research article impact factor

Google Scholar Metrics

Google Scholar Metrics provide an easy way for authors to quickly gauge the visibility and influence of recent articles in scholarly publications. Scholar Metrics summarize recent citations to many publications, to help authors as they consider where to publish their new research.

To get started, you can browse the top 100 publications in several languages , ordered by their five-year h-index and h-median metrics. To see which articles in a publication were cited the most and who cited them, click on its h-index number to view the articles as well as the citations underlying the metrics.

You can also explore publications in research areas of your interest. To browse publications in a broad area of research, select one of the areas in the left column. For example: Engineering & Computer Science or Health & Medical Sciences .

To explore specific research areas, select one of the broad areas, click on the "Subcategories" link and then select one of the options. For example: Databases & Information Systems or Development Economics.

Browsing by research area is, as yet, available only for English publications. You can, of course, search for specific publications in all languages by words in their titles.

Scholar Metrics are currently based on our index as it was in July 2023 .

Available Metrics

The h-index of a publication is the largest number h such that at least h articles in that publication were cited at least h times each. For example, a publication with five articles cited by, respectively, 17, 9, 6, 3, and 2, has the h-index of 3.

The h-core of a publication is a set of top cited h articles from the publication. These are the articles that the h-index is based on. For example, the publication above has the h-core with three articles, those cited by 17, 9, and 6.

The h-median of a publication is the median of the citation counts in its h-core. For example, the h-median of the publication above is 9. The h-median is a measure of the distribution of citations to the articles in the h-core.

Finally, the h5-index , h5-core , and h5-median of a publication are, respectively, the h-index, h-core, and h-median of only those of its articles that were published in the last five complete calendar years.

We display the h5-index and the h5-median for each included publication. We also display an entire h5-core of its articles, along with their citation counts, so that you can see which articles contribute to the h5-index. And there's more! Click on the citation count for any article in the h5-core to see who cited it.

Coverage of Publications

Scholar Metrics currently cover articles published between 2018 and 2022 , both inclusive. The metrics are based on citations from all articles that were indexed in Google Scholar in July 2023 . This also includes citations from articles that are not themselves covered by Scholar Metrics.

Since Google Scholar indexes articles from a large number of websites, we can't always tell in which journal a particular article has been published. To avoid misidentification of publications, we have included only the following items:

• journal articles from websites that follow our inclusion guidelines ;
• selected conference articles in Engineering and Computer Science.

Furthermore, we have specifically excluded the following items:

• court opinions, patents, books, and dissertations;
• publications with fewer than 100 articles published between 2018 and 2022;
• publications that received no citations to articles published between 2018 and 2022.

Overall, Scholar Metrics cover a substantial fraction of scholarly articles published in the last five years. However, they don't currently cover a large number of articles from smaller publications.

Inclusion and Corrections

If you can't find the journal you're looking for, try searching by its abbreviated title or alternate title. There're sometimes several ways to refer to the same publication. (Fun fact: we've seen 959 ways to refer to PNAS.)

If you're wondering why your journal is not included, or why it has fewer citations than it surely deserves, that is often a matter of configuring your website for indexing in Google Scholar. Please refer to the inclusion manual . Also, keep in mind that Scholar Metrics only include publications with at least a hundred articles in the last five years.

• Privacy & Terms

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Gen Intern Med
• v.37(7); 2022 May

Measures of Impact for Journals, Articles, and Authors

Elizabeth m suelzer.

1 Medical College of Wisconsin, Milwaukee, WI USA

Jeffrey L. Jackson

2 Zablocki VA Medical Center, Milwaukee, WI USA

Journals and authors hope the work they do is important and influential. Over time, a number of measures have been developed to measure author and journal impact. These impact factor instruments are expanding and can be difficult to understand. The varying measures provide different perspectives and have varying strengths and weaknesses. A complete picture of impact for individual researchers and journals requires using multiple measures and does not fully capture all aspects of influence. There are only a few players in the scholarly publishing world that collect data on article citations: Clarivate Analytics, Elsevier, and Google Scholar (Table ​ (Table1). 1 ). Measures of influence for authors and journals based on article citations use one of these sources and may vary slightly because of differing journal coverage.

Citation Databases

Individual Authors

Researchers make contributions to their fields in many ways: through education, advocacy, mentorship, collaboration, reviewing grants and articles, editorial activities, and leadership. For better or worse, their impact is usually based on the number of research articles they publish and how often those articles are cited. Some activities, such as writing editorials for leading journals, book chapters, or other clinical texts; testifying before Congress; or helping to shape government or health system policy, can be highly influential, but not credited in these measures of influence.

A common problem authors have in determining their impact is duplicate names, either from being inconsistent in the name they use (e.g., Jackson JL vs Jackson J) or name changes. There are several ways to establish a persistent and unique digital identifier. Researchers should take advantage of all.

ORCID ( www.orcid.org )

Many funders require an ORCID identifier as part of grant submission. ORCID is free, and all authors can sign up to create a unique identifier. ORCID does not track measures of impact, but cooperates with other sites that do by maintaining a list of publications that authors can review for completeness and accuracy.

ResearcherID ( www.researcherid.com )

This site provides a unique identifier and pulls information from Web of Science (Clarivate) to generate an h -index. It has a dashboard that generates a Web of Science author impact plot, provides authors a year-by-year report on impact, and generates a “citation” map that shows the location of citations. ResearcherID is also used by Publons, another Clarivate product, that tracks peer review and editorial activity. Access requires a subscription.

Scopus and Web of Science

Scopus and Web of Science are independent sites that create unique identifiers for authors based on proprietary software. Identifiers are automatically assigned and may result in the creation of more than one identifier, particularly if authors have had multiple affiliations, have a common name, have changed names, or have been inconsistent in their name. Authors can review the identifiers assigned and merge different listings. Access to these databases requires a subscription.

In addition, authors can create a Google Scholar account, which will also track and assess author impact. Google Scholar is free. Authors should regularly review their account to make sure their article list is accurate.

Measures of Impact for Authors

There are a number of different measures of individual author impact; each has strengths and weaknesses (Table ​ (Table2). 2 ). All are limited in that they do not account for author effort and order. Most can be skewed by self-citation and favor those who have been publishing longer. 2

Author Measures of Influence

H-Index , developed by Jorge E. Hirsch in 2005, is defined as the number of published papers that have been cited at least h times. 3 An h -index of 40 means th.e author has 40 articles cited at least 40 times. This simple metric is widely used for evaluating an authors’ impact. Citation databases like Web of Science, Scopus (Elsevier), and Google Scholar provide h -index information in their author profiles, though the reported h -index may vary due to citation coverage. The h -index favors authors that publish a continuous stream of papers with persistent, above-average impact. It measures the cumulative impact of an author’s work and combines quantity and quality. However, it does not account for the author effort and order, is biased against early-career researchers with fewer publications, and can be skewed by self-citation.

G-Index , created in 2006 by Leo Egghe, is defined as the largest number such that the top “ g ” articles received together at least g 2 citations. 4 This metric favors highly cited articles; a single highly cited article will increase the g -index considerably, while only increasing the h -index by 1.

i-10-Index , calculated by Google Scholar, is a straightforward metric that shows the number of publications with at least 10 citations.

Measures of Impact for Individual Articles

This is an NIH dashboard of bibliometrics for articles. iCite has three modules: Influence, Translation, and Open Citations. Influence is based on a relative citation ratio (RCR), comparing article citations to the median for NIH–funded publications, the value of which is set at 1.0. Among NIH–funded studies, the 90 th percentile for RCR is 3.81. Among all studies, the 90 th percentile is 2.24. Individual paper influence is reported and can be used to select manuscripts that best represent one’s work. Translation provides a measure of translation from bench to bedside by breaking down whether most of the author’s publications are molecular/cellular, animal, or human. Citations provide a count of the total citations and give citation statistics (mean, median, SE, maximum) as well as a list of the citing articles for each paper.

Alternative measures of influence

There are measures of influence of individual articles that are not based on citations. They provide a snapshot of article impact in a number of alternate venues, such as public policy documents, news articles, blogs, and social media.

Altmetric tracks more than 15 different sources, including public policy documents, news articles, blog posts, mentions in syllabi, reference managers, and social networks, such as Twitter and Facebook. The results are weighted; some sources, such as news articles, get greater weight. For example, in 2020, the weights of the various sources were news stories: 8, blogs: 5, Q&A forums: 2.5, Twitter: 1, Google: 1, and Facebook: 0.25. Altmetrics can be displayed as a “badge,” a symbol with a number in the middle of a circle with the strands colored to reflect the elements that went into the score. Researchers can sign up to create an altmetric badge for their articles ( www.altmetric.com ). To create a badge, the article must have a DOI number. Altmetrics for any specific article reflects popular interest in the topic rather than scientific importance. At JGIM, article altmetrics do not correlate with citations. Altmetrics can accumulate quickly; many metrics, such as Twitter and Facebook mentions, tend to occur within days of publication, while citations can take years. Altmetrics can be applied to scholarly products other than research publications, such as curricula and software. However, altmetrics can be gamed; “popular” topics tend to get more play than others. It is still unclear how to use altmetrics; most rank and tenure committees do not include these measures in promotion deliberations.

PlumX Analytics

PlumX gathers metrics into 5 categories: citations, usage, captures, mentions, and social media. Citations include traditional citations as well as ones that may have societal impact, such as policy documents. Usage measures views, downloads, and measures of how often the article is read. Captures indicate that a reader is planning on coming back to the article; it can indicate future citations. Mentions refer to news articles, blog posts, and other public mentions of the paper. PlumX Social Media refers to tweets and Facebook likes and shares, among several sources. It provides a picture of how much public attention articles are getting. PlumX analytics suffer from the same issues as altmetrics and citations. PlumX analytics are embedded in several platforms, including Mendeley, Science Direct, and Scopus and on many open-access journal platforms.

Measures of Impact for Journals

Historically, there were many reasons why certain journals rose to the top: highly respected editors, a long publishing history, and a track record of influential work policy makers and clinicians cared about. In 1975, Thompson Reuters debuted SCI Journal Citation Reports , ranking journals based on article citations. 5 Subsequently, this has been the primary basis for journal prestige.

Journal evaluation metrics that use citation data favor some disciplines over others. Disciplines vary widely in the amount of research output, the number of citations that are normally included in papers, and the tendency of a discipline to cite recent articles. 6 For example, Acta Poetica focuses on literary criticism. Its impact factor would be a poor measure of the journal’s influence. In addition, one needs to consider where the evaluation tool is collecting their data. Databases like Web of Science and Scopus may have stronger coverage of some disciplines, impacting the citation metrics that are generated. 6

Some resources assign journals to subject categories, making it possible to compare journals within their discipline. A good analogy is points scored in sporting events. Seven points in American football is a poor offensive outing, while 7 points in European football is a juggernaut. Comparing journals within the same discipline provides better information about the journal’s relative importance.

Journal Citation Reports

Journal Impact Factor (JIF). This is published annually by Clarivate and uses citation data from Web of Science. It has been the “gold standard” for measuring journal impact since its creation. 7 Journal editors nervously await release of their impact factor every summer. The JIF is calculated by dividing the total number of citations in the previous 2 years by the number of “source” articles published the following year. JGIM had 2810 citations in 2020 for articles published in 2018 and 2019; 548 of these articles were categorized as source material. Dividing 2810/548 yields our 2020 impact factor of 5.128. Not everything journals publish is considered source material. Clarivate does not provide guidance to journals on how they decide what types of material to count. In general, letters and editorials are not included. JGIM falls in the Medicine, General & Internal and the Health Care Sciences & Services categories, ranking 27 th and 11 th , respectively, in each. Seeking high JIF has led some journals to reduce the number of articles they publish, increase the amount of non-source papers, and focus on work they believe will be highly cited. The JIF is also susceptible to journal self-citation.

Journal Citation Indicator (JCI) is a normalized metric that debuted in 2021; a score of 1.0 means that journal articles were cited on average the same as other journals in that category. 8 JGIM has a JCI of 1.48 (Table ​ (Table3), 3 ), meaning we have a 48% more citation impact than other journals in our category. Based on the JCI, JGIM ranks 23 rd in Medicine, General & Internal and 15 th in Health Care Sciences & Services.

Journal Measures of Impact

* Source articles: articles that are counted in the denominator

5-Year Impact Factor is the average number of times articles published in the previous 5 years were cited in the indexed year. It gives information on the sustained influence of journal publications. JGIM’s 2020 score was 6.070, meaning that articles published in 2014–2019 were cited an average of 6 times in 2020.

Immediacy Index is the number of citations that occur in the year of publication. Journals with high immediacy index scores are rapidly cited. JGIM has a score of 1.861. This measure has been criticized for penalizing articles published later in the year.

Eigenfactor Score , a metric created in 2007 by Carl Bergstrom and Jevin West of the University of Washington, is based on the number of times articles from a journal over the past 5 years have been cited in the indexed year and gives citations in highly cited journals more weight than lesser cited ones. Self-citations by the journal are excluded. JGIM’s 2020 eigenfactor score was 0.02895. This measure suffers from being difficult to understand.

The Normalized Eigenfactor Score provides a normalized metric of the Eigenfactor Score, setting a score of 1 as the average for all journals. Like the Eigenfactor Score, citations that come from highly cited journals carry more weight than citations from less cited journals and journal self-citations are excluded. JGIM’s score is 6.07, meaning that JGIM was sixfold more influential than the average journal in the Web of Science database.

Article Influence . This measure is calculated by dividing the Eigenfactor Score by the number of a journal’s articles over the first 5 years after publication. It is calculated by multiplying the Eigenfactor Score by 0.01 and dividing by the number of articles in the journal, then normalized as a fraction of all articles in all publications, such that the mean is 1.0. JGIM’s most recent influence score is 2.579. This indicates that JGIM is more than twice as influential as the average journal.

CiteScore is calculated by dividing the number of citations from documents (articles, reviews, conference papers, book chapters, and data papers) over the previous 4 years by the number of articles indexed in Scopus published by the journal during those years. JGIM’s CiteScore is 4.6. Cite scores are calculated on a monthly basis. Among 122 internal medicine journals, JGIM is ranked 40 th by the CiteScore.

SCImago Journal Rank (SJR) also uses Scopus data and weights citations according to the prestige of the citing journal, taking into account the thematic closeness of the citing and cited journals. 9 It is calculated based on citations in 1 year to articles published in the previous 3 years. JGIM’s SJR is 1.746, which puts us 13 th on the list of “internal medicine” journals.

SCImago H-Index calculates the number of journal articles ( h ) that have been cited at least h times. It is the same calculation used to evaluate authors; SCImago calculates the journal h -index using Scopus citation data. JGIM has an h -index of 180, meaning that 180 of our articles have been cited more than 180 times. The h -index measures the productivity and impact of journal publications.

Source Normalized Impact per Paper (SNIP) compares each journal’s citations per article with the citations expected in its field. It allows a comparison of the journal’s impact across fields, because it adjusts for the likelihood of journal articles in that field being cited. JGIM’s SNIP is 1.471 which ranks us as 23 rd among 112 internal medicine journals.

Google Scholar

H5-index. Google Scholar calculates an H5-index for journals, which is the number of articles in the last 5 years with at least h citations. Google Scholar classifies JGIM as a primary care health journal. JGIM has an H5-index of 65, making it the top-ranked journal in this category. Google Scholar does not make available the citation sources; consequently, it is difficult to tell how complete the data is.

Journal Altmetrics

Like individual articles, altmetrics can be generated for journals. They have the same advantages and disadvantages as individual article altmetrics. In 2020, JGIM had 2.5 million downloads, 61 k linkouts, and 33 k social media mentions. Journal editors may have a poor understanding of altmetrics and struggle to know what to do with the data. Altimetrics reflect popular interest. For example, in 2020, the COVID pandemic captured public interest; articles focused on aspects of the pandemic received considerable public attention. For JGIM, the top altimetric article examined the impact of masking on preventing the spread of COVID and had an altmetric score of 4829.

JGIM is interested in these measures to ensure that we (like our authors) are having an impact. However, we are not obsessed on these measures and will continue to put forward what feels most important and relevant for academic general internists.

Declarations

The authors had no conflicts of interest with this article.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

University of Illinois Chicago

University library, search uic library collections.

Find items in UIC Library collections, including books, articles, databases and more.

Advanced Search

Search UIC Library Website

Find items on the UIC Library website, including research guides, help articles, events and website pages.

• Search Collections
• Search Website

Measuring Your Impact: Impact Factor, Citation Analysis, and other Metrics: Citation Analysis

• Measuring Your Impact

Citation Analysis

Find your h-index.

• Other Metrics/ Altmetrics
• Journal Impact Factor (IF)
• Selecting Publication Venues

About Citation Analysis

What is Citation Analysis?

The process whereby the impact or "quality" of an article is assessed by counting the number of times other authors mention it in their work.

Citation analysis invovles counting the number of times an article is cited by other works to measure the impact of a publicaton or author.  The caviat however, there is no single citation analysis tools that collects all publications and their cited references.  For a thorough analysis of the impact of an author or a publication, one needs to look in multiple databases to find all possible cited references. A number of resources are available at UIC  that identify cited works including: Web of Science, Scopus, Google Scholar, and other databases with limited citation data.

Citation Analysis - Why use it?

To find out how much impact a particular article or author has had, by showing which other authors cited the work within their own papers.  The H-Index is one specific method utilizing citation analysis to determine an individuals impact.

Web of Science

Web of Science provides citation counts for articles indexed within it.  It i ndexes over 10,000 journals in the arts, humanities,  sciences, and social sciences.

• Enter the name of the author in the top search box (e.g. Smith JT).  
• Select Author from the drop-down menu on the right.
• To ensure accuracy for popular names, enter Univ Illinois in the middle search box, then select “Address” from the field drop down menu on the right.  (You might have to add the second search box by clicking "add another field" before you enter the address)
• Click on Search
• a list of publications by that author name will appear.   To the right of each citation, the number of times the article has been cited will appear.   Click the number next to "times cited" to view the articles that have cited your article

Scopus provide citation counts for articles indexed within it (limited to article written in 1996 and after).   It indexes o ver 15,000 journals from over 4,000 international publishers across the disciplines.

• Once in Scopus, click on the Author search tab.
• Enter the name of the author in the search box.  If you are using initials for the first and/or middle name, be sure to enter periods after the initials (e.g. Smith J.T.). 
• To ensure accuracy if it is a popular name, you may enter University of Illinois in the affiliation field.  
• If more than one profile appears, click on your profile (or the profile of the person you are examining). 
• Once you click on the author's profile, a list of the publications will appear and to the right of each ctation, the number of times the article has been cited will appear.  
• Click the number to view the articles that have cited your article

 Dimensions (UIC does not subscribe but parts are free to use)

• Indexes over 28000 journals
• Does not display h-index in Dimensions but can calculate or if faculty, look in MyActivities
• Includes Altmetrics score
• Google Scholar

Google Scholar provides citation counts for articles found within Google Scholar.  Depending on the discipline and cited article, it may find more cited references than Web of Science or Scopus because overall, Google Scholar is indexing more journals and more publication types than other databases. Google Scholar is not specific about what is included in its tool but information is available on how Google obtains its content .   Limiting searches to only publications by a specific author name is complicated in Google Scholar.  Using Google Scholar Citations and creating your own profile will make it easy for you to create a list of publications included in Google Scholar.   Using your Google Scholar Citations account, you can see the citation counts for your publications and have GS calculate your h-index.  (You can also search Google Scholar by author name and the title of an article to retrieve citation information for a specific article.)

• Using your google (gmail) account, create a profile of all your articles captured in Google Scholar.  Follow the prompt on the scrren to set up your profile.   Once complete, this will show all the times the articles have been cited by other documents in Google Scholar and your h-index will be provided.  Its your choice whether you make your profile public or private but if you make it public, you can link to it from your own webpages.

Try Harzing's Publish or Perish Tool in order to more selectively examine published works by a specific author.

Databases containing limited citation counts:

• PubMed Central
• Science Direct
• SciFinder Scholar

About the H-index

The h-index is an index to quantify an individual’s scientific research output ( J.E. Hirsch )   The h-index is an index that attempts to measure both the scientific productivity and the apparent scientific impact of a scientist. The index is based on the set of the researcher's most cited papers and the number of citations that they have received in other people's publications ( Wikipedia )  A scientist has index h if h of [his/her] Np papers have at least h citations each, and the other (Np − h) papers have at most h citations each.

Find your h-index at:

Below are instructions for obtaining your h-index from Web of Science, Scopus, and Google Scholar.

Web of Science provides citation counts for articles indexed within it.  It indexes over 12,000 journals in the arts, humanities,  sciences, and social sciences.  To find an author's h-index in WOS:

• To ensure accuracy for popular names, add an additional search box and enter "Univ Illinois" and then select “Address” from the field drop down menu on the right.
• Click on Citation Report on the right hand corner of the results page.  The H-index is on the right of the screen.
• If more than one profile appears, click on your profile (or the profile of the person you are examining).  Under the Research section, you will see the h-index listed.
• If you have worked at more than one place, your name may appear twice with 2 separate h-index ratings.  Select the check box next to each relevent profile, and click show documents.

  Google Scholar

• Using your google (gmail) account, create a profile of all your articles captured in Google Scholar.  Follow the prompt on the screen to set up your profile.   Once complete, this will show all the times the articles have been cited by other documents in Google Scholar and your h-index will be provided.  Its your choice whether you make your profile public or private but if you make it public, you can link to it from your own webpages.
• See  Albert Einstein's
• Harzing’s Publish or Perish (POP) 
• Publish or Perish Searches Google Scholar.  After searching by your name, deselect from the list of articles retrieved those that you did not author.  Your h-index will appear at the top of the tool.  Note:This tool must be downloaded to use
• << Previous: Measuring Your Impact
• Next: Find Your H-Index >>
• Last Updated: Mar 13, 2024 12:51 PM
• URL: https://researchguides.uic.edu/if

University Library, University of Illinois at Urbana-Champaign

Introduction to Impact Factor and Other Research Metrics

• Types of Metrics

Impact Factor

• Identifying Journals
• More Resources

More Information

• About Journal Impact Factor Visit this article published by Thomson Reuters on journal impact factor to learn more about the bibliometric and how it measures importance.
• Annual Reviews Rankings in JCR Visit this page to see how Annual Review Journals currently rank in Journal Citation Reports.
• SCImago Journal and Country Rank The SCImago Journal & Country Rank is a publicly available portal that includes the journals and country scientific indicators developed from the information contained in the Scopus database.

Ask a Librarian

Impact factor,  or Journal Impact Factor,  is a measure of the frequency with which the "average article" published in a given scholarly journal has been cited in a particular year or period and is often used to measure or describe the importance of a particular journal to its field. Impact factor was originally developed by Eugene Garfield, the founder of Institute of Scientific Information, which is now a part of Clarivate Analytics. Journal Impact Factor can be found in the  Journal Citation Reports or the JCR, as it's commonly known. Over the years various organizations have been created similar journal-level metrics, such as  SCImago Journal & Country Rank .

This page describes how to find impact factor in Journal Citation Reports .

Journal Citation Reports

Clarivate Analytics (formerly Institute for Scientific Information (ISI)) ranks, evaluates, and compares journals within subject categories and publishes the results in Journal Citation Reports . Journal Citation Reports provides ranking for journals in science, technology, and the social sciences. For every journal, the database collects and/or calculates information such as:

• citation/article counts
• impact factor
• immediacy index
• cited half-life
• citing half-life
• source data listing
• citing journal listing
• cited journal listing
• publisher information
• subject categories

Find Journal Citation Reports (JCR)

Follow the instructions below to find the Journal Citation Reports using the Library's resources.

• Begin at the Library homepage .
• Click on the orange tab that says "Find Materials," then scroll down until you see a laptop icon with the words "Databases by Subject and A-Z"; click on the icon.
• Type journal citations reports in the search box on the left side of the screen and then click on the magnifying glass to search that title.
• Your result will say "Journal Citation Reports"; click on it. It might ask you to provide your Net ID and password if you are off campus.

