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Significance of the Study – Examples and Writing Guide

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Significance of the Study

Definition:

Significance of the study in research refers to the potential importance, relevance, or impact of the research findings. It outlines how the research contributes to the existing body of knowledge, what gaps it fills, or what new understanding it brings to a particular field of study.

In general, the significance of a study can be assessed based on several factors, including:

• Originality : The extent to which the study advances existing knowledge or introduces new ideas and perspectives.
• Practical relevance: The potential implications of the study for real-world situations, such as improving policy or practice.
• Theoretical contribution: The extent to which the study provides new insights or perspectives on theoretical concepts or frameworks.
• Methodological rigor : The extent to which the study employs appropriate and robust methods and techniques to generate reliable and valid data.
• Social or cultural impact : The potential impact of the study on society, culture, or public perception of a particular issue.

Types of Significance of the Study

The significance of the Study can be divided into the following types:

Theoretical Significance

Theoretical significance refers to the contribution that a study makes to the existing body of theories in a specific field. This could be by confirming, refuting, or adding nuance to a currently accepted theory, or by proposing an entirely new theory.

Practical Significance

Practical significance refers to the direct applicability and usefulness of the research findings in real-world contexts. Studies with practical significance often address real-life problems and offer potential solutions or strategies. For example, a study in the field of public health might identify a new intervention that significantly reduces the spread of a certain disease.

Significance for Future Research

This pertains to the potential of a study to inspire further research. A study might open up new areas of investigation, provide new research methodologies, or propose new hypotheses that need to be tested.

How to Write Significance of the Study

Here’s a guide to writing an effective “Significance of the Study” section in research paper, thesis, or dissertation:

• Background : Begin by giving some context about your study. This could include a brief introduction to your subject area, the current state of research in the field, and the specific problem or question your study addresses.
• Identify the Gap : Demonstrate that there’s a gap in the existing literature or knowledge that needs to be filled, which is where your study comes in. The gap could be a lack of research on a particular topic, differing results in existing studies, or a new problem that has arisen and hasn’t yet been studied.
• State the Purpose of Your Study : Clearly state the main objective of your research. You may want to state the purpose as a solution to the problem or gap you’ve previously identified.
• Contributes to the existing body of knowledge.
• Addresses a significant research gap.
• Offers a new or better solution to a problem.
• Impacts policy or practice.
• Leads to improvements in a particular field or sector.
• Identify Beneficiaries : Identify who will benefit from your study. This could include other researchers, practitioners in your field, policy-makers, communities, businesses, or others. Explain how your findings could be used and by whom.
• Future Implications : Discuss the implications of your study for future research. This could involve questions that are left open, new questions that have been raised, or potential future methodologies suggested by your study.

Significance of the Study in Research Paper

The Significance of the Study in a research paper refers to the importance or relevance of the research topic being investigated. It answers the question “Why is this research important?” and highlights the potential contributions and impacts of the study.

The significance of the study can be presented in the introduction or background section of a research paper. It typically includes the following components:

• Importance of the research problem: This describes why the research problem is worth investigating and how it relates to existing knowledge and theories.
• Potential benefits and implications: This explains the potential contributions and impacts of the research on theory, practice, policy, or society.
• Originality and novelty: This highlights how the research adds new insights, approaches, or methods to the existing body of knowledge.
• Scope and limitations: This outlines the boundaries and constraints of the research and clarifies what the study will and will not address.

Suppose a researcher is conducting a study on the “Effects of social media use on the mental health of adolescents”.

The significance of the study may be:

“The present study is significant because it addresses a pressing public health issue of the negative impact of social media use on adolescent mental health. Given the widespread use of social media among this age group, understanding the effects of social media on mental health is critical for developing effective prevention and intervention strategies. This study will contribute to the existing literature by examining the moderating factors that may affect the relationship between social media use and mental health outcomes. It will also shed light on the potential benefits and risks of social media use for adolescents and inform the development of evidence-based guidelines for promoting healthy social media use among this population. The limitations of this study include the use of self-reported measures and the cross-sectional design, which precludes causal inference.”

Significance of the Study In Thesis

The significance of the study in a thesis refers to the importance or relevance of the research topic and the potential impact of the study on the field of study or society as a whole. It explains why the research is worth doing and what contribution it will make to existing knowledge.

For example, the significance of a thesis on “Artificial Intelligence in Healthcare” could be:

• With the increasing availability of healthcare data and the development of advanced machine learning algorithms, AI has the potential to revolutionize the healthcare industry by improving diagnosis, treatment, and patient outcomes. Therefore, this thesis can contribute to the understanding of how AI can be applied in healthcare and how it can benefit patients and healthcare providers.
• AI in healthcare also raises ethical and social issues, such as privacy concerns, bias in algorithms, and the impact on healthcare jobs. By exploring these issues in the thesis, it can provide insights into the potential risks and benefits of AI in healthcare and inform policy decisions.
• Finally, the thesis can also advance the field of computer science by developing new AI algorithms or techniques that can be applied to healthcare data, which can have broader applications in other industries or fields of research.

Significance of the Study in Research Proposal

The significance of a study in a research proposal refers to the importance or relevance of the research question, problem, or objective that the study aims to address. It explains why the research is valuable, relevant, and important to the academic or scientific community, policymakers, or society at large. A strong statement of significance can help to persuade the reviewers or funders of the research proposal that the study is worth funding and conducting.

Here is an example of a significance statement in a research proposal:

Title : The Effects of Gamification on Learning Programming: A Comparative Study

Significance Statement:

This proposed study aims to investigate the effects of gamification on learning programming. With the increasing demand for computer science professionals, programming has become a fundamental skill in the computer field. However, learning programming can be challenging, and students may struggle with motivation and engagement. Gamification has emerged as a promising approach to improve students’ engagement and motivation in learning, but its effects on programming education are not yet fully understood. This study is significant because it can provide valuable insights into the potential benefits of gamification in programming education and inform the development of effective teaching strategies to enhance students’ learning outcomes and interest in programming.

Examples of Significance of the Study

Here are some examples of the significance of a study that indicates how you can write this into your research paper according to your research topic:

Research on an Improved Water Filtration System : This study has the potential to impact millions of people living in water-scarce regions or those with limited access to clean water. A more efficient and affordable water filtration system can reduce water-borne diseases and improve the overall health of communities, enabling them to lead healthier, more productive lives.

Study on the Impact of Remote Work on Employee Productivity : Given the shift towards remote work due to recent events such as the COVID-19 pandemic, this study is of considerable significance. Findings could help organizations better structure their remote work policies and offer insights on how to maximize employee productivity, wellbeing, and job satisfaction.

Investigation into the Use of Solar Power in Developing Countries : With the world increasingly moving towards renewable energy, this study could provide important data on the feasibility and benefits of implementing solar power solutions in developing countries. This could potentially stimulate economic growth, reduce reliance on non-renewable resources, and contribute to global efforts to combat climate change.

Research on New Learning Strategies in Special Education : This study has the potential to greatly impact the field of special education. By understanding the effectiveness of new learning strategies, educators can improve their curriculum to provide better support for students with learning disabilities, fostering their academic growth and social development.

Examination of Mental Health Support in the Workplace : This study could highlight the impact of mental health initiatives on employee wellbeing and productivity. It could influence organizational policies across industries, promoting the implementation of mental health programs in the workplace, ultimately leading to healthier work environments.

Evaluation of a New Cancer Treatment Method : The significance of this study could be lifesaving. The research could lead to the development of more effective cancer treatments, increasing the survival rate and quality of life for patients worldwide.

When to Write Significance of the Study

The Significance of the Study section is an integral part of a research proposal or a thesis. This section is typically written after the introduction and the literature review. In the research process, the structure typically follows this order:

• Title – The name of your research.
• Abstract – A brief summary of the entire research.
• Introduction – A presentation of the problem your research aims to solve.
• Literature Review – A review of existing research on the topic to establish what is already known and where gaps exist.
• Significance of the Study – An explanation of why the research matters and its potential impact.

In the Significance of the Study section, you will discuss why your study is important, who it benefits, and how it adds to existing knowledge or practice in your field. This section is your opportunity to convince readers, and potentially funders or supervisors, that your research is valuable and worth undertaking.

Advantages of Significance of the Study

The Significance of the Study section in a research paper has multiple advantages:

• Establishes Relevance: This section helps to articulate the importance of your research to your field of study, as well as the wider society, by explicitly stating its relevance. This makes it easier for other researchers, funders, and policymakers to understand why your work is necessary and worth supporting.
• Guides the Research: Writing the significance can help you refine your research questions and objectives. This happens as you critically think about why your research is important and how it contributes to your field.
• Attracts Funding: If you are seeking funding or support for your research, having a well-written significance of the study section can be key. It helps to convince potential funders of the value of your work.
• Opens up Further Research: By stating the significance of the study, you’re also indicating what further research could be carried out in the future, based on your work. This helps to pave the way for future studies and demonstrates that your research is a valuable addition to the field.
• Provides Practical Applications: The significance of the study section often outlines how the research can be applied in real-world situations. This can be particularly important in applied sciences, where the practical implications of research are crucial.
• Enhances Understanding: This section can help readers understand how your study fits into the broader context of your field, adding value to the existing literature and contributing new knowledge or insights.

Limitations of Significance of the Study

The Significance of the Study section plays an essential role in any research. However, it is not without potential limitations. Here are some that you should be aware of:

• Subjectivity: The importance and implications of a study can be subjective and may vary from person to person. What one researcher considers significant might be seen as less critical by others. The assessment of significance often depends on personal judgement, biases, and perspectives.
• Predictability of Impact: While you can outline the potential implications of your research in the Significance of the Study section, the actual impact can be unpredictable. Research doesn’t always yield the expected results or have the predicted impact on the field or society.
• Difficulty in Measuring: The significance of a study is often qualitative and can be challenging to measure or quantify. You can explain how you think your research will contribute to your field or society, but measuring these outcomes can be complex.
• Possibility of Overstatement: Researchers may feel pressured to amplify the potential significance of their study to attract funding or interest. This can lead to overstating the potential benefits or implications, which can harm the credibility of the study if these results are not achieved.
• Overshadowing of Limitations: Sometimes, the significance of the study may overshadow the limitations of the research. It is important to balance the potential significance with a thorough discussion of the study’s limitations.
• Dependence on Successful Implementation: The significance of the study relies on the successful implementation of the research. If the research process has flaws or unexpected issues arise, the anticipated significance might not be realized.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How To Write Significance of the Study (With Examples) 

Whether you’re writing a research paper or thesis, a portion called Significance of the Study ensures your readers understand the impact of your work. Learn how to effectively write this vital part of your research paper or thesis through our detailed steps, guidelines, and examples.

Related: How to Write a Concept Paper for Academic Research

Table of Contents

What is the significance of the study.

The Significance of the Study presents the importance of your research. It allows you to prove the study’s impact on your field of research, the new knowledge it contributes, and the people who will benefit from it.

Related: How To Write Scope and Delimitation of a Research Paper (With Examples)

Where Should I Put the Significance of the Study?

The Significance of the Study is part of the first chapter or the Introduction. It comes after the research’s rationale, problem statement, and hypothesis.

Related: How to Make Conceptual Framework (with Examples and Templates)

Why Should I Include the Significance of the Study?

The purpose of the Significance of the Study is to give you space to explain to your readers how exactly your research will be contributing to the literature of the field you are studying 1 . It’s where you explain why your research is worth conducting and its significance to the community, the people, and various institutions.

How To Write Significance of the Study: 5 Steps

Below are the steps and guidelines for writing your research’s Significance of the Study.

1. Use Your Research Problem as a Starting Point

Your problem statement can provide clues to your research study’s outcome and who will benefit from it 2 .

Ask yourself, “How will the answers to my research problem be beneficial?”. In this manner, you will know how valuable it is to conduct your study. 

Let’s say your research problem is “What is the level of effectiveness of the lemongrass (Cymbopogon citratus) in lowering the blood glucose level of Swiss mice (Mus musculus)?”

Discovering a positive correlation between the use of lemongrass and lower blood glucose level may lead to the following results:

• Increased public understanding of the plant’s medical properties;
• Higher appreciation of the importance of lemongrass  by the community;
• Adoption of lemongrass tea as a cheap, readily available, and natural remedy to lower their blood glucose level.

Once you’ve zeroed in on the general benefits of your study, it’s time to break it down into specific beneficiaries.

2. State How Your Research Will Contribute to the Existing Literature in the Field

Think of the things that were not explored by previous studies. Then, write how your research tackles those unexplored areas. Through this, you can convince your readers that you are studying something new and adding value to the field.

3. Explain How Your Research Will Benefit Society

In this part, tell how your research will impact society. Think of how the results of your study will change something in your community. 

For example, in the study about using lemongrass tea to lower blood glucose levels, you may indicate that through your research, the community will realize the significance of lemongrass and other herbal plants. As a result, the community will be encouraged to promote the cultivation and use of medicinal plants.

4. Mention the Specific Persons or Institutions Who Will Benefit From Your Study

Using the same example above, you may indicate that this research’s results will benefit those seeking an alternative supplement to prevent high blood glucose levels.

5. Indicate How Your Study May Help Future Studies in the Field

You must also specifically indicate how your research will be part of the literature of your field and how it will benefit future researchers. In our example above, you may indicate that through the data and analysis your research will provide, future researchers may explore other capabilities of herbal plants in preventing different diseases.

Tips and Warnings

• Think ahead . By visualizing your study in its complete form, it will be easier for you to connect the dots and identify the beneficiaries of your research.
• Write concisely. Make it straightforward, clear, and easy to understand so that the readers will appreciate the benefits of your research. Avoid making it too long and wordy.
• Go from general to specific . Like an inverted pyramid, you start from above by discussing the general contribution of your study and become more specific as you go along. For instance, if your research is about the effect of remote learning setup on the mental health of college students of a specific university , you may start by discussing the benefits of the research to society, to the educational institution, to the learning facilitators, and finally, to the students.
• Seek help . For example, you may ask your research adviser for insights on how your research may contribute to the existing literature. If you ask the right questions, your research adviser can point you in the right direction.
• Revise, revise, revise. Be ready to apply necessary changes to your research on the fly. Unexpected things require adaptability, whether it’s the respondents or variables involved in your study. There’s always room for improvement, so never assume your work is done until you have reached the finish line.

Significance of the Study Examples

This section presents examples of the Significance of the Study using the steps and guidelines presented above.

Example 1: STEM-Related Research

Research Topic: Level of Effectiveness of the Lemongrass ( Cymbopogon citratus ) Tea in Lowering the Blood Glucose Level of Swiss Mice ( Mus musculus ).

Significance of the Study .

This research will provide new insights into the medicinal benefit of lemongrass ( Cymbopogon citratus ), specifically on its hypoglycemic ability.

Through this research, the community will further realize promoting medicinal plants, especially lemongrass, as a preventive measure against various diseases. People and medical institutions may also consider lemongrass tea as an alternative supplement against hyperglycemia. 

Moreover, the analysis presented in this study will convey valuable information for future research exploring the medicinal benefits of lemongrass and other medicinal plants.  

Example 2: Business and Management-Related Research

Research Topic: A Comparative Analysis of Traditional and Social Media Marketing of Small Clothing Enterprises.

Significance of the Study:

By comparing the two marketing strategies presented by this research, there will be an expansion on the current understanding of the firms on these marketing strategies in terms of cost, acceptability, and sustainability. This study presents these marketing strategies for small clothing enterprises, giving them insights into which method is more appropriate and valuable for them. 

Specifically, this research will benefit start-up clothing enterprises in deciding which marketing strategy they should employ. Long-time clothing enterprises may also consider the result of this research to review their current marketing strategy.

Furthermore, a detailed presentation on the comparison of the marketing strategies involved in this research may serve as a tool for further studies to innovate the current method employed in the clothing Industry.

Example 3: Social Science -Related Research.

Research Topic:  Divide Et Impera : An Overview of How the Divide-and-Conquer Strategy Prevailed on Philippine Political History.

Significance of the Study :

Through the comprehensive exploration of this study on Philippine political history, the influence of the Divide et Impera, or political decentralization, on the political discernment across the history of the Philippines will be unraveled, emphasized, and scrutinized. Moreover, this research will elucidate how this principle prevailed until the current political theatre of the Philippines.

In this regard, this study will give awareness to society on how this principle might affect the current political context. Moreover, through the analysis made by this study, political entities and institutions will have a new approach to how to deal with this principle by learning about its influence in the past.

In addition, the overview presented in this research will push for new paradigms, which will be helpful for future discussion of the Divide et Impera principle and may lead to a more in-depth analysis.

Example 4: Humanities-Related Research

Research Topic: Effectiveness of Meditation on Reducing the Anxiety Levels of College Students.

Significance of the Study: 

This research will provide new perspectives in approaching anxiety issues of college students through meditation. 

Specifically, this research will benefit the following:

 Community – this study spreads awareness on recognizing anxiety as a mental health concern and how meditation can be a valuable approach to alleviating it.

Academic Institutions and Administrators – through this research, educational institutions and administrators may promote programs and advocacies regarding meditation to help students deal with their anxiety issues.

Mental health advocates – the result of this research will provide valuable information for the advocates to further their campaign on spreading awareness on dealing with various mental health issues, including anxiety, and how to stop stigmatizing those with mental health disorders.

Parents – this research may convince parents to consider programs involving meditation that may help the students deal with their anxiety issues.

Students will benefit directly from this research as its findings may encourage them to consider meditation to lower anxiety levels.

Future researchers – this study covers information involving meditation as an approach to reducing anxiety levels. Thus, the result of this study can be used for future discussions on the capabilities of meditation in alleviating other mental health concerns.

Frequently Asked Questions

1. what is the difference between the significance of the study and the rationale of the study.

Both aim to justify the conduct of the research. However, the Significance of the Study focuses on the specific benefits of your research in the field, society, and various people and institutions. On the other hand, the Rationale of the Study gives context on why the researcher initiated the conduct of the study.

Let’s take the research about the Effectiveness of Meditation in Reducing Anxiety Levels of College Students as an example. Suppose you are writing about the Significance of the Study. In that case, you must explain how your research will help society, the academic institution, and students deal with anxiety issues through meditation. Meanwhile, for the Rationale of the Study, you may state that due to the prevalence of anxiety attacks among college students, you’ve decided to make it the focal point of your research work.

2. What is the difference between Justification and the Significance of the Study?

In Justification, you express the logical reasoning behind the conduct of the study. On the other hand, the Significance of the Study aims to present to your readers the specific benefits your research will contribute to the field you are studying, community, people, and institutions.

Suppose again that your research is about the Effectiveness of Meditation in Reducing the Anxiety Levels of College Students. Suppose you are writing the Significance of the Study. In that case, you may state that your research will provide new insights and evidence regarding meditation’s ability to reduce college students’ anxiety levels. Meanwhile, you may note in the Justification that studies are saying how people used meditation in dealing with their mental health concerns. You may also indicate how meditation is a feasible approach to managing anxiety using the analysis presented by previous literature.

3. How should I start my research’s Significance of the Study section?

– This research will contribute… – The findings of this research… – This study aims to… – This study will provide… – Through the analysis presented in this study… – This study will benefit…

Moreover, you may start the Significance of the Study by elaborating on the contribution of your research in the field you are studying.

4. What is the difference between the Purpose of the Study and the Significance of the Study?

The Purpose of the Study focuses on why your research was conducted, while the Significance of the Study tells how the results of your research will benefit anyone.

Suppose your research is about the Effectiveness of Lemongrass Tea in Lowering the Blood Glucose Level of Swiss Mice . You may include in your Significance of the Study that the research results will provide new information and analysis on the medical ability of lemongrass to solve hyperglycemia. Meanwhile, you may include in your Purpose of the Study that your research wants to provide a cheaper and natural way to lower blood glucose levels since commercial supplements are expensive.

5. What is the Significance of the Study in Tagalog?

In Filipino research, the Significance of the Study is referred to as Kahalagahan ng Pag-aaral.

• Draft your Significance of the Study. Retrieved 18 April 2021, from http://dissertationedd.usc.edu/draft-your-significance-of-the-study.html
• Regoniel, P. (2015). Two Tips on How to Write the Significance of the Study. Retrieved 18 April 2021, from https://simplyeducate.me/2015/02/09/significance-of-the-study/

Written by Jewel Kyle Fabula

in Career and Education , Juander How

Jewel Kyle Fabula

Jewel Kyle Fabula is a Bachelor of Science in Economics student at the University of the Philippines Diliman. His passion for learning mathematics developed as he competed in some mathematics competitions during his Junior High School years. He loves cats, playing video games, and listening to music.

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Significance of a Study: Revisiting the “So What” Question

• Open Access
• First Online: 03 December 2022

Cite this chapter

You have full access to this open access chapter

• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  

Part of the book series: Research in Mathematics Education ((RME))

18k Accesses

Every researcher wants their study to matter—to make a positive difference for their professional communities. To ensure your study matters, you can formulate clear hypotheses and choose methods that will test them well, as described in Chaps. 1, 2, 3 and 4. You can go further, however, by considering some of the terms commonly used to describe the importance of studies, terms like significance, contributions, and implications. As you clarify for yourself the meanings of these terms, you learn that whether your study matters depends on how convincingly you can argue for its importance. Perhaps most surprising is that convincing others of its importance rests with the case you make before the data are ever gathered. The importance of your hypotheses should be apparent before you test them. Are your predictions about things the profession cares about? Can you make them with a striking degree of precision? Are the rationales that support them compelling? You are answering the “So what?” question as you formulate hypotheses and design tests of them. This means you can control the answer. You do not need to cross your fingers and hope as you collect data.

