example of background study in research

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What is the Background of the Study and How to Write It

What is the Background of the Study in Research? 

The background of the study is the first section of a research paper and gives context surrounding the research topic. The background explains to the reader where your research journey started, why you got interested in the topic, and how you developed the research question that you will later specify. That means that you first establish the context of the research you did with a general overview of the field or topic and then present the key issues that drove your decision to study the specific problem you chose.

Once the reader understands where you are coming from and why there was indeed a need for the research you are going to present in the following—because there was a gap in the current research, or because there is an obvious problem with a currently used process or technology—you can proceed with the formulation of your research question and summarize how you are going to address it in the rest of your manuscript.

Why is the Background of the Study Important?

No matter how surprising and important the findings of your study are, if you do not provide the reader with the necessary background information and context, they will not be able to understand your reasons for studying the specific problem you chose and why you think your study is relevant. And more importantly, an editor who does not share your enthusiasm for your work (because you did not fill them in on all the important details) will very probably not even consider your manuscript worthy of their and the reviewers’ time and will immediately send it back to you.

To avoid such desk rejections , you need to make sure you pique the reader’s interest and help them understand the contribution of your work to the specific field you study, the more general research community, or the public. Introducing the study background is crucial to setting the scene for your readers.

Table of Contents:

• What is “Background Information” in a Research Paper?
• What Should the Background of a Research Paper Include?
• Where Does the Background Section Go in Your Paper?

Background of the Study Structure

Before writing your study background, it is essential to understand what to include. The following elements should all be included in the background and are presented in greater detail in the next section:

• A general overview of the topic and why it is important (overlaps with establishing the “importance of the topic” in the Introduction)
• The current state of the research on the topic or on related topics in the field
• Controversies about current knowledge or specific past studies that undergird your research methodology
• Any claims or assumptions that have been made by researchers, institutions, or politicians that might need to be clarified
• Methods and techniques used in the study or from which your study deviated in some way

Presenting the Study Background

As you begin introducing your background, you first need to provide a general overview and include the main issues concerning the topic. Depending on whether you do “basic” (with the aim of providing further knowledge) or “applied” research (to establish new techniques, processes, or products), this is either a literature review that summarizes all relevant earlier studies in the field or a description of the process (e.g., vote counting) or practice (e.g., diagnosis of a specific disease) that you think is problematic or lacking and needs a solution.

Example s of a general overview

If you study the function of a Drosophila gene, for example, you can explain to the reader why and for whom the study of fly genetics is relevant, what is already known and established, and where you see gaps in the existing literature. If you investigated how the way universities have transitioned into online teaching since the beginning of the Covid-19 pandemic has affected students’ learning progress, then you need to present a summary of what changes have happened around the world, what the effects of those changes have been so far, and where you see problems that need to be addressed. Note that you need to provide sources for every statement and every claim you make here, to establish a solid foundation of knowledge for your own study. 

Describing the current state of knowledge

When the reader understands the main issue(s), you need to fill them in more specifically on the current state of the field (in basic research) or the process/practice/product use you describe (in practical/applied research). Cite all relevant studies that have already reported on the Drosophila gene you are interested in, have failed to reveal certain functions of it, or have suggested that it might be involved in more processes than we know so far. Or list the reports from the education ministries of the countries you are interested in and highlight the data that shows the need for research into the effects of the Corona-19 pandemic on teaching and learning.

Discussing controversies, claims, and assumptions

Are there controversies regarding your topic of interest that need to be mentioned and/or addressed? For example, if your research topic involves an issue that is politically hot, you can acknowledge this here. Have any earlier claims or assumptions been made, by other researchers, institutions, or politicians, that you think need to be clarified?

Mentioning methodologies and approaches

While putting together these details, you also need to mention methodologies : What methods/techniques have been used so far to study what you studied and why are you going to either use the same or a different approach? Are any of the methods included in the literature review flawed in such a way that your study takes specific measures to correct or update? While you shouldn’t spend too much time here justifying your methods (this can be summarized briefly in the rationale of the study at the end of the Introduction and later in the Discussion section), you can engage with the crucial methods applied in previous studies here first.

When you have established the background of the study of your research paper in such a logical way, then the reader should have had no problem following you from the more general information you introduced first to the specific details you added later. You can now easily lead over to the relevance of your research, explain how your work fits into the bigger picture, and specify the aims and objectives of your study. This latter part is usually considered the “ statement of the problem ” of your study. Without a solid research paper background, this statement will come out of nowhere for the reader and very probably raise more questions than you were planning to answer.   

Where does the study background section go in a paper?

Unless you write a research proposal or some kind of report that has a specific “Background” chapter, the background of your study is the first part of your introduction section . This is where you put your work in context and provide all the relevant information the reader needs to follow your rationale. Make sure your background has a logical structure and naturally leads into the statement of the problem at the very end of the introduction so that you bring everything together for the reader to judge the relevance of your work and the validity of your approach before they dig deeper into the details of your study in the methods section .

Consider Receiving Professional Editing Services

Now that you know how to write a background section for a research paper, you might be interested in our AI text editor at Wordvice AI. And be sure to receive professional editing services , including academic editing and proofreading , before submitting your manuscript to journals. On the Wordvice academic resources website, you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

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What is the Background of a Study and How Should it be Written?

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Table of Contents

The background of a study is one of the most important components of a research paper. The quality of the background determines whether the reader will be interested in the rest of the study. Thus, to ensure that the audience is invested in reading the entire research paper, it is important to write an appealing and effective background. So, what constitutes the background of a study, and how must it be written?

What is the background of a study?

The background of a study is the first section of the paper and establishes the context underlying the research. It contains the rationale, the key problem statement, and a brief overview of research questions that are addressed in the rest of the paper. The background forms the crux of the study because it introduces an unaware audience to the research and its importance in a clear and logical manner. At times, the background may even explore whether the study builds on or refutes findings from previous studies. Any relevant information that the readers need to know before delving into the paper should be made available to them in the background.

How is a background different from the introduction?

The introduction of your research paper is presented before the background. Let’s find out what factors differentiate the background from the introduction.

• The introduction only contains preliminary data about the research topic and does not state the purpose of the study. On the contrary, the background clarifies the importance of the study in detail.
• The introduction provides an overview of the research topic from a broader perspective, while the background provides a detailed understanding of the topic.
• The introduction should end with the mention of the research questions, aims, and objectives of the study. In contrast, the background follows no such format and only provides essential context to the study.

How should one write the background of a research paper?

The length and detail presented in the background varies for different research papers, depending on the complexity and novelty of the research topic. At times, a simple background suffices, even if the study is complex. Before writing and adding details in the background, take a note of these additional points:

• Start with a strong beginning: Begin the background by defining the research topic and then identify the target audience.
• Cover key components: Explain all theories, concepts, terms, and ideas that may feel unfamiliar to the target audience thoroughly.
• Take note of important prerequisites: Go through the relevant literature in detail. Take notes while reading and cite the sources.
• Maintain a balance: Make sure that the background is focused on important details, but also appeals to a broader audience.
• Include historical data: Current issues largely originate from historical events or findings. If the research borrows information from a historical context, add relevant data in the background.
• Explain novelty: If the research study or methodology is unique or novel, provide an explanation that helps to understand the research better.
• Increase engagement: To make the background engaging, build a story around the central theme of the research

Avoid these mistakes while writing the background:

• Ambiguity: Don’t be ambiguous. While writing, assume that the reader does not understand any intricate detail about your research.
• Unrelated themes: Steer clear from topics that are not related to the key aspects of your research topic.
• Poor organization: Do not place information without a structure. Make sure that the background reads in a chronological manner and organize the sub-sections so that it flows well.

Writing the background for a research paper should not be a daunting task. But directions to go about it can always help. At Elsevier Author Services we provide essential insights on how to write a high quality, appealing, and logically structured paper for publication, beginning with a robust background. For further queries, contact our experts now!

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What Is Background in a Research Paper?

So you have carefully written your research paper  and probably ran it through your colleagues ten to fifteen times. While there are many elements to a good research article, one of the most important elements for your readers is the background of your study.

What is Background of the Study in Research

The background of your study will provide context to the information discussed throughout the research paper . Background information may include both important and relevant studies. This is particularly important if a study either supports or refutes your thesis.

Why is Background of the Study Necessary in Research?

The background of the study discusses your problem statement, rationale, and research questions. It links  introduction to your research topic  and ensures a logical flow of ideas.  Thus, it helps readers understand your reasons for conducting the study.

Providing Background Information

The reader should be able to understand your topic and its importance. The length and detail of your background also depend on the degree to which you need to demonstrate your understanding of the topic. Paying close attention to the following questions will help you in writing background information:

• Are there any theories, concepts, terms, and ideas that may be unfamiliar to the target audience and will require you to provide any additional explanation?
• Any historical data that need to be shared in order to provide context on why the current issue emerged?
• Are there any concepts that may have been borrowed from other disciplines that may be unfamiliar to the reader and need an explanation?

Related: Ready with the background and searching for more information on journal ranking? Check this infographic on the SCImago Journal Rank today!

Is the research study unique for which additional explanation is needed? For instance, you may have used a completely new method

How to Write a Background of the Study

The structure of a background study in a research paper generally follows a logical sequence to provide context, justification, and an understanding of the research problem. It includes an introduction, general background, literature review , rationale , objectives, scope and limitations , significance of the study and the research hypothesis . Following the structure can provide a comprehensive and well-organized background for your research.

Here are the steps to effectively write a background of the study.

1. Identify Your Audience:

Determine the level of expertise of your target audience. Tailor the depth and complexity of your background information accordingly.

2. Understand the Research Problem:

Define the research problem or question your study aims to address. Identify the significance of the problem within the broader context of the field.

3. Review Existing Literature:

Conduct a thorough literature review to understand what is already known in the area. Summarize key findings, theories, and concepts relevant to your research.

4. Include Historical Data:

Integrate historical data if relevant to the research, as current issues often trace back to historical events.

5. Identify Controversies and Gaps:

Note any controversies or debates within the existing literature. Identify gaps , limitations, or unanswered questions that your research can address.

6. Select Key Components:

Choose the most critical elements to include in the background based on their relevance to your research problem. Prioritize information that helps build a strong foundation for your study.

7. Craft a Logical Flow:

Organize the background information in a logical sequence. Start with general context, move to specific theories and concepts, and then focus on the specific problem.

8. Highlight the Novelty of Your Research:

Clearly explain the unique aspects or contributions of your study. Emphasize why your research is different from or builds upon existing work.

Here are some extra tips to increase the quality of your research background:

Example of a Research Background

Here is an example of a research background to help you understand better.

The above hypothetical example provides a research background, addresses the gap and highlights the potential outcome of the study; thereby aiding a better understanding of the proposed research.

What Makes the Introduction Different from the Background?

Your introduction is different from your background in a number of ways.

• The introduction contains preliminary data about your topic that  the reader will most likely read , whereas the background clarifies the importance of the paper.
• The background of your study discusses in depth about the topic, whereas the introduction only gives an overview.
• The introduction should end with your research questions, aims, and objectives, whereas your background should not (except in some cases where your background is integrated into your introduction). For instance, the C.A.R.S. ( Creating a Research Space ) model, created by John Swales is based on his analysis of journal articles. This model attempts to explain and describe the organizational pattern of writing the introduction in social sciences.

Points to Note

Your background should begin with defining a topic and audience. It is important that you identify which topic you need to review and what your audience already knows about the topic. You should proceed by searching and researching the relevant literature. In this case, it is advisable to keep track of the search terms you used and the articles that you downloaded. It is helpful to use one of the research paper management systems such as Papers, Mendeley, Evernote, or Sente. Next, it is helpful to take notes while reading. Be careful when copying quotes verbatim and make sure to put them in quotation marks and cite the sources. In addition, you should keep your background focused but balanced enough so that it is relevant to a broader audience. Aside from these, your background should be critical, consistent, and logically structured.

Writing the background of your study should not be an overly daunting task. Many guides that can help you organize your thoughts as you write the background. The background of the study is the key to introduce your audience to your research topic and should be done with strong knowledge and thoughtful writing.

The background of a research paper typically ranges from one to two paragraphs, summarizing the relevant literature and context of the study. It should be concise, providing enough information to contextualize the research problem and justify the need for the study. Journal instructions about any word count limits should be kept in mind while deciding on the length of the final content.

The background of a research paper provides the context and relevant literature to understand the research problem, while the introduction also introduces the specific research topic, states the research objectives, and outlines the scope of the study. The background focuses on the broader context, whereas the introduction focuses on the specific research project and its objectives.

When writing the background for a study, start by providing a brief overview of the research topic and its significance in the field. Then, highlight the gaps in existing knowledge or unresolved issues that the study aims to address. Finally, summarize the key findings from relevant literature to establish the context and rationale for conducting the research, emphasizing the need and importance of the study within the broader academic landscape.

The background in a research paper is crucial as it sets the stage for the study by providing essential context and rationale. It helps readers understand the significance of the research problem and its relevance in the broader field. By presenting relevant literature and highlighting gaps, the background justifies the need for the study, building a strong foundation for the research and enhancing its credibility.

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Organizing Your Social Sciences Research Paper

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Background information identifies and describes the history and nature of a well-defined research problem with reference to contextualizing existing literature. The background information should indicate the root of the problem being studied, appropriate context of the problem in relation to theory, research, and/or practice , its scope, and the extent to which previous studies have successfully investigated the problem, noting, in particular, where gaps exist that your study attempts to address. Background information does not replace the literature review section of a research paper; it is intended to place the research problem within a specific context and an established plan for its solution.

Fitterling, Lori. Researching and Writing an Effective Background Section of a Research Paper. Kansas City University of Medicine & Biosciences; Creating a Research Paper: How to Write the Background to a Study. DurousseauElectricalInstitute.com; Background Information: Definition of Background Information. Literary Devices Definition and Examples of Literary Terms.

Importance of Having Enough Background Information

Background information expands upon the key points stated in the beginning of your introduction but is not intended to be the main focus of the paper. It generally supports the question, what is the most important information the reader needs to understand before continuing to read the paper? Sufficient background information helps the reader determine if you have a basic understanding of the research problem being investigated and promotes confidence in the overall quality of your analysis and findings. This information provides the reader with the essential context needed to conceptualize the research problem and its significance before moving on to a more thorough analysis of prior research.

Forms of contextualization included in background information can include describing one or more of the following:

• Cultural -- placed within the learned behavior of a specific group or groups of people.
• Economic -- of or relating to systems of production and management of material wealth and/or business activities.
• Gender -- located within the behavioral, cultural, or psychological traits typically associated with being self-identified as male, female, or other form of  gender expression.
• Historical -- the time in which something takes place or was created and how the condition of time influences how you interpret it.
• Interdisciplinary -- explanation of theories, concepts, ideas, or methodologies borrowed from other disciplines applied to the research problem rooted in a discipline other than the discipline where your paper resides.
• Philosophical -- clarification of the essential nature of being or of phenomena as it relates to the research problem.
• Physical/Spatial -- reflects the meaning of space around something and how that influences how it is understood.
• Political -- concerns the environment in which something is produced indicating it's public purpose or agenda.
• Social -- the environment of people that surrounds something's creation or intended audience, reflecting how the people associated with something use and interpret it.
• Temporal -- reflects issues or events of, relating to, or limited by time. Concerns past, present, or future contextualization and not just a historical past.

Background information can also include summaries of important research studies . This can be a particularly important element of providing background information if an innovative or groundbreaking study about the research problem laid a foundation for further research or there was a key study that is essential to understanding your arguments. The priority is to summarize for the reader what is known about the research problem before you conduct the analysis of prior research. This is accomplished with a general summary of the foundational research literature [with citations] that document findings that inform your study's overall aims and objectives.

NOTE : Research studies cited as part of the background information of your introduction should not include very specific, lengthy explanations. This should be discussed in greater detail in your literature review section. If you find a study requiring lengthy explanation, consider moving it to the literature review section.

ANOTHER NOTE : In some cases, your paper's introduction only needs to introduce the research problem, explain its significance, and then describe a road map for how you are going to address the problem; the background information basically forms the introduction part of your literature review. That said, while providing background information is not required, including it in the introduction is a way to highlight important contextual information that could otherwise be hidden or overlooked by the reader if placed in the literature review section.

Background of the Problem Section: What do you Need to Consider? Anonymous. Harvard University; Hopkins, Will G. How to Write a Research Paper. SPORTSCIENCE, Perspectives/Research Resources. Department of Physiology and School of Physical Education, University of Otago, 1999; Green, L. H. How to Write the Background/Introduction Section. Physics 499 Powerpoint slides. University of Illinois; Pyrczak, Fred. Writing Empirical Research Reports: A Basic Guide for Students of the Social and Behavioral Sciences . 8th edition. Glendale, CA: Pyrczak Publishing, 2014; Stevens, Kathleen C. “Can We Improve Reading by Teaching Background Information?.” Journal of Reading 25 (January 1982): 326-329; Woodall, W. Gill. Writing the Background and Significance Section. Senior Research Scientist and Professor of Communication. Center on Alcoholism, Substance Abuse, and Addictions. University of New Mexico.

Structure and Writing Style

Providing background information in the introduction of a research paper serves as a bridge that links the reader to the research problem . Precisely how long and in-depth this bridge should be is largely dependent upon how much information you think the reader will need to know in order to fully understand the problem being discussed and to appreciate why the issues you are investigating are important.

From another perspective, the length and detail of background information also depends on the degree to which you need to demonstrate to your professor how much you understand the research problem. Keep this in mind because providing pertinent background information can be an effective way to demonstrate that you have a clear grasp of key issues, debates, and concepts related to your overall study.

The structure and writing style of your background information can vary depending upon the complexity of your research and/or the nature of the assignment. However, in most cases it should be limited to only one to two paragraphs in your introduction.

Given this, here are some questions to consider while writing this part of your introduction :

• Are there concepts, terms, theories, or ideas that may be unfamiliar to the reader and, thus, require additional explanation?
• Are there historical elements that need to be explored in order to provide needed context, to highlight specific people, issues, or events, or to lay a foundation for understanding the emergence of a current issue or event?
• Are there theories, concepts, or ideas borrowed from other disciplines or academic traditions that may be unfamiliar to the reader and therefore require further explanation?
• Is there a key study or small set of studies that set the stage for understanding the topic and frames why it is important to conduct further research on the topic?
• Y our study uses a method of analysis never applied before;
• Your study investigates a very esoteric or complex research problem;
• Your study introduces new or unique variables that need to be taken into account ; or,
• Your study relies upon analyzing unique texts or documents, such as, archival materials or primary documents like diaries or personal letters that do not represent the established body of source literature on the topic?

Almost all introductions to a research problem require some contextualizing, but the scope and breadth of background information varies depending on your assumption about the reader's level of prior knowledge . However, despite this assessment, background information should be brief and succinct and sets the stage for the elaboration of critical points or in-depth discussion of key issues in the literature review section of your paper.

Writing Tip

Background Information vs. the Literature Review

Incorporating background information into the introduction is intended to provide the reader with critical information about the topic being studied, such as, highlighting and expanding upon foundational studies conducted in the past, describing important historical events that inform why and in what ways the research problem exists, defining key components of your study [concepts, people, places, phenomena] and/or placing the research problem within a particular context. Although introductory background information can often blend into the literature review portion of the paper, essential background information should not be considered a substitute for a comprehensive review and synthesis of relevant research literature.

Hart, Cris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage, 1998; Pyrczak, Fred. Writing Empirical Research Reports: A Basic Guide for Students of the Social and Behavioral Sciences . 8th edition. Glendale, CA: Pyrczak Publishing, 2014.

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What is the Background in a Research Paper?

An effective Background section in your manuscript establishes the context for your study. And while original research requires novel findings, providing the necessary background information for these findings may be just as important. It lets your readers know that your findings are novel, important, and worthy of their time and attention.

Updated on October 3, 2022

A good Background section explains the history and nature of your research question in relation to existing literature – a “state of the art.” This section, along with the rationale, helps readers understand why you chose to study this problem and why your study is worthwhile. This article will show you how to do this.

Read on to better understand the:

• Real purpose of the Background section
• Typical length of a Background section and its placement
• Elements of an effective Background

What is the Background section of a research paper?