Find the Impact Factor

• Once in the database you either search by journal title (if you know which journal you want to see) or browse by category, which will let you view journals by JIF by discipline.
• On the left side you can choose search criteria, like impact factor range, year, and if the journal is open access.
• It is important to choose the right edition based on your subject area, as you won't be able to see specific journals if you choose the wrong one. Once you have finished selecting what to search, click Submit.
• You can't access impact factors from last year because the calculations only happen every two years (i.e. if the current year is 2021 the farthest you can go back is 2020). Most people choose the most current year they can access.
• Journals limited by the subject area, publisher, or geographic region.
• View all journals in order to browse.
• Search for a specific journal if you already know its title

Once you find a journal, the JCR gives you information about the journal, including the journal's abbreviations, how often it is published each year, the publisher, and the ISSN. 

Controversy

Many people have questioned the legitimacy of impact factor. Here are a few reasons why:

• Impact factor focuses purely on the numbers. There is no consideration of qualitative elements that have become important in today's world.
• Impact factor fails to incorporate more recent ways of sharing and using research, including Twitter mentions and posts, citation management downloads, and news and community information.
• Because impact factor is based on citations in only indexed journals , it fails to incorporate statistics from journals that might not be indexed and other sources like conference papers (which are important in the social sciences).
• Basic or summary information is usually cited the most in academia. That means that journals that publish articles with basic information are more likely to have higher impact factors. Journals that publish obscure or innovative information might not have as high of an impact factor.
• Some argue that impact factor is encouraging scholars to stick with mainstream topics and research.
• Scholars don't always have to cite something for it to be influential. Sometimes researchers just read something and it influences them, regardless of if they cite it in a future paper or piece of research.
• The journals in the JCR are mostly published in English. This means that many international sources aren't included in the conversation.
• It has been argued that journals have the ability to skew impact factor for their own journal. Before publishing an author, they will ask the author to cite more articles within their journal so that their impact factor goes up. This is NOT a common occurrence but instead something we should be aware of.
• << Previous: Types of Metrics
• Next: Identifying Journals >>
• Last Updated: Feb 28, 2024 12:49 PM
• URL: https://guides.library.illinois.edu/impact

• Research Process

What is Journal Impact Factor?

• 3 minute read
• 236.9K views

Table of Contents

Daunted by the idea of choosing the right journal for your paper? Don’t be. Metrics have become an everyday word in scholarship, in general. Within its many fields of research – if not all of them – they provide important data about a journal’s impact and relevance among its readers. In an era of information proliferation, it has become increasingly important to know where to capture the most attention and interest of your target audience.

So, whenever you are in doubt about which journal suits you better, don’t forget to browse its metrics; they will certainly help you with the decision-making process. Start, for example, with the Journal Impact Factor.

Impact factor (IF) is a measure of the number of times an average paper in a journal is cited, during a year. Clarivate Analytics releases the Journal Impact Factors annually as part of the Web of Science Journal Citation Reports®. Only journals listed in the Science Citation Index Expanded® (SCIE) and Social Sciences Citation Index® (SSCI) receive an Impact Factor.

What is a good impact factor for a scientific journal?

Impact Factors are used to measure the importance of a journal by calculating the number of times selected articles are cited within a particular year. Hence, the higher the number of citations or articles coming from a particular journal, or impact factor, the higher it is ranked. IF is also a powerful tool if you want to compare journals in the subject category.

Measuring a Journal Impact Factor:

• CiteScore metrics – helps to measure journal citation impact. Free, comprehensive, transparent and current metrics calculated using data from Scopus®, the largest abstract and citation database of peer-reviewed literature.
• SJR – or SCImago Journal Rank, is based on the concept of a transfer of prestige between journals via their citation links.
• SNIP – or Source Normalized Impact per Paper, is a sophisticated metric that accounts for field-specific differences in citation practices.
• JIF – or Journal Impact Factor is calculated by Clarivate Analytics as the average of the sum of the citations received in a given year to a journal’s previous two years of publications, divided by the sum of “citable” publications in the previous two years.
• H-index – Although originally conceived as an author-level metric, the H -index has been being applied to higher-order aggregations of research publications, including journals.

Deciding the perfect journal for your paper is an important step. Metrics are excellent tools to guide you through the process. However, we also recommend you not neglect a perfectly written text, not only scientific and grammatically but also fitting the chosen journal’s requirements and scope. At Elsevier, we provide text-editing services that aim to amend and adjust your manuscript, to increase its chances of a successful acceptance by your target journal. Although each journal has its own editorial team, the overall quality, language and whether the article is innovative may also play a role.

Language Editing Services by Elsevier Author Services:

We know that, as an academic researcher, you have many things to do to stay relevant.

Writing relevant manuscripts is a crucial part of your endeavors.

That’s why we, at Elsevier Author Service s, support you throughout your publication journey with a suite of products and services to help improve your manuscript before submission.

Check our video Reach the highest standard with Elsevier Author Services to learn more about Author Services.

Find more about What is Journal Impact Factor? on Pinterest:

• Manuscript Preparation

How to Choose Keywords for a Manuscript?

• Publication Recognition

What is a Corresponding Author?

You may also like.

Descriptive Research Design and Its Myriad Uses

Five Common Mistakes to Avoid When Writing a Biomedical Research Paper

Making Technical Writing in Environmental Engineering Accessible

To Err is Not Human: The Dangers of AI-assisted Academic Writing

When Data Speak, Listen: Importance of Data Collection and Analysis Methods

Choosing the Right Research Methodology: A Guide for Researchers

Why is data validation important in research?

Writing a good review article

Input your search keywords and press Enter.

ScienceDirect Support Center

To post social content, you must have a display name. The page will refresh upon submission. Any pending input will be lost.

Where do I find the Impact Factor of a journal?

The Impact Factor is a measure of scientific influence of scholarly journals. It measures the average number of citations received in a particular year by papers published in the journal during the two preceding years and is produced by a publisher called Thomson Reuters. The Impact Factor can be found on the Journal home page of journals that have an Impact Factor. 

Please note: Not all journals have an Impact Factor.

Follow these steps to find the Impact Factor of a journal:

• Search for a journal using the  ‘Journal/book title’  field on the ScienceDirect homepage or browse journal titles by selecting ' Journals & Books ' in the top right corner.
• Click the journal title to navigate to the journal’s home page.
• The Impact Factor and Journal CiteScore are mentioned in the header on the right side of the page.

Was this answer helpful?

Thank you for your feedback, it will help us serve you better. If you require assistance, please scroll down and use one of the contact options to get in touch.

Help us to help you:

Thank you for your feedback!

• Why was this answer not helpful?
• It was hard to understand / follow.
• It did not answer my question.
• The solution did not work.
• There was a mistake in the answer.
• Feel free to leave any comments below: Please enter your feedback to submit this form

Related Articles:

• What are Article Metrics?
• Where can I find the DOI?
• What can I do on a journal home page?
• Discover content from other publishers on ScienceDirect
• What are the technical requirements for ScienceDirect?

For further assistance:

Evaluating Information Sources

• Evaluate Your Sources
• Publication Types and Bias
• Reading Scholarly Articles

Journal Impact Factors

Links summary.

• Author Impact / Citations
• Author H-index
• Author h-index Options
• Author Citation Reports in Web of Science
• What are metrics?
• Cited Articles = Confusing Statistics
• Predatory Publishing

According to Journal Citation Reports (JCR) , an  impact factor is a ratio focusing on original research. 

Impact factor = # of citations to all items published in that journal in the past two years (divided by) # of articles and reviews published over those past two years referencing those citations

For example, if a journal has an impact factor of 2.5, this means in the indexed year each article published was cited on average 2.5 times in the previous two years in that journal.

Impact factor is used for journals only.

JCR only includes  12,000 journals and conference proceedings from over 3,300 publishers.

• InCitesTM: Journal Citation Reports® This Web of Science hosted database is a citation-based research evaluation tool for journal performance metrics with the goal of offering a systematic and objective means to evaluate the journals based on citation data.
• Tips for Using JCR Tips for using the Web of Science InCitesTM Journal Citation Reports

Metrics on the Web

• Eigenfactor® Project
• Google Scholar - Metrics
• InCitesTM: Journal Citation Reports®
• PlumX Metrics

Research Guides 

• Impact Metrics and Scholarly Attribution (UCLA)
• Research Impact Metrics (UM)
• Spreading the Word: Publishing Your Research & Extending Your Impact (USC)
• << Previous: Reading Scholarly Articles
• Next: Predatory Publishing >>
• Last Updated: Mar 8, 2024 1:17 PM
• URL: https://libguides.usc.edu/evaluate

• All subject areas
• Agricultural and Biological Sciences
• Arts and Humanities
• Biochemistry, Genetics and Molecular Biology
• Business, Management and Accounting
• Chemical Engineering
• Computer Science
• Decision Sciences
• Earth and Planetary Sciences
• Economics, Econometrics and Finance
• Engineering
• Environmental Science
• Health Professions
• Immunology and Microbiology
• Materials Science
• Mathematics
• Multidisciplinary
• Neuroscience
• Pharmacology, Toxicology and Pharmaceutics
• Physics and Astronomy
• Social Sciences
• All subject categories
• Acoustics and Ultrasonics
• Advanced and Specialized Nursing
• Aerospace Engineering
• Agricultural and Biological Sciences (miscellaneous)
• Agronomy and Crop Science
• Algebra and Number Theory
• Analytical Chemistry
• Anesthesiology and Pain Medicine
• Animal Science and Zoology
• Anthropology
• Applied Mathematics
• Applied Microbiology and Biotechnology
• Applied Psychology
• Aquatic Science
• Archeology (arts and humanities)
• Architecture
• Artificial Intelligence
• Arts and Humanities (miscellaneous)
• Assessment and Diagnosis
• Astronomy and Astrophysics
• Atmospheric Science
• Atomic and Molecular Physics, and Optics
• Automotive Engineering
• Behavioral Neuroscience
• Biochemistry
• Biochemistry, Genetics and Molecular Biology (miscellaneous)
• Biochemistry (medical)
• Bioengineering
• Biological Psychiatry
• Biomaterials
• Biomedical Engineering
• Biotechnology
• Building and Construction
• Business and International Management
• Business, Management and Accounting (miscellaneous)
• Cancer Research
• Cardiology and Cardiovascular Medicine
• Care Planning
• Cell Biology
• Cellular and Molecular Neuroscience
• Ceramics and Composites
• Chemical Engineering (miscellaneous)
• Chemical Health and Safety
• Chemistry (miscellaneous)
• Chiropractics
• Civil and Structural Engineering
• Clinical Biochemistry
• Clinical Psychology
• Cognitive Neuroscience
• Colloid and Surface Chemistry
• Communication
• Community and Home Care
• Complementary and Alternative Medicine
• Complementary and Manual Therapy
• Computational Mathematics
• Computational Mechanics
• Computational Theory and Mathematics
• Computer Graphics and Computer-Aided Design
• Computer Networks and Communications
• Computer Science Applications
• Computer Science (miscellaneous)
• Computer Vision and Pattern Recognition
• Computers in Earth Sciences
• Condensed Matter Physics
• Conservation
• Control and Optimization
• Control and Systems Engineering
• Critical Care and Intensive Care Medicine
• Critical Care Nursing
• Cultural Studies
• Decision Sciences (miscellaneous)
• Dental Assisting
• Dental Hygiene
• Dentistry (miscellaneous)
• Dermatology
• Development
• Developmental and Educational Psychology
• Developmental Biology
• Developmental Neuroscience
• Discrete Mathematics and Combinatorics
• Drug Discovery
• Drug Guides
• Earth and Planetary Sciences (miscellaneous)
• Earth-Surface Processes
• Ecological Modeling
• Ecology, Evolution, Behavior and Systematics
• Economic Geology
• Economics and Econometrics
• Economics, Econometrics and Finance (miscellaneous)
• Electrical and Electronic Engineering
• Electrochemistry
• Electronic, Optical and Magnetic Materials
• Emergency Medical Services
• Emergency Medicine
• Emergency Nursing
• Endocrine and Autonomic Systems
• Endocrinology
• Endocrinology, Diabetes and Metabolism
• Energy Engineering and Power Technology
• Energy (miscellaneous)
• Engineering (miscellaneous)
• Environmental Chemistry
• Environmental Engineering
• Environmental Science (miscellaneous)
• Epidemiology
• Experimental and Cognitive Psychology
• Family Practice
• Filtration and Separation
• Fluid Flow and Transfer Processes
• Food Animals
• Food Science
• Fuel Technology
• Fundamentals and Skills
• Gastroenterology
• Gender Studies
• Genetics (clinical)
• Geochemistry and Petrology
• Geography, Planning and Development
• Geometry and Topology
• Geotechnical Engineering and Engineering Geology
• Geriatrics and Gerontology
• Gerontology
• Global and Planetary Change
• Hardware and Architecture
• Health Informatics
• Health Information Management
• Health Policy
• Health Professions (miscellaneous)
• Health (social science)
• Health, Toxicology and Mutagenesis
• History and Philosophy of Science
• Horticulture
• Human Factors and Ergonomics
• Human-Computer Interaction
• Immunology and Allergy
• Immunology and Microbiology (miscellaneous)
• Industrial and Manufacturing Engineering
• Industrial Relations
• Infectious Diseases
• Information Systems
• Information Systems and Management
• Inorganic Chemistry
• Insect Science
• Instrumentation
• Internal Medicine
• Issues, Ethics and Legal Aspects
• Leadership and Management
• Library and Information Sciences
• Life-span and Life-course Studies
• Linguistics and Language
• Literature and Literary Theory
• LPN and LVN
• Management Information Systems
• Management, Monitoring, Policy and Law
• Management of Technology and Innovation
• Management Science and Operations Research
• Materials Chemistry
• Materials Science (miscellaneous)
• Maternity and Midwifery
• Mathematical Physics
• Mathematics (miscellaneous)
• Mechanical Engineering
• Mechanics of Materials
• Media Technology
• Medical and Surgical Nursing
• Medical Assisting and Transcription
• Medical Laboratory Technology
• Medical Terminology
• Medicine (miscellaneous)
• Metals and Alloys
• Microbiology
• Microbiology (medical)
• Modeling and Simulation
• Molecular Biology
• Molecular Medicine
• Nanoscience and Nanotechnology
• Nature and Landscape Conservation
• Neurology (clinical)
• Neuropsychology and Physiological Psychology
• Neuroscience (miscellaneous)
• Nuclear and High Energy Physics
• Nuclear Energy and Engineering
• Numerical Analysis
• Nurse Assisting
• Nursing (miscellaneous)
• Nutrition and Dietetics
• Obstetrics and Gynecology
• Occupational Therapy
• Ocean Engineering
• Oceanography
• Oncology (nursing)
• Ophthalmology
• Oral Surgery
• Organic Chemistry
• Organizational Behavior and Human Resource Management
• Orthodontics
• Orthopedics and Sports Medicine
• Otorhinolaryngology
• Paleontology
• Parasitology
• Pathology and Forensic Medicine
• Pathophysiology
• Pediatrics, Perinatology and Child Health
• Periodontics
• Pharmaceutical Science
• Pharmacology
• Pharmacology (medical)
• Pharmacology (nursing)
• Pharmacology, Toxicology and Pharmaceutics (miscellaneous)
• Physical and Theoretical Chemistry
• Physical Therapy, Sports Therapy and Rehabilitation
• Physics and Astronomy (miscellaneous)
• Physiology (medical)
• Plant Science
• Political Science and International Relations
• Polymers and Plastics
• Process Chemistry and Technology
• Psychiatry and Mental Health
• Psychology (miscellaneous)
• Public Administration
• Public Health, Environmental and Occupational Health
• Pulmonary and Respiratory Medicine
• Radiological and Ultrasound Technology
• Radiology, Nuclear Medicine and Imaging
• Rehabilitation
• Religious Studies
• Renewable Energy, Sustainability and the Environment
• Reproductive Medicine
• Research and Theory
• Respiratory Care
• Review and Exam Preparation
• Reviews and References (medical)
• Rheumatology
• Safety Research
• Safety, Risk, Reliability and Quality
• Sensory Systems
• Signal Processing
• Small Animals
• Social Psychology
• Social Sciences (miscellaneous)
• Social Work
• Sociology and Political Science
• Soil Science
• Space and Planetary Science
• Spectroscopy
• Speech and Hearing
• Sports Science
• Statistical and Nonlinear Physics
• Statistics and Probability
• Statistics, Probability and Uncertainty
• Strategy and Management
• Stratigraphy
• Structural Biology
• Surfaces and Interfaces
• Surfaces, Coatings and Films
• Theoretical Computer Science
• Tourism, Leisure and Hospitality Management
• Transplantation
• Transportation
• Urban Studies
• Veterinary (miscellaneous)
• Visual Arts and Performing Arts
• Waste Management and Disposal
• Water Science and Technology
• All regions / countries
• Asiatic Region
• Eastern Europe
• Latin America
• Middle East
• Northern America
• Pacific Region
• Western Europe
• ARAB COUNTRIES
• IBEROAMERICA
• NORDIC COUNTRIES
• Afghanistan
• Bosnia and Herzegovina
• Brunei Darussalam
• Czech Republic
• Dominican Republic
• Netherlands
• New Caledonia
• New Zealand
• Papua New Guinea
• Philippines
• Puerto Rico
• Russian Federation
• Saudi Arabia
• South Africa
• South Korea
• Switzerland
• Syrian Arab Republic
• Trinidad and Tobago
• United Arab Emirates
• United Kingdom
• United States
• Vatican City State
• Book Series
• Conferences and Proceedings
• Trade Journals

• Citable Docs. (3years)
• Total Cites (3years)

Follow us on @ScimagoJR Scimago Lab , Copyright 2007-2024. Data Source: Scopus®

Cookie settings

Cookie Policy

Legal Notice

Privacy Policy

Impact factors: What they are, where to find them, how to use them

• Introduction to impact factors
• Which UC Merced databases include impact factors?
• Video tutorials on impact factors
• Need additional help?

Instruction & Outreach Librarian

More on Citation Metrics

Wikipedia has an excellent collection of articles on various types and aspects of citation metrics, including impact factors, H-indexing, citation analysis and more. Click the image below if you're interested in learning more.

This guide was created in support of the Fall 2022 UC Merced Library workshop, "Impact factors: What they are, where to find them, and how to use them." Here, we'll discuss impact factors with emphasis on their use in writing theses and dissertations for degree credit at UC Merced.

What is an impact factor?

The impact factor measures the number of times a journal article has been cited by researchers in a given year. It's used to measure the importance of a scholarly journal -- that is, its importance to the discipline or field its articles cover, and by extension, the researchers working in that discipline or field -- by measuring the number of times articles in that journal are cited.

• Generally, impact factors are the best way to determine a journal's relative importance in a particular field or discipline. Your own research will be more readily accepted if it's based on the top journals -- meaning, the journals with the highest impact factors -- in your field.
• Impact factors are not perfect, and can be gamed, so to speak. Many journals will attempt to increase their impact factors by requiring that authors whose work is accepted for publication include citations to articles published in those journals.

Creating the impact factor

• Journal impact factors are calculated on the total number of citable articles in the two most recent, previous years. So it's not possible to get a journal impact factor for the present year. In 2022, the most recent journal impact factors will have been calculated on 2020 and 2021.
• Because journal impact factors are calculated on two years of article citations, it's not possible to calculate an impact factor for new journals.
• An impact factor of 10 is an excellent impact factor and indicates that the journal is of major importance in a field or discipline.
• An impact factor of 3 is considered to be good.
• Average impact factors for most journals are less than 1. However, this doesn't indicate that a journal is of poor quality. It may be a journal that publishes research in a field that is not noted for research.
• Next: Which UC Merced databases include impact factors? >>
• Last Updated: Oct 20, 2022 10:28 AM
• URL: https://libguides.ucmerced.edu/impact-factors

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts

Journal Metrics

This page provides information on peer review performance and citation metrics for  Scientific Data . Our  quick reference guide to journal metrics  is also available for download.

2023 Peer Review Metrics

Submission to first editorial decision: the median time (in days) from when a submission is received to when a first editorial decision about whether the paper was sent out for formal review or not is sent to the authors.

Submission to Accept: the median time (in days) from the submission date to the final editorial acceptance date.

Submission to first editorial decision - 27

Submission to Accept - 159

2022 Journal Metrics

On this page you will find a suite of citation-based metrics for  Scientific Data . Brief definitions for each of the metrics used to measure the influence of our journals are included below the journal metrics. Data has been produced by Clarivate Analytics.

For recently launched journals, metrics are calculated from available citation data. If a metric uses multiple years of data, new journals may have partial metrics.

While the metrics presented here are not intended to be a definitive list, we hope that they will prove to be informative. The page is updated on an annual basis.

2-year Impact Factor - 9.8

5-year Impact Factor - 10.8

Immediacy index - 0.9

Eigenfactor® score - 0.04464

Article Influence Score - 33

2023 Usage Metrics

Article-level metrics are also available on each article page, allowing readers to track the reach of individual papers.

6,408,593 Downloads 

14,885 Altmetric mentions

Definitions

2-year impact factor.

The Journal Impact Factor is defined as all citations to the journal in the current JCR year to items published in the previous two years, divided by the total number of scholarly items (these comprise articles, reviews, and proceedings papers) published in the journal in the previous two years. (Courtesy of  Clarivate Analytics )

5-year Impact Factor

The 5-year journal Impact Factor, available from 2007 onward, is the average number of times articles from the journal published in the past five years have been cited in the JCR year. It is calculated by dividing the number of citations in the JCR year by the total number of articles published in the five previous years. (Courtesy of  Clarivate Analytics )

Immediacy index

The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. (Courtesy of  Clarivate Analytics )

Eigenfactor® Score

The  Eigenfactor Score  calculation is based on the number of times articles from the journal published in the past five years have been cited in the JCR year, but it also considers which journals have contributed these citations so that highly cited journals will influence the network more than lesser cited journals. References from one article in a journal to another article from the same journal are removed, so that  Eigenfactor Scores  are not influenced by journal self-citation. (Courtesy of  Clarivate Analytics )

Article Influence Score

The  Article Influence Score  determines the average influence of a journal's articles over the first five years after publication. It is calculated by multiplying the  Eigenfactor Score  by 0.01 and dividing by the number of articles in the journal, normalized as a fraction of all articles in all publications. This measure is roughly analogous to the  5-Year Journal Impact Factor  in that it is a ratio of a journal's citation influence to the size of the journal's article contribution over a period of five years. (Courtesy of  Clarivate Analytics )

Downloads reflect the number of times full text or PDF versions of articles are accessed directly from the journal website. Downloads are defined as HTML, LookInside, PDF and Epub clicks. Please note that this does not include article downloads from mirror databases such as PubMed Central.

Altmetric mentions

Total number of mentions (e.g. Twitter, Facebook, Reddit, Blogs, News articles, Policy documents and Faculty of 1000 reviews) for articles published in the specified timeframe, as provided by  Altmetric .

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• Download PDF
• Share X Facebook Email LinkedIn
• Permissions

Exploring and Reducing the Impact of Neighborhoods on Health Disparities

• 1 Zablocki Veterans Affairs Medical Center, Milwaukee, Wisconsin
• 2 Medical College of Wisconsin, Milwaukee
• Original Investigation Neighborhood Determinants and Symptom Severity Among Individuals With Psychosis Oladunni Oluwoye, PhD; Megan Puzia, MS; Ari Lissau, BA; Ofer Amram, PhD; Douglas L. Weeks, PhD JAMA Network Open

Health is a complex construct, the consequence of an interplay of a myriad of factors—some known (smoking, exercise, diet, and stress), but most unknown. Health care professionals are motivated to help patients live long, healthy, happy lives. However, the root causes of good health have remained stubbornly elusive. Historically, clinicians have focused on diagnosing and controlling disease, achieving specific targets such as blood pressure and blood glucose control, preventing unhealthy complications, and minimizing devastating outcomes. However, this has not always led to good health; more factors are at play. In addition to expected variances in health, there are striking disparities in health worldwide. Certain groups of people die younger and bear the brunt of poor outcomes. 1 For example, Black US residents have nearly double the rate of end-stage kidney disease and kidney-related deaths than White US residents. 2

Initially, clinicians were blamed for the disparate outcomes. 3 However, as the health care system tested interventions to reduce disparities, it became clear that factors not in control of the health care system contributed to health and health outcomes. There is a web of influences across multiple domains involving a dynamic interaction of genetics, behaviors, as well as sociocultural determinants. A good place to explore these factors is the neighborhood in which an individual resides. Important contributors to health, life circumstances, and social determinants of health cluster within neighborhoods. Assessing factors at the neighborhood level provides a more nuanced exploration of health outcome influences compared with assessing elements at the individual level. 4

Neighborhood characteristics—including racial and ethnic composition, environmental exposures, as well as the social and physical or built environments—are correlated with health outcomes and have been identified as important aspects to consider in research and clinical management. 4 , 5 Similarly, evidence has shown that socioeconomic status is associated with physical and social environments within neighborhoods and is a potential target for health intervention to reduce disparities. 5 Living in neighborhoods where there is concentrated poverty, poor educational and vocational opportunities, high unemployment rates, higher rates of crime and violence, limited or no access to healthy food options, and reduced greenspace all contribute to poor health and suboptimal outcomes.

