research study meaning in research

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Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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10 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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Research: Meaning and Purpose

• First Online: 27 October 2022

Cite this chapter

• Kazi Abusaleh 4 &
• Akib Bin Anwar 5  

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The objective of the chapter is to provide the conceptual framework of the research and research process and draw the importance of research in social sciences. Various books and research papers were reviewed to write the chapter. The chapter defines ‘research’ as a deliberate and systematic scientific investigation into a phenomenon to explore, analyse, and predict about the issues or circumstances, and characterizes ‘research’ as a systematic and scientific mode of inquiry, a way to testify the existing knowledge and theories, and a well-designed process to answer questions in a reliable and unbiased way. This chapter, however, categorizes research into eight types under four headings, explains six steps to carry out a research work scientifically, and finally sketches the importance of research in social sciences.

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Research Design and Methodology

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Abusaleh, K., Anwar, A.B. (2022). Research: Meaning and Purpose. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_2

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Definition of research

 (Entry 1 of 2)

Definition of research  (Entry 2 of 2)

transitive verb

intransitive verb

• disquisition
• examination
• exploration
• inquisition
• investigation
• delve (into)
• inquire (into)
• investigate
• look (into)

Examples of research in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'research.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Middle French recerche , from recercher to go about seeking, from Old French recerchier , from re- + cerchier, sercher to search — more at search

1577, in the meaning defined at sense 3

1588, in the meaning defined at transitive sense 1

Phrases Containing research

• marketing research
• market research
• operations research
• oppo research

research and development

• research park
• translational research

Dictionary Entries Near research

Cite this entry.

“Research.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/research. Accessed 17 May. 2024.

Kids Definition

Kids definition of research.

Kids Definition of research  (Entry 2 of 2)

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Meaning of research in English

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• He has dedicated his life to scientific research.
• He emphasized that all the people taking part in the research were volunteers .
• The state of Michigan has endowed three institutes to do research for industry .
• I'd like to see the research that these recommendations are founded on.
• It took months of painstaking research to write the book .
• absorptive capacity
• dream something up
• modularization
• nanotechnology
• non-imitative
• operations research
• think outside the box idiom
• think something up
• uninventive
• study What do you plan on studying in college?
• major US She majored in philosophy at Harvard.
• cram She's cramming for her history exam.
• revise UK I'm revising for tomorrow's test.
• review US We're going to review for the test tomorrow night.
• research Scientists are researching possible new treatments for cancer.
• The amount of time and money being spent on researching this disease is pitiful .
• We are researching the reproduction of elephants .
• She researched a wide variety of jobs before deciding on law .
• He researches heart disease .
• The internet has reduced the amount of time it takes to research these subjects .
• adjudication
• analytically
• interpretable
• interpretive
• interpretively
• investigate
• reinvestigate
• reinvestigation
• risk assessment
• run over/through something
• run through something

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If you are on hold when using the phone, you are waiting to speak to someone.

Searching out and tracking down: talking about finding or discovering things

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• Open access
• Published: 09 May 2024

Older adults at greater risk for Alzheimer’s disease show stronger associations between sleep apnea severity in REM sleep and verbal memory

• Kitty K. Lui 1 , 2 ,
• Abhishek Dave 2 , 3 ,
• Kate E. Sprecher 4 , 5 , 6 , 7 ,
• Miranda G. Chappel-Farley 8 , 9 ,
• Brady A. Riedner 10 ,
• Margo B. Heston 6 , 7 ,
• Chase E. Taylor 11 ,
• Cynthia M. Carlsson 12 , 13 , 6 , 7 ,
• Ozioma C. Okonkwo 12 , 13 , 6 , 7 ,
• Sanjay Asthana 12 , 13 , 6 , 7 ,
• Sterling C. Johnson 12 , 13 , 6 , 7 ,
• Barbara B. Bendlin 12 , 13 , 6 , 7 ,
• Bryce A. Mander 2 , 3 , 9 &
• Ruth M. Benca 10 , 14 , 2 , 5 , 9  

Alzheimer's Research & Therapy volume  16 , Article number:  102 ( 2024 ) Cite this article

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Obstructive sleep apnea (OSA) increases risk for cognitive decline and Alzheimer’s disease (AD). While the underlying mechanisms remain unclear, hypoxemia during OSA has been implicated in cognitive impairment. OSA during rapid eye movement (REM) sleep is usually more severe than in non-rapid eye movement (NREM) sleep, but the relative effect of oxyhemoglobin desaturation during REM versus NREM sleep on memory is not completely characterized. Here, we examined the impact of OSA, as well as the moderating effects of AD risk factors, on verbal memory in a sample of middle-aged and older adults with heightened AD risk.

Eighty-one adults (mean age:61.7 ± 6.0 years, 62% females, 32% a polipoprotein E ε4 allele ( APOE4) carriers, and 70% with parental history of AD) underwent clinical polysomnography including assessment of OSA. OSA features were derived in total, NREM, and REM sleep. REM-NREM ratios of OSA features were also calculated. Verbal memory was assessed with the Rey Auditory Verbal Learning Test (RAVLT). Multiple regression models evaluated the relationships between OSA features and RAVLT scores while adjusting for sex, age, time between assessments, education years, body mass index (BMI), and APOE4 status or parental history of AD. The significant main effects of OSA features on RAVLT performance and the moderating effects of AD risk factors (i.e., sex, age, APOE4 status, and parental history of AD) were examined.

Apnea–hypopnea index (AHI), respiratory disturbance index (RDI), and oxyhemoglobin desaturation index (ODI) during REM sleep were negatively associated with RAVLT total learning and long-delay recall. Further, greater REM-NREM ratios of AHI, RDI, and ODI (i.e., more events in REM than NREM) were related to worse total learning and recall. We found specifically that the negative association between REM ODI and total learning was driven by adults 60 + years old. In addition, the negative relationships between REM-NREM ODI ratio and total learning, and REM-NREM RDI ratio and long-delay recall were driven by APOE4 carriers.

Greater OSA severity, particularly during REM sleep, negatively affects verbal memory, especially for people with greater AD risk. These findings underscore the potential importance of proactive screening and treatment of REM OSA even if overall AHI appears low.

Obstructive sleep apnea (OSA) is characterized by recurrent pharyngeal airway collapses that cause complete (apneas) or partial (hypopneas) cessations of airflow that lead to sleep fragmentation and intermittent hypoxemia [ 1 ]. Meta-analyses have shown that sleep-disordered breathing (SDB), including OSA, increases incidence of Alzheimer’s disease (AD), and people with AD were five times more likely to have OSA [ 2 , 3 ]. Proposed mechanisms suggest that OSA accelerates expression of AD pathologies, medial temporal lobe (MTL) degeneration, and memory impairment through OSA-related hypoxemia [ 4 , 5 , 6 , 7 , 8 ]. Though, some have reported that the cognitive consequences of OSA are diminished in older age [ 9 ]. Verbal memory deficits, specifically learning and recall of word list, is considered as the most sensitive marker of early cognitive changes associated with AD [ 10 , 11 , 12 ], and OSA-related hypoxemia could exacerbate AD risk through its impact on the hippocampus, a brain region critical for the formation and processing of episodic memories and is especially vulnerable to injury from oxygen deprivation [ 13 , 14 ]. While OSA severity has been linked to poor verbal memory performance, especially word lists learning tests [ 8 , 15 ], the exact OSA features driving these relationships, as well as whether respiratory events occurring during non-rapid eye movement (NREM) or rapid eye movement (REM) sleep are more damaging, have remained unclear.

During REM sleep, there is higher neurometabolic demand in regions impacted in early AD compared to NREM sleep [ 16 , 17 , 18 ]. In addition, during REM sleep, there is an increased susceptibility of upper airway collapse due to inhibition of the genioglossus muscle (the major upper airway dilator muscle that helps stabilize breathing) [ 19 ]. There are also lower hypoxic and hypercapnic respiratory drives during REM sleep which results in longer durations of apneas and hypopneas, and more instances of oxyhemoglobin desaturation than in NREM sleep [ 20 ]. These specific features of REM sleep and OSA events during REM sleep allude to the possibility that OSA during REM sleep may impart greater cognitive consequences, however, this has yet to be fully examined.

Furthermore, AD risk factors, such as older age, female sex, and apolipoprotein E ε4 ( APOE4 ) genotype have been implicated in OSA as well. OSA and AD are both more prevalent in the aging population [ 21 ]. Roughly 40–80% of people with AD carry at least one APOE4 allele and older adults with APOE4 may have increased risk for SDB, although this has not been consistently reported [ 22 , 23 ]. Moreover, the effects of APOE4 on the associations between OSA and memory have remained unclear [ 24 , 25 ], though it appears that OSA’s effects on memory networks may be stronger in β-amyloid positive older adults [ 26 ]. There are also sex differences in both AD and OSA risk. Women are nearly twice as likely to be diagnosed with AD and have a more severe disease progression that is characterized by faster memory decline and more pathological tau accumulation [ 27 , 28 , 29 , 30 ]. While men are at increased risk for OSA, OSA prevalence substantially rises in post-menopausal women, yet women typically remain underdiagnosed [ 31 , 32 ]. There is some evidence that suggests women with OSA may develop stronger OSA-related memory impairments than men with OSA, though, results have been inconsistent [ 33 , 34 , 35 ]. OSA is also expressed differentially by sex, with apneas and hypopneas more likely to occur in REM sleep in women than men [ 36 , 37 ]. Thus, there is a possibility of interplay between OSA and AD risk factors of age, sex, and APOE4 , that may synergistically cause stronger memory impairments. However, these interactions have yet to be fully examined.

Here, in the current study, we sought to examine OSA expression separately during REM and NREM sleep as it related to verbal memory, and whether AD risk factors moderated these relationships. We combined clinical polysomnography (PSG) with verbal memory measured by the Rey Auditory Verbal Learning Test (RAVLT) in a cohort of middle- and older- aged adults enriched for parental history and genetic risk for AD. We aimed to extend the current literature by testing the following hypotheses: 1) greater OSA severity, particularly during REM sleep, is associated with impaired verbal learning and delayed recall and 2) that in significant associations, the relationships are stronger among older adults, women, and/or individuals with increased genetic and parental risk for AD.

Clinical methods

One hundred fifteen cognitively unimpaired middle- and older-aged adults from the Wisconsin Alzheimer’s Disease Research Center (ADRC) Clinical Core, a prospective cohort study enriched for probable parental history of AD relative to the general population [ 38 ], were enrolled in a sub-study, the Predicting Alzheimer’s from Metabolic Markers and Sleep (PAMMS). From their ADRC visit, participants underwent cognitive assessments of declarative and semantic memory, attention, executive function, language, and visuospatial processing using the National Alzheimer’s Coordinating Center Uniform Data Set (UDS) neuropsychological battery version 3 and additional assessments [ 39 ]. Clinical diagnosis of cognitively unimpaired status was determined using the 2011 National Institute on Aging-Alzheimer’s Association (NIA-AA) workgroup diagnostic criteria and confirmed by multidisciplinary consensus conference (Table S 1 ) [ 40 , 41 ].

Of the 115 participants in PAMMS, 89 underwent a subcomponent of the study with polysomnography (PSG) with high-density EEG (hdEEG). This portion of the study excluded for individuals with a past history or current neurological, psychiatric, medical conditions, or treatments that impacted their cognition, or hindered their ability to complete any aspects of the study protocol, taking medications known to influence sleep or sleep electroencephalography (EEG), including antipsychotic medications, non-selective serotonin reuptake inhibitors (SSRIs) antidepressants, neuroleptics, chronic anxiolytics, sedative hypnotics, and stimulants, and was currently undergoing treatment for SDB (e.g. continuous positive airway pressure). Participants completed PSG with hdEEG approximately within 1 year of cognitive assessment. RAVLT scores were taken from their cognitive assessment at the ADRC. In addition, participant’s APOE 4 genotyped by DNA extraction from whole blood samples using competitive allele-specific PCR based KASP genotyping for rs429358, as previously reported [ 42 ]. The final sample size consisted of 81 participants with valid PSG data and RAVLT scores (e.g., oxyhemoglobin saturation levels not recorded the whole night would be considered invalid data).

Polysomnography

To assess sleep and OSA severity, participants underwent clinical PSG with 256-channel hdEEG. A thorough description of PSG with hdEEG recording and sleep scoring has been previously described [ 43 ]. From PSG, sleep architecture measures of total sleep time (TST), time in bed (TIB), sleep onset latency, wake after sleep onset (WASO), and percent of TST spent in N1, N2, N3, and REM were derived. Additionally, clinical measures reflecting sleep disorder characteristics were calculated, including apnea–hypopnea index (AHI; number of apneas and hypopneas per hour), respiratory disturbance index (RDI; number of apneas, hypopneas, and respiratory-related arousals per hour), oxyhemoglobin desaturation index (ODI; number of oxyhemoglobin desaturations ≥ 4% per hour). The AHI is a traditional measure for OSA diagnosis, with the American Academy of Sleep Medicine (AASM) clinical criterion of an AHI ≥ 5 for at least mild OSA [ 44 , 45 ]. The RDI captures additional information regarding respiratory events that do not meet criteria for a hypopnea, yet still lead to an arousal, and thus disrupt continuous sleep [ 44 , 45 ]. The AASM criteria for OSA diagnosis is RDI ≥ 5, if daytime sleepiness is present, and RDI ≥ 15 if not. The ODI is a clinically informative measure on the frequency of drops in oxyhemoglobin saturation levels, a marker of intermittent hypoxemia, which has been associated with poor cardiovascular outcomes, including elevated stroke risk, and increased risk of mortality [ 46 , 47 , 48 ].

Furthermore, nadir blood oxyhemoglobin saturation level, mean blood oxyhemoglobin saturation, duration of time spent with < 90% blood oxyhemoglobin saturation, and periodic leg movements during sleep index (PLMSI; number of PLMs per hour) were analyzed. These measures besides PLMSI, also reflects the degree of hypoxemia that occurs during sleep, and are additionally diagnostically important for OSA [ 45 , 47 ]. This study included participants with and without OSA and used the clinical measures of OSA as continuous variables for statistical analyses.

In addition, AHI, RDI, ODI, and duration of time spent with < 90% oxyhemoglobin saturation in REM and NREM sleep were also derived. To measure whether an individual had more REM or NREM OSA features throughout the night, ratios of AHI, RDI, and ODI between REM and NREM sleep were also calculated [ 49 , 50 , 51 , 52 ]. The NREM/REM ratios provide context of whether there is predominance of OSA events in a specific sleep stage across the sleep period. There are well characterized physiological differences in REM versus NREM sleep; specifically, OSA in REM sleep leads to more severe OSA events and has been linked to poor cardiovascular outcomes [ 7 , 16 , 17 , 18 , 19 , 20 , 53 , 54 , 55 , 56 ]. While some studies reported no significant differences between those with OSA events predominantly in NREM sleep versus REM sleep in clinical features such as BMI, daytime sleepiness, and depression [ 50 , 51 ], there have been established physiological and polysomnographic differences in those that express OSA events more in REM sleep and in those that express OSA events more in NREM sleep [ 51 , 52 ]. For instance, in predominant NREM OSA, ventilatory control is worse with higher loop gain, whereas in predominant REM OSA, the upper airway is more collapsible. Furthermore, people with more predominant NREM OSA had longer sleep onset latency, less sleep efficiency, and lower mean oxyhemoglobin saturation. There has yet to be an examination of the ratio of REM OSA events to NREM OSA event as it relates to memory.

Rey Auditory Verbal Learning Test (RAVLT)

The RAVLT is a standard neuropsychological assessment for verbal memory that it MTL-dependent, a sensitive marker of memory impairment in preclinical AD, and commonly used in AD research and clinical practice for diagnosis [ 57 , 58 , 59 , 60 ]. Further, OSA severity has been linked to poor verbal memory performance [ 8 ].

The test includes one learning phase, two recall phases, and one recognition phase. During the learning phase, a list of 15 words is read to the participant five times, and the participant repeat the words they remember after each trial. An interference list of 15 words is then read aloud once, and the short-delay recall ability is assessed after the interference list. Long-delay recall is then assessed after 20 min. A total learning score is derived by summing the number of remembered words in trials 1 through 5 (range: 0–75). Short-delay recall was measured as total number of words recalled after the interference list (range: 0–15), and long-delay recall was measured as total number of words after the 20-min delay (range: 0–15).

Statistical analyses

The purpose of this study was to comprehensively examine the distinct features of OSA (e.g., number of OSA-related events per hour or time spent during sleep with blood oxyhemoglobin levels in hypoxemia) across the whole night and broken down by distinct sleep stages (NREM versus REM), since there are physiological differences in these brain states [ 16 , 17 , 18 , 19 , 20 ]. Thus, the OSA characteristics that were analyzed were AHI, RDI, and ODI, and sleep duration spent with < 90% blood oxyhemoglobin saturation for total sleep, REM sleep, and NREM sleep. Total sleep nadir blood oxyhemoglobin saturation, total sleep mean blood oxyhemoglobin saturation, and WASO were also analyzed. RAVLT measures included total learning, short-delay recall, and long-delay recall. Normality of variables were analyzed with a Shapiro–Wilk test. All AHI, RDI, and ODI measures were log-transformed with a constant added to meet normality assumptions.

Independent sample t-tests were conducted to analyze group differences by sex, APOE4 status, and parental history of AD. Student’s t-tests were used if assumptions of normality and variance were met. Mann Whitney U-tests were used if assumptions of normality were violated. Kendall rank correlation was conducted on the associations between age and OSA characteristics. Paired samples t-tests were used to analyze REM versus NREM AHI, RDI, ODI, and duration spent with < 90% blood oxyhemoglobin saturation. Student’s t-test was used if assumptions of normality and Wilcoxon signed-rank test was used if assumptions of normality were violated.

Multiple linear regression models were used to analyze the relationships between OSA characteristics (predictors) and RAVLT (outcomes), controlling for sex, age, time between PSG and RAVLT, APOE4 status, body mass index (BMI), and years of education. One participant’s BMI was not measured and another's APOE4 status was not obtained, and thus were not included in statistical analyses that included those measures. Across all 15 models, the Benjamin-Hochberg method for False Discovery Rate (FDR) correction was used to correct multiple comparisons [ 61 ]. Regressions were repeated, substituting APOE4 status for parental history of AD.

To further understand the relative impact of OSA during REM sleep against NREM sleep on verbal memory performance, post hoc analyses included using the Steiger’s Z test to directly compare the correlation strengths of the associations between REM and NREM sleep apnea features and RAVLT scores [ 62 ]. Also, REM-NREM AHI, RDI, and ODI ratios were calculated, and the ratios were log-transformed with a constant added to meet normality assumptions. Multiple linear regression models were then used to analyze the associations between the OSA feature by sleep stage ratios and RAVLT scores while controlling for the same covariates. Regressions were repeated substituting APOE4 status for parental history of AD as a covariate.

For significant sleepstage findings, we wanted to account for the possible effects of sleep duration in that sleep stage on the significant associations between OSA features and RAVLT measures. Thus, we conducted follow-up analyses that included percent of time spent in that sleep stage in the models. Also, to disentangle whether total number of OSA events in that sleep stage or sleep duration in that sleep stage (i.e., the values that go into calculating AHI/RDI/ODI) was driving the detected effects, we conducted linear regression models with those separate measures as predictors. Total number of apnea/hypopneas, respiratory disturbances, and oxyhemoglobin desaturations during REM sleep were all log-transformed to meet assumptions of normality.

For significant associations between OSA characteristics and verbal memory, we investigated the moderating effects of sex, age, APOE4 status, and parental history of AD ( APOE4 status and parental history of AD in separate regression models) on those relationships. To further probe significant interactions with either APOE4 status or parental history of AD, we grouped participants into three groups consisting of 1) people with no APOE4 or parental history of AD, 2) people with either APOE 4 or parental history of AD, and 3) people with both APOE4 and parental history of AD. Analysis of covariance (ANCOVA) was used to analyze interactions between AD risk group and OSA features as it predicted verbal memory while controlling for the same covariates mentioned above. Slopes of the relationship between OSA features and verbal memory between the 3 groups were compared and Tukey’s method was used to correct for multiple comparison [ 63 ]. For significant interactions with age, Johnson-Neyman intervals and simple slope analyses were used to determine how much of the sample was driving the significant moderating effect [ 64 , 65 , 66 ]. All statistical analysis was conducted on JASP (Version 0.17.3) and RStudio (Version 2021.09.2).