The Background section is an essential element of every study, answering:

• What do we already know about the topic?
• How does your study relate to what's been done so far in your field?
• What is its scope?
• Why does the topic warrant your interest and their interest?
• How did you develop the research question that you'll later introduce?

In grant writing, a Background section is often referred to as the “state of the art,” and this is a useful term to have in mind when writing this part of your paper.

What comes next?

After you make the above points,

• Formulate your research question/hypothesis . Research aims and objectives should be closely related to how you'll fill the gap you've identified in the literature. Your research gap is the central theme of your article and why people should read it.
• Summarize how you'll address it in the paper . Your methodology needs to be appropriate for addressing the “problem” you've identified.
• Describe the significance of your study . Show how your research fits into the bigger picture.

Note that the Background section isn't the same as the research rationale. Rather, it provides the relevant information the reader needs so they can follow your rationale. For example, it

• Explains scientific terms
• Provides available data and statistics on the topic
• Describes the methods used so far on your topic. Especially if these are different from what you're going to do. Take special care here, because this is often where peer reviewers focus intently.

This is a logical approach to what comes after the study's background. Use it and the reader can easily follow along from the broader information to the specific details that come later. Crucially, they'll have confidence that your analysis and findings are valid.

Where should the background be placed in a research paper?

Usually, the background comes after the statement of the problem, in the Introduction section. Logically, you need to provide the study context before discussing the research questions, methodology, and results.

The background can be found in:

The abstract

The background typically forms the first few sentences of the abstract. Why did you do the study? Most journals state this clearly. In an unstructured (no subheadings) abstract, it's the first sentence or two. In a structured abstract, it might be called the Introduction, Background, or State-of-the-Art.

PLOS Medicine , for example, asks for research article abstracts to be split into three sections: Background, Methods and Findings, and Conclusions. Journals in the humanities or social sciences might not clearly ask for it because articles sometimes have a looser structure than STEM articles.

The first part of the Introduction section

In the journal Nature , for example, the Introduction should be around 200 words and include

• Two to three sentences giving a basic introduction to the field.
• The background and rationale of the study are stated briefly.
• A simple phrase “Here we show ...”, or “In this study, we show ....” (to round out the Introduction).

The Journal of Organic Chemistry has similar author guidelines.

The Background as a distinct section

This is often the case for research proposals or some types of reports, as discussed above. Rather than reviewing the literature, this is a concise summary of what's currently known in the field relevant to the question being addressed in this proposed study.

How long should the Background section be?

As mentioned, there's no set length for the Background section. It generally depends on the journal and the content of your manuscript. Check the journal's author guidelines, the research center, granting agency, etc. If it's still not clear or if the instructions are contradictory, email or phone them directly.

The length of your background will depend on:

The manuscript length and content

A book-length study needs a more extensive Background than a four-page research article. Exploring a relatively unknown method or question might also need a longer Background.

For example, see this Frontiers article on the applications of artificial intelligence for developing COVID-19 vaccines. It has a seven-paragraph long Background (1,200 words) in a separate section. The authors need to discuss earlier successful uses of machine learning for therapy discovery to make a convincing case.

An academic paper published in an international journal is usually around 5,000 words. Your paper needs to be balanced, with appropriate text lengths used for the different sections: It would make no sense to have a 300-word introduction and then 4,000 words for the methods, for example. In a 5,000-word manuscript, you'll be able to use about 1,500 for the introduction, which includes the background.

How much you need to show your understanding of the topic

A lengthy grant application might need a longer Background (sub-)section. That's because if they're going to grant you money, they need a very good reason to. You'll need to show that the work is both interesting and doable. The Background is where you can do this.

What should the Background of a research manuscript include?

The Background of a research paper needs to show two things:

The study's territory ( scope )

First, provide a general overview of the field. Scientists in most disciplines should find it relatively easy to understand. Be broad, keep it interesting. Don't go into the specifics of your particular study.

Let's look at two examples:

• one from basic research (seeking to generate new knowledge)
• one from applied research (trying to solve or improve existing processes or products)

Applied research

This Frontiers in Artificial Intelligence article explores how AI can help discover treatments for COVID-19.

The background of the study can be found (i) in the abstract and (ii) in a separate section discussed at the end of this article. The abstract starts with this general overview: “SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic.” ( Arshadi et al., 2020 ). This is broad, and it's interesting. This is a topic that many researchers (even from outside this specific area) may want to learn more about.

Think of any theories, models, concepts, or terms (maybe borrowed from different disciplines) that may be unfamiliar to your reader. Be sure to clarify them in plainer language, if necessary.

For example, this systematic review looks at the connections of physician burnout with career engagement and quality of patient care. The Background is in the Introduction section. It starts by defining what burnout is:

• “Burnout is defined as a syndrome related to work that involves three key dimensions.” ( Hodkinson et al., 2022 )

The authors go on to explain its three aspects: emotional exhaustion, depersonalization, and a sense of reduced personal accomplishment.

Basic research

Imagine you're investigating how universities' moves to online teaching during the COVID-19 pandemic impacted students' learning outcomes in the United Kingdom. The overview could be:

• The COVID-19 pandemic and the ensuing lockdown generated tremendous challenges across the higher education sector. University campuses were forced to close. Face-to-face teaching and assessment transitioned into a virtual format.

2. The niche in the field (motivation)

To establish the niche in your field, describe what drove you to explore this specific topic.

• Explain how (un)successfully previous studies have investigated the problem.
• Note the knowledge gap or present a problem with a currently used process/practice/product.

After setting the stage, the abstract of the Frontiers in Artificial Intelligence article identifies a problem:

• “At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense.” ( Arshadi et al., 2020 )

The authors need to support their claim that computational methods can help discover new COVID-19 treatments. They do so by referring to previous research findings:

• “In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies.” ( Arshadi et al., 2020 )

Going back to the study on students' learning outcomes after universities introduced e-learning. The background section will next identify and describe the current knowledge gap and your proposed method of fixing it. It may be something like:

• Existing literature and studies by the UK Department for Education reveal x + y changes and effects on teaching and learning. Yet they provide little to no information on students' learning outcomes. Understanding the impact of online teaching and assessments on student outcomes is key to adopting future teaching practices and ensuring students from disadvantaged backgrounds are not left behind.

How is the background different from the literature review?

Both the background and literature review sections compile previous studies that are relevant and important to the topic.

Despite their similarities, they're different in scope and aims.

Overall, the research background could be seen as a small part of the detailed critical discussion in the literature review. Almost always, primary research articles do not include a detailed literature review.

How is the Background different from the Introduction section?

Although often part of the Introduction, the Background differs from the Introduction in scope and aim.

Breakdown of the Background in published articles

Consider this systematic review looking at the connections of physician burnout with career engagement and quality of patient care.

The Background is placed in the Introduction section. It's critical, consistent, and logically structured, moving from general to specific information.

You can also check out the summary paragraph breakdown provided by Nature. (Nature's “summary paragraph” is essentially an abstract.)

And if you're looking for some help, or have an article that's finished but needs a pre-submission review click here to connect with one of our expert AJE editors.

Gareth Dyke, PhD

Director of Global Content

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PHILO-notes

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How to Write the Background of the Study in Research (Part 1)

Background of the Study in Research: Definition and the Core Elements it Contains

Before we embark on a detailed discussion on how to write the background of the study of your proposed research or thesis, it is important to first discuss its meaning and the core elements that it should contain. This is obviously because understanding the nature of the background of the study in research and knowing exactly what to include in it allow us to have both greater control and clear direction of the writing process.

So, what really is the background of the study and what are the core elements that it should contain?

The background of the study, which usually forms the first section of the introduction to a research paper or thesis, provides the overview of the study. In other words, it is that section of the research paper or thesis that establishes the context of the study. Its main function is to explain why the proposed research is important and essential to understanding the main aspects of the study.

The background of the study, therefore, is the section of the research paper or thesis that identifies the problem or gap of the study that needs to addressed and justifies the need for conducting the study. It also articulates the main goal of the study and the thesis statement, that is, the main claim or argument of the paper.

Given this brief understanding of the background of the study, we can anticipate what readers or thesis committee members expect from it. As we can see, the background of the study should contain the following major points:

1) brief discussion on what is known about the topic under investigation; 2) An articulation of the research gap or problem that needs to be addressed; 3) What the researcher would like to do or aim to achieve in the study ( research goal); 4) The thesis statement, that is, the main argument or contention of the paper (which also serves as the reason why the researcher would want to pursue the study); 5) The major significance or contribution of the study to a particular discipline; and 6) Depending on the nature of the study, an articulation of the hypothesis of the study.

Thus, when writing the background of the study, you should plan and structure it based on the major points just mentioned. With this, you will have a clear picture of the flow of the tasks that need to be completed in writing this section of your research or thesis proposal.

Now, how do you go about writing the background of the study in your proposed research or thesis?

The next lessons will address this question.

How to Write the Opening Paragraphs of the Background of the Study?

To begin with, let us assume that you already have conducted a preliminary research on your chosen topic, that is, you already have read a lot of literature and gathered relevant information for writing the background of your study. Let us also assume that you already have identified the gap of your proposed research and have already developed the research questions and thesis statement. If you have not yet identified the gap in your proposed research, you might as well go back to our lesson on how to identify a research gap.

So, we will just put together everything that you have researched into a background of the study (assuming, again, that you already have the necessary information). But in this lesson, let’s just focus on writing the opening paragraphs.

It is important to note at this point that there are different styles of writing the background of the study. Hence, what I will be sharing with you here is not just “the” only way of writing the background of the study. As a matter of fact, there is no “one-size-fits-all” style of writing this part of the research or thesis. At the end of the day, you are free to develop your own. However, whatever style it would be, it always starts with a plan which structures the writing process into stages or steps. The steps that I will share with below are just some of the most effective ways of writing the background of the study in research.

So, let’s begin.

It is always a good idea to begin the background of your study by giving an overview of your research topic. This may include providing a definition of the key concepts of your research or highlighting the main developments of the research topic.

Let us suppose that the topic of your study is the “lived experiences of students with mathematical anxiety”.

Here, you may start the background of your study with a discussion on the meaning, nature, and dynamics of the term “mathematical anxiety”. The reason for this is too obvious: “mathematical anxiety” is a highly technical term that is specific to mathematics. Hence, this term is not readily understandable to non-specialists in this field.

So, you may write the opening paragraph of your background of the study with this:

“Mathematical anxiety refers to the individual’s unpleasant emotional mood responses when confronted with a mathematical situation.”

Since you do not invent the definition of the term “mathematical anxiety”, then you need to provide a citation to the source of the material from which you are quoting. For example, you may now say:

“Mathematical anxiety refers to the individual’s unpleasant emotional mood responses when confronted with a mathematical situation (Eliot, 2020).”

And then you may proceed with the discussion on the nature and dynamics of the term “mathematical anxiety”. You may say:

“Lou (2019) specifically identifies some of the manifestations of this type of anxiety, which include, but not limited to, depression, helplessness, nervousness and fearfulness in doing mathematical and numerical tasks.”

After explaining to your readers the meaning, nature, and dynamics (as well as some historical development if you wish to) of the term “mathematical anxiety”, you may now proceed to showing the problem or gap of the study. As you may already know, the research gap is the problem that needs to be addressed in the study. This is important because no research activity is possible without the research gap.

Let us suppose that your research problem or gap is: “Mathematical anxiety can negatively affect not just the academic achievement of the students but also their future career plans and total well-being. Also, there are no known studies that deal with the mathematical anxiety of junior high school students in New Zealand.” With this, you may say:

“If left unchecked, as Shapiro (2019) claims, this problem will expand and create a total avoidance pattern on the part of the students, which can be expressed most visibly in the form of cutting classes and habitual absenteeism. As we can see, this will negatively affect the performance of students in mathematics. In fact, the study conducted by Luttenberger and Wimmer (2018) revealed that the outcomes of mathematical anxiety do not only negatively affect the students’ performance in math-related situations but also their future career as professionals. Without a doubt, therefore, mathematical anxiety is a recurring problem for many individuals which will negatively affect the academic success and future career of the student.”

Now that you already have both explained the meaning, nature, and dynamics of the term “mathematical anxiety” and articulated the gap of your proposed research, you may now state the main goal of your study. You may say:

“Hence, it is precisely in this context that the researcher aims to determine the lived experiences of those students with mathematical anxiety. In particular, this proposed thesis aims to determine the lived experiences of the junior high school students in New Zealand and identify the factors that caused them to become disinterested in mathematics.”

Please note that you should not end the first paragraph of your background of the study with the articulation of the research goal. You also need to articulate the “thesis statement”, which usually comes after the research goal. As is well known, the thesis statement is the statement of your argument or contention in the study. It is more of a personal argument or claim of the researcher, which specifically highlights the possible contribution of the study. For example, you may say:

“The researcher argues that there is a need to determine the lived experiences of these students with mathematical anxiety because knowing and understanding the difficulties and challenges that they have encountered will put the researcher in the best position to offer some alternatives to the problem. Indeed, it is only when we have performed some kind of a ‘diagnosis’ that we can offer practicable solutions to the problem. And in the case of the junior high school students in New Zealand who are having mathematical anxiety, determining their lived experiences as well as identifying the factors that caused them to become disinterested in mathematics are the very first steps in addressing the problem.”

If we combine the bits and pieces that we have written above, we can now come up with the opening paragraphs of your background of the study, which reads:

As we can see, we can find in the first paragraph 5 essential elements that must be articulated in the background of the study, namely:

1) A brief discussion on what is known about the topic under investigation; 2) An articulation of the research gap or problem that needs to be addressed; 3) What the researcher would like to do or aim to achieve in the study (research goal); 4) The thesis statement , that is, the main argument or claim of the paper; and 5) The major significance or contribution of the study to a particular discipline. So, that’s how you write the opening paragraphs of your background of the study. The next lesson will talk about writing the body of the background of the study.

How to Write the Body of the Background of the Study?

If we liken the background of the study to a sitting cat, then the opening paragraphs that we have completed in the previous lesson would just represent the head of the cat.

This means we still have to write the body (body of the cat) and the conclusion (tail). But how do we write the body of the background of the study? What should be its content?

Truly, this is one of the most difficult challenges that fledgling scholars faced. Because they are inexperienced researchers and didn’t know what to do next, they just wrote whatever they wished to write. Fortunately, this is relatively easy if they know the technique.

One of the best ways to write the body of the background of the study is to attack it from the vantage point of the research gap. If you recall, when we articulated the research gap in the opening paragraphs, we made a bold claim there, that is, there are junior high school students in New Zealand who are experiencing mathematical anxiety. Now, you have to remember that a “statement” remains an assumption until you can provide concrete proofs to it. This is what we call the “epistemological” aspect of research. As we may already know, epistemology is a specific branch of philosophy that deals with the validity of knowledge. And to validate knowledge is to provide concrete proofs to our statements. Hence, the reason why we need to provide proofs to our claim that there are indeed junior high school students in New Zealand who are experiencing mathematical anxiety is the obvious fact that if there are none, then we cannot proceed with our study. We have no one to interview with in the first. In short, we don’t have respondents.

The body of the background of the study, therefore, should be a presentation and articulation of the proofs to our claim that indeed there are junior high school students in New Zealand who are experiencing mathematical anxiety. Please note, however, that this idea is true only if you follow the style of writing the background of the study that I introduced in this course.

So, how do we do this?

One of the best ways to do this is to look for literature on mathematical anxiety among junior high school students in New Zealand and cite them here. However, if there are not enough literature on this topic in New Zealand, then we need to conduct initial interviews with these students or make actual classroom observations and record instances of mathematical anxiety among these students. But it is always a good idea if we combine literature review with interviews and actual observations.

Assuming you already have the data, then you may now proceed with the writing of the body of your background of the study. For example, you may say:

“According to records and based on the researcher’s firsthand experience with students in some junior high schools in New Zealand, indeed, there are students who lost interest in mathematics. For one, while checking the daily attendance and monitoring of the students, it was observed that some of them are not always attending classes in mathematics but are regularly attending the rest of the required subjects.”

After this sentence, you may insert some literature that will support this position. For example, you may say:

“As a matter of fact, this phenomenon is also observed in the work of Estonanto. In his study titled ‘Impact of Math Anxiety on Academic Performance in Pre-Calculus of Senior High School’, Estonanto (2019) found out that, inter alia, students with mathematical anxiety have the tendency to intentionally prioritize other subjects and commit habitual tardiness and absences.”

Then you may proceed saying:

“With this initial knowledge in mind, the researcher conducted initial interviews with some of these students. The researcher learned that one student did not regularly attend his math subject because he believed that he is not good in math and no matter how he listens to the topic he will not learn.”

Then you may say:

“Another student also mentioned that she was influenced by her friends’ perception that mathematics is hard; hence, she avoids the subject. Indeed, these are concrete proofs that there are some junior high school students in New Zealand who have mathematical anxiety. As already hinted, “disinterest” or the loss of interest in mathematics is one of the manifestations of a mathematical anxiety.”

If we combine what we have just written above, then we can have the first two paragraphs of the body of our background of the study. It reads:

“According to records and based on the researcher’s firsthand experience with students in some junior high schools in New Zealand, indeed there are students who lost interest in mathematics. For one, while checking the daily attendance and monitoring of the students, it was observed that some of them are not always attending classes in mathematics but are regularly attending the rest of the required subjects. As a matter of fact, this phenomenon is also observed in the work of Estonanto. In his study titled ‘Impact of Math Anxiety on Academic Performance in Pre-Calculus of Senior High School’, Estonanto (2019) found out that, inter alia, students with mathematical anxiety have the tendency to intentionally prioritize other subjects and commit habitual tardiness and absences.

With this initial knowledge in mind, the researcher conducted initial interviews with some of these students. The researcher learned that one student did not regularly attend his math subject because he believed that he is not good in math and no matter how he listens to the topic he will not learn. Another student also mentioned that she was influenced by her friends’ perception that mathematics is hard; hence, she avoids the subject. Indeed, these are concrete proofs that there are some junior high school students in New Zealand who have mathematical anxiety. As already hinted, “disinterest” or the loss of interest in mathematics is one of the manifestations of a mathematical anxiety.”

And then you need validate this observation by conducting another round of interview and observation in other schools. So, you may continue writing the body of the background of the study with this:

“To validate the information gathered from the initial interviews and observations, the researcher conducted another round of interview and observation with other junior high school students in New Zealand.”

“On the one hand, the researcher found out that during mathematics time some students felt uneasy; in fact, they showed a feeling of being tensed or anxious while working with numbers and mathematical problems. Some were even afraid to seat in front, while some students at the back were secretly playing with their mobile phones. These students also show remarkable apprehension during board works like trembling hands, nervous laughter, and the like.”

Then provide some literature that will support your position. You may say:

“As Finlayson (2017) corroborates, emotional symptoms of mathematical anxiety involve feeling of helplessness, lack of confidence, and being nervous for being put on the spot. It must be noted that these occasionally extreme emotional reactions are not triggered by provocative procedures. As a matter of fact, there are no personally sensitive questions or intentional manipulations of stress. The teacher simply asked a very simple question, like identifying the parts of a circle. Certainly, this observation also conforms with the study of Ashcraft (2016) when he mentions that students with mathematical anxiety show a negative attitude towards math and hold self-perceptions about their mathematical abilities.”

And then you proceed:

“On the other hand, when the class had their other subjects, the students show a feeling of excitement. They even hurried to seat in front and attentively participating in the class discussion without hesitation and without the feeling of being tensed or anxious. For sure, this is another concrete proof that there are junior high school students in New Zealand who have mathematical anxiety.”

To further prove the point that there indeed junior high school students in New Zealand who have mathematical anxiety, you may solicit observations from other math teachers. For instance, you may say:

“The researcher further verified if the problem is also happening in other sections and whether other mathematics teachers experienced the same observation that the researcher had. This validation or verification is important in establishing credibility of the claim (Buchbinder, 2016) and ensuring reliability and validity of the assertion (Morse et al., 2002). In this regard, the researcher attempted to open up the issue of math anxiety during the Departmentalized Learning Action Cell (LAC), a group discussion of educators per quarter, with the objective of ‘Teaching Strategies to Develop Critical Thinking of the Students’. During the session, one teacher corroborates the researcher’s observation that there are indeed junior high school students in New Zealand who have mathematical anxiety. The teacher pointed out that truly there were students who showed no extra effort in mathematics class in addition to the fact that some students really avoided the subject. In addition, another math teacher expressed her frustrations about these students who have mathematical anxiety. She quipped: “How can a teacher develop the critical thinking skills or ability of the students if in the first place these students show avoidance and disinterest in the subject?’.”