Dr Oluwoye and colleagues 4 focus on neighborhood determinants of mental health, namely symptom severity among individuals with psychosis. They identified 3 types of neighborhoods as having disparate impacts on mental health: urban low-risk, urban high-risk, and rural. Urban low-risk neighborhoods had high income, educational attainment, and access to transportation and health care but also high levels of environmental exposures. Urban high-risk neighborhoods had low income and educational attainment and low access to health care with high access to transportation and environmental exposures. Rural areas had average income, average-to-low educational attainment, and limited access to health care and transportation but low levels of environmental exposure. In addition to well-established correlates of depression and anxiety, they found that urban high-risk neighborhoods had worse mental health compared with urban low-risk or rural areas. This finding is not surprising, given the challenges of day-to-day life that can occur in neighborhoods with higher risk profiles. For example, neighborhoods with high crime and gun violence cause chronic stress for those living there, a potent influencer of mental health. Furthermore, urban high-risk neighborhoods have decreased walkability, high pollution, poor food environments, inadequate recreational space for physical activity, high stress, low civic engagement, and decreased social connectedness. 5 , 6 Their conclusions supported the need for assessing and understanding the cumulative impact of neighborhood factors on health to improve health outcomes.

The important question is what can be done to minimize risks for poor health outcomes in neighborhoods with higher risk profiles and limited resources? Fifteen years ago, the World Health Organization stated that “the unequal distribution of health-damaging experiences is not in any sense a natural phenomenon but is the result of a toxic combination of poor social policies and programs, unfair economic arrangements, and bad politics.” 7 They outlined several steps that could close this gap: improve daily living conditions; tackle the inequitable distribution of power, money, and resources; measure and understand the problem; and assess the impact of action on reducing health disparities and improving health outcomes. These reforms are beyond the power of individual clinicians, requiring a collaborative effort between the individuals most impacted by neighborhood determinants, specialists in multiple disciplines, and interdisciplinary teams to reduce inequities in health and improve outcomes. However, closing the gap will likely require significant changes in sociopolitical, cultural, and economic systems, beyond the power of the health care system.

Research to identify and address neighborhood influences on specific health consequences is an important step toward improving both mental and physical health outcomes. At present, most research on neighborhoods has been exploratory. 6 The harder task will be to design and evaluate the impact of tailored neighborhood interventions on reducing disparities and improving outcomes that are specific to community needs. Research like that of Dr Oluwoye and colleagues 4 can help lay the groundwork for next steps.

Accepted for Publication: March 28, 2024.

Published: May 15, 2024. doi:10.1001/jamanetworkopen.2024.10206

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Jackson JL et al. JAMA Network Open .

Corresponding Author: Jeffrey L. Jackson, MD, MPH, Medical College of Wisconsin, 5000 W National Ave, Milwaukee WI 53295 ( [email protected] ).

Conflict of Interest Disclosures: None reported.

See More About

Jackson JL , Williams JS. Exploring and Reducing the Impact of Neighborhoods on Health Disparities. JAMA Netw Open. 2024;7(5):e2410206. doi:10.1001/jamanetworkopen.2024.10206

Manage citations:

© 2024

Select Your Interests

Customize your JAMA Network experience by selecting one or more topics from the list below.

• Academic Medicine
• Acid Base, Electrolytes, Fluids
• Allergy and Clinical Immunology
• American Indian or Alaska Natives
• Anesthesiology
• Anticoagulation
• Art and Images in Psychiatry
• Artificial Intelligence
• Assisted Reproduction
• Bleeding and Transfusion
• Caring for the Critically Ill Patient
• Challenges in Clinical Electrocardiography
• Climate and Health
• Climate Change
• Clinical Challenge
• Clinical Decision Support
• Clinical Implications of Basic Neuroscience
• Clinical Pharmacy and Pharmacology
• Complementary and Alternative Medicine
• Consensus Statements
• Coronavirus (COVID-19)
• Critical Care Medicine
• Cultural Competency
• Dental Medicine
• Dermatology
• Diabetes and Endocrinology
• Diagnostic Test Interpretation
• Drug Development
• Electronic Health Records
• Emergency Medicine
• End of Life, Hospice, Palliative Care
• Environmental Health
• Equity, Diversity, and Inclusion
• Facial Plastic Surgery
• Gastroenterology and Hepatology
• Genetics and Genomics
• Genomics and Precision Health
• Global Health
• Guide to Statistics and Methods
• Hair Disorders
• Health Care Delivery Models
• Health Care Economics, Insurance, Payment
• Health Care Quality
• Health Care Reform
• Health Care Safety
• Health Care Workforce
• Health Disparities
• Health Inequities
• Health Policy
• Health Systems Science
• History of Medicine
• Hypertension
• Images in Neurology
• Implementation Science
• Infectious Diseases
• Innovations in Health Care Delivery
• JAMA Infographic
• Law and Medicine
• Leading Change
• Less is More
• LGBTQIA Medicine
• Lifestyle Behaviors
• Medical Coding
• Medical Devices and Equipment
• Medical Education
• Medical Education and Training
• Medical Journals and Publishing
• Mobile Health and Telemedicine
• Narrative Medicine
• Neuroscience and Psychiatry
• Notable Notes
• Nutrition, Obesity, Exercise
• Obstetrics and Gynecology
• Occupational Health
• Ophthalmology
• Orthopedics
• Otolaryngology
• Pain Medicine
• Palliative Care
• Pathology and Laboratory Medicine
• Patient Care
• Patient Information
• Performance Improvement
• Performance Measures
• Perioperative Care and Consultation
• Pharmacoeconomics
• Pharmacoepidemiology
• Pharmacogenetics
• Pharmacy and Clinical Pharmacology
• Physical Medicine and Rehabilitation
• Physical Therapy
• Physician Leadership
• Population Health
• Primary Care
• Professional Well-being
• Professionalism
• Psychiatry and Behavioral Health
• Public Health
• Pulmonary Medicine
• Regulatory Agencies
• Reproductive Health
• Research, Methods, Statistics
• Resuscitation
• Rheumatology
• Risk Management
• Scientific Discovery and the Future of Medicine
• Shared Decision Making and Communication
• Sleep Medicine
• Sports Medicine
• Stem Cell Transplantation
• Substance Use and Addiction Medicine
• Surgical Innovation
• Surgical Pearls
• Teachable Moment
• Technology and Finance
• The Art of JAMA
• The Arts and Medicine
• The Rational Clinical Examination
• Tobacco and e-Cigarettes
• Translational Medicine
• Trauma and Injury
• Treatment Adherence
• Ultrasonography
• Users' Guide to the Medical Literature
• Vaccination
• Venous Thromboembolism
• Veterans Health
• Women's Health
• Workflow and Process
• Wound Care, Infection, Healing

Get the latest research based on your areas of interest.

Others also liked.

• Register for email alerts with links to free full-text articles
• Access PDFs of free articles
• Manage your interests
• Save searches and receive search alerts

This paper is in the following e-collection/theme issue:

Published on 14.5.2024 in Vol 26 (2024)

Understanding the Impact of Communicating Uncertainty About COVID-19 in the News: Randomized Between-Subjects Factorial Experiment

Authors of this article:

Original Paper

• Rui Zhao 1 , PhD   ; 
• Xuerong Lu 2 , PhD   ; 
• Jiayi Yang 3 , PhD   ; 
• Biao Li 4 , PhD  

1 School of Journalism and Communication, Minzu University of China, Beijing, China

2 School of Communication, Oregon State University, Corvallis, OR, United States

3 School of Chinese Language and Literature, University of International Business and Economics, Beijing, China

4 School of Journalism, Renmin University of China, Beijing, China

Corresponding Author:

Biao Li, PhD

School of Journalism

Renmin University of China

19 Zhong Guancun Street

Beijing, 100081

Phone: 86 18810386586

Email: [email protected]

Background: Whether and how the uncertainty about a public health crisis should be communicated to the general public have been important and yet unanswered questions arising over the past few years. As the most threatening contemporary public health crisis, the COVID-19 pandemic has renewed interest in these unresolved issues by both academic scholars and public health practitioners.

Objective: The aim of this study was to investigate the impact of communicating uncertainty about COVID-19–related threats and solutions on individuals’ risk perceptions and misinformation vulnerability, as well as the sequential impact of these effects on health information processing and preventative behavioral intentions.

Methods: A 2×2 (threat uncertainty [presence vs absence]×solution uncertainty [presence vs absence]) full-fractional between-subjects online experiment was conducted with 371 Chinese adults. Focusing on the discussion of whether the asymptomatic cases detected during the COVID-19 pandemic would further lead to an uncontrolled pandemic, news articles were manipulated in terms of whether the infectiousness of asymptomatic cases and the means to control the transmission are presented in terms of their certainty or uncertainty. Participants were randomly assigned to one of the four experimental conditions, being instructed to read one news article. After reading the news article assigned, participants were asked to respond to a series of questions to assess their cognitive and behavioral responses.

Results: Individuals were more susceptible to believing false COVID-19–related information when a certain threat and uncertain solution were framed in the news article. Moreover, individuals’ perceptions of crisis severity increased when exposed to news information containing uncertain solutions. Both misinformation vulnerability and perceived severity were positively associated with information processing. Information seeking was positively associated with protective behavioral intention, whereas information avoidance was negatively associated with protective behavioral intention.

Conclusions: Our findings imply that uncertainty, depending on its aspect, can be effectively communicated to the public during an emerging public health crisis. These results have theoretical and practical implications for health communicators and journalists. Given its limited influence on individuals’ cognitive and behavioral responses, uncertainty related to a health threat should be disseminated to meet the public’s expectation of information transparency. However, caution is advised when communicating uncertainty related to potential solutions, as this factor exhibited a mixed impact on individual responses during a crisis.

Introduction

The question of how to communicate uncertainty to the general public has been raked up during the COVID-19 pandemic, which is considered to be the most threatening public health crisis that emerged over the past 10 years, characterized by a high level of uncertainty. Since its outbreak, news coverage of COVID-19 has largely been emphasizing the “unknowns” about the source, infectivity, treatment, prevention, and control measures of the virus [ 1 ]. However, whether (or not) uncertainties should be communicated to the general public remains a controversial issue, given the general low tolerance of the public for uncertainty along with a high expectation for information transparency. On the one hand, uncertainty is an undesirable experience in which people fear losing control of their lives, leading to negative consequences [ 2 , 3 ]. On the other hand, uncertainty may also have positive effects, as some scholars suggest that when uncertainty is perceived, people tend to actively seek for information to ease this feeling, and in this process can gain more information and a deeper understanding of the event [ 4 ]. Thus, it is important to understand whether the uncertainty presented in news articles influences individuals’ cognitive and behavioral intentions during public health emergencies.

In actual news framing, uncertainty does not appear as a monolithic entity, and each new challenge presented by COVID-19 involves different aspects (eg, threats and solutions) with varying degrees of uncertainty [ 5 ]. According to the Centers for Disease Control and Prevention, the uncertainties related to threats and solutions are the two greatest concerns among the public during public health emergencies [ 6 ]. Therefore, this study focused on the uncertainties related to threats and solutions associated with COVID-19 that the news media might (or might not) communicate to the public, with the goal of exploring how this communication of uncertainty might influence individuals’ health behaviors.

Impacts of Uncertainty on Risk Perception and Misinformation Vulnerability

Risk perception is always associated with uncertainty in the public health context. It is assumed that individuals will only begin to manage uncertainty through information processing or preventive behaviors when they perceive a given issue to be associated with a certain level of risk [ 7 ]. The perceived risk by individuals includes the severity and susceptibility of a public health crisis. Severity refers to the magnitude of harm caused by the threat, whereas susceptibility refers to the probability of occurrence of a threat to a specific subject [ 8 ]. Empirically, Lalot et al [ 9 ] found that individuals’ uncertainty about how the novel coronavirus would affect people significantly increased their perceived threat of the pandemic. Pine et al [ 10 ] found that the partial and changing information would cultivate the uncertainty surrounding COVID-19, which would further influence individuals’ risk perception of the pandemic.

Moreover, the potential impact of uncertainty on exacerbating the misinformation effect has been raised as a concern in recent years. Lu et al [ 11 ] found that communicating uncertainty about preliminary evidence related to COVID-19 was positively associated with the number of likes and retweets of related misinformation on social media. Consequently, the communication of uncertainty during a pandemic might unexpectedly facilitate engagement with misinformation.

To better understand the impact of threat uncertainty and solution uncertainty that are communicated to the general public during a public health crisis, we established the following research question: How, if at all, does the uncertainty frame of a threat and solution exert main and interaction effects on individuals’ risk perceptions and misinformation vulnerability?

Impacts of Uncertainty on Information Processing

Information seeking is regarded as a key communication outcome during a public health crisis, guiding individuals to understand public health issues and consequently adopt appropriate health behaviors (eg, [ 7 , 12 ]). Information seeking refers to individuals’ active information-searching activity through human interaction [ 13 ], online inquiry [ 14 ], and passive observation [ 15 ]. Theoretical and empirical evidence suggests that individuals tend to engage in information-seeking behaviors when experiencing psychological discomfort such as confusion and anxiety resulting from exposure to uncertainty (eg, [ 16 , 17 ]). Although the motivations for information-seeking behaviors according to various demographic characteristics such as age, gender, and health status have been extensively examined in the public health context (eg, [ 12 , 18 ]), little is known about how uncertainty communicated by the media and experts influences individuals’ information-seeking behavior.

Case et al [ 19 ] pointed out that individuals might also engage in information avoidance to reduce feelings of uncertainty. They found that the mental discomfort that arises due to uncertainty, especially in a health context, could facilitate information-avoidance behaviors. Information avoidance refers to an individual’s absence from or ignorance of information and its source [ 4 ].

Thus, we further aimed to understand whether (or not) and how the uncertainty framed in the news would motivate individuals’ information-seeking and information-avoiding behavior differently.

Additionally, the relationship between risk perception and information processing has been documented in the health risk literature. Goodall and Reed [ 20 ] suggested that individuals would seek more information when the perceived threat is high. Conversely, Jiang et al [ 21 ] found that a higher risk perception of the COVID-19 pandemic would reduce individuals’ information-seeking behavior. They also found that individuals’ propensity to believe COVID-19 misinformation would also influence their additional information-seeking behavior.

Therefore, we sought to examine how, if at all, uncertainty framed in severity and susceptibility influences information processing (ie, information seeking and information avoidance) through risk perceptions and misinformation vulnerability.

Information Seeking and Avoidance Influence Preventive Behaviors

In the context of COVID-19, studies on how organizations and individuals perceive risks during crises have centered on the changes in preventive behaviors during infectious disease outbreaks and how these behavioral shifts can be facilitated by engagement in informational behaviors.

Previous studies suggest that information seeking through different channels and sources is positively associated with preventive behaviors during crises (eg, [ 22 , 23 ]). Individuals who engage in more effortful information seeking and processing are more likely to develop risk-related cognitions, attitudes, and behaviors [ 24 ]. However, the health information environment in a pandemic is often filled with uncertain information, false claims, or even conspiracy theories [ 25 ], which can bias people’s pandemic-related beliefs and impede their adoption of effective actions [ 26 ].

Nevertheless, this situation does not imply that refraining from active information seeking is a wise choice. By contrast, the impact of information avoidance on preventive behaviors is not less significant than that of information seeking [ 27 ]. While information avoidance minimizes the chances of interaction with unnecessary information, it simultaneously diminishes the opportunities to receive relevant information. From a cognitive perspective, individuals have limited capacity to process information, and if not adequately addressed, the outcome can be information overload [ 28 ]. Avoiding information acquisition may lead individuals to make preventive decisions based on limited information [ 29 ]. Particularly when faced with uncertain information, information avoidance may lead to incorrect preventive behaviors. Therefore, we sought to determine how, if at all, preventative behavioral intentions might be associated with information seeking (1) and information avoidance (2) separately.

Research Design

This study adopted a controlled experiment approach. Based on the question “ Will asymptomatic cases lead to an uncontrolled epidemic? ” a 2×2 (threat certainty vs uncertainty×solution certainty vs uncertainty) online experiment was designed using asymptomatic cases, an emerging challenge in the COVID-19 epidemic, as a risk scenario. Data were collected in May 2020 through this anonymous online experiment. Participants were recruited from Sojump, which is the largest online survey platform in China. As the context of the experiment, at this time, China was gradually implementing measures to prevent and control the COVID-19 epidemic. However, at the same time, the detection of an increasing number of asymptomatic cases was raising concern. On March 31, 2020, China’s National Health Commission announced that as of the following day (April 1), it would disclose the detection, transition, and management of asymptomatic cases in its daily briefings on the epidemic to respond to these societal concerns in a timely manner [ 30 ]. The risk threat posed by the emerging challenge and COVID-19 prevention measures were still being explored and discussed at that time and were fraught with uncertainties. Based on this context, we developed 4 simulated online news reports based on real news coverage and expert interview data; the details of the simulations are provided in Multimedia Appendix 1 with a brief summary in Table 1 .

Although the content orientation of the simulated news coverage differed across the 4 scenarios, the format, word count, structural design, and information volume of the news coverage remained consistent. Participants were randomly assigned to one of the scenarios and were asked to read the simulated news coverage before completing the questionnaire. A total of 592 people participated in the study online and 371 valid questionnaires were obtained after postchecking, including 88 valid questionnaires for condition 1, 99 for condition 2, 88 for condition 3, and 96 for condition 4.

Figure 1 shows the general flow of participants in the study. Participants were from a wide range of age groups. The sample included a relatively equal sex ratio (with 41.80% of the sample identifying as female), and the majority of the participants had an education level of college degree or above (73%). Table 2 summarizes the main sociodemographic characteristics of the sample.

Ethical Considerations

The Institutional Review Board office of Renmin University of China approved the research protocol, and responses were collected via Sojump. Before the experiment, participants were asked if they agree to participate in the research voluntarily for receiving compensation. Participants received a reward of RMB 7 (US $1). The final data set is anonymized, without any identifiable private information connected to participants.

Perceived Severity

Perceived severity was measured with five items adapted from a previous study [ 8 ]: (1) I think asymptomatic cases are a serious problem for us; (2) At some point in the future, we could all be threatened by asymptomatic cases; (3) I think asymptomatic cases have very serious consequences; (4) I think asymptomatic cases are a very serious problem; and (4) I think the presence of asymptomatic cases is a serious threat to my health (mean 5.26, SD 1.18; Cronbach α=0.89).

Perceived Susceptibility

Perceived susceptibility was measured with two items taken from an existing scale [ 8 ]: (1) I feel that I am also at risk of being infected by asymptomatic cases and (2) I feel that I may also be infected by asymptomatic cases (mean 4.86, SD 1.76; Pearson r =0.83, P <.001).

Information Seeking

Information seeking was measured based on the scale developed by Brashers et al [ 31 ] with the following eight items: (1) I would like to learn more information on asymptomatic cases than what is provided in this report; (2) I may discuss asymptomatic cases with people close to me (eg, friends, family); (3) I may ask my doctor about asymptomatic cases; (4) I may seek other news reports about asymptomatic cases; (5) I may pay close attention to news reports about asymptomatic cases that I encounter in the future; (6) I may check the internet for other information about asymptomatic cases; (7) I am likely to pay close attention to information on asymptomatic cases that I encounter on the internet in the future; and (8) I may read the scientific research literature on asymptomatic cases (mean 5.46, SD 1.30; Cronbach α=0.94).

Information Avoidance

Information avoidance was measured using the scales developed by Afifi and Weiner [ 13 ] and Evans et al [ 14 ] with the following six items: (1) I may try to change the subject if people close to me (eg, friends and family) discuss the issue of asymptomatic cases; (2) I may try to change the subject if my doctor discusses asymptomatic cases; (3) I may avoid exposure to other news reports about asymptomatic cases; (4) I may avoid exposure to information on the internet about asymptomatic cases; (5) I may try not to think too much about asymptomatic cases; and (6) I may try to forget about asymptomatic cases (mean 2.76, SD 1.73; Cronbach α=0.96).

Willingness to Adopt Preventive Behaviors

Willingness to adopt preventive behaviors was measured via 10 items using a 7-point Likert scale (1=strongly agree to 7=strongly disagree), which measured respondents’ willingness to adopt a range of preventive behaviors such as strengthening protection, following health instructions, trying more preventive measures, and getting vaccinated. The specific items were: (1) I have decided to strengthen my preventive measures against COVID-19 immediately; (2) I intend to strengthen my protective measures against COVID-19 in the future; (3) I will pay more attention to asymptomatic cases; (4) I will persuade people around me to pay more attention to asymptomatic cases; (5) I will strictly follow professional health instructions to prevent catching the disease; (6) I will persuade people around me to strictly follow professional health instructions to prevent disease; (7) I will try as many ways as possible to prevent disease; (8) I will persuade those around me to try as many ways as possible to prevent disease; (9) I will get vaccinated as soon as a COVID-19 vaccine is developed; and (10) I will persuade those around me to get vaccinated as soon as a COVID-19 vaccine is developed (mean 5.54, SD 1.27; Cronbach α=0.89).

Vulnerability to False Information

Vulnerability to false information was measured by presenting respondents with 10 pieces of false news on COVID-19 (with five real news articles provided as distractors) to measure their trust in the false news. Sample items included the following:

Isatis root is suitable for treating conditions such as the common cold and viral influenza with heat symptoms. It has an antiviral effect and can help to prevent COVID-19.

Tobacco particles are at the nanometer scale and can evenly cover the surface of lung cells, forming a barrier that keeps viruses out of the body. Therefore, smoking can reduce the infection of the novel coronavirus.

The novel coronavirus is primarily a respiratory infection virus. Gargling with saline solution can eliminate the novel coronavirus bacteria that enter through the mouth.

These items were measured on a scale of 1 (strongly disbelieve) to 10 (strongly believe) (mean 3.68, SD 1.46; Cronbach α=0.89).

Data Analysis

To answer the four research questions, ANOVA was performed to examine the main and interaction effects of the manipulated independent variables (ie, uncertainty) on dependent outcomes (ie, risk perceptions and misinformation vulnerability). The mediation model was used as a posthoc analysis to estimate statistically significant differences between experimental conditions on outcomes. SPSS (version 28.0) and PROCESS (version 4.2) were used for all statistical analyses.

Effects of Uncertainty Framing in News Coverage

According to the ANOVA results ( Figure 2 ), there was a significant interaction effect of threat uncertainty and solution uncertainty on individuals’ vulnerability to misinformation ( F 1, 367 =5.10, P =.02; partial η 2 =0.01). Figure 2 provides the complete data on group comparisons. Specifically, individuals who read news containing a certain threat and uncertain solution (mean 3.86, SD 0.15) were more likely to believe misinformation than those who read news containing a certain threat and certain solution (mean 3.47, SD 0.16). However, there was neither a significant main effect of threat uncertainty (partial η 2 =0.00) nor a significant main effect of solution uncertainty (partial η 2 =0.00) on individuals’ vulnerability to false information ( Table 3 ).

In terms of information processing ( Table 4 ), the threat uncertainty of health information showed a significant effect in promoting information-seeking behavior (partial η 2 =0.012). This implies that people who read news with the presence of an uncertain threat were more likely to search for additional relevant information than those who read news with absence of an uncertain threat. However, the uncertainty framing in the news, regardless of the type of uncertainty, did not affect individuals’ information avoidance behaviors.

