good clinical research papers

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How to do high-quality clinical research 1: First steps

Affiliation.

• 1 Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
• PMID: 29299959
• DOI: 10.1177/1747493017750923

This is the first paper in a series of five on how to do good quality clinical research. It sets the scene for the four papers that follow. The aims of the series are to: promote reliable clinical research to inform clinical practice; help people new to research to get started (at any stage of their career); create teaching resources for experienced researchers; and help clinicians working in resource-poor settings to conduct research. We set out in this paper the skills clinicians need to run research projects that are relevant to their clinical practice. We focus on how to get the right training in research methodology, choose and refine a good research question, and then how to ensure the methods and data analysis plan are correct for the question being asked.

Keywords: Research; epidemiology; mentoring; project management; statistics; training.

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JAY SIWEK, M.D., MARGARET L. GOURLAY, M.D., DAVID C. SLAWSON, M.D., AND ALLEN F. SHAUGHNESSY, PHARM.D.

Am Fam Physician. 2002;65(2):251-258

Traditional clinical review articles, also known as updates, differ from systematic reviews and meta-analyses. Updates selectively review the medical literature while discussing a topic broadly. Nonquantitative systematic reviews comprehensively examine the medical literature, seeking to identify and synthesize all relevant information to formulate the best approach to diagnosis or treatment. Meta-analyses (quantitative systematic reviews) seek to answer a focused clinical question, using rigorous statistical analysis of pooled research studies. This article presents guidelines for writing an evidence-based clinical review article for American Family Physician . First, the topic should be of common interest and relevance to family practice. Include a table of the continuing medical education objectives of the review. State how the literature search was done and include several sources of evidence-based reviews, such as the Cochrane Collaboration, BMJ's Clinical Evidence , or the InfoRetriever Web site. Where possible, use evidence based on clinical outcomes relating to morbidity, mortality, or quality of life, and studies of primary care populations. In articles submitted to American Family Physician , rate the level of evidence for key recommendations according to the following scale: level A (randomized controlled trial [RCT], meta-analysis); level B (other evidence); level C (consensus/expert opinion). Finally, provide a table of key summary points.

American Family Physician is particularly interested in receiving clinical review articles that follow an evidence-based format. Clinical review articles, also known as updates, differ from systematic reviews and meta-analyses in important ways. 1 Updates selectively review the medical literature while discussing a topic broadly. An example of such a topic is, “The diagnosis and treatment of myocardial ischemia.” Systematic reviews comprehensively examine the medical literature, seeking to identify and synthesize all relevant information to formulate the best approach to diagnosis or treatment. Examples are many of the systematic reviews of the Cochrane Collaboration or BMJ's Clinical Evidence compendium. Meta-analyses are a special type of systematic review. They use quantitative methods to analyze the literature and seek to answer a focused clinical question, using rigorous statistical analysis of pooled research studies. An example is, “Do beta blockers reduce mortality following myocardial infarction?”

The best clinical review articles base the discussion on existing systematic reviews and meta-analyses, and incorporate all relevant research findings about the management of a given disorder. Such evidence-based updates provide readers with powerful summaries and sound clinical guidance.

In this article, we present guidelines for writing an evidence-based clinical review article, especially one designed for continuing medical education (CME) and incorporating CME objectives into its format. This article may be read as a companion piece to a previous article and accompanying editorial about reading and evaluating clinical review articles. 1 , 2 Some articles may not be appropriate for an evidence-based format because of the nature of the topic, the slant of the article, a lack of sufficient supporting evidence, or other factors. We encourage authors to review the literature and, wherever possible, rate key points of evidence. This process will help emphasize the summary points of the article and strengthen its teaching value.

Topic Selection

Choose a common clinical problem and avoid topics that are rarities or unusual manifestations of disease or that have curiosity value only. Whenever possible, choose common problems for which there is new information about diagnosis or treatment. Emphasize new information that, if valid, should prompt a change in clinical practice, such as the recent evidence that spironolactone therapy improves survival in patients who have severe congestive heart failure. 3 Similarly, new evidence showing that a standard treatment is no longer helpful, but may be harmful, would also be important to report. For example, patching most traumatic corneal abrasions may actually cause more symptoms and delay healing compared with no patching. 4

Searching the Literature

When searching the literature on your topic, please consult several sources of evidence-based reviews ( Table 1 ) . Look for pertinent guidelines on the diagnosis, treatment, or prevention of the disorder being discussed. Incorporate all high-quality recommendations that are relevant to the topic. When reviewing the first draft, look for all key recommendations about diagnosis and, especially, treatment. Try to ensure that all recommendations are based on the highest level of evidence available. If you are not sure about the source or strength of the recommendation, return to the literature, seeking out the basis for the recommendation.

In particular, try to find the answer in an authoritative compendium of evidence-based reviews, or at least try to find a meta-analysis or well-designed randomized controlled trial (RCT) to support it. If none appears to be available, try to cite an authoritative consensus statement or clinical guideline, such as a National Institutes of Health Consensus Development Conference statement or a clinical guideline published by a major medical organization. If no strong evidence exists to support the conventional approach to managing a given clinical situation, point this out in the text, especially for key recommendations. Keep in mind that much of traditional medical practice has not yet undergone rigorous scientific study, and high-quality evidence may not exist to support conventional knowledge or practice.

Patient-Oriented vs. Disease-Oriented Evidence

With regard to types of evidence, Shaughnessy and Slawson 5 – 7 developed the concept of Patient-Oriented Evidence that Matters (POEM), in distinction to Disease-Oriented Evidence (DOE). POEM deals with outcomes of importance to patients, such as changes in morbidity, mortality, or quality of life. DOE deals with surrogate end points, such as changes in laboratory values or other measures of response. Although the results of DOE sometimes parallel the results of POEM, they do not always correspond ( Table 2 ) . 2 When possible, use POEM-type evidence rather than DOE. When DOE is the only guidance available, indicate that key clinical recommendations lack the support of outcomes evidence. Here is an example of how the latter situation might appear in the text: “Although prostate-specific antigen (PSA) testing identifies prostate cancer at an early stage, it has not yet been proved that PSA screening improves patient survival.” (Note: PSA testing is an example of DOE, a surrogate marker for the true outcomes of importance—improved survival, decreased morbidity, and improved quality of life.)

Evaluating the Literature

Evaluate the strength and validity of the literature that supports the discussion (see the following section, Levels of Evidence). Look for meta-analyses, high-quality, randomized clinical trials with important outcomes (POEM), or well-designed, nonrandomized clinical trials, clinical cohort studies, or case-controlled studies with consistent findings. In some cases, high-quality, historical, uncontrolled studies are appropriate (e.g., the evidence supporting the efficacy of Papanicolaou smear screening). Avoid anecdotal reports or repeating the hearsay of conventional wisdom, which may not stand up to the scrutiny of scientific study (e.g., prescribing prolonged bed rest for low back pain).

Look for studies that describe patient populations that are likely to be seen in primary care rather than subspecialty referral populations. Shaughnessy and Slawson's guide for writers of clinical review articles includes a section on information and validity traps to avoid. 2

Levels of Evidence

Readers need to know the strength of the evidence supporting the key clinical recommendations on diagnosis and treatment. Many different rating systems of varying complexity and clinical relevance are described in the medical literature. Recently, the third U.S. Preventive Services Task Force (USPSTF) emphasized the importance of rating not only the study type (RCT, cohort study, case-control study, etc.), but also the study quality as measured by internal validity and the quality of the entire body of evidence on a topic. 8

While it is important to appreciate these evolving concepts, we find that a simplified grading system is more useful in AFP . We have adopted the following convention, using an ABC rating scale. Criteria for high-quality studies are discussed in several sources. 8 , 9 See the AFP Web site ( www.aafp.org/afp/authors ) for additional information about levels of evidence and see the accompanying editorial in this issue discussing the potential pitfalls and limitations of any rating system.

Level A (randomized controlled trial/meta-analysis): High-quality randomized controlled trial (RCT) that considers all important outcomes. High-quality meta-analysis (quantitative systematic review) using comprehensive search strategies.

Level B (other evidence): A well-designed, nonrandomized clinical trial. A nonquantitative systematic review with appropriate search strategies and well-substantiated conclusions. Includes lower quality RCTs, clinical cohort studies, and case-controlled studies with non-biased selection of study participants and consistent findings. Other evidence, such as high-quality, historical, uncontrolled studies, or well-designed epidemiologic studies with compelling findings, is also included.

Level C (consensus/expert opinion): Consensus viewpoint or expert opinion.

Each rating is applied to a single reference in the article, not to the entire body of evidence that exists on a topic. Each label should include the letter rating (A, B, C), followed by the specific type of study for that reference. For example, following a level B rating, include one of these descriptors: (1) nonrandomized clinical trial; (2) nonquantitative systematic review; (3) lower quality RCT; (4) clinical cohort study; (5) case-controlled study; (6) historical uncontrolled study; (7) epidemiologic study.

Here are some examples of the way evidence ratings should appear in the text:

“To improve morbidity and mortality, most patients in congestive heart failure should be treated with an angiotensin-converting enzyme inhibitor. [Evidence level A, RCT]”

“The USPSTF recommends that clinicians routinely screen asymptomatic pregnant women 25 years and younger for chlamydial infection. [Evidence level B, non-randomized clinical trial]”

“The American Diabetes Association recommends screening for diabetes every three years in all patients at high risk of the disease, including all adults 45 years and older. [Evidence level C, expert opinion]”

When scientifically strong evidence does not exist to support a given clinical recommendation, you can point this out in the following way:

“Physical therapy is traditionally prescribed for the treatment of adhesive capsulitis (frozen shoulder), although there are no randomized outcomes studies of this approach.”

Format of the Review

Introduction.

The introduction should define the topic and purpose of the review and describe its relevance to family practice. The traditional way of doing this is to discuss the epidemiology of the condition, stating how many people have it at one point in time (prevalence) or what percentage of the population is expected to develop it over a given period of time (incidence). A more engaging way of doing this is to indicate how often a typical family physician is likely to encounter this problem during a week, month, year, or career. Emphasize the key CME objectives of the review and summarize them in a separate table entitled “CME Objectives.”

The methods section should briefly indicate how the literature search was conducted and what major sources of evidence were used. Ideally, indicate what predetermined criteria were used to include or exclude studies (e.g., studies had to be independently rated as being high quality by an established evaluation process, such as the Cochrane Collaboration). Be comprehensive in trying to identify all major relevant research. Critically evaluate the quality of research reviewed. Avoid selective referencing of only information that supports your conclusions. If there is controversy on a topic, address the full scope of the controversy.

The discussion can then follow the typical format of a clinical review article. It should touch on one or more of the following subtopics: etiology, pathophysiology, clinical presentation (signs and symptoms), diagnostic evaluation (history, physical examination, laboratory evaluation, and diagnostic imaging), differential diagnosis, treatment (goals, medical/surgical therapy, laboratory testing, patient education, and follow-up), prognosis, prevention, and future directions.

The review will be comprehensive and balanced if it acknowledges controversies, unresolved questions, recent developments, other viewpoints, and any apparent conflicts of interest or instances of bias that might affect the strength of the evidence presented. Emphasize an evidence-supported approach or, where little evidence exists, a consensus viewpoint. In the absence of a consensus viewpoint, you may describe generally accepted practices or discuss one or more reasoned approaches, but acknowledge that solid support for these recommendations is lacking.

In some cases, cost-effectiveness analyses may be important in deciding how to implement health care services, especially preventive services. 10 When relevant, mention high-quality cost-effectiveness analyses to help clarify the costs and health benefits associated with alternative interventions to achieve a given health outcome. Highlight key points about diagnosis and treatment in the discussion and include a summary table of the key take-home points. These points are not necessarily the same as the key recommendations, whose level of evidence is rated, although some of them will be.

Use tables, figures, and illustrations to highlight key points, and present a step-wise, algorithmic approach to diagnosis or treatment when possible.

Rate the evidence for key statements, especially treatment recommendations. We expect that most articles will have at most two to four key statements; some will have none. Rate only those statements that have corresponding references and base the rating on the quality and level of evidence presented in the supporting citations. Use primary sources (original research, RCTs, meta-analyses, and systematic reviews) as the basis for determining the level of evidence. In other words, the supporting citation should be a primary research source of the information, not a secondary source (such as a nonsystematic review article or a textbook) that simply cites the original source. Systematic reviews that analyze multiple RCTs are good sources for determining ratings of evidence.

The references should include the most current and important sources of support for key statements (i.e., studies referred to, new information, controversial material, specific quantitative data, and information that would not usually be found in most general reference textbooks). Generally, these references will be key evidence-based recommendations, meta-analyses, or landmark articles. Although some journals publish exhaustive lists of reference citations, AFP prefers to include a succinct list of key references. (We will make more extensive reference lists available on our Web site or provide links to your personal reference list.)

