhang Z. College students’ loneliness and problematic social networking use: Chain mediation of social self-efficacy and social anxiety. Shanghai Normal University; 2019.

Zhu B. Discussion on mechanism of social networking addiction——Social anxiety, craving and excitability. Liaoning Normal University; 2017.

Blackwell D, Leaman C, Tramposch R, Osborne C, Liss M. Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Pers Indiv Differ. 2017;116:69–72.

Chen A. From attachment to addiction: the mediating role of need satisfaction on social networking sites. Comput Hum Behav. 2019;98:80–92.

Chen Y, Zhong S, Dai L, Deng Y, Liu X. Attachment anxiety and social networking sites addiction in college students: a moderated mediating model. Chin J Clin Psychol. 2019;27(3):497–500.

Li J. The relations among problematic social networks usage behavior, Childhood Trauma and adult attachment in University students. Hunan Agricultural University; 2020.

Liu C, Ma J-L. Adult attachment orientations and social networking site addiction: the Mediating effects of Online Social Support and the fear of missing out. Front Psychol. 2019;10:2629–2629.

Mo S, Huang W, Xu Y, Tang Z, Nie G. The impact of medical students’ attachment anxiety on the use of problematic social networking sites during the epidemic. Psychol Monthly. 2022;17(9):1–4.

Teng X. The effect of attachment anxiety on problematic mobile social network use: the role of loneliness and self-control. Harbin Normal University; 2021.

Worsley JD, Mansfield R, Corcoran R. Attachment anxiety and problematic social media use: the Mediating Role of Well-Being. Cyberpsychology Behav Social Netw. 2018;21(9):563–8.

Wu Z. The effect of adult attachment on problematic social network use: the chain mediating effect of loneliness and fear of missing out. Jilin University; 2022.

Xia N. The impact of attachment anxiety on adolescent problem social networking site use: a moderated mediation model. Shihezi University; 2022.

Young L, Kolubinski DC, Frings D. Attachment style moderates the relationship between social media use and user mental health and wellbeing. Heliyon 2020, 6(6).

Bakioglu F, Deniz M, Griffiths MD, Pakpour AH. Adaptation and validation of the online-fear of missing out inventory into Turkish and the association with social media addiction, smartphone addiction, and life satisfaction. BMC Psychol. 2022;10(1):154–154.

Bendayan R, Blanca MJ. Spanish version of the Facebook Intrusion Questionnaire (FIQ-S). Psicothema. 2019;31(2):204–9.

Blachnio A, Przepiorka A. Facebook intrusion, fear of missing out, narcissism, and life satisfaction: a cross-sectional study. Psychiatry Res. 2018;259:514–9.

Casale S, Rugai L, Fioravanti G. Exploring the role of positive metacognitions in explaining the association between the fear of missing out and social media addiction. Addict Behav. 2018;85:83–7.

Chen Y, Zhang Y, Zhang S, Wang K. Effect of fear of’ missing out on college students negative social adaptation: Chain¬ - mediating effect of rumination and problematic social media use. China J Health Psychol. 2022;30(4):581–6.

Cheng S, Zhang X, Han Y. Relationship between fear of missing out and phubbing on college students: the chain intermediary effect of intolerance of uncertainty and problematic social media use. China J Health Psychol. 2022;30(9):1296–300.

Cui Q, Wang J, Zhang J, Li W, Li Q. The relationship between loneliness and negative emotion in college students: the chain-mediating role of fear of missing out and social network sites addiction. J Jining Med Univ. 2022;45(4):248–51.

Ding Q, Wang Z, Zhang Y, Zhou Z. The more gossip, the more addicted: the relationship between interpersonal curiosity and social networking sites addiction tendencies in college students. Psychol Dev Educ. 2022;38(1):118–25.

Fabris MA, Marengo D, Longobardi C, Settanni M. Investigating the links between fear of missing out, social media addiction, and emotional symptoms in adolescence: the role of stress associated with neglect and negative reactions on social media. Addict Behav. 2020;106:106364.

Fang J, Wang X, Wen Z, Zhou J. Fear of missing out and problematic social media use as mediators between emotional support from social media and phubbing behavior. Addict Behav. 2020;107:106430.

Gao Z. The study on the relationship and intervention among fear of missing out self-differentiation and problematic social media use of college students. Yunnan Normal University; 2021.

Gioia F, Fioravanti G, Casale S, Boursier V. The Effects of the Fear of Missing Out on People’s Social Networking Sites Use During the COVID-19 Pandemic: The Mediating Role of Online Relational Closeness and Individuals’ Online Communication Attitude. Front Psychiatry 2021, 12.

Gu X. Study on the Inhibitory Effect of Mindfulness Training on Social Media Addiction of College Students. Wuhan University; 2020.

Gugushvili N, Taht K, Schruff-Lim EM, Ruiter RA, Verduyn P. The Association between Neuroticism and problematic social networking sites Use: the role of fear of missing out and Self-Control. Psychol Rep 2022:332941221142003–332941221142003.

Hou J. The study on FoMO and content social media addiction among young people. Huazhong University of Science and Technology; 2021.

Hu R, Zhang B, Yang Y, Mao H, Peng Y, Xiong S. Relationship between college students’ fear of missing and wechat addiction: a cross-lagged analysis. J Bio-education. 2022;10(5):369–73.

Hu G. The relationship between basic psychological needs satisfaction and the use of problematic social networks by college students: a moderated mediation model and online intervention studies. Jiangxi Normal University; 2020.

Jiang Y, Jin T. The relationship between adolescents’ narcissistic personality and their use of problematic mobile social networks: the effects of fear of missing out and positive self-presentation. Chin J Special Educ 2018(11):64–70.

Li J. The effect of positive self-presentation on social networking sites on problematic use of social networking sites: a moderated mediation model. Henan University; 2020.

Li J, Zhang Y, Zhang X. The impact of Freshmen Social Exclusion on problematic Social Network Use: a Moderated Mediation Model. J Heilongjiang Vocat Inst Ecol Eng. 2023;36(1):118–22.

Li M. The relationship between fear of missing out and social media addiction among middle school students——The moderating role of self-control. Kashi University; 2022.

Li R, Dong X, Wang M, Wang R. A study on the relationship between fear of missing out and social network addiction. New Educ Era 2021(52):122–3.

Li Y. Fear of missing out or social avoidance? The influence of peer exclusion on problematic social media use among adolescents in Guangdong Province and Macao. Guangzhou University; 2020.

Ma J, Liu C. The effect of fear of missing out on social networking sites addiction among college students: the mediating roles of social networking site integration use and social support. Psychol Dev Educ. 2019;35(5):605–14.

Mao H. A follow-up study on the mechanism of the influence of university students’ Qi deficiency quality on WeChat addiction. Hunan University of Chinese Medicine; 2021.

Mao Y. The effect of dual filial piety to the college students ’internet social dependence: the mediation of maladaptive cognition and fear of missing out. Huazhong University of Science and Technology; 2021.

Moore K, Craciun G. Fear of missing out and personality as predictors of Social networking sites usage: the Instagram Case. Psychol Rep. 2021;124(4):1761–87.

Niu J. The relationship of college students’ basic psychological needs and social media dependence: the mediating role of fear of missing out. Huazhong University of Science and Technology; 2021.

Pi L, Li X. Research on the relationship between loneliness and problematic mobile social media usage: evidence from variable-oriented and person-oriented analyses. China J Health Psychol. 2023;31(6):936–42.

Pontes HM, Taylor M, Stavropoulos V. Beyond Facebook Addiction: the role of cognitive-related factors and Psychiatric Distress in Social networking site addiction. Cyberpsychol Behav Soc Netw. 2018;21(4):240–7.

Quaglieri A, Biondi S, Roma P, Varchetta M, Fraschetti A, Burrai J, Lausi G, Marti-Vilar M, Gonzalez-Sala F, Di Domenico A et al. From Emotional (Dys) Regulation to Internet Addiction: A Mediation Model of Problematic Social Media Use among Italian Young Adults. Journal of Clinical Medicine 2022, 11(1).

Servidio R, Koronczai B, Griffiths MD, Demetrovics Z. Problematic smartphone Use and Problematic Social Media Use: the predictive role of Self-Construal and the Mediating Effect of Fear Missing Out. Front Public Health 2022, 10.

Sheldon P, Antony MG, Sykes B. Predictors of problematic social media use: personality and life-position indicators. Psychol Rep. 2021;124(3):1110–33.

Sun C, Li Y, Kwok SYCL, Mu W. The relationship between intolerance of uncertainty and problematic Social Media Use during the COVID-19 pandemic: a serial mediation model. Int J Environ Res Public Health 2022, 19(22).

Tang Z. The relationship between loneliness and problematic social networks use among college students: the mediation of fear of missing out and the moderation of social support. Jilin University; 2022.

Tomczyk Ł, Selmanagic-Lizde E. Fear of missing out (FOMO) among youth in Bosnia and Herzegovina — Scale and selected mechanisms. Child Youth Serv Rev. 2018;88:541–9.

Unal-Aydin P, Ozkan Y, Ozturk M, Aydin O, Spada MM. The role of metacognitions in cyberbullying and cybervictimization among adolescents diagnosed with major depressive disorder and anxiety disorders: a case-control study. Clinical Psychology & Psychotherapy; 2023.

Uram P, Skalski S. Still logged in? The Link between Facebook Addiction, FoMO, Self-Esteem, Life satisfaction and loneliness in social media users. Psychol Rep. 2022;125(1):218–31.

Varchetta M, Fraschetti A, Mari E, Giannini AM. Social Media Addiction, fear of missing out (FoMO) and online vulnerability in university students. Revista Digit De Investigación en Docencia Universitaria. 2020;14(1):e1187.

Wang H. Study on the relationship and intervention between fear of missing and social network addiction in college students. Yunnan Normal University; 2021.

Wang M, Yin Z, Xu Q, Niu G. The relationship between shyness and adolescents’ social network sites addiction: Moderated mediation model. Chin J Clin Psychol. 2020;28(5):906–9.

Wegmann E, Oberst U, Stodt B, Brand M. Online-specific fear of missing out and internet-use expectancies contribute to symptoms of internet-communication disorder. Addict Behav Rep. 2017;5:33–42.

Wegmann E, Brandtner A, Brand M. Perceived strain due to COVID-19-Related restrictions mediates the Effect of Social needs and fear of missing out on the risk of a problematic use of Social Networks. Front Psychiatry 2021, 12.

Wei Q. Negative emotions and problematic social network sites usage: the mediating role of fear of missing out and the moderating role of gender. Central China Normal University; 2018.

Xiong L. Effect of social network site use on college students’ social network site addiction: A moderated mediation model and attention bias training intervention study. Jiangxi Normal University; 2022.

Yan H. The influence of college students’ basic psychological needs on social network addiction: The intermediary role of fear of missing out. Wuhan University; 2020.

Yan H. The status and factors associated with social media addiction among young people——Evidence from WeChat. Chongqing University; 2021.

Yang L. Research on the relationship of fear of missing out, excessive use of Wechat and life satisfaction. Beijing Forestry University; 2020.

Yin Y, Cai X, Ouyang M, Li S, Li X, Wang P. FoMO and the brain: loneliness and problematic social networking site use mediate the association between the topology of the resting-state EEG brain network and fear of missing out. Comput Hum Behav. 2023;141:107624.

Zhang C. The parental rejection and problematic social network sites with adolescents: the chain mediating effect of basic psychological needs and fear of missing out. Central China Normal University; 2022.

Zhang J. The influence of basic psychological needs on problematic mobile social networks usage of adolescent: a moderated mediation model. Liaocheng University; 2020.

Zhang Y, Chen Y, Jin J, Yu G. The relationship between fear of missing out and social media addiction: a cross-lagged analysis. Chin J Clin Psychol. 2021;29(5):1082–5.

Zhang Y, Jiang W, Ding Q, Hong M. Social comparison orientation and social network sites addiction in college students: the mediating role of fear of missing out. Chin J Clin Psychol. 2019;27(5):928–31.

Zhou J, Fang J. Social network sites support and addiction among college students: a moderated mediation model. Psychology: Techniques Appl. 2021;9(5):293–9.

Andreassen CS, Torsheim T, Brunborg GS, Pallesen S. Development of a Facebook addiction scale. Psychol Rep. 2012;110(2):501–17.

Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, Pallesen S. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behav. 2016;30(2):252.

Elphinston RA, Noller P. Time to face it! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. Cyberpsychology Behav Social Netw. 2011;14(11):631–5.

Caplan SE. Theory and measurement of generalized problematic internet use: a two-step approach. Comput Hum Behav. 2010;26(5):1089–97.

Jiang Y. Development of problematic mobile social media usage assessment questionnaire for adolescents. Psychology: Techniques Appl. 2018;6(10):613–21.

Wang X. College students’ social network addiction tendency: Questionnaire construction and correlation research. Master’s thesis Southwest University; 2016.

Derogatis LR. Brief symptom inventory 18. Johns Hopkins University Baltimore; 2001.

Lovibond PF, Lovibond SH. The structure of negative emotional states: comparison of the Depression anxiety stress scales (DASS) with the Beck Depression and anxiety inventories. Behav Res Ther. 1995;33(3):335–43.

Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7.

Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica. 1983;67(6):361–70.

Spielberger CD, Gonzalez-Reigosa F, Martinez-Urrutia A, Natalicio LF, Natalicio DS. The state-trait anxiety inventory. Revista Interamericana de Psicologia/Interamerican Journal of Psychology 1971, 5(3&4).

Marteau TM, Bekker H. The development of a six-item short‐form of the state scale of the Spielberger State—trait anxiety inventory (STAI). Br J Clin Psychol. 1992;31(3):301–6.

Leary MR. Social anxiousness: the construct and its measurement. J Pers Assess. 1983;47(1):66–75.

Liebowitz MR. Social phobia. Modern problems of pharmacopsychiatry 1987.

Alkis Y, Kadirhan Z, Sat M. Development and validation of social anxiety scale for social media users. Comput Hum Behav. 2017;72:296–303.

La Greca AM, Stone WL. Social anxiety scale for children-revised: factor structure and concurrent validity. J Clin Child Psychol. 1993;22(1):17–27.

Fenigstein A, Scheier MF, Buss AH. Public and private self-consciousness: Assessment and theory. J Consult Clin Psychol. 1975;43(4):522.

Mattick RP, Clarke JC. Development and validation of measures of social phobia scrutiny fear and social interaction anxiety. Behav Res Ther. 1998;36(4):455–70.

Peters L, Sunderland M, Andrews G, Rapee RM, Mattick RP. Development of a short form Social Interaction anxiety (SIAS) and Social Phobia Scale (SPS) using nonparametric item response theory: the SIAS-6 and the SPS-6. Psychol Assess. 2012;24(1):66.

Brennan KA, Clark CL, Shaver PR. Self-report measurement of adult attachment: an integrative overview. Attachment Theory Close Relationships. 1998;46:76.

Wei M, Russell DW, Mallinckrodt B, Vogel DL. The experiences in Close Relationship Scale (ECR)-short form: reliability, validity, and factor structure. J Pers Assess. 2007;88(2):187–204.

Bartholomew K, Horowitz LM. Attachment styles among young adults: a test of a four-category model. J Personal Soc Psychol. 1991;61(2):226.

Przybylski AK, Murayama K, DeHaan CR, Gladwell V. Motivational, emotional, and behavioral correlates of fear of missing out. Comput Hum Behav. 2013;29(4):1841–8.

Xiaokang S, Yuxiang Z, Xuanhui Z. Developing a fear of missing out (FoMO) measurement scale in the mobile social media environment. Libr Inform Service. 2017;61(11):96.

Bown M, Sutton A. Quality control in systematic reviews and meta-analyses. Eur J Vasc Endovasc Surg. 2010;40(5):669–77.

Turel O, Qahri-Saremi H. Problematic use of social networking sites: antecedents and consequence from a dual-system theory perspective. J Manage Inform Syst. 2016;33(4):1087–116.

Chou H-TG, Edge N. They are happier and having better lives than I am: the impact of using Facebook on perceptions of others’ lives. Cyberpsychology Behav Social Netw. 2012;15(2):117–21.

Beyens I, Frison E, Eggermont S. I don’t want to miss a thing: adolescents’ fear of missing out and its relationship to adolescents’ social needs, Facebook use, and Facebook related stress. Comput Hum Behav. 2016;64:1–8.