Sequential Mediation Analysis

Serial linear regression with PROCESS macro model 81 was used to analyze how the dummy-coded variables (1=uncertain threat and certain solution, 2=certain threat and uncertain solution, 3=uncertain threat and uncertain solution, and reference (0)=certain threat and certain solution) influence preventive behavioral intention through the factors perceived severity, information seeking, and information avoidance. A significant serial mediation model was detected, in which the perceived severity and information seeking would sequentially mediate the relationship between the exposure to news containing a certain threat and an uncertain solution (point estimate 0.17, SE 0.08; 95% CI 0.02-0.33) or news containing an uncertain threat and an uncertain solution (point estimate 0.18, SE 0.08; 95% CI 0.03-0.34) and the protective behavioral intention (see Figure 3 for the path significance and coefficients).

The mediation models obtained with PROCESS macro model 81, including misinformation vulnerability, information seeking, and information avoidance as the three mediators, were established with the same three dummy-coded comparison variables described above. Although the serial mediating effect of misinformation vulnerability and information processing on the relationship was nonsignificant, significant associations between misinformation vulnerability on information processing and protective behavioral intention were detected (see Figure 4 for the path significance and coefficients).

Principal Findings

This study explored the impact of incorporating different levels of uncertainty in news articles on individuals’ risk perception, susceptibility to misinformation, subsequent information processing, and intention toward preventive behaviors. These factors play a crucial role in guiding individuals to protect themselves during a public health crisis.

First, as expected, the findings showed that individuals would perceive the crisis to be of greater severity when reading news framed withing a context of solution uncertainty, regardless of whether or not the threat of the crisis was framed as certain or uncertain in the news article. Furthermore, the perceived severity would in turn motivate individuals’ information-seeking and information-avoidance behaviors. As suggested by Gudykunst [ 32 ], the experience of fear and concern is also associated with uncertainty. Individuals’ fear might be evoked when they fail to find a solution to solve the risky problem, which would consequently increase their perception of the severity of the health threat. Meanwhile, to cope with the fear, individuals would either seek more information or avoid more information [ 31 ].

Second, individuals tended to believe false information during a public health crisis, especially after reading a news article containing certain threats and uncertain solutions. Furthermore, posthoc regression analysis suggested that both information seeking and information avoidance were positively associated with misinformation vulnerability. As expected, information seeking was positively associated with protective behavioral intentions, while information avoidance was negatively associated with protective behavioral intention. That said, communicating crisis uncertainty in a news article would be risky in terms of increasing the public’s vulnerability to misinformation. Moreover, misinformation vulnerability would further motivate information avoidance, which would consequently dissuade individuals’ intention to adopt preventive behaviors. However, communicating crisis uncertainty could also be beneficial because the increased misinformation vulnerability that arises after reading news with related uncertainty could simultaneously motivate both expected health information–seeking and protective behaviors.

Third, our findings suggest that compared to an uncertain threat, uncertain solutions are more potentially problematic. An uncertain solution with either an uncertain or certain threat had a greater impact on individuals’ perceived severity of the crisis. Moreover, an uncertain solution with an uncertain threat led to higher vulnerability to misinformation. Although both perceived severity and misinformation vulnerability could motivate expected health information–seeking or protective behavioral intentions, they were likely to trigger information avoidance, which would further impair the protective behavioral intention. Given these conflicting findings, more research is needed to understand the mechanism behind the effects of communicating solution uncertainty, especially during a public health crisis.

Implications and Limitations

A few limitations of this study should be noted to provide inspiring suggestions for further research. First, the generalizability of the findings needs to be addressed. This study is based on an exceptionally unique context of a public health event. In April 2020, the ambiguity surrounding the perceived risk of COVID-19 and the appropriate preventive measures was prevalent. Different regions in China implemented varying degrees of epidemic control measures, leading to divergent strategies. Media coverage of “asymptomatic carriers” and other aspects of the COVID-19 pandemic often exhibited inconsistent or contradictory information. Thus, the diverse experimental scenarios were based on natural contexts for presenting four distinct threat-and-solution scenarios and would not induce a perceptual conflict for the participants. However, when extrapolating to other research topics, it is imperative to consider the coherence between experimental scenarios and real-world settings.

Second, the impact of the research topic on participants with different characteristics requires further discussion. This study endeavored to achieve a balanced representation of participants with respect to sex, education level, and income bracket. Consequently, an exhaustive examination of the differential effects stemming from various sociodemographic factors on the outcomes was not performed. However, in distinct research inquiries, this divergence may prove consequential. Hence, future research endeavors could delve deeper into understanding the perception of information uncertainty and management behaviors across populations with diverse backgrounds and characteristics.

Third, although this type of experimental design typically includes 80-90 participants per group to achieve sufficient statistical power, the unexpected smaller effect size in this study resulted in a reduced statistical power of approximately 60%. This diminutive effect size may compromise the study’s sensitivity in detecting meaningful relationships, thereby affecting the reliability and generalizability of the findings. To enhance statistical power and ensure robust results in similar experimental studies in China, a larger sample size is recommended.

Regarding the implications, this study aimed to elucidate the impact of health information uncertainty on individuals’ information-processing mechanisms. The results thus provide further evidence for the impacts of individuals’ perceptions and behaviors underlying uncertainty management theory. Amid public health events inundated with uncertain information, individuals’ perceptions and behaviors related to uncertainty management often determine their attitudes toward addressing health threats and the potential adoption of health measures. Therefore, comprehending this process contributes to facilitating more effective health communication between the public health system and the general public. Faced with uncertain public health events, participants such as public health institutions, media, and the general public should all take into account the implications of information uncertainty, ensuring the effective dissemination of information throughout all stages of the crisis.

Acknowledgments

This work was supported by the National Social Science Fund of China (project name: Construction and Impact Research of Trust Mechanisms in Health Communication during the “Information Epidemic”; project number 22CXW022).

Conflicts of Interest

None declared.

Simulated news articles presented to the participants in the online experiment under four conditions varying in threat and solution uncertainty about the COVID-19 pandemic.

• Capurro G, Jardine CG, Tustin J, Driedger M. Communicating scientific uncertainty in a rapidly evolving situation: a framing analysis of Canadian coverage in early days of COVID-19. BMC Public Health. Nov 29, 2021;21(1):2181. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Büchter RB, Betsch C, Ehrlich M, Fechtelpeter D, Grouven U, Keller S, et al. Communicating uncertainty in written consumer health information to the public: parallel-group, web-based randomized controlled trial. J Med Internet Res. Aug 10, 2020;22(8):e15899. [ FREE Full text ] [ CrossRef ] [ Medline ]
• van den Bos K, Ham J, Lind EA, Simonis M, van Essen WJ, Rijpkema M. Justice and the human alarm system: the impact of exclamation points and flashing lights on the justice judgment process. J Exp Soc Psychol. Mar 2008;44(2):201-219. [ CrossRef ]
• Afifi WA, Weiner JL. Toward a theory of motivated information management. Commun Theory. May 2004;14(2):167-190. [ CrossRef ]
• Chu TH, Yeo TED, Su Y. Effects of exposure to COVID-19 news and information: a meta-analysis of media use and uncertainty-related responses during the pandemic. Journalism Mass Commun Quart. Jan 04, 2022;99(1):89-112. [ CrossRef ]
• Bed bugs FAQs. Centers for Disease Control and Prevention. URL: https://www.cdc.gov/parasites/bedbugs/faqs.html [accessed 2020-09-16]
• So J, Kuang K, Cho H. Information seeking upon exposure to risk messages: predictors, outcomes, and mediating roles of health information seeking. Commun Res. Nov 24, 2016;46(5):663-687. [ CrossRef ]
• Witte K, Cameron KA, McKeon JK, Berkowitz JM. Predicting risk behaviors: development and validation of a diagnostic scale. J Health Commun. Oct 29, 1996;1(4):317-341. [ CrossRef ] [ Medline ]
• Lalot F, Abrams D, Travaglino GA. Aversion amplification in the emerging COVID‐19 pandemic: the impact of political trust and subjective uncertainty on perceived threat. Commun Appl Soc Psy. Dec 13, 2020;31(2):213-222. [ CrossRef ]
• Pine KH, Lee M, Whitman SA, Chen Y, Henne K. Making sense of risk information amidst uncertainty: individuals' perceived risks associated with the COVID-19 pandemic. 2021. Presented at: CHI '21: CHI Conference on Human Factors in Computing Systems; May 8-13, 2021; Yokohama, Japan. [ CrossRef ]
• Lu J, Zhang M, Zheng Y, Li Q. Communication of uncertainty about preliminary evidence and the spread of its inferred misinformation during the COVID-19 pandemic-a Weibo case study. Int J Environ Res Public Health. Nov 13, 2021;18(22):11933. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zhao X, Fan J, Basnyat I, Hu B. Online health information seeking using "#COVID-19 Patient Seeking Help" on Weibo in Wuhan, China: descriptive study. J Med Internet Res. Oct 15, 2020;22(10):e22910. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Afifi WA, Weiner JL. Seeking information about sexual health: applying the theory of motivated information management. Human Comm Res. Jan 2006;32(1):35-57. [ CrossRef ]
• Evans BM, Kairam S, Pirolli P. Do your friends make you smarter?: an analysis of social strategies in online information seeking. Inf Process Manage. Nov 2010;46(6):679-692. [ CrossRef ]
• Hogan TP, Brashers DE. The theory of communication and uncertainty management: implications from the wider realm of information behavior. In: Afifi T, Afifi W, editors. Uncertainty, Information Management, and Disclosure Decisions: Theories and Applications. New York. Routledge/Taylor & Francis Group; 2009;45-66.
• Charpentier CJ, Cogliati Dezza I, Vellani V, Globig LK, Gädeke M, Sharot T. Anxiety increases information-seeking in response to large changes. Sci Rep. May 05, 2022;12(1):7385. [ CrossRef ] [ Medline ]
• Keller AM, Taylor HA, Brunyé TT. Uncertainty promotes information-seeking actions, but what information? Cogn Res Princ Implic. Sep 07, 2020;5(1):42. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Brainin E, Neter E. Refined analysis of older eHealth users from an agency perspective: quantitative telephone interview study. JMIR Aging. Apr 26, 2023;6:e40004. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Case DO, Andrews JE, Johnson JD, Allard SL. Avoiding versus seeking: the relationship of information seeking to avoidance, blunting, coping, dissonance, and related concepts. J Med Libr Assoc. Jul 2005;93(3):353-362. [ FREE Full text ] [ Medline ]
• Goodall CE, Reed P. Threat and efficacy uncertainty in news coverage about bed bugs as unique predictors of information seeking and avoidance: an extension of the EPPM. Health Commun. Jan 2013;28(1):63-71. [ CrossRef ] [ Medline ]
• Jiang S, Liu PL, Ngien A, Wu Q. The effects of worry, risk perception, information-seeking experience, and trust in misinformation on COVID-19 fact-checking: a survey study in China. Chinese J Commun. Sep 12, 2022;16(2):132-149. [ CrossRef ]
• Liu BF, Viens J. Crisis and risk communication scholarship of the future: reflections on research gaps. J Int Crisis Ris Commun Res. Mar 9, 2020;3(1):7-14. [ CrossRef ]
• Liu BF, Xu S, Rhys Lim J, Egnoto M. How publics’ active and passive communicative behaviors affect their tornado responses: an integration of STOPS and SMCC. Public Relat Rev. Nov 2019;45(4):101831. [ CrossRef ]
• Griffin RJ, Dunwoody S, Neuwirth K. Proposed model of the relationship of risk information seeking and processing to the development of preventive behaviors. Environ Res. Feb 1999;80(2 Pt 2):S230-S245. [ CrossRef ] [ Medline ]
• Bode L, Vraga EK. See something, say something: correction of global health misinformation on social media. Health Commun. Sep 2018;33(9):1131-1140. [ CrossRef ] [ Medline ]
• Tan ASL, Lee C, Chae J. Exposure to health (mis)information: lagged effects on young adults' health behaviors and potential pathways. J Commun. Jul 06, 2015;65(4):674-698. [ CrossRef ]
• Soroya SH, Farooq A, Mahmood K, Isoaho J, Zara S. From information seeking to information avoidance: understanding the health information behavior during a global health crisis. Inf Process Manag. Mar 2021;58(2):102440. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sharit J, Czaja SJ. Overcoming older adult barriers to learning through an understanding of perspectives on human information processing. J Appl Gerontol. Mar 30, 2020;39(3):233-241. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Dai B, Ali A, Wang H. Exploring information avoidance intention of social media users: a cognition–affect–conation perspective. Internet Res. Jul 14, 2020;30(5):1455-1478. [ CrossRef ]
• "Asymptomatic infections" enter daily bulletin: epidemic prevention and control must be realistic and pragmatic. Xinhua News Agency. URL: http://www.xinhuanet.com/politics/2020-04/02/c_1125803965.htm [accessed 2020-04-02]
• Brashers DE, Goldsmith DJ, Hsieh E. Information seeking and avoiding in health contexts. Human Comm Res. Apr 2002;28(2):258-271. [ CrossRef ]
• Gudykunst W. Anxiety/uncertainty management (AUM) theory: current status. In: Wiseman R, editor. Intercultural Communication Theory. New York. Sage Publications; 1995;8-58.

Edited by G Eysenbach, L Beri; submitted 16.08.23; peer-reviewed by S Su, X Xu, A Chang; comments to author 06.12.23; revised version received 08.01.24; accepted 12.04.24; published 14.05.24.

©Rui Zhao, Xuerong Lu, Jiayi Yang, Biao Li. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

• Open access
• Published: 08 May 2024

The relationship between childhood adversity and sleep quality among rural older adults in China: the mediating role of anxiety and negative coping

• Yuqin Zhang 1 ,
• Chengwei Lin 2 ,
• Hongwei Li 1 ,
• Xueyan Zhou 4 ,
• Ying Xiong 5 ,
• Jin Yan 1 ,
• Mengxue Xie 1 ,
• Xueli Zhang 6 ,
• Chengchao Zhou 7 &
• Lian Yang 1  

BMC Psychiatry volume  24 , Article number:  346 ( 2024 ) Cite this article

222 Accesses

Metrics details

Studies have revealed the effects of childhood adversity, anxiety, and negative coping on sleep quality in older adults, but few studies have focused on the association between childhood adversity and sleep quality in rural older adults and the potential mechanisms of this influence. In this study, we aim to evaluate sleep quality in rural older adults, analyze the impact of adverse early experiences on their sleep quality, and explore whether anxiety and negative coping mediate this relationship.

Data were derived from a large cross-sectional study conducted in Deyang City, China, which recruited 6,318 people aged 65 years and older. After excluding non-agricultural household registration and lack of key information, a total of 3,873 rural older adults were included in the analysis. Structural equation modelling (SEM) was used to analyze the relationship between childhood adversity and sleep quality, and the mediating role of anxiety and negative coping.

Approximately 48.15% of rural older adults had poor sleep quality, and older adults who were women, less educated, widowed, or living alone or had chronic illnesses had poorer sleep quality. Through structural equation model fitting, the total effect value of childhood adversity on sleep quality was 0.208 (95% CI: 0.146, 0.270), with a direct effect value of 0.066 (95% CI: 0.006, 0.130), accounting for 31.73% of the total effect; the total indirect effect value was 0.142 (95% CI: 0.119, 0.170), accounting for 68.27% of the total effect. The mediating effects of childhood adversity on sleep quality through anxiety and negative coping were significant, with effect values of 0.096 (95% CI: 0.078, 0.119) and 0.024 (95% CI: 0.014, 0.037), respectively. The chain mediating effect of anxiety and negative coping between childhood adversity and sleep quality was also significant, with an effect value of 0.022 (95% CI: 0.017, 0.028).

Conclusions

Anxiety and negative coping were important mediating factors for rural older adult’s childhood adversity and sleep quality. This suggests that managing anxiety and negative coping in older adults may mitigate the negative effects of childhood adversity on sleep quality.

Peer Review reports

The global population is entering an aging stage, and China has the fastest rate of population aging in the world. According to China’s seventh national census, in 2020, 191 million individuals were aged 65 years and older, accounting for 13.50% of the total population [ 1 ], and the proportion of people aged 65 and above in rural is 6.6% higher than in urban [ 2 ]. In addition, China’s long-standing urban-rural dual structure has resulted in inequality in economic, medical, and educational development, leading to significant differences in the health status of China’s urban and rural older populations [ 3 , 4 ]. Relevant studies have found that, urban residents have a higher survival rate [ 5 ], better self-assessed health status and better self-assessed self-care ability than rural dwellers [ 6 ]. Therefore, to reduce health inequalities among older adults, the health status of rural older adults is an important focus.

Good quality sleep has been found to be essential for health [ 7 , 8 , 9 ]. However, sleep problems are prevalent among the older population [ 10 , 11 ]. Gulia and Tatineny have reported that the current prevalence of sleep disorders in the global older population is 30–40% [ 12 , 13 ]. In a systematic review, Lu reported that the overall prevalence of poor sleep among the older population in China had reached 35.9% [ 14 ]. In the rural older adults, the prevalence of sleep disorders is more than 40% [ 15 ], even as high as 58.40% [ 16 ].There are various factors that affect sleep quality [ 17 , 18 ]. Adverse childhood experiences (ACEs) are stressful and/or traumatic experiences that occur during childhood [ 19 ]. There is growing evidence that ACEs may lead to sleep problems in adulthood [ 20 , 21 ] and that the influence can last up to 50 years [ 22 ]. For example, emotional abuse and neglect experienced early in life impede the development of individuals’ social relationships later in life and negatively affect the subjective sleep quality of older adults [ 23 ]. A study by Dorji. found that older adults with multiple (≥ 7) ACEs had a higher incidence of insomnia [ 24 ]. Although previous investigations have indicated the relationship between childhood adversity and sleep quality in older adults, they have ignored possible potential mechanisms for this relationship.

Previous studies have found that anxiety negatively affects sleep quality in older adults [ 25 ], whereas a good mental state can improve their sleep quality. Notably, childhood adversity may be associated with increased anxiety symptoms in late adulthood [ 26 ]. Raposo have reported that older adults who experienced childhood adversity were more likely to suffer from anxiety (OR = 1.48; 95%CI = 1.20–1.83) [ 27 ]. Considering the relationships among anxiety, childhood adversity, and sleep quality, one aim of this study was to verify whether anxiety mediates the relationship between childhood adversity and sleep quality.

A coping style refers to a psychological and behavioral strategy adopted by an individual in response to changes in the internal and external environment [ 28 ]. Negative coping is usually positively associated with sleep disorders [ 29 , 30 ]. Coping style usually evolves over time and may be influenced by exposure to childhood adversity; for instance, people exposed to early adverse experiences show predominantly emotion-focused and avoidance coping styles, such as denial and disengagement [ 31 , 32 ]. In addition to childhood adversity, negative emotions or psychological states also can influence individuals’ coping strategies [ 33 ]. For example, Orgeta reported that older adults with high levels of anxiety were more likely to adopt dysfunctional coping [ 34 ]. Therefore, we hypothesized that anxiety affects coping styles in older adults and that negative coping may be a potential mediator between childhood adversity and sleep quality.

Stress is defined as the process of adaptive and coping responses when an individual faces or perceives threatening or challenging environmental changes [ 35 ]. People respond to stress with either problem-focused coping or emotion-focused coping [ 36 ]. Stress can be caused by many factors, such as early adversities, and the result of stress is adaptive or maladaptive psychosomatic responses. Based on the above, we constructed a structural equation model of a large cross-sectional dataset to explore the effects of childhood adversity on sleep quality, with childhood adversity as the stressor and anxiety and negative coping as mediators.

Research methods

Research population.

The data were sourced from a large-scale cross-sectional study conducted in 2022 that recruited older adults aged 65 years and older living in 6 districts and counties in Deyang City, Sichuan Province. Using a multistage stratified random cluster sampling method, townships (streets) were randomly selected from six county (districts), administrative villages (communities) were randomly selected from each sample township (streets), finally, people over 65 years old were selected randomly in each chosen village or community. The inclusion criteria were as follows: (1) individuals aged ≥ 65 years; (2) permanent residents in the survey area (those who have lived in the area for 6 months or more); (3) those who signed an informed consent form and agreed to take the questionnaire survey. The exclusion criteria were as follows: (1) unwilling to participate in research; (2) individuals identified by local village doctors who are unable to answer questions independently and have a history of dementia;3) other reasons for not participating in the study. The household registration system is a very important factor affecting the unequal social welfare rights and privileges of urban and rural residents in China [ 37 ], which is associated with poor health [ 38 ]. In this study, rural means that residents with agricultural household registration. A total of 6318 respondents were recruited, excluding non-agricultural household registration (2345) and missing main information (100), and finally included 3873 for analysis. The study was approved by the Medical Ethics Committee of the Affiliated Hospital of Chengdu University of Chinese Medicine, and all participants signed an informed consent form before taking the survey.

Measurement tools

General information.

This includes the age, gender, education level, marital status, chronic disease status, and exercise status of the participating older adults.

Childhood adversity

Childhood adversity was measured using the Adverse Childhood Experiences Scale developed by the Centers for Disease Control and Prevention (USA). The scale contains three major dimensions (abuse, neglect, and household dysfunction) and ten subdimensions including emotional abuse, physical abuse, sexual abuse, and emotional neglect. Higher ACE scores indicate more severe ACE exposure [ 19 , 39 ]. The internal consistency coefficients of the abuse, neglect, and household dysfunction subscales in this study were 0.790, 0.732, and 0.778, respectively.

• Sleep quality

Sleep quality was evaluated using the revised Chinese-version Pittsburgh Sleep Quality Index (PSQI). The scale consists of seven dimensions including subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbance, use of sleep medication, and daytime dysfunction. A PSQI score of ≥ 7 is generally considered to indicate poor sleep quality [ 15 , 40 , 41 ]. The internal consistency coefficient of the scale in this study was 0.754.

Anxiety in older adults was measured using the Self-Rating Anxiety Scale (SAS). The scale consists of 20 items and is rated on a 4-point scale. An SAS score of 50 or more is considered to be indicative of anxiety symptoms [ 42 ]. The internal consistency coefficient of this scale in this study was 0.831.

Trait coping style

Negative coping was measured using the Trait Coping Style Questionnaire (TCSQ). The scale consists of 20 questions in 2 dimensions—negative coping and positive coping—and is rated on a 5-point scale. The negative coping and positive coping scores are the sum of the scores for each item in the corresponding dimensions. A positive total score indicates a predominantly positive attitude toward coping with events, whereas a negative score indicates a predominantly negative coping style [ 43 , 44 ]. Only the negative coping dimension of the scale, which has an internal consistency coefficient of 0.929, was selected in this study.

Statistical analysis

The variables in the study were descriptively analyzed using the mean, standard deviation, frequency (n), and constituent ratio (%), and difference tests were conducted using t-tests and the Kruskal-Wallis H test. Spearman’s correlation was used to analyze whether there were correlations between sleep quality and the variables. Finally, a multiple-mediator structural equation model was constructed to analyze the effects of anxiety and negative coping on the relationship between sleep quality and childhood adversity, and the bootstrap method was applied to verify the mediating effect. After the initial establishment of the model, we evaluated the fit degree of the structural equation model and adjusted the model via calculating indicators such as standardized root-mean-square residual (SRMR ≤ 0.08), root-mean-square error of approximation (RMSEA ≤ 0.08), goodness of fit index (GFI ≥ 0.90), comparative fit index (CFI ≥ 0.90), normed fit index (NFI ≥ 0.90) according to the studies by Wen and Kang [ 45 , 46 ]. Data were analyzed using SPSS 25.0 and AMOS 24.0 software, and a P value < 0.05 was considered to be statistically significant. The bootstrap CI was set to 95%, and the bootstrap sample size was 5000. If the 95% CI interval does not contain 0, it indicates a significant mediating effect.

Research results

Comparison of the general information and sleep quality scores of the study participants.

A total of 3,873 older adults were included in this study. The mean participant age was 72.84 ± 6.13 years, ranging from a minimum of 65 years to a maximum of 99 years. The mean PISQ score was 6.94 ± 3.88, and older adults with poor sleep quality (PSQI score ≥ 7) accounted for 48.15%. The mean ACE score was 2.09 ± 1.16, the mean SAS score was 44.13 ± 9.84, and the mean TCSQ negative coping score was 21.88 ± 8.23.