Again, if we combine what we have just written above, then we can now have the remaining parts of the body of the background of the study. It reads:

So, that’s how we write the body of the background of the study in research . Of course, you may add any relevant points which you think might amplify your content. What is important at this point is that you now have a clear idea of how to write the body of the background of the study.

How to Write the Concluding Part of the Background of the Study?

Since we have already completed the body of our background of the study in the previous lesson, we may now write the concluding paragraph (the tail of the cat). This is important because one of the rules of thumb in writing is that we always put a close to what we have started.

It is important to note that the conclusion of the background of the study is just a rehashing of the research gap and main goal of the study stated in the introductory paragraph, but framed differently. The purpose of this is just to emphasize, after presenting the justifications, what the study aims to attain and why it wants to do it. The conclusion, therefore, will look just like this:

“Given the above discussion, it is evident that there are indeed junior high school students in New Zealand who are experiencing mathematical anxiety. And as we can see, mathematical anxiety can negatively affect not just the academic achievement of the students but also their future career plans and total well-being. Again, it is for this reason that the researcher attempts to determine the lived experiences of those junior high school students in New Zealand who are experiencing a mathematical anxiety.”

If we combine all that we have written from the very beginning, the entire background of the study would now read:

If we analyze the background of the study that we have just completed, we can observe that in addition to the important elements that it should contain, it has also addressed other important elements that readers or thesis committee members expect from it.

On the one hand, it provides the researcher with a clear direction in the conduct of the study. As we can see, the background of the study that we have just completed enables us to move in the right direction with a strong focus as it has set clear goals and the reasons why we want to do it. Indeed, we now exactly know what to do next and how to write the rest of the research paper or thesis.

On the other hand, most researchers start their research with scattered ideas and usually get stuck with how to proceed further. But with a well-written background of the study, just as the one above, we have decluttered and organized our thoughts. We have also become aware of what have and have not been done in our area of study, as well as what we can significantly contribute in the already existing body of knowledge in this area of study.

Please note, however, as I already mentioned previously, that the model that I have just presented is only one of the many models available in textbooks and other sources. You are, of course, free to choose your own style of writing the background of the study. You may also consult your thesis supervisor for some guidance on how to attack the writing of your background of the study.

Lastly, and as you may already know, universities around the world have their own thesis formats. Hence, you should follow your university’s rules on the format and style in writing your research or thesis. What is important is that with the lessons that you learned in this course, you can now easily write the introductory part of your thesis, such as the background of the study.

• Open access
• Published: 14 May 2024

Investigating young children’s physical activity through time and place

• T. Remmers 1 ,
• P. Koolwijk 2 ,
• I. Fassaert 1 , 8 ,
• J. Nolles 3 ,
• W. de Groot 3 ,
• S. B. Vos 1 , 4 ,
• S. I. de Vries 2 , 5 ,
• R. Mombarg 3 , 6 &
• D. H. H. Van Kann 1 , 7  

International Journal of Health Geographics volume  23 , Article number:  12 ( 2024 ) Cite this article

257 Accesses

Metrics details

Previous research indicates the start of primary school (4-5-year-old) as an essential period for the development of children’s physical activity (PA) patterns, as from this point, the age-related decline of PA is most often observed. During this period, young children are exposed to a wider variety of environmental- and social contexts and therefore their PA is influenced by more diverse factors. However, in order to understand children’s daily PA patterns and identify relevant opportunities for PA promotion, it is important to further unravel in which (social) contexts throughout the day, PA of young children takes place.

We included a cross-national sample of 21 primary schools from the Startvaardig study. In total, 248 children provided valid accelerometer and global positioning (GPS) data. Geospatial analyses were conducted to quantify PA in (social) environments based on their school and home. Transport-related PA was evaluated using GPS speed-algorithms. PA was analysed at different environments, time-periods and for week- and weekend days separately.

Children accumulated an average of 60 min of moderate-to-vigorous PA (MVPA), both during week- and weekend days. Schools contributed to approximately half of daily MVPA during weekdays. During weekends, environments within 100 m from home were important, as well as locations outside the home-school neighbourhood. Pedestrian trips contributed to almost half of the daily MVPA.

Conclusions

We identified several social contexts relevant for children’s daily MVPA. Schools have the potential to significantly contribute to young children’s PA patterns and are therefore encouraged to systematically evaluate and implement parts of the school-system that stimulate PA and potentially also learning processes. Pedestrian trips also have substantial contribution to daily MVPA of young children, which highlights the importance of daily active transport in school- and parental routines.

Early childhood (i.e. from birth until five years old) has recently become a prominent health-promotion target group as there is increased recognition that establishing health-supporting environments in early childhood can reduce subsequent population-level risk factors and disease [ 1 ]. Within these health-supporting environments, physical activity (PA) and sedentary behaviours contribute to the development of children’s physical-, psychosocial and cognitive abilities [ 2 , 3 , 4 ]. The consistency, quality and timing of these interconnected behaviours are formed in early childhood and the accompanied habits tend to track from childhood through adolescence [ 5 , 6 ].

In early childhood, the role of PA is of particular interest because through PA a child interacts with the surrounding environment and experiences the capabilities of its own body. By doing so, PA acts as an initiator of various learning processes [ 7 ]. In addition, sufficient- and appropriate variation of PA leads to the development of fundamental motor skills [ 8 ] which are important building blocks for more complex motor skills later in life [ 9 , 10 , 11 ]. Research suggests that PA and the development of motor skills may be more intertwined with cognitive development than previously assumed [ 12 , 13 , 14 ]. In addition, more PA in early childhood is associated with a broad range of favourable indicators relating to cardiometabolic-, skeletal- and morphological health [ 15 , 16 , 17 ]. In 2020, the WHO formulated specific international guidelines for early childhood [ 18 ]. For 3-4-year-old children, at least 180 daily minutes of PA (of which 60 min of moderate-to-vigorous intensity) and no more than 60 min of daily sedentary screen time are recommended [ 19 ]. Before five- to six years of age, children seem to be sufficiently active, especially at light intensity [ 20 , 21 ]. However, already around the age of 6 years, children’s PA levels decline while sedentary activities such as screen-related behaviours increase [ 21 ]. To understand the mechanism behind this age-related decline, it is vital to gain more insight in the daily PA patterns of young children [ 22 , 23 , 24 ].

Previous longitudinal studies showed that the onset of primary school is crucial in the development of healthy PA patterns of children, as notable increases in sedentary patterns were observed in this phase [ 25 , 26 ]. In primary school, children are exposed to a wider social- and physical environment (both in- and out of school), extending the potential of barriers and affordances for PA. Also, previous research showed that sedentary time predominantly increased during school hours, suggesting that in-school practices are probably responsible for decreasing PA [ 27 , 28 , 29 ]. Other studies have reported that variability between children’s PA was highest out of school [ 28 , 30 , 31 ]. This illustrates that the start of primary school is an interesting phase in which a complex and dynamic system of environmental factors have great influence of children’s emerging PA patterns [ 26 , 32 , 33 ]. In addition, the context in which PA occurs greatly influences the potential of these factors in influencing PA [ 34 ]. For example, children’s PA at school and PA at home are influenced by different environmental factors [ 34 ]. This means that in order to understand children’s PA patterns and how to effectively promote PA, more contextual information about the type of PA is essential [ 35 ]. However, investigating context-specific PA of young children is complex, because they predominantly perform PA in short sporadic bursts, sometimes without clear motives [ 36 ]. This makes the application of subjective assessment (e.g. parental recall) challenging and susceptible for social-desirability bias [ 37 , 38 ]. On the other hand, objective measurements (e.g. accelerometry) fail to capture essential contextual information (e.g. location) about the type of PA performed [ 16 ]. One way of overcoming these issues is by combined accelerometer and GPS methodology, which simultaneously combines information about PA and the geographic context [ 39 ]. Previous studies that have used this methodology in young children are scarce and have either focused on places for PA within childcare centers [ 40 ] or residential neighbourhoods separately [ 41 ]. Results showed that within childcare centers, larger open areas with portable equipment (e.g. balls, toys) were associated with children’s PA-hotspots [ 40 ] and that approximately 60% of the daily moderate-to-vigorous PA (MVPA) of 3-year-old Western-Australian children occurred < 500 m from their home, while 30% of daily MVPA occurred outside their neighbourhood (> 1600 m from their home) [ 41 ]. Although this provides valuable insights in where children’s PA takes place within the childcare and neighbourhood context, integrated information from both contexts is warranted to evaluate the degree to which each of these contexts contribute to children’s daily PA. Therefore, the purpose of this study was to investigate context-specific PA patterns of 4-6-year-old children (i.e. onset of primary school in the Netherlands) to improve our understanding of how to effectively promote these PA patterns.

Design and participants

In this cross-sectional study, a convenience sample of 21 primary schools in medium- to large scale cities of the Netherlands (i.e. 5 schools located in Eindhoven, 7 schools in the vicinity of The Hague, 9 schools in the vicinity of Groningen) were selected from the cross-national ‘Start Vaardig’ project (Dutch for ‘Skilful Start’). The three cities lie relatively close to each other (i.e. 370 km of driving distance to visit all three cities), with comparable climate during the period of measurement. Participating schools represented a wide variation of predominantly suburban areas in the north, middle, and south of the Netherlands (Fig.  1 ). In terms of PA- or transport related geography (e.g. percentage greenness, flat land, degree of urbanization) the suburban areas of the participating schools were comparable. The Dutch primary school system ranges from grade 1 (for 4-year-old children) till grade 8 (for 12-year-old children), and in our study children from grades 1 and 2 were eligible for participation. Schools provided detailed information about schedules and break times.

Geographical distribution of participating schools

All participating schools were visited four times by a team of two trained researchers. At the first visit, children and teachers were informed about the project and shown how to wear the accelerometer (Actigraph GT3X+, Pensacola, FL, USA) and GPS devices (Qstarz BT1000XT, Tapei, Taiwan). Children were provided with a written information letter and informed consent form. Parents were given the possibility to sign and return the written informed consent form to their child’s teacher or to sign online. Teachers were provided with additional written instruction about the purpose of the project and how to collect the informed consent forms. At the second visit, consent forms were collected, and reminders were handed out to the children. At the third visit, children received the devices with verbal instruction and parents were provided with written instructions. We instructed children to wear the devices at the right hip using an elastic belt during waking hours, for six consecutive days (containing two weekend days). We instructed to only remove the devices during sleep or water-related activities (e.g. swimming, showering) and to recharge the GPS logger every day just before going to bed. Additionally, parents of all participating children received a paper questionnaire, as well as an online version of the questionnaire. At the fourth and last visit, devices were returned, and paper questionnaires were collected.

Data collection took place between the 26th of May and the 15th of July 2021, in-between restrictions caused by the COVID-19 pandemic. Daily average temperature was 18.1 degrees Celsius (SD = 3.1) with average precipitation of 3.7 mm per day (69% of days with < 1 mm). Sunset times during this period were between 21:38 and 21:48 h ( www.timeanddate.com ). Ethical approval was obtained by the Ethical Research Committee of the VU Medical Centre in Amsterdam, the Netherlands (VCWE-2020-137).

Measurements

Parents provided socio-demographic information in the questionnaire, such as their child’s date of birth and gender, postal code and number of spouses. In addition, questions were asked about the frequency and reason that the accelerometer- and GPS devices were taken off (e.g. swimming, showering, discomfort) as well as the days on which their child did not sleep at home during the night.

Numerous studies have supported validity and accuracy of the accelerometer and GPS devices [ 42 , 43 , 44 , 45 ]. We used the manufacturer’s software to initialize devices and export data to CSV-files, for the accelerometer (Actilife version 6.13.4) and GPS logger (QTravel version 1.54) separately. Devices were set to record data every 10 s epochs. GPS loggers were initialized to record data between 6 AM and 10 PM to optimize battery life and storage capacity and to stop logging when storage capacity was full. We processed accelerometer data using R-package GGIR (version 3.0–1) [ 46 ], which included algorithms regarding autocalibration of accelerometers [ 47 ] and standard weartime detection algorithms. Namely, non-wear time was investigated per 15-minute time blocks, while the definition of non-wear time was based on the standard deviation (i.e. <13 milli gravity (mg)) and range (i.e. <50 mg) of the 60-minute time window that centered each 15-minute time block. Intensity-classification of PA was based on the vertical-axis classification of Evenson et al. (2008) [ 48 ] and were adjusted for the 10 s epoch by linear interpolation. We processed combined GPS and accelerometer output using the HABITUS (hbGPS) software [ 49 , 50 ], inspired by functionality from the earlier PALMS system [ 44 , 51 ]. GPS data was cleaned by removing outliers based on (1) missing values in speed estimates, (2) speed greater than 130 km/h with a speed-difference > 30 km/h, and (3) elevation change between successive values > 1000 m [ 50 ]. Trips were identified by a consistent speed of at least 1 kmph for any sequence of three successive datapoints (i.e. 30 s). We furthermore identified trip pause points with insufficient speed (see sentence above) for a maximum of 2 min. When the pause time exceeded 2 min, we classified this as a trip end point. Alternatively, we treated this as one common trip. We subsequently removed trips with (1) distance < 100 m, (2) duration < 60 s, (3) no available GPS data (time gaps) of > 30 s between each datapoint and the preceding datapoint. GPS data were exported as latitude, longitude, and trip-characteristics. Finally, accelerometer- and GPS data were matched based on timestamp of the accelerometer. Trip mode was based on the 90th percentile speed-thresholds of 1, 10- and 35 kmph for walking, cycling and vehicle respectively [ 52 ].

Data analyses

In total, 358 parents (26.2% from total potential sample) provided written informed consent for their child to participate in combined accelerometer and GPS measurements. After accounting for participant refusal and device malfunctioning, our sample of analysis consisted of 311 4-6-year-old children (84.5% from the sample of parents with informed consent, see Fig.  2 ). Next, a total of 281 parents filled in the questionnaire at the start of the study and 248 children provided valid combined accelerometer- and GPS data (i.e. sensor-data), defined as weekdays with 8 h- and weekend days with 6 h of combined accelerometer and GPS data. We defined these criteria because during weekend days, we observed less weartime due to a delayed start of weartime in the morning. From the 281 children with questionnaire-data, 85 children had insufficient sensor-data. From the 248 children with valid sensor-data, questionnaire-data were missing for 52 children. Consequently, for 196 children we had both valid sensor-data and questionnaire-data. Slight differences between the drop-out percentage between the cities were caused by the fact that in Eindhoven, accelerometers were handed out to the classroom teacher for individual children from parents that provided informed consent but were absent during the day of measurement (e.g. often due to COVID-19 restrictions). This led to an increased number of participants not meeting the 3-day valid data criteria, whereas in Groningen and The Hague, these children were considered missing a-priori and not treated as drop-out.

We imported combined accelerometer and GPS datasets for each school into ArcGIS Pro version 3.1.0 (ESRI, Redlands, CA, USA) for additional geospatial analyses. We geocoded the location of schools based on the school’s registry and extracted polygons of the school building and surrounding parcel. For the residential location of children, parents provided their six-digit postal code (i.e. identifies street-level area of 15- to 20 addresses without house number). In addition, we extracted the average centroid point of GPS data on week- and weekend days between 6- and 8 AM, during days that the child slept at home. These locations were validated by calculating Euclidean distances between the centroid point and the six-digit postal codes that parents provided in the questionnaire (median distance was 52.2 m for weekdays and 54.1 for weekend days). Next, for each datapoint, we calculated Euclidean- (i.e. < 10 m) and network distances (i.e. remaining distance categories) between children’s home and school. To investigate distances of children’s datapoints based on the combined home-school environment (not based on home and school separately), we integrated these distance-categories from both home and school (see Fig.  3 ). In addition, based on the Dutch national registry of large-scale topography (i.e. BGT), polygons identified as parks, sports terrains and public playgrounds were extracted and we subsequently performed ‘spatial join’ analyses to identify the datapoints that were within 10-meters from these parks, sports terrains, or playground parcels.

Example of distance-categories integrating both home and school locations

Parents indicated that children were awake for an average of 12 h per day and that water activities such as swimming were the primary reason for non-weartime during waking hours, while 11 parents (8.0%) reported their child experiencing discomfort when wearing the devices. Finally, only data points containing both valid accelerometer- and GPS data were retained, which resulted in a final sample of 248 children (1017 days of measurement; with 762 weekdays and 255 weekend days). We used days as the unit of analysis as this allows variation between days within children. We presented PA as minutes and percentage in light (LPA) and moderate-to-vigorous (MVPA) intensities.

Slightly more boys (54.3%) than girls participated in the study. The mean age of children was 5.56-year-old (SD = 0.75). Almost all children had either one- (57.8%) or two or more siblings (34.5%), and 61 children (40.0%) had at least one older sibling. Parents reported that 82.3% of the children slept home for all days during measurement. In total, 49.8% reported that the child had visited afterschool childcare at school for at least one day during measurement and 15.7% had visited afterschool childcare outside the school’s parcel (e.g. childcare at other location or other organization). Regarding the use of bicycles, 45.2% indicated that their child was able to cycle without supervision. In terms of organized sports, 55.6% of the children was a member of a sports club, while 30.6%- and 49.5% participated in organized sports and swimming lessons during the measurement period, respectively (see Table  1 ).

School start times were 8.30 am (19 schools) and 8.40 am (2 schools), while school bell times ranged from 2.00 pm to 3.15 pm. In total, 15 schools used a shortened schedule on Wednesdays (i.e. bell times ranging from 12.00 am to 12.35 pm) and 5 schools used a shortened schedule on Fridays (i.e. bell times ranging from 12.00 am to 12.30 pm). All schools provided breaks at the school parcel, so children were not allowed to leave school before school bell time. On average children lived at 2.76 km (SD = 0.33 km) pedestrian network-distance from their school (median = 604 m). Alternatively, when categorized in distance-categories, 30.8% lived within 400 m, 29.1% lived between 400- and 800 m (i.e., approximately 8 min walking time) and 40.1% lived more than 800 m from their school.

When looking at the temporal distribution of PA, average daily weartime of combined sensor-data was 713.26 min (SD = 116.07) during weekdays and 670.34 min (SD = 117.38) during weekend days, while children performed an average of 63.00 min (8.9%) and 65.37 min (9.8%) of MVPA during week- and weekend days, respectively. On weekdays, children spent an average of 294.88 min (SD = 78.90) in the temporal school-schedule, which makes the average distribution of time during weekdays approximately 50% for combined before- and in school and 50% for afterschool till sleep (data not shown). Within weekdays, schooltime contributed to almost half of the daily MVPA (i.e. 29 min), while after school time periods approximately contributed to the other half (Fig.  4 ). The minutes of MVPA after school, as well as its relative percentage, gradually declined during the day. During weekends, the absolute and relative contribution of MVPA slightly increased across the day, with the most active part in the early afternoon. After 16:00 h, intensity of MVPA dropped to 7.2% on average.

Temporal distribution of mean daily minutes of MVPA in week- and weekend days

When looking at the geographical distribution of PA during weekdays, percentages of LPA and MVPA were about twice as high at school versus at home (Table  2 ). At school, children spent on average 21.99 min in MVPA, which is 10.7% from the total daily weartime at school. Very little time was spent in the overlapping home-school neighbourhood and in the home neighbourhood outside the school neighbourhood. During weekdays, the vast majority of weartime was spent at- or close to home and school parcels. Children reported most daily minutes of MVPA at their school-parcel (i.e. approximately 22 min). Another 5.8 min of MVPA occurred within 100 m from their school, summing up to approximately 28 min. Also, compared to all other environments, the absolute- and relative contribution of LPA was highest at school, meaning that children were least sedentary at- and around their school (data not shown). At home, absolute- and relative contributions of LPA, as well as MVPA, were lower. Children spent more LPA and MVPA outside their home (but within 100 m from home) compared to their direct home location. In addition, children spent on average 70 min outside the home-school neighbourhood, with a relatively high amount of 7.5 min in MVPA (i.e. 10.6% of time spent outside home-school neighbourhood). Obviously, during weekend days the influence of school on PA disappeared, but this resulted in higher absolute- and slightly higher relative contribution of the home location in children’s LPA and MVPA (Table  2 ). Children spent especially more time at the ‘close to home’ location, resulting in approximately 23 min of MVPA. The percentage of MVPA that occurred at home remained relatively stable (i.e. 4.5% during weekdays versus 5.8% during weekend days). During weekends children also spent more time outside the home-school neighbourhood, while the percentage MVPA remained stable compared to weekdays. This resulted in another 23 min of MVPA performed outside the home-school neighbourhood (i.e. 12.0% of time spent at this location).