Di Blasi M, Gullo S, Mancinelli E, Freda MF, Esposito G, Gelo OCG, Lagetto G, Giordano C, Mazzeschi C, Pazzagli C. Psychological distress associated with the COVID-19 lockdown: a two-wave network analysis. J Affect Disord. 2021;284:18–26.

Yang X, Hu H, Zhao C, Xu H, Tu X, Zhang G. A longitudinal study of changes in smart phone addiction and depressive symptoms and potential risk factors among Chinese college students. BMC Psychiatry. 2021;21(1):252.

Kuss DJ, Griffiths MD. Social networking sites and addiction: ten lessons learned. Int J Environ Res Public Health. 2017;14(3):311.

Ryan T, Chester A, Reece J, Xenos S. The uses and abuses of Facebook: a review of Facebook addiction. J Behav Addictions. 2014;3(3):133–48.

Elhai JD, Levine JC, Dvorak RD, Hall BJ. Non-social features of smartphone use are most related to depression, anxiety and problematic smartphone use. Comput Hum Behav. 2017;69:75–82.

Jackson LA, Wang J-L. Cultural differences in social networking site use: a comparative study of China and the United States. Comput Hum Behav. 2013;29(3):910–21.

Ahrens LM, Mühlberger A, Pauli P, Wieser MJ. Impaired visuocortical discrimination learning of socially conditioned stimuli in social anxiety. Soc Cognit Affect Neurosci. 2014;10(7):929–37.

Elhai JD, Yang H, Montag C. Fear of missing out (FOMO): overview, theoretical underpinnings, and literature review on relations with severity of negative affectivity and problematic technology use. Brazilian J Psychiatry. 2020;43:203–9.

Barker V. Older adolescents’ motivations for social network site use: the influence of gender, group identity, and collective self-esteem. Cyberpsychology Behav. 2009;12(2):209–13.

Krasnova H, Veltri NF, Eling N, Buxmann P. Why men and women continue to use social networking sites: the role of gender differences. J Strateg Inf Syst. 2017;26(4):261–84.

Palmer J. The role of gender on social network websites. Stylus Knights Write Showc 2012:35–46.

Vannucci A, Flannery KM, Ohannessian CM. Social media use and anxiety in emerging adults. J Affect Disord. 2017;207:163–6.

Primack BA, Shensa A, Sidani JE, Whaite EO, yi Lin L, Rosen D, Colditz JB, Radovic A, Miller E. Social media use and perceived social isolation among young adults in the US. Am J Prev Med. 2017;53(1):1–8.

Twenge JM, Campbell WK. Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Prev Med Rep. 2018;12:271–83.

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This research was supported by the Social Science Foundation of China (Grant Number: 23BSH135).

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Exploring age and gender variations in root canal morphology of maxillary premolars in Saudi sub population: a cross-sectional CBCT study

• Mohmed Isaqali Karobari 1 , 2 ,
• Azhar Iqbal 3 ,
• Rumesa Batul 1 ,
• Abdul Habeeb Adil 1 ,
• Jamaluddin Syed 4 , 5 ,
• Hmoud Ali Algarni 3 ,
• Meshal Aber Alonazi 3 &
• Tahir Yusuf Noorani 6 , 7  

BMC Oral Health volume  24 , Article number:  543 ( 2024 ) Cite this article

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In complex teeth like maxillary premolars, endodontic treatment success depends on a complete comprehension of root canal anatomy. The research on mandibular premolars’ root canal anatomy has been extensive and well-documented in existing literature. However, there appears to be a notable gap in available data concerning the root canal anatomy of maxillary premolars. This study aimed to explore the root canal morphology of maxillary premolars using cone-beam computed tomography (CBCT) imaging, considering age and gender variations.

From 500 patient CBCT scans, 787 maxillary premolar teeth were evaluated. The sample was divided by gender and age (10–20, 21–30, 31–40, 41–50, 51–60, and 61 years and older). Ahmed et al. classification system was used to record root canal morphology.

The most frequent classifications for right maxillary 1st premolars were 2 MPM 1 B 1 L 1 (39.03%) and 1 MPM 1 (2.81%), while the most frequent classifications for right maxillary 2nd premolars were 2 MPM 1 B 1 L 1 (39.08%) and 1 MPM 1 (17.85%). Most of the premolars typically had two roots (left maxillary first premolars: 81.5%, left maxillary second premolars: 82.7%, right maxillary first premolars: 74.4%, right maxillary second premolars: 75.7%). Left and right maxillary 1st premolars for classes 1 MPM 1 and 1 MPM 1–2−1 showed significant gender differences. For classifications 1 MPM 1 and 1 MPM 1–2−1 , age-related changes were seen in the left and right maxillary first premolars.

This study provides novel insights into the root canal anatomy of maxillary premolars within the Saudi population, addressing a notable gap in the literature specific to this demographic. Through CBCT imaging and analysis of large sample sizes, the complex and diverse nature of root canal morphology in these teeth among Saudi individuals is elucidated. The findings underscore the importance of CBCT imaging in precise treatment planning and decision-making tailored to the Saudi population. Consideration of age and gender-related variations further enhances understanding and aids in personalized endodontic interventions within this demographic.

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Introduction

The morphology and variability of root canal systems play a crucial role in the success of endodontic treatment [ 1 , 2 ]. Understanding the intricacies of root canal anatomy is essential for effective diagnosis, treatment planning, and applying appropriate techniques. The research on mandibular premolars’ root canal anatomy has been extensive and well-documented in existing literature [ 3 , 4 ]. However, there appears to be a notable gap in available data concerning the root canal anatomy of maxillary premolars [ 5 , 6 , 7 , 8 , 9 ].

Maxillary premolars present unique challenges due to their anatomical complexity, including multiple canals, isthmuses, and accessory canals [ 10 , 11 ]. Accurately identifying and classifying root canal systems in maxillary premolars is crucial for diagnosis and achieving optimal treatment outcomes [ 12 ].

Despite the importance of understanding root canal morphology, there remains a gap in knowledge concerning maxillary premolars. This lack of comprehensive information on the root canal morphology of maxillary premolars hinders endodontic practitioners’ ability to deliver precise and successful treatments [ 13 ]. This study aims to fill this gap by conducting an investigation using cone-beam computed tomography (CBCT) imaging. CBCT, as a non-invasive and highly accurate imaging technique, offers the advantage of providing detailed three-dimensional representations of root canal systems, which were previously not easily achievable through conventional radiographs [ 14 ]. The high-resolution images obtained through CBCT will provide valuable data to enhance the knowledge and clinical management of root canal anatomy in these teeth, leading to better-informed treatment decisions and reduced complications [ 4 , 15 ].

By analyzing a large sample size of CBCT images, we aim to comprehensively understand the root canal configuration in maxillary premolars, considering factors such as age and gender [ 16 ]. The findings of this study will contribute to enhancing the knowledge and clinical management of root canal anatomy in maxillary premolars, improving treatment success rates, and reducing complications.

By elucidating the variations and complexities of root canal morphology in maxillary premolars, this study will aid dental professionals in making informed decisions regarding treatment approaches, instrument selection, and the application of advanced endodontic techniques [ 17 , 18 ]. Furthermore, the results will provide valuable insights for dental educators, researchers, and students, facilitating the development of standardized protocols and guidelines for managing root canal systems in maxillary premolars.

Methodology

Study design.

This study employed a retrospective cross-sectional design to comprehensively investigate the root canal morphology of maxillary premolars using cone-beam computed tomography (CBCT) imaging. This design allows for the examination of a large sample size and facilitates the analysis of root canal anatomy variations among different age groups and genders. By retrospectively analyzing CBCT images, the study aimed to elucidate the complex root canal anatomy of maxillary premolars and identify potential factors influencing their variability.

Ethical consideration

Ethical approval was obtained from the Local Committee of Bioethics for Research at the Dentistry College, King Abdul-Aziz University (Ethical Approval No. 025-02-22). Informed consent was obtained from the Committee of Bioethics for Research, College of Dentistry, King Abdul-Aziz University, Jeddah, Saudi Arabia, considering the retrospective nature of the study. This ensured that the study adhered to ethical standards and protected the rights and confidentiality of the participants. Additionally, the study complied with all relevant regulations and guidelines regarding the use of patient data for research purposes.

Sample size determination

The sample size for this study was determined using G Power 3.1.9.4 software, considering a chi-square test for goodness-of-fit, statistical power analysis, and an a priori approach. A comprehensive sample of 500 patient records was obtained, resulting in the evaluation of 787 maxillary premolar teeth. This large sample size enhances the statistical power of the study and allows for robust analysis of root canal morphology variations. It also increases the generalizability of the findings to the target population.

Inclusion and exclusion criteria

Inclusion criteria were carefully defined to ensure the selection of appropriate teeth for analysis. Healthy maxillary premolars with small carious or restorative crowns, fully formed root apex, and defect-free radiographic images were included in the study. Exclusion criteria were applied to eliminate potential confounding factors, including root canal-treated teeth, fractured upper and lower posterior teeth, post and core restorations, calcification, resorption defects, and anomalies of crown and root. These criteria helped ensure the homogeneity of the study sample and the validity of the results.

Imaging technique

CBCT images were acquired using the iCAT scanner system (Imaging Sciences International, Hatfield, PA, USA), a widely recognized and reliable imaging device in dentistry. Standardized imaging parameters (120 KVp, 5–7 mA) were employed to ensure consistent image quality across all scans. The use of CBCT allowed for the acquisition of detailed three-dimensional representations of root canal anatomy, enabling precise analysis and classification. High-resolution images obtained through CBCT provided valuable data for evaluating root canal morphology.

Calibration and reliability

Prior to data collection, calibration was conducted involving an expert endodontist and an observer. The observer underwent rigorous training to accurately identify and classify root canal morphology. Calibration involved the examination of 50 CBCT images, with discrepancies resolved through discussion to achieve consensus. The kappa test was utilized to determine the level of agreement between observers, and intra- and interobserver reliability was assessed. Furthermore, specimens were assessed independently by observers following calibration to minimize bias and ensure consistency in the evaluations. A high kappa value (0.8) was obtained, indicating substantial to almost perfect reliability, thereby ensuring the validity of the data collected. This rigorous calibration process helped minimize observer bias and enhance the reliability of the study findings.

Root and canal analysis

Root canal morphology was recorded and classified according to the classification system proposed by Ahmed et al. in 2017. This classification system provides a standardized framework for describing root canal configurations, facilitating comparisons across studies. The obtained CBCT images were meticulously analyzed, with root canal morphology recorded for each maxillary premolar (Fig.  1 ). The images were divided into age groups (10 to 20, 21 to 30, 31 to 40, 41 to 50, 51 to 60, and 61 years above) and categorized by gender (males and females) to explore variations in root canal anatomy. Detailed analysis of each image was conducted to identify the number of roots, canals, and any anatomical variations present.

New classification system for root canal morphology of maxillary left second premolar classified using the new classification system, described as code 1 25 1 . The code consists of three components, the tooth number – Yellow color arrow, number of roots – blue color arrow and the root canal configuration – green color arrow. The number of roots is added as a superscript before the tooth number, so it is single root and tooth number (25). Description of root canal configuration is written as superscript after the tooth number on the course of the root canal starting from the orifices [O], passing through the canal [C], ending by the foramen [F], so it is single canal

Statistical analysis

Statistical analysis was performed using SPSS version 26 software. Descriptive statistics, including mean frequency and standard deviation, were calculated to summarize the data. The association between root canal morphology and age/gender was analyzed using the chi-square test or Fisher exact test, depending on the distribution of the data. Significance levels were set at p  ≤ 0.05 to determine the statistical significance of the findings. Additionally, subgroup analyses were conducted to explore potential interactions between age, gender, and root canal morphology.

The distribution of maxillary premolars according to Ahmed’s classification was examined. Table  1 presents the distribution of premolars based on the classification categories. For right maxillary 1st premolars, the majority belonged to 2 MPM 1 B 1 L 1 (39.03%) and 1 MPM 1 (2.81%) categories. Similarly, for right maxillary 2nd premolars, 2 MPM 1 B 1 L 1 (39.08%) and 1 MPM 1 (17.85%) were the most prevalent categories.

Table  2 displays the distribution of maxillary premolars based on the number of roots. The majority of premolars had two roots (73.33% for left maxillary 1st premolars, 24.45% for left maxillary 2nd premolars, 74.03% for right maxillary 1st premolars, and 24.32% for right maxillary 2nd premolars) (Figs.  2 , 3 and 4 ).

CBCT View (Sagittal and axial) of left maxillary second premolar showing the code 1 MPM 1

CBCT View (Sagittal and axial) maxillary first and second premolars showing the canal variations

CBCT View (Sagittal and axial) maxillary first and second premolars showing the canal variations in more than one root

Tables  3 and 4 present the distribution of left and right maxillary 1st and 2nd premolars, respectively, based on gender. In Table  3 , significant gender differences were observed for the classification 1 MPM 1 ( p  = 0.515) and 1 MPM 1–2−1 ( p  = 0.010*) for both left maxillary 1st and 2nd premolars. The number of males and females for MPM 1 in left maxillary 1st premolars was 121 and 88, respectively, while for 1 MPM 1 in left maxillary 2nd premolars, it was 111 and 72, respectively. Similarly, for 1 MPM 1–2−1 in left maxillary 1st premolars, the number of males and females was 3 and 3, respectively, whereas for left maxillary 2nd premolars, it was 30 and 21, respectively.

Table  4 indicates significant gender differences for the classification MPM 1 ( p  = 0.032*) and 1 MPM 1–2−1 ( p  = 0.003*) in the right maxillary 1st premolars. The number of males and females for 1 MPM 1 in the right maxillary 1st premolars was 122 and 84, respectively, while for 1 MPM 1 in the right maxillary 2nd premolars, it was 115 and 70, respectively. Additionally, the number of males and females for 1 MPM 1–2−1 in right maxillary 1st premolars was 10 and 11, respectively, whereas, for right maxillary 2nd premolars, it was 33 and 18, respectively.

Tables  5 and 6 demonstrate the distribution of left and right maxillary 1st and 2nd premolars, respectively, based on age groups. In Table  5 , significant differences were observed for the classification 1 MPM 1 ( p  = 0.053) and 1 MPM 1–2−1 ( p  = 0.002*) in left maxillary 1st premolars. The number of premolars in each age group for 1 MPM 1 in left maxillary 1st premolars ranged from 1 to 7, whereas for 1 MPM 1–2−1, it ranged from 0 to 3. For left maxillary 2nd premolars, significant differences were observed for the classification 1 MPM 1 ( p  = 0.002*) and 1 MPM 1–2−1 ( p  = 0.002*). The number of premolars in each age group for 1 MPM 1 in left maxillary 2nd premolars ranged from 6 to 38, whereas for 1 MPM 1–2−1, it ranged from 4 to 23.

In Table  6 , significant differences were observed for the classification 1 MPM 1 ( p  = 0.055) and MPM 1 ( p  = 0.002*) in the right maxillary 1st and 2nd premolars, respectively. The number of premolars in each age group for 1 MPM 1 in the right maxillary 1st premolars ranged from 1 to 6, whereas for 1 MPM 1–2−1, it ranged from 0 to 15. For right maxillary 2nd premolars, significant differences were observed for the classification 1 MPM 1 ( p  = 0.002*) and 1 MPM 1–2−1 ( p  = 0.002*). The number of premolars in each age group for 1 MPM 1 in the right maxillary 2nd premolars ranged from 6 to 36, whereas for 1 MPM 1–2−1 , it ranged from 3 to 15.

The present study aimed to investigate the root canal morphology of maxillary premolars using cone-beam computed tomography (CBCT) imaging. By analyzing a large sample size of CBCT images, we sought to provide a comprehensive understanding of the complex and variable root canal configuration in maxillary premolars, considering factors such as gender and age.

As mentioned in the literature [ 11 , 19 ], our findings revealed a diverse range of root canal configurations in maxillary premolars. Multiple canals, isthmuses, and accessory canals in these teeth pose a challenge to endodontic treatment, as it necessitates thorough exploration, disinfection, and meticulous instrumentation [ 20 ]. Recognizing such complex anatomy underscores the importance of employing advanced imaging techniques, such as CBCT, to accurately visualize and assess root canal morphology [ 21 , 22 ].