You may use the following checklist to ensure the completeness of your evidence-based review article; use the source list of reviews to identify important sources of evidence-based medicine materials.

Checklist for an Evidence-Based Clinical Review Article

The topic is common in family practice, especially topics in which there is new, important information about diagnosis or treatment.

The introduction defines the topic and the purpose of the review, and describes its relevance to family practice.

A table of CME objectives for the review is included.

The review states how you did your literature search and indicates what sources you checked to ensure a comprehensive assessment of relevant studies (e.g., MEDLINE, the Cochrane Collaboration Database, the Center for Research Support, TRIP Database).

Several sources of evidence-based reviews on the topic are evaluated ( Table 1 ) .

Where possible, POEM (dealing with changes in morbidity, mortality, or quality of life) rather than DOE (dealing with mechanistic explanations or surrogate end points, such as changes in laboratory tests) is used to support key clinical recommendations ( Table 2 ) .

Studies of patients likely to be representative of those in primary care practices, rather than subspecialty referral centers, are emphasized.

Studies that are not only statistically significant but also clinically significant are emphasized; e.g., interventions with meaningful changes in absolute risk reduction and low numbers needed to treat. (See http://www.cebm.net/index.aspx?o=1116 .) 11

The level of evidence for key clinical recommendations is labeled using the following rating scale: level A (RCT/meta-analysis), level B (other evidence), and level C (consensus/expert opinion).

Acknowledge controversies, recent developments, other viewpoints, and any apparent conflicts of interest or instances of bias that might affect the strength of the evidence presented.

Highlight key points about diagnosis and treatment in the discussion and include a summary table of key take-home points.

Use tables, figures, and illustrations to highlight key points and present a step-wise, algorithmic approach to diagnosis or treatment when possible.

Emphasize evidence-based guidelines and primary research studies, rather than other review articles, unless they are systematic reviews.

The essential elements of this checklist are summarized in Table 3 .

Siwek J. Reading and evaluating clinical review articles. Am Fam Physician. 1997;55:2064-2069.

Shaughnessy AF, Slawson DC. Getting the most from review articles: a guide for readers and writers. Am Fam Physician. 1997;55:2155-60.

Pitt B, Zannad F, Remme WJ, Cody R, Castaigne A, Perez A, et al. The effect of spironolactone on morbidity and mortality in patients with severe heart failure. N Engl J Med. 1999;341:709-17.

Flynn CA, D'Amico F, Smith G. Should we patch corneal abrasions? A meta-analysis. J Fam Pract. 1998;47:264-70.

Slawson DC, Shaughnessy AF, Bennett JH. Becoming a medical information master: feeling good about not knowing everything. J Fam Pract. 1994;38:505-13.

Shaughnessy AF, Slawson DC, Bennett JH. Becoming an information master: a guidebook to the medical information jungle. J Fam Pract. 1994;39:489-99.

Slawson DC, Shaughnessy AF. Becoming an information master: using POEMs to change practice with confidence. Patient-oriented evidence that matters. J Fam Pract. 2000;49:63-7.

Harris RP, Helfand M, Woolf SH, Lohr KN, Mulrow CD, Teutsch SM, et al. Methods Work Group, Third U.S. Preventive Services Task Force. Current methods of the U.S. Preventive Services Task Force. A review of the process. Am J Prev Med. 2001;20(3 suppl):21-35.

CATbank topics: levels of evidence and grades of recommendations. Retrieved November 2001, from: http://www.cebm.net/ .

Saha S, Hoerger TJ, Pignone MP, Teutsch SM, Helfand M, Mandelblatt JS. for the Cost Work Group of the Third U.S. Preventive Services Task Force. The art and science of incorporating cost effectiveness into evidence-based recommendations for clinical preventive services. Am J Prev Med. 2001;20(3 suppl):36-43.

Evidence-based medicine glossary. Retrieved November 2001, from: http://www.cebm.net/index.aspx?o=1116 .

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Clinical Research: An Overview of Study Types, Designs, and Their Implications in the Public Health Perspective

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Clinical Research Paper Topics

This page aims to provide a comprehensive list of clinical research paper topics spanning various subfields of clinical research. It further guides students on how to choose a fitting topic and how to write an effective clinical research paper. Additionally, it introduces iResearchNet’s writing services, offering a platform for students to order a custom clinical research paper. The ultimate goal of this resource is to support students in their quest for academic excellence and successful navigation through the realm of clinical research.

100 Clinical Research Paper Topics

Navigating through the extensive field of clinical research may seem daunting. To streamline this process, we present a comprehensive list of clinical research paper topics divided into ten categories. Each category features ten engaging and relevant topics that cover a wide range of current issues in the field of clinical research.

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1. Disease Prevention and Management:

• The Role of Clinical Research in the Prevention of Cardiovascular Diseases
• Advancements in Clinical Strategies for Diabetes Management
• Impact of Clinical Research in Cancer Prevention
• Novel Clinical Approaches to Managing Obesity
• Prevention and Control of Sexually Transmitted Infections: Clinical Perspectives
• Clinical Research in the Battle Against Alzheimer’s Disease
• Impact of Lifestyle Changes on Disease Prevention: Clinical Evidence
• Role of Vaccination in the Prevention of Infectious Diseases: A Clinical Overview
• Clinical Trials in the Prevention and Management of Mental Health Disorders
• Osteoporosis: Prevention and Management Strategies in Clinical Research

2. Clinical Trials:

• The Importance of Randomization in Clinical Trials
• Ethical Considerations in Conducting Clinical Trials
• The Role of Placebos in Clinical Trials
• Clinical Trials in Pediatric Populations
• Patient Recruitment Strategies in Clinical Trials
• Advances in Oncology: The Role of Clinical Trials
• The Impact of Clinical Trials on Drug Development
• Adaptive Design in Clinical Trials
• Challenges in the Conduct of Clinical Trials during a Pandemic
• Clinical Trials: From Bench to Bedside

3. Ethical Issues in Clinical Research:

• Informed Consent in Clinical Research
• Confidentiality and Privacy Issues in Clinical Research
• The Ethics of Using Animals in Clinical Research
• Ethical Dilemmas in Genetic Testing and Clinical Research
• Protection of Vulnerable Groups in Clinical Research
• Balancing Benefit and Risk in Clinical Research
• Ethical Aspects of Research using Human Tissue Samples
• Data Integrity and Ethical Considerations in Clinical Research
• Ethical Challenges in Conducting Research on Rare Diseases
• Conflict of Interest in Clinical Research

4. Technological Advancements in Clinical Research:

• The Role of Artificial Intelligence in Clinical Research
• Use of Mobile Technologies in Clinical Trials
• Big Data and Its Impact on Clinical Research
• The Potential of Virtual Reality in Clinical Trials
• The Role of Blockchain Technology in Clinical Research
• Wearable Technology and Patient Monitoring in Clinical Trials
• The Impact of Genomics and Precision Medicine on Clinical Research
• Telemedicine and Its Role in Clinical Research
• The Role of Robotic Surgery in Clinical Trials
• Application of Nanotechnology in Clinical Research

5. Pharmacological Clinical Research:

• Clinical Trials and Drug Development
• Clinical Research in Antibiotic Resistance
• Adverse Drug Reactions: Monitoring and Management in Clinical Research
• Clinical Research in Psychopharmacology
• Pharmacogenetics and Personalized Medicine
• Clinical Research in Opioid Use and Addiction
• Clinical Trials for Novel Vaccines
• Clinical Research and the Development of Anticancer Drugs
• Pediatric Pharmacology and the Challenges in Clinical Research
• Over-the-counter Drugs and Self-Medication: A Clinical Research Perspective

6. Health Policy and Clinical Research:

• The Impact of Health Policy on Clinical Research
• Health Insurance Policies and Accessibility to Clinical Trials
• The Role of Policy in Regulating Clinical Trials
• How Health Policies Influence Patient Participation in Clinical Trials
• Policies for Ethical Conduct in Clinical Research
• Health Policy and Its Influence on Drug Development in Clinical Research
• The Impact of Global Health Policies on Clinical Research
• Policy Considerations for Genetic Testing in Clinical Research
• The Role of Policy in the Prevention of Clinical Research Misconduct
• The Impact of Health Policy Changes on the Scope of Clinical Research

7. Mental Health and Clinical Research:

• Clinical Research on the Efficacy of Cognitive Behavioral Therapy
• Role of Clinical Trials in Developing New Psychotropic Medications
• Advances in Clinical Research for Treating Post-Traumatic Stress Disorder
• The Role of Clinical Research in Understanding the Biology of Depression
• Clinical Research Approaches in Managing Childhood Autism
• Clinical Trials in the Development of Therapies for Schizophrenia
• Impact of Clinical Research on the Treatment of Anxiety Disorders
• Clinical Research on Mental Health in the Elderly
• Novel Clinical Approaches to Treating Eating Disorders
• The Role of Clinical Research in Suicidal Behavior Studies

8. Public Health and Clinical Research:

• The Role of Clinical Research in Advancing Public Health
• The Impact of Public Health Policies on Clinical Research
• Clinical Research on Preventive Measures in Public Health
• Clinical Trials and Their Significance in Public Health Improvement
• The Role of Clinical Research in Reducing Health Disparities
• Clinical Research in the Field of Occupational Health
• The Impact of Environmental Health Factors: A Clinical Research Perspective
• Clinical Research in the Control and Prevention of Epidemics
• The Role of Public Health Interventions in Clinical Research
• Global Public Health and the Need for International Clinical Research

9. Pediatric Clinical Research:

• Ethical Considerations in Pediatric Clinical Research
• The Role of Clinical Trials in Advancing Pediatric Medicine
• Clinical Research on Pediatric Oncology
• The Impact of Clinical Research on Pediatric Neurological Disorders
• Clinical Research on Rare Genetic Disorders in Children
• The Role of Clinical Research in Understanding and Treating Autism in Children
• Clinical Trials in the Development of Pediatric Vaccines
• The Impact of Clinical Research on Neonatal Care
• Challenges and Opportunities in Pediatric Clinical Trials
• Clinical Research in Pediatric Cardiology

10. Clinical Research in the Era of COVID-19:

• The Role of Clinical Research in Understanding COVID-19
• Clinical Trials for the Development of COVID-19 Vaccines
• Impact of the COVID-19 Pandemic on Ongoing Clinical Trials
• The Role of Clinical Research in Understanding Long-COVID
• Clinical Trials for COVID-19 Treatments: Successes and Challenges
• The Impact of COVID-19 on Mental Health: A Clinical Research Perspective
• Clinical Research on the Effect of COVID-19 in Children
• Lessons from COVID-19: Clinical Research for Future Pandemics
• The Role of Telemedicine in Clinical Research During COVID-19
• Ethical Considerations for Clinical Research During a Global Pandemic

This comprehensive list ensures that students have a broad spectrum of contemporary and significant topics to choose from, allowing them to find a subject that fits their interests and the current demands of the field.