Transport-related pedestrian trips were responsible for approximately 45% (i.e. 26 min) and 38% (i.e. 22 min) of children’s average daily MVPA during weekdays and weekend days respectively (Table  3 ). Higher daily mean minutes of pedestrian trips were found during weekdays compared to weekends. During weekdays, additional analyses revealed that slightly more minutes of daily pedestrian trips were observed during in-school time zones (82.25, SD = 80.22 min) compared to afterschool time zones (66.29, SD = 61.36 min). The influence of bicycle- and motorized trips to MVPA was substantially lower. In general, this also means that approximately 30 min of MVPA during weekdays- and 34 min during weekend days was spent relatively stationary (i.e. not identified by GPS-based algorithm as a transport trip). The percentage MVPA was higher in pedestrian trips compared to stationary activities (e.g. 12.3% versus 6.8%, respectively).

Public open spaces equipped for PA (i.e. parks, sports terrains, and playgrounds) played a minor role in young children’s daily PA patterns. Although playgrounds showed a relatively high percentage of time spent in MVPA, absolute time spent at playgrounds was relatively low (2.8 and 5.1 daily minutes during week- and weekend days, respectively).

This study demonstrated context-specific PA patterns of young children by investigating their PA through space and time. More specifically, we showed that at the onset of primary school, half of children’s daily amount of MVPA during weekdays occurred at school or within 100 m from school, while the other half was divided between home or within 100 m from home and environments outside children’s home-school neighbourhood. During weekend days, from the daily amount of MVPA (i.e., approximately 65 min), slightly over half was performed at home or within 100 m from home. Only a marginal part of total daily MVPA occurred outside the home-school neighbourhood. These findings are in line with the study of Bai and colleagues, who showed that about 60% of 3-year-old children’s daily weekday MVPA of approximately 76 min occurred within 500 m from their home [ 41 ]. Furthermore, this shows that although the school-context was responsible for over 50% of MVPA during weekdays, these children seem to be able to reallocate this with PA around home and outside the home-school neighbourhood during weekend days. This is not in line with the Structured Days Hypothesis [ 53 ], stating that the presence of structure and routine of pre-scheduled activities (e.g. physical education, active travel, limited screen time) may positively influence children’s PA. Future studies are encouraged to further unravel within-person mechanisms (both between-day and within-day) in order to tailor future PA interventions [ 54 , 55 ].

Our study demonstrated the importance of pedestrian-trips in daily MVPA of young children. Urban planners, school boards, policy-makers and health scientists are encouraged to co-develop initiatives that persuade parents and children to use active mobility instead of passive forms while exploiting the potential of supportive social- and physical environments [ 56 ]. Sensitivity analyses revealed that during weekdays most of time spent in pedestrian trips were during school time. However, we also showed that in our sample, cycling played a minor role in daily MVPA, which is in contrast with studies in older Dutch children [ 57 ] showing that cycling being one of the major contributors to daily PA in Dutch children [ 58 ]. This is in accordance with the long history of normalization of daily cycling mobility in the Netherlands [ 59 , 60 ]. Children usually learn to cycle around the age of 5–7 years [ 61 ]. According to our questionnaire-data, parents reported that most of the children in our study was technically already able to ride a bike with- (32%) or without supervision (45%), but 98.5% reported supervision of parents- or siblings in home-school trips. An alternative explanation for this finding may be the use of the uniaxial signal of our hip-worn accelerometer in our study, as this underestimates PA during cycling [ 62 ]. Future studies, especially in older populations, are encouraged to improve measurement of cycling (e.g. using alternative placement, tri-axial signals, or multiple measurements). In addition, future studies may continue to distinguish between transport trips and relative stationary PA (i.e. not identified as a trip), potentially also leading to associations with motor development of young children.

The present study contributed to the understanding of how children’s integrated school- and home environments contribute to their daily PA, in both week- and weekend days. Previous studies have investigated PA from either one of these environments [ 63 , 64 ], but to our knowledge, this is the first study that applied this combination of contexts. In particular, this study showed that especially during weekends, a considerable proportion of MVPA was performed > 800 m from both home- and school locations. This is again in line with preschool-data from Bai and colleagues, who showed that almost 30% of daily MVPA occurred at residential locations outside children’s neighbourhoods [ 41 ]. Our data showed that young children’s daily exposure during both week- and weekend days in parks, sports terrains, and playgrounds was very low but the percentage of MVPA at these locations was relatively high. This may require specific interventions focusing on increasing young children’s exposure at these environments, potentially as a multi-component involving both the home/family- and the school setting [ 65 ]. In addition, based on the same findings regarding the low daily minutes of PA that occurred in parks, sports terrains, and playgrounds, it seems unlikely that MVPA outside home-school neighbourhood would relate to these specific locations. Furthermore, it seems also unlikely that afterschool childcare or care by grandparents outside the neighbourhood may be responsible for this, since our questionnaire-data showed that only 15% of the parents reported a visit to non-school childcare for at least one day during measurement. Another suggestion may be that these children often participated in pre-arranged play sessions at a friend’s house outside their own neighbourhood or more informal play-spaces around their residential neighbourhood, but future research should provide additional insight in this type of affordance [ 41 ].

One of the strengths of this study is the inclusion of multiple study-sites surrounding three cities in the Netherlands, which allowed us to study children’s PA patterns in diverse settings, increasing the variability in environmental exposure [ 34 ]. In addition, the use of the combined accelerometer GPS methodology allowed us to objectively monitor context-specific PA patterns throughout multiple days, minimizing potential recall bias. The additional use of geospatial analyses yielded further understanding of where young children are active. Although efforts were made to include a diverse and representative sample of young children by recruiting schools from multiple Dutch cities and the fact that daily PA of our sample was relatively comparable to international literature, it still may be that wearing accelerometer- and GPS devices was most interesting for active children or parents that perceive their child as relatively active. Future technological advancements such as smaller wrist-worn devices may have potential to be suitable and interesting for all children. Another potential weakness of this study was the use of a descriptive approach that elaborated on mean daily patterns for all children in multiple contexts, while future studies may implement a more evaluative approach to investigate differences between subgroups of children or evaluate determinants of specific behaviours (e.g. active transport to- and from school) or environments. For example, the relative contribution of school times to children’s daily PA may vary between types of children and environments where they live, allowing increased tailoring of PA intervention to the target group. We showed that MVPA at- and around children’s home was low. As previous research indicated that there is a lack of knowledge about facilitators and barriers in the home-based family environment (e.g. related to practices of both active and sedentary behaviours) [ 66 ] Previous studies showed that parents act as key gatekeepers for children’s spatial freedom [ 67 , 68 ], while this study demonstrated the importance of the environment within 100 m from home. Hence, it seems essential to get a better understanding of how parental rearing-constructs such as perception of traffic safety or ‘stranger danger’, but also social- and environmental factors influence parent-practices regarding independent mobility and, in turn, influences children’s PA [ 69 ]. Indications from our questionnaire data show that approximately 50% of the parents allowed their child to independently play in their neighbourhood, while 27% allowed their child to independently travel to visit family or friends. Supervision of siblings or peers increased the percentages above to approximately 68% and 42%, respectively. Future studies are encouraged to progress this field by combining data from parents (e.g. child-rearing constructs) and objective PA- and location data from children, with specific interest for home and school environments.

Our sample of 5.5-year-old children reported approximately 29 min of weekday MVPA during schooltime. Conversely, a previous review suggested that in older children, less than a quarter reached 30 min schooltime MVPA and that adolescents even reported lower levels [ 70 ]. Additionally, a previous study showed that in a sample of 7-11-year-old children with relatively low motor competence, school was the least active time period of their day. compared to before- and after school [ 71 ]. In turn, recent research showed that longer-term integration of PA in curricula, such as active breaks and physically active learning, fosters important pre-requisites of academic learning (e.g. time on task) [ 72 , 73 ]. Therefore, schools are well-suited for addressing important PA-related health inequalities of young children [ 74 ] and are therefore encouraged to implement evidence-based policies and to systematically evaluate which parts of the school-system hamper and stimulate their pupil’s PA as well as academic performance.

Overall, our study demonstrated the importance of schools in supporting PA of young children during weekdays. During weekends, the environment within 100 m from young children’s home was important, as well as locations outside the home-school neighbourhood. During- week and weekend days, walking contributed to almost half of the daily MVPA, emphasizing the importance of active school transportation- but also habitual daily walking and cycling (during week- and weekend days) for sustainable and PA promotion in children.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Dutch Organization for Scientific Research

Physical Activity

Moderate-to-vigorous Physical Activity

Global Positioning System

Geographic Information System

Personal Activity Location Measurement System

Human Activity Behavior Identification Tool and data Unification System

Software R-package to process raw accelerometer data

Standard Deviation

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Acknowledgements

We are grateful to all schools and children participating in the Start Vaardig study.

The Start Vaardig study was funded by the Dutch Organization for Scientific Research (NWO) (RAAKPRO03.123).

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T. Remmers, I. Fassaert, S. B. Vos & D. H. H. Van Kann

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TR analyzed and interpreted the data and drafted the original manuscript. PK assisted in the ethical approval and managed data collection. IF managed data collection and assisted in analyses. JN managed data collection and assisted in drafting the manuscript. WdG assisted in data collection and assisted in ethical approval. SV supervised the project and assisted in funding. SdV managed the project and supervised the funding. RM supervised the project and assisted in funding. DVK supervised the project, assisted in funding, and assisted in drafting the manuscript. All authors read and approved the final manuscript.

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Remmers, T., Koolwijk, P., Fassaert, I. et al. Investigating young children’s physical activity through time and place. Int J Health Geogr 23 , 12 (2024). https://doi.org/10.1186/s12942-024-00373-8

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Procrastination, depression and anxiety symptoms in university students: a three-wave longitudinal study on the mediating role of perceived stress

• Anna Jochmann 1 ,
• Burkhard Gusy 1 ,
• Tino Lesener 1 &
• Christine Wolter 1  

BMC Psychology volume  12 , Article number:  276 ( 2024 ) Cite this article

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It is generally assumed that procrastination leads to negative consequences. However, evidence for negative consequences of procrastination is still limited and it is also unclear by which mechanisms they are mediated. Therefore, the aim of our study was to examine the harmful consequences of procrastination on students’ stress and mental health. We selected the procrastination-health model as our theoretical foundation and tried to evaluate the model’s assumption that trait procrastination leads to (chronic) disease via (chronic) stress in a temporal perspective. We chose depression and anxiety symptoms as indicators for (chronic) disease and hypothesized that procrastination leads to perceived stress over time, that perceived stress leads to depression and anxiety symptoms over time, and that procrastination leads to depression and anxiety symptoms over time, mediated by perceived stress.

To examine these relationships properly, we collected longitudinal data from 392 university students at three occasions over a one-year period and analyzed the data using autoregressive time-lagged panel models.

Procrastination did lead to depression and anxiety symptoms over time. However, perceived stress was not a mediator of this effect. Procrastination did not lead to perceived stress over time, nor did perceived stress lead to depression and anxiety symptoms over time.

Conclusions

We could not confirm that trait procrastination leads to (chronic) disease via (chronic) stress, as assumed in the procrastination-health model. Nonetheless, our study demonstrated that procrastination can have a detrimental effect on mental health. Further health outcomes and possible mediators should be explored in future studies.

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Introduction

“Due tomorrow? Do tomorrow.”, might be said by someone who has a tendency to postpone tasks until the last minute. But can we enjoy today knowing about the unfinished task and tomorrow’s deadline? Or do we feel guilty for postponing a task yet again? Do we get stressed out because we have little time left to complete it? Almost everyone has procrastinated at some point when it came to completing unpleasant tasks, such as mowing the lawn, doing the taxes, or preparing for exams. Some tend to procrastinate more frequently and in all areas of life, while others are less inclined to do so. Procrastination is common across a wide range of nationalities, as well as socioeconomic and educational backgrounds [ 1 ]. Over the last fifteen years, there has been a massive increase in research on procrastination [ 2 ]. Oftentimes, research focuses on better understanding the phenomenon of procrastination and finding out why someone procrastinates in order to be able to intervene. Similarly, the internet is filled with self-help guides that promise a way to overcome procrastination. But why do people seek help for their procrastination? Until now, not much research has been conducted on the negative consequences procrastination could have on health and well-being. Therefore, in the following article we examine the effect of procrastination on mental health over time and stress as a possible facilitator of this relationship on the basis of the procrastination-health model by Sirois et al. [ 3 ].

Procrastination and its negative consequences

Procrastination can be defined as the tendency to voluntarily and irrationally delay intended activities despite expecting negative consequences as a result of the delay [ 4 , 5 ]. It has been observed in a variety of groups across the lifespan, such as students, teachers, and workers [ 1 ]. For example, some students tend to regularly delay preparing for exams and writing essays until the last minute, even if this results in time pressure or lower grades. Procrastination must be distinguished from strategic delay [ 4 , 6 ]. Delaying a task is considered strategic when other tasks are more important or when more resources are needed before the task can be completed. While strategic delay is viewed as functional and adaptive, procrastination is classified as dysfunctional. Procrastination is predominantly viewed as the result of a self-regulatory failure [ 7 ]. It can be understood as a trait, that is, as a cross-situational and time-stable behavioral disposition [ 8 ]. Thus, it is assumed that procrastinators chronically delay tasks that they experience as unpleasant or difficult [ 9 ]. Approximately 20 to 30% of adults have been found to procrastinate chronically [ 10 , 11 , 12 ]. Prevalence estimates for students are similar [ 13 ]. It is believed that students do not procrastinate more often than other groups. However, it is easy to examine procrastination in students because working on study tasks requires a high degree of self-organization and time management [ 14 ].

It is generally assumed that procrastination leads to negative consequences [ 4 ]. Negative consequences are even part of the definition of procrastination. Research indicates that procrastination is linked to lower academic performance [ 15 ], health impairment (e.g., stress [ 16 ], physical symptoms [ 17 ], depression and anxiety symptoms [ 18 ]), and poor health-related behavior (e.g., heavier alcohol consumption [ 19 ]). However, most studies targeting consequences of procrastination are cross-sectional [ 4 ]. For that reason, it often remains unclear whether an examined outcome is a consequence or an antecedent of procrastination, or whether a reciprocal relationship between procrastination and the examined outcome can be assumed. Additionally, regarding negative consequences of procrastination on health, it is still largely unknown by which mechanisms they are mediated. Uncovering such mediators would be helpful in developing interventions that can prevent negative health consequences of procrastination.

The procrastination-health model

The first and only model that exclusively focuses on the effect of procrastination on health and the mediators of this effect is the procrastination-health model [ 3 , 9 , 17 ]. Sirois [ 9 ] postulates three pathways: An immediate effect of trait procrastination on (chronic) disease and two mediated pathways (see Fig.  1 ).

Adopted from the procrastination-health model by Sirois [ 9 ]

The immediate effect is not further explained. Research suggests that procrastination creates negative feelings, such as shame, guilt, regret, and anger [ 20 , 21 , 22 ]. The described feelings could have a detrimental effect on mental health [ 23 , 24 , 25 ].

The first mediated pathway leads from trait procrastination to (chronic) disease via (chronic) stress. Sirois [ 9 ] assumes that procrastination creates stress because procrastinators are constantly aware of the fact that they still have many tasks to complete. Stress activates the hypothalamic-pituitary-adrenocortical (HPA) system, increases autonomic nervous system arousal, and weakens the immune system, which in turn contributes to the development of diseases. Sirois [ 9 ] distinguishes between short-term and long-term effects of procrastination on health mediated by stress. She believes that, in the short term, single incidents of procrastination cause acute stress, which leads to acute health problems, such as infections or headaches. In the long term, chronic procrastination, as you would expect with trait procrastination, causes chronic stress, which leads to chronic diseases over time. There is some evidence in support of the stress-related pathway, particularly regarding short-term effects [ 3 , 17 , 26 , 27 , 28 ]. However, as we mentioned above, most of these studies are cross-sectional. Therefore, the causal direction of these effects remains unclear. To our knowledge, long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress have not yet been investigated.

The second mediated pathway leads from trait procrastination to (chronic) disease via poor health-related behavior. According to Sirois [ 9 ], procrastinators form lower intentions to carry out health-promoting behavior or to refrain from health-damaging behavior because they have a low self-efficacy of being able to care for their own health. In addition, they lack the far-sighted view that the effects of health-related behavior only become apparent in the long term. For the same reason, Sirois [ 9 ] believes that there are no short-term, but only long-term effects of procrastination on health mediated by poor health-related behavior. For example, an unhealthy diet leads to diabetes over time. The findings of studies examining the behavioral pathway are inconclusive [ 3 , 17 , 26 , 28 ]. Furthermore, since most of these studies are cross-sectional, they are not suitable for uncovering long-term effects of trait procrastination on (chronic) disease mediated by poor health-related behavior.

In summary, previous research on the two mediated pathways of the procrastination-health model mainly found support for the role of (chronic) stress in the relationship between trait procrastination and (chronic) disease. However, only short-term effects have been investigated so far. Moreover, longitudinal studies are needed to be able to assess the causal direction of the relationship between trait procrastination, (chronic) stress, and (chronic) disease. Consequently, our study is the first to examine long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress, using a longitudinal design. (Chronic) disease could be measured by a variety of different indicators (e.g., physical symptoms, diabetes, or coronary heart disease). We choose depression and anxiety symptoms as indicators for (chronic) disease because they signal mental health complaints before they manifest as (chronic) diseases. Additionally, depression and anxiety symptoms are two of the most common mental health complaints among students [ 29 , 30 ] and procrastination has been shown to be a significant predictor of depression and anxiety symptoms [ 18 , 31 , 32 , 33 , 34 ]. Until now, the stress-related pathway of the procrastination-health model with depression and anxiety symptoms as the health outcome has only been analyzed in one cross-sectional study that confirmed the predictions of the model [ 35 ].

The aim of our study is to evaluate some of the key assumptions of the procrastination-health model, particularly the relationships between trait procrastination, (chronic) stress, and (chronic) disease over time, surveyed in the following analysis using depression and anxiety symptoms.

In line with the key assumptions of the procrastination-health model, we postulate (see Fig.  2 ):

Procrastination leads to perceived stress over time.

Perceived stress leads to depression and anxiety symptoms over time.

Procrastination leads to depression and anxiety symptoms over time, mediated by perceived stress.

The section of the procrastination-health model we examined

Materials and methods

Our study was part of a health monitoring at a large German university Footnote 1 . Ethical approval for our study was granted by the Ethics Committee of the university’s Department of Education and Psychology. We collected the initial data in 2019. Two occasions followed, each at an interval of six months. In January 2019, we sent out 33,267 invitations to student e-mail addresses. Before beginning the survey, students provided their written informed consent to participate in our study. 3,420 students took part at the first occasion (T1; 10% response rate). Of these, 862 participated at the second (T2) and 392 at the third occasion (T3). In order to test whether dropout was selective, we compared sociodemographic and study specific characteristics (age, gender, academic semester, number of assessments/exams) as well as behavior and health-related variables (procrastination, perceived stress, depression and anxiety symptoms) between the participants of the first wave ( n  = 3,420) and those who participated three times ( n  = 392). Results from independent-samples t-tests and chi-square analysis showed no significant differences regarding sociodemographic and study specific characteristics (see Additional file 1: Table S1 and S2 ). Regarding behavior and health-related variables, independent-samples t-tests revealed a significant difference in procrastination between the two groups ( t (3,409) = 2.08, p  < .05). The mean score of procrastination was lower in the group that participated in all three waves.