In our study, age emerged as a significant factor influencing the root canal morphology of maxillary premolars. The categorization into different age groups allowed for a nuanced exploration of these variations, corroborating previous research [ 23 , 24 , 25 ]. The age-specific analysis revealed noteworthy trends in the prevalence of certain root canal configurations. For instance, in left maxillary 1st premolars, the marginal significance ( p  = 0.053) for 1MPM1 suggests a potential shift in root canal anatomy with increasing age. This finding prompts further investigation into the underlying reasons for such variations across age groups. Similarly, the significant difference ( p  = 0.002*) observed in 1MPM1-2-1 in both left and right maxillary 1st premolars indicates distinct patterns in root canal morphology among different age brackets. This finding raises questions about whether these differences are attributed to developmental changes, wear and tear, or other factors associated with aging. These age-related changes can be attributed to factors such as dentin deposition and secondary dentin formation, which may alter the shape and complexity of the root canal system over time. Therefore, endodontists should consider these age-related variations when planning and performing root canal procedures, particularly in older patients [ 26 ]. Younger age groups may exhibit features associated with incomplete root development and open apices, while older age groups may show signs of maturation, closure of apices, and increased calcification [ 27 ]. The correlations between age-related changes in root canal morphology and systemic conditions enhance the clinical context. Systemic factors, such as hormonal changes, metabolic disorders, or medication use, may influence dental development and impact root canal anatomy differently across age groups [ 28 ]. Practitioners should consider these age-related nuances during treatment planning and execution, adjusting their approaches to accommodate the potential variations in root canal anatomy. For example, younger patients may exhibit different anatomical features compared to older individuals, influencing decisions related to instrumentation and obturation techniques.

Furthermore, our study identified gender-based differences in root canal morphology. This finding aligns with Ahmed et al. [ 19 ], who reported similar gender differences in maxillary premolars. Their study revealed a higher prevalence of multiple canals in males than females, which supports our observations of significant gender variations in root canal morphology. However, it is worth noting that Ahmed et al. did not mention the specific classification code 1 MPM 1–2−1 in their study, making a direct comparison somewhat limited.

Likewise, Cleghorn et al. [ 11 ] found that the prevalence of multiple canals in maxillary first premolars ranged from 30 to 73%, a range consistent with our findings. Shi et al., while studying the Chinese population [ 23 ], also noted significant differences in the number of roots and gender in both maxillary first and second premolars.

In a study conducted by Mashyakhy et al. [ 29 ] in a Saudi population, highly statistically significant differences in canal configurations were observed between genders in maxillary teeth. Similarly, Martins et al. [ 30 ] reported a gender difference in the root canal morphology of the Portuguese population. However, it is essential to mention that some contrasting results were found in specific subpopulations. For instance, no significant difference in root canal morphology was noted in the Malaysian subpopulation [ 31 ] and the German subpopulation [ 32 ].

In summary, our study adds to the existing body of literature by providing further evidence of gender-related variations in root canal morphology, and it is in line with previous research in this field.

This study’s utilization of CBCT imaging provided valuable insights into the three-dimensional morphology of maxillary premolars. CBCT has emerged as a powerful diagnostic tool in endodontics, enabling the visualization of intricate root canal anatomy [ 33 ]. Accurately assessing root canal morphology facilitates precise treatment planning, guiding clinicians in determining the appropriate access, instrumentation, and obturation techniques [ 34 ]. The present study has several advantages, reinforcing its conclusions’ reliability and veracity. First and foremost, a large sample size was used in the study, with 500 cone-beam computed tomography (CBCT) images in total, 1230 maxillary premolars included. This large sample size improves the study’s statistical power and broadens the applicability of the results to the intended population.

The study employed qualified endodontists and observers calibrated to evaluate root canal morphology to achieve precise and reliable analysis. To determine the classification of root canal morphology, 50 CBCT images were examined as part of the calibration process. The research boosted the consistency and accuracy of the results by creating a smooth decision-making process that reduced the possibility of observer bias.

In the present study, a standardized classification scheme was used. This classification system offers a reliable and standardized method for classifying root canal morphology. The study’s findings may be easily compared and integrated with those of other research utilizing the same approach because it used a recognized classification system. Understanding root canal morphology in maxillary premolars is ultimately enhanced by this, making it easier for future research and enabling meta-analyses.

Additionally, the study compared its findings to pertinent literature, enabling a thorough interpretation of the data in light of earlier research. The study offers important insights into the heterogeneity of root canal morphology in maxillary premolars by comparing the consistency or divergence of results across different populations and studies. The scientific knowledge base is expanded, and this topic is better understood thanks to the comparative method.

Strengths of our study

One of the key strengths of our study is the large sample size, which enhances the statistical power and generalizability of our findings. Additionally, the utilization of cone-beam computed tomography (CBCT) imaging allowed for detailed three-dimensional analysis of root canal morphology, providing valuable insights into the complexity of maxillary premolars. Our rigorous calibration process, involving expert endodontists and observers, ensured the reliability and accuracy of our data collection and analysis. Furthermore, by considering age and gender variations, we were able to explore the influence of demographic factors on root canal anatomy, contributing to a more nuanced understanding of this topic.

Limitations

Despite these strengths, our study also has several limitations that warrant consideration. Firstly, the retrospective nature of the study may introduce selection bias and limit the generalizability of the findings. Additionally, the study focused on a specific population, which may limit its applicability to other ethnic groups or regions. Furthermore, the reliance on CBCT imaging, while providing detailed anatomical information, is subject to radiation exposure and cost constraints. Moreover, the inclusion and exclusion criteria applied in the study may have inadvertently excluded certain teeth or patient populations, potentially affecting the representativeness of the sample.

Future research endeavors should explore the relationship between root canal morphology and treatment outcomes in maxillary premolars to enhance our knowledge further. Long-term follow-up studies can provide valuable insights into the success rates and potential complications associated with different root canal configurations. Furthermore, advancements in imaging modalities and treatment techniques, such as guided endodontics and regenerative approaches, hold promise for overcoming the challenges posed by complex root canal anatomy.

This study provides novel insights into the root canal anatomy of maxillary premolars within the Saudi population, addressing a notable gap in the literature specific to this demographic. Through CBCT imaging and analysis of large sample sizes, the complex and diverse nature of root canal morphology in these teeth among Saudi individuals is elucidated. The findings underscore the importance of CBCT imaging in precise treatment planning and decision-making tailored to the Saudi population. Consideration of age and gender-related variations further enhances understanding and aids in personalized endodontic interventions within this demographic. Moving forward, these findings inform clinical practice within the Saudi community, emphasizing the need for customized approaches to optimize treatment outcomes.

Data availability

All data supporting the findings of this study are available from the corresponding author upon reasonable request.

Schilder H. Cleaning and shaping the root canal. Dental Clin N Am. 1974;18(2):269–96.

Article   CAS   Google Scholar  

Karobari MI, et al. Application of two systems to classify the root and canal morphology in the human dentition: a national survey in India. Journal of Dental Education; 2023.

Vertucci FJ. Root canal anatomy of the human permanent teeth Oral surgery, oral medicine, oral pathology, 1984. 58(5): pp. 589–599.

Karobari MI, et al. Evaluation of root and canal morphology of mandibular premolar amongst Saudi subpopulation using the new system of classification: a CBCT study. BMC Oral Health. 2023;23(1):1–11.

Article   Google Scholar  

Martins JN, et al. Worldwide Assessment of the Root and Root Canal characteristics of Maxillary Premolars–A Multi-center Cone-Beam Computed Tomography cross-sectional study with Meta-analysis. J Endod. 2024;50(1):31–54.

Article   PubMed   Google Scholar  

Ahmed HMA. A critical analysis of laboratory and clinical research methods to study root and canal anatomy. Int Endod J. 2022;55:229–80.

Mashyakhy M. Anatomical evaluation of maxillary premolars in a Saudi population: an in vivo cone-beam computed tomography study. J Contemp Dent Pract. 2021;22(3):284–9.

Merhej M-J et al. Root and Root Canal Morphology of Premolars in a Sample of the Lebanese Population: Clinical Considerations 2022.

Karobari MI et al. Assessment of Root Canal Morphology of Maxillary Premolars: A CBCT Study Exploring Age and Gender Variations 2023.

Pécora JD, et al. Internal anatomy, direction and number of roots and size of human mandibular canines. Braz Dent J. 1993;4(1):53–7.

PubMed   Google Scholar  

Cleghorn BM, et al. Root and root canal morphology of the human permanent maxillary first molar: a literature review. J Endod. 2006;32(9):813–21.

Karobari MI, et al. Root and root canal morphology classification systems. Int J Dent. 2021;2021:1–6.

Google Scholar  

Tian YY, et al. Root and canal morphology of maxillary first premolars in a Chinese subpopulation evaluated using cone-beam computed tomography. Int Endod J. 2012;45(11):996–1003.

Karobari MI, et al. Roots and root canals characterization of permanent mandibular premolars analyzed using the cone beam and micro computed tomography—a systematic review and metanalysis. J Clin Med. 2023;12(6):2183.

Article   PubMed   PubMed Central   Google Scholar  

Iqbal A, et al. Evaluation of root canal morphology in permanent maxillary and mandibular anterior teeth in Saudi subpopulation using two classification systems: a CBCT study. BMC Oral Health. 2022;22(1):171.

Karobari MI, et al. Root and canal morphology of the anterior permanent dentition in Malaysian population using two classification systems: a CBCT clinical study. Australian Endodontic J. 2021;47(2):202–16.

Versiani MA, et al. Root and root canal morphology of four-rooted maxillary second molars: a micro–computed tomography study. J Endod. 2012;38(7):977–82.

Neelakantan P, et al. Cone-beam computed tomography study of root and canal morphology of maxillary first and second molars in an Indian population. J Endod. 2010;36(10):1622–7.

Ahmad IA, et al. Root and root canal morphology of maxillary first premolars: a literature review and clinical considerations. J Endod. 2016;42(6):861–72.

Dastgerdi AC, et al. Isthmuses, accessory canals, and the direction of root curvature in permanent mandibular first molars: an in vivo computed tomography study. Volume 45. Restorative Dentistry & Endodontics; 2020. 1.

Scarfe WC et al. Use of cone beam computed tomography in endodontics International journal of dentistry, 2009. 2009.

Karobari MI, et al. Root and root canal configuration characterization using microcomputed tomography: a systematic review. J Clin Med. 2022;11(9):2287.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Shi Z-Y, et al. Root canal morphology of maxillary premolars among the elderly. Chin Med J. 2017;130(24):2999–3000.

Hu R, et al. Aging changes of the root canal morphology in maxillary first premolars observed by cone-beam computerized tomography. Zhonghua Kou Qiang Yi Xue Za Zhi = Zhonghua Kouqiang Yixue Zazhi = Chin J Stomatology. 2016;51(4):224–9.

CAS   Google Scholar  

Thomas R, et al. Root canal morphology of maxillary permanent first molar teeth at various ages. Int Endod J. 1993;26(5):257–67.

Article   CAS   PubMed   Google Scholar  

Mashyakhy M, et al. Root and root canal morphology differences between genders: a comprehensive in-vivo CBCT study in a Saudi population. Acta Stomatol Croatica. 2019;53(3):213.

Nuni E, et al. Endodontic Treatment for Young Permanent Teeth , in contemporary endodontics for children and adolescents . Springer; 2023. pp. 281–321.

Gilroy FG. Perceptions of general health and root canal treatment in New Zealand general dental practice. University of Otago; 2020.

Martins JN, et al. Gender influence on the number of roots and root canal system configuration in human permanent teeth of a Portuguese subpopulation. Quintessence Int. 2018;49(2):103–11.

Pan JYY, et al. Root canal morphology of permanent teeth in a Malaysian subpopulation using cone-beam computed tomography. BMC Oral Health. 2019;19(1):1–15.

Bürklein S, et al. Evaluation of the root canal anatomy of maxillary and mandibular premolars in a selected German population using cone-beam computed tomographic data. J Endod. 2017;43(9):1448–52.

Yoza T, et al. Cone-beam computed tomography observation of maxillary first premolar canal shapes. Anat Cell Biology. 2021;54(4):424–30.

Kulinkovych-Levchuk K, et al. Guided endodontics: a Literature Review. Int J Environ Res Public Health. 2022;19(21):13900.

Karobari MI, et al. Micro computed tomography (Micro-CT) characterization of root and root canal morphology of mandibular first premolars: a systematic review and meta-analysis. BMC Oral Health. 2024;24(1):1.

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Department of Dental Research, Center for Global Health Research, Saveetha Medical College, and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India

Mohmed Isaqali Karobari, Rumesa Batul & Abdul Habeeb Adil

Department of Restorative Dentistry & Endodontics, Faculty of Dentistry, University of Puthisastra, Phnom Penh, 12211, Cambodia

Mohmed Isaqali Karobari

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Azhar Iqbal, Hmoud Ali Algarni & Meshal Aber Alonazi

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Jamaluddin Syed

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Conservative Dentistry Unit, School of Dental Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian, 16150, Kota Bharu, Kelantan, Malaysia

Tahir Yusuf Noorani

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Conception and design of the study: MIK, and TYN. Acquisition of data: AZ and SJ. Analysis and interpretation of data: RB and AHA. Drafting the article: MIK, RB, AHA and SJ. Revising it critically for important intellectual content: MIK, AZ, HAA, MAA and TYN. All authors approved the final submitted version.

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Ethical approval for this retrospective study was obtained from the Local Committee of Bioethics for Research at the Dentistry College, King Abdul-Aziz University, with Ethical Approval No. 025-02-22. Informed consent was obtained from the Committee of Bioethics for Research, College of Dentistry, King Abdul-Aziz University, Jeddah, Saudi Arabia, considering the study’s retrospective nature. Before any investigation or treatment, the patients signed a general consent form, allowing the use of findings in future studies and publications without revealing personal information. The informed consent was obtained from all subjects and/or their legal guardian(s).

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Karobari, M.I., Iqbal, A., Batul, R. et al. Exploring age and gender variations in root canal morphology of maxillary premolars in Saudi sub population: a cross-sectional CBCT study. BMC Oral Health 24 , 543 (2024). https://doi.org/10.1186/s12903-024-04310-w

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Neighborhood based computational approaches for the prediction of lncRNA-disease associations

• Mariella Bonomo 1 &
• Simona E. Rombo 1 , 2  

BMC Bioinformatics volume  25 , Article number:  187 ( 2024 ) Cite this article

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Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming.

We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966.

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Source code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git

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Introduction

More than \(98\%\) of the human genome consists of non-coding regions, considered in the past as “junk” DNA. However, in the last decades evidence has been shown that non-coding genome elements often play an important role in regulating various critical biological processes [ 1 ]. An important class of non-coding molecules which have started to receive great attention in the last few years is represented by long non-coding RNAs (lncRNAs), that is, RNAs not translated into functional proteins, and longer than 200 nucleotides.

LncRNAs have been found to interplay with other molecules in order to perform important biological tasks, such as modulating chromatin function, regulating the assembly and function of membraneless nuclear bodies, interfering with signalling pathways [ 2 , 3 ]. Many of these functions ultimately affect gene expression in diverse biological and physiopathological contexts, such as in neuronal disorders, immune responses and cancer. Therefore, the alteration and dysregulation of lncRNAs have been associated with the occurrence and progress of many complex diseases [ 4 ].

The discovery of novel lncRNA-disease associations (LDAs) may provide valuable input to the understanding of disease mechanisms at lncRNA level, as well as to the detection of disease biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, verifying that a specific lncRNA may have a role in the occurrence/progress of a given disease is an expensive process, therefore the number of disease-related lncRNAs verified by traditional biological experiments is yet very limited. Computational approaches for the prediction of potential LDAs can effectively decrease the time and cost of biological experiments, allowing for the identification of the most promising lncRNA-disease pairs to be further verified in laboratory (see [ 5 ] for a comprehensive review on the topic). Such approaches often train predictive models on the basis of the known and experimentally validated lncRNA-disease pairs (e.g., [ 6 , 7 , 8 , 9 ]). In other cases, they rely on the analysis of lncRNAs related information stored in public databases, such as their interaction with other types of molecules (e.g., [ 10 , 11 , 12 , 13 , 14 , 15 ]). As an example, large amounts of lncRNA-miRNA interactions have been collected in public databases, and plenty of experimentally confirmed miRNA-disease associations are available as well. However, although non-coding RNA function and its association with human complex diseases have been widely studied in the literature (see [ 16 , 17 , 18 ]), how to provide biologists with more accurate and ready-to-use software tools for LDAs prediction is yet an open challenge, due to the specific characteristics of lncRNAs (e.g., they are much less characterized than other non-coding RNAs.)