The results of univariate analysis showed that among the different gender populations, women had poorer sleep quality and a statistically significantly higher PSQI score than men at 7.44 ± 3.98 (t = 8.845, p  < 0.001). The PSQI score increased with age: that of adults aged 80 years and older was 7.32 ± 4.01, and the difference was statistically significant (H = 11.125, p  = 0.004). Regarding the groups with different educational levels, the highest PSQI score was found among illiteracy individuals (7.39 ± 4.01), with a statistically significant difference (H = 39.885, p  < 0.001). Sleep quality varied among older adults with different marital statuses, and the worst sleep quality was found in widowed older adults, with a PSQI score of 7.52 ± 4.00, which presented a statistically significant difference (H = 39.582, p  < 0.001). Older adults living alone had the worst sleep quality with a statistically significantly different PSQI score of 7.46 ± 3.90 (H = 20.904, p  < 0.001). Older adults with chronic diseases had poor sleep quality with a statistically significantly different PSQI score of 7.4 ± 3.95 (t=-8.83, p  < 0.001) (Table  1 ).

Association of sleep quality with childhood adversity, anxiety, and negative coping in rural older adults

The relevant analysis results indicated that the PSQI score was positively correlated with the ACE score ( r  = 0.092, P  < 0.01). The PSQI score was positively correlated with the SAS score and negative coping score ( r  = 0.279 and r  = 0.239, respectively; both P  < 0.01). The ACE score was positively correlated with the SAS score and negative coping score ( r  = 0.217 and r  = 0.133, respectively; both P  < 0.01). There was also a positive correlation between the SAS score and negative coping score ( r  = 0.351, P  < 0.01) (Table  2 ).

Analysis of mediating effects

Goodness-of-fit indices and path coefficients for the theoretical model of older adults’ sleep quality.

Based on the results of the above analyses, a structural equation model was constructed with childhood adversity as the independent variable, anxiety and negative coping as the mediating variables, and sleep quality as the dependent variable. The final model was screened according to the following model fitting indices: SRMR = 0.05, RMSEA = 0.06, GFI = 0.97, CFI = 0.90 and NFI = 0.89. The results of the fitting indices indicated that the model was well fitted. The differences in each of the standardized path coefficients in the model were statistically significant (all P  < 0.05) (Fig.  1 ).

Serial mediation models for childhood adversity, anxiety, negative coping and sleep quality

Bootstrap test of the theoretical model of older adults’ sleep quality

Table  3 demonstrates the results of structural modeling: (1) The total effect value of childhood adversity on sleep quality was 0.208 (95% CI: 0.146, 0.270), with a direct effect value of 0.066 (95% CI: 0.006, 0.130), accounting for 31.73% of the total effect, and a total indirect effect value of 0.142 (95% CI: 0.119, 0.170), accounting for 68.27% of the total effect. (2) The mediating effect of anxiety on the association between childhood adversity and sleep quality was significant, with a path effect value of 0.096 (95% CI: 0.078, 0.119), accounting for 46.15% of the total effect. (3) The mediating effect of negative coping on the association between childhood adversity on sleep quality was significant, with a path effect value of 0.024 (95% CI: 0.014, 0.037), accounting for 11.54% of the total effect. (4) The multiple mediating effects of anxiety and negative coping on the association between childhood adversity on sleep quality were also significant, with a pathway effect value of 0.022 (95% CI: 0.017, 0.028), accounting for 10.58% of the total effect (Table  3 ).

Current status and influencing factors of sleep quality in older adults

The proportion of older adults with poor sleep quality (PSQI score ≥ 7) was 48.15%, which is similar to the results of previous studies [ 15 , 16 ]. Due to gradual aging, the sleep-wake cycle of the older adults is disordered, and the efficiency of the circadian rhythm mechanism is reduced, which leads to changes in their sleep duration, sleep architecture, and sleep depth [ 12 ]. Furthermore, the occurrence of a variety of sleep problems such as sleep disruption, early sleep onset, and early awakening [ 47 , 48 , 49 ], result in a general decline in the sleep quality of older adults. We also found that gender, educational level, marital status, residency status, and chronic diseases were influencing factors of sleep quality. First, women have poorer sleep quality than men, which is in accordance with the established viewpoint [ 50 , 51 ]. Poor sleep quality and an increased risk of sleep disorders in older women may be due to the following reasons: (1) women are at a disadvantage in terms of socioeconomic factors, such as education and personal income [ 52 ]; (2) women are more susceptible to somatic [ 53 ] and psychiatric [ 54 , 55 ] disorders than men; and (3) women experience changes in secreted reproductive hormones [ 56 ]. Second, differences in sleep quality among older adults with different educational levels may be due to the fact that well-educated older adults have a higher sense of wellness and are more likely to access healthcare knowledge, which in turn leads to a better sleep state [ 57 ]. Third, the poorer sleep quality in widowed older adults and those living alone than in others may be related to loneliness and lack of social support leading to mood disorders, which in turn may cause reduced sleep efficiency and quality [ 58 ]. Finally, having a chronic disease is also a risk factor for poor sleep quality in older adults, which may be related to the physical discomfort caused by chronic diseases, the side effects of medications, and the associated financial pressure and psychological burden [ 59 ].

Direct effect of childhood adversity on sleep quality in older adults

The present study found that childhood adversity had a direct effect on sleep quality. Early life experiences, such as abuse, poverty, or the death of a parent, can affect sleep not only in childhood and adolescence but also in adulthood [ 60 , 61 ]. Childhood is an important phase for significant development of the hypothalamic-pituitary-adrenal (HPA) axis and the brain [ 58 ], and adverse events experienced during childhood can lead to long-term changes in the HPA axis response to stress (e.g., hyperactivity) and interfere with normal neurodevelopment in childhood and adolescence [ 62 ], increasing the risk of developing psychiatric disorders such as depression and post-traumatic stress disorder, which indirectly affect sleep in adulthood [ 63 ]. In addition, people exposed to ACEs are more likely to adopt unhealthy lifestyles and behaviors [ 64 , 65 ], and these changes may directly affect the sleep-wake cycle and lead to sleep problems.

Mediating effect of anxiety between childhood adversity and sleep quality in older adults

Sleep problems are not only a precursor but also a consequence of mental illness [ 66 , 67 ]. Our study found that anxiety could partially explain the relationship between childhood adversity and sleep disorders. Extensive studies have confirmed that exposure to adverse experiences in early life can increase an individual’s risk of developing psychiatric disorders such as anxiety and depression [ 68 , 69 ]. Anxiety is thus associated with a variety of sleep problems, with higher levels of anxiety corresponding to more severe sleep disorders [ 25 , 70 , 71 ]. Furthermore, anxiety has been found to mediate the effects of childhood adversity on sleep quality. For example, Amarneh found that elevated levels of anxiety sensitivity may explain the relationship between child maltreatment and adult sleep disorders among psychiatric hospitalizations [ 72 ]. Haimov found that COVID-19-related anxiety mediated the association between the number of childhood adversities and adult sleep quality [ 73 ]. The findings of our study further support the mediating role of anxiety on the effects of childhood adversity on sleep quality in older adults, suggesting that actively intervening in older adults’ anxiety states may mitigate the effects of childhood adversity on their sleep quality.

Mediating effect of negative coping between childhood adversity and sleep quality in older adults

Our results also identified a significant mediating effect of negative coping in the action of childhood adversity on sleep quality. Individuals’ exposure to environmental stressors early in life can compromise their adaptive coping strategies [ 74 ] and thus further affect sleep [ 75 ]. This result can be explained by the theory of stress. This theory states that when facing stressful events, people may take measures to disengage from threatening stimuli and generate associated thoughts and emotions (i.e., reducing activity and sleeping longer to minimize exposure to the stressor and the associated maladaptive emotions and thoughts) as well as adopt emotion-focused coping (i.e., regulating emotional responses to problems). However, such approaches may increase alertness and thus produce physiological arousal, disrupting or reducing sleep, which in turn affects sleep quality [ 76 ].

Finally, we founded that childhood adversity affected sleep quality in older adults through anxiety and negative coping. As mentioned above, stressful life events in childhood are associated with an increased risk of anxiety disorders in adulthood. Under the influence of such negative emotions, individuals are more inclined to adopt negative coping, which in turn affects the sleep quality in older adults. The above results facilitate a deeper understanding of the relationships among childhood adversity, anxiety, negative coping, and sleep quality and provide clues for exploring the potential mechanisms of how childhood adversity affects sleep quality in older adults.

Research limitations

In this study, the theoretical structural equation model fit the data well and provided an epidemiologic basis for the associations among childhood adversity, anxiety, negative coping, and sleep quality. However, there are several limitations. First, the results for the main variables in this study were obtained via self-report from the respondents and thus may be subject to unavoidable recall bias. Second, this study utilized a cross-sectional research design, which does not allow for a more precise determination of the causal relationship between variables. Third, this study explored the relationship between ACEs and PSQI scores but did not determine a dose-response relationship or whether different types of childhood adversities have different effects on sleep quality. Finally, the effects of drugs (such as antidepressants and anti-inflammatory drugs) on sleep quality were ignored in this study.

To sum up, anxiety and negative coping not only had direct effects on sleep quality but also played mediating roles in the association between childhood adversity and sleep quality, with a chained multiple mediating effect. These findings suggest that timely intervention for anxiety symptoms and negative coping states in older adults may mitigate the negative impact of childhood adversity on sleep quality.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Adverse Childhood Experiences

the revised Chinese-version Pittsburgh Sleep Quality Index

Self-Rating Anxiety Scale

Trait Coping Style Questionnaire

Structural equation modelling

confidence interval

root mean square error of approximation

the hypo-thalamic pituitary adrenal axis

Bulletin of the Seventh National. Census (No. 5) - National Bureau of Statistics. https://www.stats.gov.cn/sj/tjgb/rkpcgb/qgrkpcgb/202302/t20230206_1902005.html . Accessed 2 Jan 2024.

China’s Population Aging and Regional Variation | SpringerLink. https://link.springer.com/chapter/10.1007/978-3-030-98032-0_2 . Accessed 2 Jan 2024.

Yuan L, Yu B, Gao L, Du M, Lv Y, Liu X, et al. Decomposition analysis of health inequalities between the urban and rural oldest-old populations in China: evidence from a national survey. SSM Popul Health. 2023;21:101325.

Article   PubMed   Google Scholar  

Zhang J, Li D, Gao J. Health disparities between the Rural and Urban Elderly in China: a cross-sectional study. Int J Environ Res Public Health. 2021;18:8056.

Article   PubMed   PubMed Central   Google Scholar  

Yu P, Song X, Shi J, Mitnitski A, Tang Z, Fang X, et al. Frailty and survival of older Chinese adults in urban and rural areas: results from the Beijing Longitudinal Study of Aging. Arch Gerontol Geriatr. 2012;54:3–8.

Yang Y, Wang S, Chen L, Luo M, Xue L, Cui D, et al. Socioeconomic status, social capital, health risk behaviors, and health-related quality of life among Chinese older adults. Health Qual Life Outcomes. 2020;18:291.

Ramar K, Malhotra RK, Carden KA, Martin JL, Abbasi-Feinberg F, Aurora RN, et al. Sleep is essential to health: an American Academy of Sleep Medicine position statement. J Clin Sleep Med. 2021;17:2115–9.

Luyster FS, Strollo PJ, Zee PC, Walsh JK. Boards of directors of the American Academy of Sleep Medicine and the Sleep Research Society. Sleep: a health imperative. Sleep. 2012;35:727–34.

Hall MH, Brindle RC, Buysse DJ. Sleep and cardiovascular disease: emerging opportunities for psychology. Am Psychol. 2018;73:994–1006.

Besedovsky L, Lange T, Haack M. The Sleep-Immune Crosstalk in Health and Disease. Physiol Rev. 2019;99:1325–80.

Liu E, Feng Y, Yue Z, Zhang Q, Han T. Differences in the health behaviors of elderly individuals and influencing factors: evidence from the Chinese longitudinal healthy longevity survey. Int J Health Plann Manage. 2019;34:e1520–32.

Gulia KK, Kumar VM. Sleep disorders in the elderly: a growing challenge. Psychogeriatrics. 2018;18:155–65.

Tatineny P, Shafi F, Gohar A, Bhat A. Sleep in the Elderly. Mo Med. 2020;117:490–5.

PubMed   PubMed Central   Google Scholar  

Lu L, Wang S-B, Rao W, Zhang Q, Ungvari GS, Ng CH, et al. The prevalence of Sleep disturbances and Sleep Quality in older Chinese adults: a Comprehensive Meta-Analysis. Behav Sleep Med. 2019;17:683–97.

Jia G, Yuan P. The association between sleep quality and loneliness in rural older individuals: a cross-sectional study in Shandong Province, China. BMC Geriatr. 2020;20:180.

Li G, Zhu Z, Hu M, He J, Yang W, Zhu J, et al. Effects of carbon dioxide and green space on sleep quality of the elderly in rural areas of Anhui Province, China. Environ Sci Pollut Res Int. 2022;29:21107–18.

Dong X, Wang Y, Chen Y, Wang X, Zhu J, Wang N, et al. Poor sleep quality and influencing factors among rural adults in Deqing, China. Sleep Breath. 2018;22:1213–20.

Gamaldo AA, Wright RS, Aiken-Morgan AT, Allaire JC, Thorpe RJ, Whitfield KE. The Association between Subjective Memory complaints and Sleep within older African American adults. J Gerontol B Psychol Sci Soc Sci. 2019;74:202–11.

Dube SR, Anda RF, Felitti VJ, Chapman DP, Williamson DF, Giles WH. Childhood abuse, household dysfunction, and the risk of attempted suicide throughout the life span: findings from the adverse childhood experiences Study. JAMA. 2001;286:3089–96.

Article   CAS   PubMed   Google Scholar  

Sheehan CM, Li L, Friedman EM. Quantity, timing, and type of childhood adversity and sleep quality in adulthood. Sleep Health. 2020;6:246–52.

Kajeepeta S, Gelaye B, Jackson CL, Williams MA. Adverse childhood experiences are associated with adult sleep disorders: a systematic review. Sleep Med. 2015;16:320–30.

Sullivan K, Rochani H, Huang L-T, Donley DK, Zhang J. Adverse childhood experiences affect sleep duration for up to 50 years later. Sleep. 2019;42:zsz087.

Poon CYM, Knight BG. Impact of childhood parental abuse and neglect on sleep problems in old age. J Gerontol B Psychol Sci Soc Sci. 2011;66:307–10.

Dorji N, Dunne M, Deb S. Adverse childhood experiences: association with physical and mental health conditions among older adults in Bhutan. Public Health. 2020;182:173–8.

Yu J, Rawtaer I, Fam J, Jiang M-J, Feng L, Kua EH, et al. Sleep correlates of depression and anxiety in an elderly Asian population. Psychogeriatrics. 2016;16:191–5.

Lähdepuro A, Savolainen K, Lahti-Pulkkinen M, Eriksson JG, Lahti J, Tuovinen S, et al. The impact of early life stress on anxiety symptoms in late adulthood. Sci Rep. 2019;9:4395.

Raposo SM, Mackenzie CS, Henriksen CA, Afifi TO. Time does not heal all wounds: older adults who experienced childhood adversities have higher odds of mood, anxiety, and personality disorders. Am J Geriatr Psychiatry. 2014;22:1241–50.

Zhao X, Li J, Huang Y, Jin Q, Ma H, Wang Y, et al. Genetic variation of FYN contributes to the molecular mechanisms of coping styles in healthy chinese-Han participants. Psychiatr Genet. 2013;23:214–6.

Li Y, Cong X, Chen S, Li Y. Relationships of coping styles and psychological distress among patients with insomnia disorder. BMC Psychiatry. 2021;21:255.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Otsuka Y, Kaneita Y, Itani O, Nakagome S, Jike M, Ohida T. Relationship between stress coping and sleep disorders among the general Japanese population: a nationwide representative survey. Sleep Med. 2017;37:38–45.

Solberg MA, Peters RM, Resko SM, Templin TN. Does coping mediate the relationship between adverse childhood experiences and Health outcomes in Young adults? J Child Adolesc Trauma. 2023;16:1–13.

Sheffler JL, Piazza JR, Quinn JM, Sachs-Ericsson NJ, Stanley IH. Adverse childhood experiences and coping strategies: identifying pathways to resiliency in adulthood. Anxiety Stress Coping. 2019;32:594–609.

Bjørkløf GH, Engedal K, Selbæk G, Kouwenhoven SE, Helvik A-S. Coping and depression in old age: a literature review. Dement Geriatr Cogn Disord. 2013;35:121–54.

Orgeta V, Orrell M. Coping styles for anxiety and depressive symptoms in Community-Dwelling older adults. Clin Gerontologist. 2014;37:406–17.

Article   Google Scholar  

Cohen S, Gianaros PJ, Manuck SB. A stage model of stress and disease. Perspect Psychol Sci. 2016;11:456–63.

Folkman S, Stress. Appraisal and Coping. In: Gellman MD, Turner JR, editors. Encyclopedia of behavioral medicine. New York, NY: Springer New York; 2013. pp. 1913–5.

Chapter   Google Scholar  

Wu X. The Household Registration System and Rural-Urban Educational Inequality in Contemporary China. Chin Sociol Rev. 2011;44:31–51.

Zhang Z, Treiman DJ. Social origins, hukou conversion, and the wellbeing of urban residents in contemporary China. Soc Sci Res. 2013;42:71–89.

Su S, Wang X, Pollock JS, Treiber FA, Xu X, Snieder H, et al. Adverse childhood experiences and blood pressure trajectories from childhood to Young Adulthood: the Georgia stress and heart study. Circulation. 2015;131:1674–81.

Li J, Yao Y, Dong Q, Dong Y, Liu J, Yang L, et al. Characterization and factors associated with sleep quality among rural elderly in China. Arch Gerontol Geriatr. 2013;56:237–43.

Nyqvist F, Cattan M, Conradsson M, Näsman M, Gustafsson Y. Prevalence of loneliness over ten years among the oldest old. Scand J Public Health. 2017;45:411–8.

Yue T, Li Q, Wang R, Liu Z, Guo M, Bai F, et al. Comparison of hospital anxiety and Depression Scale (HADS) and Zung Self-Rating Anxiety/Depression scale (SAS/SDS) in evaluating anxiety and depression in patients with psoriatic arthritis. Dermatology. 2019;236:170–8.

Luo Y, Wang H. Correlation research on psychological health impact on nursing students against stress, coping way and social support. Nurse Educ Today. 2009;29:5–8.

Shao R, He P, Ling B, Tan L, Xu L, Hou Y, et al. Prevalence of depression and anxiety and correlations between depression, anxiety, family functioning, social support and coping styles among Chinese medical students. BMC Psychol. 2020;8:38.

Wen Zhonglin H, Jietai M. Structural equation model testing cutoff criterion for goodness of fit indices and Chi-square test. Acta Physiol Sinica. 2004;36:186–94.

Google Scholar  

Kang H, Ahn J-W. Model setting and interpretation of results in Research using Structural equation modeling: a checklist with guiding questions for reporting. Asian Nurs Res (Korean Soc Nurs Sci). 2021;15:157–62.

PubMed   Google Scholar  

Moraes W, Piovezan R, Poyares D, Bittencourt LR, Santos-Silva R, Tufik S. Effects of aging on sleep structure throughout adulthood: a population-based study. Sleep Med. 2014;15:401–9.

Feinsilver SH. Normal and abnormal sleep in the Elderly. Clin Geriatr Med. 2021;37:377–86.

Ohayon MM, Carskadon MA, Guilleminault C, Vitiello MV. Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep. 2004;27:1255–73.

Madrid-Valero JJ, Martínez-Selva JM, Ribeiro do Couto B, Sánchez-Romera JF, Ordoñana JR. Age and gender effects on the prevalence of poor sleep quality in the adult population. Gac Sanit. 2017;31:18–22.

Hwang H, Kim KM, Yun C-H, Yang KI, Chu MK, Kim W-J. Sleep state of the elderly population in Korea: Nationwide cross-sectional population-based study. Front Neurol. 2022;13:1095404.

Lallukka T, Sares-Jäske L, Kronholm E, Sääksjärvi K, Lundqvist A, Partonen T, et al. Sociodemographic and socioeconomic differences in sleep duration and insomnia-related symptoms in Finnish adults. BMC Public Health. 2012;12:565.

Murtagh KN, Hubert HB. Gender differences in physical disability among an elderly cohort. Am J Public Health. 2004;94:1406–11.

Moieni M, Irwin MR, Jevtic I, Olmstead R, Breen EC, Eisenberger NI. Sex differences in depressive and socioemotional responses to an inflammatory challenge: implications for sex differences in depression. Neuropsychopharmacology. 2015;40:1709–16.

Asher M, Aderka IM. Gender differences in social anxiety disorder. J Clin Psychol. 2018;74:1730–41.

Lin CM, Davidson TM, Ancoli-Israel S. Gender differences in obstructive sleep apnea and treatment implications. Sleep Med Rev. 2008;12:481–96.

Li N, Xu G, Chen G, Zheng X. Sleep quality among Chinese elderly people: a population-based study. Arch Gerontol Geriatr. 2020;87:103968.

Yue Z, Zhang Y, Cheng X, Zhang J. Sleep quality among the Elderly in 21st Century Shandong Province, China: a ten-year comparative study. Int J Environ Res Public Health. 2022;19:14296.

Suzuki K, Miyamoto M, Hirata K. Sleep disorders in the elderly: diagnosis and management. J Gen Fam Med. 2017;18:61–71.

Counts CJ, Grubin FC, John-Henderson NA. Childhood socioeconomic status and risk in early family environments: predictors of global sleep quality in college students. Sleep Health. 2018;4:301–6.

Hall Brown TS, Belcher HME, Accardo J, Minhas R, Briggs EC. Trauma exposure and sleep disturbance in a sample of youth from the National Child Traumatic Stress Network Core Data Set. Sleep Health. 2016;2:123–8.

Hulme PA. Childhood sexual abuse, HPA axis regulation, and mental health: an integrative review. West J Nurs Res. 2011;33:1069–97.

Berens AE, Jensen SKG, Nelson CA. Biological embedding of childhood adversity: from physiological mechanisms to clinical implications. BMC Med. 2017;15:135.

Shonkoff JP, Garner AS, Committee on Psychosocial Aspects of Child and Family Health. Committee on Early Childhood, Adoption, and Dependent Care, section on developmental and behavioral pediatrics. The lifelong effects of early childhood adversity and toxic stress. Pediatrics. 2012;129:e232–246.

Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the global burden of Disease Study 2010. Lancet. 2012;380:2224–60.

Asarnow LD, Soehner AM, Harvey AG. Circadian rhythms and psychiatric illness. Curr Opin Psychiatry. 2013;26:566–71.

Jansson-Fröjmark M, Lindblom K. A bidirectional relationship between anxiety and depression, and insomnia? A prospective study in the general population. J Psychosom Res. 2008;64:443–9.

McKay MT, Cannon M, Chambers D, Conroy RM, Coughlan H, Dodd P, et al. Childhood trauma and adult mental disorder: a systematic review and meta-analysis of longitudinal cohort studies. Acta Psychiatr Scand. 2021;143:189–205.

Matheson SL, Shepherd AM, Pinchbeck RM, Laurens KR, Carr VJ. Childhood adversity in schizophrenia: a systematic meta-analysis. Psychol Med. 2013;43:225–38.

Chang KJ, Son SJ, Lee Y, Back JH, Lee KS, Lee SJ, et al. Perceived sleep quality is associated with depression in a Korean elderly population. Arch Gerontol Geriatr. 2014;59:468–73.

Zhu X, Hu Z, Nie Y, Zhu T, Chiwanda Kaminga A, Yu Y, et al. The prevalence of poor sleep quality and associated risk factors among Chinese elderly adults in nursing homes: a cross-sectional study. PLoS ONE. 2020;15:e0232834.

Amarneh D, Lebeaut A, Viana AG, Alfano CA, Vujanovic AA. The role of anxiety sensitivity in the Association between Childhood Maltreatment and Sleep disturbance among adults in Psychiatric Inpatient Treatment. J Nerv Ment Dis. 2023;211:306–13.