Sample characteristics

Participant demographics and RAVLT scores of the 81 participants are shown in Table  1 . Sleep architecture and OSA characteristics are shown in Table  2 . In this sample, the average age was 61.7 ± 6.0 years (age range: 44–88 years), 60% participants were female, 32.5% of them were APOE4 carriers, 69.1% had parental history of AD, and 26.3% were both APOE4 carriers and had parental history of AD. The average time between PSG and RAVLT assessments was 0.31 ± 0.50 years. Nearly half of the cohort (44.44%) had OSA (AHI ≥ 5/h) and 16.05% had moderate or severe OSA (AHI ≥ 15/h). OSA severity was significantly higher in REM sleep than in NREM sleep (AHI: (t(80) = 7.91, p <  0.001), RDI: (t(81) = 5.11, p <  0.001), ODI: (t(81) = 9.64, p <  0.001). Remarkably, the duration spent with blood oxyhemoglobin levels < 90% did not differ significantly between REM and NREM sleep stages (1.83 ± 4.57 versus 2.55 ± 7.21, z = 0.06, p =  0.96), despite the fact that participants spent significantly more of the sleep period in NREM than in REM sleep stages (NREM:279.49 ± 57.71 min versus REM:59.80 ± 28.69 min, t(80) = 36.17, p <  0.001). This resulted in the proportion of time spent with blood oxyhemoglobin levels < 90% being significantly higher during REM than in NREM sleep stages (REM: 0.04 ± 0.11 versus NREM:0.01 ± 0.03, z = 3.68, p <  0.001).

Sex, age, APOE4 status, parental history of AD effects on OSA

Overall, male participants had more severe OSA than females (see Table S 2 ). However, females did have a significantly higher REM-NREM ODI ratio (t(79) = 2.45, p =  0.02). There were no significant associations observed between age and any OSA characteristics (all p >  0.10; see Table S 3 ). APOE4 carriers had significantly lower overall AHI, RDI, and ODI; REM AHI, as well as lower NREM AHI and ODI when compared to APOE4 non-carriers (all p <  0.05; see Table S 4 ), indicating that in this cohort, APOE4 carriers did not have greater OSA severity compared to non-carriers. There was also no significant difference in OSA characteristics among participants with and without a parental history of AD (all p >  0.30; see Table S 5 ). These findings indicate that those with higher AD risk did not show evidence of greater OSA severity in this cognitively intact cohort.

Sex, age, APOE4 status, parental history of AD effects on verbal memory

As previously reported, females had higher RAVLT scores than males (all p <  0.05; see Table S 6 ) [ 67 ]. Age and verbal memory were not significantly correlated (all p >  0.10; see Table S 7 ) and RAVLT scores did not significantly differ between APOE4 carriers and non-carriers (all p >  0.20; see Table S 8 ). Interestingly, participants with parental history of AD performed better across the RAVLT compared to those without parental history of AD (all p <  0.002; see Table S 9 ).

Associations between sleep apnea and verbal memory and total learning

Total AHI (b = -4.47, p =  0.09), RDI (b = -3.70, p =  0.17, and ODI (b = -5.17, p =  0.09) were not significantly associated with total learning. Similar results were observed when substituting APOE4 status with parental history of AD in the models. When testing OSA metrics by sleep stage, we found that REM AHI (b = -4.84, p =  0.01, FDR-corrected p =  0.07), REM RDI (b = -5.65, p =  0.01, FDR-corrected p =  0.06), and REM ODI (b = -7.91, p =  0.001, FDR-corrected p =  0.02) were all significantly associated with total learning, with REM ODI as the only significant predictor following FDR correction (Fig.  1 A-C). In the models with parental history of AD as the covariate instead of APOE4 status, REM RDI and REM ODI were significant predictors of total learning after FDR correction (all FDR-corrected p <  0.05). However, these same features in NREM sleep were not significantly associated with total learning performance after adjusting for covariates (NREM AHI: (b = -1.32, p =  0.63), NREM RDI: (b = -1.47, p =  0.56), NREM ODI: (b = -2.41, p =  0.40)). In addition, WASO, TST nadir and average oxyhemoglobin desaturation, and duration spent with < 90% blood oxyhemoglobin saturation in TST, NREM, and REM were not significantly associated with total learning (all p >  0.40; See Table S 10 for full statistical details). Similar insignificant results were found with models that included parental history of AD instead of APOE4 status (all ps > 0.30; See Table S 11 for full statistical details).

Scatter plots showing the relationships of ( A ) AHI, (B ) RDI, and ( C ) ODI during REM sleep to RAVLT total learning scores while controlling for age, sex, time between assessments, years of education, BMI, and APOE4 status

Steiger’s Z test revealed that correlations between total learning and AHI (z = 1.66, p =  0.10), RDI (z = 1.29, p =  0.20), and ODI (z = 1.51, p =  0.13) did not differ significantly between REM and NREM sleep. Although there were no significant differences in correlation strengths between REM and NREM OSA features in their associations with RAVLT total learning, the regression models indicated that REM OSA features, especially REM ODI, were significant predictors of verbal learning deficits, whereas NREM OSA features were not. Further, multiple regression models revealed that the ratios of REM-NREM AHI (b = -6.07, p =  0.01), RDI (b = -6.65, p =  0.01), and ODI (b = -7.42, p =  0.01) were significantly associated with total learning (Fig.  2 A-C; see Table S 12 ). Consistent results were found in models with parental history of AD instead of APOE4 status as the covariate (all p <  0.02; see Table S 13 ). These findings indicated that greater OSA severity during REM sleep in comparison to NREM sleep was associated with diminished total learning performance.

Scatter plots showing the relationships of REM-NREM ( A ) AHI, ( B ) RDI, and ( C ) ODI ratios to RAVLT total learning scores while controlling for age, sex, time between assessments, years of education, BMI, and APOE4 status

Associations between sleep apnea and short-delay recall

Overall AHI (b = -0.84, p =  0.27), RDI (b = -0.44, p =  0.56), and ODI (b = -0.80, p =  0.37) were not significantly related to short-delay recall. REM AHI (b = -1.38, p =  0.01, FDR-corrected p =  0.21) and REM ODI (b = -1.54, p =  0.03, FDR-corrected p =  0.23) were associated with short-delay recall but were no longer significant after FDR correction (See Table S 14 for full statistical details of all predictors). In models with parental history of AD instead of APOE4 status, REM AHI was associated with short-delay recall ( p =  0.04; FDR-corrected p =  0.48), but not after FDR correction (See Table S 15 for full statistical details of all predictors). Steiger’s Z test revealed a significant difference in the correlation strengths between short-delay recall and AHI during REM sleep versus during NREM sleep (z = 2.81, p =  0.005), demonstrating that REM AHI was more strongly associated with short-delay recall than NREM AHI. Moreover, multiple regression models indicated that REM-NREM AHI (b = -2.38, p <  0.001), RDI (b = -1.77, p =  0.02), and ODI (b = -2.02, p =  0.03) ratios were negatively associated with short-delay recall, both in models featuring APOE4 status (See Table S 16 ) and parental history of AD as covariates (all p <  0.03; See Table S 17 ). Thus, individuals with more severe sleep apnea had worse short-delay recall, particularly if OSA was more prevalent during REM sleep as opposed to during NREM sleep.

Associations between sleep apnea and long-delay recall

Total AHI, RDI, and ODI were not significantly associated with long-delay recall (all p >  0.07). However, REM AHI (b = -1.96, p =  0.001, FDR-corrected p =  0.01), REM RDI (b = -1.83, p =  0.01, FDR-corrected p =  0.04), and REM ODI (b = -2.46, p =  0.001, FDR-corrected p =  0.02) were all significantly associated with worse long-delay recall (Fig.  3 A-C). In models with parental history of AD instead of APOE4 status as the covariate, REM AHI, REM RDI, and REM ODI remained significant predictors (all FDR corrected p <  0.05). Demonstrating specificity, these same OSA parameters during NREM sleep were not significantly predictive of long-delay recall (NREM AHI (b = 0.07, p =  0.93), NREM RDI (b = -0.70, p =  0.93), NREM ODI (b = -0.08, p =  0.93). Nadir and average oxyhemoglobin desaturation during total sleep, duration spent with < 90% blood oxyhemoglobin saturation across total sleep and in NREM and REM sleep stages, and WASO were also not significant predictors (all p >  0.12; See Table S 18 for full statistical results). Similar insignificant results were found with models with NREM OSA severity predicting long-delay recall that included parental history of AD instead of APOE4 status (See Table S 19 for full statistical results).

Scatter plots showing the relationships of ( A ) AHI, ( B ) RDI, and ( C ) ODI during REM sleep to RAVLT long-delay scores while controlling for age, sex, time between assessments, years of education, BMI, and APOE4 status 

Steiger’s Z tests revealed significant differences in the correlation strengths between long-delay recall and REM AHI (z = 2.90, p =  0.004) and REM ODI (z = 2.14, p =  0.03) versus NREM features, but not in RDI (z = 1.85, p =  0.07), indicating that the frequency of events and extent of oxyhemoglobin desaturations in REM sleep were more strongly associated with long-delay recall than in NREM sleep. Further, multiple regression models showed that the ratios of REM-NREM AHI (b = -2.95, p <  0.001), RDI (b = -2.62, p =  0.001), and ODI (b = -2.94, p <  0.001) were significantly negatively associated with long-delay recall (Fig.  4 A-C; See Table S 20 ). Results from models with parental history of AD in place of APOE4 status were similar (all p <  0.003; See Table S 21 ). These results complement our initial findings by demonstrating that that worse verbal memory learning and recall performance were specifically associated with greater OSA severity during REM sleep and not during NREM sleep.

Scatter plots showing the relationships of REM-NREM ( A ) AHI, ( B ) RDI, and ( C ) ODI ratios to RAVLT long-delay scores while controlling for age, sex, time between assessments, years of education, BMI, and APOE4 status

Given the significant associations between REM OSA features and verbal memory, we wanted to account for the possible influence of percentage of REM sleep in these models. We found that even when controlling for percentage of REM sleep, REM AHI, RDI, and ODI, in addition to NREM-REM AHI, NREM-REM RDI, and NREM-REM ODI ratios were still associated with RAVLT measures (all p >  0.05; See Tables S 22 and S 23 for full statistical details). Furthermore, we probed whether total number of OSA events during REM sleep or total duration of REM sleep (i.e., the two values that go into calculating OSA indices) may be driving the significant associations between REM OSA features and verbal memory. We found that REM sleep duration was not significantly associated with RAVLT performance (all p >  0.25; see Tables S 24 and S 25 ). In contrast, total amount of apneas and hypopneas in REM sleep was associated with RAVLT total learning, short-delay, and long-delay significantly or on a trend level (all p <  0.09; See Tables S 26 and S 27 for full statistical details). Total number of respiratory-related arousals in addition to apneas/hypopneas (the total number of events in RDI) in REM sleep were also related to RAVLT total learning and long-delay recall (ps < 0.05; See Tables S 28 and S 29 for full statistical details). Lastly, total number of oxyhemoglobin desaturations during REM sleep was associated with total learning and long-delay recall (all p <  0.05; see Tables S 30 and S 31 for full statistical details). All models controlled for the same covariates with either APOE 4 status or parental history of AD. These findings suggest that the impact of OSA during REM sleep on verbal memory is more strongly associated with the OSA-related events themselves rather than their effects on REM sleep duration, per se. As a control analysis, we also analyzed whether periodic limb movement of sleep index (PMLSI) was associated with RAVLT performance. We found that there was no significant association between PLSMI and verbal memory (all p <  0.06).

The moderating effects of AD risk factors on verbal memory

Next, we examined the moderating influence of sex, age, and genetic and familial risk of AD on the significant relationships between OSA variables (i.e., OSA indices in REM sleep and REM-NREM ratios) and verbal memory. We found that APOE4 carriers demonstrated a significant association between REM-NREM ODI ratio and total learning (b = -18.17, p <  0.01) as opposed to non-carriers (b = -4.12, p =  0.17; Fig.  5 A). Full statistical details with all interactions between OSA features and AD risk factors predicting RAVLT total learning (with APOE4 status as a covariate in the models) are presented in Table S 32 . In models with parental history of AD as the covariate, age significantly moderated the association between REM ODI and total learning (b = -0.47, p <  0.05). There were significant effects at the mean age and at 1SD above the mean age (all p <  0.01; Fig.  5 B), with 80% of the sample in the significant moderating range (Figure S 1 ). Full statistical details with all interactions between OSA features and AD risk factors predicting RAVLT total learning (with parental history of AD as a covariate in the models) are presented in Table S 33 . Taken together, these findings indicate that the negative impact of oxyhemoglobin desaturations during REM sleep (relative to NREM sleep) on RAVLT total learning was more pronounced in those that were APOE4 carriers and in those aged 60 or older. Further, REM-NREM RDI ratio was significantly associated with long-delay recall in APOE4 carriers (b = -5.42, p <  0.01) but not in those without APOE4 (b = -1.67, p =  0.06; Fig.  5 C). Full statistical details with all interactions between OSA features and AD risk factors predicting long-delay recall (with APOE4 status as a covariate in the models) are presented in Table S 34 . There were no significant interactions between OSA factors and AD risk factors on long-delay recall when parental history of AD was a covariate in the models (See Table S 35 ). This suggests that the association between higher REM-NREM RDI and long-delay recall was specific to individuals with genetic risk for AD.

A The association between REM-NREM ODI ratio and RAVLT total learning scores was significantly moderated by APOE4 status. Only the APOE4 carriers showed that more oxyhemoglobin saturations during REM sleep as opposed to NREM sleep was related to worse learning performance. B The association between REM ODI and RAVLT total learning scores was moderated by age. A significant moderating effect was present at the mean age and 1 SD above the mean age (in 80% of the sample). C The association between REM-NREM RDI ratio and RAVLT long-delay recall score was moderated by APOE4 status. A significant moderating effect was observed for only APOE4 carriers in that, more respiratory events during REM sleep than in NREM sleep was associated with fewer words remembered after a 20-min delay

Lastly, we binned participants into the 3 groups based on presence or absence of APOE4 status and parental history of AD: 1) people with no AD risk factors, 2) people with either APOE4 status or parental history of AD, and 3) people with both AD risk factors. We then used ANCOVA models to examine interactions between OSA characteristics and AD risk factor groups as it related to verbal memory. In individuals with both AD risk factors, we found that higher REM RDI (b = -14.08, 95%CI:[-21.53, -6.63]), REM-NREM RDI ratio (b = -23.20, 95%CI:[-35.34, -11.06]) and REM-NREM ODI (b = -23.49, 95%CI:[-35.46, -11.51]) were significantly associated with worse total learning (See Figure S 2 and Tables S 36 -S 38 for contrast testing). Similarly, in individuals that were both APOE4 positive and had parental history of AD, higher REM-NREM RDI ratio was significantly associated with lower long-delay recall (b = -8.27, 95%CI:[-12.13, -4.42]; See Figure S 3 and Table S 39 for contrast testing results). Overall, these findings suggest that more OSA-related events in REM sleep (relative to NREM sleep) strongly impaired word list learning and recall, especially for those that had both parental and genetic risk for AD.

In this study, we assessed the relationships between OSA features and verbal memory performance of a word list, and tested the moderating effects of biological sex, age, APOE4 status, and parental history of AD on these relationships. We found that greater OSA severity during REM sleep was associated with worse word list learning and delay memory recall in a cohort of cognitively unimpaired middle- and older- aged adults enriched for AD risk. Additionally, more oxyhemoglobin desaturations during REM sleep versus NREM sleep were associated with worse learning performance, specifically in those that were older than 60 years old and APOE4 carriers. Further, more respiratory events and arousals during REM sleep, as opposed to during NREM sleep, had a greater negative impact on recall performance for those who were APOE4 carriers. The negative effects of OSA during REM sleep, specifically respiratory disturbances and oxyhemoglobin desaturations, on verbal memory seemed to be most prominent in those that had a parent with AD and was an APOE4 carrier. Since AD risk factors (e.g., female sex, older age, or genetic or familial risk) were not associated with more severe OSA in this current study, these findings were not simply driven by increased OSA severity in individuals with AD risk factors. Though it is possible this could be related to lower survival from conversion to mild cognitive impairment (MCI) or AD in older adults with AD risk and more severe OSA [ 9 , 21 , 68 , 69 ]. That being said, our results support the hypothesis that the memory consequences of OSA are particularly important for cognitively intact older adults with AD risk factors (older age, APOE4 positivity, and parental history of AD), particularly when OSA events occur during REM sleep.

OSA predominantly expressed in REM sleep is a common condition and REM-sleep related physiological changes lead to increased susceptibility to airway collapse, with longer durations of apneas and hypopneas and more severe oxyhemoglobin desaturations [ 19 , 20 , 53 , 70 ]. This is consistent with our findings that demonstrated higher AHI, RDI, and ODI scores during REM sleep relative to NREM sleep and extends the current literature by demonstrating that OSA events in REM sleep were more strongly linked with verbal memory performance than OSA events in NREM sleep. However, future investigations comparing samples enriched for more severe REM or NREM OSA are needed to determine whether it is specifically REM OSA severity that negatively impacts verbal memory performance.

REM OSA may impact verbal memory learning and recall via active disruption of memory processing or through long-term damage to brain structures and brain network function relevant for memory processing during REM sleep. While considerable attention has been given to the role of NREM sleep features in memory processing [ 71 ], there is evidence that REM sleep also supports memory. Neuroplastic processes needed for both memory consolidation and forgetting has been observed during REM sleep, in addition, hippocampal replay also occurs during this sleep stage [ 72 , 73 ]. Further, behaviorally, REM sleep has been linked to both emotional and spatial navigational memory [ 74 ]. Metabolic demand is also greater during REM sleep as opposed to wake and NREM sleep, including in memory-relevant regions, such as the MTL [ 17 , 18 ]. Therefore, OSA events in REM sleep could potentially cause memory deficits through both 1) transient disruptions in cerebral glucose metabolism in memory networks actively supporting memory processing during REM sleep and 2) long term degeneration of memory networks resultant from the presence of repeated hypoxia during high metabolic demand.

Varga and colleagues demonstrated the acute cognitive consequences of REM OSA in which they found that when withdrawing positive airway pressure (PAP) treatment specifically during REM sleep, spatial memory performance was reduced when compared to continued PAP treatment during REM sleep [ 16 ]. Although, the impact of withdrawing treatment during NREM sleep was not assessed, these findings indicated that REM OSA could cause transient MTL dysfunction by actively disrupting memory formation and consolidation even prior to neurodegenerative processes.

The effects of intermittent hypoxemia during OSA is a likely contributor to the hippocampal atrophy reported in people with OSA and explains memory impairments observed in OSA [ 5 , 6 , 7 , 8 , 9 , 13 , 14 ]. A possible mechanism of OSA’s impact on the hippocampus is through the presence of AD pathologies, with evidence supporting that hypoxemia exacerbates expression of both β-amyloid and tau that will in turn cause neurodegeneration and cognitive deficits [ 75 , 76 , 77 ]. Another potential mechanism is that hypoxia and sleep fragmentation specifically in REM sleep could accelerate neurodegeneration and cognitive decline via a vascular pathway [ 54 , 55 , 56 , 78 , 79 ]. REM sleep is characterized by increased sympathetic activation, decreased vagal tone, and cardiovascular instability, and REM OSA has been linked to poor cardiovascular health [ 20 , 54 , 55 , 56 ]. While REM sleep has shown to have high cerebral blood flow in memory-relevant brain areas, REM OSA severity has also been associated with reduced regional cerebral blood flow in those regions [ 78 , 79 ]. In addition, older adults with cardiovascular risk factors were more likely to have memory deficits [ 80 ]. Thus, it is possible that the compounded effects of REM OSA and vascular dysfunction greatly increases oxidative stress, neuroinflammation, blood brain barrier breakdown, and/or endothelial dysfunction causing neurodegenerative-associated memory deficits in older adults [ 20 , 77 ].