We propose three novel computational approaches for the prediction of LDAs, relying on the use of known lncRNA-miRNA interactions (LMIs) and miRNA-disease associations (MDAs). In particular, we model the problem of LDAs prediction as a neighborhood analysis performed on tripartite graphs, where the three sets of vertices represent lncRNAs, miRNAs and diseases, respectively, and vertices are linked according to LMIs and MDAs. Based on the assumption that similar lncRNAs interact with similar diseases [ 12 ], the first approach proposed here (NGH) aims at identifying novel LDAs by analyzing the behaviour of lncRNAs which are neighbors , in terms of their intermediate relationships with miRNAs. The main idea here is that neighborhood analysis automatically guides towards the detection of similar behaviours, and without the need of using a-priory known LDAs for training. Therefore, differently than other approaches from the literature, those proposed here do not involve verified LDAs in the prediction step, thus avoiding possible biases due to the fact that the number and variety of verified LDAs is yet very limited. The second presented approach (CF) relies on collaborative filtering, applied on the basis of common miRNAs shared by different lncRNAs. We have also explored the combination of neighborhood analysis with collaborative filtering, showing that this notably improves the LDAs prediction accuracy. Indeed, the third approach we have designed (NGH-CF) boosts NGH with collaborative filtering, and it is the best performing one, although also NGH and CF have been able to reach high accuracy values across all the different considered validation tests. In particular, Fig.  1 summarizes the research flowchart explained above.

Flowchart of the research pipeline. The miRNA-lncRNA interactions and miRNA-disease associations are exploited for the construction of the tripartite graph. The tripartite graph, in its turn, is at the basis of both neighborhood analysis and collaborative filtering steps, from which the three proposed approaches are obtained: NGH from neighborhood analysis, CF from collaborative filtering, NGH-CF from the combination of the two ones. Each prediction approach returns in output a LDAs rank

The proposed approaches have been exhaustively validated on both synthetic and real datasets, and the result is that they outperform (also significantly) the other methods from the literature. The experimental analysis shows that the improvement in accuracy achieved by the methods proposed here is due to their ability in capturing specific situations neglected by competitors. Examples of that are represented by true LDAs, detected by our approaches and not by the other approaches in the literature, where the involved lncRNA does not present intermediate molecules in common with the associated disease, although its neighbor lncRNAs share a large number of miRNAs with that disease. Moreover, it is shown that our approaches are robust to noise obtained by perturbing a controlled percentage of lncRNA-miRNA interactions and miRNA-disease associations, with NGH-CF the best one also for robustness. The obtained experimental results show that the prediction methods proposed here may effectively support biologists in selecting significant associations to be further verified in laboratory.

Novel putative LDAs coming from the consensus of the three proposed methods, and not yet registered in the available databases as experimentally verified, are provided. Interestingly, the core of novel LDAs returned with highest score by all three approaches finds evidence in the recent literature, while many other high scored predicted LDAs involve less studied lncRNAs, thus providing useful insights for their better characterization.

A first group of approaches aim at using existing true validated cases to train the prediction system, in order to make it able to correctly detect novel cases.

In [ 19 ] a Laplacian Regularized Least Squares is proposed to infer candidates LDAs ( LRLSLDA ) by applying a semi-supervised learning framework. LRLSLDA assumes that similar diseases tend to correlate with functionally similar lncRNAs, and vice versa. Thus, known LDAs and lncRNA expression profiles are combined to prioritize disease-associated lncRNA candidates by LRLSLDA, which does not require negative samples (i.e., confirmed uncorrelated LDAs). In [ 20 ] the method SKF-LDA is proposed that constructs a lncRNA-disease correlation matrix, based on the known LDAs. Then, it calculates the similarity between lncRNAs and that between diseases, according to specific metrics, and integrates such data. Finally, a predicted LDA matrix is obtained by the Laplacian Regularized Least Squares method. The method ENCFLDA [ 6 ] combines matrix decomposition and collaborative filtering. It uses matrix factorization combined with elastic networks and a collaborative filtering algorithm, making the prediction model more stable and eliminating the problem of data over-fitting. HGNNLDA recently proposed in [ 21 ] is based on hypergraph neural network, where the associations are modeled as a lncRNA-drug bipartite graph to build lncRNA hypergraph and drug hypergraph. Hypergraph convolution is then used to learn correlation of higher-order neighbors from the lncRNA and drug hypergraphs. LDAI-ISPS proposed in [ 22 ] is a LDAs inference approach based on space projections of integrated networks, recostructing the disease (lncRNA) integrated similarities network via integrating multiple information, such as disease semantic similarities, lncRNA functional similarities, and known LDAs. A space projection score is finally obtained via vector projections of the weighted networks. In [ 7 ] a consensual prediction approach called HOPEXGB is presented, to identify disease-related miRNAs and lncRNAs by high-order proximity preserved embedding and extreme gradient boosting. The authors build a heterogeneous disease-miRNA-lncRNA (DML) information network by linking lncRNA, miRNA, and disease nodes based on their correlation, and generate a negative dataset based on the similarities between unknown and known associations, in order to reduce the false negative rate in the data set for model construction. The method MAGCNSE proposed in [ 23 ] builds multiple feature matrices based on semantic similarity and disease Gaussian interaction profile kernel similarity of both lncRNAs and diseases. MAGCNSE adaptively assigns weights to the different feature matrices built upon the lncRNAs and diseases similarities. Then, it uses a convolutional neural network to further extract features from multi-channel feature matrices, in order to obtain the final representations of lncRNAs and diseases that is used for the LDAs prediction task.

LDAFGAN [ 8 ] is a model designed for predicting associations between long non-coding RNAs (lncRNAs) and diseases. This method is based on a generative and a discriminative networks, typically implemented as multilayer fully connected neural networks, which generate synthetic data based on some underlying distribution. The generative and discriminative networks are trained together in an adversarial manner. The generative network tries to generate realistic representations of lncRNA-disease associations, while the discriminative network tries to distinguish between real and fake associations. This adversarial training process helps the generative network learn to generate more realistic associations. Once the model is trained, it can predict associations between new lncRNAs and diseases without requiring associated data for those specific lncRNAs. The model captures the data distribution during training, which enables it to make predictions even for unseen lncRNAs. The approach GCNFORMER [ 9 ] is based on graph convolutional network and transformer. First, it integrates the intraclass similarity and interclass connections between miRNAs, lncRNAs and diseases, building a graph adjacency matrix. Then, the method extracts the features between various nodes, by a graph convolutional network. To obtain the global dependencies between inputs and outputs, a transformer encoder with a multiheaded attention mechanism to forecast lncRNA-disease associations is finally applied.

As for the approaches summarized above, it is worth to point out that they may suffer of the fact that the experimentally verified LDAs are still very limited, therefore the training set may be rather incomplete and not enough diversified. For this reason, when such approaches are applied for de novo LDAs prediction, their performance may drastically go down [ 12 ].

Other approaches from the literature use intermediate molecules (e.g., miRNA) to infer novel LDAs. Such approaches are the most related to those we propose here.

The author in [ 11 ] proposes HGLDA , relying on HyperGeometric distribution for LDAs inference, that integrates MDAs and LMIs information. HGLDA has been successfully applied to predict Breast Cancer, Lung Cancer and Colorectal Cancer-related lncRNAs. NcPred [ 10 ] is a resource propagation technique, using a tripartite network where the edges associate each lncRNA with a disease through its targets. The algorithm proposed in [ 10 ] is based on a multilevel resource transfer technique, which computes the weights between each lncRNA-disease pair and, at each step, considers the resource transferred from the previous step. The approach in [ 24 ], referred to as LDA-TG for short in the following, is the antecedent of the approaches proposed here. It relies on the construction of a tripartite graph, built upon MDAs and LMIs. A score is assigned to each possible LDA ( l ,  d ) by considering both their respective interactions with common miRNAs, and the interactions with miRNAs shared by the considered disease d and other lncRNAs in the neighborhood of l on the tripartite graph. The approaches proposed here differ from LDA-TG for two main reasons. First, the score of LDA-TG is different from the one we introduce here, that allows to reach a better accuracy. Second, a further step based on collaborative filtering is considered here, which also improves the accuracy performance. A method for LDAs prediction relying on a matrix completion technique inspired by recommender systems is presented in [ 14 ]. A two-layer multi-weighted nearest-neighbor prediction model is adopted, using a method similar to memory-based collaborative filtering. Weights are assigned to neighbors for reassigning values to the target matrix, that is an adjacency matrix consisting of lncRNAs, diseases and miRNA. SSMF-BLNP [ 25 ] is based on the combination of selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP). In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. In BLNP, the initial LDAs are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation.

A third category includes approaches based on integrative frameworks, proposed to take into account different types of information related to lncRNAs, such as their interactions with other molecules, their involvement in disorders and diseases, their similarities. This may improve the prediction step, taking into account simultaneously independent factors.

IntNetLncSim [ 26 ] relies on the construction of an integrated network that comprises lncRNA regulatory data, miRNA-mRNA and mRNA-mRNA interactions. The method computes a similarity score for all pairs of lncRNAs in the integrated network, then analyzes the information flow based on random walk with damping. This allows to infer novel LDAs by exploring the function of lncRNAs. SIMCLDA [ 12 ] identifies LDAs by using inductive matrix completion, based on the integration of known LDAs, disease-gene interactions and gene-gene interactions. The main idea in [ 12 ] is to extract feature vectors of lncRNAs and diseases by principal component analysis, and to calculate the interaction profile for a new lncRNA by the interaction profiles. MFLDA [ 27 ] is a Matrix Factorization based LDAs prediction model that first encodes directly (or indirectly) relevant data sources related to lncRNAs or diseases in individual relational data matrices, and presets weights for these matrices. Then, it simultaneously optimizes the weights and low-rank matrix tri-factorization of each relational data matrix. RWSF-BLP , proposed in [ 28 ], applies a random walk-based multi-similarity fusion method to integrate different similarity matrices, mainly based on semantic and expression data, and bidirectional label propagation. The framework LRWRHLDA is proposed in [ 15 ] based on the construction of a global multi-layer network for LDAs prediction. First, four isomorphic networks including a lncRNA similarity network, a disease similarity network, a gene similarity network and a miRNA similarity network are constructed. Then, six heterogeneous networks involving known lncRNA-disease, lncRNA-gene, lncRNA-miRNA, disease-gene, disease-miRNA, and gene-miRNA associations are built to design the multi-layer network. In [ 29 ] the LDAP-WMPS LDA prediction model is proposed, based on weight matrix and projection score. LDAP-WMPS consists on three steps: the first one computes the disease projection score; the second step calculates the lncRNA projection score; the third step fuses the disease projection score and the lncRNA projection score proportionally, then it normalizes them to get the prediction score matrix.

For most of the approaches summarized above, the performance is evaluated using the LOOCV framework, such that each known LDA is left out in turn as a test sample, and how well this test sample is ranked relative to the candidate samples (all the LDAs without the evidence to confirm their relationships) is computed.

The main goal of the research presented here is to provide more accurate computational methods for the prediction of novel LDAs, candidate for experimental validation in laboratory. To this aim, external information on both molecular interactions (e.g., lncRNA-miRNA interactions) and genotype-phenotype associations (e.g., miRNA-disease associations) is assumed to be available. Indeed, while only a restricted number of validated LDAs is yet available, large amounts of interactions between lncRNAs and other molecules (e.g., miRNAs, genes, proteins), as well as associations between these other molecules and diseases, are known and annotated in curated databases.

A commonly recognized assumption is that lncRNAs with similar behaviour in terms of their molecular interactions with other molecules, may also reflect such a similarity for their involvement in the occurrence and progress of disorders and diseases [ 12 ]. This is even more effective if the correlation with diseases is “mediated” by the molecules they interact with. Based on this observation, we have designed three novel prediction methods that all consider the notion of lncRNA “neighbors”, intended as lncRNAs which share common mediators among the molecules they physically interact with. Here, we focus on miRNAs as mediator molecules. However, the proposed approaches are general enough to allow also the inclusion of other different molecules. Relationships among lncRNAs, mediators and diseases are modeled through tripartite graphs in all the proposed approaches (see Fig.  1 that illustrates the flowchart of the presented research pipeline).

Problem statement Let \({\mathcal {L}}=\{l_1, l_2, \ldots , l_h\}\) be a set of lncRNAs and \({\mathcal {D}}=\{d_1, d_2, \ldots , d_k\}\) be a set of diseases. The goal is to return an ordered set of triplets \({\mathcal {R}}=\{\langle l_x, d_y, s_{xy}\rangle \}\) (with \(x\in [1,h]\) , and \(y\in [1,k]\) ), ranked according to the score \(s_{xy}\) .

The top triplets in \({\mathcal {R}}\) correspond to those pairs \((l_x, d_y)\) with most chances to represent putative LDAs which may be considered for further analysis in laboratory, while the triplets in the bottom correspond to lncRNAs and diseases which are unlikely to be related each other. A key aspect for the solution of the problem defined above is the score computation, that is the main aim of the approaches introduced in the following.

NGH: neighborhood based approach

A model of tripartite graph is adopted here to take into account that lncRNAs interacting with common mediators may be involved in common diseases.

Let \(T_{LMD}=\langle I, A \rangle\) be a tripartite graph defined on the three sets of disjoint vertexes L , M and D , such that \((l,m) \in I\) are edges between vertexes \(l \in L\) and \(m \in M\) , \((m,d) \in A\) are edges between vertexes \(m \in M\) and \(d \in D\) , respectively. In particular, L is associated to a set of lncRNAs, M to a set of miRNA and D to a set of diseases. Moreover, edges of the type ( l ,  m ) represent molecular interactions between lncRNAs and miRNA, experimentally validated in laboratory; edges of the type ( m ,  d ) correspond to known miRNA-disease associations, according to the existing literature. In both cases, interactions and associations annotated and stored in public databases may be taken into account.

The following definitions hold.

Definition 1

(Neighbors) Two lncRNAs \(l_h, l_k \in L\) are neighbors in \(T_{LMD}=\langle I, A \rangle\) if there exists at least a \(m_x \in M\) such that \((l_h, m_x) \in I\) and \((l_k, m_x) \in I\) .

Definition 2

(Prediction Score) The Prediction Score for the pair \((l_i,d_j)\) such that \(l_i \in L\) and \(d_j \in D\) is defined as:

\(M_{l_i}\) is the set of annotated miRNA interacting with \(l_i\) ,

\(M_{d_j}\) is the set of miRNA found to be associated to \(d_j\) ,

\(M_{l_x}\) is the set of miRNA interacting with the neighbor \(l_x\) of \(l_i\) (for each neighbor of \(l_i\) ),

\(\alpha\) is a real value in [0, 1] used to balance the two terms of the formula.

Definition 3

(Normalized prediction score) The Normalized Prediction Score for the pair \((l_i,d_j)\) such that \(l_i \in L\) , \(d_j \in D\) and \(s_{ij}\) is the Prediction Score for \((l_i,d_j)\) , is defined as:

NGH-CF: NGH extended with collaborative filtering

We remark that the main idea here is trying to infer the behaviour of a lncRNA, from that of its neighbors. Moreover, it is worth to point out that the notion of neighbor is related to the presence of miRNAs interacting with the same lncRNAs. However, not all the miRNA-lncRNA interactions have already been discovered, and miRNA-disease associations as well. This intuitively reminds to a typical context of data incompleteness where Collaborative Filtering may be successful in supporting the prediction process [ 30 ].

In more detail, what to be encoded by the Collaborative Filter is that lncRNAs presenting similar behaviours in terms of interactions with miRNAs, should reflect such a similarity also in their involvement with the occurrence and progress of diseases, mediated by those miRNAs. To this aim, a matrix R is considered here such that each element \(r_{ij}\) represents if (or to what extent) the lncRNA i and the disease j may be considered related. We call R relationship matrix (it is also known as rating matrix in other contexts, such as for example in the prediction of user-item associations). How to obtain \(r_{ij}\) is at the basis of the two variants of the approach presented in this section.