The mean age of the longitudinal respondents was 24.1 years ( SD  = 5.5 years), the youngest participants were 17 years old, the oldest one was 59 years old. The majority of participants was female (74.0%), 7 participants identified neither as male nor as female (1.8%). The respondents were on average enrolled in the third year of studying ( M  = 3.9; SD  = 2.3). On average, the students worked about 31.2 h ( SD  = 14.1) per week for their studies, and an additional 8.5 h ( SD  = 8.5) for their (part-time) jobs. The average income was €851 ( SD  = 406), and 4.9% of the students had at least one child. The students were mostly enrolled in philosophy and humanities (16.5%), education and psychology (15.8%), biology, chemistry, and pharmacy (12.5%), political and social sciences (10.6%), veterinary medicine (8.9%), and mathematics and computer science (7.7%).

We only used established and well evaluated instruments for our analyses.

• Procrastination

We adopted the short form of the Procrastination Questionnaire for Students (PFS-4) [ 36 ] to measure procrastination. The PFS-4 assesses procrastination at university as a largely stable behavioral disposition across situations, that is, as a trait. The questionnaire consists of four items (e.g., I put off starting tasks until the last moment.). Each item was rated on a 5-point scale ((almost) never = 1 to (almost) always = 5) for the last two weeks. All items were averaged, with higher scores indicating a greater tendency to procrastinate. The PFS-4 has been proven to be reliable and valid, showing very high correlations with other established trait procrastination scales, for example, with the German short form of the General Procrastination Scale [ 37 , 38 ]. We also proved the scale to be one-dimensional in a factor analysis, with a Cronbach’s alpha of 0.90.

Perceived stress

The Heidelberger Stress Index (HEI-STRESS) [ 39 ] is a three-item measure of current perceived stress due to studying as well as in life in general. For the first item, respondents enter a number between 0 (not stressed at all) and 100 (completely stressed) to indicate how stressed their studies have made them feel over the last four weeks. For the second and third item, respondents rate on a 5-point scale how often they feel “stressed and tense” and as how stressful they would describe their life at the moment. We transformed the second and third item to match the range of the first item before we averaged all items into a single score with higher values indicating greater perceived stress. We proved the scale to be one-dimensional and Cronbach’s alpha for our study was 0.86.

Depression and anxiety symptoms

We used the Patient Health Questionnaire-4 (PHQ-4) [ 40 ], a short form of the Patient Health Questionnaire [ 41 ] with four items, to measure depression and anxiety symptoms. The PHQ-4 contains two items from the Patient Health Questionnaire-2 (PHQ-2) [ 42 ] and the Generalized Anxiety Disorder Scale-2 (GAD-2) [ 43 ], respectively. It is a well-established screening scale designed to assess the core criteria of major depressive disorder (PHQ-2) and generalized anxiety disorder (GAD-2) according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). However, it was shown that the GAD-2 is also appropriate for screening other anxiety disorders. According to Kroenke et al. [ 40 ], the PHQ-4 can be used to assess a person’s symptom burden and impairment. We asked the participants to rate how often they have been bothered over the last two weeks by problems, such as “Little interest or pleasure in doing things”. Response options were 0 = not at all, 1 = several days, 2 = more than half the days, and 3 = nearly every day. Calculated as the sum of the four items, the total scores range from 0 to 12 with higher scores indicating more frequent depression and anxiety symptoms. The total scores can be categorized as none-to-minimal (0–2), mild (3–5), moderate (6–8), and severe (9–12) depression and anxiety symptoms. The PHQ-4 was shown to be reliable and valid [ 40 , 44 , 45 ]. We also proved the scale to be one-dimensional in a factor analysis, with a Cronbach’s alpha of 0.86.

Data analysis

To test our hypotheses, we performed structural equation modelling (SEM) using R (Version 4.1.1) with the package lavaan. All items were standardized ( M  = 0, SD  = 1). Due to the non-normality of some study variables and a sufficiently large sample size of N near to 400 [ 46 ], we used robust maximum likelihood estimation (MLR) for all model estimations. As recommended by Hu and Bentler [ 47 ], we assessed the models’ goodness of fit by chi-square test statistic, root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), Tucker-Lewis index (TLI), and comparative fit index (CFI). A non-significant chi-square indicates good model fit. Since chi-square is sensitive to sample size, we also evaluated fit indices less sensitive to the number of observations. RMSEA and SRMR values of 0.05 or lower as well as TLI and CFI values of 0.97 or higher indicate good model fit. RMSEA values of 0.08 or lower, SRMR values of 0.10 or lower, as well as TLI and CFI values of 0.95 or higher indicate acceptable model fit [ 48 , 49 ]. First, we conducted confirmatory factor analysis for the first occasion, defining three factors that correspond to the measures of procrastination, perceived stress, and depression and anxiety symptoms. Next, we tested for measurements invariance over time and specified the measurement model, before testing our hypotheses.

Measurement invariance over time

To test for measurement invariance over time, we defined one latent variable for each of the three occasions, corresponding to the measures of procrastination, perceived stress, and depression and anxiety symptoms, respectively. As recommended by Geiser and colleagues [ 50 ], the links between indicators and factors (i.e., factor loadings and intercepts) should be equal over measurement occasions; therefore, we added indicator specific factors. A first and least stringent step of testing measurement invariance is configural invariance (M CI ). It was examined whether the included constructs (procrastination, perceived stress, depression and anxiety symptoms) have the same pattern of free and fixed loadings over time. This means that the assignment of the indicators to the three latent factors over time is supported by the underlying data. If configural invariance was supported, restrictions for the next step of testing measurement invariance (metric or weak invariance; M MI ) were added. This means that each item contributes to the latent construct to a similar degree over time. Metric invariance was tested by constraining the factor loadings of the constructs over time. The next step of testing measurement invariance (scalar or strong invariance; M SI ) consisted of checking whether mean differences in the latent construct capture all mean differences in the shared variance of the items. Scalar invariance was tested by constraining the item intercepts over time. The constraints applied in the metric invariance model were retained [ 51 ]. For the last step of testing measurement invariance (residual or strict invariance; M RI ), the residual variables were also set equal over time. If residual invariance is supported, differences in the observed variables can exclusively be attributed to differences in the variances of the latent variables.

We used the Satorra-Bentler chi-square difference test to evaluate the superiority of a more stringent model [ 52 ]. We assumed the model with the largest number of invariance restrictions – which still has an acceptable fit and no substantial deterioration of the chi-square value – to be the final model [ 53 ]. Following previous recommendations, we considered a decrease in CFI of 0.01 and an increase in RMSEA of 0.015 as unacceptable to establish measurement invariance [ 54 ]. If a more stringent model had a significant worse chi-square value, but the model fit was still acceptable and the deterioration in model fit fell within the change criteria recommended for CFI and RMSEA values, we still considered the more stringent model to be superior.

Hypotheses testing

As recommended by Dormann et al. [ 55 ], we applied autoregressive time-lagged panel models to test our hypotheses. In the first step, we specified a model (M 0 ) that only included the stabilities of the three variables (procrastination, perceived stress, depression and anxiety symptoms) over time. In the next step (M 1 ), we added the time-lagged effects from procrastination (T1) to perceived stress (T2) and from procrastination (T2) to perceived stress (T3) as well as from perceived stress (T1) to depression and anxiety symptoms (T2) and from perceived stress (T2) to depression and anxiety symptoms (T3). Additionally, we included a direct path from procrastination (T1) to depression and anxiety symptoms (T3). If this path becomes significant, we can assume a partial mediation [ 55 ]. Otherwise, we can assume a full mediation. We compared these nested models using the Satorra-Bentler chi-square difference test and the Akaike information criterion (AIC). The chi-square difference value should either be non-significant, indicating that the proposed model including our hypotheses (M 1 ) does not have a significant worse model fit than the model including only stabilities (M 0 ), or, if significant, it should be in the direction that M 1 fits the data better than M 0 . Regarding the AIC, M 1 should have a lower value than M 0 .

Table  1 displays the means, standard deviations, internal consistencies (Cronbach’s alpha), and stabilities (correlations) of all study variables. The alpha values of procrastination, perceived stress, and depression and anxiety symptoms are classified as good (> 0.80) [ 56 ]. The correlation matrix of the manifest variables used for the analyses can be found in the Additional file 1: Table  S3 .

We observed the highest test-retest reliabilities for procrastination ( r  ≥ .74). The test-retest reliabilities for depression and anxiety symptoms ( r  ≥ .64) and for perceived stress ( r  ≥ .54) were a bit lower (see Table  1 ). The pattern of correlations shows a medium to large but positive relationship between procrastination and depression and anxiety symptoms [ 57 , 58 ]. The association between procrastination and perceived stress was small, the one between perceived stress and depression and anxiety symptoms very large (see Table  1 ).

Confirmatory factor analysis showed an acceptable to good fit (x 2 (41) = 118.618, p  < .001; SRMR = 0.042; RMSEA = 0.071; TLI = 0.95; CFI = 0.97). When testing for measurement invariance over time for each construct, the residual invariance models with indicator specific factors provided good fit to the data (M RI ; see Table  2 ), suggesting that differences in the observed variables can exclusively be attributed to differences of the latent variables. We then specified and tested the measurement model of the latent constructs prior to model testing based on the items of procrastination, perceived stress, and depression and anxiety symptoms. The measurement model fitted the data well (M M ; see Table  3 ). All items loaded solidly on their respective factors (0.791 ≤ β ≤ 0.987; p  < .001).

To test our hypotheses, we analyzed the two models described in the methods section.

The fit of the stability model (M 0 ) was acceptable (see Table  3 ). Procrastination was stable over time, with stabilities above 0.82. The stabilities of perceived stress as well as depression and anxiety symptoms were somewhat lower, ranging from 0.559 (T1 -> T2) to 0.696 (T2 -> T3) for perceived stress and from 0.713 (T2 -> T3) to 0.770 (T1 -> T2) for depression and anxiety symptoms, respectively.

The autoregressive mediation model (M 1 ) fitted the data significantly better than M 0 . The direct path from procrastination (T1) to depression and anxiety symptoms (T3) was significant (β = 0.16; p  < .001), however, none of the mediated paths (from procrastination (T1) to perceived stress (T2) and from perceived stress (T2) to depression and anxiety symptoms (T3)) proved to be substantial. Also, the time-lagged paths from perceived stress (T1) to depression and anxiety symptoms (T2) and from procrastination (T2) to perceived stress (T3) were not substantial either (see Fig.  3 ).

To examine whether the hypothesized effects would occur over a one-year period rather than a six-months period, we specified an additional model with paths from procrastination (T1) to perceived stress (T3) and from perceived stress (T1) to depression and anxiety symptoms (T3), also including the stabilities of the three constructs as in the stability model M 0 . The model showed an acceptable fit (χ 2 (486) = 831.281, p  < .001; RMSEA = 0.048; SRMR = 0.091; TLI = 0.95; CFI = 0.95), but neither of the two paths were significant.

Therefore, our hypotheses, that procrastination leads to perceived stress over time (H1) and that perceived stress leads to depression and anxiety symptoms over time (H2) must be rejected. We could only partially confirm our third hypothesis, that procrastination leads to depression and anxiety over time, mediated by perceived stress (H3), since procrastination did lead to depression and anxiety symptoms over time. However, this effect was not mediated by perceived stress.

Results of the estimated model including all hypotheses (M 1 ). Note Non-significant paths are dotted. T1 = time 1; T2 = time 2; T3 = time 3. *** p  < .001

To sum up, we tried to examine the harmful consequences of procrastination on students’ stress and mental health. Hence, we selected the procrastination-health model by Sirois [ 9 ] as a theoretical foundation and tried to evaluate some of its key assumptions in a temporal perspective. The author assumes that trait procrastination leads to (chronic) disease via (chronic) stress. We chose depression and anxiety symptoms as indicators for (chronic) disease and postulated, in line with the key assumptions of the procrastination-health model, that procrastination leads to perceived stress over time (H1), that perceived stress leads to depression and anxiety symptoms over time (H2), and that procrastination leads to depression and anxiety symptoms over time, mediated by perceived stress (H3). To examine these relationships properly, we collected longitudinal data from students at three occasions over a one-year period and analyzed the data using autoregressive time-lagged panel models. Our first and second hypotheses had to be rejected: Procrastination did not lead to perceived stress over time, and perceived stress did not lead to depression and anxiety symptoms over time. However, procrastination did lead to depression and anxiety symptoms over time – which is in line with our third hypothesis – but perceived stress was not a mediator of this effect. Therefore, we could only partially confirm our third hypothesis.

Our results contradict previous studies on the stress-related pathway of the procrastination-health model, which consistently found support for the role of (chronic) stress in the relationship between trait procrastination and (chronic) disease. Since most of these studies were cross-sectional, though, the causal direction of these effects remained uncertain. There are two longitudinal studies that confirm the stress-related pathway of the procrastination-health model [ 27 , 28 ], but both studies examined short-term effects (≤ 3 months), whereas we focused on more long-term effects. Therefore, the divergent findings may indicate that there are short-term, but no long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress.

Our results especially raise the question whether trait procrastination leads to (chronic) stress in the long term. Looking at previous longitudinal studies on the effect of procrastination on stress, the following stands out: At shorter study periods of two weeks [ 27 ] and four weeks [ 28 ], the effect of procrastination on stress appears to be present. At longer study periods of seven weeks [ 59 ], three months [ 28 ], six months, and twelve months, as in our study, the effect of procrastination on stress does not appear to be present. There is one longitudinal study in which procrastination was a significant predictor of stress symptoms nine months later [ 34 ]. The results of this study should be interpreted with caution, though, because the outbreak of the COVID-19 pandemic fell within the study period, which could have contributed to increased stress symptoms [ 60 ]. Unfortunately, Johansson et al. [ 34 ] did not report whether average stress symptoms increased during their study. In one of the two studies conducted by Fincham and May [ 59 ], the COVID-19 pandemic outbreak also fell within their seven-week study period. However, they reported that in their study, average stress symptoms did not increase from baseline to follow-up. Taken together, the findings suggest that procrastination can cause acute stress in the short term, for example during times when many tasks need to be completed, such as at the end of a semester, but that procrastination does not lead to chronic stress over time. It seems possible that students are able to recover during the semester from the stress their procrastination caused at the end of the previous semester. Because of their procrastination, they may also have more time to engage in relaxing activities, which could further mitigate the effect of procrastination on stress. Our conclusions are supported by an early and well-known longitudinal study by Tice and Baumeister [ 61 ], which compared procrastinating and non-procrastinating students with regard to their health. They found that procrastinators experienced less stress than their non-procrastinating peers at the beginning of the semester, but more at the end of the semester. Additionally, our conclusions are in line with an interview study in which university students were asked about the consequences of their procrastination [ 62 ]. The students reported that, due to their procrastination, they experience high levels of stress during periods with heavy workloads (e.g., before deadlines or exams). However, the stress does not last, instead, it is relieved immediately after these periods.

Even though research indicates, in line with the assumptions of the procrastination-health model, that stress is a risk factor for physical and mental disorders [ 63 , 64 , 65 , 66 ], perceived stress did not have a significant effect on depression and anxiety symptoms in our study. The relationship between stress and mental health is complex, as people respond to stress in many different ways. While some develop stress-related mental disorders, others experience mild psychological symptoms or no symptoms at all [ 67 ]. This can be explained with the help of vulnerability-stress models. According to vulnerability-stress models, mental illnesses emerge from an interaction of vulnerabilities (e.g., genetic factors, difficult family backgrounds, or weak coping abilities) and stress (e.g., minor or major life events or daily hassles) [ 68 , 69 ]. The stress perceived by the students in our sample may not be sufficient enough on its own, without the presence of other risk factors, to cause depression and anxiety symptoms. However, since we did not assess individual vulnerability and stress factors in our study, these considerations are mere speculation.

In our study, procrastination led to depression and anxiety symptoms over time, which is consistent with the procrastination-health model as well as previous cross-sectional and longitudinal evidence [ 18 , 21 , 31 , 32 , 33 , 34 ]. However, it is still unclear by which mechanisms this effect is mediated, as perceived stress did not prove to be a substantial mediator in our study. One possible mechanism would be that procrastination impairs affective well-being [ 70 ] and creates negative feelings, such as shame, guilt, regret, and anger [ 20 , 21 , 22 , 62 , 71 ], which in turn could lead to depression and anxiety symptoms [ 23 , 24 , 25 ]. Other potential mediators of the relationship between procrastination and depression and anxiety symptoms emerge from the behavioral pathway of the procrastination-health model, suggesting that poor health-related behaviors mediate the effect of trait procrastination on (chronic) disease. Although evidence for this is still scarce, the results of one cross-sectional study, for example, indicate that poor sleep quality might mediate the effect of procrastination on depression and anxiety symptoms [ 35 ].

In summary, we found that procrastination leads to depression and anxiety symptoms over time and that perceived stress is not a mediator of this effect. We could not show that procrastination leads to perceived stress over time, nor that perceived stress leads to depression and anxiety symptoms over time. For the most part, the relationships between procrastination, perceived stress, and depression and anxiety symptoms did not match the relationships between trait procrastination, (chronic) stress, and (chronic) disease as assumed in the procrastination-health model. Explanations for this could be that procrastination might only lead to perceived stress in the short term, for example, during preparations for end-of-semester exams, and that perceived stress may not be sufficient enough on its own, without the presence of other risk factors, to cause depression and anxiety symptoms. In conclusion, we could not confirm long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress, as assumed for the stress-related pathway of the procrastination-health model.

Limitations and suggestions for future research

In our study, we tried to draw causal conclusions about the harmful consequences of procrastination on students’ stress and mental health. However, since procrastination is a trait that cannot be manipulated experimentally, we have conducted an observational rather than an experimental study, which makes causal inferences more difficult. Nonetheless, a major strength of our study is that we used a longitudinal design with three waves. This made it possible to draw conclusions about the causal direction of the effects, as in hardly any other study targeting consequences of procrastination on health before [ 4 , 28 , 55 ]. Therefore, we strongly recommend using a similar longitudinal design in future studies on the procrastination-health model or on consequences of procrastination on health in general.

We chose a time lag of six months between each of the three measurement occasions to examine long-term effects of procrastination on depression and anxiety symptoms mediated by perceived stress. However, more than six months may be necessary for the hypothesized effects to occur [ 72 ]. The fact that the temporal stabilities of the examined constructs were moderate or high (0.559 ≤ β ≤ 0.854) [ 73 , 74 ] also suggests that the time lags may have been too short. The larger the time lag, the lower the temporal stabilities, as shown for depression and anxiety symptoms, for example [ 75 ]. High temporal stabilities make it more difficult to detect an effect that actually exists [ 76 ]. Nonetheless, Dormann and Griffin [ 77 ] recommend using shorter time lags of less than one year, even with high stabilities, because of other influential factors, such as unmeasured third variables. Therefore, our time lags of six months seem appropriate.

It should be discussed, though, whether it is possible to detect long-term effects of the stress-related pathway of the procrastination-health model within a total study period of one year. Sirois [ 9 ] distinguishes between short-term and long-term effects of procrastination on health mediated by stress, but does not address how long it might take for long-term effects to occur or when effects can be considered long-term instead of short-term. The fact that an effect of procrastination on stress is evident at shorter study periods of four weeks or less but in most cases not at longer study periods of seven weeks or more, as we mentioned earlier, could indicate that short-term effects occur within the time frame of one to three months, considering the entire stress-related pathway. Hence, it seems appropriate to assume that we have examined rather long-term effects, given our study period of six and twelve months. Nevertheless, it would be beneficial to use varying study periods in future studies, in order to be able to determine when effects can be considered long-term.

Concerning long-term effects of the stress-related pathway, Sirois [ 9 ] assumes that chronic procrastination causes chronic stress, which leads to chronic diseases over time. The term “chronic stress” refers to prolonged stress episodes associated with permanent tension. The instrument we used captures perceived stress over the last four weeks. Even though the perceived stress of the students in our sample was relatively stable (0.559 ≤ β ≤ 0.696), we do not know how much fluctuation occurred between each of the three occasions. However, there is some evidence suggesting that perceived stress is strongly associated with chronic stress [ 78 ]. Thus, it seems acceptable that we used perceived stress as an indicator for chronic stress in our study. For future studies, we still suggest the use of an instrument that can more accurately reflect chronic stress, for example, the Trier Inventory for Chronic Stress (TICS) [ 79 ].