We found that AD risk factors including older age and both parental and genetic risk for AD all exacerbated the effect of OSA severity during REM sleep on word list learning and recall. While it has been reported that the associations between OSA and cognition are weaker in older age, our findings suggested in contrast, that the relationship between oxygen desaturations in REM sleep and verbal memory were actually strongest in older individuals [ 9 ]. As our cohort consisted of individuals with undiagnosed OSA and we are unaware of the true age of OSA onset, it is quite possible that some of the older participants may have had untreated OSA longer than the younger participants. We thus cannot discount that our findings may be more related to the consequences of the duration of untreated OSA than age of OSA onset, per se.

While it is remains unclear whether APOE4 status increases risk for SDB, our findings suggest that APOE4 carriers may be more vulnerable to the impact of OSA, especially during REM sleep, on memory function. Other studies have reported similar findings in that, in APOE4 carriers, OSA severity was associated with worse memory and executive function and had increased odds of cognitive decline [ 25 , 81 , 82 , 83 ]. Furthermore, disrupted sleep and APOE4 status may synergistically exacerbate expression of hallmark AD pathologies of β-amyloid and tau [ 84 , 85 ].

The combined effects of parental history of AD and APOE4 positivity has shown to have strong negative effects on learning and memory [ 86 , 87 ]. In addition, older age, family history of AD, and APOE4 status have been linked to a smaller hippocampus and greater accumulation of pathological β-amyloid and tau [ 88 , 89 ]. Moreover, in a subset of this cohort, we found that increasing age was related to elevated cerebral spinal fluid (CSF) markers of tau phosphorylation and neuroinflammation, which were then associated with impaired sleep-dependent memory [ 43 ]. This points to the possibility that accumulation of AD pathologies might be intensified by REM-related OSA leading to poor memory function, with the effects strongest or even just specific to those that are older and with parental and/or genetic risk for AD. Alternatively, REM OSA may contribute to cognitive impairment through cerebrovascular disease, and may be a factor to the common comorbidity of AD and vascular cognitive impairment [ 90 ]. Prospective studies will be necessary to investigate whether REM OSA accelerates expression of AD pathologies or promotes cognitive impairment through cerebrovascular dysfunction, or both, as well as why individuals with AD risk factors and OSA may be more cognitively impaired.

We did not find sex-specific effects in the associations between OSA and memory. In this specific cohort, males presented with more severe NREM and REM OSA, and had worse verbal memory performance than females. The lack of a sex effect could be due to a cognitively healthy sample that included females with a less severe OSA presentation. While females have increased risk for AD and present with greater levels of pathological tau in regions associated with AD compared to males, it is possible that the negative effects of OSA on verbal memory performance may be more exaggerated only once women are tau and/or β-amyloid positive, due to the female verbal memory advantage [ 28 , 29 , 67 , 91 , 92 ]. This verbal memory advantage tends to be diminished when women progress from MCI to AD who present with steeper memory decline than men [ 30 ]. In support of this possibility, the average age of this cohort was < 65 years old and in a subsample of 58 participants from this cohort, they were almost entirely β-amyloid and tau negative (based on CSF assessment) [ 43 ]. Future studies are needed that combine multimodal neuroimaging, sleep apnea testing, and other cognitive measures, to examine this in more detail. Regardless, it is important to state that despite the cohort being largely β-amyloid and tau negative, AD risk still remained a significant moderator of OSA-memory relationships, indicating that these effects cannot be entirely explained by and may even precede β-amyloid positivity, despite recent findings [ 26 ].

Some limitations of this study should be addressed. This was a cross-sectional study that found correlational relationships between OSA characteristics during REM sleep and verbal memory. Longitudinal studies will be necessary to examine how treatment of REM-related OSA would affect memory decline and progression to MCI or AD. Given the study sample size and number of analyses computed, it is possible that the study was underpowered to detect some significant associations. However, the focus of the current study was to contrast the relative strengths of associations between OSA features and memory when events occurred during NREM or REM sleep. While we think these effects are likely robust, it will be important to replicate these findings in a larger study. Further, the memory testing and sleep measurements did not typically occur on the same day. While we controlled for time between measurements, this study does not directly address memory processing that occurs over a night of sleep, but rather informs upon sleep abnormalities and memory associations at the trait level of individual differences. Another limitation is that this study had exclusions of multiple medications that are commonly taken by older adults, which could potentially bias the sample and reduce the generalizability of the results. It is also important to note that this cohort was mostly White (88%) and that these findings may not be generalizable, since racial/ethnic disparities and differences exist for both OSA and AD risk factors and the relationships between these risk factors are not well studied in underrepresented populations [ 93 , 94 ].

Our findings further support the possibility that OSA could be a modifiable risk factor for AD through its impact on one of the more sensitive markers of AD, impairment in word list learning and recall. A future direction is to extend our analyses to examine OSA’s relationship with other cognitive measures that are impaired in AD, including verbal memory of stories, nonverbal memory, and executive function. This will further elucidate OSA as a contributor to AD and provide further support that treatment may reduce risk for cognitive decline [ 9 ]. There is some evidence that continuous positive airway pressure (CPAP) adherence decreases the odds of AD dementia and slowed cognitive decline [ 95 , 96 , 97 ]. Importantly, a systematic review reported that PAP treatment adherence only covers mostly the first half of the night, which could potentially leave much of REM sleep OSA untreated, since REM sleep dominates the latter half of the night [ 98 ]. It will be critical for future investigations to examine whether more aggressive OSA treatment that covers the entire sleep period would mitigate cognitive impairment and AD risk in individuals with OSA. With growth of the aging population, there is a need for interventions targeting prevention of MCI and AD, and early diagnosis and effective treatment of OSA may be one approach that could reduce risk for neurodegenerative diseases and cognitive dysfunction associated with AD.

In conclusion, these findings suggest that more severe OSA during REM sleep and more REM OSA events as opposed to NREM OSA events were linked to worse verbal memory performance. This relationship was particularly true for older adults and individuals with a genetic risk for and parental history of AD. This suggests that the negative memory consequences of OSA, specifically when OSA events occur during REM sleep, are particularly impactful in individuals with multiple AD risk factors. The findings emphasize the importance of a thorough OSA screening with sleep recording capable of assessing sleep stage specific expression of OSA, as certain individuals may have high REM AHI, while presenting with a low overall AHI. This is particularly important given that most ambulatory, non-PSG methods for assessing OSA do not include the capacity to assess REM versus NREM sleep specific OSA expression. Without sleep stage characterization of OSA, individuals that are more susceptible to memory decline, especially those with AD risk factors, may miss the opportunity to be referred for comprehensive neurological/neuropsychological evaluation and aggressive OSA treatment that may delay cognitive decline and/or AD onset.

Availability of data and materials

The data are available upon reasonable request and can be obtained by completing a Wisconsin Alzheimer’s Disease Research Center resource request: https://www.adrc.wisc.edu/apply-resources .

Abbreviations

Alzheimer’s disease

Alzheimer’s Disease Research Center

Apnea–hypopnea Index

Analysis of covariance

Apolipoprotein E ε4

Body mass index

Continuous Positive Airway Pressure

Cerebral spinal fluid

False Discovery Rate

High-density electroencephalography

Mild cognitive impairment

Medial temporal lobe

Non-rapid eye movement

Oxyhemoglobin Desaturation Index

Obstructive Sleep Apnea

Positive airway pressure

Periodic leg movements during sleep index

Rey Auditory Verbal Learning Test

Respiratory Disturbance Index

Rapid eye movement

Sleep Disordered Breathing

Total sleep time

Time in bed

Wake after sleep onset

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Acknowledgements

We would like to thank the research participants and staff of the Wisconsin ADRC and Wisconsin Sleep for their contributions to the study.

This research was supported by grants R56 AG052698, R01 AG027161, R01 AG021155, ADRC P50 AG033514, R01 AG037639, K01 AG068353, and National Research Service Award F31 AG048732 from the National Institute on Aging, and by the Clinical and Translational Science Award (CTSA) program, through the NIH National Center for Advancing Translational Sciences (NCATS), grant UL1TR000427.

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Kitty K. Lui

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Kitty K. Lui, Abhishek Dave, Bryce A. Mander & Ruth M. Benca

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Contributions

K.K.L analyzed the data and wrote the manuscript. A.D. and M.G.C. aided in analyzing the data and writing the manuscript. K.E.S. conducted the experiments, overseeing sleep data collection as part of her dissertation, and aided in data analysis and writing the manuscript. B.A.R. aided in sleep study data collection, provided data analytic tools, and aided in manuscript preparation. M.H. aided in collection and analysis of demographic, and neuropsychological data. C.T. aided in data collection. C.M.C., O.C.O., S.A., and S.C.J. aided data collection, neuropsychological screening, and manuscript preparation. B.B.B. aided study design, provided the subject pool, and aided in data analysis and manuscript preparation. R.M.B. designed the study, oversaw clinical sleep screening procedures, aided in data collection and analysis, and aided writing the manuscript. Lastly, B.A.M. aided in data analysis and writing the manuscript.

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Dr. Mander has served as a consultant for Eisai Co., Ltd. Dr. Benca has served as a consultant for Eisai, Genomind, Idorsia, Jazz, Merck, and Sunovion. Dr. Riedner has several patents related to sleep technology jointly held by the Wisconsin Alumni Research Foundation and Philips Healthcare, and in addition to grant support, has given several lectures sponsored by Philips Healthcare.

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Lui, K.K., Dave, A., Sprecher, K.E. et al. Older adults at greater risk for Alzheimer’s disease show stronger associations between sleep apnea severity in REM sleep and verbal memory. Alz Res Therapy 16 , 102 (2024). https://doi.org/10.1186/s13195-024-01446-3

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New cardiac research will save women’s lives by improving detection of heart failure

By: Communications

An important new study has advanced how heart failure is detected in women – meaning more female patients can be diagnosed and at an earlier stage.

Researchers led by teams from the Universities of East Anglia (UEA), Sheffield and Leeds, have been able to fine-tune how magnetic resonance imaging (MRI) is used to detect heart failure in women’s hearts, making it more accurate.    Lead author Dr Pankaj Garg , of the University of East Anglia’s Norwich Medical School and a consultant cardiologist at the Norfolk and Norwich University Hospital, said: “By refining the method for women specifically, we were able to diagnose 16.5pc more females with heart failure.      “This could have huge impact in the NHS, which diagnoses around 200,000 patients with heart failure each year.      “This improved method will increase early detection, meaning more women can get life-saving treatment sooner."       In 2022, UEA and the University of Sheffield published research which showed how using MRI scans could be used to detect heart failure and which led to this technique being widely employed by medics.  When a heart starts to fail, it is unable to pump blood out effectively, and so the pressure in the heart rises.    Co-author Dr Gareth Matthews , of the University of East Anglia’s Norwich Medical School, said: “Currently one of the best ways of diagnosing heart failure is to measure pressures inside the heart with a tube called a catheter.     “While this is very accurate, it is an invasive procedure, and therefore carries risks for patients, which limits its use.    “For this reason, doctors tend to use echocardiograms, which are based on ultrasound, to assess heart function, but this is inaccurate in up to 50 per cent of cases. Using MRI, we can get much more accurate images of how the heart is working.”    The team was able to create an equation which allowed them to non-invasively derive the pressure in the heart using an MRI scanner.    However, previous use of this method wasn’t as accurate as the researchers would have liked in diagnosing heart failure in women, especially in early or borderline disease. 

You can learn more about Dr Garg's work on using MRI to detect heart disease here: https://www.uea.ac.uk/health/revolutionising-heart-disease-diagnosis   Co-author Professor Andy Swift, of the University of Sheffield’s School of Medicine and Population Health, said: “Women’s hearts are biologically different to men’s.     “Our work suggests that in heart failure women's hearts may respond differently in response to increases in pressure.”    Heart failure can be classed differently, depending on the amount of blood squeezed out of the main chamber of the heart with every beat, known as the heart’s ejection fraction.    Women suffer disproportionately from a type of heart failure where the pumping function of the heart is preserved but the ability of the heart to relax and fill with blood is impaired.     Echocardiography really struggles to diagnose this type of heart failure. The improvements in diagnosis from this new work will enable more of this particular cohort to be diagnosed more accurately and hopefully drive better treatments.    Co-author Dr Peter Swoboda, of the University of Leeds’ Faculty of Medicine and Health, said: “The symptoms of heart failure, like breathlessness and fatigue, can have a devastating effect on patients’ quality of life.     “We are increasingly recognising the importance of early diagnosis and, early treatment can improve symptoms and life expectancy.     “This research will help diagnose heart failure in women more quickly and get them established on life-saving treatments sooner.”    The Government’s Health and Social Care Secretary, Victoria Atkins, said: "Heart failure is a devastating condition affecting hundreds of thousands of women in the UK, so this research is a hugely positive development that could make it possible for thousands of people to get diagnosed and treated at an earlier stage.     “For the second year of our Women’s Health Strategy for England, I have been clear that we need more research to look at the differences between how conditions affect men and women.       “I am delighted that this government-backed research has met this challenge so that we can get life-saving treatment to women faster."    The research was a collaboration between the University of East Anglia, the University of Leeds, the University of Sheffield, the Norfolk and Norwich University Hospital NHS Foundation Trust, the National Heart Research Institute Singapore, Duke-NUS Medical School in Singapore, Queen Mary University of London, the National Institute for Health and Care Research’s Sheffield Biomedical Research Centre, the University of Amsterdam and Kocaeli City Hospital in Turkey.    It was funded by the National Institute for Health and Care Research (NIHR) Sheffield  Biomedical Research Centre, the Wellcome Trust, and the National Medical Research Council (NMRC).     “Sex-specific cardiac magnetic resonance pulmonary capillary wedge pressure” is published in the European Heart Journal Open.   

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Competency gap among graduating nursing students: what they have achieved and what is expected of them

• Majid Purabdollah 1 , 2 ,
• Vahid Zamanzadeh 2 , 3 ,
• Akram Ghahramanian 2 , 4 ,
• Leila Valizadeh 2 , 5 ,
• Saeid Mousavi 2 , 6 &
• Mostafa Ghasempour 2 , 4  

BMC Medical Education volume  24 , Article number:  546 ( 2024 ) Cite this article

Metrics details

Nurses’ professional competencies play a significant role in providing safe care to patients. Identifying the acquired and expected competencies in nursing education and the gaps between them can be a good guide for nursing education institutions to improve their educational practices.

In a descriptive-comparative study, students’ perception of acquired competencies and expected competencies from the perspective of the Iranian nursing faculties were collected with two equivalent questionnaires consisting of 85 items covering 17 competencies across 5 domains. A cluster sampling technique was employed on 721 final-year nursing students and 365 Iranian nursing faculties. The data were analyzed using descriptive statistics and independent t-tests.

The results of the study showed that the highest scores for students’ acquired competencies and nursing faculties’ expected competencies were work readiness and professional development, with mean of 3.54 (SD = 0.39) and 4.30 (SD = 0.45), respectively. Also, the lowest score for both groups was evidence-based nursing care with mean of 2.74 (SD = 0.55) and 3.74 (SD = 0.57), respectively. The comparison of competencies, as viewed by both groups of the students and the faculties, showed that the difference between the two groups’ mean scores was significant in all 5 core-competencies and 17 sub-core competencies ( P  < .001). Evidence-based nursing care was the highest mean difference (mean diff = 1) and the professional nursing process with the lowest mean difference (mean diff = 0.70).

The results of the study highlight concerns about the gap between expected and achieved competencies in Iran. Further research is recommended to identify the reasons for the gap between the two and to plan how to reduce it. This will require greater collaboration between healthcare institutions and nursing schools.

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Introduction| Background

Nursing competence refers to a set of knowledge, skills, and behaviors that are necessary to successfully perform roles or responsibilities [ 1 ]. It is crucial for ensuring the safe and high-quality care of patients [ 2 , 3 , 4 , 5 ]. However, evaluating nursing competence is challenging due to the complex, dynamic, and multi factorial nature of the clinical environment [ 3 ]. The introduction of nursing competencies and their assessment as a standard measure of clinical performance at the professional level has been highlighted by the Association of American Colleges of Nursing [ 6 , 7 ]. As a result, AACN (2020) introduces competence assessment as an emerging concept in nursing education [ 7 ].

On the other hand, the main responsibility of nursing education is to prepare graduates who have the necessary competencies to provide safe and quality care [ 3 ]. Although it is believed that it is impossible to teach everything to students, acquiring some competencies requires entering a real clinical setting and gaining work experience [ 8 ]. However, nursing students are expected to be competent to ensure patient safety and quality of care after graduation [ 9 ]. To the extent that the World Health Organization (WHO), while expressing concern about the low quality of nursing education worldwide, has recommended investing in nursing education and considers that the future to require nurses who are theoretically and clinically competent [ 5 ]. Despite efforts, the inadequate preparation of newly graduated nursing students and doubts about the competencies acquired in line with expectations to provide safe care for entering the nursing setting have become a global concern [ 10 , 11 , 12 , 13 ]. The results of studies in this field are different. The results of Amsalu et al. showed that the competence of newly graduated nursing students to provide quality and safe care was not satisfactory [ 14 ]. Some studies have also highlighted shortcomings in students’ “soft” skills, such as technical competency, critical thinking, communication, teamwork, helping roles, and professionalism [ 15 ]. Additionally, prior research has indicated that several nursing students have an unrealistic perception of their acquired competencies before entering the clinical setting and they report a high level of competence [ 2 ]. In other study, Hickerson et al. showed that the lack of preparation of nursing students is associated with an increase in patient errors and poor patient outcomes [ 16 ]. Some studies also discussed nursing competencies separately; Such as patient safety [ 17 ], clinical reasoning [ 18 ], interpersonal communication [ 19 ], and evidence-based care competence [ 20 ].

On the other hand, the growing need for safe nursing care and the advent of new educational technologies, the emergence of infectious diseases has increased the necessity of nursing competence. As a result, the nursing profession must be educated to excellence more than ever before [ 5 , 21 , 22 ]. Therefore, the self-assessment of students’ competence levels as well as the evaluation of nursing managers about the competencies expected from them is an essential criterion for all healthcare stakeholders, educators, and nursing policymakers to ensure the delivery of safe, and effective nursing care [ 9 , 23 , 24 ].

However, studies of nurse managers’ perceptions of the competence of newly graduated nursing students are limited and mostly conducted at the national level. Hence, further investigation is needed in this field [ 25 , 26 ]. Some other studies have been carried out according to the context and the needs of societies [ 3 , 26 , 27 , 28 ]. The results of some other studies in the field of students’ self-assessment of perceived competencies and managers’ and academic staff’s assessment of expected competency levels are different and sometimes contradictory, and there is the “academic-clinical gap” between expected and achieved competencies [ 25 , 29 , 30 ]. A review of the literature showed that this gap has existed for four decades, and the current literature shows that it has not changed much over time. The academe and practice settings have also been criticized for training nurses who are not sufficiently prepared to fully engage in patient care [ 1 ]. Hence, nursing managers must understand the expected competencies of newly graduated students, because they have a more complete insight into the healthcare system and the challenges facing the nursing profession. Exploration of these gaps can reveal necessities regarding the work readiness of nursing graduates and help them develop their competencies to enter the clinical setting [ 1 , 25 ].