Due to the fact that R is usually a very sparse matrix, it can be factored into other two matrices L and D such that R \(\approx\) \(L\) \(^T\) \(D\) . In particular, matrix factorization models map both lncRNAs and diseases to a joint latent factor space F of dimensionality f , such that each lncRNA i is associated with a vector \(l_i \in F\) , each disease j with a vector \(d_j \in F\) , and their relationships are modeled as inner products in that space. Indeed, for each lncRNA i , the elements of \(l_i\) measure the extent to which it possesses those latent factors, and the same holds for each disease j and the corresponding elements of \(d_j\) . The resulting dot product in the factor space captures the affinity between lncRNA i and disease j , with reference to the considered latent factors. To this aim, there are two important tasks to be solved:

Mapping lncRNAs and diseases into the corresponding latent factors vectors.

Fill the matrix R , that is, the training set.

To learn the factor vectors \(l_i\) and \(d_j\) , a possible choice is to minimize the regularized squared error on the set of known relationships:

where \(\chi\) is the set of ( i ,  j ) pairs for which \(r_{ij}\) is not equal to zero in the matrix R . To this aim, we apply the ALS technique [ 31 ], which rotates between fixing the \(l_i\) ’s and fixing the \(d_j\) ’s. When all \(l_i\) ’s are fixed, the system recomputes the \(d_j\) ’s by solving a least-squares problem, and vice versa.

Filling the matrix R is performed according to two different criteria, resulting in the two different variants of the approach presented in this section, namely, CF and NGH-CF, respectively. According to the first criteria (CF), \(r_{ij}\) is set equal to 1 if the lncRNA i and the disease j share at least one miRNA in common, to 0 otherwise. The second variant (NGH-CF) works instead as a booster to improve the accuracy of NGH. In this latter case, the matrix R is filled by the normalized score ( 2 ). For both variants, the considered score to rank the predicted LDAs is given by the final value returned by the ALS technique applied on the corresponding matrix R .

Validation methodologies

We remark that the proposed approaches for LDAs prediction return a rank of LDAs, sorted according to the score that is characteristic of the considered approach, such that top triplets may be assumed as the most promising putative LDAs for further analysis in laboratory. As in other contexts [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], the performance of a prediction tool may be evaluated using suitable external criteria . Here, an external criterion relies on the existence of LDAs that are known to be true from the literature or, even better, from public repositories, where associations already verified in laboratory are annotated. A gold standard is constructed, containing only such true LDAs. The putative LDAs returned by the prediction method can thus be compared against those in the gold standard. In order to work properly, this validation methodology requires the gold standard information to be independent on that considered, in its turn, from the method under evaluation during its prediction task. This is satisfied in our case, due to the fact that all three approaches introduced in the previous sections do not exploit any type of knowledge referred to known LDAs during prediction, relying instead on known miRNA-lncRNA interactions and miRNA-disease associations, which come from independent sources.

According to the above mentioned validation methodology, the proposed approaches can be validated with references to the Receiver Operating Characteristics (ROC) analysis [ 34 ]. In particular, each predicted LDA is associated to a label, that is true if that association is contained in the considered gold standard, and false otherwise.

By varying the threshold value, it is possible to compute the true positive rate (TPR) and the false positive rate (FPR), by refferring to the percentage of the true/false predictions whose ranking is higher/below than the considered threshold value. ROC curve can be drawn by plotting TPR versus FPR at different threshold values. The Area Under ROC Curve (ROC-AUC) is further calculated to evaluate the performance of the tested methods. ROC-AUC equal to 1 indicates perfect performance, ROC-AUC equal to 0.5 random performance.

Similarly to the ROC curve, the Precision-Recall (PR) curve can be drawn as well, combining the positive predictive value (PPV, Precision), i.e., the fraction of predicted LDAs which are true in the gold standard, and the TPR (Recall), in a single visualization, at the threshold varying. The higher on y-axis the obtained curve is, the better the prediction method performance. The Area Under PR curve (AUPR) is more sensitive than AUC to the improvements for the positive class prediction [ 35 ], that is important for the case studied here. Indeed, only true LDAs are known, therefore no negative samples are included in the gold standard.

Another important measure useful to evaluate the prediction accuracy of a method and that can be considered here is the F1-score, defined as the harmonic mean of Precision and Recall to symmetrically represent both metrics in a single one.

We have validated the proposed approaches on both syntetic and real datasets, as explained below.

Synthetic data

A synthetic dataset has been built with 15 lncRNAs, 35 miRNA and 10 diseases, such that three different sets of LDAs may be identified, as follows (see also Table 1 , where the characteristics of each LDA are summarized).

Set 1: 26 LDAs, such that each lncRNA has from 3 to 4 miRNAs shared with the same disease (strongly linked lncRNAs) .

Set 2: 16 LDAs, each lncRNA having only one miRNA shared with a disease, and from 2 to 5 neighbors that are strongly linked with that same disease (directly linked lncRNAs and strong neighborhood) .

Set 3: 12 LDAs involving lncRNAs without any miRNA in common with a certain disease, and a number between 2 and 5 neighbors that are strongly linked with that same disease (only strong neighborhood) .

Experimentally verified data downloaded from starBase [ 36 ] and from HMDD [ 37 ] have been considered for the lncRNA-miRNA interactions and for the miRNA-disease associations, respectively. In particular, the latest version of HMDD, updated at 2019, has been used. Overall, \(1,\!114\) lncRNAs, \(1,\!058\) miRNAs, 885 diseases, \(10,\!112\) lncRNA-miRNA interactions and \(16,\!904\) miRNA-disease associations have been included in the analysis.

In order to evaluate the prediction accuracy of the approaches proposed here against those from the literature, three different gold standards have been considered. A first gold standard dataset GS1 has been obtained from the LncRNA-Disease database [ 38 ], resulting in 183 known and verified LDAs. A second, more restrictive, gold standard GS2 with 157 LDAs has been built by the intersection of data from [ 38 ] and [ 39 ]. Finally, also a larger gold standard dataset GS3 has been included in the analysis, by extracting LDAs from MNDRv2.0 database [ 40 ], where associations both experimentally verified and retrieved from manual literature curation are stored, resulting in 408 known LDAs.

Comparison on real data

The approaches proposed here have been compared against other approaches from the literature, over the three different gold standards described in the previous Section. In particular, all approaches considered from the literature have been run according to the default setting of their parameters, reported on the corresponding scientific publications and/or on their manual instructions.

Our approaches have been compared at first on GS1 against those approaches taking exactly the same input than ours, that are HGLDA [ 11 ], ncPred [ 10 ] and LDA-TG [ 24 ]. In particular, we have implemented HGLDA and used the corresponding p-value score, corrected by FDR as suggested by [ 11 ], for the ROC analysis. Moreover, we have normalized also the scores returned by ncPred and LDA-TG for the predicted LDAs, according to the formula in Definition 3 . Indeed, we have observed experimentally that such a normalization improves the accuracy of both methods from the literature, resulting in a better AUC. As for the novel approaches proposed here, the Normalized Prediction Score has been considered for NGH, while the approximated rating score resulting from ALS [ 31 ] is used for both CF and NGH-CF. Figure  2 shows the AUC scored by each method on GS1, while in Fig.  3 the different ROC curves are plotted. In particular, NGH scores a value of AUC equal to 0.914, thus outperforming the other three methods previously presented in the literature, i.e., HGLDA, ncPred and LDA-TG, that reach 0.876, 0.886 and 0.866, respectively (we remark also that performance of both ncPred and LDA-TG has been slightly improved with respect to their original one, by normalizing their scores). As for the novel approaches based on collaborative filtering, they both present a better accuracy than the others, with CF having AUC equal to 0.957 and NGH-CF to 0.966, respectively. Therefore, these results confirm that taking into account the collaborative effects of lncRNAs and miRNAs is useful to improve LDAs prediction, and the most successful approach is NGH-CF, that is, the neighborhood based approach boosted by collaborative filtering.

Comparison of the scored AUC on GS1

ROC curves for the compared methods on GS1

Another interesting issue is represented by the “agreement” between the different methods taking the same input, in terms of the returned best scoring LDAs. Table 2 shows the Jaccard Index computed between the proposed approaches and those receiving the same input, on the top \(5\%\) LDAs in the corresponding ranks, sorted from the best to the worst score values for each method. It emerges that results by HGLDA and ncPred have a small match with the other approaches (at most 0.23), while NGH-CF has high agreement with CF (0.74), as well as with NGH and LDA-TG (both 0.70). LDA-TG and CF present a sufficient match in their best predictions (0.59). This latter comparison based on agreement shows that approaches based on neighborhood analysis share a larger set of LDAs, in the top part of their ranks.

The proposed approaches have been compared also against other two recent methods from the literature, i.e., SIMCLDA and HGNNLDA, which receive in input different data than ours, including mRNA and drugs. For this reason, the more restrictive gold standard GS2 has been exploited for the comparison, where only lncRNAs and diseases having some correspondences with the additional input data of SIMCLDA and HGNNLDA are included. Figure  4 shows the comparison of the scored AUC on GS2, while Fig.  5 the corresponding ROC curves. In particular, the behaviour of all approaches previously tested does not change significantly on this other gold standard, moreover all the other approaches overcome SIMCLDA. On the other hand, HGNNLDA has a better performance than HGLDA, NcPred and LDA-TG, although it has a worse accuracy than NGH, CF and NGH-CF. The former confirms its superiority with regards to all considered approaches.

Comparison of the scored AUC on GS2

ROC curves for the compared methods on GS2

The proposed approaches have been compared also against LDAP-WMPS on GS3. Figure  6 shows the AUC values scored by all compared approaches on GS3, while Fig.  7 the corresponding ROC curves. In particular, the behaviour of all approaches previously tested does not change on this other gold standard, and LDAP-WMPS has better performance than the other approaches except for NGH, CF, NGH-CF and HGNNLDA.

Comparison of the scored AUC on GS3

ROC curves for the compared methods on GS3

The AUPR values scored by the compared methods on GS1, GS2, and GS3 are shown in Fig.  8 , while the corresponding PR-curves are plotted in Fig.  9 . In particular, for GS1 results are analogous to the ROC analysis, with NGH-CF the best performing one, followed by CF and NGH, while HGLDA is the worst. On GS2, NGH-CF and CF keep their superiority, followed by SMCLDA and NGH, while HGLDA is yet the worst one. On GS3, NGH-CF is the first, Cf the second and both HGNNLDA and LDAP-WMPS outperform NGH, while HGLDA in this case slightly outperforms LDA-TG, ncPred and SMCLDA, which results to be the worst one.

AUPR hystogram for the compared methods on GS1, GS2, GS3

Precision-recall curves for the compared methods on GS1,GS2,GS3

Figures 10 , 11 and 12 show the F1-score values obtained, for all methods compared on GS1, GS2 and GS3, respectively, at the varying of a threshold fixed on the method score. In Tables 3 , 4 and 5 it is shown, for each gold standard, the highest value of F1-score obtained by each considered method, as well as the corresponding Precision and Recall values, and the minimum threshold value for which the highest F1-score value has been reached. On GS1 and GS2, the three best performing approaches are NGH-CF, CF and NGH, in this order. On GS3 the order is the same, and LDAP-WMPS performs equally to NGH.

F1-score for the compared methods on GS1

F1-Score for the compared methods on GS2

F1-Score for the compared methods on GS3

Robustness analysis

The main aim of the analysis discussed here is to measure to what extent the proposed methods are able to correctly recognize verified LDAs, even if part of the existing associations are missed, i.e., the sets of known and verified lncRNA-miRNA interactions and miRNA-disease associations are not complete. This is important to verify that the proposed approaches can provide reliable predictions also in presence of data incompleteness, that is often the case when lncRNAs are involved. Therefore, the robustness of each proposed method has been evaluated by performing progressive alterations of the input associations coming from the real datasets, according to the following three different criteria.

Progressively eliminate the \(5\%\) , \(10\%\) , \(15\%\) and \(20\%\) of lncRNA-miRNA interactions from the input data.

Progressively eliminate the \(5\%\) , \(10\%\) , \(15\%\) and \(20\%\) of miRNA-disease associations from the input data.

Progressively eliminate the \(5\%\) , \(10\%\) , \(15\%\) and \(20\%\) of both lncRNA-miRNA interactions and miRNA-disease associations (half and half), from the input data.

Tests summarized above have been performed for 20 times each. Tables 6 , 7 and 8 show the mean of the AUC values for NGH, CF and NGH-CF, respectively, over the 20 tests. In particular, all methods perform well on the three test typologies at \(5\%\) , the worst being NGH-CF, which however presents an average AUC equal to 0.84 for case 1), that is still a high value. NGH-CF is also the method that presents the best robustness on case 3), keeping the value of 0.92 also at \(20\%\) , while CF is the worst performing in case 3), indeed its average AUC decreases from 0.95 at \(5\%\) to 0.63 already at \(10\%\) , and then to 0.50 at \(20\%\) . This behaviour in case 3), where both lncRNA-miRNA interactions and miRNA-disease associations are progressively eliminated, deserves some observations. Indeed, results show that the combination of neighborhood analysis and collaborative filtering is the most robust one with regards to this perturbation, while collaborative filtering alone is the worst performing. On the other hand, CF results to be the most robust in case 1), where only lncRNA-miRNA interactions are eliminated, and this is due to the fact that CF does not take into account how many miRNAs are shared by pairs of lncRNAs. As for case 2), performance of all methods is comparable and generally good, possibly in consideration of the fact that a large number of miRNA-disease associations are available, therefore discarding small percentages of them does not affect largely the final prediction.

Comparison on specific situations

In this section further experimental tests are described, showing how well the considered methods perform in detecting specific situations, depicted through the synthetic dataset first, and then searched for in the real data. In particular, the basic observation here is that prediction approaches from the literature usually fail in detecting true LDAs, when the involved lncRNAs and diseases do not have a large number of shared miRNAs (referring to those approaches taking the same input than ours). The novel approaches we propose are particularly effective in managing the situation depicted above, through neighborhood analysis and collaborative filtering, allowing to detect similar behaviours shared by different lncRNAs, depending on the miRNAs they interact with.

For each set of LDAs defined in the synthetic data (i.e., set 1, set 2, and set 3), and for each tested method (i.e., HGLDA, NCPRED, NHG, CF, NGH-CF), Table 9 shows the percentage of LDAs in that set which is recognized at the top \(10\%\) , \(20\%\) , \(30\%\) , \(50\%\) of the rank of all LDAs, sorted by the score returned by the considered method. As an example, for HGLDA the \(32\%\) of LDAs of set 1 are located in the top \(10\%\) of its rank, where instead none LDAs in set 2 or 3 find place.

Looking at these results some interesting considerations come out. First of all, for the methods HGLDA, NCPRED, NHG and CF most associations of the set 1 are located in the top \(50\%\) of their corresponding ranks, while NGH-CF has a different behaviour. Indeed, it locates a lower number of such LDAs in the highest part of its rank than the other approaches, possibly due to the fact that it leaves room for a larger number of associations in the other two sets in the top ranked positions. As for LDAs in the set 2, all methods recognize some of them already in the top \(10\%\) , except for HGLDA, as alredy highlighted. The approaches able to recognize the larger percentages of these associations at the top \(50\%\) of their rank are NGH and NGH-CF. LDAs in the set 3 are the most difficult to recognize, due to the fact that the lncRNA and the disease do not share any miRNA in common. Indeed, the worst performing methods in this case are HGLDA, which is able to locate some of these associations only at the top \(50\%\) (according to the percentages we considered here), and NCPRED, which performs slightly better although it reaches the same percentage of located associations than HGLDA at \(50\%\) (the \(28\%\) ). As expected, approaches based on neighborhood analysis and collaborative filtering perform better, with the best one resulting to be NGH-CF.

In the previous section we have shown that all methods proposed here are able to detect specific situations, characterized by the fact that a lncRNA may have very few (or none) common miRNAs with a disease, and its neighbors share instead a large set of miRNAs with that disease. We have checked if this case occurs among the verified LDAs that our approaches find and their competitors do not. Table 10 shows, only by meaning of example, 10 experimentally verified LDAs, included in GS1, that are top ranked for the novel approaches proposed here, whereas they are in the bottom rank of the other approaches from the literature compared on GS1. Six out of such LDAs do not present any common miRNAs between the lncRNA and the disease, while four share only one miRNA. All involved lncRNAs present neighbors with a large number of miRNAs in common with the disease in that LDA, in accordance with the hypothesis that the ability in capturing this situation allows to obtain a better accuracy.