It is also possible that the occasions were inconveniently chosen, as they all took place in a critical academic period near the end of the semester, just before the examination period began. We chose a similar period in the semester for each occasion for the sake of comparability. However, it is possible that, during this preparation periods, stress levels peaked and procrastinators procrastinated less because they had to catch up after delaying their work. This could have introduced bias to the data. Therefore, in future studies, investigation periods should be chosen that are closer to the beginning or in the middle of a semester.

Furthermore, Sirois [ 9 ] did not really explain her understanding of “chronic disease”. However, it seems clear that physical illnesses, such as diabetes or cardiovascular diseases, are meant. Depression and anxiety symptoms, which we chose as indicators for chronic disease, represent mental health complaints that do not have to be at the level of a major depressive disorder or an anxiety disorder, in terms of their quantity, intensity, or duration [ 40 ]. But they can be viewed as precursors to a major depressive disorder or an anxiety disorder. Therefore, given our study period of one year, it seems appropriate to use depression and anxiety symptoms as indicators for chronic disease. At longer study periods, we would expect these mental health complaints to manifest as mental disorders. Moreover, the procrastination-health model was originally designed to be applied to physical diseases [ 3 ]. Perhaps, the model assumptions are more applicable to physical diseases than to mental disorders. By applying parts of the model to mental health complaints, we have taken an important step towards finding out whether the model is applicable to mental disorders as well. Future studies should examine additional long-term health outcomes, both physical and psychological. This would help to determine whether trait procrastination has varying effects on different diseases over time. Furthermore, we suggest including individual vulnerability and stress factors in future studies in order to be able to analyze the effect of (chronic) stress on (chronic) diseases in a more differentiated way.

Regarding our sample, 3,420 students took part at the first occasion, but only 392 participated three times, which results in a dropout rate of 88.5%. At the second and third occasion, invitation e-mails were only sent to participants who had indicated at the previous occasion that they would be willing to participate in a repeat survey and provided their e-mail address. This is probably one of the main reasons for our high dropout rate. Other reasons could be that the students did not receive any incentives for participating in our study and that some may have graduated between the occasions. Selective dropout analysis revealed that the mean score of procrastination was lower in the group that participated in all three waves ( n  = 392) compared to the group that participated in the first wave ( n  = 3,420). One reason for this could be that those who have a higher tendency to procrastinate were more likely to procrastinate on filling out our survey at the second and third occasion. The findings of our dropout analysis should be kept in mind when interpreting our results, as lower levels of procrastination may have eliminated an effect on perceived stress or on depression and anxiety symptoms. Additionally, across all age groups in population-representative samples, the student age group reports having the best subjective health [ 80 ]. Therefore, it is possible that they are more resilient to stress and experience less impairment of well-being than other age groups. Hence, we recommend that future studies focus on other age groups as well.

It is generally assumed that procrastination leads to lower academic performance, health impairment, and poor health-related behavior. However, evidence for negative consequences of procrastination is still limited and it is also unclear by which mechanisms they are mediated. In consequence, the aim of our study was to examine the effect of procrastination on mental health over time and stress as a possible facilitator of this relationship. We selected the procrastination-health model as a theoretical foundation and used the stress-related pathway of the model, assuming that trait procrastination leads to (chronic) disease via (chronic) stress. We chose depression and anxiety symptoms as indicators for (chronic) disease and collected longitudinal data from students at three occasions over a one-year period. This allowed us to draw conclusions about the causal direction of the effects, as in hardly any other study examining consequences of procrastination on (mental) health before. Our results indicate that procrastination leads to depression and anxiety symptoms over time and that perceived stress is not a mediator of this effect. We could not show that procrastination leads to perceived stress over time, nor that perceived stress leads to depression and anxiety symptoms over time. Explanations for this could be that procrastination might only lead to perceived stress in the short term, for example, during preparations for end-of-semester exams, and that perceived stress may not be sufficient on its own, that is, without the presence of other risk factors, to cause depression and anxiety symptoms. Overall, we could not confirm long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress, as assumed for the stress-related pathway of the procrastination-health model. Our study emphasizes the importance of identifying the consequences procrastination can have on health and well-being and determining by which mechanisms they are mediated. Only then will it be possible to develop interventions that can prevent negative health consequences of procrastination. Further health outcomes and possible mediators should be explored in future studies, using a similar longitudinal design.

Data availability

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

University Health Report at Freie Universität Berlin.

Abbreviations

Comparative fit index

Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

Generalized Anxiety Disorder Scale-2

Heidelberger Stress Index

Hypothalamic-pituitary-adrenocortical

Robust maximum likelihood estimation

Short form of the Procrastination Questionnaire for Students

Patient Health Questionnaire-2

Patient Health Questionnaire-4

Root mean square error of approximation

Structural equation modeling

Standardized root mean square residual

Tucker-Lewis index

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Conceptualization: A.J., B.G., T.L.; methodology: B.G., A.J.; validation: B.G.; formal analysis: A.J., B.G.; investigation: C.W., T.L., B.G.; data curation: C.W., T.L., B.G.; writing–original draft preparation: A.J., B.G.; writing–review and editing: A.J., T.L., B.G., C.W.; visualization: A.J., B.G.; supervision: B.G., T.L.; project administration: C.W., T.L., B.G.; All authors contributed to the article and approved the submitted version.

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Jochmann, A., Gusy, B., Lesener, T. et al. Procrastination, depression and anxiety symptoms in university students: a three-wave longitudinal study on the mediating role of perceived stress. BMC Psychol 12 , 276 (2024). https://doi.org/10.1186/s40359-024-01761-2

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Prefrontal tDCS for improving mental health and cognitive deficits in patients with Multiple Sclerosis: a randomized, double-blind, parallel-group study

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Background: Multiple Sclerosis (MS) is an autoimmune disease associated with physical disability, psychological impairment, and cognitive dysfunctions. Consequently, the disease burden is substantial, and treatment choices are limited. In this randomized, double-blind study, we used repeated prefrontal electrical stimulation and assessed mental health-related variables (including quality of life, sleep, psychological distress) and cognitive dysfunctions (psychomotor speed, working memory, attention/vigilance) in 40 patients with MS. Methods: The patients were randomly assigned (block randomization method) to two groups of sham (n=20), or 1.5-mA (n=20) transcranial direct current stimulation (tDCS) targeting the left dorsolateral prefrontal cortex (F3) and right frontopolar cortex (Fp2) with anodal and cathodal stimulation respectively (electrode size: 25 cm2). The treatment included 10 sessions of 20 minutes stimulation delivered every other day. Outcome measures were quality of life, sleep quality, psychological distress, and performance on a neuropsychological test battery dedicated to cognitive dysfunctions in MS (psychomotor speed, working memory, and attention). All outcome measures were examined pre-intervention and post-intervention. Both patients and technicians delivering the stimulation were unaware of the study hypotheses and the type of stimulation being used. Results: The active protocol significantly improved quality of life and reduced sleep difficulties and psychological distress compared to the sham group. The active protocol, furthermore, improved psychomotor speed, attention and vigilance, and some aspects of working memory performance compared to the sham protocol. Improvement in mental health outcome measures was significantly associated with better cognitive performance. Conclusions: Modulation of prefrontal regions with tDCS ameliorates secondary clinical symptoms and results in beneficial cognitive effects in patients with MS. These results support applying prefrontal tDCS in larger trials for improving mental health and cognitive dysfunctions in MS.

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Michael Nitsche is a member of the Scientific Advisory Boards of Neuroelectrics and Precisis. All other authors declare no competing interests

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NCT06401928

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This study did not receive any funding

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All patients gave their written consent to participate in the study. The protocol was conducted in accordance with the latest version of the Declaration of Helsinki and was approved by the Institutional Review Board and ethical committee at the Mohaghegh Ardabili University. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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Does health voucher intervention increase antenatal consultations and skilled birth attendances in Cameroon? Results from an interrupted time series analysis

• Isidore Sieleunou   ORCID: orcid.org/0000-0001-7264-4540 1 , 2 &
• Roland Pascal Enok Bonong   ORCID: orcid.org/0000-0002-9552-5365 2  

BMC Health Services Research volume  24 , Article number:  602 ( 2024 ) Cite this article

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Limited access to health services during the antenatal period and during childbirth, due to financial barriers, is an obstacle to reducing maternal and child mortality. To improve the use of health services in the three regions of Cameroon, which have the worst reproductive, maternal, neonatal, child and adolescent health indicators, a health voucher project aiming to reduce financial barriers has been progressively implemented since 2015 in these three regions. Our research aimed to assess the impact of the voucher scheme on first antenatal consultation (ANC) and skilled birth attendance (SBA).

Routine aggregated data by month over the period January 2013 to May 2018 for each of the 33 and 37 health facilities included in the study sample were used to measure the effect of the voucher project on the first ANC and SBA, respectively. We estimated changes attributable to the intervention in terms of the levels of outcome indicators immediately after the start of the project and over time using an interrupted time series regression. A meta-analysis was used to obtain the overall estimates.

Overall, the voucher project contributed to an immediate and statistically significant increase, one month after the start of the project, in the monthly number of ANCs (by 26%) and the monthly number of SBAs (by 57%). Compared to the period before the start of the project, a statistically significant monthly increase was observed during the project implementation for SBAs but not for the first ANCs. The results at the level of health facilities (HFs) were mixed. Some HFs experienced an improvement, while others were faced with the status quo or a decrease.

Conclusions

Unlike SBAs, the voucher project in Cameroon had mixed results in improving first ANCs. These limited effects were likely the consequence of poor design and implementation challenges.

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Reducing maternal, newborn, and child mortality is one of the world's top public health priorities. The third of the seventeen Sustainable Development Goals (SDGs) reflects the international commitment to improving maternal and child health. By 2030, the goals include reducing the global maternal mortality ratio to less than 70 per 100,000 live births, neonatal mortality to 12 per 1,000 live births at most, and under-five mortality to less than 25 per 1,000 live births [ 1 ].

However, despite considerable improvements in recent decades, maternal mortality has remained a major public health concern globally, with more than 295,000 maternal deaths in 2017 and sub-Saharan Africa (SSA) alone accounting for approximately 66% of this global picture [ 2 ]. On the other hand, despite dramatic reductions in child mortality over the last 30 years, the global burden of child deaths has remained immense, with a total of 5.2 million under-five deaths in 2019, representing an average of 14,000 deaths every day [ 3 ].

While from 2000 to 2017, the global maternal mortality ratio (MMR) decreased by 38% [ 2 ], Cameroon's MMR skyrocketed from 511 in 1998 to 782 in 2011 before declining to 467 in 2018 [ 4 ].

A priority toward ending preventable maternal and child deaths is to improve access to and use of quality health services and qualified nurses at birth [ 5 , 6 ]. One of the basic elements is the presence of pregnant women at antenatal consultations. Previous studies have shown that performing prenatal consultations reduces the risk of neonatal mortality [ 7 , 8 ].

However, women in developing countries encounter significant barriers to accessing conventional health services, including poor education, physical and financial barriers, and limited voice and decision-making power [ 9 , 10 ]. The poor quality of available health services offers a further disincentive [ 6 ]. This translates to only half of parturient women receiving skilled assistance at delivery and many fewer receiving postpartum cares [ 6 ].

In Cameroon, the country’s comparatively slow reduction in maternal and child mortalities is likely due to insufficient coverage of reproductive, maternal, neonatal, child and adolescent health (RMNCAH) services; for instance, in 2018, an estimated 65% of women in Cameroon attended at least four antenatal consultations (ANC) visits, 69% gave birth with the assistance of qualified personnel, and 59% received postnatal care (PNC) [ 11 ]. In addition, these general estimates hide enormous disparities. Overall, 65% of the pregnant women who attended the four ANCs included more than 79% of those in urban areas but only 52% of those in rural areas. Moreover, while this rate was 91% in the richest quintile, only one-third (37%) of the poorest pregnant women attended the four ANCs [ 11 ].

The complexity of barriers to accessing care in developing countries indicates that any solution to improving maternal health service utilization must be comprehensive and address both supply- and demand-side health system constraints. This is particularly important in a context such as Cameroon where household out-of-pocket (OOP) spending was the single largest source of financing for the health sector, at 71 percent of total health spending in 2017, well above the WHO benchmark of 15-20 percent, and exceeding the average for SSA (33 percent) and countries of similar income such as Kenya (24 percent) and Ghana (40 percent) [ 12 ].

As ability to pay remains an important determinant of women’s access to healthcare, many countries have sought to improve coverage of maternal services by reducing financial barriers to seeking services [ 13 , 14 ]. Strategies implemented at the country level include national health insurance and user fee removals/exemptions, and at the subnational level, community-based health insurance, health vouchers and conditional cash transfers [ 15 ].

Given that limited access to emergency obstetric and neonatal care (EmONC) is a major contributor to high maternal mortality [ 16 ], increasing pregnant women's use of health facilities for assisted delivery could help reduce maternal and new born morbidity and mortality, as previous studies have indicated [ 17 , 18 ].

In recent years, there has been growing interest in the use of vouchers and other innovative financing mechanisms to increase access to EmONCs for low-income women [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. By providing a financial or in-kind reward conditioned on the achievement of agreed-upon performance goals, vouchers are described as a promising holistic approach to foster the use of cost-effective services by the poor and other disadvantaged populations [ 22 ].

Vouchers can act on the demand side, the supply side, or both sides. Demand-side incentives encourage service use not only by reducing the financial burden but also by offering women a choice of providers and informing them of the benefits of using maternal health services. Supply-side incentives aim to improve the quality and responsiveness of service delivery.

To date, findings from the few assessments of reproductive health voucher programs suggest that, if implemented well, they have the potential to improve both assisted and facility-based deliveries [ 19 , 20 , 22 , 24 , 26 ]. Yet, there is a paucity of evidence based on rigorous evaluation studies, making it challenging to draw consistent conclusions about the impact of voucher initiatives and to make subsequent policy recommendations.

The current study evaluated a pilot voucher program in Cameroon, a country where approximately 39% of all deliveries took place at home at the time of the program’s inception [ 27 ]. The research aimed to assess the impact of the voucher scheme on first antenatal consultation and skilled birth attendance (SBA). In the following, we present a brief description of the Cameroon voucher program. We then present our data and methods, followed by the results. We end with a discussion of the study’s results, as well as the implications of these findings.

Voucher program in Cameroon

Results from the 2014 Multiple Indicator Cluster Survey (MICS) indicate an enormous disparity in health outcomes among Cameroon's ten regions, with the three northern regions (Adamawa, North, Far North) bearing the brunt of the disease burden [ 27 ]. For example, while the Far North and North regions represented 27.5% of the total population of children under five years in 2014, both regions accounted for 63% of the total excess mortality during the same period [ 27 ]. In addition, while 65% of women nationwide gave birth with the help of qualified personnel, only 29%, 36% and 53% in the Far North, North and Adamawa regions, respectively, gave birth in the same conditions. Moreover, these three regions featuring the lowest frequencies of ANCs and assisted deliveries, were home to more than 60% of the country’s poorest population [ 28 ].

Initiated in 2015, the voucher programme is a government programme, supported with funding from German and French partners, that aims to reduce financial barriers to maternal and neonatal care in the three northern regions of Cameroon.

Under the project, (poor) women can purchase subsidized vouchers for 6000 FCFA (≈$11), a co-payment of 10% of the actual cost of the service package estimated at 60,000 FCFA (approximately USD109), that covered the cost of a benefit package including services for pregnant women and their new-borns up to 42 days after delivery. In addition, beneficiaries are provided with transportation from their house to the nearest health facility and transportation from health centers to referral hospitals. Health facilities offering services for the voucher scheme are compensated for extra costs incurred. All pregnant women living within the 3 northern regions of Cameroon were eligible for the programme. To be included in the programme, health facilities are required to meet minimum quality standards based on national guidelines for the provision of maternal care. Women can redeem vouchers at any participating facility, and the contracted facilities submit claims to be reimbursed at standard rates for each service provided.

At its inception, the programme implementation was outsourced to the ‘Centre International de Développement et de Recherche’ (CIDR), an international organization. Since November 2018, the management of the scheme has been transferred to a national entity: the Regional Funds for Health Promotion (RFHP). A transfer protocol signed between the ministry of public health (MPH) and CIDR made provisions for the training of the RFHP personnel to take over the implementation.

Study design, data source and study sample

To achieve the study objectives, we used a quasi-experimental study design. Specifically, for each health facility (HF) that was enrolled in the health voucher project, the potential effect of the project was measured using an analysis of interrupted time series [ 29 , 30 , 31 , 32 ]. This method compares changes in the indicators of interest before and after the start of the intervention. It is based on the fundamental assumption that, in the absence of intervention, the trend of the interest indicator remains unchanged over time [ 31 ]. It is desirable to have at least 12 observation points for the indicator or variable of interest before and after the start of the intervention, respectively [ 29 ].

We used secondary data from the monitoring and evaluation system database populated by the three regional implementing agencies of the health voucher project, let by the CIDR-CARE prior to the transfer of the project to the RFHP that began in 2018. These databases were updated quarterly by trained research assistants after monthly data collection from the registries of all health facilities enrolled in the project. Data quality control was carried out jointly by the team from the MPH in charge of monitoring project implementation and by the project team. The data used in this study are monthly aggregates of the variables of interest over the period from January 2013 to May 2018 (i.e., 65 months of observation).

The database contains information on 42 health facilities (HFs) enrolled in the health voucher project, spread across three regions: 12 HFs in the Adamawa region, 15 in the North region and 14 in the Far North region. These HFs were sequentially enrolled in the health voucher project and not at the same time. In the Adamawa region, activities started in 9 HFs in May 2015 and in 3 HFs in March 2016. In the North region, the implementation of activities started in May 2015 in one HF, in June 2015 in 5 HFs and in July 2016 in 9 HFs. For the Far North region, the intervention started in 4 HFs in June 2015, in 3 HFs in March 2016 and in 7 HFs in July 2016. For the analysis of each outcome, HFs included in the sample were those with at least 90% data completeness over the selected period. Thus, the sample sizes for analysis of the outcomes associated with the first antenatal consultation and assisted deliveries were 33 and 37, respectively.

Study variables

Two dependent variables were considered for this evaluation: (i) the monthly number of first ANC visits in each HF and (ii) the monthly number of SBA in each HF.

Covariables

X it : a time-dependent dichotomous variable that takes the value 0 for the months before the start of the health voucher project in HF i and 1 after the start of the project.

T t : time variable measured in months, with values ranging from 0 (January 2013) to 64 (May 2018).

X it *(T t -θ i ): interaction variable between the variables X it and T t centered on the value corresponding to the month of project start in HF i (θ i ).

Statistical analysis

Descriptive analysis.

To explore the outcomes, we used descriptive statistics (mean, median, standard deviation, interquartile range, absolute frequency, relative frequency) and trend curves.

Statistical modeling

For each HF and for each outcome, the estimation of the effects of the health voucher project was carried out using a negative binomial regression.

Since both outcomes are count variables, the choice of negative binomial regression instead of Poisson regression, which is the classic model for this type of variable, was considered to overcome the violation of the fundamental assumption underlying Poisson regression, which states that the mean is equal to the variance. Let Y it be the value of the considered outcome observed in HF i at time t. Y it follows a Poisson distribution with parameter μ it (Y it ~Poisson (μ it )). The general equation of the model used is shown below:

The other parameters of the model are described below.

β 0 = intercept (value of the dependent variable at month 1 of follow-up);

β 1 = slope of the outcome trajectory before the start of the health voucher project;

β 2 = change in the level of outcome at the end of the first month of implementation of the health voucher project;

β 3 = difference between the slope of the outcome trajectory after and before the start of the health voucher project;

variable γ it is the term that differentiates Poisson regression from negative binomial regression. In other words, e γit follows a gamma distribution with mean 1 and variance α (e γit ~ gamma (1/α, α)), with α being the overdispersion parameter.

The coefficient β 2 assesses the immediate effect of the project and β 3 assesses the effect of the project over time.

The graphs used to explore the evolution of outcomes over time highlighted the presence of seasonality. Thus, 11 dichotomous variables were considered in the different models. Equation ( 1 ) becomes log (μ it ) = β 0 + β 1 T t + β 2 X it + β 3 X it *(T t -θ i ) + ɸ 1 February + ɸ 2 March + ɸ 3 April + ɸ 4 May + ɸ 5 June + ɸ 6 July + ɸ 7 August + ɸ 8 September + ɸ 9 October + ɸ 10 November + ɸ 11 December + γ it .