Although research has been carried out on this topic in other countries, the educational system in those countries varies from that of Iran’s nursing education [ 31 , 32 ]. Iran’s nursing curriculum has tried to prepare nurses who have the necessary competencies to meet the care needs of society. Despite the importance of proficiency in nursing education, many nursing graduates often report feeling unprepared to fulfill expected competencies and they have deficiencies in applying their knowledge and experience in practice [ 33 ]. Firstly, the failure to define and identify the expected competencies in the nursing curriculum of Iran led to the absence of precise and efficient educational objectives. Therefore, it is acknowledged that the traditional nursing curriculum of Iran focuses more on lessons organization than competencies [ 34 ]. Secondly, insufficient attention has been given to the scheduling, location, and level of competencies in the nursing curriculum across different semesters [ 35 ]. Thirdly, the large volume of content instead of focusing on expected competencies caused nursing graduates challenged to manage complex situations [ 36 ]. Therefore, we should not expect competencies such as critical thinking, clinical judgment, problem-solving, decision-making, management, and leadership from nursing students and graduates in Iran [ 37 ]. Limited research has been conducted in this field in Iran. Studies have explored the cultural competence of nursing students [ 38 ] and psychiatric nurses [ 39 ]. Additionally, the competence priorities of nurses in acute care have been investigated [ 40 ], as well as the competency dimensions of nurses [ 41 ].

In Iran, after receiving the diploma, the students participate in a national exam called Konkur. Based on the results of this exam, they enter the field of nursing without conducting an aptitude test interview and evaluating individual and social characteristics. The 4-year nursing curriculum in Iran has 130 units including 22 general, 54 specific, 15 basic sciences, and 39 internship units. In each semester, several workshops are held according to the syllabus [ 42 ]. Instead of the expected competencies, a list of general competencies is specified as learning outcomes in the program. Accepted students based on their rank in the exam and their choice in public and Islamic Azad Universities (non-profit), are trained with a common curriculum. Islamic Azad Universities are not supported by government funding and are managed autonomously, this problem limits the access to specialized human resources and sufficient educational fields, and the lower salaries of faculty members in Azad Universities compared to the government system, students face serious challenges. Islamic Azad Universities must pay exorbitant fees to medical universities for training students in clinical departments and medical training centers, doubling these Universities’ financial problems. In some smaller cities, these financial constraints cause students to train in more limited fields of clinical training and not experience much of what they have learned in the classroom in practice and the real world of nursing. The evaluation of learners in the courses according to the curriculum is based on formative and summative evaluation with teacher-made tests, checklists, clinical assignments, conferences, and logbooks. The accreditation process of nursing schools includes two stages internal evaluation, which is done by surveying students, professors and managers of educational groups, and external accreditation is done by the nursing board. After completing all their courses, to graduate, students must participate in an exam called “Final”, which is held by each faculty without the supervision of an accreditation institution, the country’s assessment organization or the Ministry of Health, and obtain at least a score of 10 out of 20 to graduate.

Therefore, we conducted this comprehensive study as the first study in Iran to investigate the difference between the expected and perceived competence levels of final year nursing students. The study’s theoretical framework is based on Patricia Benner’s “From Novice to Expert” model [ 43 ].

Materials and methods

The present study had the following three objectives:

Determining self-perceived competency levels from the perspective of final year nursing students in Iran.

Determining expected levels of competency from the perspective of nursing faculties in Iran.

To determine the difference between the expected competencies from the perspective of nursing faculties and the achieved competencies from the perspective of final-year nursing students.

This study is a descriptive-comparative study.

First, we obtained a list of all nursing schools in the provinces of Iran from the Ministry of Health ( n  = 31). From 208 Universities, 72 nursing schools were randomly selected using two-stage cluster sampling. Among the selected faculties, we chose 721 final-year nursing students and 365 nursing faculties who met the eligibility criteria for the study. Final-year nursing students who consented to participate in the study were selected. Full-time faculty members with at least 2 years of clinical experience and nurse managers with at least 5 years of clinical education experience were also included. In this study, nursing managers, in addition to their educational roles in colleges, also have managerial roles in the field of nursing. Some of these roles include nursing faculty management, nursing board member, curriculum development and review, planning and supervision of nursing education, evaluation, and continuous improvement of nursing education. The selection criteria were based on the significant role that managers play in nursing education and curriculum development [ 44 ]. Non-full-time faculty members and managers without clinical education experience were excluded from the study.

The instrument used in this study is a questionnaire developed and psychometrically tested in a doctoral nursing dissertation [ 45 ]. To design the tool, the competencies expected of undergraduate nursing students in Iran and worldwide were first identified through a scoping review using the methodology recommended by the Joanna Briggs Institute (JBI) and supported by the PAGER framework. Summative content analysis by Hsieh and Shannon (2005) was used for analysis, which included: counting and comparing keywords and content, followed by interpretation of textual meaning. In the second step, the results of the first step were used to create tool statements. Then the validity of the instrument was checked by face validity, content validity (determination of the ratio and index of content validity), and validity of known groups. Its reliability was also checked by internal consistency using Cronbach’s alpha method and stability using the test-retest method. The competency questionnaire comprises 85 items covering 17 competencies across 5 domains: “individualized care” (4 competencies with 21 items), “evidence-based nursing care” (2 competencies with 10 items), “professional nursing process” (3 competencies with 13 items), “nursing management” (2 competencies with 16 items), and “work readiness and professional development” (6 competencies with 25 items) [ 45 ]. “The Bondy Rating Scale was utilized to assess the competency items, with ratings ranging from 1 (Dependent) to 5 (Independent) on a 5-point Likert scale [ 46 ]. The first group (nursing students) was asked to indicate the extent to which they had acquired each competency. The second group (nursing faculties) was asked to specify the level to which they expected nursing students to achieve each competency.

Data collection

First, the researcher contacted the deans and managers of the selected nursing schools by email to obtain permission. After explaining the aims of the study and the sampling method, we obtained the telephone number of the representative of the group of final year nursing students and also the email of the faculty members. The representative of the student group was then asked to forward the link to the questionnaire to 10 students who were willing to participate in the research. Informed consent for students to participate in the online research was provided through the questionnaires, while nursing faculty members who met the eligibility criteria for the study received an informed consent form attached to the email questionnaire. The informed consent process clarified the study objectives and ensured anonymity of respondent participation in the research, voluntary agreement to participate and the right to revoke consent at any time. An electronic questionnaire was then sent to 900 final year nursing students and 664 nursing faculties (from 4 March 2023 to 11 July 2023). Reminder emails were sent to nursing faculty members three times at two-week intervals. The attrition rate in the student group was reported to be 0 (no incomplete questionnaires). However, four questionnaires from nursing faculty members were discarded because of incomplete responses. Of the 900 questionnaires sent to students and 664 sent to nursing faculties, 721 students and 365 nursing faculty members completed the questionnaire. The response rates were 79% and 66% respectively.

Data were analyzed using SPSS version 22. Frequencies and percentages were used to report categorical variables and mean and standard deviations were used for quantitative variables. The normality of the quantitative data was confirmed using the Shapiro-Wilk and Skewness tests. An independent t-test was used for differences between the two groups.

Data analysis revealed that out of 721 students, 441 (61.20%) was female. The mean and deviation of the students’ age was 22.50 (SD = 1.21). Most of the students 577 (80%) were in their final semester. Also, of the total 365 faculties, the majority were female 253 (69.31%) with a mean of age 44.06 (SD = 7.46) and an age range of 22–65. The academic rank of most nursing faculty members 156 (21.60%) was assistant professor (Table  1 ).

The results of the study showed that in both groups the highest scores achieved by the students and expected by the nursing faculty members were work readiness and professional development with a mean and standard deviation of 3.54 (0.39) and 4.30 (0.45) respectively. The lowest score for both groups was also evidence-based nursing care with a mean and standard deviation of 2.74 (0.55) for students and 3.74 (0.57) for nursing faculty members (Table  2 ).

Also, the result of the study showed that the highest expected competency score from the nursing faculty members’ point of view was the safety subscale. In other words, faculty members expected nursing students to acquire safety competencies at the highest level and to be able to provide safe care independently according to the rating scale (Mean = 4.51, SD = 0.45). The mean score of the competencies achieved by the students was not above 3.77 in any of the subscales and the highest level of competency achievement according to self-report of students was related to safety competencies (mean = 3.77, SD = 0.51), preventive health services (mean = 3.69, SD = 0.79), values and ethical codes (mean = 3.67, SD = 0.77), and procedural/clinical skills (mean = 3.67, SD = 0.71). The other competency subscales from the perspective of the two groups are presented in Table  3 , from highest to lowest score.

The analysis of core competencies achieved and expected from both students’ and nursing faculty members’ perspectives revealed that, firstly, there was a significant difference between the mean scores of the two groups in all five core competencies ( P  < .001) and that the highest mean difference was related to evidence-based care with mean diff = 1 and the lowest mean difference was related to professional care process with mean diff = 0.70 (Table  4 ).

Table  5 indicates that there was a significant difference between the mean scores achieved by students and nursing faculty members in all 5 core competencies and 17 sub-core Competencies ( p  < .001).

The study aimed to determine the difference between nursing students’ self-perceived level of competence and the level of competence expected of them by their nursing faculty members. The study results indicate that students scored highest in work readiness and professional development. However, they were not independent in this competency and required support. The National League for Nursing (NLN) recognizes nursing professional development as the goal of nursing education programs [ 47 ] However, Aguayo-Gonzalez [ 48 ] believes that the appropriate time for professional development is after entering a clinical setting. This theme includes personal characteristics, legality, clinical/ procedural skills, patient safety, preventive health services, and mentoring competence. Personality traits of nursing students are strong predictors of coping with nursing stress, as suggested by Imus [ 49 ]. These outcomes reflect changes in students’ individual characteristics during their nursing education. Personality changes, such as the need for patience and persistence in nursing care and understanding the nurse identity prepare students for the nursing profession, which is consistent with the studies of Neishabouri et al. [ 50 ]. Although the students demonstrated a higher level of competence in this theme, an examination of the items indicates that they can still not adapt to the challenges of bedside nursing and to use coping techniques. This presents a concerning issue that requires attention and resolution. Previous studies have shown that nursing education can be a very stressful experience [ 51 , 52 , 53 ].

Of course, there is no consensus on the definition of professionalism and the results of studies in this field are different. For example, Akhtar et al. (2013) identified common viewpoints about professionalism held by nursing faculty and students, and four viewpoints emerged humanists, portrayers, facilitators, and regulators [ 54 ]. The findings of another study showed that nursing students perceived vulnerability, symbolic representation, role modeling, discontent, and professional development are elements that show their professionalism [ 55 ]. The differences indicate that there may be numerous contextual variables that affect individuals’ perceptions of professionalism.

The legal aspects of nursing were the next item in this theme that students needed help with. The findings of studies regarding the legal competence of newly graduated nursing students are contradictory reported that only one-third of nurse managers were satisfied with the legal competence of newly graduated nursing students [ 56 , 57 ]. Whereas the other studies showed that legality was the highest acquired competence for newly graduated nursing students [ 58 , 59 ]. However, the results of this study indicated that legality may be a challenge for newly graduated nursing students. Benner [ 43 ] highlighted the significant change for new graduates in that they now have full legal and professional responsibility for the patient. Tong and Epeneter [ 60 ] also reported that facing an ethical dilemma is one of the most stressful factors for new graduates. Therefore, the inexperience of new graduates cannot reduce the standard of care that patients expect from them [ 60 ]. Legal disputes regarding the duties and responsibilities of nurses have increased with the expansion of their roles. This is also the case in Iran. Nurses are now held accountable by law for their actions and must be aware of their legal obligations. To provide safe healthcare services, it is essential to know of professional, ethical, and criminal laws related to nursing practice. The nursing profession is accountable for the quality of services delivered to patients from both professional and legal perspectives. Therefore, it is a valuable finding that nurse managers should support new graduates to better deal with ethical dilemmas. Strengthening ethical education in nursing schools necessitates integrating real cases and ethical dilemmas into the curriculum. Especially, Nursing laws are missing from Iran’s undergraduate nursing curriculum. By incorporating authentic case studies drawn from clinical practice, nursing schools provide students with opportunities to engage in critical reflection, ethical analysis, and moral deliberation. These real cases challenge students to apply ethical principles to complex and ambiguous situations, fostering the development of ethical competence and moral sensitivity. Furthermore, ethical reflection and debriefing sessions during clinical experiences enable students to discuss and process ethical challenges encountered in practice, promoting self-awareness, empathy, and professional growth. Overall, by combining theoretical instruction with practical application and the use of real cases, nursing schools can effectively prepare future nurses to navigate ethical dilemmas with integrity and compassion.

However, the theme of evidence-based nursing care was the lowest scoring, indicating that students need help with this theme. The findings from studies conducted in this field are varied. A limited number of studies reported that nursing students were competent to implement evidence-based care [ 61 ], while other researchers reported that nursing students’ attitudes toward evidence-based care to guide clinical decisions were largely negative [ 20 , 62 ]. The principal barriers to implementing evidence-based care are lack of authority to change patient care policy, slow dissemination of evidence and lack of time at the bedside to implement evidence [ 10 ], and lack of knowledge and awareness of the process of searching databases and evaluating research [ 63 ]. While the European Higher Education Area (EHEA) framework and the International Council of Nurses Code of Ethics introduce the ability to identify, critically appraise, and apply scientific information as expected learning outcomes for nursing students [ 64 , 65 ], the variation in findings highlights the complexity of the concept of competence and its assessment [ 23 ]. Evidence-Based Nursing (EBN) education for nursing students is most beneficial when it incorporates a multifaceted approach. Interactive workshops play a crucial role, providing students with opportunities to critically appraise research articles, identify evidence-based practices, and apply them to clinical scenarios. Simulation-based learning further enhances students’ skills by offering realistic clinical experiences in a safe environment. Additionally, clinical rotations offer invaluable opportunities for students to observe and participate in evidence-based practices under the guidance of experienced preceptors. Journal clubs foster a culture of critical thinking and ongoing learning, where students regularly review and discuss current research articles. Access to online resources such as databases and evidence-based practice guidelines allows students to stay updated on the latest evidence and best practices. To bridge the gap between clinical practice and academic theory, collaboration between nursing schools and healthcare institutions is essential. This collaboration can involve partnerships to create clinical learning environments that prioritize evidence-based practice, inter professional education activities to promote collaboration across disciplines, training and support for clinical preceptors, and continuing education opportunities for practicing nurses to strengthen their understanding and application of EBN [ 66 ]. By implementing these strategies, nursing education programs can effectively prepare students to become competent practitioners who integrate evidence-based principles into their clinical practice, ultimately improving patient outcomes.

The study’s findings regarding the second objective showed that nursing faculty members expected students to achieve the highest level of competence in work readiness and professional development, and the lowest in evidence-based nursing care competence. The results of the studies in this area revealed that there is a lack of clarity about the level of competence of newly graduated nursing students and that confusion about the competencies expected of them has become a major challenge [ 13 , 67 ]. Evidence of nurse managers’ perceptions of newly graduated nursing student’s competence is limited and rather fragmented. There is a clear need for rigorous empirical studies with comprehensive views of managers, highlighting the key role of managers in the evaluation of nurse competence [ 1 , 9 ]. Some findings also reported that nursing students lacked competence in primary and specialized care after entering a real clinical setting [ 68 ] and that nursing managers were dissatisfied with the competence of students [ 30 ].

The results of the present study on the third objective confirmed the gap between expected and achieved competence requirements. The highest average difference was related to evidence-based nursing care, and the lowest mean difference was related to the professional nursing process. The findings from studies in this field vary. For instance, Brown and Crookes [ 13 ] reported that newly graduated nursing students were not independent in at least 26 out of 30 competency domains. Similar studies have also indicated that nursing students need a structured program after graduation to be ready to enter clinical work [ 30 ]. It can be stated that the nursing profession does not have clear expectations of the competencies of newly graduated nursing students, and preparing them for entry into clinical practice is a major challenge for administrators [ 13 ]. These findings can be explained by the Duchscher transition shock [ 69 ]. It is necessary to support newly graduated nursing students to develop their competence and increase their self-confidence.

The interesting but worrying finding was the low expectations of faculty members and the low scores of students in the theme of evidence-based care. However, nursing students need to keep their competencies up to date to provide safe and high-quality care. The WHO also considers the core competencies of nurse educators to be the preparation of effective, efficient, and skilled nurses who can teach the evidence-based learning process and help students apply it clinically [ 44 ]. The teaching of evidence-based nursing care appears to vary across universities, and some clinical Faculties do not have sufficient knowledge to support students. In general, it can be stated that the results of the present study are in line with the context of Iran. Some of the problems identified include a lack of attention to students’ academic talent, a lack of a competency-based curriculum, a gap between theory and clinical practice, and challenges in teaching and evaluating the achieved competencies [ 42 ].

Strengths and limitations

The study was conducted on a national level with a sizable sample. It is one of the first studies in Iran to address the gap between students’ self-perceived competence levels and nursing faculty members’ expected competency levels. Nevertheless, one of the limitations of the study is the self-report nature of the questionnaire, which may lead to social desirability bias. In addition, the COVID-19 pandemic coinciding with the student’s first and second years could potentially impact their educational quality and competencies. The limitations established during the outbreak negatively affected the nursing education of students worldwide.

Acquiring nursing competencies is the final product of nursing education. The current study’s findings suggest the existence of an academic-practice gap, highlighting the need for educators, faculty members, and nursing managers to collaborate in bridging the potential gap between theory and practice. While nursing students were able to meet some expectations, such as value and ethical codes, there is still a distance between expectations and reality. Especially, evidence-based care was identified as one of the weaknesses of nursing students. It is recommended that future research investigates the best teaching strategies and more objective assessments of competencies. The findings of this study can be used as a guide for the revision of undergraduate nursing education curricula, as well as a guide for curriculum development based on the development of competencies expected of nursing students. Nursing managers can identify existing gaps and plan to fill them and use them for the professionalization of students. This requires the design of educational content and objective assessment tools to address these competencies at different levels throughout the academic semester. This significant issue necessitates enhanced cooperation between healthcare institutions and nursing schools. Enhancing nursing education requires the implementation of concrete pedagogical strategies to bridge the gap between theoretical knowledge and practical skills. Simulation-based learning emerges as a pivotal approach, offering students immersive experiences in realistic clinical scenarios using high-fidelity simulators [ 70 ]. Interprofessional education (IPE) is also instrumental, in fostering collaboration among healthcare professionals and promoting holistic patient care. Strengthening clinical preceptorship programs is essential, with a focus on providing preceptors with formal training and ongoing support to facilitate students’ clinical experiences and transition to professional practice [ 71 ]. Integrating evidence-based practice (EBP) principles throughout the curriculum cultivates critical thinking and inquiry skills among students, while technology-enhanced learning platforms offer innovative ways to engage students and support self-directed learning [ 72 ]. Diverse and comprehensive clinical experiences across various healthcare settings ensure students are prepared for the complexities of modern healthcare delivery. By implementing these practical suggestions, nursing education programs can effectively prepare students to become competent and compassionate healthcare professionals.

Data availability

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

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Acknowledgements

The authors extend their gratitude to all the nursing students and faculties who took part in this study.

This article is part of research approved with the financial support of the deputy of research and technology of Tabriz University of Medical Sciences.

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M P: conceptualized the study, data collection, analysis and interpretation, drafting of manuscript; V Z: conceptualized the study, analysis and interpretation, drafting of manuscript; LV: conceptualized the study, data collection and analysis, manuscript revision; A Gh: conceptualized the study, data collection, analysis, and drafting of manuscript; S M: conceptualized the study, analysis, and drafting of manuscript; M Gh: data collection, analysis, and interpretation, drafting of manuscript; All authors read and approved the final manuscript.

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Purabdollah, M., Zamanzadeh, V., Ghahramanian, A. et al. Competency gap among graduating nursing students: what they have achieved and what is expected of them. BMC Med Educ 24 , 546 (2024). https://doi.org/10.1186/s12909-024-05532-w

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Original research article, consumption of fruits and vegetables and its association with sleep duration among finnish adult population: a nationwide cross-sectional study.

• 1 Doctoral School of Health Sciences, University of Helsinki, Helsinki, Finland
• 2 Finnish Institute for Health and Welfare, Helsinki, Finland
• 3 Master School, Health and Wellbeing, Turku University of Applied Sciences, Turku, Finland

Introduction: Sleep and diet are crucial determinants of overall health and wellbeing, with the potential to mutually influence each other. This study examined the association between sleep duration and fruits and vegetables (FV) consumption among Finnish adults.