Survival analysis has been also performed by one of the TCGA Computational Tools, that is, TANRIC [ 41 ], on four of the pairs in Table 10 . In particular, those lncRNAs and diseases available in TANRIC have been chosen. Results are reported in Figures 13 , 14 , 15 and 16 , showing that the over-expression of the considered lncRNA determines a lower survival probability over the time, for all four considered cases.

Survival analysis related to SNHG16 and bladder neoplasm

Survival analysis related to CBR3-AS1 and prostate neoplasm

Survival analysis related to MALAT1 and bladder neoplasm

Survival analysis related to MEG3 and breast neoplasm

In the previous sections the effectiveness and robustness of the proposed approaches have been illustrated, showing that all three are able to return reliable predictions, as well as to detect specific situations which may occur in true predictions and are missed by competitors. Here we provide a discussion on some novel LDAs predicted by NGH, CF and NGH-CF.

Table 11 shows seven LDAs which are not present in the considered gold standards, and that have been returned by all three methods proposed here, with highest score. The first of these associations is between CDKN2B-AS1 and LEUKEMIA, confirmed by recent literature [ 42 , 43 ]. Indeed, CDKN2B-AS1 was found to be highly expressed in pediatric T-ALL peripheral blood mononuclear cells [ 42 ], moreover genome-wide association studies show that it is associated to Chronic Lymphocytic Leukaemia risk in Europeans [ 43 ]. As for the second association between DLEU2 and LEUKEMIA, DLEU2 is a long non-coding transcript with several splice variants, which has been identified by [ 44 ] through a comprehensive sequencing of a commonly deleted region in leukemia (i.e., the 13q14 region). Different investigations reported up regulation of this lncRNA in several types of cancers. The lncRNA H19 regulates GLIOMA angiogenesis [ 45 , 46 ], while MAP3K14 is one of the well-recognized biomarkers in the prognosis of renal cancer, which is reminiscent of the pancreatic metastasis from renal cell carcinoma [ 47 ]. MEG3 has been recently found to be important for the prediction of LEUKEMIA risk [ 48 ]. Multiple studies have shown that MIR155HG is highly expressed in diffuse large B-cell (DLBC) lymphoma and primary mediastinal B-cell lymphoma, and in chronic lymphocytic leukemia. The transcription factor MYB activates MIR155HG activity, which causes the epigenetic state of MIR155HG to be dysregulated and causes an abnormal increase in MIR155 [ 49 ]. Also the last top-ranked association in Table 11 between TUG1 and NON-SMALL CELL LUNG CARCINOMA has found evidence in the literature [ 50 , 51 , 52 ].

Tables 12 , 13 , and 14 show the top 100 (sorted by the scores returned by each method) novel LDA predictions that NGH and CF, NGH and NGH-CF, CF and NGH-CF have in common, respectively. Many of the lncRNAs involved in such top-ranked LDAs are not yet characterized in the literature, therefore results presented here may be considered a first attempt to provide novel knowledge about them, through their inferred association with known diseases.

We have explored the application of neighborhood analysis, combined with collaborative filtering, for the improvement of LDAs prediction accuracy. The three approaches proposed here have been evaluated and compared first against their direct competitors from the literature, i.e., the other methods which also use lncRNA-miRNA interactions and miRNA-disease associations, without exploiting a priori known LDAs. It results that all methods proposed here are able to outperform direct competitors, the best one (NGH-CF) also significantly (AUC equal to 0.966 against the 0.886 by NCPRED). In particular, it has been shown that the improvement in accuracy is due to the fact that our approaches capture specific situations neglected by competitors, relying on similar lncRNAs behaviour in terms of their interactions with the considered intermediate molecules (i.e., miRNAs). The proposed approaches have been then compared also against other recent methods, taking different inputs (e.g., integrative approaches), and the experimental evaluation shows that they are able to outperform them as well.

It is worth pointing out the importance of providing reliable data in input to the LDAs prediction approaches. As discussed in this manuscript, information on the lncRNAs relationships with other molecules, and between intermediate molecules and diseases, is provided in input to the proposed approaches. Reliable datasets have been used to perform the experimental analysis provided here. However, as the user may provide also different input datasets, it is important to point out that the reliability of the obtained predictions strictly depends on that of input information.

As neighborhood analysis has resulted to be effective in characterizing lncRNAs with regards to their association with known diseases, we plan to apply it also for predicting possible common functions among lncRNAs, for example by clustering them according to their interactions, which has shown to be successful for other types of molecules [ 53 ]. Moreover, due to the success of integrative approaches on the analysis of biological data [ 54 ], we expect that including other types of intermediate molecules, such as for example genes and proteins, in the main pipeline of the proposed approaches may further improve their accuracy.

In conclusion, the use of reliable input data and the integration of different types of information coming from molecular interactions seem to be the most promising future directions for LDAs prediction.

Availability of data and materials

The source code is available at: https://github.com/marybonomo/LDAsPredictionApproaches.git In particular, executable software for NGH, CF, and NGH-CF are provided, as well as syntetic and real input datasets used here; the three different gold standard datasets GS1, GS2, GS3; the final obtained results.

Medico-Salsench E, et al. The non-coding genome in genetic brain disorders: New targets for therapy? Essays Biochem. 2021;65(4):671–83.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Statello L, Guo CJ, Chen LL, et al. Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol. 2021;22:96–118.

Article   CAS   PubMed   Google Scholar  

Zhao H, Shi J, Zhang Y, et al. LncTarD: a manually-curated database of experimentally-supported functional lncRNA–target regulations in human diseases. Nucl Acids Res. 2019;48(D1):D118–D126. ISSN: 0305-1048.

Liao Q, et al. Large-scale prediction of long non-coding RNA functions in a coding-non-coding gene co- expression network. Nuc Acids Res. 2011;39:3864–78.

Article   CAS   Google Scholar  

Chen X, et al. Long non-coding RNAs and complex diseases: from experimental results to computational models. Brief Bioinf. 2017;18(4):558–76.

CAS   Google Scholar  

Wang B, et al. lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering. Sci Rep. 2022;12:7.

Google Scholar  

He J, et al. HOPEXGB: a consensual model for predicting miRNA/lncRNA-disease associations using a heterogeneous disease-miRNA-lncRNA information network. J Chem Inf Model 2023

Zhong H, et al. Association filtering and generative adversarial networks for predicting lncRNA-associated disease. BMC Bioinf. 2023;24(1):234.

Dengju Y, et al. GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations. BMC Bioinf. 2024;25(1):5.

Article   Google Scholar  

Alaimo S, Giugno R, Pulvirenti A. ncPred: ncRNA-disease association prediction through Tripartite network-based inference. Front Bioeng Biot. 2014;2:71.

Chen X. Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA. Sci Rep. 2015;5:13186.

Lu C, et al. Prediction of lncRNA-disease associations based on inductive matrix completion. Bioinformatics. 2018;34(19):3357–64.

Xuan Z, Li J, Yu X, Feng J, et al. A probabilistic matrix factorization method for identifying lncRNA-disease associations. Genes 2019;10(2)

Du X, et al. lncRNA-disease association prediction method based on the nearest neighbor matrix completion model. Sci Rep. 2022;12(1):21653.

Wang L, et al. Prediction of lncRNA-disease association based on a Laplace normalized random walk with restart algorithm on heterogeneous networks. BMC Bioinf. 2022;23(1):1–20.

Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models. Brief Bioinf. 2022;23(5):bbac358.

Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion. Brief Bioinf. 2022;23(6):bbac397.

Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models. Brief Bioinf. 2022;23(6):bbac407.

Chen X, Yan G. Novel human lncRNA-disease association inference based on lncRNA expression profiles. Bioinformatics. 2013;29(20):2617–24.

Xie G, et al. SKF-LDA: similarity kernel fusion for predicting lncRNA-disease association. Mol Therapy-Nucleic Acids. 2019;18:45–55.

Liu D, et al. HGNNLDA: predicting lncRNA-drug sensitivity associations via a dual channel hypergraph neural network. IEEE/ACM transactions on computational biology and bioinformatics, 2023;1–11.

Zhang Y, et al. LDAI-ISPS: lncRNA-disease associations inference based on integrated space projection scores. Int J Molecular Sci. 2020;21(4):1508.

Liang Y, et al. MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model. BMC Bioinf. 2022;23(1):189.

Bonomo M, La Placa A, Rombo SE. Prediction of lncRNA-disease associations from tripartite graphs. In: Heterogeneous data management, polystores, and analytics for healthcare - VLDB workshops, poly 2020 and DMAH 2020, virtual event, August 31 and September 4, 2020, Revised Selected Papers. Springer, Berlin, 2020;205–210. ISSN: 978-3-030-71054-5

Xie G, et al. Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation. Brief Bioinform. 2023;24(1):bbac595.

Article   PubMed   Google Scholar  

Cheng L, et al. ntNetLncSim: an integrative network analysis method to infer human lncRNA functional similarity. Oncotarget. 2016;7(30):47864–74.

Article   PubMed   PubMed Central   Google Scholar  

Guangyuan F, et al. Matrix factorization-based data fusion for the prediction of lncRNA-disease associations. Bioinformatics. 2018;34:1529–37.

Xie G, et al. RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation. Mol Genet Genom. 2021;296:473–83.

Wang B, et al. lncRNA-disease association prediction based on the weight matrix and projection score. PLOS One. 2023;18(1): e0278817.

Duan R, Jiang C, Jain HK. Combining review-based collaborative filtering and matrix factorization: a solution to rating’s sparsity problem”. Decis Support Syst 2022;156:113748. ISSN: 0167–9236.

Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer. 2009;42(8):30–7.

Parida L, Pizzi C, Rombo SE. Irredundant tandem motifs. Theoret Comput Sci. 2014;525:89–102.

Bonomo M, et al. Topological ranks reveal functional knowledge encoded in biological networks: a comparative analysis. Brief Bioinform. 2022;23(3):bbac101.

Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27(8):861–74.

Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLOS One. 2015;10(3): e0118432.

Li J, et al. starBase v2. 0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2013;42:D92–7.

Li Y, et al. HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res. 2014;42:D1070–4.

Chen G, et al. LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic Acids Res. 2013;41:D983–6.

Gao Y, et al. Lnc2Cancer 3.0: an updated resource for experimentally supported lncRNA/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data. Nucleic Acids Res. 2021;49(D1):D1251–8.

Cui T, et al. MNDR v2. 0: an updated resource of ncRNA-disease associations in mammals. Nucleic Acids Res. 2018;46(D1):D371–4.

CAS   PubMed   Google Scholar  

Li J, et al. TANRIC: an interactive open platform to explore the function of lncRNAs in cancer. Cancer Res. 2015;75(18):3728–37.

Chen L, et al. lncRNA CDKN2B-AS1 contributes to tumorigenesis and chemoresistance in pediatric T-cell acute lymphoblastic leukemia through miR-335-3p/TRAF5 axis. In: Anti-cancer drugs, Wolters Kluwer Health, Inc. (2020)

Song C, et al. CDKN2B-AS1: an indispensable long non-coding RNA in multiple diseases. Current Pharm Des. 2020;26(41):5335–46.

Ghafouri-Fard S, et al. Deleted in lymphocytic leukemia 2 (DLEU2): an lncRNA with dissimilar roles in different cancers. Biomed Pharmacother. 2021;133: 111093.

Jia P, et al. Long non-coding RNA H19 regulates glioma angiogenesis and the biological behavior of glioma-associated endothelial cells by inhibiting microRNA-29a. Cancer Lett. 2016;381(2):359–69.

Liu Z, et al. LncRNA H19 promotes glioma angiogenesis through miR-138/HIF-1 α /VEGFaxis. Neoplasma. 2020;67(1):111–8.

Zhou S, et al. A novel immune-related gene prognostic Index (IRGPI) in pancreatic adenocarcinoma (PAAD) and its implications in the tumor microenvironment. Cancers. 2022;14(22):5652.

Pei J, et al. Novel contribution of long non-coding RNA MEG3 genotype to prediction of childhood leukemia risk. Cancer Genom Proteom. 2022;19(1):27–34.

Peng L, et al. MIR155HG is a prognostic biomarker and associated with immune infiltration and immune checkpoint molecules expression in multiple cancers. Cancer Med. 2019;8(17):7161–73.

Zhang E, et al. P53-regulated long non-coding RNA TUG1 affects cell proliferation in human non-small cell lung cancer, partly through epigenetically regulating HOXB7 expression. Cell Death Dis. 2014;5(5):e1243–e1243.

Lin P, et al. Long noncoding RNA TUG1 is downregulated in non-small cell lung cancer and can regulate CELF1 on binding to PRC2. BMC Cancer. 2016;16:1–10.

Niu Y, et al. Long non-coding RNA TUG1 is involved in cell growth and chemoresistance of small cell lung cancer by regulating LIMK2b via EZH2. Mol Cancer. 2017;16(1):1–13.

Pizzuti C, Rombo SE. An evolutionary restricted neighborhood search clustering approach for PPI networks. Neurocomputing. 2014;145:53–61.

Rombo SE, Ursino D (2021) Integrative bioinformatics and omics data source interoperability in the next-generation sequencing era

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Acknowledgements

The authors are grateful to the Anonymous Reviewers, for the constructive and useful suggestions that allowed to significantly improve the quality of this manuscript. Some of the results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga .

PRIN “multicriteria data structures and algorithms: from compressed to learned indexes, and beyond”, Grant No. 2017WR7SHH, funded by MIUR (closed). “Modelling and analysis of big knowledge graphs for web and medical problem solving” (CUP: E55F22000270001), “Computational Approaches for Decision Support in Precision Medicine” (CUP:E53C22001930001), and “Knowledge graphs e altre rappresentazioni compatte della conoscenza per l’analisi di big data” (CUP: E53C23001670001), funded by INdAM GNCS 2022, 2023, 2024 projects, respectively. “Models and Algorithms relying on knowledge Graphs for sustainable Development goals monitoring and Accomplishment - MAGDA” (CUP: B77G24000050001), funded by the European Union under the PNRR program related to “Future Artificial Intelligence - FAIR”.

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MB and SER equally contributed to the research presented in this manuscript. MB implemented and run the software, SER performed the analysis of results. Both authors wrote and reviewed the entire manuscript.

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Bonomo, M., Rombo, S.E. Neighborhood based computational approaches for the prediction of lncRNA-disease associations. BMC Bioinformatics 25 , 187 (2024). https://doi.org/10.1186/s12859-024-05777-8

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Nurses constitute the largest body of healthcare professionals globally, positioning them at the forefront of enhancing patient safety. Despite their crucial role, there is a notable gap in the literature regarding the comprehension and competency of nursing students in patient safety within Egypt. This gap underscores the urgent need for research to explore how nursing students perceive patient safety and the extent to which these competencies are integrated into their clinical and educational experiences. Understanding these perspectives is essential for developing targeted interventions that can significantly improve patient safety outcomes. The objective of this study was to fill this gap by assessing the perspectives of nursing intern students on patient safety competencies, thereby contributing to the global efforts in enhancing patient safety education and practice.

In this research, a cross-sectional study design was employed to investigate the topic at hand. A purposive sample of 266 nursing intern students was enrolled from the Faculty of Nursing at Mansoura University. The data were collected using a patient safety survey. Subsequently, the collected data underwent analysis through the application of descriptive and inferential statistical techniques using SPSS-20 software.

Among the studied intern nursing students, we found that 55.3% and 59.4% of the involved students agreed that they could understand the concept of patient safety and the burden of medical errors. Regarding clinical safety issues, 51.1% and 54.9% of the participating students agreed that they felt confident in what they had learned about identifying patients correctly and avoiding surgical errors, respectively. Concerning error reporting issues, 40.2% and 37.2% of the involved students agreed that they were aware of error reports and enumerated the barriers to incident reporting, respectively. There was a statistically significant difference between the nursing student patient safety overview domain and their age ( p  = 0.025).

Conclusions

Our study's compelling data demonstrated that intern students who took part in the patient safety survey scored higher overall in all patient safety-related categories. However, problems with error reporting showed the lowest percentage. The intern students would benefit from additional educational and training workshops to increase their perspectives on patients' safety competencies.