You have full access to this open access chapter,  Download chapter PDF

Part I. Setting the Groundwork

One of the most common questions asked of researchers is “So what?” What difference does your study make? Why are the findings important? The “so what” question is one of the most basic questions, often perceived by novice researchers as the most difficult question to answer. Indeed, addressing the “so what” question continues to challenge even experienced researchers. It is not always easy to articulate a convincing argument for the importance of your work. It can be especially difficult to describe its importance without falling into the trap of making claims that reach beyond the data.

That this issue is a challenge for researchers is illustrated by our analysis of reviewer comments for JRME . About one-third of the reviews for manuscripts that were ultimately rejected included concerns about the importance of the study. Said another way, reviewers felt the “So what?” question had not been answered. To paraphrase one journal reviewer, “The manuscript left me unsure of what the contribution of this work to the field’s knowledge is, and therefore I doubt its significance.” We expect this is a frequent concern of reviewers for all research journals.

Our goal in this chapter is to help you navigate the pressing demands of journal reviewers, editors, and readers for demonstrating the importance of your work while staying within the bounds of acceptable claims based on your results. We will begin by reviewing what we have said about these issues in previous chapters. We will then clarify one of the confusing aspects of developing appropriate arguments—the absence of consensus definitions of key terms such as significance, contributions, and implications. Based on the definitions we propose, we will examine the critical role of alignment for realizing the potential significance of your study. Because the importance of your study is communicated through your evolving research paper, we will fold suggestions for writing your paper into the discussion of creating and executing your study.

We laid the groundwork in Chap. 1 for what we consider to be important research in education:

In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to support the improvement of learning opportunities for all students…. If there is no way to imagine a connection to improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Of course, you might prefer another “ultimate purpose” for research in education. That’s fine. The critical point is that the argument for the importance of the hypotheses you are testing should be connected to the value of a long-term goal you can describe. As long as most of the educational community agrees with this goal, and you can show how testing your hypotheses will move the field forward to achieving this goal, you will have developed a convincing argument for the importance of your work.

In Chap. 2 , we argued the importance of your hypotheses can and should be established before you collect data. Your theoretical framework should carry the weight of your argument because it should describe how your hypotheses will extend what is already known. Your methods should then show that you will test your hypotheses in an appropriate way—in a way that will allow you to detect how the results did, and did not, confirm the hypotheses. This will, in turn, allow you to formulate revised hypotheses. We described establishing the importance of your study by saying, “The importance can come from the fact that, based on the results, you will be able to offer revised hypotheses that help the field better understand an issue relevant for improving all students’ learning opportunities.”

The ideas from Chaps. 1 , 2 , and 3 go a long way toward setting the parameters for what counts as an important study and how its importance can be determined. Chapter 4 focused on ensuring that the importance of a study can be realized. The next section fills in the details by proposing definitions for the most common terms used to claim importance: significance, contributions, and implications.

You might notice that we do not have a chapter dedicated to discussing the presentation of the findings—that is, a “results” chapter. We do not mean to imply that presenting results is trivial. However, we believe that if you follow our recommendations for writing your evolving research paper, presenting the results will be quite straightforward. The key is to present your results so they can be most easily compared with your predictions. This means, among other things, organizing your presentation of results according to your earlier presentation of hypotheses.

Part II. Clarifying Importance by Revisiting the Definitions of Key Terms

What does it mean to say your findings are significant? Statistical significance is clear. There are widely accepted standards for determining the statistical significance of findings. But what about educational significance? Is this the same as claiming that your study makes an important contribution? Or, that your study has important implications? Different researchers might answer these questions in different ways. When key terms like these are overused, their definitions gradually broaden or shift, and they can lose their meaning. That is unfortunate, because it creates confusion about how to develop claims for the importance of a study.

By clarifying the definitions, we hope to clarify what is required to claim that a study is significant , that it makes a contribution , and that it has important implications . Not everyone defines the terms as we do. Our definitions are probably a bit narrower or more targeted than those you may encounter elsewhere. Depending on where you want to publish your study, you may want to adapt your use of these terms to match more closely the expectations of a particular journal. But the way we define and address these terms is not antithetical to common uses. And we believe ridding the terms of unnecessary overlap allows us to discriminate among different key concepts with respect to claims for the importance of research studies. It is not necessary to define the terms exactly as we have, but it is critical that the ideas embedded in our definitions be distinguished and that all of them be taken into account when examining the importance of a study.

We will use the following definitions:

Significance: The importance of the problem, questions, and/or hypotheses for improving the learning opportunities for all students (you can substitute a different long-term goal if its value is widely shared). Significance can be determined before data are gathered. Significance is an attribute of the research problem , not the research findings .

Contributions : The value of the findings for revising the hypotheses, making clear what has been learned, what is now better understood.

Implications : Deductions about what can be concluded from the findings that are not already included in “contributions.” The most common deductions in educational research are for improving educational practice. Deductions for research practice that are not already defined as contributions are often suggestions about research methods that are especially useful or methods to avoid.

Significance

The significance of a study is built by formulating research questions and hypotheses you connect through a careful argument to a long-term goal of widely shared value (e.g., improving learning opportunities for all students). Significance applies both to the domain in which your study is located and to your individual study. The significance of the domain is established by choosing a goal of widely shared value and then identifying a domain you can show is connected to achieving the goal. For example, if the goal to which your study contributes is improving the learning opportunities for all students, your study might aim to understand more fully how things work in a domain such as teaching for conceptual understanding, or preparing teachers to attend to all students, or designing curricula to support all learners, or connecting learning opportunities to particular learning outcomes.

The significance of your individual study is something you build ; it is not predetermined or self-evident. Significance of a study is established by making a case for it, not by simply choosing hypotheses everyone already thinks are important. Although you might believe the significance of your study is obvious, readers will need to be convinced.

Significance is something you develop in your evolving research paper. The theoretical framework you present connects your study to what has been investigated previously. Your argument for significance of the domain comes from the significance of the line of research of which your study is a part. The significance of your study is developed by showing, through the presentation of your framework, how your study advances this line of research. This means the lion’s share of your answer to the “So what?” question will be developed as part of your theoretical framework.

Although defining significance as located in your paper prior to presenting results is not a definition universally shared among educational researchers, it is becoming an increasingly common view. In fact, there is movement toward evaluating the significance of a study based only on the first sections of a research paper—the sections prior to the results (Makel et al., 2021 ).

In addition to addressing the “So what?” question, your theoretical framework can address another common concern often voiced by readers: “What is so interesting? I could have predicted those results.” Predictions do not need to be surprising to be interesting and significant. The significance comes from the rationales that show how the predictions extend what is currently known. It is irrelevant how many researchers could have made the predictions. What makes a study significant is that the theoretical framework and the predictions make clear how the study will increase the field’s understanding toward achieving a goal of shared value.

An important consequence of interpreting significance as a carefully developed argument for the importance of your research study within a larger domain is that it reveals the advantage of conducting a series of connected studies rather than single, disconnected studies. Building the significance of a research study requires time and effort. Once you have established significance for a particular study, you can build on this same argument for related studies. This saves time, allows you to continue to refine your argument across studies, and increases the likelihood your studies will contribute to the field.

Contributions

As we have noted, in fields as complicated as education, it is unlikely that your predictions will be entirely accurate. If the problem you are investigating is significant, the hypotheses will be formulated in such a way that they extend a line of research to understand more deeply phenomena related to students’ learning opportunities or another goal of shared value. Often, this means investigating the conditions under which phenomena occur. This gets complicated very quickly, so the data you gather will likely differ from your predictions in a variety of ways. The contributions your study makes will depend on how you interpret these results in light of the original hypotheses.

Contributions Emerge from Revisions to your Hypotheses

We view interpreting results as a process of comparing the data with the predictions and then examining the way in which hypotheses should be revised to more fully account for the results. Revising will almost always be warranted because, as we noted, predictions are unlikely to be entirely accurate. For example, if researchers expect Outcome A to occur under specified conditions but find that it does not occur to the extent predicted or actually does occur but without all the conditions, they must ask what changes to the hypotheses are needed to predict more accurately the conditions under which Outcome A occurred. Are there, for example, essential conditions that were not anticipated and that should be included in the revised hypotheses?

Consider an example from a recently published study (Wang et al., 2021 ). A team of researchers investigated the following research question: “How are students’ perceptions of their parents’ expectations related to students’ mathematics-related beliefs and their perceived mathematics achievement?” The researchers predicted that students’ perceptions of their parents’ expectations would be highly related to students’ mathematics-related beliefs and their perceived mathematics achievement. The rationale was based largely on prior research that had consistently found parents’ general educational expectations to be highly correlated with students’ achievement.

The findings showed that Chinese high school students’ perceptions of their parents’ educational expectations were positively related to these students’ mathematics-related beliefs. In other words, students who believed their parents expected them to attain higher levels of education had more desirable mathematics-related beliefs.

However, students’ perceptions of their parents’ expectations about mathematics achievement were not related to students’ mathematics-related beliefs in the same way as the more general parental educational expectations. Students who reported that their parents had no specific expectations possessed more desirable mathematics-related beliefs than all other subgroups. In addition, these students tended to perceive their mathematics achievement rank in their class to be higher on average than students who reported that their parents expressed some level of expectation for mathematics achievement.

Because this finding was not predicted, the researchers revised the original hypothesis. Their new prediction was that students who believe their parents have no specific mathematics achievement expectations possess more positive mathematics-related beliefs and higher perceived mathematics achievement than students who believe their parents do have specific expectations. They developed a revised rationale that drew on research on parental pressure and mathematics anxiety, positing that parents’ specific mathematics achievement expectations might increase their children’s sense of pressure and anxiety, thus fostering less positive mathematics-related beliefs. The team then conducted a follow-up study. Their findings aligned more closely with the new predictions and affirmed the better explanatory power of the revised rationale. The contributions of the study are found in this increased explanatory power—in the new understandings of this phenomenon contained in the revisions to the rationale.

Interpreting findings in order to revise hypotheses is not a straightforward task. Usually, the rationales blend multiple constructs or variables and predict multiple outcomes, with different outcomes connected to different research questions and addressed by different sets of data. Nevertheless, the contributions of your study depend on specifying the differences between your original hypotheses and your revised hypotheses. What can you explain now that you could not explain before?

We believe that revising hypotheses is an optimal response to any question of contributions because a researcher’s initial hypotheses plus the revisions suggested by the data are the most productive way to tie a study into the larger chain of research of which it is a part. Revised hypotheses represent growth in knowledge. Building on other researchers’ revised hypotheses and revising them further by more explicitly and precisely describing the conditions that are expected to influence the outcomes in the next study accumulates knowledge in a form that can be recorded, shared, built upon, and improved.

The significance of your study is presented in the opening sections of your evolving research paper whereas the contributions are presented in the final section, after the results. In fact, the central focus in this “Discussion” section should be a specification of the contributions (note, though, that this guidance may not fully align with the requirements of some journals).

Contributions Answer the Question of Generalizability

A common and often contentious, confusing issue that can befuddle novice and experienced researchers alike is the generalizability of results. All researchers prefer to believe the results they report apply to more than the sample of participants in their study. How important would a study be if the results applied only to, say, two fourth-grade classrooms in one school, or to the exact same tasks used as measures? How do you decide to which larger population (of students or tasks) your results could generalize? How can you state your claims so they are precisely those justified by the data?

To illustrate the challenge faced by researchers in answering these questions, we return to the JRME reviewers. We found that 30% of the reviews expressed concerns about the match between the results and the claims. For manuscripts that ultimately received a decision of Reject, the majority of reviewers said the authors had overreached—the claims were not supported by the data. In other words, authors generalized their claims beyond those that could be justified.

The Connection Between Contributions and Generalizability

In our view, claims about contributions can be examined productively alongside considerations of generalizability. To make the case for this view, we need to back up a bit. Recall that the purpose of research is to understand a phenomenon. To understand a phenomenon, you need to determine the conditions under which it occurs. Consequently, productive hypotheses specify the conditions under which the predictions hold and explain why and how these conditions make a difference. And the conditions set the parameters on generalizability. They identify when, where, and for whom the effect or situation will occur. So, hypotheses describe the extent of expected generalizability, and revised hypotheses that contain the contributions recalibrate generalizability and offer new predictions within these parameters.

An Example That Illustrates the Connection

In Chap. 4 , we introduced an example with a research question asking whether second graders improve their understanding of place value after a specially designed instructional intervention. We suggested asking a few second and third graders to complete your tasks to see if they generated the expected variation in performance. Suppose you completed this pilot study and now have satisfactory tasks. What conditions might influence the effect of the intervention? After careful study, you developed rationales that supported three conditions: the entry level of students’ understanding, the way in which the intervention is implemented, and the classroom norms that set expectations for students’ participation.

Suppose your original hypotheses predicted the desired effect of the intervention only if the students possessed an understanding of several concepts on which place value is built, only if the intervention was implemented with fidelity to the detailed instructional guidelines, and only if classroom norms encouraged students to participate in small-group work and whole-class discussions. Your claims of generalizability will apply to second-grade settings with these characteristics.

Now suppose you designed the study so the intervention occurred in five second-grade classrooms that agreed to participate. The pre-intervention assessment showed all students with the minimal level of entry understanding. The same well-trained teacher was employed to teach the intervention in all five classrooms, none of which included her own students. And you learned from prior observations and reports of the classroom teachers that three of the classrooms operated with the desired classroom norms, but two did not. Because of these conditions, your study is now designed to test one of your hypotheses—the desired effect will occur only if classroom norms encouraged students to participate in small-group work and whole-class discussions. This is the only condition that will vary; the other two (prior level of understanding and fidelity of implementation) are the same across classrooms so you will not learn how these affect the results.

Suppose the classrooms performed equally well on the post-intervention assessments. You expected lower performance in the two classrooms with less student participation, so you need to revise your hypotheses. The challenge is to explain the higher-than-expected performance of these students. Because you were interested in understanding the effects of this condition, you observed several lessons in all the classrooms during the intervention. You can now use this information to explain why the intervention worked equally well in classrooms with different norms.

Your revised hypothesis captures this part of your study’s contribution. You can now say more about the ways in which the intervention can help students improve their understanding of place value because you have different information about the role of classroom norms. This, in turn, allows you to specify more precisely the nature and extent of the generalizability of your findings. You now can generalize your findings to classrooms with different norms. However, because you did not learn more about the impact of students’ entry level understandings or of different kinds of implementation, the generalizability along these dimensions remains as limited as before.

This example is simplified. In many studies, the findings will be more complicated, and more conditions will likely be identified, some of which were anticipated and some of which emerged while conducting the study and analyzing the data. Nevertheless, the point is that generalizability should be tied to the conditions that are expected to affect the results. Further, unanticipated conditions almost always appear, so generalizations should be conservative and made with caution and humility. They are likely to change after testing the new predictions.

Contributions Are Assured When Hypotheses Are Significant and Methods Are Appropriate and Aligned

We have argued that the contributions of your study are produced by the revised hypotheses you can formulate based on your results. Will these revisions always represent contributions to the field? What if the revisions are minor? What if your results do not inform revisions to your hypotheses?

We will answer these questions briefly now and then develop them further in Part IV of this chapter. The answer to the primary question is “yes,” your revisions will always be a contribution to the field if (1) your hypotheses are significant and (2) you crafted appropriate methods to test the hypotheses. This is true even if your revisions are minor or if your data are not as informative as you expected. However, this is true only if you meet the two conditions in the earlier sentence. The first condition (significant hypotheses) can be satisfied by following the suggestions in the earlier section on significance. The second condition (appropriate methods) is addressed further in Part III in this chapter.

Implications

Before examining the role of methods in connecting significance with important contributions, we elaborate briefly our definition of “implications.” We reserve implications for the conclusions you can logically deduce from your findings that are not already presented as contributions. This means that, like contributions, implications are presented in the Discussion section of your research paper.

Many educational researchers present two types of implications: implications for future research and implications for practice. Although we are aware of this common usage, we believe our definition of “contributions” cover these implications. Clarifying why we call these “contributions” will explain why we largely reserve the word “implications” for recommendations regarding methods.

Implications for Future Research

Implications for future research often include (1) recommendations for empirical studies that would extend the findings of this study, (2) inferences about the usefulness of theoretical constructs, and (3) conclusions about the advisability of using particular kinds of methods. Given our earlier definitions, we prefer to label the first two types of implications as contributions.

Consider recommendations for empirical studies. After analyzing the data and presenting the results, we have suggested you compare the results with those predicted, revise the rationales for the original predictions to account for the results, and make new predictions based on the revised rationales. It is precisely these new predictions that can form the basis for recommending future research. Testing these new predictions is what would most productively extend this line of research. It can sometimes sound as if researchers are recommending future studies based on hunches about what research might yield useful findings. But researchers can do better than this. It would be more productive to base recommendations on a careful analysis of how the predictions of the original study could be sharpened and improved.

Now consider inferences about the usefulness of theoretical constructs. Our argument for labeling these inferences as contributions is similar. Rationales for predictions are where the relevant theoretical constructs are located. Revisions to these rationales based on the differences between the results and the predictions reveal the theoretical constructs that were affirmed to support accurate predictions and those that must be revised. In our view, usefulness is determined through this revision process.

Implications that do not come under our meaning of contributions are in the third type of implications, namely the appropriateness of methods for generating rich contributions. These kinds of implications are produced by your evaluation of your methods: research design, sampling procedures, tasks, data collection procedures, and data analyses. Although not always included in the discussion of findings, we believe it would be helpful for researchers to identify particular methods that were useful for conducting their study and those that should be modified or avoided. We believe these are appropriately called implications.

Implications for Practice

If the purpose of research is to better understand how to improve learning opportunities for all students, then it is appropriate to consider whether implications for improving educational practice can be drawn from the results of a study. How are these implications formulated? This is an important question because, in our view, these claims often come across as an afterthought, “Oh, I need to add some implications for practice.” But here is the sobering reality facing researchers: By any measure, the history of educational research shows that identifying these implications has had little positive effect on practice.

Perhaps the most challenging task for researchers who attempt to draw implications for practice is to interpret their findings for appropriate settings. A researcher who studied the instructional intervention for second graders on place value and found that average performance in the intervention classrooms improved more than in the textbook classrooms might be tempted to draw implications for practice. What should the researcher say? That second-grade teachers should adopt the intervention? Such an implication would be an overreach because, as we noted earlier, the findings cannot be generalized to all second-grade classrooms. Moreover, an improvement in average performance does not mean the intervention was better for all students.

The challenge is to identify the conditions under which the intervention would improve the learning opportunities for all students. Some of these conditions will be identified as the theoretical framework is built because the predictions need to account for these conditions. But some will be unforeseen, and some that are identified will not be informed by the findings. Recall that, in the study described earlier, a condition of entry level of understanding was hypothesized but the design of the study did not allow the researcher to draw any conclusions about its effect.

What can researchers say about implications for practice given the complexities involved in generalizing findings to other settings? We offer two recommendations. First, because it is difficult to specify all the conditions under which a phenomenon occurs, it is rarely appropriate to prescribe an educational practice. Researchers cannot anticipate the conditions under which individual teachers operate, conditions that often require adaptation of a suggested practice rather than implementation of a practice as prescribed.

Our second recommendation comes from returning to the purpose for educational research—to understand more fully how to improve learning opportunities for all students (or to achieve another goal of widely shared value). As we have described, understanding comes primarily from building and reevaluating rationales for your predictions. If you reach a new understanding related to improving learning opportunities, an understanding that could have practical implications, we recommend you share this understanding as an implication for practice.

For example, suppose the researcher who found better average performance of second graders after the intervention on place value had also studied several conditions under which performance improved. And suppose the researcher found that most students who did not improve their performance misunderstood a concept that appeared early in the intervention (e.g., the multiplicative relationship between positional values of a numeral). An implication for practice the researcher might share would be to describe the potential importance of understanding this concept early in the sequence of activities if teachers try out this intervention.

If you use our definitions, these implications for practice would be presented as contributions because they emerge directly from reevaluating and revising your rationales. We believe it is appropriate to use “Contributions” as the heading for this section in the Discussion section of your research paper. However, if editors prefer “Implications” we recommend following their suggestion.

We want to be clear that the terms you use for the different ways your study is important is not critical. We chose to define the terms significance, contributions, and implications in very specific and not universally shared ways to distinguish all the meanings of importance you should consider. Some of these can be established through your theoretical framework, some by the revisions of your hypotheses, and some by reflecting on the value of particular methods. The important thing, from our point of view, is that the ideas we defined for each of these terms are distinguished and recognized as specific ways of determining the importance of your study.

Part III. The Role of Methods in Determining Contributions

We have argued that every part of the study (and of the evolving research paper) should be aligned. All parts should be connected through a coherent chain of reasoning. In this chapter, we argue that the chain of reasoning is not complete until the methods are presented and the results are interpreted and discussed. The methods of the study create a bridge that connects the introductory material (research questions, theoretical framework, literature review, hypotheses) with the results and interpretations.

The role that methods play in scientific inquiry is to ensure that your hypotheses will be tested appropriately so the significance of your study will yield its potential contributions. To do this, the methods must do more than follow the standard guidelines and be technically correct (see Chap. 4 ). They must also fit with the surrounding parts of the study. We call this coherence.