Methods: The study analyzed data from the National FinHealth 2017 Study involving 5,043 adults aged 18 years and above. Participants reported their habitual sleep duration, and dietary consumption through a validated self-administered questionnaire. Confounders such as demographic, socio-economic factors, and chronotype were considered. A sensitivity analysis, which excluded energy under-reporters, was conducted to validate the findings.

Results: Mean dietary consumption was compared across three sleep duration categories (short, normal, long), revealing that short sleepers consumed 37 g/d fewer FV, and long sleepers consumed 73 g/d fewer FV than normal sleepers. Binary logistic regression analyses consistently demonstrated significant negative association between FV consumption and both short and long sleep duration across all models, even when adjusted for a range of covariates. Linear regression analyses revealed a positive but non-significant association between sleep duration and FV consumption that became significant when excluding energy under-reporters, particularly in model 1.

Discussion: This study suggests a consistent pattern where deviation from normal sleep duration was associated with decreased FV consumption, suggesting the need for considering sleep patterns in dietary intervention. The substantial role of accurate energy reporting in explaining these associations is highlighted. Further research, including longitudinal studies, is needed to better understand the mechanisms underlying these associations.

Introduction

Sleep and diet are critical determinants of overall health and wellbeing ( 1 ). It is recommended that a healthy adult should sleep between 7 to 9 h a night ( 2 , 3 ). However, the modern landscape, shaped by lifestyle dynamics and environmental triggers such as sedentary habits and dietary choices, has led to a concerning surge in adult sleep deprivation ( 4 , 5 ). Finland, for instance, has witnessed an upswing in sporadic insomnia symptoms ( 6 ) accompanied by reduced sleep duration in adult population ( 7 ). This emerging public health concern is amplified by the link between inadequate sleep and adverse health outcomes, including cardiovascular diseases ( 8 ), cognitive malfunction ( 9 ), and increased all-cause mortality ( 10 ).

Recognizing the intricate relationship between health outcomes and dietary choices ( 11 ), substantial efforts have been directed toward enhancing people’s food-related knowledge ( 12 – 14 ). However, certain aspects of modern life, such as time constraints, easily accessible fast foods, stress, and inadequate sleep, collectively contribute to unhealthy food choices ( 15 , 16 ). The World Health Organization (WHO) recommends a daily consumption of at least 400 g of fruits and vegetables, a guideline underpinned by the well-established benefits of substantial FV intake in mitigating the risk of chronic diseases ( 17 – 19 ). The recently updated Nordic Nutrition Recommendations for 2023 advocate an even higher daily consumption of at least 500 to 800 g of vegetables, fruits, and berries, with half of the consumption coming from vegetables ( 20 ). Unfortunately, data from many countries, including Finland shows non-compliance ( 21 – 24 ). In Finland, for example, only 14% of men and 22% of women met the current national recommendation of consuming a minimum of 500 g of vegetables, fruits, and berries per day ( 25 , 26 ).

While the significance of adequate sleep and consumption of FV for overall health is becoming more evident through numerous studies, a notable gap exists in comprehending the potential interplay between these factors within the general population. Existing research has primarily concentrated on specific demographic segments, including pregnant women, adolescents, children, young adults, and the elderly, often with limited sample sizes ( 27 – 33 ). Consequently, a comprehensive understanding of these associations across a broader spectrum of the population remains elusive. To bridge this gap, our study embarked on a nationwide cross-sectional analysis encompassing a larger and more diverse adult cohort.

Chronotype, which reflects an individual’s preference for timing of activities during the day (morning or evening preference), has demonstrated its influence on both sleep patterns and dietary choices in prior research ( 34 – 37 ). However, previous studies investigating the link between sleep duration and FV consumption have often overlooked chronotype as a potential confounder. Including chronotype in the analysis allows us to delve into how an individual’s innate preference for activity timing interacts with their sleep duration and dietary behaviors. This aspect of our study is pivotal as it contributes an additional layer of understanding when examining the association between sleep duration and dietary behaviors.

Our study provides a holistic perspective on the interactions between sleep duration and fruits and vegetable consumption. We aim to specifically explore how sleep duration influence fruits and vegetable consumption and vice-versa . Additionally, we will investigate the role of chronotype as a potential confounder in the associations between sleep duration and fruits and vegetables consumption.

The data for this study were derived from the National FinHealth 2017 Study, which is a population-based cross-sectional health survey. It comprises of a representative sample of individuals aged 18 and older residing in Finland. Participants were selected through stratified random sampling methods based on gender, age, and geographical location. Out of the eligible sample ( n  = 10,247) who received an invitation letter to a health examination and to complete a self-administered questionnaire by mail, 58.1% participated in the health examination. During this examination, participants were provided with additional questionnaires, including the Food Frequency Questionnaire (FFQ), which could also be completed electronically. Of those who took part in the health examination 86.1% returned the FFQ. Exclusions from the study were made for various reasons: incomplete FFQs (110 cases), duplicate responses (9 cases) and withdrawal of consent (7 cases). To ensure the credibility of the dietary data, FFQs that reported extreme and implausible energy intake values were also excluded (51 FFQs). This was based on daily energy intake thresholds that marked the lowest and highest 0.5% of sex-specific energy intake for monitoring dietary intake in large cohort studies ( 38 , 39 ). After these exclusions, the total sample size was narrowed to 5,125 participants. A further exclusion was made due to missing information on sleep duration (80 cases), resulting in a final analytical sample of 5,043 adults for this study. The pattern of missing data was evaluated using Little’s MCAR test, which yielded a Chi-Square statistic of 2.167 with one degree of freedom ( p  = 0.14), suggesting that the missingness was random. As a result, listwise deletion was employed to handle missing data in the analysis.

The study protocol and method of the FinHealth 2017 study is described in detail elsewhere ( 40 , 41 ). Ethical approval for the FinHealth 2017 Study was obtained from the Coordinating Ethics Committee for the Hospital District of Helsinki and Uusimaa (Reference 37/13/03/00/2016), and all participants provided written informed consent. In addition, for this study a permission was acquired from the institutional review board at the Finnish Institute for Health and Welfare (THL) to further analyze the dataset (Reference FT2019_025). All procedures were performed in accordance with the valid guidelines and regulations as well as the Declaration of Helsinki and its amendments.

Study variables

This study examined two pivotal variables: sleep duration and total fruit and vegetable consumption (TFVC). Sleep duration was assessed through a self-administered questionnaire. Participants were asked to report their habitual sleep duration with the question, “How many hours do you sleep in 24 h?” They were instructed to provide their response in hours and minutes, reflecting their average sleep pattern. For subsequent analysis, minutes were converted into hours, and the total sleep duration was then computed. The resulting total sleep duration was further classified into three categories: short sleepers (less than 7 h per day), normal sleepers (7–9 h per day, the reference group), and long sleepers (more than 9 h per day).

The primary dietary variable of interest was the consumption of fruits (including citrus fruits, apple, berries, other fresh and canned fruits) and vegetables (including green leafy vegetables, root-vegetables, cabbages, mushrooms, legumes, fruit vegetables, other fresh and canned vegetables). The dietary information was gathered using a validated 134-item self-administered semi-quantitative food frequency questionnaire (FFQ) ( 42 – 44 ). Participants reported their habitual diet over the past 12 months, indicating the average consumption of each food item with a scale of 10 frequency categories ranging from none to six or more times a day (none, less than once a month, 1–3 times a month, once a week, 2–4 times a week, 5–6 times a week, once a day, 2–3 times a day, 4–5 times a day, and 6 or more times a day). The portion sizes were predefined and expressed in common household and natural units (e.g., glass, or slice). The average daily food consumption (g/day) and energy intake were calculated using the FINESSI software of THL and the Finnish National Food Composition Database (FINELI) ( 39 ).

Background variables

The FinHealth 2017 Study included information on background variables including gender, age, education, employment, household income, cohabitation, the number of household members, the number of living children, Body Mass Index (BMI), physical activity level, smoking, alcohol consumption, total energy intake and chronotype. These variables were included in the statistical regression models as control variables, reflecting their established associations with sleep duration in previous literature.

For further analysis, age, education, employment, household income, cohabitation, body-mass index (BMI), physical activity level and chronotypes were grouped into different categories. Participants were categorized by age into younger adults (18–34 years), middle-aged adults (35–64 years), or older adults (65 years or older). BMI was grouped in four categories as follows: BMI < 18.5 (underweight), BMI 18.5–24.9 (normal weight), BMI 25.0–29.9 (overweight), or BMI > 30.0 (obese).

Physical activity levels over the previous 12 months were assessed by four categories: inactive (light activities such as reading and watching television); moderately active (walking, gardening or other activities ≥ 4 h/week); active (running, swimming or other physically demanding activities ≥ 3 h/week); or very active (competition or other heavy sports several times/week). The categories “active” and “very active” were combined for the present study as only a few participants were classified as “very active.”

Chronotype was assessed using self-evaluation questions with four available options: “definitely a morning type,” “rather more a morning than an evening type,” “rather more an evening than a morning type” and “definitely an evening type.” Participant’s response was further categorized into morning type, intermediate type (combining “rather more a morning than an evening type” and “rather more an evening than a morning type,” and evening type during analysis).

Education levels were expressed as basic (elementary/basic/lower secondary education), intermediate (vocational/upper secondary/high school/non-university lower education), or higher (college/university education). Working status was categorized as employed (paid job/self-employed/unpaid employment in a family-owned business, apprenticeship, and paid internship), or outside of work (unemployed/student/unpaid internship/retired/on family leave/stay-at-home mother/father).

Household income was grouped based on annual income before tax reduction as low (lowest through 35,000 €), middle (35,001 to 60,000 €), or high (60,001 € and higher). Participants in married/cohabitated/registered partnership were grouped as living together, whereas those who were single/separated/divorced/widowed were grouped as living alone.

Statistical analyses

Descriptive statistics were calculated, and FV consumption compared between sleep categories using analyses of covariance, followed by the Bonferroni multiple comparisons post-hoc test with adjustments for age, gender and energy intake were made where relevant.

Linear regression analysis was employed to explore the potential influence of sleep duration on FV consumption, employing three models with increasing levels of covariate adjustment. Model 1 adjusted for basic covariates such as age, gender and total energy intake. Model 2 further adjusted for an expanded set of covariates, including BMI, education, employment, marital status, household income, the number of household members, the number of living children, smoking, and alcohol intake. Model 3 included all the covariates from Model 2, along with chronotype as an additional covariate.

Binary logistic regression analysis was undertaken to explore the potential influence of FV consumption across different sleep categories: short sleep vs. normal sleep, and long sleep vs. normal sleep, adjusting for the same set of covariates used in the linear regression analysis. To estimate the magnitude of differences between normal sleepers and short or long sleepers, effect sizes were calculated using Cohen’s d and the effect-size correlation rYλ.

A sensitivity analysis was conducted by repeating both the linear and logistic regression analyses after excluding participants identified as energy under-reporters. Energy misreporting was assessed by calculating the ratio of reported energy intake to predicted Basal Metabolic Rate (BMR). Participants with an energy intake to BMR ratio (EI:BMR) of 1.14 or less were considered under-reporters and were excluded from this secondary analysis ( 39 ).

All statistical analyses were performed using the IBM Statistical Package for Social Sciences (SPSS) Statistics, Version 28 (International Business Machines Corporation, Armonk, NY, United States).

Table 1 depicts the distribution of the study population across different sleep duration categories. Of the 5,043 participants, 21% were short sleepers, 76.1% were normal sleepers, and the remaining 2.9% were long sleepers. The mean sleep duration for short sleepers was 6.0 h (SD = 0.6), for normal sleepers 7.7 h (SD = 0.6), and for long sleepers 10.1 h (SD = 0.7). The majority of the participants identified themselves as intermediate types (61.7%), while 22.4% identified as morning types and 15.9% as evening types. The mean age of the participants was 55 years (SD = 16.0), with 55.9% being female, and 71.2% being in marital, cohabiting, or registered relationships. The education levels showed diversity: 19.2% held basic education, 49.3% intermediate, and 31.5% higher education. Regarding employment, half of the participants were employed, and the other half were not part of the workforce. The participants’ income levels varied, with 36.4% falling within the low-income bracket, 32.0% within middle income, and 312.6% within higher income bracket. Household configurations displayed variety, with 71.6% having 1–2 members, 26.8% having 3–5 members, and 1.7% exceeding 5 members. In terms of children in the household, 22.5% had none, 69.2% had 1–3 children, and 8.3% had more than 3 children. Regarding substance use, 17.1% smoked daily, and 20.9% consumed alcohol at a risk level. Notably, nearly half of the participants were overweight (40.4%) or obese (25.1%). Physical activity levels varied, with 46.9% of participants reporting a moderately active level followed by 29.0% who were active and 24.1% who were inactive.

Table 1 . Study population by sleep categories.

Table 2 displays the mean differences in the consumption of various FV across different sleep duration categories. It was observed that normal sleepers had higher consumption of FV including all sub-groups when compared to both short and long sleepers. These differences were statistically significant after adjusting for covariates including age, gender, and energy intake with significance maintained at the 0.05 level following Bonferroni correction for multiple comparisons.

Table 2 . Mean difference in consumption of fruits and vegetables across sleep duration categories.

Effect sizes measured by Cohen’s d, were small but notable. For normal vs. short sleepers, Cohen’s d was 0.16 and for normal vs. long sleepers, it was 0.21. Corresponding effect-size correlations were 0.08 and 0.10, respectively, suggesting a small yet positive association between sleep duration and FVs consumption.

In the vegetable sub-group, significant differences were observed in the consumption of green leafy vegetables, root vegetables and fruit vegetables (e.g., tomatoes, cucumbers), between normal and short sleepers. Similarly, for normal vs. long sleepers, significant differences were again noted for green leafy vegetables and fruit vegetables. However, other fresh and canned vegetables such as cabbage, mushroom, onion, peas and beans did not exhibit significant differences.

In the fruit sub-groups, a significant mean difference was observed in the consumption of berries and other fresh and canned fruits between normal and short sleepers. Conversely, for normal vs. long sleepers, the only significant difference was observed in apple consumption.

Table 3 presents the results of linear regression analyses exploring the association between sleep duration and TFVC, along with its sub-groups. Initially, the association between sleep duration and TFVC was observed to be positive across all models, though it did not reach statistical significance. This trend persisted even with the inclusion of additional covariates with slight decrease in the strength of the association in Models 2 and 3, which did not significantly impact the associations.

Table 3 . Association between sleep duration and FV consumption.

However, when a sensitivity analysis was conducted by excluding energy under reporters ( n  = 1,926)-the association between sleep duration and TFVC in Model 1 became statistically significant (B = 11.9, 95% CI [2.6, 21.3], p  = 0.012), indicating a stronger relationship after removing potential bias from underreporting. Although this significance was nearly reached in Models 2 and 3 ( p  = 0.06), the proximity to significance suggests that energy underreporting might be attenuating the true magnitude of the observed relationships.

In the subgroup analysis, the pattern was somewhat similar, with certain food groups such as root vegetables and other fresh and canned vegetables showing a significant increase in the strength of association in the sensitivity analysis compared to the full cohort. For instance, root vegetables showed a significant association with sleep duration in Model 1 after excluding underreporters (B = 1.6, p  = 0.029). The association for other fresh and canned vegetables also became significant (B = 2.6, p  = 0.015 in Model 1) in the sensitivity analysis.

Conversely, no significant associations were observed for green leafy vegetables, fruit vegetables, citrus fruits, apples, berries, and other fresh and canned fruits after excluding energy underreporters, similar to the original analysis.

Table 4 presents the outcomes of binary logistic regression models that examined how the consumption of various fruits and vegetables is associated with the likelihood of short or long sleep durations, using normal sleep as the reference category.

Table 4 . Association between FV consumption and sleep duration categories.

For individuals with short sleep duration, a consistent negative association with TFVC was observed across all models. This pattern suggests that short sleepers tend to consume fewer FV compared to those with normal sleep duration ( p  < 0.001). A similar negative trend was observed for specific sub-groups such as green leafy vegetables, root vegetables, fruit vegetables, and berries, though the strength and significance of association varied across the models. Conversely, no significant associations were found for the consumption of other fresh and canned vegetables and citrus fruits ( p  > 0.05), except for apple consumption, which showed a negative association with short sleep only in Model 1.

Among long sleepers, similar negative associations were reported with TFVC that were significant in Models 1 and 3. The consumption of green leafy vegetables showed a notable reduction in model 1, and TFVC showed a consistent negative association across all models. However, no significant associations were found for root vegetables, other fresh and canned vegetables, and citrus fruits in any models. The relationship with apple consumption remained significantly negative across all models for long sleepers, while the associations for berries and other fresh and canned fruits did not maintain significance in Models 2 and 3.

Unlike linear regression, in the sensitivity analysis, the logistic regression revealed a persistent negative association between short sleep duration and total fruit and vegetable consumption (Beta = −0.001, p  < 0.001 across all models), indicating that the findings were stable even when potentially biased self-reported energy intake was accounted for. Similarly, the negative associations for specific sub-groups such as green leafy vegetables, root vegetables, and fruit vegetables remained significant, with minor variations in Beta coefficients and Wald statistics. For long sleepers, the sensitivity analysis generally showed a slight decrease in the strength of associations, yet the negative trend in total fruit and vegetable consumption remained significant in Models 1 and 3, with similar patterns observed for specific sub-groups. This sensitivity analysis result confirms the validity of our original findings, suggesting that the associations between TFVC and sleep duration are not substantially altered when controlling for energy underreporting. The detailed outcomes of the sensitivity analysis are presented in Supplementary Table 2 .

Overall, the results indicate that both short and long sleep durations were associated with a decrease in the consumption of certain fruits and vegetables when compared to normal sleep durations.

This study examined the associations between FV consumption and sleep duration in a population-based cohort of Finnish adults, revealing a significant association between sleep duration and FV consumption. This included various sub-groups such as green leaf, root and fruit vegetables. Our findings are in line with existing literature, which consistently demonstrates reduced FV consumption among individuals with either inadequate or excessive sleep durations ( 27 , 29 , 30 , 32 , 33 , 45 ). A closely related study by Noorwali et al. ( 31 ), focused on similar exposure and outcome variables in UK adults aged 19–65. Both studies categorized sleep duration into short, normal, and long sleepers, enabling direct comparisons. However, distinctions in dietary assessment methods exist. Noorwali et al. using a 4-day food diary validated with biomarkers, while our study employed a 12-month FFQ, assessing habitual diet and providing insights into long-term dietary patterns. Despite these methodological differences, both studies consistently reported reduced FV consumption among individuals with inadequate or excessive sleep durations.

One possible explanation for the observed negative association between short and long sleepers and FV consumption, as supported by previous studies, is that individuals with normal sleep durations are more likely to adopt healthy lifestyles. These lifestyles are often characterized by high FV consumption, regular physical activity, and better quality of life ( 46 ). Hormonal mechanisms might also play a role. Certain FVs such as cherries, kiwi, tomatoes, and cucumbers contain high levels of melatonin, a hormone vital for regulating circadian rhythms and sleep patterns ( 47 ). The significant associations we observed between sleep duration and specific FV sub-groups, such as root, fruit, and green leafy vegetables, further support these notions.

Moreover, fruits and vegetables serve as abundant sources of a variety of micronutrients and non-nutrient bioactive compounds, including vitamins, phytochemicals like (poly) phenolic compounds and carotenoids, vital minerals such as potassium, calcium, and magnesium, and dietary fiber. Their impact on human health is significant, attributed to their medicinal properties such as anti-inflammatory, antimicrobial, antioxidant, anticancer, and their preventive effects against various chronic diseases ( 48 ). It is plausible to suggest that the broader spectrum of health-promoting properties inherent in various fruits and vegetables, as outlined by their bioactive components, may have influenced the observed associations between sleep duration and specific FV-subgroups.