Choosing Clinical Research Paper Topics

Choosing the right clinical research paper topic is a critical first step in the writing process. The topic you choose can set the tone for your entire project and greatly affect your experience. Here are ten tips to guide you in making the right choice:

• Understand the Assignment: Before diving into topic selection, ensure you fully understand your assignment’s parameters. Are there specific guidelines about the type of topic you should choose? Is there a certain page or word limit? Do you need to incorporate specific resources? Understanding these requirements can help you tailor your topic appropriately.
• Passion and Interest: Choose a topic that genuinely interests you. Writing a research paper can be a long process, and your passion for the subject can keep you motivated. It also makes it easier to delve deeper and provide new insights.
• Scope of the Topic: Consider the breadth of your topic. Is it too broad to be adequately covered in your paper, or is it too narrow that it lacks sufficient material for research? Aim for a balance where your topic can be fully addressed within the constraints of your assignment.
• Relevance: Opt for topics that are relevant to your field of study and the current climate of research. These could be emerging trends, pressing issues, or gaps in the existing literature that need to be filled.
• Originality: Try to select a topic that is unique and original. While you don’t have to reinvent the wheel, having a fresh perspective or a novel aspect can make your paper more interesting.
• Availability of Resources: Before finalizing a topic, ensure there are enough resources available for you to research. These could be scholarly articles, books, reliable online sources, or primary research materials.
• Consult with Your Instructor or Advisor: If you’re struggling with choosing a topic, don’t hesitate to consult your instructor or advisor. They can provide valuable guidance, help you refine your ideas, and point you towards resources you may not have considered.
• Future Implications: Consider how the topic you choose might impact your future career or academic goals. Is it something that could contribute to your long-term research goals or lead to a specialization?
• Practicality: Consider the feasibility of your topic. Do you have the time and resources to complete this research? For instance, if your topic requires conducting surveys or interviews, will you have access to the required sample population?
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• Consensus Statement
• Published: 19 July 2024

Reporting guidelines for precision medicine research of clinical relevance: the BePRECISE checklist

• Siew S. Lim 1 ,
• Zhila Semnani-Azad   ORCID: orcid.org/0000-0001-7822-5072 2 ,
• Mario L. Morieri   ORCID: orcid.org/0000-0001-6864-0547 3 , 4 ,
• Ashley H. Ng 5 , 6 , 7 ,
• Abrar Ahmad 8 ,
• Hugo Fitipaldi 8 ,
• Jacqueline Boyle 1 ,
• Christian Collin 9 ,
• John M. Dennis   ORCID: orcid.org/0000-0002-7171-732X 10 ,
• Claudia Langenberg   ORCID: orcid.org/0000-0002-5017-7344 7 , 11 ,
• Ruth J. F. Loos 12 , 13 ,
• Melinda Morrison 14 ,
• Michele Ramsay   ORCID: orcid.org/0000-0002-4156-4801 15 ,
• Arun J. Sanyal   ORCID: orcid.org/0000-0001-8682-5748 16 ,
• Naveed Sattar   ORCID: orcid.org/0000-0002-1604-2593 17 ,
• Marie-France Hivert   ORCID: orcid.org/0000-0001-7752-2585 18 ,
• Maria F. Gomez   ORCID: orcid.org/0000-0001-6210-3142 8 ,
• Jordi Merino   ORCID: orcid.org/0000-0001-8312-1438 12 , 19 , 20 , 21 ,
• Deirdre K. Tobias 2 , 22 ,
• Michael I. Trenell 23 ,
• Stephen S. Rich   ORCID: orcid.org/0000-0003-3872-7793 24 ,
• Jennifer L. Sargent 25 &
• Paul W. Franks   ORCID: orcid.org/0000-0002-0520-7604 2 , 26  

Nature Medicine volume  30 ,  pages 1874–1881 ( 2024 ) Cite this article

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Precision medicine should aspire to reduce error and improve accuracy in medical and health recommendations by comparison with contemporary practice, while maintaining safety and cost-effectiveness. The etiology, clinical manifestation and prognosis of diseases such as obesity, diabetes, cardiovascular disease, kidney disease and fatty liver disease are heterogeneous. Without standardized reporting, this heterogeneity, combined with the diversity of research tools used in precision medicine studies, makes comparisons across studies and implementation of the findings challenging. Specific recommendations for reporting precision medicine research do not currently exist. The BePRECISE (Better Precision-data Reporting of Evidence from Clinical Intervention Studies & Epidemiology) consortium, comprising 23 experts in precision medicine, cardiometabolic diseases, statistics, editorial and lived experience, conducted a scoping review and participated in a modified Delphi and nominal group technique process to develop guidelines for reporting precision medicine research. The BePRECISE checklist comprises 23 items organized into 5 sections that align with typical sections of a scientific publication. A specific section about health equity serves to encourage precision medicine research to be inclusive of individuals and communities that are traditionally under-represented in clinical research and/or underserved by health systems. Adoption of BePRECISE by investigators, reviewers and editors will facilitate and accelerate equitable clinical implementation of precision medicine.

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Precision medicine represents an evolution in the long history of evidence-based medicine and healthcare. Spanning disease classifications and risk factor boundaries, precision medicine is underpinned by four key ‘pillars’ (prevention, diagnosis, treatment and prognosis) 1 , 2 . The overarching objective of precision medicine is to reduce error and improve accuracy in medical and health recommendations compared with contemporary approaches 3 . Precision medicine solutions should meet or improve on existing standards for safety. They should also be compatible with the individual’s preferences, capabilities and needs and tailored to the cultural and societal conditions of the population. Furthermore, precision medicine should be cost-effective and enhance health equity by increasing access to better medical and healthcare practices for the people most in need.

Cardiometabolic diseases are the leading causes of mortality globally 4 . With this burden projected to worsen over the coming decades 5 , innovative approaches to disease prevention, diagnosis and treatment are urgently needed. A plethora of precision medicine approaches are being explored in translational and clinical research. However, translating, scaling and implementing these findings for clinical practice have proved difficult. The heterogeneous nature of disease presentation and the etiology of cardiometabolic diseases contribute to these challenges, as does the range and diversity of clinical information, molecular data types and computational analyses used in precision medicine research.

The ability to synthesize data and reproduce research findings are tenets of the modern scientific process, which help maximize progress in evidence-based healthcare and medicine. The ‘Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine’ 3 was supported by a series of systematic evidence reviews 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 . The report focused on key dimensions of precision diabetes medicine, including evidence for prevention, diagnosis, treatment and prognosis in monogenic forms of diabetes, gestational diabetes, and type 1 and type 2 diabetes. A key finding from the report and the systematic evidence reviews underpinning it is that the published literature on precision diabetes medicine lacks evidence standardization or benchmarking against contemporary standards and often overlooks under-represented populations, who tend to bear the greatest burden of diabetes and its complications.

In the present report, we present reporting guidelines for clinically relevant precision medicine research, using common cardiometabolic diseases as the example. We first evaluated a representative sample of the literature on precision medicine in cardiometabolic diseases, determining that the quality of evidence reporting is low, akin to the level previously observed for precision diabetes medicine 3 . We then generated consensus guidelines and a corresponding checklist for reporting of research germane to precision medicine. The purpose of these guidelines is to improve reporting standards so that: (1) evidence can be combined and synthesized in a way that yields meaningful insights from collective efforts; (2) claims of clinical utility can be benchmarked against contemporary standards; and (3) end-user engagement and health equity will be strengthened.

Scoping review

The literature search focused on identifying precision medicine publications using the term ‘precision medicine’ and associated proxy nomenclature, among other keywords and phrases ( Supplementary Methods ). The search identified 2,679 publications, of which 13 were excluded owing to duplication. The remaining 2,666 papers were screened, of which 47 were randomly selected (through computer-generated, random-number sequence) for full text review and quality assessment. The summary (count and percentage) of each quality assessment item across all papers and the quality assessment results for each paper are shown in Supplementary Tables 2 and 3 . This quality assessment yielded a median score of 6 (interquartile range = 4–7) with none of the papers achieving a positive quality evaluation across all 11 items (Fig. 1 ).

Median scores of 47 published precision medicine manuscripts randomly selected for full text review and quality assessment through computer-generated, random-number sequence. IQR, Interquartile range.

A summary of the itemized evidence reporting quality is shown in Supplementary Table 2 . Most abstracts (81%) reported findings relevant to the four pillars of precision medicine (prevention, diagnosis, treatment and/or prognosis) and provided sufficient detail in the methods sections to determine whether the study was designed to test hypotheses on precision medicine (77%), details about participant eligibility (75%) and descriptions of standard reporting definitions (70%). The items that were less frequently reported were the description of patient and public involvement and engagement (PPIE) in determining the impact and utility of precision medicine (15%), the inclusion of the term ‘precision medicine’ in the title or abstract (17%), the reporting of measures of discriminative or predictive accuracy (23%), the description of the approach used to control risk of false-positive reporting (28%), the reporting of effect estimates with 95% confidence intervals and units underlying effect estimates (57%) and the reporting of a statistical test for comparisons of subgroups (for example, interaction test) (60%).

Stakeholder survey

Delphi panel demographics.

Of the 23 Delphi panelists, 22 (96%) completed Delphi survey 1, 18 (78%) and attended the full-panel consensus meeting and 22 (96%) completed Delphi survey 2. All panelists engaged in further extensive dialog around key topics through online communication.

Delphi results

The initial checklist in Delphi survey 1 contained 68 items. After Delphi survey 1 and the full-panel consensus meeting, 2 items were added, resulting in 70 items in Delphi survey 2. At the Consensus meeting, it was determined that the checklist should be used together with existing relevant checklists. These include the CONSORT (Consolidated Standards of Reporting Trials) 17 and STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) 18 checklists for interventional trials and observational studies, respectively. This led to a recommendation to remove items covered in established checklists (Supplementary Fig. 1 ). The scoring from Delphi survey 1, Delphi survey 2 and notes from the Consensus meetings are as shown in Supplementary Table 4 . After Delphi survey 2, the consensus was to retain 25 items across 6 core categories.

Guidelines finalization

The executive oversight committee reviewed the panel scores and free-text comments from all the rounds of Delphi surveys to determine the final checklist items and wording. The group discussed five items with inconsistent consensus (between 70% and 80% consensus), resulting in the removal of one item because it overlapped conceptually with another item (17b and 17g in Supplementary Table 4 ). It was also determined that ‘health equity’ should be included as an overarching theme, thereby encouraging users of the checklist to consider this topic more broadly when describing precision medicine research. This resulted in removal of two items.

The final checklist comprised 23 items that the executive oversight committee concluded are unique and essential for reporting standards in precision medicine. The final BePRECISE checklist is presented in Table 1 , with a downloadable version of the checklist available online ( https://www.be-precise.org , and https://www.equator-network.org/reporting-guidelines/ ).

Explanation of checklist Items

The checklist and the explanation of each item are presented in Table 1 . The BePRECISE checklist is intended to complement existing guidelines such as CONSORT 17 , STROBE 18 and PRISMA (Preferred Reporting System for Systematic Reviews and Meta-Analyses) 19 .

These reporting guidelines use the terms ‘precision medicine’ and ‘personalized medicine’ as defined in the ‘Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine’ 3 , as follows:

‘Precision medicine focuses on minimizing errors and improving accuracy in medical decisions and health recommendations. It seeks to maximize efficacy, cost-effectiveness, safety, access for those in need and compliance compared with contemporary evidence-based medicine. Precision medicine emphasizes tailoring diagnostics or therapeutics (prevention or treatment) to subgroups of populations sharing similar characteristics.’

Personalized medicine refers to ‘the use of a person’s own data to objectively gauge the efficacy, safety, and tolerability of therapeutics, and, subjectively, to tailor health recommendations and/or medical decisions to the individual’s preferences, circumstances, and capabilities’.

Accordingly, personalized medicine can be viewed as being nested within the broader concept of precision medicine.

Equity and PPIE (E1–E4)

Equity, diversity and inclusivity considerations and the involvement of patients and public is a crosscutting theme in this checklist. Where relevant, papers should include a description of how equity has been considered, including diversity and inclusivity of study participants, and whether there was PPIE. Cohort selection biases and probable risks when extrapolating the study’s results to other populations should be clearly described.

The selection of participants should consider racial, ethnic, ancestral, geographic and sociodemographic characteristics 20 , and include an explanation for the inclusion or exclusion of groups that are typically under-represented in clinical research (E1 and E2). Race and ethnicity are social constructs but, as they are categories recognized by some government and health authorities in contexts that are relevant to precision medicine, we have elected to retain inclusion of these somewhat controversial terms here.

PPIE in any part of the study should be described, including but not limited to design, conduct and reporting (E3).

Where possible, and ideally with guidance from those with lived experience, the potential impact of the research findings on the target population(s) should be discussed (E4). Consider co-writing these aspects with PPIE representatives.

Title and abstract (1.1–1.4)

In the title and/or abstract, the term ‘precision medicine’ should be included to highlight that the research is relevant to precision medicine (1.1). Given that precision medicine is an approach that can be used in several research contexts, the study design (for example, randomized clinical trial (RCT), retrospective observational) and the research question should be stated clearly (1.2). Use of the terms ‘prevention’, ‘diagnostics’, ‘treatment’ or ‘prognostics’ is needed to highlight which pillar of precision medicine the study concerns 3 (1.3). To ensure transparency about generalizability and/or applicability of the findings to a specific population or subgroup, the study population must be described (1.4).

Background and objectives (2.1–2.2)

The background should clearly describe the rationale for the chosen precision medicine approach, including the context and prior work that led to it and the specific hypothesis being tested (2.1). To provide the reader with greater context, papers should also state the nature and objective of the precision medicine study as ‘etiological’, ‘discovery’, ‘predictive’ and/or ‘confirmatory’ (2.2).

Methods (general)

Although this reporting guide focuses on clarifying elements of papers that are germane to precision medicine, authors are strongly encouraged to ensure that methods also adhere to other appropriate reporting guidelines (for example, CONSORT and STROBE), with the overarching goal of ensuring that the study protocol described therein could, in principle, be accurately reproduced by third-party investigators.

Methods (3.1–3.7)

Methods should describe the aspects of a study design relating to precision medicine in such detail that the design can be understood and replicated (3.1). The rationale for the choice of primary outcome should be clearly stated (3.2).