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Introduction

Patient safety refers to the perspectives, beliefs, attitudes, and values shared among members of the health community that focus on the prevention of errors and adverse effects on patients associated with health care [ 1 ]. In addition to becoming more efficient, health care has also grown more complicated due to the increased use of novel tools, medications, and therapies [ 2 ]. Medical errors (MEs) are a major public health concern that endangers patient safety significantly. Research conducted in Australia found that 16.6% of all admissions resulted in preventable negative outcomes, with approximately 5% of cases involving an iatrogenic injury ending in death [ 3 ].

Adverse event incidence rates varied from 2.9% to 16.6%. About 5% to 13% of the patients in these situations passed away, but 25% to 50% of them were thought to have been avoidable [ 4 ]. MEs can occur in any care setting, including hospitals, health centers, clinics, and laboratories; thus, they can negatively affect patient safety [ 5 ].

Medical errors raise hospital and medical expense costs in both wealthy and underdeveloped nations, which lowers the standard of healthcare systems. The most common errors that practitioners should exercise great care to avoid are catheter-associated urinary tract infections, central line bloodstream infections, adverse drug events, falls, pressure ulcers, obstetrical adverse events, venous thrombosis, surgical site infections, and the development of ventilator-associated pneumonia. Errors can be prevented by changing the healthcare system to make it more difficult for practitioners to perform incorrect actions and easier for them to do correct ones [ 6 ].

More time is spent with patients by nurses than by any other healthcare practitioner, making them the largest profession in the health sector. Therefore, in addition to advocating for patient safety, nurses can significantly reduce errors [ 7 ]. Students’ perspectives are how students think to respond about what they have done or about what they learned [ 8 ]. The viewpoints of nursing students can shed light on how nursing education helps prepare students to give safe care both while they are enrolled in school and after they become practitioners. Their identification of the strengths and limitations of curriculum and teaching practices can help guide our efforts to enhance nurse education and improve healthcare systems [ 9 ].

Therefore, nursing college students must comprehend and develop patient safety competency, as this fosters patients' recuperation, averts unfavorable situations, and has been a global priority for academic and healthcare institutions. Additionally, ensuring patient safety not only improves healthcare outcomes but also enhances the reputation and credibility of healthcare institutions. By prioritizing patient safety, nursing colleges can produce competent and skilled nurses who contribute to the overall development and progress of the healthcare industry [ 10 ].

Consequently, we investigated how nursing college final-year students perceived their level of patient safety competency. These results will be useful in formulating plans to raise students' proficiency in patient safety among health professionals.

Significance of the study

Patient safety issues have become a priority in health policy and healthcare management. It was reported that MEs are the third principal cause of death in the USA, with an estimated 251,000 deaths annually. Patient safety is considered an endemic concern by the WHO. However, literature reports that nursing students might need more knowledge and skills to enhance patient safety. Moreover, the students need help managing errors that might occur [ 11 ].

Also, nursing curricula need more emphasis on patient safety. Graduate nurses should have sufficient knowledge to recognize potential safety risks [ 12 ]. Sufficient knowledge will increase nursing students' confidence to protect patients from potential harm, errors, and avoidable injuries [ 13 ]. Thus, it is imperative to evaluate nursing students’ perspectives on patients' safety competencies.

Aim of the study

The study aims to evaluate nursing students’ perspectives on patients' safety competencies.

Research objectives

Assess nursing students' knowledge regarding patient safety competencies.

Evaluate nursing students' perspectives on patient safety competencies.

Research questions

What are nursing students’ perspectives on patients' safety competencies?

Research design

A descriptive cross-sectional design was utilized in this study. Descriptive cross-sectional studies explain things or how things are related to each other at a specific time [ 14 ]. A descriptive cross-sectional design was suitable for assessing nursing students’ perspectives on patients' safety competencies in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STORBE) statement ( Appendix ).

This study was conducted at the Faculty of Nursing, Mansoura University, Egypt.

Study sample

A purposive sample of 266 internship nursing students from both genders was included in the study. Purposive sampling was chosen due to its effectiveness in identifying and selecting individuals that meet a predefined set of characteristics essential for the research question. This approach ensured that the participants had a foundational understanding of nursing practices and were in the process of applying these concepts in a clinical environment, making their perspectives on patient safety both unique and immediately relevant. Students were chosen because they have received sufficient training to practice nursing care, and it is also important to investigate nursing safety considerations among these students before offering complete care to patients.

The appropriate sample size for this investigation was determined using the Steven K. Thompson equation [ 15 ]. There are 516 students enrolled in nursing internships overall, according to the Student Affairs administration. A minimum of 221 students should be included in the sample size for this study, according to the previously provided data. As the confidence level is 95%, the error proportion is 0.05, and the probabilities are 50%, add 20% for better data and follow-up drop. So the final number should be 266 nursing students.

Inclusion criteria include intern nursing students of both genders who are enrolled in the orientation program in the faculty of nursing at Mansoura University, willing to participate, and signing informed consent. Exclusion criteria include students who have a nursing diploma before joining the faculty of nursing, as those students have more knowledge and clinical experience than other students.

One tool was used in this study to collect pertinent data.

Patient Safety Survey (PSS)

Our literature review revealed that while there are several established tools for assessing patient safety competencies, most are tailored to qualified healthcare professionals or general nursing students, without a specific focus on internship nursing students in the Egyptian context. Furthermore, our study aimed to explore nuanced aspects of patient safety competencies, including students' perspectives on error reporting and clinical safety issues specific to their internship experiences. These nuances were not adequately covered by existing tools. Therefore, to capture the specific competencies and perspectives of our target population accurately, we decided to develop PSS. Researchers developed this survey after reviewing national and international literature reviews [ 16 , 17 , 18 ]. This survey consists of 24 items, divided into two parts. Part one is used to assess internship nursing students’ socio-demographic data. This data includes four items: student name, age, gender, and residence.

Part two is designed to measure internship nursing students’ perspectives regarding patient safety issues. This part covers students’ perspectives in three domains: an overview of patient safety (five items), clinical safety issues (10 items), and error reporting (five items). A 5-point Likert scale, with one representing "strongly disagree" and five representing "strongly agree," was used to gauge the students' perspectives .

Validity and reliability

The researcher developed the study tool after reviewing national and international literature [ 16 , 17 , 18 ]. The content validity of the PSS was rigorously evaluated through a structured process involving a panel of seven experts in nursing education, patient safety, and research methodology. These experts were selected based on their extensive experience and contributions to the field, ensuring a comprehensive assessment of the tool's content. Initially, the development of the survey items was informed by an extensive review of both national and international literature on patient safety competencies. This ensured that the content of the tool was grounded in the latest research and best practices in the field. The draft version of the PSS was then presented to the expert panel for evaluation. Each expert independently assessed the relevance, clarity, and comprehensiveness of the survey items, using a standardized scale to rate each item.

Based on the expert ratings, the Content Validity Index (CVI) for the tool was calculated. The CVI provides a quantitative measure of the degree to which experts agree that the survey items are relevant and representative of the construct of patient safety competencies. For our tool, the CVI was calculated at 0.82, indicating a high level of agreement among experts and confirming the content validity of the PSS. A CVI of 0.82 suggests that the majority of the items were deemed relevant and essential for assessing patient safety competencies among nursing students.

In addition to assessing content relevance, the expert panel also provided feedback on the face validity of the tool, focusing on the clarity, simplicity, and readability of the items. This process ensured that the survey would be easily understood by the target population of nursing intern students. Following the expert panel review, several adjustments were made to enhance the clarity and respondent-friendliness of the survey. For instance, the original binary response format was modified to a five-point Likert scale to allow for a more nuanced expression of respondents' perspectives. Additionally, based on expert suggestions, specific items, such as “I know the institution of medicine report, To Error is Human, and its recommendations," were added to enrich the tool's comprehensiveness and relevance. The reliability of the tools was tested using Cronbach’s alpha coefficient (0.89 for the patient safety survey, part two).

Pilot study

A pilot study was conducted with 27 participants, representing 10% of the total sample, to test the tool's applicability in the research setting. Feedback from the pilot study identified potential issues and challenges. Modifications were made to the survey tool, ensuring relevance and comprehensibility and addressing practical issues.

Data collection

Ethical approval was obtained from the Research Ethics Committee of the Faculty of Nursing, Mansoura University . The study tool, a patient safety survey, was developed by the researcher based on a recent relevant literature review. A panel of seven experts in the associated fields evaluated the study instrument for face- and content-related validity, and any necessary adjustments were made in response. The reliability of the tools was tested using Cronbach’s alpha coefficient (0.89 for the patient safety survey, part two). A pilot study was carried out with 27 (10%) of the study sample to test the feasibility and applicability of the study tool, and it will be excluded from the study sample. The necessary modifications were made accordingly. The researchers introduced themselves to the selected internship nursing students. The researchers explained the nature and purpose of this study to the study sample. After accepting to participate in this study, the researchers started to collect students’ socio-demographic data and their perspectives regarding patient safety issues using the study tool. Each student was given the appropriate time to answer the patient safety survey (about 20–30 min). The data was collected from January to February 2024.

To avoid bias in the study, we employed a purposive sampling strategy to select a representative sample of internship nursing students from Mansoura University. This strategy was chosen based on specific inclusion and exclusion criteria designed to minimize selection bias and ensure that our sample accurately reflected the population of interest. Additionally, to address potential information bias, we rigorously developed and validated the Patient Safety Survey. The survey underwent a pilot study to identify and correct any ambiguities, further enhancing the reliability and validity of the data collected. The uniform application of a 5-point Likert scale across all survey items was a deliberate choice to provide a consistent measure of nursing students' perspectives, thereby reducing measurement bias. Additionally, we standardized the training for all researchers involved in data collection to ensure uniform survey administration. We took several measures to minimize response bias, including guaranteeing anonymity and confidentiality for all participants and making participation entirely voluntary. These steps were intended to foster an environment where students felt comfortable providing honest and accurate responses without fear of repercussions.

Statistical analysis of the data

The computer was fed data, and IBM SPSS software package version 20.0 was used for analysis. [IBM Corp. Armonk, NY] Numbers and percentages were used to describe the qualitative data. The distribution's normality was confirmed using the Kolmogorov–Smirnov test. The range (minimum and maximum), mean, standard deviation, and median were used to characterize quantitative data. The results were deemed significant at the 5% level. Student t-test: to compare two examined categories for quantitative variables that are regularly distributed. F-test (ANOVA): for normally distributed quantitative variables, to compare between more than two categories. Pearson coefficient: to correlate between two normally distributed quantitative variables.

Ethical considerations and human rights

The Research Ethical Committee of the Faculty of Nursing at Mansoura University in Egypt provided ethical permission (No.0526). After being fully informed about the purpose of the study, each intern nursing student who was enrolled gave their informed consent. The pupils were reminded by the researcher that participation is completely voluntary. Throughout the whole study, confidentiality, privacy, safety, and anonymity were guaranteed. Every participant was free to leave the research at any moment. The survey did not include participant names or any other type of identifying information. The Helsinki Declaration and other pertinent rules and regulations carry out every procedure.

Demographic characteristics

The study included a total of 266 students. About 57.9% of the involved students were aged 22, and 65% of them were female. Moreover, 64.7% of the enrolled students lived in rural areas. All the involved students (100%) were from Mansoura University (Table  1 ).

Students’ distribution according to the patient safety overview domain

Among the studied intern nursing students, we found that 55.3%, 59.4%, 40.6%, 41.7%, and 49.6% of the involved students agreed that they can understand the concept of patient safety, understand the burden of medical errors, differentiate between errors, adverse events, close call/near miss, and sentinel events, know the Institution of Medicine report “To Error is Human” and its recommendations, and are aware of the ethical aspect of patient safety. The total score of the patient safety overview domain (mean ± SD) was 19.76 ± 2.69 (Table  2 ).

Distribution of the studied students according to clinical safety issues

Regarding clinical safety issues, 50.4%, 51.1%, 54.9%, 52.3%, and 52.3% of the participating students agreed that they felt confident in what they had learned about curbing infection spread, identifying patients correctly, avoiding surgical errors, using medicines safely, and preventing venous thromboembolism, respectively. In addition, 51.1%, 52.3%, 47.7%, 48.1%, and 48.5% of the participating students agreed that they felt confident in what they had learned about customizing hospital discharges, using good hospital design principles, assembling better teams and rapid response systems, sharing data for quality improvement, and fostering an open-communication culture (Table  3 ).

Distribution of the studied students according to error reporting issues domain

Concerning their error reporting, 40.2%, 50%, 37.2%, 44.7%, and 41% of the involved students agreed that they were aware of error reports, understood the importance of incident reports, enumerated the barriers to incident reporting, listed the features of an incident report, and differentiated between manual and electronic incidence reports (Table  4 ).

Relation between nursing students’ perspectives toward patient safety, their gender, and their age

Regarding gender, there was no statistically significant difference between nursing students' perceptions of patient safety and their gender ( p  > 0.05). At the same time, there was a statistically significant difference between the nursing student patient safety overview domain and their age ( p  = 0.025) (Table  5 ).

Correlation among nursing students’ perspectives domains toward patient safety

There were very high positive correlations between the overall patient safety score and its three domains: the patient safety overview domain ( r  = 0.806, p  < 0.001), the clinical safety issues domain ( r  = 0.932, p  < 0.001), and the error reporting domain ( r  = 0.842, p  < 0.001). Moreover, there was a statistically significant difference between the patient safety overview domain and the clinical safety issues domain ( p  < 0.001) with a high positive correlation ( r  = 0.659). In addition, there was a moderately positive correlation between the patient safety overview domain and the error reporting domain with a statistically significant difference ( r  = 0.543, p  < 0.001). Also, there was a high positive correlation between the clinical safety issues domain and the error reporting domain ( r  = 0.660, p  < 0.001) (Table  6 ).

Nursing students are the foundation upon which nursing care for patients will be built, and patient safety must be considered the cornerstone of the student’s education before graduation to prepare them well to provide the best care with the highest quality and efficiency [ 19 ]. Working across professions in clinical fields requires an early understanding of the responsibilities of different healthcare providers and the extent of nursing students' engagement [ 20 ].

Using a self-reported approach, we evaluated nursing students' perspectives of patient safety competency concerning safety overview, clinical safety issues, and error reporting issues. Our study's compelling data demonstrated that intern students who took part in the patient safety survey scored higher overall in all patient safety-related categories. When it came to clinical safety considerations, the students received the highest percentage of points. On the other hand, problems with error reporting showed the lowest percentage.

The clinical safety dimension, with its focus primarily on infection control, patient identification, safe medication administration, and waste disposal, might be the most familiar to students, as our students start clinical training from the first academic level in the hospital with regular and varied evaluations that help them to have a comprehensive understanding of nursing students' proficiency in infection control and patient identification. Another possible explanation for this is that combining written assessments, practical evaluations, simulations, and real-world clinical experiences in our faculty allows educators to gauge students' competence and readiness for professional practice, which increases their knowledge base.

This is in line with the results of a study in Portugal, which reported a high perception of students in terms of infection control [ 21 ]. Another study conducted in Saudi Arabia indicated a modest perception among nursing students regarding infection prevention [ 22 ]. Regarding the error reporting issue, this is because students were worried about disciplinary actions, damage to their reputation, or a potential impact on their academic and professional future. Also, the majority of our students are from rural areas with a blame culture present that can discourage open communication about error reporting.

Another significant aspect of the safety overview domain is that students have a deeper perspective on the burden of medication errors and the concept of patient safety. This finding might relate to prior exposure to patient safety-related topics. This is in harmony with those of Chan 2019, who reported students had a good perception of general terms and the concept of safety [ 23 ]. Another study assessing medical students’ knowledge, skills, and attitudes also reported high perceptions of students regarding general aspects of patient safety [ 24 ].

Another interesting finding regarding clinical safety issues is that the high perspective and confidence percentage about avoiding surgical error and the lowered perspective percentage represented assembling better teams and rapid response systems. We attribute this superiority in preventing surgical errors to the fact that the majority of respondents work part-time in the surgical and plastic surgery hospitals spread across the governorate, which gave them practical experience in this part. In combination with education, experience, mentorship, and a supportive healthcare culture, this contributes to nursing interns developing a positive perception regarding avoiding surgical errors. Following the present results, a previous study in Turkey demonstrated that nurses who formerly received preparation on patient safety had a higher statistical percentage [ 25 ]. However, the findings of the current study do not support the previous research that reported that pre-licensure nursing students have little knowledge regarding perioperative care and should be well-trained again [ 26 ].