In our analysis, we observed that including a comprehensive set of covariates, did not significantly change the observed association between sleep duration and FV consumption. This suggests that the relationship between sleep duration and FV consumption is robust and remains consistent across different models. In our study, we expanded beyond the conventional covariates, such as age, gender, BMI, physical activity level and socioeconomic status, by incorporating chronotype as a covariate in Model 3. This addition did not significantly alter the associations, indicating that these dietary behaviors are associated with sleep duration independently of the chronotype. The inclusion of chronotype as a covariate was informed by emerging research indicating its influence on dietary patterns. Studies have shown that evening chronotypes are often associated with unhealthy dietary behaviors, including a propensity for obesity-related eating habits ( 34 , 49 , 50 ). However, our findings suggest that while chronotype may play a role in general dietary preferences and behaviors, its impact on the specific relationship between sleep duration and FV consumption is minimal. This is evident from the consistency of the association between sleep duration and various types of FV consumption, as indicated by the similar B values and p -values observed across Models 2 and 3 in both linear and logistic regression analyses.

Furthermore, we examined the likelihood of individuals falling into specific sleep duration categories based on their dietary habits, providing insights into the predictive value of sleep patterns for FV consumption. Our finding highlights that sleep duration categories can serve as predictive factors for FV consumption, albeit with relatively small effect sizes. A similar finding was observed in another study, which reported that shorter sleep duration at night was associated with lower FV consumption the following day ( 33 ). This reinforces the idea that sleep patterns may have relevance in understanding dietary choices, as well as in formulating dietary interventions. Additional research including longitudinal studies is essential to understand these complex relationship and to ascertain its implications on public health and lifestyle intervention.

Strengths and limitations

A notable strength of our study is the utilization of a large, randomly selected population-based sample, which enhances the statistical power and potential generalizability of our findings. Nonetheless, it is important to acknowledge certain limitations. One key aspect is our approach to managing missing data. We employed listwise deletion based on the assumption, supported by Little’s MCAR test (Chi-Square = 1.400, df = 2, p  = 0.497) that the data was missing completely at random (MCAR). However, the potential for non-random missingness cannot be entirely ruled out. While listwise deletion is a valid approach under the MCAR condition, the potential for non-random missingness remains, and if the missing data are not MCAR, it could introduce bias and affect the generalizability of our results.

Furthermore, the reliance on self-reported data for sleep duration and food consumption may introduce recall and reporting biases such as under-or over-reporting. To address this issue, our statistical analyses included adjustments for factors like age, gender, and total energy intake. These adjustments, particularly for energy intake, help to some extent in mitigating biases related to dietary reporting. However, it is important to note that such adjustments cannot fully eliminate the inherent limitations of self-reported dietary data. Additionally, the Finnish Food Frequency Questionnaire (FFQ), though widely validated and ensuring comprehensive coverage, presents inherent limitations. Its length, comprising 134-items, may result in participant fatigue and consequent reporting errors influenced by participant characteristics.

These limitations, combined with the cross-sectional nature of our study, imply that we can identify associations, but causality cannot be inferred.

In summary, this study highlights a significant link between sleep duration and FV consumption among adults. The associations persisted across most FV subgroups, even after conducting sensitivity analysis, suggesting a strong and consistent relationship. While the findings indicate modest effect sizes, they carry substantial statistical and practical implications. Targeted interventions focusing on FV sub-groups with pronounced associations, such as green leafy vegetables and fruit vegetables can lead to impactful behavior change. Additional research, particularly longitudinal studies, is needed to better understand these associations and their public health implications, especially in regions with similar population structures and dietary patterns to Finland.

Data availability statement

The data analyzed in this study is subject to the following licenses/restrictions: this study used data from National FinHealth Study conducted by THL Finland. Data can be made available from THL upon reasonable request. Requests to access these datasets should be directed to [email protected] .

Ethics statement

The studies involving humans were approved by THL Ethics Committee [email protected] . The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

AT: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. TL: Conceptualization, Supervision, Writing – review & editing. MM: Methodology, Validation, Writing – review & editing. TP: Conceptualization, Methodology, Resources, Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Open access publication was funded by Helsinki University Library.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2024.1319821/full#supplementary-material

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41. Paalanen, L, Männistö, S, Virtanen, MJ, Knekt, P, Räsänen, L, Montonen, J, et al. Validity of a food frequency questionnaire varied by age and body mass index. J Clin Epidemiol . (2006) 59:994–1001. doi: 10.1016/j.jclinepi.2006.01.002

42. Kaartinen, NE, Tapanainen, H, Valsta, LM, Similä, ME, Reinivuo, H, Korhonen, T, et al. Relative validity of a FFQ in measuring carbohydrate fractions, dietary glycaemic index and load: exploring the effects of subject characteristics. Br J Nutr . (2012) 107:1367–75. doi: 10.1017/S0007114511004296

43. Männistö, S, Virtanen, M, Mikkonen, T, and Pietinen, P. Reproducibility and validity of a food frequency questionnaire in a case-control study on breast cancer. J Clin Epidemiol . (1996) 49:401–9. doi: 10.1016/0895-4356(95)00551-X

44. Reinivuo, H, Hirvonen, T, Ovaskainen, ML, Korhonen, T, and Valsta, LM. Dietary survey methodology of FINDIET 2007 with a risk assessment perspective. Public Health Nutr . (2010) 13:915–9. doi: 10.1017/S1368980010001096

45. Noorwali, EA, Cade, JE, Burley, VJ, and Hardie, LJ. The relationship between sleep duration and fruit/vegetable intakes in UK adults: a cross-sectional study from the National Diet and nutrition survey. BMJ Open . (2018a) 8:1–9. doi: 10.1136/bmjopen-2017-020810

46. Meng, X, Li, Y, Li, S, Zhou, Y, Gan, RY, Xu, DP, et al. Dietary sources and bioactivities of melatonin. Nutrients . (2017) 9:1–64. doi: 10.3390/nu9040367

47. Jyväkorpi, SK, Urtamo, A, Kivimäki, M, and Strandberg, TE. Associations of sleep quality, quantity and nutrition in oldest-old men the Helsinki businessmen study (HBS). Eur Geriatr Med . (2021) 12:117–22. doi: 10.1007/s41999-020-00421-z

48. Karasawa, MMG, and Mohan, C. Fruits as prospective reserves of bioactive compounds: a review. Nat Prod Bioprospect . (2018) 8:335–46. doi: 10.1007/s13659-018-0186-6

49. Maukonen, M, Kanerva, N, Partonen, T, Kronholm, E, Tapanainen, H, Kontto, J, et al. Chronotype diffences in timing of energy and macronutrieint intakes: a population-based study in adults. Obes Soc . (2017) 25:608–15. doi: 10.1002/oby.21747

50. Mazri, FH, Manaf, ZA, Shahar, S, and Mat Ludin, AF. The association between Chronotype and dietary pattern among adults: a scoping review. Int J Environ Res Public Health . (2019) 17:68. doi: 10.3390/ijerph17010068

Keywords: fruits and vegetable consumption, sleep duration, chronotype, dietary habits, public health nutrition

Citation: Thapa A, Lahti T, Maukonen M and Partonen T (2024) Consumption of fruits and vegetables and its association with sleep duration among Finnish adult population: a nationwide cross-sectional study. Front. Nutr . 11:1319821. doi: 10.3389/fnut.2024.1319821

Received: 11 October 2023; Accepted: 09 April 2024; Published: 16 May 2024.

Reviewed by:

Copyright © 2024 Thapa, Lahti, Maukonen and Partonen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Anupa Thapa, [email protected]

New cardiac research will save women's lives by improving detection of heart failure

An important new study has advanced how heart failure is detected in women -- meaning more female patients can be diagnosed and at an earlier stage.

Researchers led by teams from the Universities of East Anglia (UEA), Sheffield and Leeds, have been able to fine-tune how magnetic resonance imaging (MRI) is used to detect heart failure in women's hearts, making it more accurate.

Lead author Dr Pankaj Garg, of the University of East Anglia's Norwich Medical School and a consultant cardiologist at the Norfolk and Norwich University Hospital, said: "By refining the method for women specifically, we were able to diagnose 16.5pc more females with heart failure.

"This could have huge impact in the NHS, which diagnoses around 200,000 patients with heart failure each year.

"This improved method will increase early detection, meaning more women can get life-saving treatment sooner."

In 2022, UEA and the University of Sheffield published research which showed how using MRI scans could be used to detect heart failure and which led to this technique being widely employed by medics.

When a heart starts to fail, it is unable to pump blood out effectively, and so the pressure in the heart rises.

Co-author Dr Gareth Matthews, of the University of East Anglia's Norwich Medical School, said: "Currently one of the best ways of diagnosing heart failure is to measure pressures inside the heart with a tube called a catheter.

"While this is very accurate, it is an invasive procedure, and therefore carries risks for patients, which limits its use.

"For this reason, doctors tend to use echocardiograms, which are based on ultrasound, to assess heart function, but this is inaccurate in up to 50 per cent of cases. Using MRI, we can get much more accurate images of how the heart is working."

The team was able to create an equation which allowed them to non-invasively derive the pressure in the heart using an MRI scanner.

However, previous use of this method wasn't as accurate as the researchers would have liked in diagnosing heart failure in women, especially in early or borderline disease.

Co-author Professor Andy Swift, of the University of Sheffield's School of Medicine and Population Health, said: "Women's hearts are biologically different to men's.

"Our work suggests that in heart failure women's hearts may respond differently in response to increases in pressure."

Heart failure can be classed differently, depending on the amount of blood squeezed out of the main chamber of the heart with every beat, known as the heart's ejection fraction.

Women suffer disproportionately from a type of heart failure where the pumping function of the heart is preserved but the ability of the heart to relax and fill with blood is impaired.

Echocardiography really struggles to diagnose this type of heart failure. The improvements in diagnosis from this new work will enable more of this particular cohort to be diagnosed more accurately and hopefully drive better treatments.

Co-author Dr Peter Swoboda, of the University of Leeds' Faculty of Medicine and Health, said: "The symptoms of heart failure, like breathlessness and fatigue, can have a devastating effect on patients' quality of life.

"We are increasingly recognising the importance of early diagnosis and, early treatment can improve symptoms and life expectancy.

"This research will help diagnose heart failure in women more quickly and get them established on life-saving treatments sooner."

The Government's Health and Social Care Secretary, Victoria Atkins, said: "Heart failure is a devastating condition affecting hundreds of thousands of women in the UK, so this research is a hugely positive development that could make it possible for thousands of people to get diagnosed and treated at an earlier stage.

"For the second year of our Women's Health Strategy for England, I have been clear that we need more research to look at the differences between how conditions affect men and women.

"I am delighted that this government-backed research has met this challenge so that we can get life-saving treatment to women faster."

The research was a collaboration between the University of East Anglia, the University of Leeds, the University of Sheffield, the Norfolk and Norwich University Hospital NHS Foundation Trust, the National Heart Research Institute Singapore, Duke-NUS Medical School in Singapore, Queen Mary University of London, the National Institute for Health and Care Research's Sheffield Biomedical Research Centre, the University of Amsterdam and Kocaeli City Hospital in Turkey.

It was funded by the National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre, the Wellcome Trust, and the National Medical Research Council (NMRC).

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Journal Reference :

• Ciaran Grafton-Clarke, Gareth Matthews, Rui Li, Hosamadin Assadi, Peter Swoboda, Chris Sawh, Vassilios S Vassiliou, Andrew J Swift, Pankaj Garg. 32 Sex-specific cardiac magnetic resonance pulmonary capillary wedge pressure model predicts outcomes in heart failure: a multi-centre study . European Heart Journal Open , 2024 DOI: 10.1136/heartjnl-2024-BSCMR.29

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70 years after brown v. board of education, new research shows rise in school segregation.

As the nation prepares to mark the 70th anniversary of the landmark U.S. Supreme Court ruling in Brown v. Board of Education , a new report from researchers at Stanford and USC shows that racial and economic segregation among schools has grown steadily in large school districts over the past three decades — an increase that appears to be driven in part by policies favoring school choice over integration.

Analyzing data from U.S. public schools going back to 1967, the researchers found that segregation between white and Black students has increased by 64 percent since 1988 in the 100 largest districts, and segregation by economic status has increased by about 50 percent since 1991.

The report also provides new evidence about the forces driving recent trends in school segregation, showing that the expansion of charter schools has played a major role.  

The findings were released on May 6 with the launch of the Segregation Explorer , a new interactive website from the Educational Opportunity Project at Stanford University. The website provides searchable data on racial and economic school segregation in U.S. states, counties, metropolitan areas, and school districts from 1991 to 2022. 

“School segregation levels are not at pre- Brown levels, but they are high and have been rising steadily since the late 1980s,” said Sean Reardon , the Professor of Poverty and Inequality in Education at Stanford Graduate School of Education and faculty director of the Educational Opportunity Project. “In most large districts, school segregation has increased while residential segregation and racial economic inequality have declined, and our findings indicate that policy choices – not demographic changes – are driving the increase.” 

“There’s a tendency to attribute segregation in schools to segregation in neighborhoods,” said Ann Owens , a professor of sociology and public policy at USC. “But we’re finding that the story is more complicated than that.”

Assessing the rise

In the Brown v. Board decision issued on May 17, 1954, the U.S. Supreme Court ruled that racially segregated public schools violated the Equal Protection Clause of the Fourteenth Amendment and established that “separate but equal” schools were not only inherently unequal but unconstitutional. The ruling paved the way for future decisions that led to rapid school desegregation in many school districts in the late 1960s and early 1970s.

Though segregation in most school districts is much lower than it was 60 years ago, the researchers found that over the past three decades, both racial and economic segregation in large districts increased. Much of the increase in economic segregation since 1991, measured by segregation between students eligible and ineligible for free lunch, occurred in the last 15 years.

White-Hispanic and white-Asian segregation, while lower on average than white-Black segregation, have both more than doubled in large school districts since the 1980s. 

Racial-economic segregation – specifically the difference in the proportion of free-lunch-eligible students between the average white and Black or Hispanic student’s schools – has increased by 70 percent since 1991. 

School segregation is strongly associated with achievement gaps between racial and ethnic groups, especially the rate at which achievement gaps widen during school, the researchers said.  

“Segregation appears to shape educational outcomes because it concentrates Black and Hispanic students in higher-poverty schools, which results in unequal learning opportunities,” said Reardon, who is also a senior fellow at the Stanford Institute for Economic Policy Research and a faculty affiliate of the Stanford Accelerator for Learning . 

Policies shaping recent trends 

The recent rise in school segregation appears to be the direct result of educational policy and legal decisions, the researchers said. 

Both residential segregation and racial disparities in income declined between 1990 and 2020 in most large school districts. “Had nothing else changed, that trend would have led to lower school segregation,” said Owens. 

But since 1991, roughly two-thirds of districts that were under court-ordered desegregation have been released from court oversight. Meanwhile, since 1998, the charter sector – a form of expanded school choice – has grown.

Expanding school choice could influence segregation levels in different ways: If families sought schools that were more diverse than the ones available in their neighborhood, it could reduce segregation. But the researchers found that in districts where the charter sector expanded most rapidly in the 2000s and 2010s, segregation grew the most. 

The researchers’ analysis also quantified the extent to which the release from court orders accounted for the rise in school segregation. They found that, together, the release from court oversight and the expansion of choice accounted entirely for the rise in school segregation from 2000 to 2019.

The researchers noted enrollment policies that school districts can implement to mitigate segregation, such as voluntary integration programs, socioeconomic-based student assignment policies, and school choice policies that affirmatively promote integration. 

“School segregation levels are high, troubling, and rising in large districts,” said Reardon. “These findings should sound an alarm for educators and policymakers.”

Additional collaborators on the project include Demetra Kalogrides, Thalia Tom, and Heewon Jang. This research, including the development of the Segregation Explorer data and website, was supported by the Russell Sage Foundation, the Robert Wood Johnson Foundation, and the Bill and Melinda Gates Foundation.   

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• Published: 16 May 2024

The Egyptian pyramid chain was built along the now abandoned Ahramat Nile Branch

• Eman Ghoneim   ORCID: orcid.org/0000-0003-3988-0335 1 ,
• Timothy J. Ralph   ORCID: orcid.org/0000-0002-4956-606X 2 ,
• Suzanne Onstine 3 ,
• Raghda El-Behaedi 4 ,
• Gad El-Qady 5 ,
• Amr S. Fahil 6 ,
• Mahfooz Hafez 5 ,
• Magdy Atya 5 ,
• Mohamed Ebrahim   ORCID: orcid.org/0000-0002-4068-5628 5 ,
• Ashraf Khozym 5 &
• Mohamed S. Fathy 6  

Communications Earth & Environment volume  5 , Article number:  233 ( 2024 ) Cite this article

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• Archaeology
• Geomorphology
• Hydrogeology
• Sedimentology

The largest pyramid field in Egypt is clustered along a narrow desert strip, yet no convincing explanation as to why these pyramids are concentrated in this specific locality has been given so far. Here we use radar satellite imagery, in conjunction with geophysical data and deep soil coring, to investigate the subsurface structure and sedimentology in the Nile Valley next to these pyramids. We identify segments of a major extinct Nile branch, which we name The Ahramat Branch, running at the foothills of the Western Desert Plateau, where the majority of the pyramids lie. Many of the pyramids, dating to the Old and Middle Kingdoms, have causeways that lead to the branch and terminate with Valley Temples which may have acted as river harbors along it in the past. We suggest that The Ahramat Branch played a role in the monuments’ construction and that it was simultaneously active and used as a transportation waterway for workmen and building materials to the pyramids’ sites.

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Introduction.

The landscape of the northern Nile Valley in Egypt, between Lisht in the south and the Giza Plateau in the north, was subject to a number of environmental and hydrological changes during the past few millennia 1 , 2 . In the Early Holocene (~12,000 years before present), the Sahara of North Africa transformed from a hyper-arid desert to a savannah-like environment, with large river systems and lake basins 3 , 4 due to an increase in global sea level at the end of the Last Glacial Maximum (LGM). The wet conditions of the Sahara provided a suitable habitat for people and wildlife, unlike in the Nile Valley, which was virtually inhospitable to humans because of the constantly higher river levels and swampy environment 5 . At this time, Nile River discharge was high, which is evident from the extensive deposition of organic-rich fluvial sediment in the Eastern Mediterranean basin 6 . Based on the interpretation of archeological material and pollen records, this period, known as the African Humid Period (AHP) (ca. 14,500–5000 years ago), was the most significant and persistent wet period from the early to mid-Holocene in the eastern Sahara region 7 , with an annual rainfall rate of 300–920 mm yr −1   8 . During this time the Nile would have had several secondary channels branching across the floodplain, similar to those described by early historians (e.g., Herodotus).

During the mid-Holocene (~10,000–6000 years ago), freshwater marshes were common within the Nile floodplain causing habitation to be more nucleated along the desert margins of the Nile Valley 9 . The desert margins provided a haven from the high Nile water. With the ending of the AHP and the beginning of the Late Holocene (~5500 years ago to present), rainfall greatly declined, and the region’s humid phase gradually came to an end with punctuated short wet episodes 10 . Due to increased aridity in the Sahara, more people moved out of the desert towards the Nile Valley and settled along the edge of the Nile floodplain. With the reduced precipitation, sedimentation increased in and around the Nile River channels causing the proximal floodplain to rise in height and adjacent marshland to decrease in the area 11 , 12 estimated the Nile flood levels to have ranged from 1 to 4 m above the baseline (~5000 BP). Inhabitants moved downhill to the Nile Valley and settled in the elevated areas on the floodplain, including the raised natural levees of the river and jeziras (islands). This was the beginning of the Old Kingdom Period (ca. 2686 BCE) and the time when early pyramid complexes, including the Step Pyramid of Djoser, were constructed at the margins of the floodplain. During this time the Nile discharge was still considerably higher than its present level. The high flow of the river, particularly during the short-wet intervals, enabled the Nile to maintain multiple branches, which meandered through its floodplain. Although the landscape of the Nile floodplain has greatly transformed due to river regulation associated with the construction of the Aswan High Dam in the 1960s, this region still retains some clear hydro-geomorphological traces of the abandoned river channels.