To enable readers to assess bias and interpret the study findings, this section should state how the participants were identified and enrolled in the study (4.1) and (if applicable) how a subset of a broader group of participants was selected from an existing study (3.3). Any markers used for stratification or prediction should be explicitly stated with an explanation of how the marker(s) was(were) chosen (3.4).

The sample size and how it was derived should be described, for example, following a priori power calculations, or if the sample size was limited primarily by availability or cost, and any implications that this might have for type 2 error (3.5). Authors should also describe attempts to minimize false-positive discovery, especially when multiple testing has occurred (3.5).

If any replication and/or validation analyses were undertaken, a clear description should be given of the approach, including whether these analyses were planned and relevant datasets identified before or after conclusion of primary analyses (3.6), in addition to justification for the sample size and choice of replication cohort (3.7).

Results (4.1–4.4)

The number of participants in the study should be provided, along with a table of baseline characteristics (4.1). If the analysis involves comparison (rather than discovery) of subgroups, the baseline characteristics and numbers of participants should be provided by the subgroup.

Results from any statistical tests done should be reported. Any comparisons of subgroups should include appropriate test statistics, which may include tests of interaction and heterogeneity, and in cluster analyses tests of probability for cluster assignment (for example, relative entropy statistic) (4.2).

Key findings should be benchmarked against current reference standards or practice, if they exist, so that the reader can determine the likely benefit of translating the study’s findings into clinical practice. This may include, for example, the comparison of the new and existing approaches using tests of discriminative (cross-sectional) or predictive (prospective) accuracy, or estimation of net reclassification or changes in numbers needed to treat. If benchmarking has not been done, a clear explanation should be given (4.3).

If validation and/or replication analyses were undertaken, the results of all such attempts at analyses should be clearly described (4.4).

Discussion (5.1–5.2)

The paper should include a balanced and nuanced discussion of any limitations to the interpretation and/or implementation of the reported findings. The limitations section should consider biases that might prevent fair and equitable generalization of the study’s findings to other populations, particularly to groups that are under-represented within the published literature. Authors are also encouraged to consider other potential biases that might arise with stratified and subgroup analyses (5.1).

If there is a direct clinical implication of the study’s findings, authors should describe how their findings might be applied in clinical practice. This might, for example, include an explanation of how any algorithms, technologies or risk markers that stem directly from the research might benefit clinical practice.

The BePRECISE guidelines are intended to enhance publication of research on precision medicine by improving quality and standardization of reporting. In turn, it is anticipated that this will help improve and accelerate the impact of precision medicine research on the health and well-being of target populations and individuals.

BePRECISE was initiated to follow up on recommendations from the ‘Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine’ 3 . The report, founded on 16 systematic evidence reviews summarizing research described in >100,000 published papers, found a low degree of standardization across the published literature, with a broad absence of key information needed for benchmarking against contemporary standards, validation analyses and meaningful interpretation of research findings.

Implementation of the checklist

These reporting guidelines were derived through structured evaluation and consensus processes undertaken by subject-matter experts in precision medicine for complex traits. The report is premised on cardiometabolic disease translational research but is relevant to translation of research in other complex diseases. These guidelines are directed toward authors describing translational research in precision medicine, as well as for journal editors handling submissions in this field. These guidelines may also be of value to funding agencies, policy advisers and health educators.

The BePRECISE guidelines are designed to be used together with existing study-specific checklists such as CONSORT 17 , STROBE 18 and STORMS (Strengthening the Organization and Reporting of Microbiome Studies) 21 . Publications relevant to precision medicine cover diverse topics and study designs; thus, to accommodate this diversity, we recommend that authors elaborate on relevant details related to checklist items to facilitate manuscript evaluations by journal editors and peer reviewers who will determine whether a given paper has addressed the BePRECISE checklist criteria.

Health equity

Precision medicine has the potential to improve health equity by making health advice and medical therapies more accessible to those in most need and by being more effective and acceptable to the recipient than contemporary clinical approaches. Nevertheless, as the ‘inverse care law’ 22 highlights, the best healthcare often reaches those who need it least. We believe that precision medicine research should place emphasis on the development of solutions for people in greatest need, regardless of who or where they are.

Ensuring representation of underserved populations, where the disease burden can be high, is important because determining the effectiveness of precision medicine solutions requires data from the target populations. Research in population genetics provides clear evidence of this, where the predictive accuracy of polygenic burden scores can be low when applied outside the data-source population, even when these populations are geographically proximal 23 , 24 . Raising awareness of these challenges by discussing them in the health literature and, ultimately, by addressing them through improved study design could facilitate enhancement of health equity using precision medicine approaches.

Promoting equity through precision medicine requires awareness of the many biases. For this reason, the BePRECISE guidelines place emphasis on equity, diversity and inclusion as an overarching concept throughout the checklist.

As with health equity, the BePRECISE guidelines position PPIE as a crosscutting theme to motivate its consideration in all elements of precision medicine translational research. We encourage those using the BePRECISE checklist to follow existing guidance on PPIE 25 . Ensuring that the eventual recipients of precision medicine solutions are adequately represented in the planning, execution and reporting of precision medicine research will help maximize the translational value of the research. Ideally, research teams should include members of the communities that will eventually benefit from this work, including in leadership roles, although to achieve this will often require long-term capacity strengthening. This engagement will help ensure that the relevance and utility of the research output are maximized. It will also strengthen the potential for target populations to determine their own health trajectories. Where this is not immediately achievable, establishing authentic partnerships with representatives from these target populations should be prioritized. This may involve community consultations, training opportunities and co-creation of research proposals with assigned community members, through dissemination and translation of research findings. Moreover, the selection of study participants should be done equitably and result in study cohorts that are representative of the populations who are the focus of the research 26 . The use of patient-reported outcome measures and patient-reported experience measures should be considered during the research design and execution phases, and reported in research papers wherever possible following established guidelines 27 , 28 . Doing so will amplify the patient voice and maximize the relevance of the research to the target populations and individuals.

Cost-effectiveness

The translation of precision medicine research into practice will invariably depend on it being cost-effective, affordable and accessible. This initial version of the BePRECISE checklist does not include checklist items pertaining directly to these important factors. The consensus view was that such analyses are sufficiently complex to stand alone and are likely to be outside the scope of most current precision medicine research. This topic may be revisited in subsequent versions of the checklist.

Strengths and limitations

We believe that implementation of the BePRECISE checklist in the context of academic publishing will strengthen standardization of reporting across precision medicine research, ultimately enabling improved and equitable translation of research findings into the clinical and public health settings. The checklist will also encourage investigators to improve study design, particularly with respect to health equity. Other strengths include rigor of our consensus methods and the diverse range of societal backgrounds and expertise of our group.

We acknowledge that precision medicine in many complex diseases is relatively nascent (with the exception of precision oncology), with the needs of the field and stakeholders evolving. We plan to evaluate uptake of the checklist among journals and authors to assess whether items should be added or removed from the checklist as the field matures. An additional limitation is that the BePRECISE consensus group is small by comparison with similar efforts in other fields of research. We will involve a larger group of experts with broader global and technical representation in future efforts, including increased representation from low- and middle-income countries and individuals with more diverse lived experiences. Additional technical expertise may also be needed from other disciplines, including health economics and health systems administration, for example.

We acknowledge that journal formatting requirements and procedures may not always entirely align with the checklist specifications. We removed a checklist item for provision of a plain language summary, for example, because many journal formats are presently unable to accommodate this type of additional material. However, we hope that in the future editors and publishers of medical and scientific journals will include space for this incredibly important component that facilitates scientific communication with the public.

We defer to editorial and reviewer discretion in implementation of the BePRECISE checklist. Although the BePRECISE checklist items are included to support best scientific practices, at least in the short term, some ongoing precision medicine studies will not have addressed the health equity or PPIE considerations in their design. We do not expect that insufficient attention to these items would be a sole reason for not considering a manuscript for review, unless blatant disregard for participant and/or community safety, privacy or respect has occurred in the study design and/or conduct. Over time, however, we hope that health equity and PPIE will be considered as standard practice in precision medicine research and implementation.

Conclusions

The BePRECISE reporting guidelines have been generated through a structured consensus process to address the need for better reporting of clinical translational research in precision medicine in common complex diseases. The burgeoning literature on this topic is reported inconsistently, impeding the assimilation, syntheses and interpretation of evidence. There is a general lack of benchmarking against contemporary standards, a situation that makes it impossible to determine whether new precision medicine approaches might be beneficial, feasible and sustainable. Moreover, very little existing precision medicine research has incorporated PPIE or focused on the groups within societies most in need of innovative precision medicine solutions. These barriers limit the positive impact that precision medicine could have on the health and well-being of those most in need. The BePRECISE reporting guidelines are intended to help address these and other important challenges.

Consortium structure

The BePRECISE Consortium comprised an executive oversight committee (S.S.L., Z.S.-A., M.L.M., A.H.N., S.S.R., J.L.S. and P.W.F.), which oversaw the full process, with representation across key domain areas, and an evidence evaluation group (Z.S.-A., M.L.M., A.A., H.F., M.-F.H., M.F.G., J.M., D.K.T., M.I.T., S.S.R., J.L.S. and P.W.F.), which undertook the scoping review to determine current reporting standards. All consortium members participated in a Delphi consensus process 29 . The Consortium chair and co-chair were P.W.F. and S.S.L., respectively (Supplementary Table 1 ).

Protocols and registrations

A scoping review protocol was developed before initiating the literature review or consensus activities and was registered in the Open Science Framework (http://osf-registrations-nh4g2-v1). The consensus process followed the EQUATOR (Enhancing the QUAlity and TRansparency of health Research) Network recommendations for reporting guidelines development ( https://www.equator-network.org/library/equator-network-reporting-guideline-manual ) and was registered with EQUATOR as ‘Reporting guidelines under development’ ( https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-other-study-designs ). The final BePRECISE guidelines are available on the Equator website ( https://www.equator-network.org/reporting-guidelines/ ).

The purpose of the scoping review was to determine whether the published literature on precision medicine in cardiometabolic diseases met a minimum threshold for reporting quality. We set the minimum expectation as a condition where most (that is, ≥50%) published papers in this domain are adequately reported. To define a study as adequately or inadequately reported (as a binary variable), members of the scoping review committee identified, through consultation, 11 key items (Supplementary Tables 2 and 3 ). Papers that met all 11 reporting criteria were deemed, a priori, to be adequately reported.

The checklist items used to assess the reporting quality of studies captured in the scoping review were determined before the Delphi surveys were undertaken. These scoping review checklist items correspond with some of those used in the Delphi surveys that formed the basis of the final BePRECISE checklist, because both the scoping review and Delphi surveys are, to varying degrees, derived from the findings of the ‘Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine’ 3 . The scoping review was intended to provide a snapshot of the quality of reporting in a subset of literature relating to precision medicine. It was not undertaken to inform the items in the BePRECISE checklist; this purpose was served by the systematic evidence reviews 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 and the Consensus report 3 described above.

Based on the findings of the precision diabetes medicine Consensus report 3 , we hypothesized that no more than 30% of currently published studies are adequately reported. This assumption was tested by full text reviewing a statistically powered, random subsample of published papers on precision medicine across cardiometabolic diseases (ʻSearch strategyʼ and ʻSample size estimationʼ). This scoping review was conducted in accordance with the PRISMA Extension for Scoping Review guidelines 30 to identify and assess the current literature on precision medicine in cardiometabolic diseases and was completed before the ‘guidelines consensus process’ described below.

Sample size estimation

The literature search was not intended to be a comprehensive evaluation of the published evidence, but instead to provide an unbiased representation of this literature. To determine how many papers should be reviewed as a representative sample of the published literature, an a priori sample size calculation was performed using SAS software v.9.4 (SAS Institute). Given the scenario described, we used a two-sided test with a type 1 error threshold (critical α) of 0.05, assuming a null hypothesis proportion of 0.50, which corresponds to our minimum expectation, an expected number of adequately reported papers of <30% and nominal power of 80%. This calculation determined that 47 randomly selected papers should be full text reviewed to ascertain whether the assumed proportion of adequately reported studies is significantly lower than the prespecified null proportion (that is, to infer that the quality of papers reported in this field is lower than the minimum expectation).

Search strategy

We searched the PubMed database ( https://pubmed.ncbi.nlm.nih.gov ) to identify relevant articles published in the past 5 years (January 2019 to January 2024). The search strategy incorporated keywords and terms ( https://www.ncbi.nlm.nih.gov/mesh ) in human epidemiological cohorts and clinical trials representing: (1) precision medicine, (2) cardiometabolic diseases and (3) clinical translation (see Supplementary Methods for the detailed search strategy). The search was constrained to publications written in English. Conference abstracts, case reports, study protocols, reviews and animal studies were excluded.