Haimov I, Szepsenwol O, Cohen A. Associations between Childhood stressors, COVID-19-Related anxiety, and sleep quality of adults during the Third Wave of the COVID-19 pandemic in Israel. Nat Sci Sleep. 2022;14:1665–75.

Mayo D, Corey S, Kelly LH, Yohannes S, Youngquist AL, Stuart BK, et al. The role of Trauma and Stressful Life events among individuals at Clinical High Risk for psychosis: a review. Front Psychiatry. 2017;8:55.

Ren Z, Zhang X, Shen Y, Li X, He M, Shi H, et al. Associations of negative life events and coping styles with sleep quality among Chinese adolescents: a cross-sectional study. Environ Health Prev Med. 2021;26:85.

Sadeh A, Gruber R. Stress and sleep in adolescence: a clinical-developmental perspective. Adolescent sleep patterns: Biological, social, and psychological influences. New York, NY, US: Cambridge University Press; 2002. pp. 236–53.

Download references

Acknowledgements

We thank the responsible person of local health work, all participants and the staff of data reduction for their cooperation.

This work was funded by the research projects of “Investigation on health status and risk factors of the elderly over 65 years old in Deyang City” (No.301021062) of Chengdu University of Traditional Chinese Medicine.

Author information

Authors and affiliations.

School of Public Health, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, Sichuan, China

Yuqin Zhang, Hongwei Li, Jin Yan, Mengxue Xie & Lian Yang

Sichuan Provincial Center for Disease Control and Prevention, No.6, Zhongxue Road, Wuhou District, Chengdu, 610041, China

Chengwei Lin

Hospital of Chengdu University of Traditional Chinese Medicine, Deyang Integrated Traditional Chinese and Western Medicine Hospital, Deyang, 618000, China

Centre for Aging Health Service of Deyang City, Deyang, 618000, China

Xueyan Zhou

Health Commission of Deyang City, Deyang, 618000, China

Sichuan Provincial Health Information Center, Chengdu, 610015, Sichuan, China

Xueli Zhang

Centre for Health Management and Policy Research,School of Public Health, Cheeloo College of Medicine,Shandong University, NHC Key Lab of Health Economics and Policy Research, Shandong University, Jinan, 250012, China

Chengchao Zhou

You can also search for this author in PubMed   Google Scholar

Contributions

YQ Z, CW L and HW L were responsible for conception and design of the study. L L, XY Z and Y X were involved in recruiting the participants. YQ Z and CW L did the statistical analysis and were involved in manuscript preparation and drafting the article.J Y , MX X, and XL Z were involved in editing and revising the manuscript. CC Z and L Y were responsible for the critical revision of the manuscript. All authors have contributed to and have approved the final manuscript.

Corresponding authors

Correspondence to Chengchao Zhou or Lian Yang .

Ethics declarations

Ethics approval and consent to participate.

The current study was conducted according to the guidelines of the Declaration of Helsinki, approved by the Medical Ethics Committee of the Affiliated Hospital of Chengdu University of Chinese Medicine (Approval no.2023KL-011). All the participants completed informed consent forms before recruitment to the study. For illiterate participants their guardians (usually immediate family members, for example, son, daughter, son and daughter in law etc.) gave written informed consent for participation in the study. The ethics committee had approved the methods of giving consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Zhang, Y., Lin, C., Li, H. et al. The relationship between childhood adversity and sleep quality among rural older adults in China: the mediating role of anxiety and negative coping. BMC Psychiatry 24 , 346 (2024). https://doi.org/10.1186/s12888-024-05792-2

Download citation

Received : 03 January 2024

Accepted : 25 April 2024

Published : 08 May 2024

DOI : https://doi.org/10.1186/s12888-024-05792-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Rural older adults
• Childhood adversity experiences
• Negative coping
• Chain mediation

BMC Psychiatry

ISSN: 1471-244X

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Open access
• Published: 09 May 2024

Self-reported suicidal behaviour among people living with disabilities: prevalence and associated factors from a cross-sectional nation-wide survey in Bangladesh

• Kamrun Nahar Koly 1 ,
• Aniqua Anjum 1 ,
• Rasma Muzaffar 2 ,
• Teresa Pollard 3 ,
• Taslima Akter 4 ,
• Zakia Rahman 5 ,
• Helal Uddin Ahmed 6 &
• Julian Eaton 7 , 8  

BMC Psychology volume  12 , Article number:  231 ( 2024 ) Cite this article

216 Accesses

Metrics details

Disability marginalises a large portion of Bangladesh’s population. Global pre- and post-pandemic research evidently states that, this group is more prone to develop mental health problems, which increases the risk of self-harm and suicide among them. It is crucial to comprehend and mitigate the mental health challenges among the people with disabilities which in turn can promote their greater participation in community, and in national socioeconomic development. However, currently there is limited information available, regarding the suicidal behaviour of this group in Bangladesh. Therefore, this study aimed to investigate the prevalence and contributing factors of suicidal behaviour among people with disabilities.

A cross-sectional survey was conducted during September and October 2022, among the participants who had selected disabilities, by using probability proportional to size sampling technique across all eight divisions of Bangladesh. A semi-structured questionnaire comprising information about sociodemographic, lifestyle, health; and Suicidal Behaviour Questionnaire-Revision (SBQ-R) was used. The association between the determinants and mental health outcome was investigated using the Chi-square test, and the contributing factors were investigated using the multiple binary logistic regression.

About 10.45% of the participants reported to have suicidal behaviour (e.g., suicidal ideation, attempts, completed suicide), considering the cut-off score as 7 for the SBQ-R in the study period. Approximately, 40% respondents mentioned suicidal ideation in their lifetime, whereas, 9.01% had suicidal ideation over the past 12 months. Additionally, 8.87% of the person with disabilities, mentioned about their suicidal intent to the family members, and 5.94% reported the likelihood of suicide in the future. Being female, having multiple disabilities, and not being connected with family and friends were found to be significantly associated with suicidal behaviour.

This research demonstrates the significance of treating mental health issues and expanding accessibility to pre-existing services to lessen the impact of the limitations generated by disabilities. Policymakers can utilize this baseline findings to design large scale research and develop measures for suicide prevention, and management for at-risk groups.

Peer Review reports

Mental health is one of the most significant public health concerns worldwide [ 1 ]. Unaddressed common mental health problems often tend to increase the risk of suicidal behaviour among populations [ 2 ]. Suicidal behaviour can take various forms and intensities, including suicidal ideation, suicidal attempts, and actual suicide. Suicide claims approximately 730,000 lives globally annually, and according to the World Health Organization (WHO), over 79% of these occur in low- and middle-income countries (LMICs) [ 3 , 4 , 5 ]. In Bangladesh, reportedly eight per 100,00 people die because of suicide, leading to a total of 10,000 suicidal deaths cumulatively annually [ 6 , 7 , 8 , 9 , 10 ]. However, the actual rate is believed to be higher than the reported rate; since in Bangladesh it is common for incidents to be classified as accidental death rather than suicide, due to stigma and to avoid postmortem social repercussions [ 11 ].

Evidence suggests that disability itself is a significant risk factor for suicidal ideation [ 12 ]. Worldwide, about 15% of the population experience some form of disability such as physical disability (upper limb, lower limb), visual, speech and hearing disability [ 13 ]. A number of past studies revealed, around 5.6–10% of the Bangladeshi population have at least one form of disability [ 7 , 8 , 9 ]. Compared to persons without disabilities, persons with disabilities experience poor health and mental health outcomes [ 14 ]. Some contributing factors are: less access to healthcare facilities, lower levels of education, limited social and economic participation, and higher poverty rates for this marginalised population [ 15 ]. Chronic stress due to limitation in daily activities, stigma, discrimination, isolation from wider society, physical and financial dependency make them more vulnerable to mental health conditions [ 16 ]. Stigma related to suicide and disability, as well as lack of appropriate services, prevent them from seeking professional help reflecting double burden for this underprivileged population [ 17 ]. During the COVID-19 pandemic, additional symptoms indicating mental health issues were experienced, that could increase the risk of suicide behaviour among the person with disabilities [ 18 ]. Evidence from Higher Income Countries show that, persons with disabilities experienced increased burden (20.7–30.8%) of suicidal thoughts than the persons without disabilities (4.1–8.3%) during the COVID-19 pandemic [ 19 , 20 , 21 ]. Suicidal thoughts and behaviour differ from person to person depending on their age, sex, overall health, frequency of stressors and previous suicidal attempts or thoughts [ 14 ]. Previous studies reported that these factors influence the risk of suicidal behaviour [ 14 ].

However, Bangladesh still lacks any kind of epidemiological study concerning suicidal behaviour among people with disabilities. Although suicidality among the general population has been previously examined in several studies [ 22 , 23 , 24 , 25 , 26 ]. Those studies mainly determined the suicidal behaviour among persons with certain functional limitations like disabilities due to chronic illnesses, multiple sclerosis, Huntington disease and intellectual disabilities [ 27 ]. There is only one study conducted in Bangladesh which reported the prevalence of suicidal ideation as 23.9% among persons with disabilities [ 28 ]. However, this particular study assessed suicidal ideation in few districts of Bangladesh and did not use any assessment tool. Hence, there is a large knowledge gap about suicidal behaviour. On the contrary, our study aimed to conduct a nationwide survey to assess the prevalence and determinants of suicidal behaviour among this cohort.

Study design and settings

A cross-sectional study was conducted among people with disabilities from the largest disability rights based non-governmental organization (NGO) in Bangladesh, the Center for Disability in Development (CDD). Additionally, CDD works jointly with more than 350 national and international organizations for people with disabilities (OPDs) and disability-specific organizations (DSOs) [ 29 ]. The socio-demographical profile of participants covered the ages of 18 and 60 years and from Bangladesh’s eight divisions (highest administrative units), namely Dhaka, Chattogram, Barisal, Sylhet, Mymensingh, Khulna, Rajshahi, and Rangpur where the major CDD beneficiaries were based. The study included participants with a variety of physical (upper and lower extremity), speech, hearing, and visual impairments. As of 2021, CDD along with its collaborative partners (OPDs and DSOs) supported around 31,546 people with disabilities. A sampling frame comprising list of CDD beneficiaries was compiled using probability proportional to size (PPS) sampling. The overall sample was distributed among the selected area of beneficiaries regarding the share of the total beneficiaries per division in the selected OPDs and DSOs (Table  1 ). In addition to including each of the eight divisions, sampling was employed to determine geographical areas within each division based on the locations of the OPDs and DSOs. Savar, Mirpur, and Khilgaon from Dhaka; Bashkhali, Rangunia, and Shahid Nagar from Chattogram; Kalapara from Barisal; Dhubaura from Mymensingh; Dinajpur, Rangpur, and Mithapukur from Rangpur; Bagerhat from Khulna; Bagha from Rajshahi; and Kulaura from Sylhet were included. This study excluded those with intellectual disabilities, unable to communicate meaningfully, pregnant women, those under the age of 18, and those above the age of 60.

Sample size

It was difficult to estimate the precise prevalence of suicidal behaviour due to the scarcity of research on the mental health problems that affect people with disabilities. Therefore, the prevalence found in research with Bangladeshi mothers who have child with autism spectrum disorder (ASD) was used to estimate the sample size for this study [ 30 ]. Suicidal behaviour is an outcome of the untreated common mental health issues, that is caused by social exclusion, barriers and poor quality of life among persons with disability. So, we considered the mothers as they are closely tied with their children with ASD and encounter similar type of obstacles in Bangladesh. Hence, the sample size was calculated using the formula below considering 15.7% prevalence of suicidal behaviour.

where, n  = number of samples; z = 1.96 (99% confidence level); p = prevalence estimate (15.7%); q = (1-p); and d = precision limit or proportion of sampling error 0.05.

Considering a 99% confidence interval and a 5% margin of error, a sample size estimate of 353 was calculated for this study. For this study, a larger sample size of 353 was considered based on the assumptions of a 10% non-response rate and a 1.5% design effect. Probability proportional to size (PPS) sampling technique was used to compute the sample size.

Data collection procedure and measures

The data was collected using a semi-structured questionnaire developed in Bangla that included informed consent (see below). Based on the prior literatures, information regarding socio-demographic, lifestyle, health and disease, and healthcare seeking were included in the questionnaire [ 31 , 32 , 33 ]. During September and October 2022, the trained study team carried out face-to-face interviews to collect data. The local CDD staff enabled the team to locate the residences and contact the selected individuals from the sample list. The study participants were informed about the study objectives, the measures of protecting their anonymity and privacy of the participants. Written and verbal consents were collected from the participants and were given the choice to participate in the study. Additionally, all of participants had the flexibilities to have a caregiver who assisted them to answer during the most the interviews since it was mandatory for the person with visual, speech and hearing impairment. The research team was always accompanied by a trained CDD staff who knew sign language to support the person with the speech and hearing impairment. Some of the participants were also interviewed at the selected OPDs and DSOs to prioritize their preferences and compensated for their travel cost (100–200 BDT). The principal investigator and the co-investigators frequently checked the accuracy of the data collected. Following data collection, open responses were post-coded in accordance with the requirements. The post-coding was done based on previous literatures [ 31 , 32 , 33 ]. Participants with severe suicidal behaviour scores were given further referral to the collaborators for further mental health support.

Measurements

To assess the primary outcome of suicidal behaviour, we considered the revised version of the Suicidal Behaviour Questionnaire-Revised (SBQ-R). A Bengali translated version was previously used among Bangladeshi university students during the second wave of COVID-19 pandemic and was also validated among the persons with autism, functional and motor disabilities in United States of America [ 34 , 35 , 36 ]. It is made up of four items, representing a different dimension of suicidality. SBQ-R item 1 into lifetime suicide ideation and suicide attempts; item 2 assesses the frequency of suicidal ideation over the past twelve months; item 3 indicates into the threat of suicidal behaviour; and item 4 evaluates self-reported likelihood of suicidal behaviour. Briefly, to be consistent with existing 4-item versions, the responses of the several items of the original questions were modified. We evaluated the SBQ-R total item scores separately, then calculated the overall response by combining the final scores. Based on previous literature, dichotomous responses were formed, considering 7 as cutoff [ 34 , 36 , 37 ].

Along with the previous literature and known confounders, we included the following socio-demographic information: lifestyle, health and disease, care-seeking behaviour related factors, to determine the association with suicidal behaviour [ 31 , 32 , 33 , 34 , 35 , 36 ]. Socio-demographic information were collected, related to age, gender, educational qualification, type of disability, occupation, religion, marital status, division, and area of residence. Regarding age and educational qualification, we considered completed years. Initially, the age was collected as a continuous variable. However, to understand the age specific suicidal behaviour, we categorized age into three different groups as 18–35 years, 36 to 54 years and above 54 years. In addition, the participants were asked about their food accessibility, sleeping duration, smoking habits, receipt of family and community support, and self-care practice, to understand lifestyle patterns. Moreover, sleep duration was reported as per their actual sleeping duration which were later categorized based on standard sleeping duration by the previous studies, i.e.,7 h. Furthermore, the health and disease-related section included history of non-communicable disease (NCDs) and other health-related issues. Additionally, the health care-seeking behaviour section included different relevant questions including their usual practice and type of barriers they faced while seeking healthcare.

Statistical analysis

The study team entered, managed, cleaned, and processed all the data by using SPSS software version 26 and performed statistical analysis in SPSS software version 26 and STATA software version 13.0. Initially for descriptive statistics, both frequency and percentage were calculated. To identify the differences between the groups, we used the χ2 (Chi-square) test for categorical data and independent sample t-test for continuous data. We checked the linearity assumption between the factors and the outcome variable. We found there was a non-linear relationship between the factors and the outcome variable. Then we transformed the factors (age and educational qualification, sleeping hours, occupation) into categories. We estimated both unadjusted (crude) and adjusted odds ratio using simple and multiple logistic regression models considering different factors (age, gender, educational qualification, type of disability, occupation, religion, marital status, division, and area of residence) to see the effect of these factors on suicidal behaviour. Factors that were significant (with a p-value of less than 0.05) were considered for further estimation of the multiple logistic regression model. Assumptions of multiple logistic regression were checked, i.e., multicollinearity was checked (all included variables resulted in Variance Inflation Factor (VIF < 4), responses were independent, and responses were categorized and arranged in ascending order [ 38 , 39 ].

General characteristics of participants

A total of 355 participants were included in this study. The majority of the participants were 18–35 years old. Among the participants, fewer had education above secondary school (7.61%), higher nuclear families (77.75%),), a higher percentage of people were married (65.63%%) and 41.13% were urban residents. About 20.28% of the participants reported having multiple disabilities. Moreover, almost 45.07% of participants self-reported the presence of having NCDs. In addition, 64.31% of participants reported poor connectedness with family and friends, however, 65.63% reported that they received support from their community with their daily chores. However, 94.93% reported that they practice at least one form of self-care. Moreover, 54.93% of participants reported over-sleeping, and 72.68% stated that they faced problems with food accessibility (Table  1 ).

Prevalence of suicidal behaviour among persons with disabilities

The overall prevalence of suicidal behaviour among the persons with disabilities was 10.45%, considering the cut-off score as 7 for the SBQ-R. Whereas, about 40% reported having suicidal thoughts in their entire lifetime, 9.01% had suicidal ideation in the last 12 months, 8.87% threatened suicidal attempts and 5.94% reported a likelihood of suicidal behaviour in the future. ( Figure-1)

Prevalence of indicators of suicidal behaviour

Association between suicidal behaviour and other measures

Suicidal behavior was significantly associated with being female, having multiple disabilities, poor connection with family and friends, sleeping more than the standard duration, encountering problems in food accessibility (Table  2 ).

Logistic regression analysis

From the crude model, female participants were 2.0 (CI: 1.0, 4.0) times more likely to have suicidal behaviour compared to males. Moreover, persons with multiple disabilities are 2.1 (0.9, 4.4) times more prone to have suicidal behaviour compared to persons with one disability. The odds of having suicidal behaviour for the persons who faced food inaccessibility were 2.9 (1.5, 5.8) times higher compared to those who did not faced any inaccessibility. Furthermore, odds were increased 0.5 (CI: 0.2, 0.9) times for longer sleepers. Additionally, participants who were not connected with their family and friends were found to have more than double the odds for having suicidal behaviour (OR: 2.6; CI: 1.1, 6.11).

A multiple binary logistic regression evaluated the associated factors of suicidal behaviour. The reference group was ‘no suicidal behaviour’. Being female (OR: 2.2; CI: 1.1, 4.9), faced problem in food accessibility (OR:2.6; CI: 1.3, 5.4), and not connected with their family and friends (OR: 2.9; CI: 1.2, 7.2) were significantly more likely to have suicidal behaviour (Table  3 ).

Suicide is a major social and public health issue which has been postulated to be influenced by the presence of a disability. Moreover, a number of studies have also significantly associated suicidal behaviour and suicides with common mental health conditions [ 40 ]. Importantly, the COVID-19 pandemic also led to deteriorating mental well-being of all populations, especially vulnerable populations like people with disabilities. However, very limited studies exploring suicidal behaviour among people with special needs exists, therefore our study assessed the prevalence and determinants of suicidal behaviour among persons with disabilities in Bangladesh. The findings may influence to create the scope for evidence-based and inclusive strategies for developing action plans to reduce the incident rates of suicide among people with disabilities.

Compared to HICs (High Income Countries), many studies have reported a higher suicide prevalence among populations of LMICs like Azerbaijan and Bangladesh, but very few of these studies highlighted the suicide prevalence among people with disabilities in LMICs [ 40 ]. Informing this gap, our study findings reported an overall prevalence of suicidal behaviour among the persons with disabilities to be 10.45%. Aligning with this, the few global studies available also depicted three folds increase of suicides among people with disabilities in comparison to people without disabilities [ 41 ]. Furthermore, as per the International Classification of Functioning, Disability and Health (ICF), the inter-connected factors like functional impairment, activity limitation and restricted participation, affect the way a person with disability can access and participate in society [ 42 ]. Such limitations and exclusion from society, also render people with disabilities at greater risk of common mental health conditions [ 42 ]. Moreover, unaddressed distress and mental health issues can lead to more severe mental health conditions, which are known to increase suicidality as well as being associated with shorter lifespan for a number of other reasons [ 40 ]. Hence, early interventions like early diagnosis, patient-profile based therapy, coordination between primary and secondary care sectors at individual, community and government level should be implemented.

As per prior studies, suicide is causally a heterogeneous phenomenon, varying with the patterns of risk factors across gender, age, culture, geographic location, and other person-specific factors like relationships, educational level, income level and so on [ 43 ]. As per a number of studies in LMICs, disability is already highly stigmatized in the societies [ 44 , 45 ]. Hence, intersecting factors like having multiple disability and being females in the male dominant societies arise as additional challenges leading to suicidal behaviour [ 46 , 47 , 48 ]. Our study also reported suicidal behaviour to be significantly associated with factors like being female, having multiple disability, poor connection with family and friends, sleeping more than the standard duration, facing problems in food accessibility. Our findings were therefore coherent with existing research and theory.

Several international and national studies report a marked gender disparity in suicidal rates, with females being more prone to suicidal behaviour [ 49 ]. Although the rate of suicidal behaviour has been reported to be higher among women, the rate of successful suicide is higher among men in most global research [ 50 ]. Consistently, our study also reported being female as a significant factor contributing to suicidal behaviour. Similar to other LMICS like India and Pakistan, a patriarchal social system is dominant where women are subordinated to men both within the household and community levels [ 51 ]. In Bangladesh, women are the primary caregivers of the families and conversely considered as the economic and social burdens in their families. Owing to this, many females from impoverished families face child marriages, physical and sexual violence [ 49 ]. Moreover, the presence of disability-confounded with marital disharmony, perceived performance failure as wife, divorce based on infertility, and expulsion from the family, can instill suicidal behaviours among women with disabilities [ 52 ]. To overcome this disparity, gender-sensitive advocacy, and gender-specific mental health interventions are necessary.

Additionally,, our study findings emphasized that participants with multiple disabilities are more likely to have suicidal behaviour. Aligning with this, prior studies found that multiple disabilities increased the risk of suicidal thoughts and suicide by three to eight fold [ 53 ]. Moreover, compared to people with one disability, people with multiple disabilities are prone to increased health issues and more limitations in daily life activities, increasing their cumulative risk of being suicidal [ 48 ]. Hence, accommodations at family, community, policy, infrastructural levels and access to augmentative and alternative communication skills for health staff, leading to better access to social and health services for people with multiple disabilities is needed.

A number of prior studies across developed and resource-poor countries, reported low socio-economic status and food inaccessibility among people with disabilities are inter-related [ 54 ]. Following this, our study also stated respondents who faced food inaccessibility were more prone to suicidal behaviour. Unfortunately, few employment opportunities and the strong association between poverty and disability makes financial dependence for basic needs like food and shelter common among people with disabilities [ 55 ]. Therefore, inclusive and flexible policies to ensure access to poverty alleviation efforts, including livelihood and cash transfer programs, for person with disabilities should be ensured.

Furthermore, a plethora of research studies across the world report being connected with family and friends as a protective factor against suicidal behaviour [ 56 ]. However, many people with disabilities are deprived of their fundamental rights to participate in social and community life– for example enshrined in Article 19 of the Convention on the Rights of Persons with Disabilities– due to attitudinal barriers and stigma [ 57 ]. Additionally, family support is essential for people with disabilities to fulfill not only emotional needs, but resource provision or mobilization of supports and resources [ 58 ]. Our findings also confirmed poor connection with family and friends to be significantly associated with suicidal behaviour. Therefore, access to social rights and the exercise of community participation in the community should be encouraged.

Evidently, our study reported participants with insufficient sleeping hours are more inclined to suicidal behaviour. Likewise, previous research from LMICs highlighted that sleep disruptions, specifically insomnia symptoms and poor sleep quality significantly influence suicidal thoughts and suicide attempts [ 58 ]. To minimize this burden, this group should be educated to maintain a healthy life style including adequate sleeping time.