Coherence Across the Phases of Scientific Inquiry

Coherence means the parts of a whole are fully aligned. When doing scientific inquiry, the early parts or phases should motivate the later phases. The methods you use should be motivated or explained by the earlier phases (e.g., research questions, theoretical framework, hypotheses). Your methods, in turn, should produce results that can be interpreted by comparing them with your predictions. Methods are aligned with earlier phases when you can use the rationales contained in your hypotheses to decide what kinds of data are needed to test your predictions, how best to gather these kinds of data, and what analyses should be performed (see Chap. 4 and Cai et al., 2019a ).

For a visual representation of this coherence, see Fig. 5.1 . Each box identifies an aspect of scientific inquiry. Hypotheses (shown in Box 1) include the rationales and predictions. Because the rationales encompass the theoretical framework and the literature review, Box 1 establishes the significance of the study. Box 2 represents the methods, which we defined in Chap. 4 as the entire set of procedures you will use, including the basic design, measures for collecting data, and analytic approaches. In Fig. 5.1 , the hypothesis in Box 1 points you to the methods you will use. That is, you will choose methods that provide data for analyses that will generate results or findings (Box 3) that allow you to make comparisons against your predictions. Based on those comparisons, you will revise your hypotheses and derive the contributions and implications of your study (Box 4).

The Chain of Coherence That Runs Through All Parts of a Research Study

We intend Fig. 5.1 to carry several messages. One is that coherence of a study and the associated research paper require all aspects of the study to flow from one into the other. Each set of prior entries must motivate and justify the next one. For example, the data and analyses you intend to gather and use in Box 2 (Methods) must be those that are motivated and explained by the research question and hypothesis (prediction and rationale) in Box 1.

A second message in the figure is that coherence includes Box 4, “Discussion.” Aligned with the first three boxes, the fourth box flows from these boxes but is also constrained by them. The contributions and implications authors describe in the Discussion section of the paper cannot go beyond what is allowed by the original hypotheses and the revisions to these hypotheses indicated by the findings.

Methods Enable Significance to Yield Contributions

We begin this section by identifying a third message conveyed in Fig. 5.1 . The methods of the study, represented by Box 2, provide a bridge that connects the significance of the study (Box 1) with the contributions of the study (Box 4). The results (Box 3) indicate the nature of the contributions by determining the revisions to the original hypotheses.

In our view, the connecting role played by the methods is often underappreciated. Crafting appropriate methods aligned with the significance of the study, on one hand, and the interpretations, on the other, can determine whether a study is judged to make a contribution.

If the hypotheses are established as significant, and if appropriate methods are used to test the predictions, the study will make important contributions even if the data are not statistically significant. We can say this another way. When researchers establish the significance of the hypotheses (i.e., convince readers they are of interest to the field) and use methods that provide a sound test of these hypotheses, the data they present will be of interest regardless of how they turn out. This is why Makel et al. ( 2021 ) endorse a review process for publication that emphasizes the significance of the study as presented in the first sections of a research paper.

Treating the methods as connecting the introductory arguments to the interpretations of data prevent researchers from making a common mistake: When writing the research paper, some researchers lose track of the research questions and/or the predictions. In other words, results are presented but are not interpreted as answers to the research questions or compared with the predictions. It is as if the introductory material of the paper begins one story, and the interpretations of results ends a different story. Lack of alignment makes it impossible to tell one coherent story.

A final point is that the alignment of a study cannot be evaluated and appreciated if the methods are not fully described. Methods must be described clearly and completely in the research paper so readers can see how they flow from the earlier phases of the study and how they yield the data presented. We suggested in Chap. 4 a rule of thumb for deciding whether the methods have been fully described: “Readers should be able to replicate the study if they wish.”

Part IV. Special Considerations that Affect a Study’s Contributions

We conclude Chap. 5 by addressing two additional issues that can affect how researchers interpret the results and make claims about the contributions of a study. Usually, researchers deal with these issues in the Discussion section of their research paper, but we believe it is useful to consider them as you plan and conduct your study. The issues can be posed as questions: How should I treat the limitations of my study? How should I deal with findings that are completely unexpected?

Limitations of a Study

We can identify two kinds of limitations: (1) limitations that constrain your ability to interpret your results because of unfortunate choices you made, and (2) limitations that constrain your ability to generalize your results because of missing variables you could not fit into the scope of your study or did not anticipate. We recommend different ways of dealing with these.

Limitations Due to Unfortunate Choices

All researchers make unfortunate choices. These are mistakes that could have been prevented. Often, they are choices in how a study was designed and/or executed. Maybe the sample did not have the characteristics assumed, or a task did not assess what was expected, or the intervention was not implemented as planned. Although many unfortunate choices can be prevented by thinking through the consequences of every decision or by conducting a well-designed pilot study or two, some will occur anyway. How should you deal with them?

The consequence of unfortunate choices is that the data do not test the hypotheses as precisely or completely as hoped. When this happens, the data must be interpreted with these constraints in mind. Almost always, this limits the researcher to making fewer or narrower claims than desired about differences and similarities between the results and the predictions. Usually this means conclusions about the ways in which the rationales must be revised require extra qualifications. In other words, claims about contributions of the study must be made with extra caution.

Research papers frequently include a subsection in the Discussion called “Limitations of the Study.” Researchers often use this subsection to identify the study’s limitations by describing the unfortunate choices, but they do not always spell out how these limitations should affect the contributions of the paper. Sometimes, it appears that researchers are simply checking off a requirement to identify the limitations by saying something like “The results should be interpreted with caution.” But this does not help readers understand exactly what cautions should be applied and it does not hold researchers accountable for the limitations.

We recommend something different. We suggest you do the hard work of figuring out how the data should be interpreted in light of the limitations and share these details with the readers. You might do this when the results are presented or when you interpret them. Rather than presenting your claims about the contributions of the study and then saying readers should interpret these with “caution” because of the study’s limitations, we suggest presenting only those interpretations and claims of contributions that can be made with the limitations in mind.

One way to think about the constraints you will likely need to impose on your interpretations is in terms of generalizability. Recall that earlier in this chapter, we described the close relationship between contributions and generalizability. When generalizability is restricted, so are contributions.

Limitations Due to Missing Variables

Because of the complexity of problems, questions, and hypotheses explored in educational research, researchers are unlikely to anticipate in their studies all the variables that affect the data and results. In addition, tradeoffs often must be made. Researchers cannot study everything at once, so decisions must be made about which variables to study carefully and which to either control or ignore.

In the earlier example of studying whether second graders improve their understanding of place value after a specially designed instructional intervention, the researcher identified three variables that were expected to influence the effect of the intervention: students’ entry level of understanding, implementation of the intervention, and norms of the classrooms in which the intervention was implemented. The researcher decided to control the implementation variable by hiring one experienced teacher to implement the intervention in all the classrooms. This meant the variable of individual teacher differences was not included in the study and the researcher could not generalize to classrooms with these differences.

Some researchers might see controlling the implementation of the intervention as a limitation. We do not. As a factor that is not allowed to vary, it constrains the generalizations a researcher can make, but we believe these kinds of controlled variables are better treated as opportunities for future research. Perhaps the researcher’s observations in the classroom provided information that could be used to make some predictions about which elements of the intervention are essential and which are optional—about which aspects of the intervention must be implemented as written and which can vary with different teachers. When revising the rationales to show what was learned in this study, the researcher could include rationales for new, tentative predictions about the effects of the intervention in classrooms where implementation differed in specified ways. These predictions create a genuine contribution of the study. If you use our definitions, these new predictions, often presented under “implications for future research,” would be presented as “contributions.”

Notice that if you follow our advice, you would not need to include a separate section in the Discussion of your paper labeled “Limitations.” We acknowledge, however, that some journal editors recommend such a subsection. In this case, we suggest you include this subsection along with treating the two different kinds of limitations as we recommend. You can do both.

Dealing with Unexpected Findings

Researchers are often faced with unexpected and perhaps surprising results, even when they have developed a convincing theoretical framework, posed research questions tightly connected to this framework, presented predictions about expected outcomes, and selected methods that appropriately test these predictions. Indeed, the unexpected findings can be the most interesting and valuable products of the study. They can range from mildly surprising to “Wow. I didn’t expect that.” How should researchers treat such findings? Our answer is based on two principles.

The first principle is that the value of research does not lie in whether the predictions are completely accurate but in helping the field learn more about the explanatory power of theoretical frameworks. That is, the value lies in the increased understanding of phenomena generated by examining the ability of theoretical frameworks (or rationales) to predict outcomes and explain results. The second principle, a corollary to the first, is to treat unexpected findings in a way that is most educative for the reader.

Based on our arguments to this point, you could guess we will say there will always be unexpected findings. Predicted answers to significant research questions in education will rarely, if ever, be entirely accurate. So, you can count on dealing with unexpected findings.

Consistent with the two principles above, your goal should be to use unexpected findings to understand more fully the phenomenon under investigation. We recommend one of three different paths. The choice of which path to take depends on what you decide after reflecting again on the decisions you made at each phase of the study.

The first path is appropriate when researchers reexamine their theoretical framework in light of the unexpected findings and decide that it is still a compelling framework based on previous work. They reason that readers are likely to have been convinced by this framework and would likely have made similar predictions. In this case, we believe that it is educative for researchers to (a) summarize their initial framework, (b) present the findings and distinguish those that were aligned with the predictions from those that were not, and (c) explain why the theoretical framework was inadequate and propose changes to the framework that would have created more alignment with the unexpected findings.

Revisions to initial hypotheses are especially useful if they include explanations for why a researcher might have been wrong (and researchers who ask significant questions in domains as complex as education are almost always wrong in some way). Depending on the ways in which the revised framework differs from the original, the authors have two options. If the revised framework is an expansion of the original, it would be appropriate for the authors to propose directions for future research that would extend this study. Alternatively, if the revised framework is still largely within the scope of the original study and consists of revisions to the original hypotheses, the revisions could guide a second study to check the adequacy of the revisions. This second study could be conducted by the same researchers (perhaps before the final manuscript is written and presented as two parts of the same report) or it could be proposed in the Discussion as a specific study that could be conducted by other researchers.

The second path is appropriate when researchers reexamine their theoretical framework in light of the unexpected findings and recognize serious flaws in the framework. The flaws could result from a number of factors, including defining elements of the framework in too general a way to formulate well-grounded hypotheses, failing to include a variable, or not accounting carefully enough for the previous work in this domain, both theoretical and empirical. In many of these cases, readers would not be well served by reading a poorly developed framework and then learning that the framework, which had not been convincing, did not accurately predict the results. Before scrapping the study and starting over, we suggest stepping back and reexamining the framework. Is it possible to develop a more coherent, complete, and convincing framework? Would this framework predict the results more accurately? If the findings remain unexpected based on the predictions generated by this revised, more compelling framework, then the first path applies.

It is likely that the new framework will better predict the findings. After all, the researchers now know the findings they will report. However, it is unlikely that the framework will accurately predict all the findings. This is because the framework is not built around the findings of this study of which authors are now aware (but have not yet been presented). Frameworks are built on research and theory already published. This means the redesigned framework is built from exactly the same empirical findings and theoretical arguments available before the study was conducted. The redesigned framework also is constrained by needing to justify exactly those methods used in the study. The redesigned framework cannot justify different methods or even slightly altered methods. The task for researchers is to show how the new theoretical framework necessarily generates, using the same methods, the predictions they present in the research paper. Just as before, it is unlikely this framework can account for all the findings. Just as before, after presenting the results the researchers should explain why they believe particular hypotheses were confirmed and why others should be revised, even in small ways, based on the findings reported. Researchers can now use these findings to revise the hypotheses presented in the paper. The point we are making is that we believe it is acceptable to reconstruct frameworks before writing research reports if doing so would be more educative for the reader.

Finally, the third path becomes appropriate when researchers, in reexamining their theoretical framework, trace the problem to a misalignment between the methods they used and the theoretical framework or the research questions. Perhaps the researchers recognize that the tasks they used did not yield data that could test the predictions and address the research questions. Or perhaps the researchers realize that the sample they selected would likely have been heavily influenced by a factor they failed to take into account. In other words, the researchers decide that the unexpected findings were due to a problem with the methods they used, not with the framework or the accompanying predictions. In this case, we recommend that the researchers correct the methodological problems and conduct the study again.

Part V. A Few Suggestions for Structuring Your Discussion Section

Writing the Discussion section of your research paper can be overwhelming given all our suggestions about what to include in this section. Here are a few tips that might help you create a simple template for this section.

We recommend the Discussion begin with a brief summary of the main results, especially those you will interpret in this section. This summary should not contain new data or results not previously presented in the paper.

The Discussion could then move to presenting the contributions in the ways we have described. To do this you could point out the ways in which the results differed from the predictions and suggest revisions to your rationales that would have better predicted the results. Doing this will show how the contributions of your study extend what is known beyond the research you drew on to build your original rationale. You can then propose how to extend your contributions to research by proposing future research studies that would test your new predictions. If you believe the revisions you make to your rationales produce new insights or understandings that could be helpful for educational practitioners, you can identify these contributions to practice as well. This comprises the bulk of the Discussion section.

If you have embedded the limitations in earlier sections of the paper, you will have presented your results and interpreted your findings constrained by these limitations. If you choose (or are asked) to describe limitations in the Discussion, you could identify the limitations and then point to the ways they affected your interpretations of the findings. Finally, the Discussion could conclude with the implications of the study for methodological choices that could improve research in the domain in which your study is located or how future studies could overcome the limitations you identified.

Because we are providing guidance on writing your research paper for publication, we will reiterate here that you should investigate the expectations and conventions of the journal to which you will submit your paper. Usually, it will be acceptable to use the terms “significance,” “contributions,” and “implications” as we have defined them. However, if the editors expect you to use the terms differently, follow the editors’ expectations. Our definitions in this chapter are meant to help you think clearly about the different ways you can make a case for the importance of your research. What matters is that you have carefully built and described a coherent chain of scientific inquiry that allows your study to translate the significance of your research problem into contributions to the field.

We began the chapter with the “So what?” question. The question looks simple and straightforward but is challenging and complicated. Its simple appearance can lead researchers to believe it should have a simple answer. But it almost never does. In this chapter, we tried to address the many complications that arise when answering the question. We hope you now have some new insights and new tools for answering the question in your next study.

Cai, J., Morris, A., Hohensee, C., Hwang, S., Robison, V., Cirillo, M., Kramer, S. L., & Hiebert, J. (2019a). Choosing and justifying robust methods for educational research. Journal for Research in Mathematics Education, 50 (4), 342–348. https://doi.org/10.5951/jresematheduc.50.2.0114

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). Significance of a Study: Revisiting the “So What” Question. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_5

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What is the Significance of the Study?

• By DiscoverPhDs
• August 25, 2020

• what the significance of the study means,
• why it’s important to include in your research work,
• where you would include it in your paper, thesis or dissertation,
• how you write one
• and finally an example of a well written section about the significance of the study.

What does Significance of the Study mean?

The significance of the study is a written statement that explains why your research was needed. It’s a justification of the importance of your work and impact it has on your research field, it’s contribution to new knowledge and how others will benefit from it.

Why is the Significance of the Study important?

The significance of the study, also known as the rationale of the study, is important to convey to the reader why the research work was important. This may be an academic reviewer assessing your manuscript under peer-review, an examiner reading your PhD thesis, a funder reading your grant application or another research group reading your published journal paper. Your academic writing should make clear to the reader what the significance of the research that you performed was, the contribution you made and the benefits of it.

How do you write the Significance of the Study?

When writing this section, first think about where the gaps in knowledge are in your research field. What are the areas that are poorly understood with little or no previously published literature? Or what topics have others previously published on that still require further work. This is often referred to as the problem statement.

The introduction section within the significance of the study should include you writing the problem statement and explaining to the reader where the gap in literature is.

Then think about the significance of your research and thesis study from two perspectives: (1) what is the general contribution of your research on your field and (2) what specific contribution have you made to the knowledge and who does this benefit the most.

For example, the gap in knowledge may be that the benefits of dumbbell exercises for patients recovering from a broken arm are not fully understood. You may have performed a study investigating the impact of dumbbell training in patients with fractures versus those that did not perform dumbbell exercises and shown there to be a benefit in their use. The broad significance of the study would be the improvement in the understanding of effective physiotherapy methods. Your specific contribution has been to show a significant improvement in the rate of recovery in patients with broken arms when performing certain dumbbell exercise routines.

This statement should be no more than 500 words in length when written for a thesis. Within a research paper, the statement should be shorter and around 200 words at most.

Significance of the Study: An example

Building on the above hypothetical academic study, the following is an example of a full statement of the significance of the study for you to consider when writing your own. Keep in mind though that there’s no single way of writing the perfect significance statement and it may well depend on the subject area and the study content.

Here’s another example to help demonstrate how a significance of the study can also be applied to non-technical fields:

The significance of this research lies in its potential to inform clinical practices and patient counseling. By understanding the psychological outcomes associated with non-surgical facial aesthetics, practitioners can better guide their patients in making informed decisions about their treatment plans. Additionally, this study contributes to the body of academic knowledge by providing empirical evidence on the effects of these cosmetic procedures, which have been largely anecdotal up to this point.

The statement of the significance of the study is used by students and researchers in academic writing to convey the importance of the research performed; this section is written at the end of the introduction and should describe the specific contribution made and who it benefits.

A concept paper is a short document written by a researcher before starting their research project, explaining what the study is about, why it is needed and the methods that will be used.

Thinking about applying to a PhD? Then don’t miss out on these 4 tips on how to best prepare your application.

A science investigatory project is a science-based research project or study that is performed by school children in a classroom, exhibition or science fair.

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Scientific misconduct can be described as a deviation from the accepted standards of scientific research, study and publication ethics.

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What is the Significance of a Study? Examples and Guide

If you’re reading this post you’re probably wondering: what is the significance of a study?

No matter where you’re at with a piece of research, it is a good idea to think about the potential significance of your work. And sometimes you’ll have to explicitly write a statement of significance in your papers, it addition to it forming part of your thesis.

In this post I’ll cover what the significance of a study is, how to measure it, how to describe it with examples and add in some of my own experiences having now worked in research for over nine years.

If you’re reading this because you’re writing up your first paper, welcome! You may also like my how-to guide for all aspects of writing your first research paper .

Looking for guidance on writing the statement of significance for a paper or thesis? Click here to skip straight to that section.

What is the Significance of a Study?

For research papers, theses or dissertations it’s common to explicitly write a section describing the significance of the study. We’ll come onto what to include in that section in just a moment.

However the significance of a study can actually refer to several different things.

Working our way from the most technical to the broadest, depending on the context, the significance of a study may refer to:

• Within your study: Statistical significance. Can we trust the findings?
• Wider research field: Research significance. How does your study progress the field?
• Commercial / economic significance: Could there be business opportunities for your findings?
• Societal significance: What impact could your study have on the wider society.
• And probably other domain-specific significance!

We’ll shortly cover each of them in turn, including how they’re measured and some examples for each type of study significance.

But first, let’s touch on why you should consider the significance of your research at an early stage.

Why Care About the Significance of a Study?

No matter what is motivating you to carry out your research, it is sensible to think about the potential significance of your work. In the broadest sense this asks, how does the study contribute to the world?

After all, for many people research is only worth doing if it will result in some expected significance. For the vast majority of us our studies won’t be significant enough to reach the evening news, but most studies will help to enhance knowledge in a particular field and when research has at least some significance it makes for a far more fulfilling longterm pursuit.

Furthermore, a lot of us are carrying out research funded by the public. It therefore makes sense to keep an eye on what benefits the work could bring to the wider community.

Often in research you’ll come to a crossroads where you must decide which path of research to pursue. Thinking about the potential benefits of a strand of research can be useful for deciding how to spend your time, money and resources.

It’s worth noting though, that not all research activities have to work towards obvious significance. This is especially true while you’re a PhD student, where you’re figuring out what you enjoy and may simply be looking for an opportunity to learn a new skill.

However, if you’re trying to decide between two potential projects, it can be useful to weigh up the potential significance of each.

Let’s now dive into the different types of significance, starting with research significance.

Research Significance

What is the research significance of a study.

Unless someone specifies which type of significance they’re referring to, it is fair to assume that they want to know about the research significance of your study.

Research significance describes how your work has contributed to the field, how it could inform future studies and progress research.

Where should I write about my study’s significance in my thesis?

Typically you should write about your study’s significance in the Introduction and Conclusions sections of your thesis.

It’s important to mention it in the Introduction so that the relevance of your work and the potential impact and benefits it could have on the field are immediately apparent. Explaining why your work matters will help to engage readers (and examiners!) early on.

It’s also a good idea to detail the study’s significance in your Conclusions section. This adds weight to your findings and helps explain what your study contributes to the field.

On occasion you may also choose to include a brief description in your Abstract.

What is expected when submitting an article to a journal

It is common for journals to request a statement of significance, although this can sometimes be called other things such as:

• Impact statement
• Significance statement
• Advances in knowledge section

Here is one such example of what is expected:

Impact Statement:  An Impact Statement is required for all submissions.  Your impact statement will be evaluated by the Editor-in-Chief, Global Editors, and appropriate Associate Editor. For your manuscript to receive full review, the editors must be convinced that it is an important advance in for the field. The Impact Statement is not a restating of the abstract. It should address the following: Why is the work submitted important to the field? How does the work submitted advance the field? What new information does this work impart to the field? How does this new information impact the field? Experimental Biology and Medicine journal, author guidelines

Typically the impact statement will be shorter than the Abstract, around 150 words.