Study selection and quality assessment

Covidence software ( https://www.covidence.org ; Veritas Health Innovation) was used to manage the scoping review selection process. Studies were filtered in three stages: (1) removal of duplicate publications; (2) ascertainment of study eligibility based on title and abstract by at least two independent reviewers; and (3) full text review of 47 randomly selected studies, where at least 2 independent reviewers assessed the eligibility of each publication according to the inclusion and exclusion criteria. Each paper was further evaluated to determine whether it met the 11 predetermined quality criteria. Any conflicts were subsequently resolved by an independent reviewer.

Consensus process

The five-step consensus process was based on a modified Delphi and nominal group technique 29 . The consensus process involved: (1) completion of an initial Delphi survey (6–13 February 2024); (2) a consensus meeting (15–16 February 2024); and (3) a second Delphi survey (19–26 February 2024). Finalization of the checklist was conducted at a second consensus meeting by the executive oversight committee (5–6 March 2024), who reviewed the voting of all rounds of the Delphi survey, made final decisions about item inclusion and refined wording of the BePRECISE checklist. The executive oversight committee also evaluated the checklist against two publications on precision medicine determined through the scoping review to be of high and low quality, respectively. The final version of the checklist was circulated to all panel members for consultation and approval (13–19 March 2024).

The items in the first iteration of Delphi survey 1 were derived from existing checklists: CONSORT 17 , STROBE 18 , CONSORT-Equity 2017 extension 31 and STrengthening the REporting of Genetic Association Studies (STREGA)—an extension of the STROBE guidelines 32 . Additional items specific to precision medicine were generated based on the reporting gaps identified from the series of systematic reviews (11 published) that underpinned the ‘Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine’ 3 . The draft of Delphi survey 1 was presented to the full panel at a roundtable discussion followed by co-development with the full panel through an online document-sharing platform. The final items for Delphi survey 1, including the input sources for its development, are shown in Supplementary Table 3 .

The Delphi survey response scale had five options: ‘Completely inappropriate’, ‘Somewhat inappropriate’, ‘Neither appropriate nor inappropriate’, ‘Somewhat appropriate’ and ‘Completely appropriate’. The consensus threshold was defined a priori as at least 80% of the panel voting for ‘Completely appropriate’ or ‘Somewhat appropriate’. Items with voting scores under this consensus threshold were discussed at the Consensus meetings. The Delphi surveys were administered online and were anonymous. Panelists were invited to provide free-text comments to suggest new items (survey 1 only), suggest a change of wording for a given item or justify their voting decision. The voting scores and anonymous comments for each item from the previous consensus round were provided to panelists at the subsequent rounds, such that consensus was reached iteratively.

Delphi panel and executive oversight committee

The BePRECISE checklist panelists cover the core areas of expertise outlined in the EQUATOR Network recommendations for reporting guidelines development ( https://www.equator-network.org/library/equator-network-reporting-guideline-manual ). The panel includes subject-matter experts across relevant disease areas and with expertise in the topics highlighted as gaps in the ‘Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine’. Moreover, the BePRECISE panelist selection focused on ensuring diversity: (1) global representation (Europe, North America, sub-Saharan Africa and Australia); (2) career stages (23% early career researchers within 10 years of research experience, 27% of mid-career researchers of 11–15 years of experience and 50% of senior researchers of >20 years of experience); and (3) gender (55% of authors being female).

Accordingly, the Delphi panel comprised subject-matter experts in key cardiometabolic disorders (diabetes, obesity, cardiovascular disease, fatty liver disease, renal disease), statistics, study design (epidemiologists and clinical trialists), journal editorial, lived experience, benchmarking and technology, education and translation, health equity, community engagement and clinical practice. Several of these experts are based in or have worked extensively with investigators in low- and middle-income countries (M.R., N.S., J.L.S. and P.W.F.).

The executive oversight committee for this report consisted of multidisciplinary experts in cardiometabolic disorders, equity research, medical journal editorial and lived experience (P.W.F., S.S.L., S.S.R., J.L.S., A.H.N., Z.S.A. and M.L.M.).

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Acknowledgements

As a PPIE representative from Australia, A.H.N. was remunerated by the Cardiometabolic Health Implementation Research in Postpartum women (CHIRP) consumer group, Eastern Health Clinical School, Monash University according to the Monash Partners Remuneration and Reimbursement Guidelines for Consumer and Community Involvement Activity. The Covidence license was paid for in part by Lund University’s Medical Library (Faculty of Medicine, Lund University, Lund, Sweden). Z.S.-A. was supported by the Canadian Institutes of Health Research Fellowship; M.L.M. by the Italian Ministry of Health Grant ‘Ricerca Finalizzata 2019’ (no. GR-2019-12369702); A.A. by Swedish Heart–Lung Foundation (grant no. 20190470), Swedish Research Council (2018-02837), EU H2020-JTI-lMl2-2015-05 (grant no. 115974—BEAt-DKD) and HORIZON-RIA project (grant no. 101095146—PRIME-CKD); H.F. by EU H2020-JTI-lMl2-2015-05 (grant no. 115974—BEAt-DKD) and HORIZON-RIA project (grant no. 101095146—PRIME-CKD). J.M.D. is a Wellcome Trust Early Career Fellow (no. 227070/Z/23/Z) and is supported by the Medical Research Council (UK) (grant no. MR/N00633X/1) and the National Institute for Health and Care Research (NIHR), Exeter Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. R.J.F.L. is employed at the Novo Nordisk Foundation Center for Basic Metabolic Research, which is supported by grants from the Novo Nordisk Foundation (nos. NNF23SA0084103 and NNF18CC0034900), and in addition by personal grants from the Novo Nordisk Foundation (Laureate award no. NNF20OC0059313) and the Danish National Research Fund (Chair DNRF161). M.R. is a South African Research Chair on the Genomics and Bioinformatics of African Populations, funded by the Department of Science and Innovation. N.S. is Chair of the Obesity Mission for the Office of Life Science, UK Government. M.F.G. is supported by the Swedish Research Council (EXODIAB, no. 2009-1039), Swedish Foundation for Strategic Research (LUDC-IRC, no. 15-0067) and EU H2020-JTI-lMl2-2015-05 (grant no. 115974—BEAt-DKD). A.H.N’.s salary is supported by funding from the Medical Research Future Fund and Monash Centre for Health Research and Implementation. S.S.R. is supported by grants from the National Institute of Diabetes and Digestive and Kidney Diseases (no. R01 DK122586), the Juvenile Diabetes Research Foundation (no. 2-SRA-202201260-S-B) and the Leona M. and Harry B. Helmsley Charitable Trust (no. 2204–05134). P.W.F. is supported by grants from the Swedish Research Council (no. 2019-01348), the European Commission (ERC-2015-CoG-681742-NASCENT), and Swedish Foundation for Strategic Research (no. LUDC-IRC, 15-0067).

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Siew S. Lim & Jacqueline Boyle

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S.S.L. (co-chair), Z.S.-A., M.L.M., A.H.N., S.S.R., J.L.S. and P.W.F. (chair) formed the executive oversight committee. Z.S.-A. (lead), M.L.M., A.A., H.F., M.-F.H., M.F.G., J.M., D.K.T., M.I.T., S.S.R., J.L.S. and P.W.F. formed the evidence evaluation group. S.S.L. (lead), Z.S.-A., M.L.M., A.A., H.F., J.B., C.C., J.M.D., C.L., R.J.F.L., M.M., M.R., A.J.S., N.S., M.-F.H., M.F.G., J.M., D.K.T., M.I.T., A.H.N., S.S.R., J.L.S. and P.W.F. formed the consensus review panel. A.H.N. and C.C. were the PPIE representatives. S.S.L., Z.S.-A., M.L.M., A.H.N., S.S.R., J.L.S. and P.W.F. wrote the first draft of the manuscript. All the authors edited and approved the final version of the manuscript before submission for journal review.

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Correspondence to Paul W. Franks .

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M.L.M. has consulted for and/or received speaker honoraria from Amarin, Amgen, AstraZeneca, Boehringer Ingelheim, Daichi, Eli Lilly, Merck Sharp & Dohme, Novo Nordisk, Novartis and Servier. In the past 5 years, A.H.N. has received an investigator-initiated grant from Abbott Diabetes Care and consulting honoraria from Roche Diabetes Care, Australia and the Australian Diabetes Educators Association. There are no perceived conflicts from previous involvements on this work. C.C. is a member of the Board of Directors for the Steno Diabetes Center in Copenhagen, Denmark. The views expressed in this paper do not necessarily reflect those of the Steno Center. M.R. is a consultant on the Genentech. ‘One Roche: Race, Ethnicity and Ancestry (“REA”) Initiative’. A.J.S. received research grants (paid to the institution) from: Intercept, Lilly, Novo Nordisk, Echosense, Boehringer Ingelhiem, Pfizer, Merck, Bristol Myers Squibb, Hanmi, Madrigal, Galmed, Gilead, Salix and Malinckrodt; was a consultant for Intercept, Gilead, Merck, NGM Bio, Terns, Regeneron, Alnylam, Amgen, Genentech, Pfizer, Novo Nordisk, AstraZeneca, Salix, Malinckrodt, Lilly, Histoindex, Path AI, Rivus, Hemoshear, Northsea, 89Bio, Altimmune, Surrozen and Poxel; and had ownership interests in Tiziana, Durect, Exhalenz, GENFIT, Galmed, Northsea and Hemoshear. N.S. has consulted for and/or received speaker honoraria from Abbott Laboratories, AbbVie, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Menarini-Ricerche, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics and Sanofi; and received grant support (paid to the institution) from AstraZeneca, Boehringer Ingelheim, Novartis and Roche Diagnostics outside the submitted work. M.F.G. received financial and nonfinancial (in-kind) support (paid to the institution) from Boehringer Ingelheim Pharma, JDRF International, Eli Lilly, AbbVie, Sanofi-Aventis, Astellas, Novo Nordisk, Bayer, within EU grant H2020-JTI-lMl2-2015-05 (grant no. 115974—BEAt-DKD); also received financial and in-kind support from Novo Nordisk, Pfizer, Follicum, Coegin Pharma, Abcentra, Probi and Johnson & Johnson, within a project funded by the Swedish Foundation for Strategic Research on precision medicine in diabetes (LUDC-IRC no. 15-0067); and received personal consultancy fees from Lilly and Tribune Therapeutics AB. M.I.T. has, within the past 5 years, received consulting/honoraria from the Novo Nordisk Foundation, Abbott Nutrition, Changing Health and DAISER. This work is independent and does not represent the opinions of these organizations. S.S.R. has received consulting honoraria from Westat and investigator-initiated grants from the US National Institutes of Health, the Juvenile Diabetes Research Foundation and the Leona M. and Harry B. Helmsley Charitable Trust. J.L.S. receives consulting fees from the World Health Organization and the University of Bergen. This work was done outside these roles and the opinions expressed in these guidelines do not necessarily reflect those of the World Health Organization or the University of Bergen. J.L.S. was deputy editor of Nature Medicine until December 2023. She left employment at Springer Nature before any of her work on this Consensus Statement was initiated. P.W.F. was an employee of the Novo Nordisk Foundation at the time that these guidelines were written, although this work was done entirely within his academic capacity. The opinions expressed in these guidelines do not necessarily reflect those of the Novo Nordisk Foundation. Within the past 5 years, he has received consulting honoraria from Eli Lilly, Novo Nordisk Foundation, Novo Nordisk, UBS and Zoe, and previously had other financial interests in Zoe. He has also received investigator-initiated grants (paid to the institution) from numerous pharmaceutical companies as part of the Innovative Medicines Initiative of the European Union. The remaining authors declare no competing interests. J.A.B. received royalties from Elsevier as an editor on a medical textbook that does not impact this work. A.J.S. has stock options in Rivus, is a consultant to Boehringer Ingelhiem and Akero, and has grants from Takeda.

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Lim, S.S., Semnani-Azad, Z., Morieri, M.L. et al. Reporting guidelines for precision medicine research of clinical relevance: the BePRECISE checklist. Nat Med 30 , 1874–1881 (2024). https://doi.org/10.1038/s41591-024-03033-3

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The “what, why, and how?” of story completion in health services research: a scoping review

• Candelyn Yu Pong 1 ,
• Nicola J. Roberts 4 &
• Elaine Lum 1 , 2 , 3  

BMC Medical Research Methodology volume  24 , Article number:  159 ( 2024 ) Cite this article

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The story completion method provides a different way of doing qualitative research. We note the emergent popularity of this method in health-related research, while much remains to be negotiated in terms of best practices for such studies. This scoping review aims to provide a synthesis on how researchers have used the story completion method in health services research. We offer implications for research and practice for further discussion by the scholarly community.