Suicidal behaviours often remain unreported or underreported and we recognise that our research only reflects the tip of the iceberg. Moreover, in a resource poor setting like Bangladesh, the burden of common mental health conditions is already higher among females, low income families and people with disabilities. Suicide reduction is an indicator for achieving the United Nations Sustainable Development Goals and a multisectoral strategy involving members from diverse sectors as well as the healthcare sector is necessary to prevent suicide among people with disabilities. As part of a national suicide prevention strategy for people with disabilities, regular media workshops at the national, regional, and local levels might be emphasized. Journalists can develop a self-regulating and self-monitoring system for the compassionate reporting of suicide cases. This research findings also have the potential to guide the formulation of additional suicide prevention interventions particularly for the people marginalised community.

Strength and limitations

This was a cross-sectional study, no cause-and-effect relationships in between components were established. In order to investigate the potential risk of suicidal behaviour, longitudinal studies should be developed. Additionally, due to the lack of accessible communication resources, this study only included participants with physical, speech, hearing and visual impairment. Evidently, inclusion of psychosocial disabilities would have increased identified suicidal behaviour. Moreover, the cut-off points of these psychometric tools were established mostly among people without disability in Bangladesh context. Furthermore, suicidal behaviour is a complicated psychological phenomenon, which makes it difficult to accurately evaluate and categorize because it cannot be adequately captured by self-reported responses. However, due to self-reporting and related stigmas from positive responses, underreporting of suicide behaviours is probable. As people without impairments were not covered in our sample, it was not possible to contrast the suicidal behaviour of people with and without disabilities, which may have helped elucidate the relative impact of social factors vs. impairments and disability-specific issues.

Although during the consenting process, the trained field staff assured the participants of privacy, confidentiality and anonymity, there might be some possibilities of under reporting of the suicidal behaviour, since these issues are highly stigmatized in Bangladeshi.

To best of our knowledge, this is the first study to investigate the factors that may lead to suicidal behaviour among Bangladeshi people with disabilities. Since, it focused on the persons with disabilities, one of the most vulnerable population, who are largely understudied worldwide, these findings might be helpful for developing interventions programs for the susceptible group. Moreover, this was a population-based, large scale study which used previously validated instruments to assess suicidal behaviour. Furthermore, it covered almost all the covariates found significant in previous literatures. Moreover, the data were collected from the largest organization that works with people with disabilities, which followed a scientific approach for tracking them, which could be beneficial for designing large-scale studies.

The COVID-19 pandemic has had drastic repercussions on mental health, especially among people with disabilities. With very limited research available on this specific population, we hope our nationwide study findings will establish the foundation for further research and interventions for people with disabilities. The findings identified the risk factors associated with suicidal behaviour among people with disabilities, and the study findings might help translate the into evidence-based interventions for a more inclusive mental health care system in Bangladesh. It is essential to ensure the nation’s disability support infrastructure is more aware of this issue, and that the mental health care system can provide better accessibility for people with disabilities.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

World Health Organisation. Mental Disorders 2022 [ https://www.who.int/news-room/fact-sheets/detail/mental-disorders .

Saied AA, Shah J, Dean YE, Tanas Y, Motawea KR, Hasan W, et al. Suicide prevention in Egypt. Lancet Psychiatry. 2022;9(9):e41.

Article   PubMed   Google Scholar  

Bantjes J, Iemmi V, Coast E, Channer K, Leone T, McDaid D, et al. Poverty and suicide research in low- and middle-income countries: systematic mapping of literature published in English and a proposed research agenda. Glob Ment Health (Camb). 2016;3:e32.

WHO. Suicide worldwide in 2019: global health estimates. Geneva: World Health Organization; Licence: CC BY-NC-SA 3.0 IGO.; 2021.

Organization WH. LIVE LIFE: Preventing suicide. 2018.

Organization WH. Suicide in the world: global health estimates. World Health Organization; 2019.

WASH Accessibility for Persons with Disabilities. and Older People in Bangladesh: A Way Forward [press release]. https://www.icddrb.org/news-and-events/news?id=908&task=view : icddr,b2023.

Rashed AMT, Hossain J. Disability in Bangladesh Prevalence, Knowledge, Attitudes and Practices. 2005.

Wikipedia. Disability in Bangladesh [ https://en.wikipedia.org/wiki/Disability_in_Bangladesh .

Mashreky SR, Rahman F, Rahman A. Suicide kills more than 10,000 people every year in Bangladesh. Arch Suicide Res. 2013;17(4):387–96.

Arafat SY, Al Mamun M. Repeated suicides in the University of Dhaka (November 2018): strategies to identify risky individuals. Asian J Psychiatry. 2019;39:84–5.

Article   Google Scholar  

McConnell D, Hahn L, Savage A, Dubé C, Park E. Suicidal ideation among adults with disability in Western Canada: a brief report. Commun Ment Health J. 2016;52(5):519–26.

Krahn GL. WHO World Report on disability: a review. Disabil Health J. 2011;4(3):141–2.

Lunsky Y. Suicidality in a clinical and community sample of adults with mental retardation. Res Dev Disabil. 2004;25(3):231–43.

Beake S, Clark LL, Turner T, Bick D. A mixed methods study to develop and pilot a competency assessment tool to support midwifery care of women with intellectual disabilities. Nurse Educ Today. 2013;33(8):901–6.

Russell D, Turner RJ, Joiner TE. Physical disability and suicidal ideation: a community-based study of risk/protective factors for suicidal thoughts. Suicide Life‐Threatening Behav. 2009;39(4):440–51.

Werner S. Public stigma and the perception of rights: differences between intellectual and physical disabilities. Res Dev Disabil. 2015;38:262–71.

Banerjee D. The COVID-19 outbreak: crucial role the psychiatrists can play. Asian J Psychiatry. 2020;50:102014.

Czeisler MÉ, Board A, Thierry JM, Czeisler CA, Rajaratnam SM, Howard ME, et al. Mental health and substance use among adults with disabilities during the COVID-19 pandemic—United States, February–March 2021. Morb Mortal Wkly Rep. 2021;70(34):1142.

Czeisler MÉ, Lane RI, Wiley JF, Czeisler CA, Howard ME, Rajaratnam SM. Follow-up survey of US adult reports of mental health, substance use, and suicidal ideation during the COVID-19 pandemic, September 2020. JAMA Netw open. 2021;4(2):e2037665–e.

Article   PubMed   PubMed Central   Google Scholar  

Okoro CA, Strine TW, McKnight-Eily L, Verlenden J, Hollis ND. Indicators of poor mental health and stressors during the COVID-19 pandemic, by disability status: a cross-sectional analysis. Disabil Health J. 2021;14(4):101110.

Aktarujjaman M, Amin MB, Hossain E, Mamun MA, Roy N, Sarker B. Suicidal ideation among people with disabilities during the COVID-19 pandemic in Bangladesh: prevalence and associated factors. BJPsych Open. 2023;9(1):e3.

Jahan S, Araf K, Griffiths MD, Gozal D, Mamun MA. Depression and suicidal behaviors among Bangladeshi mothers of children with Autism Spectrum disorder: a comparative study. Asian J Psychiatr. 2020;51:101994.

Akter H, Mali B, Arafat SMY, Demography. Psychiatric Morbidity and Motives of Self-Harm: A Pilot Study in Bangladesh. 2018;7:1008-14.

Muyeed A, Mohsin M, Alam M, Mahmud S, Hossain MN, Khatun M et al. Suicidal Behaviors and Emotional Distress among University Students in Bangladesh: A Cross-sectional Study in Bangladesh2023.

Sharmin Salam S, Alonge O, Islam MI, Hoque DME, Wadhwaniya S, Ul Baset MK et al. The burden of suicide in Rural Bangladesh: magnitude and risk factors. Int J Environ Res Public Health. 2017;14(9).

Kim SH. Suicidal ideation and suicide attempts in older adults: influences of chronic illness, functional limitations, and pain. Geriatr Nurs. 2016;37(1):9–12.

Roy N, Amin MB, Mamun MA, Hossain E, Aktarujjaman M, Sarker B. Suicidal ideation among people with disabilities during the COVID-19 pandemic in Bangladesh: prevalence and associated factors. BJPsych open. 2023;9(1):e3.

Center. for Disability in Deveopment. Key Focus Areas. 2021.

Jahan S, Araf K, Griffiths MD, Gozal D, Mamun MA. Depression and suicidal behaviors among Bangladeshi mothers of children with Autism Spectrum disorder: a comparative study. Asian J Psychiatry. 2020;51:101994.

Kuvalekar K, Kamath R, Ashok L, Shetty B, Mayya S, Chandrasekaran V. Quality of life among persons with physical disability in Udupi Taluk: A Cross Sectional Study. J Family Med Prim Care. 2015;4(1):69–73.

Mahmud I. Health seeking Behaviour of Disabled people attending a Specialized. Bangladesh: Rehabilitation Centre in Dhaka; 2007.

Google Scholar  

Sultana M, Mahumud Rashidul A, Sarker Abdur R, Liu W-Z. Burden of chronic illness and associated disabilities in Bangladesh: evidence from the Household Income and Expenditure Survey. Chronic Dis Translational Med. 2017;03(02):112–22.

Rahman QM, Khan AH, Al Zubayer A, Ahmed M, Hasan MT, Rahaman A, et al. Factors associated with suicidal behavior among university students in Bangladesh after one year of COVID-19 pandemic. Heliyon. 2022;8(1):e08782.

Lund EM, Nadorff MR, Galbraith K, Thomas KB. Comparing the internal consistency, overall scores, and response patterns on the suicidal behavior questionnaire–revised in people with and without disabilities. Rehabilitation Couns Bull. 2017;62(2):108–20.

Cassidy SA, Bradley L, Cogger-Ward H, Shaw R, Bowen E, Glod M, et al. Measurement Properties of the suicidal Behaviour Questionnaire-revised in autistic adults. J Autism Dev Disord. 2020;50(10):3477–88.

Osman A, Bagge CL, Gutierrez PM, Konick LC, Kopper BA, Barrios FX. Suicidal Behaviors Questionnaire—Revised. Assessment.

Sesay R, Kpangay M, Seppeh S. An Ordinal Logistic Regression Model to identify factors influencing students academic performance at Njala University. Int J Res Sci Innov. 2021;08:91–100.

Hosmer DW Jr, Lemeshow S, Sturdivant RX. Applied logistic regression: Wiley; 2013.

Bilsen J. Suicide and youth: risk factors. Frontiers in psychiatry. 2018:540.

Marlow NM, Xie Z, Tanner R, Jo A, Kirby AV. Association between disability and suicide-related outcomes among US adults. Am J Prev Med. 2021;61(6):852–62.

Duplaga M, Szulc K. The association of internet use with wellbeing, mental health and health behaviours of persons with disabilities. Int J Environ Res Public Health. 2019;16(18):3252.

Turecki G, Brent DA. Suicide and suicidal behaviour. Lancet. 2016;387(10024):1227–39.

Scior K, Hamid A, Hastings R, Werner S, Belton C, Laniyan A, et al. Intellectual disability stigma and initiatives to challenge it and promote inclusion around the globe. J Policy Pract Intellect Disabil. 2020;17(2):165–75.

Azalde G, Braathen SH. The role of stigma in accessing education for people with disabilities in low and middle-income countries: a review of the evidence. SINTEF AS (ISBN starter med 978-82-14-). 2018.

Shahnaz A, Bagley C, Simkhada P, Kadri S. Suicidal Behaviour in Bangladesh: a Scoping Literature Review and a proposed Public Health Prevention Model. Open J Social Sci. 2017;05:254–82.

Batra R, Reio T. Gender inequality issues in India. Adv Developing Hum Resour. 2016;18:88–101.

Meltzer H, Brugha T, Dennis MS, Hassiotis A, Jenkins R, McManus S, et al. The influence of disability on suicidal behaviour. Alter. 2012;6(1):1–12.

Shahnaz A, Bagley C, Simkhada P, Kadri S. Suicidal behaviour in Bangladesh: a scoping literature review and a proposed public health prevention model. Open J Social Sci. 2017;5(07):254.

Servaty-Seib HL, Williams P, Liew CH. Interpersonal and intrapersonal predictors of suicidal thoughts and actions in first-year college students. J Am Coll Health. 2021:1–24.

Batra R, Reio TG Jr. Gender inequality issues in India. Adv Developing Hum Resour. 2016;18(1):88–101.

Manzar MD, Albougami A, Usman N, Mamun MA. Suicide among adolescents and youths during the COVID-19 pandemic lockdowns: a press media reports‐based exploratory study. J Child Adolesc Psychiatric Nurs. 2021;34(2):139–46.

Moses T. Suicide attempts among adolescents with self-reported disabilities. Child Psychiatry Hum Dev. 2018;49(3):420–33.

PubMed   Google Scholar  

Coleman-Jensen A, Nord M. Disability is an important risk factor for food insecurity. 2013.

Faria M, Santos MR, Sargento P, Branco M. The role of social support in suicidal ideation: a comparison of employed vs. unemployed people. J Mental Health. 2020;29(1):52–9.

Smith L, Shin JI, Barnett Y, Allen PM, Lindsay R, Pizzol D, et al. Association of objective visual impairment with suicidal ideation and suicide attempts among adults aged ≥ 50 years in low/middle-income countries. Br J Ophthalmol. 2022;106(11):1610–6.

Vikström L, Shah S, Janssens A. Introduction: disability, partnership, and family across time and space. Taylor & Francis; 2020. pp. 177–201.

Owusu J, Doty S, Adjaye-Gbewonyo D, Bass J, Wilcox H, Gallo J, et al. Association of sleep characteristics with suicidal ideation and suicide attempt among adults aged 50 and older with depressive symptoms in low-and middle-income countries. Sleep Health. 2020;6(1):92–9.

Download references

Acknowledgements

The authors appreciate the support of the CDD (the largest disability rights-based non-governmental organization in Bangladesh), with the completion of data collection of this project. They are also thankful to all the respondents who participated in this study voluntarily and spontaneously. Additionally, icddr, b is grateful to the Government of Bangladesh, Canada, Sweden, and the UK for providing core/unrestricted support.

The authors are thankful to Center for Disability in Development (CDD) and Christian Blind Mission (CBM) Global for the financial support for the implementation of this project of icddr, b.

Author information

Authors and affiliations.

Health System and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), 1212, Mohakhali, Dhaka, Dhaka, Bangladesh

Kamrun Nahar Koly & Aniqua Anjum

North South University, Dhaka, Bangladesh

Rasma Muzaffar

Guy’s and St. Thomas’s NHS Foundation Trust, London, UK

Teresa Pollard

Center for Disability in Development, Dhaka, Bangladesh

Taslima Akter

Christian Blind Mission (CBM) Global, Dhaka, Bangladesh

Zakia Rahman

National Institute of Mental Health, Sher-E-Bangla Nagar, Dhaka, Bangladesh

Helal Uddin Ahmed

Centre for Global Mental Health, London School of Hygiene and Tropical Medicine, London, UK

Julian Eaton

CBM Global Disability and Inclusion, Laudenbach, Germany

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: KNK Methodology: KNK, HUA; Validation: KNK, TA, ZR, JE; Formal analysis: KNK, AA; Investigation: KNK, TA, ZR; Data curation: KNK, AA; Writing—original draft preparation: KNK, AA, RM, TP; Writing—review and editing: KNK, TA, ZR, JE, TP, HUA Supervision: KNK; Project administration: KNK, ZR; Funding acquisition: KNK. All authors have read and agreed to the submitted version of the manuscript.

Corresponding author

Correspondence to Kamrun Nahar Koly .

Ethics declarations

Ethics approval and consent to participate.

The study (PR-22079) was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of icddr, b. Written informed consent was obtained from all subjects involved in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Koly, K.N., Anjum, A., Muzaffar, R. et al. Self-reported suicidal behaviour among people living with disabilities: prevalence and associated factors from a cross-sectional nation-wide survey in Bangladesh. BMC Psychol 12 , 231 (2024). https://doi.org/10.1186/s40359-024-01699-5

Download citation

Received : 13 October 2023

Accepted : 02 April 2024

Published : 09 May 2024

DOI : https://doi.org/10.1186/s40359-024-01699-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Suicidal Behaviour
• Mental Health
• Persons with disability

BMC Psychology

ISSN: 2050-7283

• General enquiries: [email protected]

A quantitative study examining the effects of sleep quality on construction workers’ performance in the city of Jeddah, Saudi Arabia

• Original Article
• Open access
• Published: 14 May 2024

Cite this article

You have full access to this open access article

• Ahmad Baghdadi   ORCID: orcid.org/0009-0009-1734-1390 1  

Poor sleep and related sleep disorders have been linked to subpar performance in sectors like health and education. Such sleep issues in the workforce negatively affect individual and organisational productivity. Despite this, the high-stress environment of the construction sector has been largely overlooked. This study delves into the role of sleep in construction field management and human resource practices, examining how a better understanding of workers' sleep patterns could enhance job performance. The research involved an online survey of 119 construction workers in the city of Jeddah in Saudi Arabia, focusing on their sleep duration and its impact on job performance, with data analysis conducted using SPSS software. The findings indicate that inadequate sleep influenced by factors such as dietary habits is significantly related to poor performance. The majority of workers are affected by poor sleep quality. The study suggests that construction management should implement health awareness campaigns to improve workers' responsiveness and awareness regarding sleep. It emphasises the need for management to develop strategies to increase sleep awareness and education in the construction industry, aiming to improve overall job performance.

Avoid common mistakes on your manuscript.

1 Introduction

The construction sector offers a significant platform for establishing sleep quality impacts among workers. It requires both physical energy and emotional concentration to perform. Thus, any lapse in sleep quality can disrupt the performance and engagement levels among the individual workers or their respective teams [ 1 ]. Construction workers experience the challenges of sleep deprivation, owing to the nature of their workplace and the responsibilities expected of them. This study explores the relevance or impacts of quality sleep on construction workers' performance and engagement levels in the city of Jeddah, Saudi Arabia. Through the assessment of the research on related subjects, it is evident that the construction sector is briefly explored. There needs to be more in the perspective of construction field management on how leadership can improve performance by targeting the aspects of quality sleep. Therefore, this study seeks to explore the components of sleep science, its relevance, and its significance in enhancing the performances of construction field workers. An ideal literature review reveals the contemporary issues, research gaps, implications and future studies that can be bridged to achieve the core expectations and goals. Achieving the objectives requires critically assessing past scholarly views, including theoretical frameworks and the sleep science that connect the variables.

Thus, this study aims to survey construction workers in the city of Jeddah, Saudi Arabia, to understand the impact of sleep quality on their performance. Thus, the purpose is to correlate sleep quality to the performances of the construction sector workers in light of whether they experience sleep deprivation or the right quality and the interventions the management can undertake. Specifically, this is an online survey of male construction workers to assess their sleep deprivation situation, which enables policy shift and management approaches towards improving performances. Similar studies have been conducted in the health, education, and related sectors to underscore the significance of ample and quality sleep on the performance of the employees. However, the main research question is: “ What are the specific impacts of sleep quality on the performance of construction workers in Jeddah, Saudi Arabia? ” Other questions include:

What are the specific activities, addictions, and patterns in your lifestyle or behaviour that contribute to sleep deprivation?

Are you aware of the impacts of quality sleep on your workplace performance? Does quality sleep matter to you at the workplace?

What recommendations can you give management to ensure policy change and a shift in management to address sleep deprivation among employees?

2 Literature review

Correlating sleep deprivation or quality to the performance and output of the workforce has been studied in the past, especially in the health and education sectors. In these areas, the primary motivation has been the performance of healthcare providers and students when subjected to sleep deprivation or inadequate sleep. Thus, a range of literature is available that offers background information on how this research can be contextualised, including the research gaps to be addressed through the execution of this study. Therefore, relevant literature that connects to the variables is researched through suitable databases, libraries, and sites that provide the relevant information, patterns, trends, and contemporaries for achieving the expected outcomes. The expectation is that these studies can help give the correct information, address the gap, and help with discussion regarding the impacts of sleep deprivation on the performance of construction sector workers.

2.1 Sleep deprivation and performances

Sleep deprivation is an expanding problem affecting general workers and construction workers. Sleep deprivation affects a worker's alertness, attention, and other cognitive functions. Nearly 93% of Indians are sleep-deprived, 58% of the people believe that their work is affected due to lack of sleep, and 87% of Indians accept that their health is affected due to lack of sleep. In the survey, the researcher found that 70% of college workers are affected by sleep deprivation, particularly chronic sleep deprivation, which has more negative consequences. However, some studies explored the subject of sleep deprivation in the context of establishing the risk factors or the behavioural patterns that cause it. For instance, sleep deprivation includes alcohol consumption and food intake as a worldwide problem, as evident in the study on college workers [ 2 ].

Signs and symptoms of sleep deprivation are often manifested in the workplace. For instance, the imminent results of sleep deprivation are biased feelings of fatigue [ 3 ]. There are connections between these symptoms and poor performance, based on the science of sleep, which is explored in many studies. For instance, a comparative analysis of the research findings in [ 4 , 5 ] shows the similarities in the context of alcohol consumption and how they impact the concentration levels among workers and create sleep deprivation. The two authors appreciate that alcoholism and substance and drug use are indeed risk factors and have the relevant symptoms to ascertain the claims. However, authors in [ 6 ] prioritised the behaviours such as taking stimulants to ensure people stay awake.

Sleep quality is directly correlated to the performances and well-being of workers, irrespective of their sectorial affiliations [ 7 ]. For instance, studies conducted in China and South Korea established that about 30% and 63% of the construction workers in these countries struggle with sleep quality [ 7 ]. Besides, the impacts of these trends on the scope of sleep routines, including the higher chances of workplace injuries and under-performances, are correlated. Work-related juries and possible fatalities are attributed to the poor or inadequate sleep quality that characterises workers in the construction sector. Similarly, construction workers and the relationship between sleep quality and their performances can be connected to their mental, emotional, and behavioural routines. Critical components of such studies demonstrate that construction workers who experience low or poor sleep quality are more likely to experience cognitive behaviour and rational abilities challenges resulting from continued sleep deprivation [ 8 ]. These outcomes underline the holistic impacts of sleep quality on performance and the health and wellness of the construction workers, which translate to their commitment and output.

2.2 Theoretical framework

This study is anchored on the significance of ample and quality sleep on workers' performance. Thus, sleep science is a crucial part of the study when exploring the available literature. For instance, Oswald's Restoration Theory of Sleep underlines the significance of getting the right amount of sleep in the workforce. This individual approach is widely applied in various sectors to underline the need for the consciousness of sleep routines and the science and studies that support the assumptions. For instance, Adeyanju explored the relevance of Oswald's theoretical framework in ascertaining students' academic performance [ 9 ]. The author used the theory's core tenets and characteristics, including that sleep is crucial for restoring the mind, body, and physical and emotional wellness. Sleep serves as a reset session, hence refreshing and restarting the mind to achieve the expected goals and objectives of learning and performance [ 9 ]. Moreover, Himashree et al. [ 10 ] correlated sleep and performance to understand the trends and how the workers are impacted [ 10 ]. The outcomes included a direct correlation, in which those with sleep deprivation showed little interest, physical synergy, and mental fatigue related to the loss of psychological capital to perform.

Restoration theory underlines the key activities individuals should take in a personal capacity to ensure optimal sleep routines. Oswald's view was that any disruption of the restoration process could have overarching mental and emotional health implications on the workers, impacting their engagement levels and performances [ 9 ]. Oswald demonstrated that disruptions to the restoration process when sleeping could have negative implications, including interference with people's ability to sleep, workers becoming more impulsive and emotional in decisions and behaviours, and having a health risk hazard as part of this strategic failure [ 10 ]. Oswald's view of sleep forms an integral part of this study as it explores how the disruptions to the restorative roles of sleep can impact the holistic nature of humans and their ultimate performances in the workplace.