Defining the study’s significance is helpful not just for the impact statement (if the journal asks for one) but also for building a more compelling argument throughout your submission. For instance, usually you’ll start the Discussion section of a paper by highlighting the research significance of your work. You’ll also include a short description in your Abstract too.

How to describe the research significance of a study, with examples

Whether you’re writing a thesis or a journal article, the approach to writing about the significance of a study are broadly the same.

I’d therefore suggest using the questions above as a starting point to base your statements on.

• Why is the work submitted important to the field?
• How does the work submitted advance the field?
• What new information does this work impart to the field?
• How does this new information impact the field?

Answer those questions and you’ll have a much clearer idea of the research significance of your work.

When describing it, try to clearly state what is novel about your study’s contribution to the literature. Then go on to discuss what impact it could have on progressing the field along with recommendations for future work.

Potential sentence starters

If you’re not sure where to start, why not set a 10 minute timer and have a go at trying to finish a few of the following sentences. Not sure on what to put? Have a chat to your supervisor or lab mates and they may be able to suggest some ideas.

• This study is important to the field because…
• These findings advance the field by…
• Our results highlight the importance of…
• Our discoveries impact the field by…

Now you’ve had a go let’s have a look at some real life examples.

Statement of significance examples

A statement of significance / impact:

Impact Statement This review highlights the historical development of the concept of “ideal protein” that began in the 1950s and 1980s for poultry and swine diets, respectively, and the major conceptual deficiencies of the long-standing concept of “ideal protein” in animal nutrition based on recent advances in amino acid (AA) metabolism and functions. Nutritionists should move beyond the “ideal protein” concept to consider optimum ratios and amounts of all proteinogenic AAs in animal foods and, in the case of carnivores, also taurine. This will help formulate effective low-protein diets for livestock, poultry, and fish, while sustaining global animal production. Because they are not only species of agricultural importance, but also useful models to study the biology and diseases of humans as well as companion (e.g. dogs and cats), zoo, and extinct animals in the world, our work applies to a more general readership than the nutritionists and producers of farm animals. Wu G, Li P. The “ideal protein” concept is not ideal in animal nutrition.  Experimental Biology and Medicine . 2022;247(13):1191-1201. doi: 10.1177/15353702221082658

And the same type of section but this time called “Advances in knowledge”:

Advances in knowledge: According to the MY-RADs criteria, size measurements of focal lesions in MRI are now of relevance for response assessment in patients with monoclonal plasma cell disorders. Size changes of 1 or 2 mm are frequently observed due to uncertainty of the measurement only, while the actual focal lesion has not undergone any biological change. Size changes of at least 6 mm or more in  T 1  weighted or  T 2  weighted short tau inversion recovery sequences occur in only 5% or less of cases when the focal lesion has not undergone any biological change. Wennmann M, Grözinger M, Weru V, et al. Test-retest, inter- and intra-rater reproducibility of size measurements of focal bone marrow lesions in MRI in patients with multiple myeloma [published online ahead of print, 2023 Apr 12].  Br J Radiol . 2023;20220745. doi: 10.1259/bjr.20220745

Other examples of research significance

Moving beyond the formal statement of significance, here is how you can describe research significance more broadly within your paper.

Describing research impact in an Abstract of a paper:

Three-dimensional visualisation and quantification of the chondrocyte population within articular cartilage can be achieved across a field of view of several millimetres using laboratory-based micro-CT. The ability to map chondrocytes in 3D opens possibilities for research in fields from skeletal development through to medical device design and treatment of cartilage degeneration. Conclusions section of the abstract in my first paper .

In the Discussion section of a paper:

We report for the utility of a standard laboratory micro-CT scanner to visualise and quantify features of the chondrocyte population within intact articular cartilage in 3D. This study represents a complimentary addition to the growing body of evidence supporting the non-destructive imaging of the constituents of articular cartilage. This offers researchers the opportunity to image chondrocyte distributions in 3D without specialised synchrotron equipment, enabling investigations such as chondrocyte morphology across grades of cartilage damage, 3D strain mapping techniques such as digital volume correlation to evaluate mechanical properties  in situ , and models for 3D finite element analysis  in silico  simulations. This enables an objective quantification of chondrocyte distribution and morphology in three dimensions allowing greater insight for investigations into studies of cartilage development, degeneration and repair. One such application of our method, is as a means to provide a 3D pattern in the cartilage which, when combined with digital volume correlation, could determine 3D strain gradient measurements enabling potential treatment and repair of cartilage degeneration. Moreover, the method proposed here will allow evaluation of cartilage implanted with tissue engineered scaffolds designed to promote chondral repair, providing valuable insight into the induced regenerative process. The Discussion section of the paper is laced with references to research significance.

How is longer term research significance measured?

Looking beyond writing impact statements within papers, sometimes you’ll want to quantify the long term research significance of your work. For instance when applying for jobs.

The most obvious measure of a study’s long term research significance is the number of citations it receives from future publications. The thinking is that a study which receives more citations will have had more research impact, and therefore significance , than a study which received less citations. Citations can give a broad indication of how useful the work is to other researchers but citations aren’t really a good measure of significance.

Bear in mind that us researchers can be lazy folks and sometimes are simply looking to cite the first paper which backs up one of our claims. You can find studies which receive a lot of citations simply for packaging up the obvious in a form which can be easily found and referenced, for instance by having a catchy or optimised title.

Likewise, research activity varies wildly between fields. Therefore a certain study may have had a big impact on a particular field but receive a modest number of citations, simply because not many other researchers are working in the field.

Nevertheless, citations are a standard measure of significance and for better or worse it remains impressive for someone to be the first author of a publication receiving lots of citations.

Other measures for the research significance of a study include:

• Accolades: best paper awards at conferences, thesis awards, “most downloaded” titles for articles, press coverage.
• How much follow-on research the study creates. For instance, part of my PhD involved a novel material initially developed by another PhD student in the lab. That PhD student’s research had unlocked lots of potential new studies and now lots of people in the group were using the same material and developing it for different applications. The initial study may not receive a high number of citations yet long term it generated a lot of research activity.

That covers research significance, but you’ll often want to consider other types of significance for your study and we’ll cover those next.

Statistical Significance

What is the statistical significance of a study.

Often as part of a study you’ll carry out statistical tests and then state the statistical significance of your findings: think p-values eg <0.05. It is useful to describe the outcome of these tests within your report or paper, to give a measure of statistical significance.

Effectively you are trying to show whether the performance of your innovation is actually better than a control or baseline and not just chance. Statistical significance deserves a whole other post so I won’t go into a huge amount of depth here.

Things that make publication in  The BMJ  impossible or unlikely Internal validity/robustness of the study • It had insufficient statistical power, making interpretation difficult; • Lack of statistical power; The British Medical Journal’s guide for authors

Calculating statistical significance isn’t always necessary (or valid) for a study, such as if you have a very small number of samples, but it is a very common requirement for scientific articles.

Writing a journal article? Check the journal’s guide for authors to see what they expect. Generally if you have approximately five or more samples or replicates it makes sense to start thinking about statistical tests. Speak to your supervisor and lab mates for advice, and look at other published articles in your field.

How is statistical significance measured?

Statistical significance is quantified using p-values . Depending on your study design you’ll choose different statistical tests to compute the p-value.

A p-value of 0.05 is a common threshold value. The 0.05 means that there is a 1/20 chance that the difference in performance you’re reporting is just down to random chance.

• p-values above 0.05 mean that the result isn’t statistically significant enough to be trusted: it is too likely that the effect you’re showing is just luck.
• p-values less than or equal to 0.05 mean that the result is statistically significant. In other words: unlikely to just be chance, which is usually considered a good outcome.

Low p-values (eg p = 0.001) mean that it is highly unlikely to be random chance (1/1000 in the case of p = 0.001), therefore more statistically significant.

It is important to clarify that, although low p-values mean that your findings are statistically significant, it doesn’t automatically mean that the result is scientifically important. More on that in the next section on research significance.

How to describe the statistical significance of your study, with examples

In the first paper from my PhD I ran some statistical tests to see if different staining techniques (basically dyes) increased how well you could see cells in cow tissue using micro-CT scanning (a 3D imaging technique).

In your methods section you should mention the statistical tests you conducted and then in the results you will have statements such as:

Between mediums for the two scan protocols C/N [contrast to noise ratio] was greater for EtOH than the PBS in both scanning methods (both  p  < 0.0001) with mean differences of 1.243 (95% CI [confidence interval] 0.709 to 1.778) for absorption contrast and 6.231 (95% CI 5.772 to 6.690) for propagation contrast. … Two repeat propagation scans were taken of samples from the PTA-stained groups. No difference in mean C/N was found with either medium: PBS had a mean difference of 0.058 ( p  = 0.852, 95% CI -0.560 to 0.676), EtOH had a mean difference of 1.183 ( p  = 0.112, 95% CI 0.281 to 2.648). From the Results section of my first paper, available here . Square brackets added for this post to aid clarity.

From this text the reader can infer from the first paragraph that there was a statistically significant difference in using EtOH compared to PBS (really small p-value of <0.0001). However, from the second paragraph, the difference between two repeat scans was statistically insignificant for both PBS (p = 0.852) and EtOH (p = 0.112).

By conducting these statistical tests you have then earned your right to make bold statements, such as these from the discussion section:

Propagation phase-contrast increases the contrast of individual chondrocytes [cartilage cells] compared to using absorption contrast. From the Discussion section from the same paper.

Without statistical tests you have no evidence that your results are not just down to random chance.

Beyond describing the statistical significance of a study in the main body text of your work, you can also show it in your figures.

In figures such as bar charts you’ll often see asterisks to represent statistical significance, and “n.s.” to show differences between groups which are not statistically significant. Here is one such figure, with some subplots, from the same paper:

In this example an asterisk (*) between two bars represents p < 0.05. Two asterisks (**) represents p < 0.001 and three asterisks (***) represents p < 0.0001. This should always be stated in the caption of your figure since the values that each asterisk refers to can vary.

Now that we know if a study is showing statistically and research significance, let’s zoom out a little and consider the potential for commercial significance.

Commercial and Industrial Significance

What are commercial and industrial significance.

Moving beyond significance in relation to academia, your research may also have commercial or economic significance.

Simply put:

• Commercial significance: could the research be commercialised as a product or service? Perhaps the underlying technology described in your study could be licensed to a company or you could even start your own business using it.
• Industrial significance: more widely than just providing a product which could be sold, does your research provide insights which may affect a whole industry? Such as: revealing insights or issues with current practices, performance gains you don’t want to commercialise (e.g. solar power efficiency), providing suggested frameworks or improvements which could be employed industry-wide.

I’ve grouped these two together because there can certainly be overlap. For instance, perhaps your new technology could be commercialised whilst providing wider improvements for the whole industry.

Commercial and industrial significance are not relevant to most studies, so only write about it if you and your supervisor can think of reasonable routes to your work having an impact in these ways.

How are commercial and industrial significance measured?

Unlike statistical and research significances, the measures of commercial and industrial significance can be much more broad.

Here are some potential measures of significance:

Commercial significance:

• How much value does your technology bring to potential customers or users?
• How big is the potential market and how much revenue could the product potentially generate?
• Is the intellectual property protectable? i.e. patentable, or if not could the novelty be protected with trade secrets: if so publish your method with caution!
• If commercialised, could the product bring employment to a geographical area?

Industrial significance:

What impact could it have on the industry? For instance if you’re revealing an issue with something, such as unintended negative consequences of a drug , what does that mean for the industry and the public? This could be:

• Reduced overhead costs
• Better safety
• Faster production methods
• Improved scaleability

How to describe the commercial and industrial significance of a study, with examples

Commercial significance.

If your technology could be commercially viable, and you’ve got an interest in commercialising it yourself, it is likely that you and your university may not want to immediately publish the study in a journal.

You’ll probably want to consider routes to exploiting the technology and your university may have a “technology transfer” team to help researchers navigate the various options.

However, if instead of publishing a paper you’re submitting a thesis or dissertation then it can be useful to highlight the commercial significance of your work. In this instance you could include statements of commercial significance such as:

The measurement technology described in this study provides state of the art performance and could enable the development of low cost devices for aerospace applications. An example of commercial significance I invented for this post

Industrial significance

First, think about the industrial sectors who could benefit from the developments described in your study.

For example if you’re working to improve battery efficiency it is easy to think of how it could lead to performance gains for certain industries, like personal electronics or electric vehicles. In these instances you can describe the industrial significance relatively easily, based off your findings.

For example:

By utilising abundant materials in the described battery fabrication process we provide a framework for battery manufacturers to reduce dependence on rare earth components. Again, an invented example

For other technologies there may well be industrial applications but they are less immediately obvious and applicable. In these scenarios the best you can do is to simply reframe your research significance statement in terms of potential commercial applications in a broad way.

As a reminder: not all studies should address industrial significance, so don’t try to invent applications just for the sake of it!

Societal Significance

What is the societal significance of a study.

The most broad category of significance is the societal impact which could stem from it.

If you’re working in an applied field it may be quite easy to see a route for your research to impact society. For others, the route to societal significance may be less immediate or clear.

Studies can help with big issues facing society such as:

• Medical applications : vaccines, surgical implants, drugs, improving patient safety. For instance this medical device and drug combination I worked on which has a very direct route to societal significance.
• Political significance : Your research may provide insights which could contribute towards potential changes in policy or better understanding of issues facing society.
• Public health : for instance COVID-19 transmission and related decisions.
• Climate change : mitigation such as more efficient solar panels and lower cost battery solutions, and studying required adaptation efforts and technologies. Also, better understanding around related societal issues, for instance this study on the effects of temperature on hate speech.

How is societal significance measured?

Societal significance at a high level can be quantified by the size of its potential societal effect. Just like a lab risk assessment, you can think of it in terms of probability (or how many people it could help) and impact magnitude.

Societal impact = How many people it could help x the magnitude of the impact

Think about how widely applicable the findings are: for instance does it affect only certain people? Then think about the potential size of the impact: what kind of difference could it make to those people?

Between these two metrics you can get a pretty good overview of the potential societal significance of your research study.

How to describe the societal significance of a study, with examples

Quite often the broad societal significance of your study is what you’re setting the scene for in your Introduction. In addition to describing the existing literature, it is common to for the study’s motivation to touch on its wider impact for society.

For those of us working in healthcare research it is usually pretty easy to see a path towards societal significance.

Our CLOUT model has state-of-the-art performance in mortality prediction, surpassing other competitive NN models and a logistic regression model … Our results show that the risk factors identified by the CLOUT model agree with physicians’ assessment, suggesting that CLOUT could be used in real-world clinicalsettings. Our results strongly support that CLOUT may be a useful tool to generate clinical prediction models, especially among hospitalized and critically ill patient populations. Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

In other domains the societal significance may either take longer or be more indirect, meaning that it can be more difficult to describe the societal impact.

Even so, here are some examples I’ve found from studies in non-healthcare domains:

We examined food waste as an initial investigation and test of this methodology, and there is clear potential for the examination of not only other policy texts related to food waste (e.g., liability protection, tax incentives, etc.; Broad Leib et al., 2020) but related to sustainable fishing (Worm et al., 2006) and energy use (Hawken, 2017). These other areas are of obvious relevance to climate change… AI-Based Text Analysis for Evaluating Food Waste Policies

The continued development of state-of-the art NLP tools tailored to climate policy will allow climate researchers and policy makers to extract meaningful information from this growing body of text, to monitor trends over time and administrative units, and to identify potential policy improvements. BERT Classification of Paris Agreement Climate Action Plans

Top Tips For Identifying & Writing About the Significance of Your Study

• Writing a thesis? Describe the significance of your study in the Introduction and the Conclusion .
• Submitting a paper? Read the journal’s guidelines. If you’re writing a statement of significance for a journal, make sure you read any guidance they give for what they’re expecting.
• Take a step back from your research and consider your study’s main contributions.
• Read previously published studies in your field . Use this for inspiration and ideas on how to describe the significance of your own study
• Discuss the study with your supervisor and potential co-authors or collaborators and brainstorm potential types of significance for it.

Now you’ve finished reading up on the significance of a study you may also like my how-to guide for all aspects of writing your first research paper .

Writing an academic journal paper

I hope that you’ve learned something useful from this article about the significance of a study. If you have any more research-related questions let me know, I’m here to help.

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The Why: Explaining the significance of your research

In the first four articles of this series, we examined The What: Defining a research project , The Where: Constructing an effective writing environment , The When: Setting realistic timeframes for your research , and The Who: Finding key sources in the existing literature . In this article, we will explore the fifth, and final, W of academic writing, The Why: Explaining the significance of your research.

Q1: When considering the significance of your research, what is the general contribution you make?

According to the Unite for Sight online module titled “ The Importance of Research ”:

“The purpose of research is to inform action. Thus, your study should seek to contextualize its findings within the larger body of research. Research must always be of high quality in order to produce knowledge that is applicable outside of the research setting. Furthermore, the results of your study may have implications for policy and future project implementation.”

In response to this TweetChat question, Twitter user @aemidr shared that the “dissemination of the research outcomes” is their contribution. Petra Boynton expressed a contribution of “easy to follow resources other people can use to help improve their health/wellbeing”.

Eric Schmieder said, “In general, I try to expand the application of technology to improve the efficiency of business processes through my research and personal use and development of technology solutions.” While Janet Salmons offered the response, “ I am a metaresearcher , that is, I research emerging qualitative methods & write about them. I hope contribution helps student & experienced researchers try new approaches.”

Despite the different contributions each of these participants noted as the significance of their individual research efforts, there is a significance to each. In addition to the importance stated through the above examples, Leann Zarah offered 7 Reasons Why Research Is Important , as follows:

• A Tool for Building Knowledge and for Facilitating Learning
• Means to Understand Various Issues and Increase Public Awareness
• An Aid to Business Success
• A Way to Prove Lies and to Support Truths
• Means to Find, Gauge, and Seize Opportunities
• A Seed to Love Reading, Writing, Analyzing, and Sharing Valuable Information
• Nourishment and Exercise for the Mind

Q1a: What is the specific significance of your research to yourself or other individuals?

The first of “ 3 Important Things to Consider When Selecting Your Research Topic ”, as written by Stephen Fiedler is to “choose something that interests you”. By doing so, you are more likely to stay motivated and persevere through inevitable challenges.

As mentioned earlier, for Salmons her interests lie in emerging methods and new approaches to research. As Salmons pointed out in the TweetChat, “Conventional methods may not be adequate in a globally-connected world – using online methods expands potential participation.”

For @aemidr, “specific significance of my research is on health and safety from the environment and lifestyle”. In contrast, Schmieder said “my ongoing research allows me to be a better educator, to be more efficient in my own business practices, and to feel comfortable engaging with new technology”.

Regardless of discipline, a personal statement can help identify for yourself and others your suitability for specific research. Some things to include in the statement are:

• Your reasons for choosing your topic of research
• The aspects of your topic of research that interest you most
• Any work experience, placement or voluntary work you have undertaken, particularly if it is relevant to your subject. Include the skills and abilities you have gained from these activities
• How your choice of research fits in with your future career plans

Q2: Why is it important to communicate the value of your research?

According to Salmons, “If you research and no one knows about it or can use what you discover, it is just an intellectual exercise. If we want the public to support & fund research, we must show why it’s important!” She has written for the SAGE MethodSpace blog on the subject Write with Purpose, Publish for Impact building a collection of articles from both the MethodSpace blog and TAA’s blog, Abstract .

Peter J. Stogios shares with us benefits to both the scientist and the public in his article, “ Why Sharing Your Research with the Public is as Necessary as Doing the Research Itself ”. Unsure where to start? Stogios states, “There are many ways scientists can communicate more directly with the public. These include writing a personal blog, updating their lab’s or personal website to be less technical and more accessible to non-scientists, popular science forums and message boards, and engaging with your institution’s research communication office. Most organizations publish newsletters or create websites showcasing the work being done, and act as intermediaries between the researchers and the media. Scientists can and should interact more with these communicators.”

Schmieder stated during the TweetChat that the importance of communicating the value of your research is “primarily to help others understand why you do what you do, but also for funding purposes, application of your results by others, and increased personal value and validation”.

In her article, “ Explaining Your Research to the Public: Why It Matters, How to Do It! ”, Sharon Page-Medrich conveys the importance, stating “UC Berkeley’s 30,000+ undergraduate and 11,000+ graduate students generate or contribute to diverse research in the natural and physical sciences, social sciences and humanities, and many professional fields. Such research and its applications are fundamental to saving lives, restoring healthy environments, making art and preserving culture, and raising standards of living. Yet the average person-in-the-street may not see the connection between students’ investigations and these larger outcomes.”

Q2a: To whom is it most difficult to explain that value?

Although important, it’s not always easy to share our research efforts with others. Erin Bedford sets the scene as she tells us “ How to (Not) Talk about Your Research ”. “It’s happened to the best of us. First, the question: ‘so, what is your research on?’ Then, the blank stare as you try to explain. And finally, the uninterested but polite nod and smile.”

Schmieder acknowledges that these polite people who care enough to ask, but often are the hardest to explain things to are “family and friends who don’t share the same interests or understanding of the subject matter.” It’s not that they don’t care about the efforts, it’s that the level to which a researcher’s investment and understanding is different from those asking about their work.

When faced with less-than-supportive reactions from friends, Noelle Sterne shares some ways to retain your perspective and friendship in her TAA blog article, “ Friends – How to deal with their negative responses to your academic projects ”.