We used the JBI methodology for scoping reviews. Six databases were searched for published literature till March 1, 2023: Medline, Embase, CINAHL, PsycINFO, SAGE Journals Online databases, and SAGE Research Methods. We included primary studies of any study design using the story completion method in health services research.

A total of 17 studies were included. Findings suggest that the story completion method is useful for research on sensitive topics, and affords the use of comparative study designs and large sample sizes which may be difficult with conventional qualitative research methods. More than 80% of included studies used story completion as the sole method. However, the data collected from this method were limited in terms of the inferences that can be drawn; and richness of participant responses may vary widely. Less than 30% of included studies reported piloting of the story stems. Most studies were conducted online and analyzed qualitatively, though the story stem design and sample size varied widely.

The story completion method, with its attendant affordances for larger sample sizes, comparative study designs, and streamlined data collection is an innovative and useful stand-alone or adjunct qualitative method for health services research.

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Qualitative methods increasingly underpin robust population health research, health services research, and implementation research [ 1 , 2 , 3 ]. The insights provided by qualitative methods allow us to appropriately design, execute, and evaluate a plethora of healthcare programs and innovations, including digital health and AI-augmented healthcare [ 4 , 5 , 6 ].

Qualitative methods used in these fields include interviews and focus groups. These methods are often time and resource intensive [ 7 ], and arguably less efficacious in eliciting uncensored views especially for topics that are socio-culturally sensitive [ 8 ]. In that, participants may adjust their positions on an issue to align with what they perceive as accepted social or cultural discourses, perhaps to avoid potential repercussions [ 9 ].

Story completion is a method not often used in population health, health services, and implementation research. Given its attributes, apart from being used on its own to explore socio-culturally sensitive topics, story completion promises to be a useful adjunct to semi-structured interviews and focus groups. The story completion method has already garnered much interest in the scholarly community, with several published discussions regarding its utility and issues [ 10 , 11 ], flexibility as a method across disciplines [ 12 ], and potential for decolonizing research methodologies [ 13 ].

What is story completion?

Story completion, first used in quantitative developmental psychology research and in psychoanalysis as a projective technique for clinical assessment, was subsequently re-developed as a qualitative method by Kitzinger for feminist research [ 8 ]. Importantly, Kitzinger re-conceptualized story completion in the mid-1990s as a way “to access not just psychological meanings but also social discourses” [ 8 ]. More recently, Clarke and colleagues re-ignited interest in this method with the publication of a special issue “Using Story Completion Methods in Qualitative Research” in the Qualitative Research in Psychology journal [ 8 ]. Although story completion originated as a pen-and-paper task, this method has been increasingly administered online. Hence, the moniker, digital story completion.

In typical story completion studies, participants are presented with one or several hypothetical scenarios that act as writing prompts (story stems) and asked to complete the story however they like (Table 1 ). In example 1, researchers used story completion as a stand-alone qualitative method to collect narratives from Australian adults regarding their views on the COVID-19 restrictions implemented, and how it affected their health and well-being [ 14 ]. The story completion method was chosen due to its ability to examine social discourses, meanings, norms, and assumptions; and researchers were interested to understand how individuals would react to constantly changing situations, such as COVID-19 restrictions [ 14 ]. In example 2, researchers also used story completion as a stand-alone method to explore how evangelical Christians perceive depression [ 15 ]. Story completion method was chosen in this case due to the stigma associated with depression or mental health in general; and as this method does not explicitly obtain respondents’ personal experiences or views, it reduces the risk of social desirability bias [ 15 ].

A key advantage of the story completion method is its ability to side-step solely direct personal experiences to include socio-cultural discourse and representations, which enables researchers to understand meaning-making frameworks of a particular social group [ 10 ]. Other notable advantages of the method include the ability to accommodate larger samples of participants relative to traditional qualitative methods, and the contentious use of comparative study designs, uncommon in qualitative research [ 10 ].

A perceived weakness of this method has to do with the invitation to participants to be imaginative when responding to the story stem, triggering some researchers (and users of research) to be concerned that “anything goes.” To alleviate this concern, we recognize the bi-directional connection between imagination and experience, where imagination is influenced by an individual’s experiences [ 16 ] and “experiences are partly constituted through the stories within [one’s] socio-cultural landscapes [ 17 ]. So, despite its apparent playfulness, story completion holds merit as a sole method and as a useful adjunct to traditional qualitative methods in multiple- or mixed-methods studies.

Rationale for this review

We observed a steady increase via PubMed in the number of studies using story completion for health-related research in the last five years. While each study justifies and explains its use, it is our opinion that much needs to be clarified and negotiated about best practices for this method. For example, how should story stems be derived? How and when to use comparator groups? How large should the sample be to yield adequate data for meaningful analysis? These questions pertain to future discussions about best practices or ‘shoulds’. However, we first need to understand the current landscape. In this scoping review we elicited how the story completion method has been used in health-related research. Specifically, we were interested in: (a) the target populations and/or health conditions, (b) the study designs used, (c) how story stems were derived, (d) how data were analyzed, (e) other research methods used to triangulate data from the story completion method, and (f) strengths and weaknesses of the method stated by study authors. Our findings serve as a useful resource or starting point for health services researchers interested in using the story completion method, when planning or designing their study.

Search strategy

This study was carried out in accordance with the JBI methodology for scoping review [ 18 ]. The protocol was published on Open Science Framework (available here: https://osf.io/rk2e6/ ) [ 19 ]. We developed a search strategy using the PRESS guidelines [ 20 ] and consulted university librarians for refinement around the following key terms: story completion and health services research (Additional File 1 ). We searched six databases: Medline, Embase, CINAHL, PsycINFO, SAGE Journals Online databases, and SAGE Research Methods for published literature till March 1, 2023.

Eligibility criteria

Inclusion criteria: a primary study of any study design using the story completion method in health services research. For the purposes of this review, we defined story completion as a type of qualitative research method where study participants are asked to complete a story based on an assigned story “stem” or opening [ 8 ], and health services research as an interdisciplinary study of scientific investigation that explores how social determinants, financial policies, organizational systems and structures, medical technology, and individual actions influence cost, access, quality of healthcare, and also our well-being and health [ 21 ]. This definition of health services research does not confine it to the provision of health services or health structures, but also includes the exploration of how social determinants and individual behaviors affect health and well-being. The World Health Organization recognizes social determinants such as social exclusion and discrimination as important factors that can affect access to healthcare and health equity in negative ways [ 22 ]. Hence, studies investigating perceptions of potentially stigmatizing conditions or sexual orientations which are likely to influence how/whether those individuals seek help/healthcare have been included. Studies were excluded if they were editorials, commentaries, discussion papers, methodological papers (non-empirical), conference papers, systematic reviews, meta-analyses, or study protocols.

Selection of studies

Three researchers (CP, NJR, EL) independently conducted title/abstract and full text screening of studies captured by the search strategy. Conflicts at both screening stages were resolved through discussion by two lead researchers (NJR, EL). Covidence ® , a web-based software for conducting reviews (Veritas Health Innovation, Melbourne, Australia) and Endnote 20 (Clarivate Analytics, PA, USA) were used for screening and managing citations respectively. Studies in languages other than English were translated using ChatGPT (OpenAI, CA, USA) and screened by two researchers (CP, EL), to determine eligibility.

Data extraction and data analysis

A standardized form was developed for data extraction using Google Forms. The following data were extracted: publication year, author, country of study, characteristics of the study population, study aim(s), study design, description of the story completion study, sample size, how story stems were derived, how data was captured and analyzed including type of analysis (e.g. Braun & Clarke’s reflexive thematic analysis, etc.), other research methods used to triangulate data (e.g. semi-structured interviews, surveys, focus groups, etc.), reported strengths and weaknesses of the story completion method, assumptions and underlying theories.

The form was piloted by three researchers (CP, NJR, EL) using three included studies, and refined accordingly. How we operationalized data extraction is shown in Additional File 2 . Two researchers (CP, EL) independently completed data extraction for the remaining studies. Publication year and sample size were extracted as numerical values. Other data points expressed as textual data were summarized rather than extracted verbatim from included studies, apart from author, country of study, and study aims. For example, data point “characteristics of the study population” were summarized as “Australia-based adults aged 18 and above during the COVID-19 pandemic”, “adolescents aged 14–25 years old with complex regional pain syndrome” and so forth. Descriptive statistics, where appropriate, were used to summarize extracted data in Excel ® (Version 1808 (Microsoft)). For example, to provide a numerical count of how many included studies were single country versus multi-country, and so forth.

The search yielded 278 studies. After removing 75 duplicates, 203 studies remained for screening. At full text screening stage there were nine studies reported in languages other than English which were translated using ChatGPT; these did not meet eligibility criteria and were excluded. A total of 17 studies were included in this review (Fig.  1 ). The list of included studies is provided as Additional File 3 .

PRISMA-scoping review flow diagram [ 18 ]

Study characteristics

The main characteristics of included studies are summarized in Table  2 . The majority were single country studies (15/17, 88·2%), originating from the European region (9/17, 52·9%) and Western Pacific region (4/17, 23·5%). Most of the studies were published between 2021 and 2023 (9/17, 52·9%). In terms of the study design used, of the 17 studies, 13 (76·5%) were qualitative, three (3/17, 17·6%) used a mixed-method design, and one (1/17, 5·9%) used a multi-method design (Table  2 ). We used the following definitions for mixed-method and multi-method studies, respectively. Mixed-method studies use two or more methods in a single research project comprising both qualitative and quantitative approaches, that involves the connection, integration, or linking of these two approaches [ 23 ]. Multi-method studies use two or more solely qualitative or solely quantitative methods in a single research project [ 24 ]. In addition, although the story completion method allows for comparative study designs, this was adopted by only three studies (3/17, 17·6%) [ 25 , 26 , 27 ].

Description of the story completion study

Most studies asked participants to complete one story stem each, with the exception of five studies (5/17, 29·4%) which asked each participant to complete either two [ 30 , 31 , 32 ] or three story stems [ 33 , 34 ]. Of these five studies, four provided multiple story stems to allow a diversity of illnesses, genders, socio-economic groups, or occupations to be included in the stem [ 30 , 32 , 33 , 34 ]; while one did not provide a rationale for having multiple stems. Story stems provided were fairly brief, consisting of two to five short sentences. Examples of story stems can be found in Table 1 .

For single story stem studies ( n  = 12), most provided the same story opening to every participant, except three (3/12, 25.0%) which adopted a comparative design where study authors developed two story stems of the same narrative but with different protagonists in terms of gender [ 25 , 26 ] or occupation [ 27 ]. Participants were allocated [ 25 ] or randomly allocated to either stem [ 26 , 27 ]. Another study (1/12, 8.3%) randomized each participant to one of three story stems pertaining to the research, with results from each stem analyzed separately [ 35 ]. Of the five multi-story stems studies, three (3/5, 60.0%) provided the story openings in the same order for participants [ 31 , 33 , 34 ]. Two studies (2/5, 40.0%) counterbalanced the order of the story openings with half the participants presented with the first story stem followed by the second story stem while the other half were presented with the second story stem followed by the first [ 30 , 32 ].

Most studies provided participant guidelines for either time (minutes) and/or length (number of words/ characters/ sentences) for story responses (10/17, 58·8%), though these varied widely among studies (Table  3 ). Sample size varied widely among studies as well; ranging from 17 to as large as 227 (Table  3 ).

Populations studied and health conditions

Study aims, populations studied, and health conditions are shown in Table  3 . Study participants were recruited from general populations or subgroups of general populations (e.g. adolescents, gay men), or were working adults in the healthcare industry and/or students in a health-related course. Most studies targeted a particular health condition (13/17, 76·5%). Of those that did, six studies focused on mental health conditions (6/17, 35·3%), three on chronic diseases (3/17, 17·6%), and one on cancer (1/17, 5·9%).

Study aims of some included studies are socio-culturally sensitive. For example, in the study by Lloyd et al 2022 one of the aims was to “explore how self-harm is perceived” [ 36 ], while Walsh et al 2010 aimed to “explore the ways in which ‘anorexic’ and ‘bulimic’ young women are discursively constructed by those who neither self-identify as ‘eating disordered’ nor are involved in ‘eating disorder’ interventions” [ 32 ].

How story stems were derived

Story stems were constructed by study authors in most studies, except for four studies (4/17, 23.5%) where study authors reported using either published literature or a theoretical framework to inform the development of story stems [ 27 , 33 , 36 , 37 ], and one study (1/17, 5.9%) which derived and modified the story stems based on the Wallace (1956) measure [ 31 , 38 ]. The Wallace measure estimates how far into the future a person typically plans (future time perspective) and consists of two types of questions concerning timeframes about future actions or outcomes [ 38 ]. For example, the first type of question may ask participants to list 10 events that will occur in their lives and the age they would expect to be for each event. The second type of question asks participants to write endings to story stems (i.e. story completion) and to indicate the duration in which the story occurred (e.g. “x” minutes, days, years).