Since the beginning of the Pharaonic era, the Nile River has played a fundamental role in the rapid growth and expansion of the Egyptian civilization. Serving as their lifeline in a largely arid landscape, the Nile provided sustenance and functioned as the main water corridor that allowed for the transportation of goods and building materials. For this reason, most of the key cities and monuments were in close proximity to the banks of the Nile and its peripheral branches. Over time, however, the main course of the Nile River laterally migrated, and its peripheral branches silted up, leaving behind many ancient Egyptian sites distant from the present-day river course 9 , 13 , 14 , 15 . Yet, it is still unclear as to where exactly the ancient Nile courses were situated 16 , and whether different reaches of the Nile had single or multiple branches that were simultaneously active in the past. Given the lack of consensus amongst scholars regarding this subject, it is imperative to develop a comprehensive understanding of the Nile during the time of the ancient Egyptian civilization. Such a poor understanding of Nile River morphodynamics, particularly in the region that hosts the largest pyramid fields of Egypt, from Lisht to Giza, limits our understanding of how changes in the landscape influenced human activities and settlement patterns in this region, and significantly restricts our ability to understand the daily lives and stories of the ancient Egyptians.

Currently, much of the original surface of the ancient Nile floodplain is masked by either anthropogenic activity or broad silt and sand sheets. For this reason, singular approaches such as on-ground searches for the remains of hidden former Nile branches are both increasingly difficult and inauspicious. A number of studies have already been carried out in Egypt to locate segments of the ancient Nile course. For instance 9 , proposed that the axis of the Nile River ran far west of its modern course past ancient cities such as el-Ashmunein (Hermopolis) 13 . mapped the ancient hydrological landscape in the Luxor area and estimated both an eastward and westward Nile migration rate of 2–3 km per 1000 years. In the Nile Delta region 17 , detected several segments of buried Nile distributaries and elevated mounds using geoelectrical resistivity surveys. Similarly, a study by Bunbury and Lutley 14 identified a segment of an ancient Nile channel, about 5000 years old, near the ancient town of Memphis ( men-nefer ). More recently 15 , used cores taken around Memphis to reveal a section of a lateral ancient Nile branch that was dated to the Neolithic and Predynastic times (ca. 7000–5000 BCE). On the bank of this branch, Memphis, the first capital of unified Egypt, was founded in early Pharaonic times. Over the Dynastic period, this lateral branch then significantly migrated eastwards 15 . A study by Toonen et al. 18 , using borehole data and electrical resistivity tomography, further revealed a segment of an ancient Nile branch, dating to the New Kingdom Period, situated near the desert edge west of Luxor. This river branch would have connected important localities and thus played a significant role in the cultural landscape of this area. More recent research conducted further north by Sheisha et al. 2 , near the Giza Plateau, indicated the presence of a former river and marsh-like environment in the floodplain east of the three great Pyramids of Giza.

Even though the largest concentration of pyramids in Egypt are located along a narrow desert strip from south Lisht to Giza, no explanation has been offered as to why these pyramid fields were condensed in this particular area. Monumental structures, such as pyramids and temples, would logically be built near major waterways to facilitate the transportation of their construction materials and workers. Yet, no waterway has been found near the largest pyramid field in Egypt, with the Nile River lying several kilometers away. Even though many efforts to reconstruct the ancient Nile waterways have been conducted, they have largely been confined to small sites, which has led to the mapping of only fragmented sections of the ancient Nile channel systems.

In this work, we present remote sensing, geomorphological, soil coring and geophysical evidence to support the existence of a long-lost ancient river branch, the Ahramat Branch, and provide the first map of the paleohydrological setting in the Lisht-Giza area. The finding of the Ahramat Branch is not only crucial to our understanding of why the pyramids were built in these specific geographical areas, but also for understanding how the pyramids were accessed and constructed by the ancient population. It has been speculated by many scholars that the ancient Egyptians used the Nile River for help transporting construction materials to pyramid building sites, but until now, this ancient Nile branch was not fully uncovered or mapped. This work can help us better understand the former hydrological setting of this region, which would in turn help us learn more about the environmental parameters that may have influenced the decision to build these pyramids in their current locations during the time of Pharaonic Egypt.

Position and morphology of the Ahramat Branch

Synthetic Aperture Radar (SAR) imagery and radar high-resolution elevation data for the Nile floodplain and its desert margins, between south Lisht and the Giza Plateau area, provide evidence for the existence of segments of a major ancient river branch bordering 31 pyramids dating from the Old Kingdom to Second Intermediate Period (2686−1649 BCE) and spanning between Dynasties 3–13 (Fig.  1a ). This extinct branch is referred to hereafter as the Ahramat Branch, meaning the “Pyramids Branch” in Arabic. Although masked by the cultivated fields of the Nile floodplain, subtle topographic expressions of this former branch, now invisible in optical satellite data, can be traced on the ground surface by TanDEM-X (TDX) radar data and the Topographic Position Index (TPI). Data analysis indicates that this lateral distributary channel lies between 2.5 and 10.25 km west from the modern Nile River. The branch appears to have a surface channel depth between 2 and 8 m, a channel length of about 64 km and a channel width of 200–700 m, which is similar to the width of the contemporary neighboring Nile course. The size and longitudinal continuity of the Ahramat Branch and its proximity to all the pyramids in the study area implies a functional waterway of great significance.

a Shows the Ahramat Branch borders a large number of pyramids dating from the Old Kingdom to the 2 nd Intermediate Period and spanning between Dynasties 3 and 13. b Shows Bahr el-Libeini canal and remnant of abandoned channel visible in the 1911 historical map (Egyptian Survey Department scale 1:50,000). c Bahr el-Libeini canal and the abandoned channel are overlain on satellite basemap. Bahr el-Libeini is possibly the last remnant of the Ahramat Branch before it migrated eastward. d A visible segment of the Ahramat Branch in TDX is now partially occupied by the modern Bahr el-Libeini canal. e A major segment of the Ahramat Branch, approximately 20 km long and 0.5 km wide, can be traced in the floodplain along the Western Desert Plateau south of the town of Jirza. Location of e is marked in white a box in a . (ESRI World Image Basemap, source: Esri, Maxar, Earthstar Geographics).

A trace of a 3 km river segment of the Ahramat Branch, with a width of about 260 m, is observable in the floodplain west of the Abu Sir pyramids field (Fig.  1b–d ). Another major segment of the Ahramat Branch, approximately 20 km long and 0.5 km wide can be traced in the floodplain along the Western Desert Plateau south of the town of Jirza (Fig.  1e ). The visible segments of the Ahramat Branch in TDX are now partially occupied by the modern Bahr el-Libeini canal. Such partial overlap between the courses of this canal, traced in the1911 historical maps (Egyptian Survey Department scale 1:50,000), and the Ahramat Branch is clear in areas where the Nile floodplain is narrower (Fig.  1b–d ), while in areas where the floodplain gets wider, the two water courses are about 2 km apart. In light of that, Bahr el-Libeini canal is possibly the last remnant of the Ahramat Branch before it migrated eastward, silted up, and vanished. In the course of the eastward migration over the Nile floodplain, the meandering Ahramat Branch would have left behind traces of abandoned channels (narrow oxbow lakes) which formed as a result of the river erosion through the neck of its meanders. A number of these abandoned channels can be traced in the 1911 historical maps near the foothill of the Western Desert plateau proving the eastward shifting of the branch at this locality (Fig.  1b–d ). The Dahshur Lake, southwest of the city of Dahshur, is most likely the last existing trace of the course of the Ahramat Branch.

Subsurface structure and sedimentology of the Ahramat Branch

Geophysical surveys using Ground Penetrating Radar (GPR) and Electromagnetic Tomography (EMT) along a 1.2 km long profile revealed a hidden river channel lying 1–1.5 m below the cultivated Nile floodplain (Fig.  2 ). The position and shape of this river channel is in an excellent match with those derived from radar satellite imagery for the Ahramat Branch. The EMT profile shows a distinct unconformity in the middle, which in this case indicates sediments that have a different texture than the overlying recent floodplain silt deposits and the sandy sediments that are adjacent to this former branch (Fig.  2 ). GPR overlapping the EMT profile from 600–1100 m on the transect confirms this. Here, we see evidence of an abandoned riverbed approximately 400 m wide and at least 25 m deep (width:depth ratio ~16) at this location. This branch has a symmetrical channel shape and has been infilled with sandy Neonile sediment different to other surrounding Neonile deposits and the underlying Eocene bedrock. The geophysical profile interpretation for the Ahramat Branch at this locality was validated using two sediment cores of depths 20 m (Core A) and 13 m (Core B) (Fig.  3 ). In Core A between the center and left bank of the former branch we found brown sandy mud at the floodplain surface and down to ~2.7 m with some limestone and chert fragments, a reddish sandy mud layer with gravel and handmade material inclusions at ~2.8 m, a gray sandy mud layer from ~3–5.8 m, another reddish sandy mud layer with gravel and freshwater mussel shells at ~6 m, black sandy mud from ~6–8 m, and sandy silt grading into clean, well-sorted medium sand dominated the profile from ~8 to >13 m. In Core B on the right bank of the former branch we found recently deposited brown sandy mud at the floodplain surface and down to ~1.5 m, alternating brown and gray layers of silty and sandy mud down to ~4 m (some reddish layers with gravel and handmade material inclusions), a black sandy mud layer from ~4–4.9 m, and another reddish sandy mud layer with gravel and freshwater mussel shells at ~5 m, before clean, well-sorted medium sand dominated the profile from 5 to >20 m. Shallow groundwater was encountered in both cores concurrently with the sand layers, indicating that the buried sedimentary structure of the abandoned Ahramat Branch acts as a conduit for subsurface water flow beneath the distal floodplain of the modern Nile River.

a Locations of geophysical profile and soil drilling (ESRI World Image Basemap, source: Esri, Maxar, Earthstar Geographics). Photos taken from the field while using the b Electromagnetic Tomography (EMT) and c Ground Penetrating Radar (GPR). d Showing the apparent conductivity profile, e showing EMT profile, and f showing GPR profiles with overlain sketch of the channel boundary on the GPR graph. g Simplified interpretation of the buried channel with the location of the two-soil coring of A and B.

It shows two-soil cores, A and core B, with soil profile descriptions, graphic core logs, sediment grain size charts, and example photographs.

Alignment of old and middle kingdom pyramids to the Ahramat Branch

The royal pyramids in ancient Egypt are not isolated monuments, but rather joined with several other structures to form complexes. Besides the pyramid itself, the pyramid complex includes the mortuary temple next to the pyramid, a valley temple farther away from the pyramid on the edge of a waterbody, and a long sloping causeway that connects the two temples. A causeway is a ceremonial raised walkway, which provides access to the pyramid site and was part of the religious aspects of the pyramid itself 19 . In the study area, it was found that many of the causeways of the pyramids run perpendicular to the course of the Ahramat Branch and terminate directly on its riverbank.

In Egyptian pyramid complexes, the valley temples at the end of causeways acted as river harbors. These harbors served as an entry point for the river borne visitors and ceremonial roads to the pyramid. Countless valley temples in Egypt have not yet been found and, therefore, might still be buried beneath the agricultural fields and desert sands along the riverbank of the Ahramat Branch. Five of these valley temples, however, partially survived and still exist in the study area. These temples include the valley temples of the Bent Pyramid, the Pyramid of Khafre, and the Pyramid of Menkaure from Dynasty 4; the valley temple of the Pyramid of Sahure from Dynasty 5, and the valley temple of the Pyramid of Pepi II from Dynasty 6. All the aforementioned temples are dated to the Old Kingdom. These five surviving temples were found to be positioned adjacent to the riverbank of the Ahramat Branch, which strongly implies that this river branch was contemporaneously functioning during the Old Kingdom, at the time of pyramid construction.

Analysis of the ground elevation of the 31 pyramids and their proximity to the floodplain, within the study area, helped explain the position and relative water level of the Ahramat Branch during the time between the Old Kingdom and Second Intermediate Period (ca. 2649–1540 BCE). Based on Fig. ( 4) , the Ahramat Branch had a high-water level during the first part of the Old Kingdom, especially during Dynasty 4. This is evident from the high ground elevation and long distance from the floodplain of the pyramids dated to that period. For instance, the remote position of the Bent and Red Pyramids in the desert, very far from the Nile floodplain, is a testament to the branch’s high-water level. On the contrary, during the Old Kingdom, our data demonstrated that the Ahramat Branch would have reached its lowest level during Dynasty 5. This is evident from the low altitudes and close proximity to the floodplain of most Dynasty 5 pyramids. The orientation of the Sahure Pyramid’s causeway (Dynasty 5) and the location of its valley temple in the low-lying floodplain provide compelling evidence for the relatively low water level proposition of the Ahramat Branch during this stage. The water level of the Ahramat Branch would have been slightly raised by the end of Dynasty 5 (the last 15–30 years), during the reign of King Unas and continued to rise during Dynasty 6. The position of Pepi II and Merenre Pyramids (Dynasty 6) deep in the desert, west of the Djedkare Isesi Pyramid (Dynasty 5), supports this notion.

It explains the position and relative water level of the Ahramat Branch during the time between the Old Kingdom and Second Intermediate Period. a Shows positive correlation between the ground elevation of the pyramids and their proximity to the floodplain. b Shows positive correlation between the average ground elevation of the pyramids and their average proximity to the floodplain in each Dynasty. c Illustrates the water level interpretation by Hassan (1986) in Faiyum Lake in correlation to the average pyramids ground elevation and average distances to the floodplain in each Dynasty. d The data indicates that the Ahramat Branch had a high-water level during the first period of the Old Kingdom, especially during Dynasty 4. The water level reduced afterwards but was raised slightly in Dynasty 6. The position of the Middle Kingdom’s pyramids, which was at lower altitudes and in close proximity to the floodplain as compared to those of the Old Kingdom might be explained by the slight eastward migration of the Ahramat Branch.

In addition, our analysis in Fig. ( 4) , shows that the Qakare Ibi Pyramid of Dynasty 8 was constructed very close to the floodplain on very low elevation, which implies that the Nile water levels were very low at this time of the First Intermediate Period (2181–2055 BCE). This finding is in agreement with previous work conducted by Kitchen 20 which implies that the sudden collapse of the Old Kingdom in Egypt (after 4160 BCE) was largely caused by catastrophic failure of the annual flood of the Nile River for a period of 30–40 years. Data from soil cores near Memphis indicated that the Old Kingdom settlement is covered by about 3 m of sand 11 . Accordingly, the Ahramat Branch was initially positioned further west during the Old Kingdom and then shifted east during the Middle Kingdom due to the drought-induced sand encroachments of the First Intermediate Period, “a period of decentralization and weak pharaonic rule” in ancient Egypt, spanning about 125 years (2181–2055 BCE) post Old Kingdom era. Soil cores from the drilling program at Memphis show dominant dry conditions during the First Intermediate Period with massive eolian sand sheets extended over a distance of at least 0.5 km from the edge of the western desert escarpment 21 . The Ahramat Branch continued to move east during the Second Intermediate Period until it had gradually lost most of its water supply by the New Kingdom.

The western tributaries of the Ahramat Branch

Sentinal-1 radar data unveiled several wide channels (inlets) in the Western Desert Plateau connected to the Ahramat Branch. These inlets are currently covered by a layer of sand, thus partially invisible in multispectral satellite imagery. In Sentinal-1 radar imagery, the valley floors of these inlets appear darker than the surrounding surfaces, indicating subsurface fluvial deposits. These smooth deposits appear dark owing to the specular reflection of the radar signals away from the receiving antenna (Fig.  5a, b ) 22 . Considering that Sentinel-1’s C-Band has a penetration capability of approximately 50 cm in dry sand surface 23 , this would suggest that the riverbed of these channels is covered by at least half a meter of desert sand. Unlike these former inlets, the course of the Ahramat Branch is invisible in SAR data due in large part to the presence of dense farmlands in the floodplain, which limits radar penetration and the detection of underlying fluvial deposition. Moreover, the radar topographic data from TDX revealed the areal extent of these inlets. Their river courses were extracted from TDX data using the Topographic Position Index (TPI), an algorithm which is used to compute the topographic slope positions and to automate landform classifications (Fig.  5c, d ). Negative TPI values show the former riverbeds of the inlets, while positive TPI signify the riverbanks bordering them.

a Conceptual sketch of the dependence of surface roughness on the sensor wavelength λ (modified after 48 ). b Expected backscatter characteristics in sandy desert areas with buried dry riverbeds. c Dry channels/inlets masked by desert sand in the Dahshur area. d The channels’ courses were extracted using TPI. Negative TPI values highlight the courses of the channels while positive TPI signify their banks.

Analysis indicated that several of the pyramid’s causeways, from Dynasties 4 and 6, lead to the inlet’s riverbanks (Fig.  6 ). Among these pyramids, are the Bent Pyramid, the first pyramid built by King Snefru in Dynasty 4 and among the oldest, largest, and best preserved ancient Egyptian pyramids that predates the Giza Pyramids. This pyramid is situated at the royal necropolis of Dahshur. The position of the Bent Pyramid, deep in the desert, far from the modern Nile floodplain, remained unexplained by researchers. This pyramid has a long causeway (~700 m) that is paved in the desert with limestone blocks and is attached to a large valley temple. Although all the pyramids’ valley temples in Egypt are connected to a water body and served as the landing point of all the river-borne visitors, the valley temple of the Bent Pyramid is oddly located deep in the desert, very distant from any waterways and more than 1 km away from the western edge of the modern Nile floodplain. Radar data revealed that this temple overlooked the bank of one of these extinct channels (called Wadi al-Taflah in historical maps). This extinct channel (referred to hereafter as the Dahshur Inlet due to its geographical location) is more than 200 m wide on average (Fig.  6 ). In light of this finding, the Dahshur Inlet, and the Ahramat Branch, are thus strongly argued to have been active during Dynasty 4 and must have played an important role in transporting building materials to the Bent Pyramid site. The Dahshur Inlet could have also served the adjacent Red Pyramid, the second pyramid built by the same king (King Snefru) in the Dahshur area. Yet, no traces of a causeway nor of a valley temple has been found thus far for the Red Pyramid. Interestingly, pyramids in this site dated to the Middle Kingdom, including the Amenemhat III pyramid, also known as the Black Pyramid, White Pyramid, and Pyramid of Senusret III, are all located at least 1 km far to the east of the Dynasty 4 pyramids (Bent and Red) near the floodplain (Fig.  6 ), which once again supports the notion of the eastward shift of the Ahramat Branch after the Old Kingdom.

a The two inlets are presently covered by sand, thus invisible in optical satellite imagery. b Radar data, and c TDX topographic data reveal the riverbed of the Sakkara Inlet due to radar signals penetration capability in dry sand. b and c show the causeways of Pepi II and Merenre Pyramids, from Dynasty 6, leading to the Saqqara Inlet. The Valley Temple of Pepi II Pyramid overlooks the inlet riverbank, which indicates that the inlet, and thus Ahramat Branch, were active during Dynasty 6. d Radar data, and e TDX topographic data, reveal the riverbed of the Dahshur Inlet with the Bent Pyramid’s causeway of Dynasty 4 leading to the Inlet. The Valley Temple of the Bent Pyramid overlooks the riverbank of the Dahshur Inlet, which indicates that the inlet and the Ahramat Branch were active during Dynasty 4 of the Old Kingdom.

Radar satellite data revealed yet another sandy buried channel (tributary), about 6 km north of the Dahshur Inlet, to the west of the ancient city of Memphis. This former fluvial channel (referred to hereafter as the Saqqara Inlet due to its geographical location) connects to the Ahramat Branch with a broad river course of more than 600 m wide. Data shows that the causeways of the two pyramids of Pepi II and Merenre, situated at the royal necropolis of Saqqara and dated to Dynasty 6, lead directly to the banks of the Saqqara Inlet (see Fig.  6 ). The 400 m long causeway of Pepi II pyramid runs northeast over the southern Saqqara plateau and connects to the riverbank of the Saqqara Inlet from the south. The causeway terminates with a valley temple that lies on the inlet’s riverbank. The 250 long causeway of the Pyramid of Merenre runs southeast over the northern Saqqara plateau and connects to the riverbank of the Saqqara Inlet from the north. Since both pyramids dated to Dynasty 6, it can be argued that the water level of the Ahramat Branch was higher during this period, which would have flooded at least the entrance of its western inlets. This indicates that the downstream segment of the Saqqara Inlet was active during Dynasty 6 and played a vital role in transporting construction materials and workers to the two pyramids sites. The fact that none of the Dynasty 5 pyramids in this area (e.g., the Djedkare Isesi Pyramid) were positioned on the Saqqara Inlet suggests that the water level in the Ahramat Branch was not high enough to enter and submerge its inlets during this period.