Q3: What methods have you used to explain your research to others (both inside and outside of your discipline)?

Schmieder stated, “I have done webinars, professional development seminars, blog articles, and online courses” in an effort to communicate research to others. The Edinburg Napier University LibGuides guide to Sharing Your Research includes some of these in their list of resources as well adding considerations of online presence, saving time / online efficiency, copyright, and compliance to the discussion.

Michaela Panter states in her article, “ Sharing Your Findings with a General Audience ”, that “tips and guidelines for conveying your research to a general audience are increasingly widespread, yet scientists remain wary of doing so.” She notes, however, that “effectively sharing your research with a general audience can positively affect funding for your work” and “engaging the general public can further the impact of your research”.

If these are affects you desire, consider CES’s “ Six ways to share your research findings ”, as follows:

• Know your audience and define your goal
• Collaborate with others
• Make a plan
• Embrace plain language writing
• Layer and link, and
• Evaluate your work

Q4: What are some places you can share your research and its significance beyond your writing?

Beyond traditional journal article publication efforts, there are many opportunities to share your research with a larger community. Schmieder listed several options during the TweetChat event, specifically, “conference presentations, social media, blogs, professional networks and organizations, podcasts, and online courses”.

Elsevier’s resource, “ Sharing and promoting your article ” provides advice on sharing your article in the following ten places:

• At a conference
• For classroom teaching purposes
• For grant applications
• With my colleagues
• On a preprint server
• On my personal blog or website
• On my institutional repository
• On a subject repository (or other non-commercial repository)
• On Scholarly Communication Network (SCN), such as Mendeley or Scholar Universe
• Social Media, such as Facebook, LinkedIn, Twitter

Nature Publishing Group’s “ tips for promoting your research ” include nine ways to get started:

• Share your work with your social networks
• Update your professional profile
• Utilize research-sharing platforms
• Create a Google Scholar profile – or review and enhance your existing one
• Highlight key and topical points in a blog post
• Make your research outputs shareable and discoverable
• Register for a unique ORCID author identifier
• Encourage readership within your institution

Finally, Sheffield Solutions produced a top ten list of actions you can take to help share and disseminate your work more widely online, as follows:

• Create an ORCID ID
• Upload to Sheffield’s MyPublications system
• Make your work Open Access
• Create a Google Scholar profile
• Join an academic social network
• Connect through Twitter
• Blog about your research
• Upload to Slideshare or ORDA
• Track your research

Q5: How is the significance of your study conveyed in your writing efforts?

Schmieder stated, “Significance is conveyed through the introduction, the structure of the study, and the implications for further research sections of articles”. According to The Writing Center at University of North Carolina at Chapel Hill, “A thesis statement tells the reader how you will interpret the significance of the subject matter under discussion”.

In their online Tips & Tools resource on Thesis Statements , they share the following six questions to ask to help determine if your thesis is strong:

• Do I answer the question?
• Have I taken a position that others might challenge or oppose?
• Is my thesis statement specific enough?
• Does my thesis pass the “So what?” test?
• Does my essay support my thesis specifically and without wandering?
• Does my thesis pass the “how and why?” test?

Some journals, such as Elsevier’s Acta Biomaterialia, now require a statement of significance with manuscript submissions. According to the announcement linked above, “these statements will address the novelty aspect and the significance of the work with respect to the existing literature and more generally to the society.” and “by highlighting the scientific merit of your research, these statements will help make your work more visible to our readership.”

Q5a: How does the significance influence the structure of your writing?

According to Jeff Hume-Pratuch in the Academic Coaching & Writing (ACW) article, “ Using APA Style in Academic Writing: Precision and Clarity ”, “The need for precision and clarity of expression is one of the distinguishing marks of academic writing.” As a result, Hume-Pratuch advises that you “choose your words wisely so that they do not come between your idea and the audience.” To do so, he suggests avoiding ambiguous expressions, approximate language, and euphemisms and jargon in your writing.

Schmieder shared in the TweetChat that “the impact of the writing is affected by the target audience for the research and can influence word choice, organization of ideas, and elements included in the narrative”.

Discussing the organization of ideas, Patrick A. Regoniel offers “ Two Tips in Writing the Significance of the Study ” claiming that by referring to the statement of the problem and writing from general to specific contribution, you can “prevent your mind from wandering wildly or aimlessly as you explore the significance of your study”.

Q6: What are some ways you can improve your ability to explain your research to others?

For both Schmieder and Salmons, practice is key. Schmieder suggested, “Practice simplifying the concepts. Focus on why rather than what. Share research in areas where they are active and comfortable”. Salmons added, “answer ‘so what’ and ‘who cares’ questions. Practice creating a sentence. For my study of the collaborative process: ‘Learning to collaborate is important for team success in professional life’ works better than ‘a phenomenological study of instructors’ perceptions’”.

In a guest blog post for Scientific American titled “ Effective Communication, Better Science ”, Mónica I. Feliú-Mójer claimed “to be a successful scientist, you must be an effective communicator.” In support of the goal of being an effective communicator, a list of training opportunities and other resources are included in the article.

Along the same lines, The University of Melbourne shared the following list of resources, workshops, and programs in their online resource on academic writing and communication skills :

• Speaking and Presenting : Resources for presenting your research, using PowerPoint to your advantage, presenting at conferences and helpful videos on presenting effectively
• Research Impact Library Advisory Service  (RILAS): Helps you to determine the impact of your publications and other research outputs for academic promotions and grant applications
• Three Minute Thesis Competition  (3MT): Research communication competition that requires you to deliver a compelling oration on your thesis topic and its significance in just three minutes or less.
• Visualise your Thesis Competition : A dynamic and engaging audio-visual “elevator pitch” (e-Poster) to communicate your research to a broad non-specialist audience in 60 seconds.

As we complete this series exploration of the five W’s of academic writing, we hope that you are adequately prepared to apply them to your own research efforts of defining a research project, constructing an effective writing environment, setting realistic timeframes for your research, finding key sources in the existing literature, and last, but not least, explaining the significance of your research.

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• Writing Tips

How to Discuss the Significance of Your Research

• 6-minute read
• 10th April 2023

Introduction

Research papers can be a real headache for college students . As a student, your research needs to be credible enough to support your thesis statement. You must also ensure you’ve discussed the literature review, findings, and results.

However, it’s also important to discuss the significance of your research . Your potential audience will care deeply about this. It will also help you conduct your research. By knowing the impact of your research, you’ll understand what important questions to answer.

If you’d like to know more about the impact of your research, read on! We’ll talk about why it’s important and how to discuss it in your paper.

What Is the Significance of Research?

This is the potential impact of your research on the field of study. It includes contributions from new knowledge from the research and those who would benefit from it. You should present this before conducting research, so you need to be aware of current issues associated with the thesis before discussing the significance of the research.

Why Does the Significance of Research Matter?

Potential readers need to know why your research is worth pursuing. Discussing the significance of research answers the following questions:

●  Why should people read your research paper ?

●  How will your research contribute to the current knowledge related to your topic?

●  What potential impact will it have on the community and professionals in the field?

Not including the significance of research in your paper would be like a knight trying to fight a dragon without weapons.

Where Do I Discuss the Significance of Research in My Paper?

As previously mentioned, the significance of research comes before you conduct it. Therefore, you should discuss the significance of your research in the Introduction section. Your reader should know the problem statement and hypothesis beforehand.

Steps to Discussing the Significance of Your Research

Discussing the significance of research might seem like a loaded question, so we’ve outlined some steps to help you tackle it.

Step 1: The Research Problem

The problem statement can reveal clues about the outcome of your research. Your research should provide answers to the problem, which is beneficial to all those concerned. For example, imagine the problem statement is, “To what extent do elementary and high school teachers believe cyberbullying affects student performance?”

Learning teachers’ opinions on the effects of cyberbullying on student performance could result in the following:

●  Increased public awareness of cyberbullying in elementary and high schools

●  Teachers’ perceptions of cyberbullying negatively affecting student performance

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●  Whether cyberbullying is more prevalent in elementary or high schools

The research problem will steer your research in the right direction, so it’s best to start with the problem statement.

Step 2: Existing Literature in the Field

Think about current information on your topic, and then find out what information is missing. Are there any areas that haven’t been explored? Your research should add new information to the literature, so be sure to state this in your discussion. You’ll need to know the current literature on your topic anyway, as this is part of your literature review section .

Step 3: Your Research’s Impact on Society

Inform your readers about the impact on society your research could have on it. For example, in the study about teachers’ opinions on cyberbullying, you could mention that your research will educate the community about teachers’ perceptions of cyberbullying as it affects student performance. As a result, the community will know how many teachers believe cyberbullying affects student performance.

You can also mention specific individuals and institutions that would benefit from your study. In the example of cyberbullying, you might indicate that school principals and superintendents would benefit from your research.

Step 4: Future Studies in the Field

Next, discuss how the significance of your research will benefit future studies, which is especially helpful for future researchers in your field. In the example of cyberbullying affecting student performance, your research could provide further opportunities to assess teacher perceptions of cyberbullying and its effects on students from larger populations. This prepares future researchers for data collection and analysis.

Discussing the significance of your research may sound daunting when you haven’t conducted it yet. However, an audience might not read your paper if they don’t know the significance of the research. By focusing on the problem statement and the research benefits to society and future studies, you can convince your audience of the value of your research.

Remember that everything you write doesn’t have to be set in stone. You can go back and tweak the significance of your research after conducting it. At first, you might only include general contributions of your study, but as you research, your contributions will become more specific.

You should have a solid understanding of your topic in general, its associated problems, and the literature review before tackling the significance of your research. However, you’re not trying to prove your thesis statement at this point. The significance of research just convinces the audience that your study is worth reading.

Finally, we always recommend seeking help from your research advisor whenever you’re struggling with ideas. For a more visual idea of how to discuss the significance of your research, we suggest checking out this video .

1. Do I need to do my research before discussing its significance?

No, you’re discussing the significance of your research before you conduct it. However, you should be knowledgeable about your topic and the related literature.

2. Is the significance of research the same as its implications?

No, the research implications are potential questions from your study that justify further exploration, which comes after conducting the research.

 3. Discussing the significance of research seems overwhelming. Where should I start?

We recommend the problem statement as a starting point, which reveals clues to the potential outcome of your research.

4. How can I get feedback on my discussion of the significance of my research?

Our proofreading experts can help. They’ll check your writing for grammar, punctuation errors, spelling, and concision. Submit a 500-word document for free today!

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How to present a research paper in PPT: best practices

A research paper presentation is frequently used at conferences and other events where you have a chance to share the results of your research and receive feedback from colleagues. Although it may appear as simple as summarizing the findings, successful examples of research paper presentations show that there is a little bit more to it.

In this article, we’ll walk you through the basic outline and steps to create a good research paper presentation. We’ll also explain what to include and what not to include in your presentation of research paper and share some of the most effective tips you can use to take your slides to the next level.

Research paper PowerPoint presentation outline

Creating a PowerPoint presentation for a research paper involves organizing and summarizing your key findings, methodology, and conclusions in a way that encourages your audience to interact with your work and share their interest in it with others. Here’s a basic research paper outline PowerPoint you can follow:

1. Title (1 slide)

Typically, your title slide should contain the following information:

• Title of the research paper
• Affiliation or institution
• Date of presentation

2. Introduction (1-3 slides)

On this slide of your presentation, briefly introduce the research topic and its significance and state the research question or objective.

3. Research questions or hypothesis (1 slide)

This slide should emphasize the objectives of your research or present the hypothesis.

4. Literature review (1 slide)

Your literature review has to provide context for your research by summarizing relevant literature. Additionally, it should highlight gaps or areas where your research contributes.

5. Methodology and data collection (1-2 slides)

This slide of your research paper PowerPoint has to explain the research design, methods, and procedures. It must also Include details about participants, materials, and data collection and emphasize special equipment you have used in your work.

6. Results (3-5 slides)

On this slide, you must present the results of your data analysis and discuss any trends, patterns, or significant findings. Moreover, you should use charts, graphs, and tables to illustrate data and highlight something novel in your results (if applicable).

7. Conclusion (1 slide)

Your conclusion slide has to summarize the main findings and their implications, as well as discuss the broader impact of your research. Usually, a single statement is enough.

8. Recommendations (1 slide)

If applicable, provide recommendations for future research or actions on this slide.

9. References (1-2 slides)

The references slide is where you list all the sources cited in your research paper.

10. Acknowledgments (1 slide)

On this presentation slide, acknowledge any individuals, organizations, or funding sources that contributed to your research.

11. Appendix (1 slide)

If applicable, include any supplementary materials, such as additional data or detailed charts, in your appendix slide.

The above outline is just a general guideline, so make sure to adjust it based on your specific research paper and the time allotted for the presentation.

Steps to creating a memorable research paper presentation

Creating a PowerPoint presentation for a research paper involves several critical steps needed to convey your findings and engage your audience effectively, and these steps are as follows:

Step 1. Understand your audience:

• Identify the audience for your presentation.
• Tailor your content and level of detail to match the audience’s background and knowledge.

Step 2. Define your key messages:

• Clearly articulate the main messages or findings of your research.
• Identify the key points you want your audience to remember.

Step 3. Design your research paper PPT presentation:

• Use a clean and professional design that complements your research topic.
• Choose readable fonts, consistent formatting, and a limited color palette.
• Opt for PowerPoint presentation services if slide design is not your strong side.

Step 4. Put content on slides:

• Follow the outline above to structure your presentation effectively; include key sections and topics.
• Organize your content logically, following the flow of your research paper.

Step 5. Final check:

• Proofread your slides for typos, errors, and inconsistencies.
• Ensure all visuals are clear, high-quality, and properly labeled.

Step 6. Save and share:

• Save your presentation and ensure compatibility with the equipment you’ll be using.
• If necessary, share a copy of your presentation with the audience.

By following these steps, you can create a well-organized and visually appealing research paper presentation PowerPoint that effectively conveys your research findings to the audience.

What to include and what not to include in your presentation

In addition to the must-know PowerPoint presentation recommendations, which we’ll cover later in this article, consider the following do’s and don’ts when you’re putting together your research paper presentation:

• Focus on the topic.
• Be brief and to the point.
• Attract the audience’s attention and highlight interesting details.
• Use only relevant visuals (maps, charts, pictures, graphs, etc.).
• Use numbers and bullet points to structure the content.
• Make clear statements regarding the essence and results of your research.

Don’ts:

• Don’t write down the whole outline of your paper and nothing else.
• Don’t put long, full sentences on your slides; split them into smaller ones.
• Don’t use distracting patterns, colors, pictures, and other visuals on your slides; the simpler, the better.
• Don’t use too complicated graphs or charts; only the ones that are easy to understand.
• Now that we’ve discussed the basics, let’s move on to the top tips for making a powerful presentation of your research paper.

8 tips on how to make research paper presentation that achieves its goals

You’ve probably been to a presentation where the presenter reads word for word from their PowerPoint outline. Or where the presentation is cluttered, chaotic, or contains too much data. The simple tips below will help you summarize a 10 to 15-page paper for a 15 to 20-minute talk and succeed, so read on!

Tip #1: Less is more

You want to provide enough information to make your audience want to know more. Including details but not too many and avoiding technical jargon, formulas, and long sentences are always good ways to achieve this.

Tip #2: Be professional

Avoid using too many colors, font changes, distracting backgrounds, animations, etc. Bullet points with a few words to highlight the important information are preferable to lengthy paragraphs. Additionally, include slide numbers on all PowerPoint slides except for the title slide, and make sure it is followed by a table of contents, offering a brief overview of the entire research paper.

Tip #3: Strive for balance

PowerPoint slides have limited space, so use it carefully. Typically, one to two points per slide or 5 lines for 5 words in a sentence are enough to present your ideas.

Tip #4: Use proper fonts and text size

The font you use should be easy to read and consistent throughout the slides. You can go with Arial, Times New Roman, Calibri, or a combination of these three. An ideal text size is 32 points, while a heading size is 44.

Tip #5: Concentrate on the visual side

A PowerPoint presentation is one of the best tools for presenting information visually. Use graphs instead of tables and topic-relevant illustrations instead of walls of text. Keep your visuals as clean and professional as the content of your presentation.

Tip #6: Practice your delivery

Always go through your presentation when you’re done to ensure a smooth and confident delivery and time yourself to stay within the allotted limit.

Tip #7: Get ready for questions

Anticipate potential questions from your audience and prepare thoughtful responses. Also, be ready to engage in discussions about your research.

Tip #8: Don’t be afraid to utilize professional help

If the mere thought of designing a presentation overwhelms you or you’re pressed for time, consider leveraging professional PowerPoint redesign services . A dedicated design team can transform your content or old presentation into effective slides, ensuring your message is communicated clearly and captivates your audience. This way, you can focus on refining your delivery and preparing for the presentation.

Lastly, remember that even experienced presenters get nervous before delivering research paper PowerPoint presentations in front of the audience. You cannot know everything; some things can be beyond your control, which is completely fine. You are at the event not only to share what you know but also to learn from others. So, no matter what, dress appropriately, look straight into the audience’s eyes, try to speak and move naturally, present your information enthusiastically, and have fun!

If you need help with slide design, get in touch with our dedicated design team and let qualified professionals turn your research findings into a visually appealing, polished presentation that leaves a lasting impression on your audience. Our experienced designers specialize in creating engaging layouts, incorporating compelling graphics, and ensuring a cohesive visual narrative that complements content on any subject.

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SIGNIFICANCE OF RESEARCH

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ORIGINAL RESEARCH

Diagnostic and prognostic significance of keloid-like collagen remodeling patterns in the extracellular matrix of colorectal cancer.

• 1 Department of Oncology, West Kazakhstan Marat Ospanov Medical University, Aktobe, Kazakhstan
• 2 Department of Radiology, West Kazakhstan Marat Ospanov Medical University, Aktobe, Kazakhstan
• 3 Department of Pathology, Karaganda Medical University, Karaganda, Kazakhstan
• 4 Department of Pathomorphology, Medical Centre of West Kazakhstan Marat Ospanov Medical University, Aktobe, Kazakhstan

Background: The desmoplastic reaction is considered a promising prognostic parameter for colorectal cancer. However, intermediate desmoplastic reaction is characterized by sizeable stromal heterogeneity, including both small amounts of keloid-like collagen (KC) in the fibrotic stroma and thick tufts of KC circumferentially surrounding cancer nests and occupying most of the fields of view. The present study aimed to evaluate the diagnostic and prognostic significance of KC histophenotyping with a quantitative visual assessment of its presence in the stroma of the invasive margin of TNM (The “tumor-node-metastasis” classification) stage II/III colorectal cancer (CRC).

Methods and results: 175 resected tumors from patients with TNM stage II/III CRC were examined. Keloid-like collagen was assessed according to Ueno H. criteria. KC was assessed at the primary tumor invasive margin using Hematoxylin & Eosin and Masson’s trichrome staining. The cut-off point for KC was examined using “the best cutoff approach by log-rank test.” Using a cutoff point of 30%, we histologically divided fibrous stroma in the invasive area into two groups: “type A”—KC ≤ 0.3 and “type B”—KC>0.3. Type A stroma was observed in 48% of patients, type B—in 52%. The association between collagen amount and 5-year recurrence-free survival (5-RFS) was assessed using Cox regression analysis. Kaplan-Meier analysis and log-rank tests were used to assess the significance of survival analysis. Analysis of categorical variables showed that increased KC in CRC stroma predicted adverse outcomes for 5-RFS (hazard ratio [HR] = 3.143, 95%, confidence interval [CI] = 1.643–6.012, p = 0.001). Moreover, in Kaplan-Meier analysis, the log-rank test showed that type B exhibited worse 5-RFS than type A ( p = 0.000).

Conclusion: KC is an independent predictor of 5-year overall and RFS in patients with TNM stage II/III CRC treated with surgery, with worse survival rates when the amount of KC increases by >30%.

Introduction

Colorectal cancer (CRC) is a disease with high morbidity and mortality, ranking third in prevalence worldwide [ 1 ]. One basis for making clinical decisions about the treatment of cancer patients, including patients with CRC, is the TNM classification [ 2 ]. However, patients with the same stage of CRC, according to the TNM classification, have different clinical outcomes. In this regard, searching for additional prognostic markers to stratify patients and improve clinical decisions is exceptionally relevant. Histological examination is highly reliable and inexpensive, so identifying additional histological criteria is a promising strategy.

Traditionally, the leading place in histological studies was occupied by the epithelial component, but modern research paradigms have gradually shifted from the tumor epithelium to the stroma [ 3 – 5 ]. Even Masson P. drew attention to proliferating fibroblasts among the inflammatory infiltrate of the tumor and described the reactions of the tumor stroma depending on the time of their appearance [ 6 ]. The prognostic value of the stroma of the invasive tumor margin was demonstrated by Ueno H [ 5 , 7 , 8 ]. The desmoplastic reaction is considered a promising prognostic parameter for colorectal cancer. However, the intermediate desmoplastic reaction is characterized by sizeable stromal heterogeneity, including both small amounts of keloid-like collagen in the fibrotic stroma and thick tufts of colloid-like collagen circumferentially surrounding cancer nests and occupying most of the visual fields.