The majority of studies did not pre-test the story stems (12/17, 70·6%); of those that did, they were either piloted to ensure clarity [ 15 , 30 , 35 , 36 ] or to prevent potential narrowing of responses [ 9 ]. Most studies adopted third-person story stem(s), except two studies (2/17, 11.8%) that used a first-person story stem to allow participants to reflect on their perceived future [ 39 , 40 ].

How data was captured and analyzed

Twelve out of 17 studies (70·6%) administered the story completion task online. The remaining five studies (5/17, 29.4%) were administered either in-person [ 31 , 34 ], a combination of both in-person and online [ 9 ], or did not report the mode of data collection [ 30 , 32 ]. Most studies analyzed the data qualitatively (14/17, 82·4%), with Braun & Clarke’s reflexive thematic analysis [ 41 ] as the most commonly used approach (12/17, 70·6%) (Table  2 ). However, three studies (3/17, 17.6%) applied a quantitative approach to the analysis of story completion data, as follows. The study by Nimbley et al. 2021 analyzed the stories collected using the Linguistic Inquiry and Word Count (LIWC) program which identified and coded words against pre-selected categories pertaining to positive or negative emotions, social, and cognitive dimensions determined by study authors [ 39 ]. The LIWC program subsequently generated quantitative data in the form of frequencies (counts) and proportions of words against these categories, which were further analyzed using statistical programs such as SPSS [ 39 ]. In the study by Jones et al. 2009, participants were asked to complete two story stems and to also indicate the duration in which the story occurred (e.g. minutes, days, years). The duration was quantitatively analyzed (salient to their research question), while the stories collected were not subjected to further analysis [ 31 ]. The study by Tichenor et al. 1977 analyzed the stories collected via deductive coding using a schema of 12 categories pre-developed by study authors, then assigning a frequency score [ 34 ]. The rates of expression for each of these categories were standardized through dividing the frequency scores by the number of words written by participants and multiplying this number by a constant of 1000 [ 34 ].

Research methods used to triangulate data

The majority of studies did not use other research methods to triangulate the data from the story completion method, with the exception of two (2/17, 11.8%) that used surveys [ 30 , 34 ] and one that used semi-structured interviews (1/17, 5·9%) [ 40 ]. Of the two studies that used surveys to triangulate data, one conducted the survey prior to the story completion task [ 30 ] whilst the other did not specify the order in which the tasks were carried out [ 34 ]. The sole study that used semi-structured interviews conducted them after the story completion task to explore the stories crafted by participants in greater detail [ 40 ].

Reported strengths and weaknesses of the story completion method

The story completion method is reported to be useful for exploring sensitive topics and vulnerable populations [ 9 , 15 , 25 , 32 , 36 , 37 , 42 ] as it does not require participants to reveal their personal experiences [ 14 , 27 , 33 ]. Instead of actual behaviors, story completion method uncovers participants’ unconscious and subconscious patterns and ways of sense-making as well as perceptions towards a given scenario, beyond their lived experiences [ 9 , 14 , 15 , 27 , 35 , 42 ], thereby reducing the risk of social desirability bias [ 15 , 36 ].

Hence, this method reportedly allows study authors the potential to obtain rich data pertaining to both individual and collective experiences of major social events and problems [ 14 , 30 ] that may not be elicited through more conventional data collection methods [ 26 , 27 ]. Additionally, data from a larger group of participants can be collected more efficiently, relative to other forms of qualitative methods [ 26 , 40 ].

However, the story completion method is not without weaknesses. Study authors discerned that as the story completion method does not explicitly obtain participants’ personal experiences [ 15 , 27 , 36 ], it limits the inferences that can be derived from the findings [ 14 , 37 ]. When crafting responses, participants may exaggerate the protagonist’s life to produce a “good” story that they would otherwise not have done in a more conventional data collection method [ 32 ] or orientate their responses to include more social elements than what they would have otherwise given due to the type of story stem provided [ 40 ].

Additionally, study authors reported that participants’ engagement with the story stem varied widely. Some would provide complex and detailed responses while others produced superficial and short stories [ 25 ], and some may misinterpret the task and provide a theoretical account of the assigned story opening instead of completing the story [ 25 , 26 ]. Study authors also noted that in common with other qualitative research, it is hard to recruit male participants [ 26 ].

Underpinning philosophy

Most studies specified the ontology, epistemology, or theoretical lens used (10/17, 58·8%). The top three were social constructionism (4/17, 23·5%), critical realism (2/17, 11·8%), and epistemic contextualism (2/17, 11·8%) (Table  2 ). Study authors deemed the story completion method to be compatible with their selected underpinning philosophy, which was in turn used to inform interpretation of the narratives collected.

This scoping review provides a synthesis of how the story completion method has been used in health services research thus far. Our findings serve as a useful resource for health services researchers interested in exploring and using the story completion method, when planning or designing their study. We found several distinct advantages of the story completion method, suggesting its usefulness as either a sole or adjunct approach to undertaking qualitative research, provided its shortcomings are mitigated.

First, the story completion method enables large sample sizes as the collection of data can be done in a relatively efficient way, compared to traditional qualitative methods such as semi-structured interviews. Several studies included in this scoping review reported sample sizes of over 100 [ 9 , 15 , 26 , 31 , 36 , 37 ], the largest being 227 [ 31 ]. In contrast, the average sample size was between 18 and 45 in a recent systematic analysis of sample sizes for interview-based studies published over a 15-year period in health research journals [ 43 ]. Second, the story completion method can accommodate comparative study designs, which is unusual in qualitative methods, and useful for systematically eliciting differences in variables salient to the research question (e.g. male/female, novice/expert, and so forth). An overview of the story completion method by Clarke et al. 2019 underscores that this advantage allows a more “nuanced understanding of how a particular phenomenon is socially constructed” [ 8 ]. Third, story completion is especially appropriate for research on sensitive topics as it minimizes the risk of social desirability bias, a common problem reported in qualitative health research literature [ 44 ]. This is because in contrast to qualitative methods such as semi-structured interviews, the story completion method allows participants to respond to socio-culturally sensitive topics as a third party and to participate anonymously (assuming the story stem references a third party e.g. “Tom”, “Ali”, and data collection was conducted via an online platform). In our review, conditions that carry social stigma such as mental health issues and eating disorders, were target health conditions among included studies.

The shortcomings of the story completion method reported by study authors are acknowledged in methodological discussions about this innovative approach [ 10 , 11 ]. The non-intrusive data collection afforded by the story completion method might limit the inferences study authors can draw from the findings as responses might not reflect participants’ lived experiences [ 14 , 15 , 27 , 36 , 37 ]. However, Clarke et al. 2019 cautions that whether this constitutes a problem depends on the ontological stance taken: “Essentialist/realist/(post)positivist researchers may be concerned that data may not reflect or predict “real-life” behaviour. By contrast, for social constructionist or critical realist researchers interested in the sociocultural meanings or discourses people draw on when writing their stories, this critique holds no water” [ 8 ].

Additionally, story completion is a fixed self-administered task unlike other qualitative research methods such as semi-structured interviews or focus groups where researchers and participants interact to co-shape the research-in-progress. Hence, some participants may misinterpret the task [ 25 , 26 ] or provide responses that fall short of the study authors’ requirements or expectations. When confronted with such data, researchers need to judge whether these responses are sufficiently meaningful to warrant inclusion in the dataset for analysis [ 11 ]. Misinterpretation of the story completion task can be mitigated by piloting to ensure clarity [ 15 , 30 , 35 , 36 ] or to prevent potential narrowing of responses [ 9 ]. Yet, piloting of story stems was conducted by less than a third of included studies.

We note that most studies used story completion as the sole method rather than as an adjunct method. For example, other quantitative or qualitative research methods were not used to triangulate data from the story completion method. Admittedly, some research questions may not require more than a single method. However, the wider literature recognizes the potentially complementary pairing of the story completion method with another method such as semi-structured interviews [ 11 , 12 ].

Most studies adopted a qualitative approach to data analysis, with Braun & Clarke’s reflexive thematic analysis [ 41 ] being the most prominently used. Story stems were brief to allow participants the freedom to construct their own stories; and story stem design varied widely, with the most common being participants completing a single third-person story stem, with the same story opening provided to all.

Since the COVID-19 pandemic, the pace of research has sped up significantly [ 45 ]. Researchers engaged in population health, health services, or implementation research have worked on ways to accelerate actionable outputs without compromising scientific rigor; for example, rapid qualitative analysis to reduce the time taken to analyze qualitative data [ 46 , 47 ] and methods to hasten on-the-ground implementation [ 48 ]. The story completion method, with its attendant affordances for larger sample sizes, comparative study designs, and streamlined data collection adds to these innovative methods.

Limitations and strengths

There are some limitations to this review. First, we may have missed capturing some studies as the search was restricted to peer-reviewed articles and we did not manually search the reference lists of included papers to identify potential studies for inclusion. Second, we did not include an assessment of the reporting quality of included studies. We attempted to assess the reporting quality of 14 out of 17 included studies which conducted qualitative analysis on collected data, using an established checklist for reporting qualitative research — the Consolidated Criteria for Reporting Qualitative Research, COREQ [ 49 ]. However, many items on the COREQ checklist were neither appropriate nor relevant to the reporting of story completion studies; for example, interview guide, repeat interviews, field notes, participant checking, and so forth. We are mindful of salient scholarly critique regarding COREQ’s trustworthiness and reliability in reflecting the quality of reporting [ 50 ]. Therefore, we could not justifiably adapt COREQ for the purposes of this study.

Strengths of this review include having an extensive search strategy and broad inclusion criteria, allowing us to retrieve as many relevant studies as possible. University librarians were consulted for refinement of search strategy and included studies were not limited to a particular search period or geographical area. We piloted our data extraction form to evaluate its ability to capture relevant study information. Issues were flagged and the form was revised accordingly prior to actual extraction by two researchers.

Implications for research and practice

Currently, there are no universally agreed best practice nor reporting standard for the story completion method in health services research. Given the various ways in which the story completion method has been used in this scoping review, we offer several suggestions for research and practice for further discussions by the scholarly community.

First, piloting is crucial and recommended by key proponents of the story completion method [ 8 , 11 ]. Story completion is a fixed task unlike other qualitative methods such as semi-structured interviews or focus groups where it is possible to iteratively modify the questions as participants co-shape the research-in-progress. Piloting is a smart way to ensure that both the instructions and story stem(s) provided to participants are clear, to prevent misinterpretations.

Second, consider using other quantitative or qualitative research methods to triangulate or corroborate the data from the story completion method if thorough investigation of the research question(s) requires more than one method. While a methodological strength of the story completion method is that it uncovers participants’ patterns and ways of sense-making beyond their lived experiences [ 9 , 14 , 15 , 27 , 35 , 42 ], this also means that there may be limited inferences that can be drawn from the findings since responses may not reflect participants’ realities. Hence, pairing story completion with a story-mediated interview, for example, may lend further insights [ 12 ].

Third, as story completion studies are markedly different from traditional qualitative research methods, having an agreed set of reporting criteria for such studies will be useful for health services researchers. For example, a minimalist set of reporting criteria could comprise the 10-item JBI critical appraisal checklist for qualitative research [ 51 ] plus a description of the study design, development of the story stems, number of participants/sample size, and how participants completed the task in terms of modality, the number of story stems per participant, and sequence of story stem presentation.

The story completion method is an exciting and innovative way of doing qualitative research, and has the potential to be used more widely. This scoping review generated a comprehensive summary of how the story completion method has been used in health-related research. Findings and suggestions for research and practice serve as useful resources for researchers interested in experimenting with and adopting the story completion method in their work.

Data availability

The dataset supporting the conclusions of this article are available in the Open Science Framework repository, https://osf.io/rk2e6/.

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Acknowledgements

We thank university librarians from these institutions for consultations on the search strategy: Queensland University of Technology, Australia and Edinburgh Napier University, United Kingdom.

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Candelyn Yu Pong & Elaine Lum

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Author contributions described according to the CRediT taxonomy as follows. Conceptualized the study: EL. Drafted, revised, and finalized the study protocol including search strategy: CYP, EL, and NJR. Conducted the study: CYP and EL. Curated and analyzed the data: CYP and EL. Interpreted the data: EL, CYP, and NJR. Supervised the study: EL. Wrote the first draft of the manuscript including data visualizations: CYP and EL. Revised the manuscript following peer review: EL. Provided critical input to the first draft of the manuscript: NJR. Provided critical input to the revised manuscript: NJR and CYP. All authors approved the final manuscript for submission.