In addition, our data analysis clearly shows that the causeways of the Khafre, Menkaure, and Khentkaus pyramids, in the Giza Plateau, lead to a smaller but equally important river bay associated with the Ahramat Branch. This lagoon-like river arm is referred to here as the Giza Inlet (Fig.  7 ). The Khufu Pyramid, the largest pyramid in Egypt, seems to be connected directly to the river course of the Ahramat Branch (Fig.  7 ). This finding proves once again that the Ahramat Branch and its western inlets were hydrologically active during Dynasty 4 of the Old Kingdom. Our ancient river inlet hypothesis is also in accordance with earlier research, conducted on the Giza Plateau, which indicates the presence of a river and marsh-like environment in the floodplain east of the Giza pyramids 2 .

The causeways of the four Pyramids lead to an inlet, which we named the Giza Inlet, that connects from the west with the Ahramat Branch. These causeways connect the pyramids with valley temples which acted as river harbors in antiquity. These river segments are invisible in optical satellite imagery since they are masked by the cultivated lands of the Nile floodplain. The photo shows the valley temple of Khafre Pyramid (Photo source: Author Eman Ghoneim).

During the Old Kingdom Period, our analysis suggests that the Ahramat Branch had a high-water level during the first part, especially during Dynasty 4 whereas this water level was significantly decreased during Dynasty 5. This finding is in agreement with previous studies which indicate a high Nile discharge during Dynasty 4 (e.g., ref. 24 ). Sediment isotopic analysis of the Nile Delta indicated that Nile flows decrease more rapidly by the end of Dynasty 4 25 , in addition 26 reported that during Dynasties 5 and 6 the Nile flows were the lowest of the entire Dynastic period. This long-lost Ahramat Branch (possibly a former Yazoo tributary to the Nile) was large enough to carry a large volume of the Nile discharge in the past. The ancient channel segment uncovered by 1 , 15 west of the city of Memphis through borehole logs is most likely a small section of the large Ahramat Branch detected in this study. In the Middle Kingdom, although previous studies implied that the Nile witnessed abundant flood with occasional failures (e.g., ref. 27 ), our analysis shows that all the pyramids from the Middle Kingdom were built far east of their Old Kingdom counterparts, on lower altitudes and in close proximity to the floodplain as compared to those of the Old Kingdom. This paradox might be explained by the fact that the Ahramat Branch migrated eastward, slightly away from the Western Desert escarpment, prior to the construction of the Middle Kingdom pyramids, resulting in the pyramids being built eastward so that they could be near the waterway.

The eastward migration and abandonment of the Ahramat Branch could be attributed to gradual tilting of the Nile delta and floodplain in lower Egypt towards the northeast due to tectonic activity 28 . A topographic tilt such as this would have accelerated river movement eastward due to the river being located in the west at a relatively higher elevation of the floodplain. While near-channel floodplain deposition would naturally lead to alluvial ridge development around the active Ahramat Branch, and therefore to lower-lying tracts of adjacent floodplain to the east, regional tilting may explain the wholesale lateral migration of the river in that direction. The eastward migration and abandonment of the branch could also be ascribed to sand incursion due to the branch’s proximity to the Western Desert Plateau, where windblown sand is abundant. This would have increased sand deposition along the riverbanks and caused the river to silt up, particularly during periods of low flow. The region experienced drought during the First Intermediate Period, prior to the Middle Kingdom. In the area of Abu Rawash north 29 and Dahshur site 11 , settlements from the Early Dynastic and Old Kingdom were found to be covered by more than 3 m of desert sands. During this time, windblown sand engulfed the Old Kingdom settlements and desert sands extended eastward downhill over a distance of at least 0.5 km 21 . The abandonment of sites at Abusir (5 th Dynasty), where the early pottery-rich deposits are covered by wind-blown sand and then mud without sherds, can be used as evidence that the Ahramat Branch migrated eastward after the Old Kingdom. The increased sand deposition activity, during the end of the Old Kingdom, and throughout the First Intermediate Period, was most likely linked to the period of drought and desertification of the Sahara 30 . In addition, the reduced river discharge caused by decreased rainfall and increased aridity in the region would have gradually reduced the river course’s capacity, leading to silting and abandonment of the Ahramat Branch as the river migrated to the east.

The Dahshur, Saqqara, and Giza inlets, which were connected to the Ahramat Branch from the west, were remnants of past active drainage systems dated to the late Tertiary or the Pleistocene when rainwater was plentiful 31 . It is proposed that the downstream reaches of these former channels (wadis) were submerged during times of high-water levels of the Ahramat Branch, forming long narrow water arms (inlets) that gave a wedge-like shape to the western flank of the Ahramat Branch. During the Old Kingdom, the waters of these inlets would have flowed westward from the Ahramat Branch rather than from their headwaters. As the drought intensified during the First Intermediate Period, the water level of the Ahramat Branch was lowered and withdrew from its western inlets, causing them to silt up and eventually dry out. The Dahshur, Saqqara, and Giza inlets would have provided a bay environment where the water would have been calm enough for vessels and boats to dock far from the busy, open water of the Ahramat Branch.

Sediments from the Ahramat Branch riverbed, which were collected from the two deep soil cores (cores A and B), show an abrupt shift from well-sorted medium sands at depth to overlying finer materials with layers including gravel, shell, and handmade materials. This indicates a step-change from a relatively consistent higher-energy depositional regime to a generally lower-energy depositional regime with periodic flash floods at these sites. So, the Ahramat Branch in this region carried and deposited well-sorted medium sand during its last active phase, and over time became inactive, infilling with sand and mud until an abrupt change led the (by then) shallow depression fill with finer distal floodplain sediment (possibly in a wetland) that was utilized by people and experienced periodic flash flooding. Validation of the paleo-channel position and sediment type using these cores shows that the Ahramat Branch has similar morphological features and an upward-fining depositional sequence as that reported near Giza, where two cores were previously used to reconstruct late Holocene Nile floodplain paleo-environments 2 . Further deep soil coring could determine how consistent the geomorphological features are along the length of the Ahramat branch, and to help explain anomalies in areas where the branch has less surface expression and where remote sensing and geophysical techniques have limitations. Considering more core logs can give a better understanding of the floodplain and the buried paleo-channels.

The position of the Ahramat Branch along the western edge of the Nile floodplain suggests it to be the downstream extension of Bahr Yusef. In fact, Bahr Yusef’s course may have initially flowed north following the natural surface gradient of the floodplain before being forced to turn west to flow into the Fayum Depression. This assumption could be supported by the sharp westward bend of Bahr Yusef’s course at the entrance to the Fayum Depression, which could be a man-made attempt to change the waterflow direction of this branch. According to Römer 32 , during the Middle Kingdom, the Gadallah Dam located at the entrance of the Fayum, and a possible continuation running eastwards, blocked the flow of Bahr Yusef towards the north. However, a sluice, probably located near the village of el-Lahun, was created in order to better control the flow of water into the Fayum. When the sluice was locked, the water from Bahr Yusef was directed to the west and into the depression, and when the sluice was open, the water would flow towards the north via the course of the Ahramat Branch. Today, the abandoned Ahramat Branch north of Fayum appears to support subsurface water flow in the buried coarse sand bed layers, however these shallow groundwater levels are likely to be quite variable due to proximity of the bed layers to canals and other waterways that artificially maintain shallow groundwater. Groundwater levels in the region are known to be variable 33 , but data on shallow groundwater could be used to further validate the delineated paleo-channel of the Ahramat Branch.

The present work enabled the detection of segments of a major former Nile branch running at the foothills of the Western Desert Plateau, where the vast majority of the Ancient Egyptian pyramids lie. The enormity of this branch and its proximity to the pyramid complexes, in addition to the fact that the pyramids’ causeways terminate at its riverbank, all imply that this branch was active and operational during the construction phase of these pyramids. This waterway would have connected important locations in ancient Egypt, including cities and towns, and therefore, played an important role in the cultural landscape of the region. The eastward migration and abandonment of the Ahramat Branch could be attributed to gradual movement of the river to the lower-lying adjacent floodplain or tilting of the Nile floodplain toward the northeast as a result of tectonic activity, as well as windblown sand incursion due to the branch’s proximity to the Western Desert Plateau. The increased sand deposition was most likely related to periods of desertification of the Great Sahara in North Africa. In addition, the branch eastward movement and diminishing could be explained by the reduction of the river discharge and channel capacity caused by the decreased precipitation and increased aridity in the region, particularly during the end of the Old Kingdom.

The integration of radar satellite data with geophysical surveying and soil coring, which we utilized in this study, is a highly adaptable approach in locating similar former buried river systems in arid regions worldwide. Mapping the hidden course of the Ahramat Branch, allowed us to piece together a more complete picture of ancient Egypt’s former landscape and a possible water transportation route in Lower Egypt, in the area between Lisht and the Giza Plateau.

Revealing this extinct Nile branch can provide a more refined idea of where ancient settlements were possibly located in relation to it and prevent them from being lost to rapid urbanization. This could improve the protection measures of Egyptian cultural heritage. It is the hope that our findings can improve conservation measures and raise awareness of these sites for modern development planning. By understanding the landscape of the Nile floodplain and its environmental history, archeologists will be better equipped to prioritize locations for fieldwork investigation and, consequently, raise awareness of these sites for conservation purposes and modern development planning. Our finding has filled a much-needed knowledge gap related to the dominant waterscape in ancient Egypt, which could help inform and educate a wide array of global audiences about how earlier inhabitants were living and in what ways shifts in their landscape drove human activity in such an iconic region.

Materials and methods

The work comprised of two main elements: satellite remote sensing and historical maps and geophysical survey and sediment coring, complemented by archeological resources. Using this suite of investigative techniques provided insights into the nature and relationship of the former Ahramat Branch with the geographical location of the pyramid complexes in Egypt.

Satellite remote sensing and historical maps

Unlike optical sensors that image the land surface, radar sensors image the subsurface due to their unique ability to penetrate the ground and produce images of hidden paleo-rivers and structures. In this context, radar waves strip away the surface sand layer and expose previously unidentified buried channels. The penetration capability of radar waves in the hyper-arid regions of North Africa is well documented 4 , 34 , 35 , 36 , 37 . The penetration depth varies according to the radar wavelength used at the time of imaging. Radar signal penetration becomes possible without significant attenuation if the surface cover material is extremely dry (<1% moisture content), fine grained (<1/5 of the imaging wavelength) and physically homogeneous 23 . When penetrating desert sand, radar signals have the ability to detect subsurface soil roughness, texture, compactness, and dielectric properties 38 . We used the European Space Agency (ESA) Sentinel-1 data, a radar satellite constellation consisting of a C-Band synthetic aperture radar (SAR) sensor, operating at 5.405 GHz. The Sentinel-1 SAR image used here was acquired in a descending orbit with an interferometric wide swath mode (IW) at ground resolutions of 5 m × 20 m, and dual polarizations of VV + VH. Since Sentinal-1 is operated in the C-Band, it has an estimated penetration depth of 50 cm in very dry, sandy, loose soils 39 . We used ENVI v. 5.7 SARscape software for processing radar imagery. The used SAR processing sequences have generated geo-coded, orthorectified, terrain-corrected, noise free, radiometrically calibrated, and normalized Sentinel-1 images with a pixel size of 12.5 m. In SAR imagery subsurface fluvial deposits appear dark owing to specular reflection of the radar signals away from the receiving antenna, whereas buried coarse and compacted material, such as archeological remains appear bright due to diffuse reflection of radar signals 40 .

Other previous studies have shown that combining radar topographic imagery (e.g., Shuttle Radar Topography Mission-SRTM) with SAR images improves the extraction and delineation of mega paleo-drainage systems and lake basins concealed under present-day topographic signatures 3 , 4 , 22 , 41 . Topographic data represents a primary tool in investigating surface landforms and geomorphological change both spatially and temporally. This data is vital in mapping past river systems due to its ability to show subtle variations in landform morphology 37 . In low lying areas, such as the Nile floodplain, detailed elevation data can detect abandoned channels, fossilized natural levees, river meander scars and former islands, which are all crucial elements for reconstructing the ancient Nile hydrological network. In fact, the modern topography in many parts of the study area is still a good analog of the past landscape. In the present study, TanDEM-X (TDX) topographic data, from the German Aerospace Centre (DLR), has been utilized in ArcGIS Pro v. 3.1 software due to its fine spatial resolution of 0.4 arc-second ( ∼ 12 m). TDX is based on high frequency X-Band Synthetic Aperture Radar (SAR) (9.65 GHz) and has a relative vertical accuracy of 2 m for areas with a slope of ≤20% 42 . This data was found to be superior to other topographic DEMs (e.g., Shuttle Radar Topography Mission and ASTER Global Digital Elevation Map) in displaying fine topographic features even in the cultivated Nile floodplain, thus making it particularly well suited for this study. Similar archeological investigations using TDX elevation data in the flat terrains of the Seyhan River in Turkey and the Nile Delta 43 , 44 allowed for the detection of levees and other geomorphologic features in unprecedented spatial resolution. We used the Topographic Position Index (TPI) module of 45 with the TDX data by applying varying neighboring radiuses (20–100 m) to compute the difference between a cell elevation value and the average elevation of the neighborhood around that cell. TPI values of zero are either flat surfaces with minimal slope, or surfaces with a constant gradient. The TPI can be computed using the following expression 46 .

Where the scaleFactor is the outer radius in map units and Irad and Orad are the inner and outer radius of annulus in cells. Negative TPI values highlight abandoned riverbeds and meander scars, while positive TPI signify the riverbanks and natural levees bordering them.

The course of the Ahramat Branch was mapped from multiple data sources and used different approaches. For instance, some segments of the river course were derived automatically using the TPI approach, particularly in the cultivated floodplain, whereas others were mapped using radar roughness signatures specially in sandy desert areas. Moreover, a number of abandoned channel segments were digitized on screen from rectified historical maps (Egyptian Survey Department scale 1:50,000 collected on years 1910–1911) near the foothill of the Western Desert Plateau. These channel segments together with the former river course segments delineated from radar and topographic data were aggregated to generate the former Ahramat Branch. In addition to this and to ensure that none of the channel segments of the Ahramat Branch were left unmapped during the automated process, a systematic grid-based survey (through expert’s visual observation) was performed on the satellite data. Here, Landsat 8 and Sentinal-2 multispectral images, Sentinal-1 radar images and TDX topographic data were used as base layers, which were thoroughly examined, grid-square by grid-square (2*2 km per a square) at a full resolution, in order to identify small-scale fluvial landforms, anomalous agricultural field patterns and irregular ditches, and determine their spatial distributions. Here, ancient fluvial channels were identified using two key aspects: First, the sinuous geometry of natural and manmade features and, second the color tone variations in the satellite imagery. For example, clusters of contiguous pixels with darker tones and sinuous shapes may signify areas of a higher moisture content in optical imagery, and hence the possible existence of a buried riverbed. Stretching and edge detection were applied to enhance contrasts in satellite images brightness to enable the visualization of traces of buried river segments that would otherwise go unobserved. Lastly, all the pyramids and causeways in the study site, along with ancient harbors and valley temples, as indicators of preexisting river channels, were digitized from satellite data and available archeological resources and overlaid onto the delineated Ahramat Branch for geospatial analysis.

Geophysical survey and sediment coring

Geophysical measurements using Ground Penetrating Radar (GPR) and Electromagnetic Tomography (EMT) were utilized to map subsurface fluvial features and validate the satellite remote sensing findings. GPR is effective in detecting changes of dielectric constant properties of sediment layers, and its signal responses can be directly related to changes in relative porosity, material composition, and moisture content. Therefore, GPR can help in identifying transitional boundaries in subsurface layers. EMT, on the other hand, shows the variations and thickness of large-scale sedimentary deposits and is more useful in clay-rich soil than GPR. In summer 2022, a geophysical profile was measured using GPR and EMT units with a total length of approximately 1.2 km. The GPR survey was conducted with a central frequency antenna of 35 MHz and a trigger interval of 5 cm. The EMT survey was performed using the multi-frequency terrain conductivity (EM–34–3) measuring system with a spacing of 10–11 meters between stations. To validate the remote sensing and geophysical data, two sediment cores with depths of 20 m (Core A) and 13 m (Core B) were collected using a deep soil driller. These cores were collected from along the geophysical profile in the floodplain. Sieving and organic analysis were performed on the sediment samples at Tanta University sediment lab to extract information about grain size for soil texture and total organic carbon. In soil texture analysis medium to coarse sediment, such as sands, are typical for river channel sediments, loamy sand and sandy loam deposits can be interpreted as levees and crevasse splays, whereas fine texture deposits, such as silt loam, silty clay loam, and clay deposits, are representative of the more distal parts of the river floodplain 47 .

Data availability

Data for replicating the results of this study are available as supplementary files at: https://figshare.com/articles/journal_contribution/Pyramids_Elevations_and_Distances_xlsx/25216259 .

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Acknowledgements

This work was funded by NSF grant # 2114295 awarded to E.G., S.O. and T.R. and partially supported by Research Momentum Fund, UNCW, to E.G. TanDEM-X data was awarded to E.G. and R.E by the German Aerospace Centre (DLR) (contract # DEM_OTHER2886). Permissions for collecting soil coring and sampling were obtained from the Faculty of Science, Tanta University, Egypt by coauthors Dr. Amr Fhail and Dr. Mohamed Fathy. Bradley Graves at Macquarie University assisted with preparation of the sedimentological figures. Hamada Salama at NRIAG assisted with the GPR field data collection.

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Eman Ghoneim

School of Natural Sciences, Macquarie University, Macquarie, NSW, 2109, Australia

Timothy J. Ralph

Department of History, The University of Memphis, Memphis, TN, 38152-3450, USA

Suzanne Onstine

Near Eastern Languages and Civilizations, University of Chicago, Chicago, IL, 60637, USA

Raghda El-Behaedi

National Research Institute of Astronomy and Geophysics (NRIAG), Helwan, Cairo, 11421, Egypt

Gad El-Qady, Mahfooz Hafez, Magdy Atya, Mohamed Ebrahim & Ashraf Khozym

Geology Department, Faculty of Science, Tanta University, Tanta, 31527, Egypt

Amr S. Fahil & Mohamed S. Fathy

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Eman Ghoneim conceived the ideas, lead the research project, and conducted the data processing and interpretations. The manuscript was written and prepared by Eman Ghoneim. Timothy J. Ralph co-supervised the project, contributed to the geomorphological and sedimentological interpretations, edited the manuscript and the figures. Suzanne Onstine co-supervised the project, contributed to the archeological and historical interpretations, and edited the manuscript. Raghda El-Behaedi contributed to the remote sensing data processing and methodology and edited the manuscript. Gad El-Qady supervised the geophysical survey. Mahfooz Hafez, Magdy Atya, Mohamed Ebrahim, Ashraf Khozym designed, collected, and interpreted the GPR and EMT data. Amr S. Fahil and Mohamed S. Fathy supervised the soil coring, sediment analysis, drafted sedimentological figures and contributed to the interpretations. All authors reviewed the manuscript and participated in the fieldwork.

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Ghoneim, E., Ralph, T.J., Onstine, S. et al. The Egyptian pyramid chain was built along the now abandoned Ahramat Nile Branch. Commun Earth Environ 5 , 233 (2024). https://doi.org/10.1038/s43247-024-01379-7

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Received : 06 December 2023

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DOI : https://doi.org/10.1038/s43247-024-01379-7

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