Regarding lack of perspective in assembling a better team and rapid response system, because interns feel hesitant to voice concerns or take charge due to hierarchical structures, insufficient resources, both in terms of staffing and equipment, may hinder the interns' ability to assemble an effective team and respond. This outcome is contrary to that of Kamran, who reported that the best score of safety was given for team functioning and response [ 27 ].

Regarding gender, there was no statistically significant difference between nursing students’ perspectives on patient safety and their gender ( p  > 0.05). This is in line with those of Ramírez, who reported that the differences in means between genders were not significantly different in the overall perspective of patient safety [ 28 ]. Additionally, those who stated that there were no discernible variations in opinions about gender and past exposure to medical errors ( p  =  > 0.05) [ 27 ]. This outcome is contrary to that stated: male students apparent competence in “working in teams” is higher than that of females [ 29 ].

Another pilot study reported that the overall patient safety grade, the number of reported events, and the number of reported events by nursing students were significantly predicted by several patient safety competence dimensions ( p  ≤ 0.05) [ 30 ].

Our results indicated that there is a significant relationship between age and patient safety. The rationale of this finding is that during the academic years, including clinical practicum, students’ ability to communicate with patients and other health professionals clearly and consistently seemed to increase with age. Similar positive student assessments about safety and age have been noted in a study by Usher, who reported highly significant scores of patient safety with age and level of students. The results are also inconsistent with those conducted in Australia and New Zealand that assess nursing students' patient safety knowledge. These results corroborate the findings of a great deal of the previous work reported a significant difference was found in the patient safety competence of nursing students with year of study [ 29 ].

Another finding that stands out from the results is that there were very high positive correlations between the overall patient safety score and the three domains. These results reflect those of another study that examined the relationship between all-cause harm and patient safety and demonstrated strong correlations between all-cause harm measures and patient safety culture [ 31 ]. These findings also lend support to previous literature, which reported that subscales of safety correlated positively with the perceived patient safety culture scale [ 32 ]. Our finding also supports evidence from previous observations that found a positive correlation between the six domains and safety-related behaviors [ 33 ].

Another finding is that there was a statistically significant difference between the patient safety overview domain and the clinical safety issues domain. The same results were reported in a cross-sectional study conducted in China that assessed the patient safety competency of Chinese nurses [ 34 ]. Also, there was a high positive correlation between the clinical safety issues domain and the error reporting domain; this finding is consistent with Mahsoon [ 35 ]. This finding is contrary to the findings of another Saudi cross-sectional study that showed a significant negative correlation [ 36 ]. Another vital aspect of patient safety that students recognized is likewise related to understanding the function of trust and error reporting in maintaining patient safety.

Nursing students ought to have a strong understanding of patient safety, grounded in the highest standards of nursing care. Students completing nursing internships knew about patient safety. This result supports the conclusion drawn from several recent studies that patient safety education improves nurses' patient safety competence. These elements could have an impact on nursing students' patient safety competence and performance. The intern students would benefit from additional educational and training workshops to increase their perspectives on patients' safety competencies. Therefore, we recommend that academic institutions and medical facilities reorganize the framework for patient safety education to begin at the earliest academic level while taking into account students' pedagogical demands and varying safety levels. This will be done to increase public awareness of patient safety education. Establishing a structured curriculum on patient safety and upholding this shift in hospital culture is also crucial if we are to optimize the impact of patient safety education. Future research in various cultural and contextual settings is necessary to enhance our understanding of the variables affecting patient safety in nursing practice and education.

Limitations

When evaluating the results, it is important to take into account the study's limitations, which include its cross-sectional design and the inclusion of only one site. An additional constraint pertains to the survey's timing, which was carried out during the internship's orientation program. The student was not entirely tasked with providing comprehensive and intense care to patients with minimal exposure to clinical safety and real-error reporting concerns. The results could have been altered if the data had been gathered closer to the internship's conclusion, when the students would have gained more clinical experience. The study was conducted at a single nursing faculty; the use of purposive sampling, while ensuring a detailed exploration of our specific research question, may also limit the generalizability of the results. Therefore, it is recommended that it be repeated across other faculties to enable generalization of results.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to protecting the confidentiality of the participants, but are available from the corresponding author upon reasonable request.

Agbar F, Zhang S, Wu Y, Mustafa M. Effect of patient safety education interventions on patient safety culture of health care professionals: Systematic review and meta-analysis. Nurse Educ Pract. 2023;67: 103565. https://doi.org/10.1016/j.nepr.2023.103565 .

Article   PubMed   Google Scholar  

World Health Organization. Global patient safety action plan 2021–2030: towards eliminating avoidable harm in health care. World Health Organization. 2021; Available at https://www.who.int/teams/integrated-health-services/patient-safety/policy/global-patient-safety-action-plan .

Doshmangir L, Najafi B, Akbari Sari A, Sergeevich GV. Incidence rate and financial burden of medical errors and policy interventions to address them: a multi-method study protocol. Health Serv Outcomes Res Method. 2022;22(2):244–52. https://doi.org/10.1007/s10742-021-00261-9 .

Article   Google Scholar  

Han Y, Kim JS, Seo Y. Cross-sectional study on patient safety culture, patient safety competency, and adverse events. West J Nurs Res. 2020;42(1):32–40. https://doi.org/10.1177/0193945919838990 .

Schwarz CM, Hoffmann M, Schwarz P, Kamolz LP, Brunner G, Sendlhofer G. A systematic literature review and narrative synthesis on the risks of medical discharge letters for patients’ safety. BMC Health Serv Res. 2019;19:1. https://doi.org/10.1186/s12913-019-3989-1 .

Article   CAS   Google Scholar  

Khammarnia M, Setoodehzadeh F. Medical error as a challenge in Iran’s health system. Health Scope. 2017;6(1):61597. https://doi.org/10.17795/jhealthscope-39743 .

Dirik HF, Samur M, Seren Intepeler S, Hewison A. Nurses’ identification and reporting of medication errors. J Clin Nurs. 2019;28(5–6):931–8. https://doi.org/10.1111/jocn.14716 .

Nadhiroh NA. EFL Students’ Perception on The Use of Google Classroom in Language Assessment and Testing Development Course at IAIN Kediri (Doctoral dissertation, IAIN Kediri). 2022. https://etheses.iainkediri.ac.id:80/id/eprint/7700 .

Tuomikoski AM, Ruotsalainen H, Mikkonen K, Kääriäinen M. Nurses’ experiences of their competence at mentoring nursing students during clinical practice: a systematic review of qualitative studies. Nurse Educ Today. 2020;85: 104258. https://doi.org/10.1016/j.nedt.2019.104258 .

Julnes SG, Myrvang T, Reitan LS, Rønning G, Vatne S. Nurse leaders’ experiences of professional responsibility towards developing nursing competence in general wards: A qualitative study. J Nurs Manag. 2022;30(7):2743–50. https://doi.org/10.1111/jonm.13745 .

Article   PubMed   PubMed Central   Google Scholar  

Abdelrahman SM, Mohamed FR. Patient safety culture as perceived by internship nursing students as perceived by internship nursing students. Int J Adv Nurs Stud. 2017;6(2):67–70. https://doi.org/10.14419/ijans.v6i2.7681 .

Mousavi-Roknabadi RS, Momennasab M, Groot G, Askarian M, Marjadi B. Medical error and under-reporting causes from the viewpoints of nursing managers: A qualitative study. Int J Prev Med. 2022;13:103. https://doi.org/10.4103/ijpvm.IJPVM_500_20 .

Alshyyab MA, Albsoul R, Fitzgerald G. Factors influencing patient safety culture in the operating room in a teaching hospital in Jordan: a qualitative descriptive study. The TQM Journal. 2023;35(7):1722–42. https://doi.org/10.1108/TQM-04-2022-0133 .

Ihudiebube-Splendor CN, Chikeme PC. A descriptive cross-sectional study: Practical and feasible design in investigating health care–seeking behaviors of undergraduates. SAGE Publications Ltd; 2020. https://doi.org/10.4135/9781529742862 .

Thompson, Steven K. Sampling. Vol. 755. John Wiley & Sons. 2012. https://doi.org/10.1111/j.1083-6101.2007.00336.x .

Dimitriadou M, Merkouris A, Charalambous A, Lemonidou C, Papastavrou E. The knowledge about patient safety among undergraduate nurse students in Cyprus and Greece: a comparative study. BMC Nurs. 2021;20(1):110. https://doi.org/10.1186/s12912-021-00610-6 .

Torkaman M, Sabzi A, Farokhzadian J. The effect of patient safety education on undergraduate nursing students’ patient safety competencies. Community Health Equity Res Policy. 2022;42(2):219–24. https://doi.org/10.1177/0272684X20974214 .

Usher K, Woods C, Parmenter G, Hutchinson M, Mannix J, Power T, Chaboyer W, Latimer S, Mills J, Siegloff L, Jackson D. Self-reported confidence in patient safety knowledge among Australian undergraduate nursing students: A multi-site cross-sectional survey study. Int J Nurs Stud. 2017;71:89–96. https://doi.org/10.1016/j.ijnurstu.2017.03.006 .

Lee SE, Dahinten VS. Evaluating a patient safety course for undergraduate nursing students: A quasi-experimental study. Collegian. 2023;30(1):75–83. https://doi.org/10.1016/j.colegn.2022.06.00 .

Doody O, Hennessy T, Bright AM. The role and key activities of Clinical Nurse Specialists and Advanced Nurse Practitioners in supporting healthcare provision for people with intellectual disability: An integrative review. Int J Nurs Stud. 2022;129: 104207. https://doi.org/10.1016/j.ijnurstu.2022.104207 .

Parreira P, Santos-Costa P, Pardal J, Neves T, Bernardes RA, Serambeque B, Sousa LB, Graveto J, Silén-Lipponen M, Korhonen U, Koponen L. Nursing Students’ Perceptions on Healthcare-Associated Infection Control and Prevention Teaching and Learning Experience in Portugal. Journal of Personalized Medicine. 2022;12(2):180. https://doi.org/10.3390/jpm12020180 .

Tumala RB, Almazan J, Abdulaziz H, Felemban EM, Alsolami F, Alquwez N, Alshammari F, Tork HM, Cruz JP. Assessment of nursing students perceptions of their training hospital’s infection prevention climate: A multi-university study in Saudi Arabia. Nurse Educ Today. 2019;81:72–7. https://doi.org/10.1016/j.nedt.2019.07.003 .

Chan R, Booth R, Strudwick G, Sinclair B. Nursing students’ perceived self-efficacy and the generation of medication errors with the use of an electronic medication administration record (eMAR) in clinical simulation. Int J Nurs Educ Scholarsh. 2019;16(1):20190014. https://doi.org/10.1515/ijnes-2019-0014 .

Abdi Z, Delgoshaei B, Ravaghi H, Heyrani A. Changing medical students’ knowledge, skills, and attitudes about patient safety. HealthMED. 2012; 6(9):3129–5. Available at https://www.researchgate.net/publication/233843540_Changing_Medical_Students'_Knowledge_Skills_and_Attitudes_about_Patient_Safety .

Ünver S, Yeniğün SC. Patient safety attitude of nurses working in surgical units: A cross-sectional study in Turkey. J Perianesth Nurs. 2020;35(6):671–5. https://doi.org/10.1016/j.jopan.2020.03.012 .

Beitz JM. Addressing the perioperative nursing shortage through education: A perioperative imperative. AORN J. 2019;110(4):403–14. https://doi.org/10.1002/aorn.12805 .

Kamran R, Bari A, Khan RA, Al-Eraky M. Patient safety awareness among undergraduate medical students in Pakistani medical school. Pak J Med Sci. 2018;34(2):305. https://doi.org/10.12669/pjms.342.14563 .

Ramírez-Torres CA, Rivera-Sanz F, Cobos-Rincon A, Sufrate-Sorzano T, Juárez-Vela R, Gea-Caballero V, Tejada Garrido CI, ColadoTello ME, Durante A, Santolalla-Arnedo I. Perception of patient safety culture among nursing students: A cross-sectional study. Nurs Open. 2023;10(12):7596–602. https://doi.org/10.1002/nop2.1995 .

Alquwez N, Cruz JP, Alshammari F, Felemban EM, Almazan JU, Tumala RB, Alabdulaziz HM, Alsolami F, Silang JP, Tork HM. A multi-university assessment of patient safety competence during clinical training among baccalaureate nursing students: A cross-sectional study. J Clin Nurs. 2019;28(9–10):1771–81. https://doi.org/10.1111/jocn.14790 .

Dominika K, Daniela B, Dominika H, Katarina Z. Nursing students’ perception of patient safety culture during the Covid-19 pandemic–results of a pilot study. Acta Medica Martiniana. 2022;22(1):45–53. https://doi.org/10.2478/acm-2022-0006 .

Sammer C, Hauck LD, Jones C, Zaiback-Aldinger J, Li M, Classen D. Examining the relationship of an all-cause harm patient safety measure and critical performance measures at the frontline of care. J Patient Saf. 2020;16(1):110. https://doi.org/10.1097/PTS.0000000000000468 .

Mihdawi M, Al-Amer R, Darwish R, Randall S, Afaneh T. The influence of nursing work environment on patient safety. Workplace health & safety. 2020;68(8):384–90. https://doi.org/10.1177/2165079920901533 .

Kong LN, Zhu WF, He S, Chen SZ, Yang L, Qi L, Peng X. Attitudes towards patient safety culture among postgraduate nursing students in China: A cross-sectional study. Nurse Educ Pract. 2019;38:1–6. https://doi.org/10.1016/j.nepr.2019.05.014 .

Yan L, Yao L, Li Y, Chen H. Assessment and analysis of patient safety competency of Chinese nurses with associate degrees: A cross-sectional study. Nurs Open. 2021;8(1):395–403. https://doi.org/10.1002/nop2.640 .

Mahsoon AN, Dolansky M. Safety culture and systems thinking for predicting safety competence and safety performance among registered nurses in Saudi Arabia: a cross-sectional study. J Res Nurs. 2021;26(1–2):19–32. https://doi.org/10.1177/17449871209761 .

Alzahrani N, Jones R, Abdel-Latif ME. Attitudes of doctors and nurses toward patient safety within emergency departments of two Saudi Arabian hospitals. BMC Health Services Research. 2018; 18:1-7.  https://orcid.org/0000-0001-9115-864 .

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Acknowledgements

The authors would like to thank the nursing student who participated in this study.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). This research did not receive explicit support from any public organizations, businesses, or the private sector.

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Yasmin Ibrahim Abdelkader Khider, Shaimaa Mohamed Elghareeb Allam & Heba Mohammed Mahmoud Elhapashy

College of Nursing, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

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YIAK: conceptualization, data collection, validation, visualization, original draft preparation, and writing; SMEA: reviewing, editing, and resources; MAZ: software, and data analysis; HMME: reviewing, editing, and resources. All the authors have read and approved the final version of the manuscript to be published.

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Khider, Y.I.A., Allam, S.M.E., Zoromba, M.A. et al. Nursing students’ perspectives on patients' safety competencies: a cross-sectional survey. BMC Nurs 23 , 323 (2024). https://doi.org/10.1186/s12912-024-01966-1

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Received : 28 February 2024

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Published : 13 May 2024

DOI : https://doi.org/10.1186/s12912-024-01966-1

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Background: Scar impairments impose a great economic burden and influence a subject's well-being and quality of life. Despite that, physiotherapy interventions are poorly investigated. Objective of the study: Provide a comprehensive overview of studies addressing physiotherapy and conservative non-invasive interventions for skin scar management, summarizing studies based on scar type, localization, patient's characteristics (e.g., age), safety and tolerance of physical interventions. The realization of an infographic will assist clinicians and patients with scars' management. Moreover, any knowledge gaps will be identified. Methods: The review will be conducted following the Joanna Briggs Institute Manual for Evidence Synthesis. MEDLINE Central, PEDro, Embase, Cochrane Library and Central Register of Controlled Trials (CENTRAL) and CINAHL and grey literature (e.g., Google Scholar) will be searched for studies considering physical therapy interventions in scars management. Every study considering conservative non-invasive physiotherapy interventions for scar management will be included. This review will look at studies carried out in any context. Articles written in English or Italian will be considered. No temporal or publication type restrictions will be placed. Selection and extraction of data will be done by three reviewers independently, any discrepancies will be resolved by a fourth reviewer. The results will be illustrated using descriptive statistics and summarized in an infographic. Ethics and dissemination: No ethics approval will be necessary.

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