Recent studies have shown that tumors can directly exploit extracellular matrix remodeling to create a microenvironment that promotes tumorigenesis and metastasis [ 9 , 10 ]. The dominant component of the extracellular matrix is collagen, and its structure is increasingly recognized as a reliable biomarker for establishing the prognosis of various types of tumors, such as prostate cancer and gastric cancer [ 11 , 12 ]. In addition, previous studies have shown that different histopatterns of extracellular collagen matrix is associated with different therapy response in colorectal and breast cancer [ 13 – 15 ]. Collagen I fibers influence the movement of macromolecules in the tumor extracellular matrix [ 16 ], and the structural characteristics of these fibers can promote or hinder cancer cell migration [ 17 ]. Collagen changes in the tumor microenvironment correlate with cancer dissemination and prognosis. Collagen regulates tumor-associated immune infiltration and is required for tumor angiogenesis, histological features of the Collagen I matrix can lead to significantly enhanced tumor metastatic potential [ 18 , 19 ]. Moreover, radial alignment of collagen at the tumor-stroma interface increases the invasiveness of cancer cells [ 20 , 21 ]. Histologically, abnormal collagen remodeling mainly results in excessive deposition and changes in the proportions and localization of collagen [ 22 – 24 ]. These morphological changes may reflect important tumor characteristics that influence patient prognosis.

Pathologists can identify stromal components, but their description and quantification is not routine and stromal factors are not always considered when making treatment decisions. This leads to different approaches in understanding the nature of individual tumors between the laboratory and the clinic. Additional evaluation of sections stained with hematoxylin and eosin does not always allow for the best determination of the various elements of the extracellular matrix. The purpose of the work was to evaluate the diagnostic and prognostic significance of histophenotyping of keloid-like collagen with a quantitative visual assessment of its presence in the stroma of the invasive margin of TNM stage II-III colorectal cancer.

Materials and methods

Tissue samples and study cohort selection.

This monocentric retrospective cohort study consecutively included resected colorectal cancer specimens with keloid-like collagen at the invasive tumor margin from all patients who underwent complete oncologic resection for TNM stage II or III colon adenocarcinoma in Aktobe (Kazakhstan) during the period since January 2001 until December 2019 All patients were observed for 5 years from the date of surgical resection.

Exclusion criteria: 1) histological diagnosis other than adenocarcinoma; 2) tumor-stroma ratio less than 50%; 3) desmoplastic reaction of the invasive tumor margin, classified as “immature,” myxoid stroma according to Ueno H. criteria [ 5 ]; 4) radiation therapy before surgery;

5) Patient death within 30 days after surgery; 6) history of malignancy within 5 years before the diagnosis of colorectal cancer (except basal cell carcinoma or cervical cancer in situ ); 7) patient refusal of treatment, highly severe clinical condition of the patient, or old age (>90 years). All tissues selected for the study were re-evaluated independently by two pathologists without their knowledge of the clinical information. All samples were anonymized before the start of the study.

Keloid-like collagen was assessed at the invasive margin of the primary tumor using hematoxylin & eosin and Masson’s trichrome staining.

In all our cases, two pathologists (Y.K.K. and A.V.Z.) with more than 10 years of experience identified the most invasive part of the removed tumor (“the invasion front”), defined as the area with the deepest tumor infiltration or the area where tumor tissue meets non-tumor tissue. On a microslide of the most invasive edge of the tumor 10 fields of microscopic view with ×40 objective magnification were selected, and then the relative amount of keloid-like collagen was calculated for each case. The cut-off point for keloid-like collagen was examined using “the best cut-off approach by log-rank test” and 30% was determined as the cut-off point. All cases, based on the ratio of colloid-like collagen in the stroma of the invasive edge of the tumor, were divided into groups “type A” (Keloid-like Collagen ≤ 0.3) and “type B” (Keloid-like Collagen > 0.3) ( Table 1 ; Figure 1 ).

Table 1 . Clinicopathological characteristics of the investigated groups.

Figure 1 . Photomicrographs of representative sections of the invasive tumor margin. (A, B) —the stroma-to-tumor ratio is more than 50%, the stroma is dense with multiple bundles of keloid-like collagen (arrows) with bright eosinophilic hyalinization around tumor nests and focal infiltration by immune cells. HE × 40. Scale bar, 500 μm (C) —stromal keloid-like collagen histopattern type B—thick tufts of hypocellular keloid-like collagen (arrows) of a homogeneous blue color, occupy more than a third of the stroma of the invasive margin of the tumor. Masson’s trichrome × 400. Scale bar, 50 μm. (D) —Stromal mature collagen histopattern type A—thin mature collagen fibers (arrows) and single keloid-like collagen fibers. Masson’s trichrome 400. Scale bar, 50 μm.

Clinical data

Clinical data were collected using software in an integrated health information system from patient medical records. These data included the sex and age of the patients, comorbidities, family history of colorectal cancer, date of surgery, data on concomitant diseases and comorbidities before surgery, localization of the tumor in the right or left side of the colon, occurrence of recurrence, date of its discovery and treatment provided.

Endpoints and definitions

The primary endpoint of this study was recurrence-free survival (RFS). RFS was defined as the time in months between the date of surgery and the date of cancer recurrence (defined as the first date of radiographic or histological diagnosis of local tumor recurrence or metastasis of colon cancer) or the date of last follow-up (with a maximum period of 5 years). Standard examinations were performed following national cancer surveillance guidelines for colon cancer after curative resection for 5 years after surgery [ 25 ]. Patients dying without cancer recurrence were censored at the date of death.

Overall survival was defined as the duration from surgery to death or last follow-up. Patients alive at the last follow-up were recorded as censored events.

Surgical resection was defined as complete single-stage removal of all gross tumors with negative surgical margins on microscopic examination. Distant metastases were determined by preoperative abdominal ultrasound or computed tomography, chest radiography or MRI (Magnetic Resonance Imaging), and intraoperative examination.

The “tumor-node-metastasis” (TNM) classification was used to determine the primary tumor stage according to the edition in effect at the time of cancer diagnosis.

Tumor budding was defined as an isolated cancer cell or cluster comprising <5 cells at the invasive front and classified into grades BD1, BD2, and BD3 according to international criteria [ 26 , 27 ].

Vascular invasion was defined as the presence of tumor cells in the muscular layer of blood vessels or invasion of the muscular vascular endothelium; lymphatic invasion was defined as the presence of nests of tumor cells in the lymphatic cavity [ 28 ].

Perineural invasion was defined as the presence of tumor cells in three layers of the nerve sheath or in close proximity to the nerve, affecting at least 33% of its entire circumference [ 29 – 31 ].

Tumor marker KRAS

Tissue blocks from all study participants were obtained from the initial surgical resection and assessed for KRAS mutational status. Sequencing of KRAS, representative tumor portions were marked histologically, and the corresponding areas on unstained tissue slides were then subjected to manual microdissection. The dissected tissues were collected into microtubes containing lysis buffer and proteinase K and incubated at 55°C for up to 2 days. KRAS mutations were determined by standard PCR followed by Sanger sequencing of exons 2 and 3 of KRAS.

Histological examination

Before the histological examination, tissue samples were fixed in 10% formaldehyde at 4°C for 24 h, washed with tap water, and dehydrated using a series of alcohols of increasing concentration (70%, 90%, 95%, 100%). The tissue samples were then immersed in xylene and embedded in paraffin blocks. Tissue sections 3 μm thick were cut using a microtome and placed on a glass slide. The slides were then deparaffinized and stained.

Hematoxylin and eosin staining procedure. Tissue sections were immersed in Mayer’s hematoxylin for 15 min and then washed with water for 5 min. After this, the sections were subjected to 1-min eosin staining.

From the tissue block, we carefully selected plain HE-stained sections that showed the representative invasive part of the primary tumor.

Masson’s trichrome staining procedure. For staining with Masson’s trichrome, a commercial kit [Trichrome dye (Masson) Bio-Optica (Italy)] was used according to the standard protocol. Collagen fibers were defined as dark-blue fibers with black nuclei.

Morphological criteria

Morphological variables were obtained as a result of repeated review of histological specimens of the tumor (surgical material) and also collected from pathologists’ reports, including tumor differentiation, tumor budding, lymphatic, venous, and perineural invasion.

Ethics statement

Due to the retrospective nature of this study, ethical review and approval was not required in accordance with the local legislation and institutional requirements.

Statistical analysis

Statistical analysis was performed using Statistica 10.0 and IBM SPSS Statistic 25.0 software (v.25.00, IBM Statistics, Armonk/NY, United States). No formal sample size calculation was performed. All eligible patients were included in the analysis. Descriptive statistics were used to present the data. Qualitative and quantitative variables were compared using the χ 2 or Student’s t-test, respectively (Mann-Whitney or Kruskal-Wallis test when the reliability conditions of Student’s t-test and χ 2 test were not met). A p -value <0.05 was considered statistically significant. The Kaplan-Meier method was used to calculate overall (OS) and relapse-free (RFS) survival. Univariate and multivariate analyses using the Cox proportional hazards regression model were performed to calculate HRs and 95% CI. The log-rank test, Kaplan-Meier method, and Cox regression (univariate) were used to estimate the cutoff value.

Study population

The clinicopathological characteristics of the study groups of patients are presented in Table 1 .

Patients were divided into groups “type A” (Keloid-like Collagen ≤0.3) and “type B” (Keloid-like Collagen > 0.3). Based on the cut-off point, 84 (48%) patients were identified as cases with a reduced amount of keloid-like collagen (type A), and 91 (52%) patients were identified as cases with an increased amount of keloid-like collagen (type B). Representative examples of images with stroma type A and type B histophenotypes are shown in Figure 1 .

Survival analyses of overall survival and recurrence-free survival

The results of the Kaplan-Meier analysis of survival curves are presented in Figure 2 .

Figure 2 . Kaplan-Meier curve features in the investigated groups [stratified by histophenotype of stromal collagen (Type A or Type B) in the colorectal cancer]. RFS—Recurrence-free survival of patients, OS—Overall survival of patients, Type A—Keloid-like Collagen less than 30% of stromal collagen fibers in the invasive tumor front (≤0.3), Type B—Keloid-like Collagen more than 30% of stromal collagen fibers in the invasive tumor front (>0.3).

In the group with the structural pattern of mature thin-structured collagen stroma type A, the following survival rates (RFS) were determined: on the 1st year it was 84.5%, on the 3rd year—72.6%, on the 5th year—67.9%; OS in the 1st year was 90.5%, in the 3rd year—84.5%, in the 5th year—84.5%.

In the group with the structural pattern of intermediate keloid-like collagen stroma type B, the following survival rates (RFS) were determined: on the 1st year it was 74.7%, on the 3rd year—48.4%, on the 5th year—37.4%; OS in the 1st year was 80.2%, in the 3rd year—63.7%, in the 5th year—59.4%.

Univariate and multivariate analysis of prognostic factors

Univariate Cox regression was performed to evaluate the prognostic significance of all clinicopathological data and keloid-like collagen assessed on all slides containing the invasive tumor margin ( Table 2 ).

Table 2 . Univariate and multivariate analyses of RFS using the Cox proportional-hazards regression model in the investigated groups.

Univariate analysis showed that among the prespecified clinicopathological prognostic factors assessed in this study, the T-stage had a significant impact on RFS: in patients with the T3 stage, the hazard ratio was 2.345 (95% CI: 1.054–5.215; p = 0.037) compared to patients at stage T2. Lymphatic invasion [HR, 2.533 (1.238–5.183), p = 0.011] and venous invasion [HR, 3.509 (1.682–7.323), p = 0.001] were also significant prognostic factors for RFS. Patients with stromal histophenotype Type B (Keloid-like Collagen > 0.3) had a significantly higher risk of relapse [HR, 3.539 (1.895–6.609), p = 0.001] than patients with stromal histophenotype Type A. In a univariate analysis, it was found that several factors, such as age, sex, race, family history of cancer, and tumor differentiation grade, did not have a statistically significant effect on RFS in patients with stage II-III colorectal cancer.

After univariate analysis, significant factors were further assessed using a multivariate Cox regression model to determine their independent prognostic value for RFS. After adjustment for other covariates, T stage [HR: 1.654, 95% CI: (0.695–3.939); p = 0.256], lymphatic invasion (HR: 1.420, 95% CI: 0.495–4.070; p = 0.514) and venous invasion (HR: 1.696, 95% CI: 0.624–4.609; p = 0.301) were not predictive of poor outcome, in contrast to the structural pattern of keloid-like collagen. Multivariate analysis showed that stromal histophenotype Type B (Keloid-like Collagen >0.3) was an independent factor significantly associated with worse RFS [HR, 3.143 (1.643–6.012), p = 0.001] in multivariate analysis ( Table 2 ).

In this study, we examined the diagnostic and prognostic significance of structural histochemical patterns of keloid-like collagen in the stroma of colorectal cancer. We analyzed the impact of clinical and histopathological prognostic factors on metastasis and survival of patients with colorectal cancer. The histopathological pattern of colon cancer between the left and right parts of the large intestine did not show significant differences, therefore, in this study, the main results of the comparative clinical and morphological assessment are presented as general data from all parts of the large intestine with a relatively uniform number of cases ( Table 1 ).

First, our results showed that increased content of keloid-like collagen in the stroma of the invasive tumor margin correlates with the presence of aggressive features, such as tumor differentiation (G3) ( p = 0.020), tumor budding (BD3) ( p = 0.036), lymphatic invasion ( p = 0.020) and venous invasion ( p = 0.023).

Second, we found that the categorization of keloid-like collagen at the invasive margin of colorectal cancer stratifies the risk of recurrence in this group of patients. Multivariate models analysis showed that the increase in keloid-like collagen in the stroma is an independent factor of postoperative relapse in patients with stage II-III colon cancer. In particular, distant metastases and postoperative relapses within 5 years after surgery in the group with a keloid-like collagen content of more than 30% were detected in 39.5% of patients, and in the group with a keloid-like collagen content of less than 30% in 15.5% of patients. These results are consistent with scientific research [ 32 – 34 ] and suggest a close relationship between keloid-like collagen and the metastatic behavior of cancer cells.

The mechanism leading to a worsening prognosis in patients with increased keloid-like collagen in the stroma is not yet fully understood. As colorectal cancer progresses the increase in the number of thick collagen fibers may initially be associated with a primary fibroproliferative reaction and, in later stages, with abnormal collagen production and defective maturation, which may promote tumor growth [ 35 ]. We believe that the tumor stroma is initially capable of exerting an inhibitory effect on malignant cells.

With continued growth, the tumor can exploit its stroma, for example, by changing its composition, which is morphologically manifested by changes in the quantitative and qualitative characteristics of collagen, in order to stimulate tumor growth and metastasis. This process occurs parallel to tumor progression and is characterized by a complex bidirectional communication between the tumor and its stroma [ 36 – 38 ]. The different rates of progression, relapse, and continued growth suggest that the in some cases tumor progression in the group with increased amount of keloid-like collagen stroma may be caused by primary stromal changes causing de novo tumor development. However, this assumption is speculative and requires further research.

Third, Kaplan-Meier analysis revealed that patients with more than 30% keloid-like collagen in the stroma of the invasive tumor margin had a significantly worse survival rate than patients with less than 30% keloid-like collagen ( p < 0.01). Our results indicate that the predictive power of keloid-like collagen categorization at the invasive margin of colorectal cancer may be independent of the anatomical TNM grade of the disease. These results suggest that stromal classification may provide reliable prognostic features and improve TNM classification for colorectal cancer. In addition, integrating histophenotype into multivariate analysis showed improved predictive capabilities, indicating that this histophenotype may be a potential addition to an outcome prediction system while simultaneously facilitating improved risk stratification for adverse outcomes. The evolving knowledge that tumor stroma plays an active role in cancer progression as it interacts with tumor and benign cells at different stages, from tumor initiation to invasion and metastasis [ 39 , 40 ], explains our findings. We recommend recording the presence of keloid-like collagen stroma as a high grade of malignancy if it constitutes more than a third of the entire extracellular matrix of the tumor’s invasive margin. Future studies are needed to discover additional features of the collagen matrix that could more accurately predict patient treatment outcomes.

The strengths of this study are the homogeneous study population and a single geographic area where diagnosis and treatment were performed in the same hospital without selection bias. Other strengths were the comprehensive histological evaluation by specialized pathologists and the availability of detailed clinicopathological information. Limitations include the relatively small sample size compared to other studies in this area and the lack of detailed cancer treatment data. However, we adjusted multivariate models for clinical and demographic characteristics. In addition, because data on the histopattern of stromal collagen were not generally available to treating physicians, treatment decisions were not made based on the specific structural patterns of collagens in the desmoplastic reaction. This study also included patients from 5 to 10 years ago, and developments in colorectal cancer treatments during this period may have influenced prognosis. For these reasons, extensive multicenter studies are needed to explore trends further. Manual assessment of the relative amount of keloid-like collagen could have a relatively high variability of results, so in the future, digital assessment of colorectal cancer extracellular matrix proteins will significantly increase accuracy and reduce the variability of results.

In summary, the results of this study indicate that keloid-like collagen stroma type B, when the relative amount of keloid-like collagen increases by more than 30% of the entire extracellular matrix in the tumor invasive front, is an independent prognostic factor in patients with TNM stage II or III colorectal cancer treated with surgery, with worse overall and recurrence-free survival rates. Determining the histophenotype of stromal keloid-like collagen will help stratify risk groups of patients and improve individual therapeutic strategies for colorectal cancer. Furthermore, these results suggest that stromal classification, in particular for stromal collagen histophenotype, may provide reliable prognostic features and improve TNM classification for colorectal cancer.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by the local commission on bioethics of the NJSC West Kazakhstan Medical University Named After Marat Ospanov. The requirement for written informed consent to participate in this study was waived by the committee due to the retrospective nature of the study.

Author contributions

Conceptualization, NI and YI; Data curation, NI, YI, YK, and AZ; Formal analysis, NI, YI, and YK; Methodology, NI, AZ, and YK; Project administration, NI; Resources, NI and YK; Software, NI, YI, YK, and AZ; Supervision, NI and YI; Validation, NI, YI, and YK; Visualization, AZ and YK; Writing, original draft, NI; Writing, review and editing, NI, YI, YK, and AZ. All authors contributed to the article and approved the submitted version.

The authors declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

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

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33. Sandberg, TP, Stuart, MPME, Oosting, J, Tollenaar, RAEM, Sier, CFM, and Mesker, WE. Increased expression of cancer-associated fibroblast markers at the invasive front and its association with tumor-stroma ratio in colorectal cancer. BMC Cancer (2019) 19(1):284. Published 2019 Mar 29. doi:10.1186/s12885-019-5462-2

34. Brown, KM, Xue, A, Smith, RC, Samra, JS, Gill, AJ, and Hugh, TJ. Cancer-associated stroma reveals prognostic biomarkers and novel insights into the tumour microenvironment of colorectal cancer and colorectal liver metastases. Cancer Med (2022) 11(2):492–506. doi:10.1002/cam4.4452

35. Arun Gopinathan, P, Kokila, G, Jyothi, M, Ananjan, C, Pradeep, L, and Humaira Nazir, S. Study of collagen birefringence in different grades of oral squamous cell carcinoma using picrosirius red and polarized light microscopy. Scientifica (Cairo). (2015) 2015:802980. doi:10.1155/2015/802980

36. Joyce, JA, and Pollard, JW. Microenvironmental regulation of metastasis. Nat Rev Cancer (2009) 9(4):239–52. doi:10.1038/nrc2618

37. Bissell, MJ, and Radisky, D. Putting tumours in context. Nat Rev Cancer (2001) 1(1):46–54. doi:10.1038/35094059

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Keywords: colorectal cancer, intermediate desmoplastic reaction, keloid-like collagen, desmoplastic reaction, extracellular matrix of colorectal cancer

Citation: Imanbayev NM, Iztleuov YM, Kamyshanskiy YK and Zhumasheva AV (2024) Diagnostic and prognostic significance of keloid-like collagen remodeling patterns in the extracellular matrix of colorectal cancer. Pathol. Oncol. Res. 30:1611789. doi: 10.3389/pore.2024.1611789

Received: 04 April 2024; Accepted: 21 May 2024; Published: 06 June 2024.

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

*Correspondence: Nauryzbay M. Imanbayev, [email protected]

† ORCID: Nauryzbay M. Imanbayev, orcid.org/0000-0002-8339-9373 ; Yerbolat M. Iztleuov, orcid.org/0000-0002-5303-8593 ; Yevgeniy K. Kamyshanskiy, orcid.org/0000-0001-8975-3612 ; Aigul V. Zhumasheva, orcid.org/0000-0001-8833-6251

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

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Five ways to drive experience-led growth in banking

Customers’ needs are changing. They expect more from service providers in the form of fast, frictionless, and personalized journeys. Their banking practices have also altered, with many of them now using digital and looking for it from their banks. Customer experience (CX) is proving to be the strategic differentiator for banks, with experience leaders outperforming laggards.

In this article, we explore how banks can improve customer experience, identify five bold moves they can make to gain the competitive advantage, and why they must act now, given the current dynamic macroeconomic environment.

Our research shows that banks that are frontrunners in customer satisfaction lead in financial metrics such as total shareholder return (TSR), increased growth, and decreased costs (Exhibit 1). We also see a positive correlation between customer satisfaction and purchasing decision—customers who are satisfied with their banking experiences say they will purchase more of that bank’s products. And satisfied customers are six times more likely to say they'll remain with a bank than dissatisfied customers are.

In this uncertain economic environment, excelling in customer experience is more important than ever for banks—the past year has seen one of the most dynamic macroeconomic conditions in the past several decades. Over the past 12 months, interest rates have risen by more than 300 basis points, mortgage originations have dropped by 60 percent, and the flow of money between financial institutions has increased four times.