2.3 Sleep deprivation and health risks

Poor sleep quality is a risk factor for mental fatigue. Studies have connected these variables to establish how inadequate sleep leads to low daytime job performance [ 11 ]. Such studies demonstrate the significance of quality sleep in daytime performances, including alertness, memory, learning, critical thinking, and effectiveness in task execution. A secondary data analysis in [ 12 ] demonstrates the factors impacting sleep quality among construction workers and how they generally lead to low performance, commitment, and motivation in their daytime commitments. Other factors explored include occupational injuries, decline in job competencies, and poor intergroup and team dynamics to accomplish tasks. This is because the affected workforce uses specific and personal methods to address the sleepiness they experience. These studies were evident in the findings across Southern India, which demonstrated the severity of inadequate sleep in the construction sector and their implications on the workforce [ 1 ]. As mentioned in other studies, quality sleep leads to higher employee self-efficacy, which translates into confidence, commitment, and performance. These factors interrelate to achieve the expected results and outcomes that directly impact the employees' commitment and the possible solutions for the workforce.

Sleep deprivation is a serious health risk factor that cuts across many sectors and areas of wellness. In the context of performances, some of the risk factors that lead to sleep deprivation and the low-quality trends beyond optimal are excessive alcohol and drug intakes, which continue to hurt the concentration and physical and mental abilities of the employees. These elements make it challenging for the workforce to gain the right mindset and physical abilities to complete construction tasks [ 4 ]. Besides, the nature of the construction workplace, including associated noise and disturbances from machine operations, can impact mental peace, which leads to sleep deprivation. According to a study on this subject matter among college workers in China, the devastating impacts of the sound environment in the construction fields can deter achieving proper sleep routines [ 2 ].

In some studies, sleep deprivation is associated with severe instances of worker frustration, limitations, and absenteeism [ 13 ]. These research works significantly impact the understanding of sleep quality and how they align with commitment and performance. Some studies have also explored the impact of overtime work routines on standard sleep patterns. For instance, Parkes' article established the implications of the overtime work model among offshore day workers, including those having sleep disorders that have long-term impacts [ 14 ]. The findings demonstrate the challenges in adjusting to the overtime work routines for either day or night-shift employees and how the administrators can address the implications on the employees' performances.

3 Methodology

Researching sleep and its implications in construction field management requires assessing workers' responsiveness to tasks. This research employs an online descriptive survey method to collect data on sleep deprivation or the quality of workers' performance in construction. Previous applications in similar aspects of the sleep domain and the project management or health contexts inform the choice and rationale for this method.

In this context, the online descriptive survey methodology is crucial in correlating sleep patterns and quality to the workers' performance or output in construction projects. This is relevant for project management and human resources to understand how to design training and awareness routines for employees to improve their sleep. In this regard, the construction workers within the city of Jeddah are sampled using stratified random sampling. They participated in this online survey to assess their responses to the correlation between the two variables. These methods are consistent and help assess the correlation based on the behaviour and responses of the construction workers.

3.1 Questionnaire and sample size

The data collected from this survey contains six sections, which the participants fill in. The initial part of the survey gathers basic respondent details such as gender, location, and age to validate the study's credibility. The second part collects demographic data on trends, behaviours, and personal characteristics, including height, weight, and lifestyle habits, aiding in categorising participants for sleep disorder research. The third section examines socio-demographic factors affecting bedtime, including time spent on social media and online activities. The fourth section queries educational background to assess awareness of sleep's impact. The fifth section probes health issues, including insomnia and mental and physical health status. Finally, the sixth section explores respondents' understanding of sleep quality, patterns, and quantity to identify trends.

These are meant to include as much reliable information as possible for the qualitative and quantitative analysis. The expectation is that the data collected directly answers the question of the sleep routines and performance of the construction workers at their sites based on the project management analytics and metrics.

3.2 Survey details and participants

The study was conducted in 2023 in Jeddah, Saudi Arabia. A stratified random sampling method was used to ensure that only the eligible participants were selected. This included the construction workers in the identified site, who were male members of this workforce. The rationale for choosing this sampling method is to ensure that only the eligible participants are selected and that the workers are included across age, gender, related variables and stratification elements. A total of 119 male participants working at the identified site were selected. The researcher then distributed the forms through the online platforms. The recruitment process of the participants included approaching their workplace, which included the human resource department for voluntary participation.

Consequently, the willing participants were informed, and their emails were given for the engagement. Despite the progressive and effective engagement with the employers, trust was challenged, especially with the job emails they considered private. However, this was a voluntary participation process, and upon explanation of the reasons for the job email, it was possible to convince the 119 to participate voluntarily.

The data is collected through the survey questionnaires that are administered online to enhance the efficiency of reaching all the participants. Besides, it gives them the flexibility of taking the study within their comfort before sending it back. The collected primary data across the six questionnaire sections are analysed through logistics regression to link sleep deprivation and construction worker performance outcomes. Besides, the socio-economic lines are used as the parameters of sleep that improve their responsiveness and suitability in understanding the outcomes. These methods are justified within the context of online surveys and how they apply to this study that investigates sleep patterns and construction worker performances. Significant themes are identified from the patterns of the responses, including how the mean, median, and regressions are contextualised to make sense of the variables and correlation.

Ethical considerations, including consent, voluntary participation, practical elaboration and explanation of the scope and methodologies of the study, and privacy and confidentiality elements are considered for this study. Moreover, the data collected from the participants were secured, especially as they were stored in the personal computer using unique passwords and access keys. Besides, an assurance of not using the responses for any other purpose rather than the study and deleting the emails after the completion was implemented. The data collection process, including filling and delivering the survey questionnaire, responded to the needs of the ethical considerations, including safety, privacy, and security.

4 Results and findings

4.1 data analysis.

This research aimed to investigate the undulations of construction workers' performance due to sleep deprivation or varying elements of sleep routines. Besides, the study sought the risk factors that impact sleep, especially among construction workers. Consequently, this research collected primary data from the participants, including those aligned to critical variables. Data analysis was carried out on demographic, socio-demographic, and sleep data. The statistical analysis used a statistical package for the social science software. Thus, the study employed SPSS (Statistical Package for the Social Sciences) based on its effectiveness as a software for social science and strategic statistical analysis methods. Consequently, the choice is based on the convenience of the tool in advancing data management, performing analysis, and improving data visualisation for comparative review.

The statistical analysis enables the identification of the relevant themes that connect the two variables, sleep deprivation and the performance of the construction workers. The statistical analysis began with descriptive analysis. All the regressions were started with demographic data and then gradually added up with socio-demographic data and sleeping problems before exams. The diversity of approaches employed in this study demonstrates the specificity of the subject matter in correlating the study's variables to understand performance and other aspects of construction worker's engagement.

4.2 Descriptive statistics

Table 1 shows the descriptive results of age and time spent on social media. This smoking habit is considered as a baseline for the results. The regression technique is used to identify the link between sleep deprivation and worker performance. The co-relation technique was used before regression analysis to filter the factors, as shown in Fig.  1 . The binary logistic regression was carried out in this study to identify the specific relationship between two variables. The descriptive statistics in Table  1 demonstrate the essential aspects, including the respondents' age, behaviour, browsing before sleeping routines, missing the workplace, substances use such as smoking, social media, and stress. The data collected are analysed in the context of statistics, standard error, and related contexts to get the right co-efficient for decision-making routines on sleep and construction workers' performances. These are represented in the statistical components, including the mean, standard deviation, and variance.

Relation between food intake and sleep timing

4.3 Regression analysis results

Table 2 represents the constructed demographic variables. The comparison between sleep factors and workers' performance includes coefficients, standard errors, Wald statistics, and degrees of freedom for the factors filtered in the co-relation technique. Where B  = constant, S.E  = standard error, W  = Wald Statistics, p  = Significance (P < 0.05). Table 2 summarises these outcomes to showcase the patterns, findings, and responses in the context of demographic variables applied in the study. These include age, gender, height, weight, smoking, and food intake time. These variables help in understanding the correlation between sleep routines and the performance of employees, especially in areas considered deprived.

Food intake is numbered in binary logistic regression. Figure  1 represents the relation between food intake and sleep: S1—food intake timing, S2 —sleeping time. S1 is coded as [(1) before 8 pm, (2) 8 pm–9 pm, (3) 9 pm–10 pm], and S2 as [(1) 4h–5h, (2) 6h–7h, (3) > 8h]. Table 3 shows that sleep deprivation was associated with worker outcomes. It also demonstrates the sleep deprivation variables, which inform crucial determinants of sleep deprivation as used in the study. However, Fig.  1 showcases the respondents' views on food intake time and sleeping routines. This is relevant in how variables relate to eating habits and their impacts on sleep time.

5 Discussion

The research findings and the literature review attempt to correlate sleep deprivation to the worker's performance in construction projects. The primary motivation is that this information is crucial for the project managers and construction personnel administrators to inform and influence the workers to adopt healthy sleep patterns. In this regard, the findings demonstrate the consistency with most of the literature reviewed in this study to establish the patterns. For instance, the descriptive statistics in Table  1 indicate the investigated variables to showcase the correlation. Consequently, the participant's age, behaviour, browsing before sleep patterns, missed workplace, smoking, social media, and stress are all responsible for an employee's performance. Generally, those with sleep challenges or deprivation tend to have negative connotations that align with their state of physical, mental, and emotional health. From the results, the study established that improper food intake and social media networking, impact construction workers' performances. These are the risk factors for creating the right environment for understanding sleep routines and their impacts on performance [ 15 ]. These results demonstrate the factors that can lead to sleep deprivation among construction workers, with the participants showcasing their implications on sleep quality.

5.1 Risk factors to sleep deprivation among workers

The findings highlight the impacts of issues with mental and emotional health risk factors, including substance use such as smoking, browsing and social media activities, and other patterns. Table 1 indicates the descriptive statistics on the key variables and the coefficient values on the implications of these aspects regarding the standard deviation, variance, and mean for each variable. Ideally, people's sleep challenges emerge from the behavioural patterns mentioned here, which hinder their sleep quality. This result underscores the increased sleep health promotion patterns that most projects and institutions use in their management practices to optimise the performances of their employees [ 16 ]. The primary motivation for using these aspects is the high illiteracy of the risk factors that impact quality sleep among the employees. Besides, Oswald's Restoration theory of sleep underscores its significance, informing the project management best practices [ 17 ]. The findings in Table  1 showcase statistical coefficients and their values that connect these variables and how the critical tenets of sleep theories can improve awareness and knowledge among construction workers. Similarly, the theoretical frameworks and the necessity for improving the rationale for knowledge and the significance of sleep are embedded here [ 10 ].

In the era of social media, online gaming, and other addictive mobile and digital apps, most people spend large chunks of their time engaged actively in these entertaining products. Consequently, most of them play till midnight or beyond, establishing the addictive nature of social media use. Thus, the socio-demographic variables indicate that most participants cited that social media use takes their time for sleeping, resulting in deprivation. These are also indicated in Table  3 , showing the impacts of social media use and related activities such as online gaming. These findings are unsurprising in the context of the literature reviewed on the subject. Sleep quality versus the quality of life domains has been studied, and these aspects are evident, including how the higher-education workers respond to these dynamics [ 18 ]. Only a tiny segment of the participants (P < 0.05) demonstrates the implications of social media use, mobile phones, and gaming as not impacting their lives and sleep routines. However, the construction human and personnel management team must develop the right educational tools to give the employees the right and necessary awareness of how their social media use at home or work might impact their sleep and restoration phases.

5.2 Behavioural and lifestyle patterns and their implications to sleep deprivation

In the context of binary logistics regression, Fig.  1 demonstrates how eating patterns can lead to sleeping challenges and the eventual performance implications. The primary purpose of this question on diet and eating habits and routines is to establish the correlation, including whether it impacts sleeping time and quality. For instance, the food intake timing can be early or late, which gives varying time to responsiveness and suitability. The correlation in these variables demonstrates that participants could take their food before 8 pm, 8–9 pm, or 9–10 pm, while S2 includes sleeping for 4–5 h, 6–7 h, or more than 8 h. These findings, as evident in Fig.  1 , demonstrate that the food intake timing, especially those who take their food late at night, have less sleep and are thus deprived. These aspects are further explored in assessing sleep deprivation in the context of curriculum exam performances and outcomes in Indian society [ 19 ]. The results show the implications of eating routines and timing of the workers in the construction sector.

The human resource management practices in the construction sector should devise a mechanism for monitoring the sleep patterns among the workers. This will help create the learning manuals and training guides to eradicate the risk factors, including the sleeping routines and associated outcomes. Specifically, Fig.  1 shows the implications of late eating patterns and engaging in disruptive activities such as social media use. Thus, despite the presence of workers or lack of absenteeism, they still show the aspects of daytime sleepiness, which hurt their engagement, concentration, and participation [ 20 ]. Performances are brain-based. Thus, any factor that impedes these aspects can easily ruin the performance of the construction workers in their respective responsibilities. The labour force in the present situation is often submerged in disruptive behaviours, especially the youths, which limits their productivity. This study demonstrates these factors' alignment with long-term sleepiness and implications [ 21 ]. The age, gender, and experience are crucial in defining their sleep routines and the understanding of the risk factors that characterise such situations. They are anticipated to have the adaptive mechanisms of ample and quality time that freshens their minds before they report to their respective workplaces.

5.3 Workplace environment as a risk to quality sleep

The nature of the workplace can impact sleep. Typically, operating the machines in construction work and the risk involved can be demanding and exhausting, which subjects the employees to being tired even with the available sleep time. Despite the evident significance of technology in improving workers' performances, there can be impediments. It takes time to learn, while the addictive trends of some apps, including mobile phones, can impact youthful employees. These components lead to sleep disorders and deprivation, while people tend to align with the technological needs and innovations in their areas. These include operating the machines and having safety patterns, which can impact the time found for resting [ 21 ]. The expectation is that the construction workers are impacted by the technological exposure that they have, their eating habits and patterns, and substances use such as smoking that directly impact their sleep times and cause deprivation [ 22 ].

5.4 Impacts of sleep and interventions by individual employs to overcome sleepiness

The findings show disparities in the appreciation of sleeping risk factors that impact individual work routines. Thus, most people appreciate the presence of such risk factors differently, calling for standardised mechanisms for human resource practitioners in the construction sector to embrace. There are varying degrees of alertness, woke, mood swings, and related manifestations of sleep disorder that different employees experience, which underline the need to use practical solutions. The analytics shown in Table  3 show these differences, showing the need for the stakeholders to have the right metrics and approaches to control the manifestation of sleep disorder in the workers and how it impacts their performances. These indicate the glaring challenges in having a uniform method of appreciating the presence of deprivation as a performance hindrance factor and developing an organisational level or sectorial approach to overcoming the outcomes. Dong and Zhang [ 23 ] defines these issues in the context of how each participant had their approaches to appreciating sleep disorders, even when working in the same sector or industry.

6 Conclusion and recommendations

This study primarily investigates the relationship between sleep deprivation and the performance of construction sector workers in Jeddah, Saudi Arabia. Utilising a sample of 119 construction workers, the study employs. Participants were surveyed online and divided into five categories to collect comprehensive data regarding their sleep patterns and their impact on work performance. This method aligns with established research standards and policies, ensuring the study's conclusions are robust and reliable. The study provides a comprehensive analysis of the impact of sleep quality on the performance of construction workers in Jeddah, Saudi Arabia. The main findings from the research indicate a significant correlation between poor sleep quality and decreased job performance among construction workers. The data analysis, particularly through binary logistic regression, highlighted specific lifestyle and behavioural patterns that contribute to sleep deprivation, such as irregular dietary habits, excessive use of social media, and engagement in shift work. These factors not only disrupt sleep patterns but also affect the workers' daily functioning and safety on the job site. The results underscore the critical need for targeted interventions that address these modifiable risk factors to enhance sleep quality among construction workers. The findings confirm a clear correlation between sleep deprivation and reduced performance among construction workers. The study underscores the importance of management strategies that assess and educate workers on optimising sleep routines to enhance overall job performance. Future research should focus on developing and implementing these management strategies effectively.

The research also delves into how personal behaviours, such as excessive engagement with social media and online gaming, can adversely affect sleep patterns. Management often unrecognises these factors, but they significantly affect worker performance. The study's results are significant for the construction sector's human resource management (HRM). They provide insights into how HRM can integrate sleep consciousness into their practices, enhancing worker performance through better sleep habits. Thus, recommendations can be applied to ensure that the study's main aspects and implications are introduced at the construction sector sites to achieve the expected outcomes, come as follows:

Improving sleep awareness among employees

Most employees are victims of their behavioural and lifestyle routines, which subject them to sleeping disorders without their awareness. This lack of appreciation for the significance of sleep is a significant risk factor for their performance, as established in the study's findings. Consequently, management should devise effective mechanisms that create a dedicated learning niche for the employees. This includes training them on the core aspects of sleep awareness and consciousness, enhancing their overall well-being and productivity.

Promote personal behaviour changes towards sleep quality

These elements directly impact the employees' performances, yet it is often considered a private matter whether they sleep late or early. Thus, despite its controversial appeal, this recommendation seeks to identify the individual challenges among the workforce and enforce the behavioural or lifestyle changes that will make them appreciate the outcomes. By addressing these personal habits, the initiative aims to enhance overall sleep quality and improve work performance and health.

Implementing regular health and safety audits

Regular health and safety audits should be conducted to enhance workplace productivity further and ensure the health and safety of construction workers. These audits will help identify potential hazards that could lead to accidents or exacerbate sleep deprivation issues among workers. By systematically assessing the work environment and practices, management can implement necessary changes to mitigate risks. This proactive approach ensures compliance with safety regulations and demonstrates a commitment to worker welfare, which can boost morale and productivity.

Development of a comprehensive wellness program

Another recommendation is developing and implementing a comprehensive wellness program focusing on sleep health. This program should offer resources and support for various aspects of health, including mental health, physical fitness, nutritional advice, and sleep education. Workshops and seminars can be organised to educate workers on the importance of maintaining a healthy lifestyle and how it impacts their work performance. Regular health screenings can also help detect any underlying issues affecting sleep, such as sleep apnea or chronic stress. By investing in employees' overall well-being, companies can improve productivity and reduce absenteeism and turnover rates.

6.1 Limitations and recommendations for future research

The study's main limitation is using male-only participants, creating a discriminative framework. A men-only survey gives insights from their perspective in an industry with female workers and administrators. These limitations can adversely affect the study's generalisation and its long-term implications. The reason behind the selection of male workers is that most of the construction in Saudi Arabia are men. This is particularly true for the projects selected for this study. However, future studies should address the roles of HRM in influencing sleep consciousness and awareness through organisational learning to impact performances. This should be a priority area for the construction work administrators to ensure they inform and educate the workers on the various aspects of sleep science and its importance.

Availability of data and materials

All data and materials are available as required.

Sathvik S, Krishnaraj L, Awuzie BO (2023) An assessment of prevalence of poor sleep quality among construction workers in Southern India. Built Environ Project Asset Manag 13(2):290–305

Article   Google Scholar  

Meng Q et al (2020) Effects of the sound environment on the sleep of college workers in China. Sci Total Environ 705:135794. https://doi.org/10.1016/j.cptl.2019.11.004

Patte KA, Qian W, Leatherdale ST (2017) Sleep duration trends and trajectories among youth in the COMPASS study. Sleep Health 3(5):309–316. https://doi.org/10.1016/j.sleh.2017.06.006

Hu N et al (2020) Alcohol consumption and incidence of sleep disorder: a systematic review and meta-analysis of cohort studies. Drug Alcohol Depend. https://doi.org/10.1016/j.drugalcdep.2020.108259

Kenney SR et al (2012) Global sleep quality as a moderator of alcohol consumption and consequences in college workers. Addict Behav 37(4):507–512. https://doi.org/10.1016/j.addbeh.2012.01.006

Article   MathSciNet   Google Scholar  

Faber NS, Häusser JA, Kerr NL (2017) Sleep deprivation impairs and caffeine enhances my performance, but not always our performance: how acting in a group can change the effects of impairments and enhancements. Pers Soc Psychol Rev 21(1):3–28

Wendimu DE, Meshesha SG (2023) Factors associated with poor sleep quality among construction workers in Arba Minch town, Ethiopia: a cross-sectional study. Health Sci Rep 6(11):e1715

Chandra SS, Loganathan K, Awuzie BO, Wang F (2023) A longitudinal study examining the association between cognitive behavior and rational abilities and the effect of sleep quality on construction laborers. Sustainability 15(7):6257

Adeyanju JO (2023) Ian Oswald’s theory of sleep as a strategy for promoting excellent students’ academic performance. Shodh Sari Int Multidiscip J 02(02):04–13. https://doi.org/10.59231/SARI7570

Himashree G, Banerjee PK, Selvamurthy W (2002) Sleep and performance-recent trends. Indian J Physiol Pharmacol 46(1):6–24

Google Scholar  

Shi J, Long L (2018) The depletion effects of sleep deprivation among employees: a new topic in organisation and management research. Adv Psychol Sci 26(5):896

Kim Y, Lee S, Lim J, Park S, Seong S, Cho Y, Kim H (2021) Factors associated with poor quality of sleep-in construction workers: a secondary data analysis. Int J Environ Res Public Health 18(5):2279

Parkes KR (2015) Sleep patterns of offshore day-workers in relation to overtime work and age. Appl Ergon 48:232–239. https://doi.org/10.1016/j.apergo.2014.12.004

Reis C et al (2020) Social timing influences sleep quality in patients with sleep disorders. Sleep Med 71:8–17. https://doi.org/10.1016/j.sleep.2020.02.019

Mnatzaganian CL, Atayee RS, Namba JM, Brandl K, Lee KC (2020) The effect of sleep quality, sleep components, and environmental sleep factors on core curriculum exam scores among pharmacy students. Curr Pharm Teach Learn 12(2):119–126

Levenson JC et al (2016) Pilot study of a sleep health promotion program for college workers. Sleep Health 2(2):167–174. https://doi.org/10.1016/j.sleh.2016.03.006

SONU Sleep (2022) Oswald's restoration theory of sleep: a brief summary. SONU Sleep. https://sonusleep.com/blogs/news/oswalds-restoration-theory-of-sleep-a-brief-summary . Accessed 29 Feb 2024

Marques DR et al (2017) Associations between sleep quality and domains of quality of life in a non-clinical sample: results from higher education workers. Sleep Health 3(5):348–356. https://doi.org/10.1016/j.sleh.2017.07.004

Mnatzaganian CL et al (2020) The effect of sleep quality, sleep components, and environmental sleep factors on core curriculum exam scores among pharmacy workers. Curr Pharm Teach Learn 12(2):119–126. https://doi.org/10.1016/j.cptl.2019.11.004

Mirghani HO, Ahmed MA, Elbadawi AS (2015) Daytime sleepiness and chronic sleep deprivation effects on academic performance among the Sudanese medical workers. J Taibah Univ Med Sci 10(4):467–470. https://doi.org/10.1016/j.jtumed.2015.05.003

Ose SO, Jensen C (2017) Youth outside the labour force—perceived barriers by service providers and service users: a mixed method approach. Children Youth Serv Rev 81:148–156. https://doi.org/10.1016/j.childyouth.2017.08.002

Phegley K (2017) Consequences of fatigue and sleep deficiency in the workplace: implications for the construction industry. Wright State University. https://corescholar.libraries.wright.edu/cgi/viewcontent.cgi?article=1211&context=mph . Accessed 15 Feb 2024

Dong Y, Zhang X (2020) Investigation of the effects of awakening daylight on the morning alertness, mood, and sleep quality of male college workers. Build Environ 180:106989. https://doi.org/10.1016/j.buildenv.2020.106989

Download references

No funds, grants, or other support was received.

Author information

Authors and affiliations.

Department of Civil and Environmental Engineering, College of Engineering and Computing in Al-Qunfudhah, Umm Al-Qura University, Mecca, Saudi Arabia

Ahmad Baghdadi

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Ahmad Baghdadi .

Ethics declarations

Conflict of interest.

The author declared that that there is no competing interest involved.

Ethical standards

Ethical considerations were meticulously adhered to throughout the research process. Participants were fully informed about the study's scope and procedures, ensuring their consent was informed and voluntary.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Baghdadi, A. A quantitative study examining the effects of sleep quality on construction workers’ performance in the city of Jeddah, Saudi Arabia. J. Umm Al-Qura Univ. Eng.Archit. (2024). https://doi.org/10.1007/s43995-024-00065-1

Download citation

Received : 19 April 2024

Accepted : 02 May 2024

Published : 14 May 2024

DOI : https://doi.org/10.1007/s43995-024-00065-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Construction workplace
• Sleep deprivation
• And workplace performances
• Find a journal
• Publish with us
• Track your research