The variables February, March … December take the value 1 if the observation relates to this month and 0 otherwise. The month of January was considered a reference.

Because the project did not start at the same time in all HFs, to obtain estimates representing the overall situation, a meta-analysis was used [ 33 ]. Thus, the pooled estimates and their confidence intervals were obtained by combining the regression coefficients of each HF using the inverse variance method. Random effects models were used to consider the strong heterogeneity highlighted by the statistics I 2 =100*(Q-df)/Q (with Q the statistics of Cochran's Q-test of heterogeneity and df the number of degrees of freedom corresponding here to the number of HFs minus one). The values 0%, 25%, 50% and 75% of the I 2 statistics represent the following levels of heterogeneity: absent, weak, moderate, and strong, respectively [ 33 , 34 ]. The incidence-rate ratio (IRR) for each HF per month as well as the aggregate estimates were graphically represented using a "forest plot". The analysis was stratified by region.

The statistical significance threshold used for interpreting the results was 5%. All the statistical analyses were performed with Stata/SE software version 14.2.

Descriptive statistics

The results in Table 1 show that the overall level of data completeness is 98.9% for the monthly number of first ANC visits and 99.3% for the monthly number of SBAs. In all regions, better data completeness was observed in the post-start period of the intervention. For the descriptive statistics of the two variables of interest, overall, the average (respectively the median) of the monthly number of first ANC visits was 58.6 (respectively 50.0). For the monthly number of SBAs, the mean and median were 52.3 and 31.0, respectively. The observed differences between the means and medians illustrate the asymmetry of the distributions of these variables. We also found that the means and medians of these two variables appeared to be greater during the implementation period of the project than during the period prior to the intervention.

Furthermore, Fig.  1 shows that there was an increasing trend over time for the monthly average of the first ANC and the monthly average of the SBA. It also emerged that the positive slope was more abrupt for SBA.

Evolution of the monthly averages of the number of first ANC visits and SBAs in the selected Health facilities between January 2013 and May 2018

Effects of the health voucher project

First antenatal consultation (anc).

Table 2 and Figure S 3 displays contrasting results. Overall, at the end of the first month of implementation of the project, controlling for other variables, a statistically significant increase of nearly 26% in the monthly number of first ANCs was observed in the 33 HFs considered in the study sample (IRR = 1.258 [95% CI: 1.075, 1.472]). A similar increase was recorded in the North region but was not statistically significant (IRR = 1.246 [95% CI: 0.976, 1.591]). In the Adamawa region, the increase was nearly 73% (IRR = 1.726 [95% CI: 1.117, 2.668]). Conversely, in the Far North region, a nonsignificant reduction of 0.2% was noted (IRR = 0.998 [95% CI: 0.882, 1.129]). These overall results hid disparities across facilities. In the Adamawa region, out of 10 HFs, there was a statistically significant increase in the monthly number of first ANCs at the end of the first month of project implementation in five HFs and a statistically significant decrease in one HF. In the Far North region, of the 10 HFs, a statistically significant increase was recorded in two HFs, and a statistically significant reduction was recorded in one HF. In the North region, of the 13 HFs, six exhibited a statistically significant increase in the aforementioned indicator and one exhibited a statistically significant decrease.

Moreover, regarding the difference between the slope of the trajectory of the first ANC after and before the start of the project, Table 2 and Figure S 4 does not show statistically significant results, either overall or by region. However, in one HF in the Adamawa region, a statistically significant increase in the slope of the trajectory of the first ANC was observed during the project implementation period compared to the situation prior to the intervention. Conversely, a statistically significant decrease was recorded in one HF. In the Far North region, no HF exhibited a statistically significant increase, but a statistically significant decrease was observed in two HFs. In the North region, two HFs exhibited a statistically significant increase, and five HFs exhibited a statistically significant decrease.

Skilled birth attendance (SBA)

Table 2 and Figure S 7 shows that by the end of the first month of implementation of the project, a statistically significant increase of nearly 57% in the monthly number of SBAs was recorded in the 37 HFs selected in the study sample, controlling for other variables (IRR = 1.566 [95% CI: 1.358, 1.806]).

A statistically significant increase in this indicator was also observed in each of the three regions. However, there were disparities between HFs. In the Adamawa region, out of 13 health facilities, there was a statistically significant increase in the monthly number of assisted deliveries at the end of the first month of project implementation in nine HFs and a statistically significant decrease in one HF. In the Far North region, of the 11 HFs, a statistically significant increase was recorded in eight HFs and a statistically significant decrease was recorded in two HFs. In the North region, of the 13 HFs, seven recorded a statistically significant increase and one a statistically significant decrease in the indicator of interest.

In addition, Table 2 and Figure S 8 indicates that, overall, the intervention had a positive effect on SBAs (IRR = 1.009 [95% CI: 1.002, 1.016]). A similar finding is observed in the three regions, with the Far North region being the only region that was statistically significant. When considering the analysis of HFs, the results are mixed. In the Adamawa region, a positive and statistically significant result was recorded for four HFs while a negative and statistically significant result was observed for three HFs. In the Far North region, statistically significant results were recorded for five HFs and all these results were positive. In the North region, two HFs recorded a positive result and three recorded a negative result.

The high values of the I 2 statistics reveal that a very large proportion of the total observed variance is due to a real difference in effect measures between HFs (Figure S 1 to S 8 ).

Our study explored the effect of the Health voucher Project on the use of health services. Overall, a statistically significant increase was observed in the number of first ANCs at the end of the first month of project implementation (success). However, this improvement was not sustained over time, with less than 10% of all HFs (3/33) experiencing an increase in ANCs.

For the SBAs, there was a statistically significant increase at the end of the first month of project implementation, with a sustained pattern over time. When looking at the individual HFs, 2/3 (65%) recorded success at the end of the first month of implementation, while 30% experienced overall improvement during the project implementation compared to the period before the start of the project.

These findings suggest that between the pre-intervention/roll-out and full implementation phases, the Cameroon voucher programme modestly increased the use of facility for ANC and SBA, consistent with previously reported results from evaluations of maternal health voucher programmes from other LMICs [ 21 , 35 , 36 , 37 , 38 ].

Our results therefore indicate that in a country such as Cameroon, where progress toward universal health coverage is still to be achieved [ 39 ], reducing financial risk by providing subsidies to offset the costs of receiving RMNCAH services may be a good cost-effective intervention to improve service utilization.

Pregnant women were more likely to use the voucher system for SBAs than for the first ANC visits. One explanation could be the late attendance of pregnant women at health facilities, as more than 70% of pregnant women in these three regions are reported to have their first contact with a health facility after the first trimester of pregnancy [ 27 ], or the late acquisition of vouchers. In-depth discussions with health care providers and direct beneficiaries are needed to better understand the realities underlying these trends.

The decrease in first ANC and SBA over time in some HFs could be explained by the increasing expansion of service coverage, with the opening of new health facilities that were not yet included in the project and that were used by some pregnant women. On the other hand, the context of growing insecurity linked to Boko Haram and other rebel groups in neighboring countries could also constitute a barrier to the use of health facilities in these regions.

It is also important to note that the voucher program is conceptually designed to target the poorest populations. In Cameroon, however, the project covers all women of reproductive age in the intervention areas, regardless of socioeconomic status. We suspect that the contribution of the 6,000 FCFA ($11 US) remains a major barrier to the use of health services for the poorest women, especially since the project covers mostly urban areas, raising the question of program equity as reported elsewhere [ 13 , 14 , 16 ]. This challenge was also highlighted in an unpublished qualitative study.

Focusing on strategies that prioritize the poorest women and strengthen community engagement can ensure equity and achieve sustainable results over time. For example, in Bangladesh and Cambodia, the voucher programme focused on those most in need and reimbursed care givers in facilities to motivate them [ 40 , 41 ]. Moreover, both countries have successfully partnered with recipient communities to improve the targeting of the poor [ 40 , 41 , 42 ].

In addition to stimulating demand, voucher schemes are often proposed as a way to improve the quality of care, as is the case in Cameroon, where health facility accreditation mechanisms are used, alongside the performance-based financing scheme implemented nationwide. However, experiences show that providers may find reimbursement rates to be unattractive and engage in practices such as providing inconsistent quality of care or ‘skimming’ programme users who require minimal intervention. Moreover, as reported in other voucher programs, the most significant problem faced by the voucher scheme in Cameroon was the delay in paying for health facilities, which led to staff demotivation and mistrust between the managers of the scheme and the beneficiaries [ 41 ] and suggested a need for greater attention to issues related to implementation in such a program [ 26 ].

This study helps to extend the body of knowledge generated by previous research on health voucher programmes in LMICs. However, in interpreting our findings, the strengths and limitations of the study design should be considered.

First, most studies on voucher programmes to date have examined the immediate or shorter-term impact of the intervention on service utilization [ 21 ]. Our study examined the immediate to longer-term effects of the intervention and used a quasi-experimental design, known as a reliable approach, to provide robust estimates of the effect of an intervention when a randomized controlled trial cannot be conducted or when a control group is lacking [ 29 , 31 ]. Unlike in cross-sectional observational studies, interrupted time series analysis allows us to estimate the dynamics of change driven by the intervention, controlling for secular changes that might have occurred in the absence of the intervention [ 29 , 43 ]. This approach thus makes it possible to observe whether the intervention has an immediate or delayed, sudden or gradual effect and whether this effect persists or is temporary. Furthermore, there is no real consensus on the number of observation points needed to use the interrupted time series method. However, the statistical power increases with the number of time points [ 30 ]. Some authors recommend 12 observation points before and after the start of the intervention [ 29 ]. In our study, only one HF had 10 observation points before the start of the project, and the others had observation points ranging from 14 and 42. During the project implementation period, the number of observations varied between 23 and 37.

At the time of the study, 81 facilities had already enrolled in the voucher project. We limited ourselves to 33 HFs for the first ANC and to 37 HFs for the SBA analysis because the data prior to the project were either unavailable or insufficient. Therefore, the results presented in this study may be a fragmented view of the project’s effect. In addition, analysis that could provide insight into the RMNCAH continuum of care was not possible due to the limited quality of data (high frequency of missing data) for some key indicators, such as the fourth ANC and postnatal consultation, as reported with other voucher programs [ 22 , 44 , 45 , 46 ].

In identifying the impact of an intervention, it is important that there are no exogenous factors influencing the results. During the implementation of the voucher program in Cameroon, there were no closures of health facilities that could have an impact on the two selected indicators. Population growth naturally leads to an increase in the number of pregnant women in absolute terms, and consequently to an increase in the number of SBAs. Because demographic data were only available for each health district and not for each health facility, estimates of expected populations or pregnant women were not included into the various negative binomial regression models as a control variable. As a result, the estimates obtained may be biased.

It is also important to point out that due to its fragility, the northern part of the country is a convergence zone of several programs and projects, including those of health. Therefore, other interventions may have also contributed to the achievement of these outcome levels. One of the most important programmes is the National Multi-sector Program to Combat Maternal, Newborn and Child Mortality, which was created in 2013.

Finally, we would like to underline that the fidelity of the program's implementation was hampered by deviations, leading for instance to extending the intervention to all women of childbearing age. At present, the program is more akin to an obstetric risk insurance system, as described for example in Mauritania [ 47 ].

This study provided important insight into the Cameroon voucher scheme. The intervention had a significant early effect on the first ANC and SBA but failed to effectively sustain these results over time for the first indicator. These mixed effects were likely the consequence of poor design and implementation challenges, including the fact that the programme did not include specific equity measures to facilitate uptake by the poorest people. This suggests that for a complex intervention such as a voucher, it is critical to properly implement practice strategies that can sustain the long-term impact of the programme.

Availability of data and materials

The data that support the findings of this study are available from the Ministry of Public Health (MPH) of Cameroon, but restrictions apply due to the terms of our contract with the MPH, and so, data are not publicly available. The corresponding author should be contacted for the process to request data access.

Abbreviations

• Antenatal consultation

Centre International de Développement et de Recherche

Emergency obstetric and neonatal care

Communauté financière africaine

Health facility

Low- and middle-income country

Maternal mortality ratio

Multiple Indicator Cluster Survey

Ministry of public health

Out-of-pocket

Post-natal care

Regional Funds for Health Promotion

Reproductive, maternal, neonatal, child and adolescent health

• Skilled birth attendance

Sustainable Development Goals

Sub Saharan Africa

United States dollar

World health organization

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Acknowledgements

We would like to thank Dr Bassirou Bouba and Dr Okala from the voucher project, Dr Yumo Habakkuk and Bashirou Ndindumouh from Research for Development International, Dr Denise Tamga from the Worlb Bank Office, and Dr Aubin Baleba from UNFPA. We are much indebted to the SPARC team for continuously reviewed our work and provided valuable comments. Finally, the authors would also like to acknowledge the work of the anonymous reviewers who provided us with extremely helpful comments and feedback.

This work was supported by the Bill & Melinda Gates Foundation [Grant number: OPP1179622].

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Isidore Sieleunou

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Isidore Sieleunou & Roland Pascal Enok Bonong

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IS and RPEB conceived and designed the study. RPEB managed the data, including quality control, provided statistical advice on study design and analyzed the data. IS drafted the manuscript, and all authors contributed substantially to its revision. All authors agreed to the final approval of the version to be published. All authors agreed to be accountable for all aspects of the work.

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Ethical approval for the study was obtained from the Cameroon National Ethics Committee for Human Health Research (CNECHHR) (N0 2020/07/1274/CE/CNERSH/SP). Administrative authorization was granted by the Cameroonian Ministry of Health (D30-607/N/MINSANTE/SG/DROS/CRSPE/BBM, N0 631-32-20). All methods were performed in accordance with the relevant guidelines and regulations. The CNECHHR waived the need for participants’ informed consent in this retrospective study because the data used were fully anonymised and aggregated.

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Sieleunou, I., Enok Bonong, R.P. Does health voucher intervention increase antenatal consultations and skilled birth attendances in Cameroon? Results from an interrupted time series analysis. BMC Health Serv Res 24 , 602 (2024). https://doi.org/10.1186/s12913-024-10962-9

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• Sex differences in risk of incident microvascular and macrovascular complications: a population-based data-linkage study among 25 713 people with diabetes
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• http://orcid.org/0000-0001-8750-9720 Alice A Gibson 1 , 2 ,
• Emma Cox 1 , 2 ,
• Francisco J Schneuer 1 , 2 , 3 ,
• Jacob Humphries 4 ,
• Crystal MY Lee 5 ,
• Joanne Gale 1 ,
• Steven Chadban 2 , 6 ,
• Mark Gillies 7 ,
• Clara K Chow 2 , 8 , 9 ,
• Stephen Colagiuri 2 , 4 ,
• Natasha Nassar 1 , 2 , 3
• 1 Menzies Centre for Health Policy and Economics, Sydney School of Public Health, Faculty of Medicine and Health , The University of Sydney , Sydney , New South Wales , Australia
• 2 Charles Perkins Centre , The University of Sydney , Sydney , New South Wales , Australia
• 3 Child Population and Translational Health Research, The Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health , The University of Sydney , Sydney , New South Wales , Australia
• 4 Faculty of Medicine and Health , The University of Sydney , Sydney , New South Wales , Australia
• 5 School of Population Health , Curtin University , Perth , Western Australia , Australia
• 6 Department of Renal Medicine, Kidney Centre , Royal Prince Alfred Hospital , Camperdown , New South Wales , Australia
• 7 Discipline of Ophthalmology and Eye Health, Save Sight Institute, Faculty of Medicine and Health , The University of Sydney , Sydney , New South Wales , Australia
• 8 Westmead Applied Research Centre, Faculty of Medicine and Health , The University of Sydney , Sydney , New South Wales , Australia
• 9 Department of Cardiology , Westmead Hospital , Westmead , New South Wales , Australia
• Correspondence to Dr Alice A Gibson, Menzies Centre for Health Policy and Economics, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia; alice.gibson{at}sydney.edu.au

Background The global prevalence of diabetes is similar in men and women; however, there is conflicting evidence regarding sex differences in diabetes-related complications. The aim of this study was to investigate sex differences in incident microvascular and macrovascular complications among adults with diabetes.

Methods This prospective cohort study linked data from the 45 and Up Study, Australia, to administrative health records. The study sample included 25 713 individuals (57% men), aged ≥45 years, with diabetes at baseline. Incident cardiovascular disease (CVD), eye, lower limb, and kidney complications were determined using hospitalisation data and claims for medical services. Multivariable Cox proportional hazards models were used to assess the association between sex and incident complications.

Results Age-adjusted incidence rates per 1000 person years for CVD, eye, lower limb, and kidney complications were 37, 52, 21, and 32, respectively. Men had a greater risk of CVD (adjusted hazard ratio (aHR) 1.51, 95% CI 1.43 to 1.59), lower limb (aHR 1.47, 95% CI 1.38 to 1.57), and kidney complications (aHR 1.55, 95% CI 1.47 to 1.64) than women, and a greater risk of diabetic retinopathy (aHR 1.14, 95% CI 1.03 to 1.26). Over 10 years, 44%, 57%, 25%, and 35% of men experienced a CVD, eye, lower limb, or kidney complication, respectively, compared with 31%, 61%, 18%, and 25% of women. Diabetes duration (<10 years vs ≥10 years) had no substantial effect on sex differences in complications.

Conclusions Men with diabetes are at greater risk of complications, irrespective of diabetes duration. High rates of complications in both sexes highlight the importance of targeted complication screening and prevention strategies from diagnosis.

• EPIDEMIOLOGY
• DIABETES MELLITUS
• CARDIOVASCULAR DISEASES
• COHORT STUDIES
• RECORD LINKAGE

Data availability statement

Data may be obtained from a third party and are not publicly available. This research was completed using data collected from the Sax Institute’s 45 and Up Study. Requests for access to data should be addressed to the corresponding author or the Sax Institute ( http://www.saxinstitute.org.au/ ).

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/jech-2023-221759

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WHAT IS ALREADY KNOWN ON THIS TOPIC

The absolute risk of cardiovascular disease appears to be higher in men with diabetes compared with women with diabetes. However, the evidence for sex differences in microvascular complications is limited and conflicting.

Further, there is little understanding of the potential impact of diabetes duration on sex differences in micro- and macrovascular complications.

WHAT THIS STUDY ADDS

Compared with women, men were at greater risk of incident cardiovascular disease, lower limb and kidney complications, and diabetic retinopathy.

Sex differences in rate of complications were similar for those with diabetes duration <10 years and ≥10 years.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE, OR POLICY

Given the high rates of complication in both sexes, this study highlights the importance of targeted complication screening and prevention strategies from the time of diagnosis.

Introduction

Diabetes leads to numerous microvascular and macrovascular complications such as loss of vision, amputation, kidney failure, myocardial infarction and stroke, placing an enormous burden on individuals and their families, healthcare systems and society in general. Globally, the prevalence of diabetes continues to escalate. An estimated 537 million people aged 20–79 years were living with diabetes in 2021, which is projected to rise to a staggering 783 million by 2045. 1 In Australia, the prevalence of diabetes has tripled over the past three decades, affecting an estimated 1.3 million (5.1%) Australians in 2018–2021. 2

Although the prevalence of diabetes is similar in men and women (worldwide prevalence of 8.9% and 8.4%, respectively), 3 the incidence and progression of diabetes-related complications appears to be more sex-specific. It is well established that the absolute risk of cardiovascular disease (CVD) is higher in men with diabetes than women with diabetes. 4 However, the evidence for sex differences in microvascular complications such as retinopathy, neuropathy and nephropathy is limited and conflicting. For instance, in the UK Prospective Diabetes Study, the incidence of retinopathy was similar in men and women; however, women had a lower relative risk of retinopathy progression (RR 0.54, 95% CI 0.37 to 0.80). 5 In the prospective DiaGene cohort study of diabetes complications, the incidence of microalbuminuria (a biomarker of nephropathy) was higher in men, and men were more likely to develop two or three microvascular complications compared with women (OR 2.42, 95% CI 1.69 to 3.45). 6

Ample duration of follow-up is required to assess long-term diabetes-related complications. Multiple studies have provided strong evidence that individuals with longer diabetes duration are at greater risk of complications 7 8 ; however, there is little understanding of the potential impact of diabetes duration on sex differences in diabetes-related complications. The aim of this study was to investigate sex differences in incident micro- and macrovascular complications among a large population-based sample of people with diabetes. We also investigated whether sex differences were modified by duration of diabetes.