Confidence is waning—more than 65 percent of customers are pessimistic about the economic outlook for the coming year, about a ten percentage point increase compared to last year. Their biggest concerns are inflation, the rising cost of goods, and savings for emergency funds.

With the dynamic macroeconomic environment and the overall pessimism consumers are feeling, customers are thinking to the future, shifting their financial practices, and reevaluating relationships with their financial institutions. We notice a move toward increasing household spending and accelerating paying down credit card debt, as well as reducing savings for retirement and emergency funds. New financial accounts are being opened at twice the average rate, and new banking relationships and switching banks are being considered (Exhibit 2).

Five critical CX moves banks can make to get ahead

Here are five get-right moves for those that want to seize the moment and become industry leaders.

Reimagine, not just de-friction, priority journeys

A typical regional bank has over 1,500 customer journeys (across business units, product lines, and customer interactions). 1 McKinsey analysis. Looked at simply, these journeys can be categorized into two broad categories—those that a bank needs to “de-friction” and those that need to be reimagined. Most journeys fall into the de-friction bucket, as streamlined, seamless experiences still matter and drive customer satisfaction. However, our research shows that the “bookend” journeys of shopping, onboarding, and problem resolution disproportionally drive the overall experience that a customer has with their bank. It is here that a bank could consider flexing its reimagination muscle (Exhibit 3).

To truly reimagine a given journey, banks can take the following steps:

• Assemble a cross-functional group that can bring diversity of experiences and thinking.
• Understand the competition, including recognizing that experience leaders also come from adjacent and other B2C industries outside purely banking—for example, a mobile payment application.
• Take inspiration from other industries as a customer’s bar for great experiences is driven by interactions and experiences outside banking.
• Leverage the concept of a zero-based design (“clean sheeting”): start with a blank canvas and imagine a new journey without considering the current state or any constraints; layer on (technical and operational) constraints afterwards.
• Co-create with customers to increase the chances of success, especially for novel signature moments.
• Push innovation to the next level. For example , how could something happen with no user-inputted data, with one click (or even no clicks)?

As a case in point, a large North American bank established an innovation factory to redesign critical banking processes and digital journeys. This brought together cross-functional teams—across product, business, technology, design, marketing, risk and compliance, legal, operations, finance, etcetera—to work on reimagining key customer journeys. Over the course of two years, more than 30 reimagined journeys were developed and rolled out. The resulting impact was a 25 to 50 percent increase in customer satisfaction of those journeys.

Radical shifts in customer behavior can be disruptive, but by delivering differentiated value for their customers, banks can take advantage of this defining moment to stand out.

Help customers migrate to digital

Most banks have highly inconsistent digital adoption. Even for banks that have similar levels of digital migration, McKinsey’s proprietary Digital Migration Index shows a two to four times variation in digital adoption of the underlying products and journeys. Our research reveals that customers who regularly use a bank’s mobile app or website (or both) have the highest average satisfaction compared to customers who use other interaction channels or infrequently use the digital channels (Exhibit 4).

So, while banks have correctly focused on building digital experiences to enable customers to bank in their channel of choice and self-serve for many interactions, there is still an opportunity for banks to actively help customers migrate to digital channels. This, in turn, will likely not only drive higher customer satisfaction, but result in a lower cost-to-serve and convenience.

Banks can actively migrate customers to digital in several ways:

• Enable: Banks can streamline enrollment into digital, seamless login, pre-authentication, and more.
• Educate: They can drive awareness of new digital offerings or features with marketing and communications, such as “how-to” videos on the website and mobile app.
• Redirect: Banks can utilize in-branch and call center or IVR intercepts to direct customers to digital channels, for example, in-branch digital and co-browse tutorials.
• Motivate: They can consider charging fees for using non-digital channels, and reward employees who redirect customers to digital channels, for instance.
• Nudge: Lastly, they can encourage customers to migrate with messaging on statements, reminders in emails or mails, gamified experiences, and so forth.

A leading Latin American bank launched a holistic digital adoption campaign to drive digital migration for its new web and mobile experiences. The bank rolled out a broad advertising campaign to encourage customers to download the new mobile app, developed incentives for recurring digital users (such as digital payments), sent out targeted customer messages after non-digital transactions were completed (for instance, in branch transfers), and thoroughly trained its front-line branch employees so they could redirect customers to digital. This broad campaign resulted in a 20 percent increase in customer satisfaction, a 5 percent increase in digitally active customers, a 25 percent increase in digital payments, and a 10 percent reduction in branch costs.

(Re)establish and (re)fortify trust

Our research shows that around 60 percent of customers currently trust that their primary bank will be helpful in navigating the next financial downturn. And this number jumps to more than 80 percent for customers who report high satisfaction with the experience their bank delivers.

Banks can take several actions to establish (or reestablish) trust

First, they can be transparent for emotionally charged interactions such as the ways fees are charged and explained (for example, on statements), the status of a loan application, and how disputes are handled. One leading payments player recently underwent a company-wide program to dramatically simplify its customer communications—from everything such as statements to the terms of loan applications to product offers on its mobile app. This program resulted not only in higher customer satisfaction (CSAT) scores, but also fewer calls coming into the contact centers (for example, for customers not understanding bills or terms and conditions clauses).

Second, they can deeply know how customers want to bank and then give them the power to interact across any channel. For example, the marketing messages they want to opt into, what channel with which they prefer to interact (email, mail, phone call, or text message), and what data they would like the bank to use when making them product offers.

Third, banks can proactively identify and help customers resolve fraud by leveraging advanced analytics. Fraud resolution is one of the most emotionally charged journeys for customers, and anything that can help them feel at ease dramatically drives trust, as well as “advocacy” by the bank on their behalf. Several banks now send text messages or emails and phone customers at the first sign of potential fraud—offering customers an opportunity to “dismiss” the alert or follow through with a fraud claim. Many banks also use this to drive advocacy by removing the charge from statements while they investigate (versus charging customers first and then refunding the charge).

And last, banks can offer a window into a customer’s financial wealth, based on customer spend and transaction history, credit bureau data, balance information, interest charges, fees, and so forth. This opens the space for banks to offer a “financial-health” score for their customers. For example, a fintech company took this to the next level by not only showing a financial-health score for its clients, but also offering advice on how to improve that score (for instance, through paying off high-interest debts and savings strategies). With this move, they aimed to become more customer-centric and develop clients’ trust.

Close the loop on measurement

“You cannot manage what you don’t measure” is a common adage in business. This is especially true for customer experience. Traditionally banks have relied on surveys, which are necessary but not sufficient to achieve these capabilities. In fact, only 16 percent of chief customer experience officers believe surveys are granular enough to act on, and only 4 percent think that surveys allow them to calculate the ROI of a decision.

Organizations that measure up well do so across four capabilities: capture (how feedback is collected and integrated), interpret (how feedback is analyzed and insights produced), act (how insights are implemented), and monitor (how dashboards are updated in near real time).

• Leaders in the industry use predictive analytics, machine learning, and big data (augmenting survey data with operational data) to overcome the well-known limitations of customer feedback. 2 “Experience DNA data and analytics platform,” McKinsey, February 2023. For example, only 7 percent on average complete surveys, 25 percent believe surveys provide timely insights to act on, while just 4 percent allow banks to quantify ROI. 3 “ Prediction: The future of CX ,” McKinsey, February 24, 2021. Banks can leverage the analytics-driven customer feedback system to personalize the experience by identifying unique customer needs and trends at scale that may go unnoticed with one-off surveys.
• They can also proactively resolve issues by ensuring that drivers of customer experience are updated in real time from all available data—as opposed to the limited survey questions that are only updated sporadically to quickly resolve trouble areas.
• Banks can predict with confidence the satisfaction for 100 percent of customers with a “single source of truth” versus the 7 to 10 percent in typical survey responses.
• Lastly, they can improve “hidden” customer interaction points; that is, quickly see how customer experience changes along a customer’s interaction with various parts of a given journey.

For example, a global bank is building a capability that scores the experience of every customer based on data such as transactions, balances, recent branch and contact center experiences, and location. It then uses machine learning to predict customer satisfaction for each customer based on their individual experience. This new capability allows the bank to dramatically improve its follow-up with customers immediately after poor service experiences and identify opportunities to deepen relationships.

Ingrain the philosophy of “customer success” in every part of the organization

Customer success is a proactive, data-led, and client-centric approach that seeks to understand client priorities and help B2B customers optimize their outcomes. The customer-success discipline is well developed in high-tech companies and the software-as-a-service (SaaS) industry but is still only slowly finding relevance in banking. Customer success equates to understanding existing B2B customers’ needs and helping them achieve their objectives (which often includes improved outcomes or experience for an end consumer or user). As a result, customer success can be successful in driving growth and reducing churn, while also increasing adoption and usage of products and services.

To implement an effective customer success model, banks can consider taking the following steps:

Build customer success capabilities: Like sales, customer success is a discipline with established practices. Setting up a customer success function requires dedicated capability building, especially if a bank is converting a team of existing client-relationship executives (such as bankers or account managers) to become customer-success managers.

Create capacity for high-value activities: In many organizations, an existing account or relationship manager is inundated with servicing requests and has limited capacity to be proactive. To create space, banks could find a way to reduce the demand on these teams to react to client “problems”, through product improvements, automation, or off-loading servicing activities to lower-cost teams.

Define the operating model with sales: Successful customer success representatives will uncover upsell and cross-sell opportunities as they work with clients to help them achieve their objectives. Therefore, it is critical to have a defined operating model and success-to-sales motion, which may differ based on the customer segmentation and coverage model (for example, teams of customer success and sales reps working together on accounts or using a model that “passes on” customer success opportunities to the sales team).

Measure customer health: A deep understanding of customer health is beneficial to customer success as it helps indicate likely-to-churn customers and assists customer success teams to prioritize how to invest their time across their customer portfolio. Banks can use all the data they have available for a customer—such as financial performance, industry trends, engagement with product and digital journeys, customer satisfaction (for instance, NPS, CSAT), product performance, and the ability to meet customer service level agreements—to develop a predictive measure of “customer health” as a key enabler of customer success.

For instance, a large wealth management player is moving to a customer-success model for its B2B business. It has introduced a “teaming” coverage model, in which large customers each have a dedicated representative for sales and customer success. The company has also defined an operating model for how sales, customer success, and sales operations will work together throughout the customer’s lifecycle. This new model has helped it better understand the needs of its customers and increased the opportunities to pursue new products and services with its existing customer base.

Bringing it all together

So how can banks achieve CX success in a competitive environment where customers want more, quickly? The good news is that we have seen companies attain leading positions by addressing three core building blocks of customer experience: a clearly defined, strong aspiration; a disciplined transformation journey; and thoughtful deployment of new capabilities such as analytics (Exhibit 5). 4 Victoria Bough, Ralph Breuer, Nicolas Maechler, and Kelly Ungerman, “ The three building blocks of successful customer experience transformation ,” McKinsey, October 27, 2020.

McKinsey research shows that this approach has delivered powerful results: a 15 to 20 percent increase in sales conversion rates, a 20 to 50 percent decline in service costs, and a 10 to 20 percent improvement in customer satisfaction. 5 Victoria Bough, Ralph Breuer, Nicolas Maechler, and Kelly Ungerman, “ The three building blocks of successful customer experience transformation ,” McKinsey, October 27, 2020.

By using these building blocks to achieve successful customer-centric transformations, and embedding the five bold moves described above, banks can take gold in the customer-experience race and attain a competitive advantage that boosts growth, lowers costs, and provides superior customer satisfaction.

Shital Chheda is a partner in McKinsey’s Chicago office; Jonathan Goldstein is an associate partner in the San Francisco office, where Robert Schiff is a senior partner; and Tim Natriello is an associate partner in the New York office.

The authors wish to thank Tim Bail, Anubhav Choudhury, Kate Ford, Alex Lapides, and Adrian Nelson for their contributions to this article.

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Knowledge management in fashion retail: key to competitive advantage and peak organizational performance

In an era where agility and innovation are paramount for success, a new study by University of South Carolina Department of Retailing Assistant Professor Md. Rafiqul Islam Rana delves into the significance of knowledge management capabilities (KMCs) within the fashion retail sector. This research, co-authored by Assistant Professor Song-yi Youn of the University of Missouri, provides fresh insights into how effectively managing knowledge can enhance competitive advantage and organizational performance, particularly amid the complexities of fashion products.

The study explores the intricate relationship between KMCs and the success of fashion retailers by analyzing survey data from 322 U.S. fashion retail professionals. The researchers utilized partial least squares structural equation modeling (PLS-SEM) to draw their conclusions.

"We hope this research encourages fashion retailers to look inward at how they manage organizational knowledge," Rana says. "Given the inherent complexity of fashion products, this study emphasizes the need to prioritize building a strong knowledge infrastructure — a culture of learning, collaborative structures, and enabling technology. With that foundation, they can effectively leverage insights around consumer data, trends, product feedback, design innovation geared toward complex products, sustainable sourcing practices, and targeted marketing strategies. Optimizing these knowledge capabilities allows retailers to better navigate product complexity while staying agile, innovative and driving peak performance in today's competitive landscape."

One pivotal finding of the study is the significant role of knowledge infrastructure capabilities (culture, structure, and technology) in boosting both competitive advantage and organizational performance. The results clearly indicate that having a robust knowledge infrastructure is crucial for fashion retailers looking to stay ahead in a highly competitive market.

On the other hand, knowledge process capabilities (acquisition, conversion, application, and protection) did not show a significant direct impact on competitive advantage but was found to improve organizational performance. This highlights that while the processes of managing knowledge may not directly influence competitive standing, they are vital for overall organizational efficiency and effectiveness.

Finally, the research examines the moderating effect of product complexity on the relationship between competitive advantage and organizational performance. The study found that higher product complexity negatively impacted this relationship. Fashion products inherently come with a level of complexity that can hinder the direct benefits of competitive advantage on performance. This complexity requires retailers to strategically enhance their knowledge management practices.

This research stands out by highlighting the interplay between product complexity and KMC in the fashion retail industry. It offers valuable insights that are beneficial for both academia and industry practitioners, adding a novel dimension to the existing body of knowledge by examining how product complexity influences the effectiveness of knowledge management strategies.

As the fashion industry continues to evolve, the findings of this study underscore the critical importance of knowledge management capabilities. By leveraging these insights, fashion retailers can better navigate the complexities of their products and the competitive landscape, ensuring sustained success and performance.

The full paper, published in the International Journal of Retail & Distribution Management , is available online .

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• Effects of moderate alcohol consumption and hypobaric hypoxia: implications for passengers’ sleep, oxygen saturation and heart rate on long-haul flights
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• Rabea Antonia Trammer 1 ,
• Daniel Rooney 1 ,
• Sibylle Benderoth 1 ,
• Martin Wittkowski 1 ,
• Juergen Wenzel 1 ,
• http://orcid.org/0000-0003-0336-6705 Eva-Maria Elmenhorst 1 , 2
• 1 Department of Sleep and Human Factors Research, Institute of Aerospace Medicine , German Aerospace Center , Cologne , Germany
• 2 Institute of Occupational, Social and Environmental Medicine, Medical Faculty , RWTH Aachen University , Aachen , Germany
• Correspondence to PD Dr. Eva-Maria Elmenhorst, Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, Cologne, Nordrhein-Westfalen, Germany; eva-maria.elmenhorst{at}dlr.de

Background Passengers on long-haul flights frequently consume alcohol. Inflight sleep exacerbates the fall in blood oxygen saturation (SpO 2 ) caused by the decreased oxygen partial pressure in the cabin. We investigated the combined influence of alcohol and hypobaric hypoxia on sleep, SpO 2 and heart rate.

Methods Two groups of healthy individuals spent either two nights with a 4-hour sleep opportunity (00:00–04:00 hours) in the sleep laboratory (n=23; 53 m above sea level) or in the altitude chamber (n=17; 753 hPa corresponding to 2438 m above sea level, hypobaric condition). Participants consumed alcohol before one of the nights (mean±SE blood alcohol concentration 0.043±0.003%). The order of the nights was counterbalanced. Two 8-hour recovery nights (23:00–07:00 hours) were scheduled between conditions. Polysomnography, SpO 2 and heart rate were recorded.

Results The combined exposure to alcohol and hypobaric condition decreased SpO 2 to a median (25th/75th percentile) of 85.32% (82.86/85.93) and increased heart rate to a median (25th/75th percentile) of 87.73 bpm (85.89/93.86) during sleep compared with 88.07% (86.50/88.49) and 72.90 bpm (70.90/78.17), respectively, in the non-alcohol hypobaric condition, 94.97% (94.59/95.33) and 76.97 bpm (65.17/79.52), respectively, in the alcohol condition and 95.88% (95.72/96.36) and 63.74 bpm (55.55/70.98), respectively, in the non-alcohol condition of the sleep laboratory group (all p<0.0001). Under the combined exposure SpO 2 was 201.18 min (188.08/214.42) below the clinical hypoxia threshold of 90% SpO 2 compared with 173.28 min (133.25/199.03) in the hypobaric condition and 0 min (0/0) in both sleep laboratory conditions. Deep sleep (N3) was reduced to 46.50 min (39.00/57.00) under the combined exposure compared with both sleep laboratory conditions (alcohol: 84.00 min (62.25/92.75); non-alcohol: 67.50 min (58.50/87.75); both p<0.003).

Conclusions The combination of alcohol and inflight hypobaric hypoxia reduced sleep quality, challenged the cardiovascular system and led to extended duration of hypoxaemia (SpO 2 <90%).

Polysomnography

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Data are available upon reasonable request. Data will be made available by the corresponding author upon reasonable request.

https://doi.org/10.1136/thorax-2023-220998

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WHAT IS ALREADY KNOWN ON THIS TOPIC

To stay in a hypobaric environment is known to decrease oxygen saturation and increase heart rate. Aeroplane passengers with cardiopulmonary diseases have an increased risk of aggravation of symptoms due to the decreased cabin pressure at cruising altitude, which is amplified during sleep. Alcohol, which is often consumed on board, has similar effects, but hypobaric hypoxia-induced changes are usually more pronounced.

WHAT THIS STUDY ADDS

This study is the first to investigate the combined impact of hypobaric hypoxia and alcohol during sleep. Effects on oxygen saturation and heart rate were supra-additive. Young and healthy individuals experienced prolonged and clinically relevant desaturations.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

We show that the on-board consumption of alcohol is an underestimated health risk that could be easily avoided. Practitioners, passengers and crew should be informed about the potential risks, and it may be beneficial to consider altering regulations to restrict the access to alcoholic beverages on board aeroplanes.

Introduction

The number of long-haul flights has been increasing for many years. While in 2002 about one billion air travellers per year were estimated, 1 in 2018 the number had quadrupled. 2 The environmental conditions during flight might pose health risks for passengers, especially those with respiratory diseases. 3 To increase passenger safety, the minimal cabin pressure on commercial flights is equivalent to 2438 m (753 hPa) as prescribed by international regulations, 1 which is defined as the lower limit of moderate altitude. 4–7 Atmospheric pressure decreases exponentially with altitude due to the diminishing mass of the overlying air column and decreasing gravitational force. This leads to a decline in arterial oxygen partial pressure (PaO 2 ), which falls to approximately 73 hPa at 2438 m, a level that corresponds to a blood oxygen saturation (SpO 2 = proportion of oxygen-saturated haemoglobin) of approximately 90% in healthy persons. 5 6 Due to the sigmoid shape of the oxygen binding curve, a further reduction of PaO 2 below 73 hPa results in a more pronounced drop in SpO 2 per linear drop in PaO 2 8 and is defined as hypobaric hypoxia. 9 Acclimatisation in the mountains can reduce the negative effects of the reduced atmospheric pressure on SpO 2 . 10–12 However, such an acclimatisation cannot be achieved during a long-haul flight.

Offering free alcoholic beverages to passengers during long-haul flights is so common that surveys were conducted to see whether passengers would prefer to book non-alcoholic flights. 13 It is therefore important to understand the implications of a combination of alcohol consumption and sleep during long-haul flights.

Alcohol acts as a potent somnogen which leads to a reduced sleep onset latency and rapid eye movement (REM) sleep duration 14–18 and may result in cognitive impairment, difficulties in concentration and memory disorder. 19 The alcohol-induced systemic vasodilatation increases heart rate during sleep. 20 Hypobaric hypoxia leads to a shortened N3 and REM sleep duration and decreased SpO 2 during sleep while the heart rate is increased. 21 We hypothesised that the combination of alcohol and hypobaric conditions would exacerbate the changes in sleep observed under single exposure conditions.

Participants

Forty-eight participants aged 18–40 years were randomly assigned to two groups stratified by age, gender and body mass index (for further details see online supplemental method ). Applicants with physical, psychological, intrinsic sleep or circadian disorders were excluded from participation in this study based on the results of questionnaires, medical history, physical examination, blood and urine tests and electrocardiography.

Supplemental material

Laboratory procedures.

The study was performed at the Institute of Aerospace Medicine of the German Aerospace Centre in Cologne. The protocol described in this paper was a segment of a larger research project that investigated a range of related research goals (see online supplemental method ). 22 This paper presents data of two experimental nights in which sleep opportunities lasted 4 hours (00:00−04:00 hours). One of the experimental nights was preceded by alcohol exposure in the evening.

The Control Group slept under conditions of normobaric normoxia in the sleep laboratory (53 m altitude) whereas the InFlight Group slept in a simulated crew-rest compartment in the altitude chamber where the pressure was decreased to 753 hPa, simulating the minimal pressure inside an aeroplane cabin at cruising altitude. In addition, realistic noise as inside a plane (70 dB(A) recorded during a flight from Cologne to Kairo) was generated.

The following two groups and conditions were compared:

Control Group: (a) one night without alcohol consumption in normobaric conditions at sea level (Control NonAlc) and (b) one night with previous alcohol consumption in normobaric conditions at sea level (Control Alc).

InFlight Group: (a) one night without alcohol consumption in hypobaric conditions (InFlight NonAlc) and (b) one night with previous alcohol consumption in hypobaric conditions (InFlight Alc).

The flowchart in figure 1 provides details of study flow.

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Study flow.

Alcohol administration

The individual amount of alcohol needed to achieve the target value of 0.06% blood alcohol concentration (BAC) was selected with reference to the usual BAC limits for driving in Western Europe (0.05%) and the USA (0.08%) and was calculated according to the modified Widmark formula of Watson et al . 23 This is equivalent to drinking two cans of beer (5%) or two glasses of wine (175 mL, 12%). At 23:15 hours the participants drank the calculated amount of pure vodka (on average 114.5 mL). Due to individual variability in resorption and absorption rates, the average BAC of the subjects measured at 23:45 hours was 0.043±0.003%. The next morning at 06:00 hours the BAC was 0.0±0.0% in both groups. All mentioned BACs were calculated from the measured breath alcohol concentrations (with Dräger Alcotest 6810) according to Pavlic et al . 24

During the nights polysomnography, SpO 2 and heart rate were monitored continuously.

Polysomnography was recorded according to the international 10–20 system (electroencephalography (EEG): C3, F3, O1, and C4, F4, O2, referenced to A2 and A1, respectively, electro-oculography, submental electromyography) as previously described. 7 One trained technician scored sleep stages and EEG arousals according to conventional criteria. 25 Heart rate was recorded with one-lead electrocardiography.

We derived the following dependent variables: total sleep time (TST), sleep efficiency (SE; TST/time in bed×100), sleep onset latency (SOL) defined as the first occurrence of any sleep stage deeper than N1 (ie, N2, N3, or REM), duration spent in sleep stages N1–3 and REM, wake after sleep onset (WASO; ie, wake duration between sleep onset and end of time in bed), number of sleep stage changes (per hour TST) and number of arousals (per hour TST). Mean heart rates were calculated per TST, N1–3 and REM.

Blood oxygen saturation (SpO 2 )

A finger tip sensor was part of the sleep recording device (PD3, DLR) and used to measure SpO 2 . From these data, average SpO 2 values were calculated per TST, N1–3 and REM as dependent variables. A further finger tip sensor (PalmSAT 2500 Series, Nonin) provided continuous online monitoring during sleep periods, ensuring the safety of the participants in the altitude chamber.

Statistical analysis

We analysed the single and combined effects of alcohol and hypobaric conditions on dependent variables using SAS version 9.4. In mixed ANOVAs with the main factors condition (alcohol, no alcohol), group (Control Group, InFlight Group) and the interaction between condition and group, we analysed dependent variables of sleep and heart rate. Individual baseline parameters of sleep in the sleep laboratory were included as covariates in the sleep analyses. Post hoc pairwise comparisons were adjusted for multiple testing according to the Tukey–Kramer test. An exploratory inclusion of sex, age and BAC into analyses did not impact the interaction results. In order to achieve a normal distribution, some parameters were transformed prior to analysis (TST, SE, N3, WASO, number of arousals). Normal distribution of residuals was assessed using Kolmogorov–Smirnov tests and Q–Q plots. The significance level was α<0.05. SpO 2 and SpO 2 <90% measured during TST and sleep stages (N1–3, REM) within and between groups were analysed with paired Wilcoxon signed rank tests and unpaired Wilcoxon rank sum tests as no normal distribution of residuals could be achieved. Bonferroni adjustment for multiple comparisons was applied and the significance level set to 0.0083 (0.05/6). The time period during sleep with SpO 2 <90% was of special interest as this threshold is used as a definition of hypoxia in clinical guidelines. 26 If not otherwise mentioned, values are given as median (25th/75th percentile).

The demographic data of the participants did not differ between the groups ( table 1 ).

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Participant demographics and blood alcohol concentration (alcohol condition)

Tables 2 and 3 provide an overview of the results.

Descriptive statistics and mixed ANOVA results: polysomnography and heart rate

Descriptive statistics: oxygen saturation

Effects of moderate alcohol consumption

Comparing the alcohol and non-alcohol conditions of the Control Group, the following isolated effects of alcohol under normobaric conditions on sleep ( figure 2 ), SpO 2 and heart rate ( figure 3 , online supplemental figures 1, 2 ) were observed: N1 (p=0.0448) and REM (p=0.0053) durations were shorter, SpO 2 during sleep was reduced (TST, N1–3, REM: p<0.0001) and heart rate accelerated (TST, N1–3, REM; p<0.0001) under alcohol exposure. During TST the median SpO 2 remained above the hypoxia threshold of 90%.

Duration of sleep stages (N1–3, REM) under two conditions (non-alcohol and alcohol) in the Control Group and InFlight Group. Mixed ANOVAs with the main factors condition, group and the interaction between condition and group and post-hoc Tukey–Kramer adjustment (α<0.05) for polysomnography. Mixed ANOVAs were adjusted for baseline sleep in the sleep laboratory as covariate. Data are from two independent groups recorded during 4-hour sleep episodes (00:00–04:00 hours) in an altitude chamber at a simulated flight level (ie, atmospheric pressure corresponding to 2438 m above sea level; n=17) and in the sleep laboratory (53 m; n=22). Box plots include mean values expressed as “X”. Whiskers represent 1.5× IQR.

Top: Oxygen saturation under non-alcohol and alcohol conditions in the Control Group and InFlight Group. Paired and unpaired Wilcoxon tests were calculated and Bonferroni adjusted (α=0.05/6=0.0083). Data are from two independent groups recorded during 4-hour sleep episodes (00:00–04:00 hours) in an altitude chamber at a simulated flight level (ie, atmospheric pressure corresponding to 2438 m above sea level; n=14) and in the sleep laboratory (53 m; n=22). Box plots include mean values expressed as “X”. Whiskers represent 1.5× IQR. Bottom: Heart rate under non-alcohol and alcohol conditions in the Control Group and InFlight Group. Mixed ANOVAs were performed with the main factors condition, group and the interaction between condition and group and post-hoc Tukey–Kramer adjustment (α<0.05). Data are from two independent groups recorded during 4-hour sleep episodes (00:00–04:00 hours) in an altitude chamber at simulated flight level (ie, atmospheric pressure corresponding to 2438 m above sea level; n=14) and in the sleep laboratory (53 m; n=22). Box plots include mean values expressed as “X”. Whiskers represent 1.5× IQR. TST, total sleep time.

Effects of hypobaric inflight conditions

Comparing the non-alcohol condition between groups, a shorter REM (p=0.0005), longer N2 duration (p<0.0001) and longer WASO (p=0.0471) were observed in the InFlight Group. SE (p=0.0165, adjusted p=0.0822), N1 (p=0.0179, adjusted p=0.0886) and N3 duration (p=0.0133, adjusted p=0.0678) only changed on trend-niveau, but TST (p=0.0223, adjusted p=0.1059) and number of arousals (p=0.0448, adjusted p=0.1903) were not significantly different after adjustment.

In the InFlight Group, SpO 2 during sleep was reduced (TST, N1–3, REM; p<0.0001) and resulted in SpO 2 <90% in 81% of TST( figure 4 ). Heart rate was accelerated (TST, N1–3, REM; p<0.002).

Comparison of blood oxygenation between non-alcohol and alcohol conditions in the Control Group and InFlight Group. Data are from two independent groups recorded during 4-hour sleep episodes (00:00–04:00 hours) in an altitude chamber at simulated flight level (ie, atmospheric pressure corresponding to 2438 m above sea level; n=14) and in the sleep laboratory (53 m; n=22). Mean±SE duration during sleep that participants spent with an oxygen saturation >90% and <90% (hypoxic state). N1–3, REM, sleep stages; TST, total sleep time.

Combined effects of alcohol and hypobaric conditions

In comparison to the normobaric, non-alcohol condition of the Control Group, N3 (p=0.0029) and REM (p<0.0001) duration decreased, N1 duration showed a decreased trend (p=0.0791) and N2 duration (p<0.0001) and WASO (p=0.0071) increased in the alcohol condition of the InFlight Group.

SpO 2 during sleep in the alcohol condition of the InFlight Group was reduced (TST, N1–3, REM; p<0.0001) and time spent with SpO 2 <90% was prolonged (TST, N1–3 REM; p<0.0001). SpO 2 during TST fell from 95.88% (95.72/96.36) in the non-alcohol condition of the Control Group to 85.32% (82.86/85.93) under the combined exposure to alcohol and hypobaric conditions. Heart rate was accelerated (TST, N1–3, REM; p<0.0001).

What does alcohol add to the effects of hypobaric conditions?

The effect of alcohol in addition to that of the hypobaric environment can be quantified when comparing the alcohol and non-alcohol conditions of the InFlight Group. Alcohol led to a shortened REM (p=0.0467), a trend to longer N2 duration (p=0.0641), a trend to shorter SOL (p=0.0827), decreased SpO 2 (TST, N1–3, REM; all p<0.0002) and increased heart rate (TST, N1–3, REM; p<0.0001) ( figures 2 and 3 ). SpO 2 during TST fell further from 88.07% (86.50/88.49) without alcohol to 85.32% (82.86/85.93) with alcohol in the InFlight Group ( figure 4 ). During N3 and REM sleep, even lower SpO 2 values were registered after previous alcohol consumption (84.84% (82.18/85.88); 84.74% (83.63/85.81)). During TST, time spent with SpO 2 <90% (p=0.0034) was 201.18 min (188.08/214.42) under the combined condition compared with 173.28 min (133.25/199.03) in the non-alcohol condition InFlight. Heart rate increased to 87.73 bpm (85.89/93.86) under the combined condition compared with 72.90 (70.90/78.17) in the non-alcohol condition InFlight. To quantify the strength of the alcohol-induced effects on SpO 2 and heart rate, a delta was calculated for each subject as the difference between conditions and compared between groups. The alcohol-induced reduction in SpO 2 (TST, N1–3, REM; p<0.0004) and increase in heart rate (TST, N1, N3, REM; p<0.03) was higher in the InFlight Group (TST SpO 2 : 3.02 (2.28/3.74), TST heart rate: 13.94 (17.11/11.60)) than in the Control Group (TST SpO 2 : 0.85 (0.56/1.41), TST heart rate: 9.96 (12.73/7.33)). In combination, hypobaric hypoxia and alcohol had a supra-additive effect on SpO 2 and heart rate.

What does the hypobaric condition add to the alcohol effect?

By comparing the alcohol conditions between the groups, the additional influence of hypobaric hypoxia was quantified. Sleep architecture under alcohol plus hypobaric conditions was characterised by a shortened REM (p=0.0015) and N3 duration (p=0.0002), while N2 duration (p<0.0001) was prolonged ( figure 2 ). Participants spent more time awake (WASO, p=0.0223). SpO 2 ( figure 3 ) was lower (TST, N1–3, REM; p<0.0001). Participants in the InFlight Group spent 95% of TST at a SpO 2 <90% in the alcohol condition compared with 0% in the Control Group in the alcohol condition (p<0.0001; figure 4 ). During sleep in the alcohol conditions, heart rate was higher in the InFlight Group compared with the Control Group (TST, N1–3, REM; p<0.0001).

Passengers frequently drink alcoholic beverages during a long-haul flight and fall asleep afterwards. Understanding the interacting effects of alcohol and sleep at altitude is therefore highly relevant. Such research provides insights for both passengers and healthcare professionals, facilitating the development of recommendations and guidelines for avoiding medical emergencies on board. The aim of this paper was to study the impact of moderate alcohol consumption and hypobaric conditions on sleep structure, SpO 2 and heart rate, with a special focus on the interacting effects of both conditions. An InFlight Group in a pressure chamber at a simulated altitude of 2438 m and a Control Group in the sleep laboratory were compared under two conditions of 4 hours of sleep: (1) sober and (2) with prior moderate alcohol consumption leading to a BAC of 0.04±0.003% just before going to sleep.

The combined impact of alcohol and hypobaric conditions led to an altered sleep architecture with shorter REM and N3 duration, prolonged N2 duration and increased WASO. During TST, SpO 2 decreased to a median of 85% which was accompanied by a compensatory increase in heart rate to a median of 88 bpm. Participants’ SpO 2 was in total 95% of TST below the clinical hypoxia threshold of 90%. Together these results indicate that, even in young and healthy individuals, the combination of alcohol intake with sleeping under hypobaric conditions poses a considerable strain on the cardiac system and might lead to exacerbation of symptoms in patients with cardiac or pulmonary diseases. Cardiovascular symptoms have a prevalence of 7% of inflight medical emergencies, with cardiac arrest causing 58% of aircraft diversions. 27 The risk of venous thromboembolism is lower in comparison, with one out of 6000 affected per flight of >4 hours. 28 It has been shown that desaturations below the hypoxia threshold were associated with worse postoperative patient outcome 29 and increased mortality risk for emergency admissions. 30 Thus, elderly people and/or people with pre-existing conditions are in danger of clinically relevant desaturations due to impaired ability for compensation, resulting in greater hypoxaemia. 5 6 31 We have previously identified sleep as a potential exacerbating factor that reduces the ability to compensate for the decreased oxygen partial pressure in the atmosphere. 7 Likewise, a study in a hypobaric chamber simulating a 20-hour flight with healthy participants not acclimatised to altitude showed that SpO 2 was lower during sleep in a coach-class aeroplane seat compared with being awake, and that even lower SpO 2 levels are to be expected in older passengers than in younger passengers. 4

In agreement with our findings on the impact of hypobaric conditions on SpO 2 (median SpO 2 of 88% during TST), a reduction in SpO 2 levels from 93.8% to 84% has been reported in response to simulated (normobaric hypoxia) and real (hypobaric hypoxia) altitude of 2000–3000 m. 12 21 32 33 Hoshikawa et al simulated an altitude of 2000 m under conditions of normobaric hypoxia with 16.4% oxygen. As in our study, the duration of N3 and REM sleep was shortened, SpO 2 decreased (89.6% vs 95.4%) and heart rate increased (55.6 bpm vs 51.3 bpm) compared with normobaric normoxia. 21 The strikingly lower heart rate under both test conditions compared with our study can be explained by the fact that only medium and long distance runners were included in the study. Latshang et al reported a reduction in slow wave sleep (NREM stages 3 and 4) and a decreased SpO 2 during sleep at 2590 m compared with 490 m. 10 As N3 and REM sleep are considered important for the recuperative value of sleep, 19 sleeping at altitude is less recuperative and refreshing and might impair cognitive functioning.

The single exposure to moderate alcohol intake just before bedtime in our study reduced median N1 and REM duration by 3.5 min and 4.5 min, respectively. A variety of potential effects of alcohol consumption on sleep (including no effects) have been reported. 18 In line with our findings, moderate, high and intoxicating doses of alcohol have been reported to reduce the duration of REM sleep. 17 34 Thakkar et al , however, stated that alcohol before bedtime irrespective of the dose shortens SOL and increases slow-wave sleep. 14 Alcohol facilitates sleep by a rapid increase in cerebral adenosine receptor availability, 16 which explains why alcohol is often used as a self-prescribed sleep aid. Accordingly, reduced SOL has been found frequently 16 18 and deemed to be the most robust effect of alcohol on sleep. 17 Alcohol has also been reported to increase N2 duration. 34 In our study SOL seemed shorter and N2 longer under the influence of alcohol, but the effects were non-significant. Alcohol intake also decreased SpO 2 by 1% to a median of 95% during TST and increased heart rate by 13 bpm to a median of 77 bpm compared with normobaric normoxic conditions without alcohol. In this case, the decrease in SpO 2 is unlikely to account for the acceleration in heart frequency. Under normoxic conditions a SpO 2 range from 96% to 98% has been defined as normal. 12 During sleep an average SpO 2 of 96.5±1.5% has been reported in healthy participants, 35 confirming the slight decrease from normal due to alcohol in our study as well as in other studies. 18 20 36 De Zambotti et al reported a dose-dependent impact of alcohol consumption just before retiring on heart rate during sleep. 37 A plausible mechanism is that alcohol induces peripheral systemic vasodilation which triggers the baroreceptor reflex resulting in an increased heart rate. 20

With different statistical comparisons, we tried to disentangle the additional and potentially synergistic impact that either alcohol had on top of hypobaric conditions or that hypobaric conditions had on top of alcohol intake. The additional exposure to alcohol primarily reduced REM duration further by 7.5 min compared with the hypobaric condition alone and showed a trend to shorten SOL and to prolong N2. It further decreased SpO 2 by 3% and led to an increase in the heart rate by 15 bpm. Comparing the change induced by alcohol intake (as the difference between both conditions) between the groups showed that alcohol also added a decrease in N3 duration to the hypoxia effect. Together these results fit well with the alterations that have been observed in response to alcohol intake alone, as discussed above. The additional exposure to the hypobaric condition reduced REM duration by 18 min, decreased N3 duration by 38 min, increased N2 duration, and WASO compared with alcohol exposure alone. It decreased SpO 2 and increased heart rate during TST by 10% and 11 bpm, respectively. Therefore, alcohol and hypobaric conditions have synergistic effects but the hypobaric condition contributes more to the observed changes than alcohol.

The results of this study refer only to a sleep duration of 4 hours, which limits the transferability to other sleep durations. However, the sleep duration was chosen to reflect realistic inflight sleep opportunities. Participants slept in supine positions, which resembles the situation of passengers travelling first and business class. Sleeping in a sitting position has been reported to impair sleep efficiency and REM duration. 38 Following the notion that hypobaric hypoxia is aggravated by sleep, passengers travelling in economy class might be affected to a lesser extent by the exposure to alcohol and hypobaric conditions. The free access of first and business class passengers to alcoholic beverages might increase the risk.

The sample examined in our study was of limited size and does not represent the average population. We derived the presented results from a subpart of a larger study, 22 so the absence of an a priori power calculation is a limitation. However, our findings are strong and robust and in line with existing literature. Even in these young and healthy subjects, critical oxygen desaturations below 90% were registered. In elderly and chronically ill people, the combined effects of alcohol consumption and hypobaric conditions on sleep architecture, SpO 2 and heart rate might be considerably stronger. Therefore, studying participants with stable treated respiratory disease is of wider public interest and is realistically feasible as it has already been done. 39 Barometric pressure was only one of several systematic differences between the sleeping environments. Factors such as personal comfort (real bed vs bunk bed as well as altitude chamber vs sleep laboratory) or ambient noise (quiet vs realistic inflight cabin noise) might have affected the outcome. Even though we provided two nights of recovery between conditions, carryover effects cannot be completely ruled out.

We conclude that the combined influence of alcohol and reduced atmospheric pressure has a supra-additive effect and even young and healthy participants suffered from clinically relevant desaturations (SpO 2 <90%) and heart rate accelerations during sleep. Since sleep quality was compromised, inflight sleep cannot be considered as fully recuperative. This is even more true for passengers travelling first and business class because they have the possibility to sleep in a horizontal position. Our findings support the recommendations of the BTS Clinical Statement on Air Travel to avoid alcohol in the 12 hours preceding and during air travel when suffering from obstructive sleep apnea syndrome or obesity hypoventilation syndrome. 3 Moreover, public awareness of this topic should be raised through patient charities, public campaigns and written health advice of airlines. Technical and economic constraints make it unlikely that an increase in cabin pressure will be implemented by airlines. 6

Statement of significance

Passengers frequently consume alcoholic beverages during long-haul flights before falling asleep. Despite this being a routinely occurring situation, the combined impact of moderate alcohol consumption and inflight hypobaric conditions on sleep, blood oxygen saturation (SpO 2 ) and heart rate is unknown. In young and healthy adults we found a decrease in SpO 2 to a median of 85% during sleep under the combined exposure that was accompanied by an increase in heart rate and disturbed sleep. Higher doses of alcohol could amplify these observed effects, potentially escalating the risk of health complications and medical emergencies during flight, especially among older individuals and those with pre-existing medical conditions. Our findings strongly suggest that the inflight consumption of alcoholic beverages should be restricted.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by the Ethics Committee of the North Rhine Medical Board, 2010100. Participants gave their signed informed consent according to the Declaration of Helsinki before taking part and were reimbursed for participation.

Acknowledgments

We are very grateful to the participants who took part in the study and for the support of our colleagues at the German Aerospace Center for the data collection. This work was part of the DLR project FIT (supported by the DLR Aeronautics Program).

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2
• Data supplement 3
• Data supplement 4

Contributors RAT: Conceptualization, formal analysis, writing - original draft, writing - review and editing. DR: Conceptualization, methodology, investigation, writing - review and editing. SB: Conceptualization, investigation, writing - review and editing. MW: Conceptualization, methodology, investigation, writing - review and editing. JW: Conceptualization, methodology, investigation, writing - review and editing. E-ME: Conceptualization, methodology, investigation, writing - review and editing, supervision, project administration, funding acquisition, guarantor.

Funding DLR Aeronautics Program (institutional funding; grant number: not applicable).

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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