Study population and data sources

We used data from The Sax Institute’s 45 and Up Study, a large prospective cohort of 267 357 men and women aged over 45 years residing in the state of New South Wales (NSW), Australia. This cohort represents approximately 11% of the NSW population aged over 45. The cohort profile and research protocol have been published in detail previously. 9 Briefly, participants of the 45 and Up Study were randomly sampled from Services Australia Medicare enrolment database, between 2005 and 2009. Participants were invited by mail and agreed to participate by completing a sex-specific self-administered questionnaire and providing written consent for linkage of their survey responses to administrative health data collections. The estimated response rate was 19%. The full baseline survey questionnaires are available at https://www.saxinstitute.org.au/our-work/45-up-study/questionnaires/ .

For this study we used data from participants’ baseline questionnaires that were linked to their corresponding medical services claims (Medicare Benefits Schedule, MBS), prescription medication (Pharmaceutical Benefits Scheme, PBS), hospital admission (Admitted Patient Data Collection, APDC) and death registry data collections (Registry of Births Deaths and Marriages). Detailed information about the datasets and linkage process is provided in the online supplemental file 1 . The 45 and Up Study was approved by the University of NSW Human Research Ethics Committee, and use of linked data for this study was approved by the NSW Population and Health Services Research Ethics Committee (Cancer Institute NSW reference: 2017/HRE0206).

Supplemental material

Study sample.

The present study includes all participants in the 45 and Up Study identified with diabetes at baseline. The online supplemental file 1 provides a detailed overview of diabetes case ascertainment. In brief, we used a combination of self-report and the multiple linked administrative data sources (MBS, PBS, APDC) to ascertain diabetes status.

Study exposures

The main exposures of interest were sex and diabetes duration at baseline. Diabetes duration at baseline was calculated using the age at first diabetes diagnosis identified from the baseline survey and categorised into <10 years or ≥10 years.

Study outcomes

Study outcomes were determined following literature review and consultation with clinical experts and defined as incident hospitalisation or treatment for the following four major groups and subgroups of diabetes-related micro- and macrovascular complications 10 :

Cardiovascular complications: ischaemic heart disease, transient ischaemic attack (TIA), stroke, heart failure, diabetic cardiomyopathy

Eye complications: diabetes with ‘any ophthalmic complication’, cataract, diabetic retinopathy

Lower limb complications: peripheral neuropathy, ulcers, cellulitis, Charcot foot, osteomyelitis, peripheral vascular disease, and minor or major amputation

Kidney complications: ‘diabetes with kidney complication’, acute kidney failure, chronic kidney disease, unspecified kidney failure, dialysis, and kidney transplant.

Diabetes-related complications were primarily ascertained from hospital admission records (APDC) using principal and additional International Statistical Classification of Diseases and Related Health Problems, Australian Modification (ICD-10-AM) diagnosis or Australian Classification of Healthcare Interventions (ACHI) procedure codes. As not all diabetes-related complications included in this analysis require hospital admission, we also included out-of-hospital treatment for complications such as home dialysis for chronic kidney disease, or retinal laser. This was identified using relevant MBS treatment items. A complete list of outcomes and associated diagnosis, procedure and treatment codes are presented in online supplemental table 1 .

Self-reported sociodemographic, lifestyle and health characteristics were identified from the baseline survey. All questions, response options and categories are provided in online supplemental table 2 . Sociodemographic characteristics included age group, socioeconomic background (Index of Relative Socioeconomic Disadvantage (IRSD) quintile), household income, highest level of education, language other than English spoken at home, country of birth, and private health insurance. The IRSD is derived from income, education, unemployment, and other census data. 11

Lifestyle and health factors included body mass index (BMI), smoking status, physical activity, fruit and vegetable consumption, family history of diabetes, and previous history of CVD (including heart disease and stroke), history of high blood pressure and blood pressure treatment, and treatment for high cholesterol. Of note, previous history of CVD was not included in the CVD complications analysis, as individuals with a prior history were excluded from this analysis.

Statistical analysis

Contingency tables were used to describe the baseline characteristics of participants, grouped by sex. For all major groups and subgroups of diabetes complications, we calculated age-adjusted incidence rates of complications per 1000 person-years, based on the subpopulation at risk (time to first event, death or end of follow-up time). We used Kaplan-Meier estimators to compare age-standardised cumulative complication rates for major outcome groups stratified by sex and duration of diabetes.

Cox proportional hazards models were used to estimate crude and adjusted hazard ratios (aHR) to assess associations between sex and incident CVD, lower limb, eye, and kidney complications. For analysing each group of complications (ie, CVD, lower limb, eye, and kidney), we excluded those with a prior history of that group of complications (ie, between January 2001 and their baseline survey date). The models for each outcome were conducted adjusting for other factors in a sequential process: (1) unadjusted, (2) adjusted for age and sex, (3) adjusted for age, sex, sociodemographics, and lifestyle, and (4) adjusted for all sociodemographic, lifestyle, and health-related factors. Person-years were calculated from the date of recruitment until incident treatment or hospitalisation, death, or end of follow-up (ie, December 2019). All models account for the competing risk of death before complication. Proportionality assumptions were verified based on the methods of Lin et al . 12

Multiple imputation was performed using full conditional specification and incorporating sociodemographic, lifestyle and health factors described above. Thirty imputations were conducted and estimates from the imputed datasets were combined by calculating the mean of the parameter of interest and standard errors adjusted for the uncertainty produced by the imputation process. The missing at random (MAR) assumption required for imputation was considered reasonable based on the missingness patterns in the data ( table 1 ) and the large number of variables included in the imputation process. 13 All analyses were performed using SAS software version 9.4 (SAS Institute Inc, Cary, NC, USA).

• View inline

Baseline sociodemographic, lifestyle and health characteristics of the cohort of participants with diabetes by sex (n=25 713).

Sample characteristics

The full baseline 45 and Up sample included 267 357 participants. There were 266 471 active participants available for this analysis. We excluded participants if they did not have diabetes at baseline (n=232 535), their diabetes status was uncertain (n=8166), or there were inconsistencies in their age, death, or baseline data (n=57). Our final sample included 25 713 participants ( online supplemental figure 1 ).

Table 1 presents the baseline characteristics of the cohort by sex, with almost half of the cohort aged 60–74 years and a slightly higher proportion of females aged 45–59 years with diabetes. A higher proportion of men were overweight (38.7% in men vs 27.8% in women), had higher educational attainment, held private health insurance, and had a history of heart disease. In terms of smoking status, although a similar proportion of men and women were current smokers, a higher proportion of men were ex-smokers (51% compared with only 29% women). Of the 19 277 (75%) people with diabetes who had an age of diagnosis, 58% had a duration of diabetes <10 years and 42% had a duration of diabetes ≥10 years at baseline. There were no meaningful differences in baseline characteristics between those with and without an age of diagnosis ( online supplemental table 3 ).

Incident CVD complications

During 177 851 person-years of follow-up, the overall incidence rate of CVD complications was 37 per 1000 person-years, which was higher among men than women (43 vs 30 per 1000) ( figure 1A ). After adjustment of covariates, compared with women, the aHR for any incident CVD complication in men was 1.51 (95% CI 1.43 to 1.59) ( figure 1B ). Among the CVD complication subgroups, associations were similar to the overall result for heart failure and stroke and stronger for myocardial infarction and other coronary heart disease ( online supplemental figure 2 ). These associations are reflected in the cumulative hazard curves which show that at 10 years’ follow-up, 44.4% (95% CI 43.0% to 45.9%) of men and 30.9% (95% CI 29.7% to 32.2%) of women with diabetes experienced a CVD complication (p<0.001) ( figure 2A ). The sex difference in rate of CVD complications at 10 years was similar, although slightly greater, for those with diabetes <10 years compared with ≥10 years’ duration ( online supplemental figure 6 , online supplemental table 4 ).

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(A) Age-adjusted incidence rates per 1000 person-years of incident diabetes-related complications by sex and (B) adjusted hazards ratio (aHR) (95% CI) for association between sex and incident diabetes-related complications. Hazards ratios are calculated from Cox proportional hazard models based on multiple imputed data adjusted for age, sociodemographics (education, SEIFA, income, language, country of birth, private insurance), lifestyle (BMI, smoking, diet and physical activity) and health history (family history of diabetes, cardiovascular disease, blood pressure and treatment for high cholesterol). BMI, body mass index; SEIFA, Socio-Economic Indexes for Areas.

Cumulative incidence of macrovascular and microvascular complications by sex: (A) CVD complications; (B) eye complications; (C) lower limb complications; (D) kidney complications. Hazard function survival curves using Kaplan-Meier methods. P values in the figures represent the results for the log-rank test. CVD, cardiovascular disease.

Incident eye complications

The incidence rate of eye complications was 52 per 1000 person years and was similar for men and women (52 vs 53 per 1000) ( figure 1A ). Compared with women, men had a lower risk of any eye complication (aHR 0.94, 95% CI 0.89 to 0.98) ( figure 1B ), with results largely influenced by the lower risk of cataract surgery among men (aHR 0.90, 95% CI 0.86 to 0.95) ( online supplemental figure 3 ). In contrast, men had a slightly greater rate and risk of diabetic retinopathy (10 vs 9 per 1000 person years; aHR 1.14, 95% CI 1.03 to 1.26) ( online supplemental figure 3 ). At 10 years’ follow-up, the cumulative incidence of eye complications was 57.0% (95% CI 55.3% to 58.8%) in men and 60.9% (95% CI 58.9% to 62.9%) in women (p<0.001) ( figure 2B ); for diabetic retinopathy these rates were 9.8% (95% CI 9.2% to 10.4%) in men and 8.9% (95% CI 8.2% to 9.5%) in women. When stratified by duration of diabetes, there was no statistical sex difference in risk of diabetic retinopathy for those with diabetes <10 years (aHR 1.12, 95% CI 0.95 to 1.31) at baseline and ≥10 years’ duration (aHR 1.16, 95% CI 0.99 to 1.36) at baseline ( online supplemental figure 6 , online supplemental table 4 ).

Incident lower limb complications

The incidence rate of lower limb complications was 21 per 1000 person years and was higher among men than women (25 vs 18 per 1000) ( figure 1A ). The risk of any lower limb complication was 1.5 times higher in men than women (aHR 1.47, 95% CI 1.38 to 1.57) ( figure 1B ), and the risks of peripheral neuropathy, ulcer and cellulitis were similar. The difference was stronger for peripheral vascular disease, with the risk of complications over two times higher for men. While the incidence was low, the risk of osteomyelitis and amputation was over 2.5-fold higher in men than in women ( online supplemental figure 4 ). The cumulative incidence of lower limb complications at 10 years was higher among men at 24.6% (95% CI 23.7% to 25.5%) versus 17.8% (95% CI 16.9% to 18.7%) in women ( figure 2C ), and this pattern was relatively similar irrespective of diabetes duration ( online supplemental figure 6 , online supplemental table 4 ).

Incident kidney complications

The incidence rate of kidney complications was 32 per 1000 person years and was higher among men than women (36 vs 26 per 1000) ( figure 1A ). The risk of any kidney complication was 1.6 times higher in men than in women (aHR 1.55, 95% CI 1.47 to 1.64) ( figure 1B ), with similar risk estimates for specific subgroups, including kidney failure, chronic kidney disease and dialysis ( online supplemental figure 5 ). This pattern of a higher risk of kidney complications in men is reflected in the cumulative incidence at 10 years, which was higher among men at 35.2% (95% CI 34.0% to 36.3%) versus 25.3% (95% CI 24.3% to 26.3%) in women ( figure 2D ). The sex difference in rate of kidney complications at 10 years was similar, although slightly greater, for those with diabetes ≥10 years compared with <10 years’ duration ( online supplemental figure 6 , online supplemental table 4 ).

Our study demonstrates that men with diabetes have a higher rate and greater risk of most diabetes-related complications compared with women, and this difference remained consistent irrespective of the duration of diabetes. For every 1000 people with diabetes, our findings suggest that an average of 37, 52, 21, and 32 people will develop CVD, eye, lower limb, and kidney complications every year. Men had a 1.5-fold increased risk of CVD, lower limb, and kidney complications, and risk of diabetic retinopathy was 14% greater in men than in women. These findings are reflected in the ~1.4 times higher 10-year rates for CVD, lower limb, and kidney complications in men compared with women.

The greater risk of CVD complications observed for men in our study is consistent with other large population-based studies in France 14 and Denmark. 15 These studies reported a higher incidence of major adverse cardiovascular events including heart failure in men with diabetes compared with women with diabetes (incidence rate (IR) 96 vs 66/1000 person-years, 14 and IR 24.9 vs 19.9/1000 person-years 15 ). Men, irrespective of diabetes status, have been shown to have a greater CVD risk factor burden than women. 16–18 A recent study using nationally representative survey data from Australians aged 45–74 years showed men had a higher average BMI, waist circumference, systolic and diastolic blood pressure, total: high density lipoprotein (HDL) cholesterol ratio, triglycerides and glycated haemoglobin (HbA1c) compared with women, and a higher proportion of men were also current or ex-smokers. 17 Our study observed similar differences in baseline characteristics, with men more likely to be overweight, have a history of heart disease or stroke, and be previous smokers. Men may also be less likely to adopt primary prevention strategies, such as healthy lifestyle change and medication use, 16 19 and to engage in health seeking behaviours, such as preventative health checks. 20 21 Further, women are known to be at lower risk of CVD complications compared with men due to the protective effects of reproductive factors such as breastfeeding and the use of hormone replacement therapy within 10 years of menopause. 22 There are important age-specific sex differences in CVD complications. Women have an older age of CVD onset compared with men, 23 and experience lower rates of CVD up until the age of 80 years. 18 It is possible that the sex differences in CVD complications observed in our study may resolve if the cohort were to be followed for a longer time.

Evidence for sex differences in microvascular diabetes complications is less conclusive than for macrovascular complications. A meta-analysis of 10 studies (nine cohort) reported an elevated, but non-significant, increase in incident chronic kidney disease among women compared with men (adjusted women-to-men relative risk ratio (WMR) 1.14, 95% CI 0.97 to 1.34), with risk particularly higher for end stage renal disease (adjusted WMR 1.38, 95% CI 1.22 to 1.55). 24 In contrast, studies from the Netherlands and UK found a higher baseline prevalence and risk of incident microalbuminuria in men. 6 25 Although no studies have examined overall lower limb complications, the risk of amputation has been shown to be greater in men than in women. 26 27 Similarly, a meta-analysis of 20 studies found that men with diabetic foot have an approximate 50% increased amputation risk compared with women. 28 In contrast to the results for CVD, kidney and lower limb complications, our study found that women with diabetes were at greater risk of eye complications. This appeared to be largely driven by the inclusion of cataracts as a sub-group, which are more prevalent in women compared with men. 29 30 Considering diabetic retinopathy specifically, our results indicate a 14% greater risk of incident retinopathy in men which is consistent with a study from Italy which showed the incidence of diabetic retinopathy to be associated with the male sex (HR 1.31, 95% CI 1.05 to 1.63). 31 The mechanisms for sex differences in microvascular complications remains under-researched, 32 but possible factors include worse glycaemic and blood pressure control and treatment, 18 and an underutilisation of medical care for microvascular complications 28 in men compared with women. Large-scale studies examining sex differences in adherence to guideline-recommended processes of care, including medication adherence and healthy lifestyle behaviours, are needed to understand these findings better.

It is well understood that individuals with longer diabetes duration are at greater risk of complications. The UK Biobank study showed that with each 5 year increase in diabetes duration, there was a 20% increase in excess risk of CVD complications for both men and women. 7 Despite the greater complication-risk with longer disease duration, we observed a similar sex difference in risk of complications for those with diabetes duration <10 years compared with those with diabetes duration ≥10 years. Few studies have examined the effect of diabetes duration on sex differences in risk of complications; however, Duarte et al found that the magnitude of the association between duration of diabetes and glycaemic control was stronger for women compared with men. 33 Only individuals with age of diagnosis reported in the baseline survey could be included in our analysis stratified by disease duration (approximately two-thirds of the full sample), which may have influenced our findings.

The strengths of this study include the large population-based sample, the long follow-up time, and use of objective linked data to identify incident diabetes-related complications, avoiding issues of loss to follow-up and self-report. The data in our study did not include diabetes complications not requiring hospitalisation, with the exception of diabetic retinopathy and home dialysis. While our analyses took into account competing risk of CVD-related death before hospitalisation, these numbers were small (n=163), with no meaningful sex differences that might have had an impact on our results ( online supplemental table 5 ). Given that diabetic kidney disease is frequently asymptomatic, unknown to patients, 34 and requires laboratory testing for detection, it is likely that the incidence of early-stage chronic kidney disease was underestimated in our study. On the other hand, as we excluded those with a prior history of complications to capture incident complications, this may have not allowed enough time for the development of end stage complications, such as limb amputations or requirement for kidney replacement therapy with dialysis or transplantation. As such, the absolute rates of complications should be interpreted with caution. The 45 and Up Study provides detailed information on sociodemographic, health and lifestyle covariates which we were able to adjust for in the analysis. However, we did not take into account all potential confounding/effect-modifying factors including glycaemic, lipid and blood pressure control, medication use 35 and adherence which may have impacted the strength of the association between sex and risk of complications. We were also not able to differentiate between type 1 and type 2 diabetes in our study, precluding an analysis by type of diabetes. Although the 45 and Up cohort are broadly representative of the Australian population aged ≥45 years, the sample does overrepresent higher income earners, people aged 80 and over, and residents of rural and remote areas, 36 which may limit the generalisability of the results. Although men have a higher absolute risk of CVD complications, studies in patients with diabetes compared to those without diabetes have shown that the relative CVD risk conferred by diabetes is greater in women. 37–39 Sex differences in relative risk of diabetes complications was not assessed in our study.

In conclusion, although men with diabetes are at greater risk of developing complications, in particular CVD, kidney and lower-limb complications, the rates of complications are high in both sexes. The similar sex difference for those with shorter compared with longer diabetes duration highlights the need for targeted complication screening and prevention strategies from the time of diabetes diagnosis. Further investigation into the underlying mechanisms for the observed sex differences in diabetes complications are needed to inform targeted interventions.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants. The 45 and Up Study was approved by the University of NSW Human Research Ethics Committee, and use of linked data for this study was approved by the NSW Population and Health Services Research Ethics Committee (Cancer Institute NSW reference: 2017/HRE0206). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

This research was completed using data collected through the 45 and Up Study (www.saxinstitute.org.au). The 45 and Up Study is managed by the Sax Institute in collaboration with major partner Cancer Council NSW and partners the Heart Foundation and the NSW Ministry of Health. We thank the many thousands of people participating in the 45 and Up Study. We also acknowledge the support of the NSW Centre for Health Record Linkage (CHeReL; http://www.cherel.org.au ).

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Supplementary materials

Supplementary data.

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• Data supplement 1

Contributors AAG, NN and SC conceived the idea for the study. All authors contributed intellectual content to the study design and interpretation of the findings. FS, JH, and JG conducted the analysis. AAG, EC, NN and JG drafted the manuscript and all authors provided edits and comments. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. All authors have approved the final article. AAG is the guarantor of this work.

Funding This work was supported by an Australian Diabetes Society Servier Research Grant, an Australian Diabetes Research Trust Grant and a NSW Health EMCR CVD Capacity Grant. AAG is supported by an Australian National Health and Medical Research Council Emerging Leader 1 Investigator Grant (APP1173784). AAG is also grateful to the NSW Cardiovascular Research Network for a Professional Development Award. NN is supported by Financial Markets Foundation for Children and by an Australian National Health and Medical Research Council Leadership 2 Investigator Grant (APP1197940). Mark Gillies is supported by an NHMRC Level 3 Investigator grant. Clara K Chow is supported by an NHMRC Leadership Investigator grant (APP1195326). Funding bodies were not involved in the study design; collection, analysis, and interpretation of data; writing the report; and the decision to submit the report for publication.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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