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Correspondence to Elaine Lum .

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Pong, C.Y., Roberts, N.J. & Lum, E. The “what, why, and how?” of story completion in health services research: a scoping review. BMC Med Res Methodol 24 , 159 (2024). https://doi.org/10.1186/s12874-024-02274-7

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

How to write and publish a scientific manuscript.

Martin R. Huecker ; Jacob Shreffler .

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Last Update: October 31, 2022 .

• Definition/Introduction

A clinician should continuously strive to increase knowledge by reviewing and critiquing papers, thoughtfully considering how to integrate new data into practice. This is the essence of evidence-based medicine (EBM). [1]  When new clinical queries arise, one should seek answers in the published literature. The ability to read a scientific or medical manuscript remains vitally important throughout the career of a clinician.

When gaps exist in the literature, clinicians should consider conducting their own research into these questions. Though typically performed by academic doctors or physician-scientists, medical research is open to all clinicians in both informal and formal methods. Anyone who treats patients can collect data on outcomes to assess the quality of care delivered (quality improvement is research). [2]  Though beyond the scope of this chapter, instruction for clinicians on how to conduct research and contribute to medical science is provided by many resources. [3] [4] [5]

Additionally, a clinician who integrates a new practice can study effects on patient outcomes, retro- or prospectively. Continuous practice improvement need not be shared with the larger population of treating providers, but dissemination to the entire scientific community allows widespread adoption, criticism, or further testing for replication of findings.

• Issues of Concern

Clinicians who seek to conduct retrospective chart reviews, prospective studies, or even randomized, controlled clinical trials should access the many resources to ensure quality methodology. [5] Once you have followed the appropriate steps to conduct a study (Table 1), you should complete the process by writing a manuscript to describe your findings and share it with other clinicians and researchers. Other resources detail the steps in undertaking writing a review article, but this StatPearls chapter will focus on Writing a Scientific Manuscript for original research. See also the StatPearls chapter for the different types of research manuscripts. [6]

• Clinical Significance

Steps to Conducting Research

• Develop a research question
• Perform a literature search
• Identify a gap in the literature
• Design a study protocol (including personnel)
• Submit to an institutional review board for approval
• Collect, responsibly store, and then analyze data
• Write a manuscript to interpret and describe your research.

After conducting a quality investigation or a study, one should put together an abstract and manuscript to share results. Researchers can write an abstract in a short amount of time, though the abstract will evolve as the full manuscript moves to completion. Many published and presented abstracts do not reach full manuscript publication. [7] [8]  Although journals and conferences do often publish abstracts, studies with important results should be published in full manuscript form to ensure dissemination and allow attempts at replication. [9]

IRB protocols, study design, and data collection and aggregation require a team effort. Those involved in the research should discuss who will contribute to the full manuscript (i.e., qualify as an author) and thus the planned order of authorship to reduce complications at the time of manuscript submission. The author, who devotes the most effort to the paper, is typically the first and corresponding author. In contrast, the last author is often the most senior member of the team, often the principal investigator of the study. All individuals listed as authors should contribute to the manuscript and overall project in some fashion. [10]

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist is perhaps the most valuable tool in the process of preparing your manuscript for submission [11] . 

Original research manuscripts have the following sections (in chronologic order): [11]

Title and Abstract

Introduction (Background and Objectives)

Methods (Design, Setting, Participants, Variables, Statistics)

Results (Participants, Descriptives, Outcomes, Subgroups)

Tables and Figures     

Discussion (Key findings, Limitations, Interpretations)

Conflict of Interest (COI), Author affiliations, Acknowledgments, Funding

Individuals involved in the IRB submission (prior to data collection) can write the introduction and methods of the manuscript before and during the process of data collection and analysis. This head start on writing makes the full manuscript composition task less formidable. The content of the introduction and methods should be well known to the study group prior to data collection and analysis. The introduction should be organized into a “problem/gap/hook” order: what problem does this study address, the precise gap in the literature, and the objectives of this study (in addressing the gap). [12]  The methods should provide enough detail such that readers who would like to replicate the study could do so.

Once data is collected and analyzed, authors can write an abstract to organize major themes of the research, understanding that the abstract will undergo edits once the manuscript is complete. Similarly, the title can change with revisions, as authors determine the most salient trends in the data. Most readers will only read the title +/- abstract. Thus these are the most important sections of the paper. The title should be concise and should directly describe the results of the trial– this correlates with more citations. The abstract must convey the crucial findings of the paper, ideally divided into sections for easier reading (unless the desired journal does not allow this). [13]

With the larger picture in mind, authors should create tables and figures that visually convey the themes of the data analysis. Working with statisticians or data experts, authors should devote a great deal of time to this component of the manuscript. Some general concepts: [14]

• Only include tables/figures that you believe are necessary.
• Make sure tables/figures are of high quality, simple, clear, with concise captions.
• Do not repeat language in results that appear in tables/figures, i.e., the tables/figures should stand alone.
• Consider how the figure will look in grayscale (in case the journal if not in color)

As with the abstract and title, the tables and figures will likely undergo further edits prior to the completion of the manuscript. The abstract and tables/figures should intuitively evolve together to convey the ‘story’ of the research project.

At this point, refer back to the introduction and methods composed during data collection. Make revisions as necessary to reflect the overall narrative of the project. Ensure you have adhered to the originally determined objectives or hypotheses. 

Next, focus on the results and discussion. The results should contain only objective data with no interpretation of significance. Describe salient results than do not already receive explanations within the figures and tables. The discussion section begins with a lead paragraph highlighting the most important findings from the study. Then the discussion interprets the current results in light of prior published literature. Ensure citation of keystone papers on this topic, including new papers that have been published since embarking on the current project. Frame your results, describing how this study adds to the literature. The discussion section usually includes study limitations. Attempt to anticipate criticisms of the methodology, the results, the organization of the manuscript itself, and the (ability to draw) conclusions. A stronger limitations section preempts journal reviewer feedback, potentially simplifying the revision/resubmission process.

The conclusion section should be concise, conveying the main take-home points from your study. You can make recommendations for current clinical practice and for future research endeavors. Finally, consider using citation management software such as Endnote or Mendeley. Though initially cumbersome, these software platforms drastically improve revision efforts and allow for easy reference reformatting.  All authors should review the manuscript multiple times, potentially sharing with other uninvolved colleagues for objective feedback. Consider who should receive acknowledgment for supporting the project and prepare to disclose conflicts of interest and funding.

Although authors should have an initial idea of which journal to submit to, once the manuscript is near completion, this decision will be more straightforward. Journal rankings are beyond the scope of this StatPearls chapter. Still, generally, one should devise a list of the journals within a specialty in order of highest to lowest impact factor (some sites categorize into tiers). High-quality prospective research and clinical trials have a higher likelihood of acceptance into the more prestigious journals within a specialty or to the high-quality general science or medicine journals. Although many journals have an option for open access publication, and numerous legitimate, open access journals now exist, beware of ‘predatory journals’ that charge a fee to publish and may not be indexed in Pubmed or other databases. [12]

Journals have diverse guidelines for formatting and submission, and the manuscript submission process can be tedious. Prior to submission, review Bordage’s paper on reasons for manuscript rejection. [15]  Most journals require a title page and cover letter, the latter of which represents an opportunity to lobby for your paper’s importance. When (not if) you experience manuscript rejections, take reviewer comments and recommendations seriously. Use this valuable feedback for resubmission to the original journal (when invited) or for subsequent submission to other journals. When submitting a requested revision, compose a point by point response to the reviewers and attach a new manuscript with tracked changes. Attempt to resubmit manuscripts as promptly as possible, keeping your work in the hands of journals (allowing you to work on other research). [14]

• Nursing, Allied Health, and Interprofessional Team Interventions

The above logistic steps will differ for review articles, case reports, editorials, and other types of submissions. [16]  However, the organization, precise methods, and adherence to journal guidelines remain important. See work by Provenzale on principles to increase the likelihood of acceptance for original and revised manuscripts. After submission, revision, resubmission, and proofing, you may experience the fulfillment of an official publication. Academics should promote their scientific work, enhancing the dissemination of research to the wider scientific community. [17] [18] [17] [19]

• Review Questions
• Access free multiple choice questions on this topic.
• Comment on this article.

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Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

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How to Write a Research Proposal: (with Examples & Templates)

Table of Contents

Before conducting a study, a research proposal should be created that outlines researchers’ plans and methodology and is submitted to the concerned evaluating organization or person. Creating a research proposal is an important step to ensure that researchers are on track and are moving forward as intended. A research proposal can be defined as a detailed plan or blueprint for the proposed research that you intend to undertake. It provides readers with a snapshot of your project by describing what you will investigate, why it is needed, and how you will conduct the research.  

Your research proposal should aim to explain to the readers why your research is relevant and original, that you understand the context and current scenario in the field, have the appropriate resources to conduct the research, and that the research is feasible given the usual constraints.  

This article will describe in detail the purpose and typical structure of a research proposal , along with examples and templates to help you ace this step in your research journey.  

What is a Research Proposal ?  

A research proposal¹ ,²  can be defined as a formal report that describes your proposed research, its objectives, methodology, implications, and other important details. Research proposals are the framework of your research and are used to obtain approvals or grants to conduct the study from various committees or organizations. Consequently, research proposals should convince readers of your study’s credibility, accuracy, achievability, practicality, and reproducibility.   

With research proposals , researchers usually aim to persuade the readers, funding agencies, educational institutions, and supervisors to approve the proposal. To achieve this, the report should be well structured with the objectives written in clear, understandable language devoid of jargon. A well-organized research proposal conveys to the readers or evaluators that the writer has thought out the research plan meticulously and has the resources to ensure timely completion.  

Purpose of Research Proposals  

A research proposal is a sales pitch and therefore should be detailed enough to convince your readers, who could be supervisors, ethics committees, universities, etc., that what you’re proposing has merit and is feasible . Research proposals can help students discuss their dissertation with their faculty or fulfill course requirements and also help researchers obtain funding. A well-structured proposal instills confidence among readers about your ability to conduct and complete the study as proposed.  

Research proposals can be written for several reasons:³  

• To describe the importance of research in the specific topic  
• Address any potential challenges you may encounter  
• Showcase knowledge in the field and your ability to conduct a study  
• Apply for a role at a research institute  
• Convince a research supervisor or university that your research can satisfy the requirements of a degree program  
• Highlight the importance of your research to organizations that may sponsor your project  
• Identify implications of your project and how it can benefit the audience  

What Goes in a Research Proposal?    

Research proposals should aim to answer the three basic questions—what, why, and how.  

The What question should be answered by describing the specific subject being researched. It should typically include the objectives, the cohort details, and the location or setting.  

The Why question should be answered by describing the existing scenario of the subject, listing unanswered questions, identifying gaps in the existing research, and describing how your study can address these gaps, along with the implications and significance.  

The How question should be answered by describing the proposed research methodology, data analysis tools expected to be used, and other details to describe your proposed methodology.   

Research Proposal Example  

Here is a research proposal sample template (with examples) from the University of Rochester Medical Center. 4 The sections in all research proposals are essentially the same although different terminology and other specific sections may be used depending on the subject.  

Structure of a Research Proposal  

If you want to know how to make a research proposal impactful, include the following components:¹  

1. Introduction  

This section provides a background of the study, including the research topic, what is already known about it and the gaps, and the significance of the proposed research.  

2. Literature review  

This section contains descriptions of all the previous relevant studies pertaining to the research topic. Every study cited should be described in a few sentences, starting with the general studies to the more specific ones. This section builds on the understanding gained by readers in the Introduction section and supports it by citing relevant prior literature, indicating to readers that you have thoroughly researched your subject.  

3. Objectives  

Once the background and gaps in the research topic have been established, authors must now state the aims of the research clearly. Hypotheses should be mentioned here. This section further helps readers understand what your study’s specific goals are.  

4. Research design and methodology  

Here, authors should clearly describe the methods they intend to use to achieve their proposed objectives. Important components of this section include the population and sample size, data collection and analysis methods and duration, statistical analysis software, measures to avoid bias (randomization, blinding), etc.  

5. Ethical considerations  

This refers to the protection of participants’ rights, such as the right to privacy, right to confidentiality, etc. Researchers need to obtain informed consent and institutional review approval by the required authorities and mention this clearly for transparency.  

6. Budget/funding  

Researchers should prepare their budget and include all expected expenditures. An additional allowance for contingencies such as delays should also be factored in.  

7. Appendices  

This section typically includes information that supports the research proposal and may include informed consent forms, questionnaires, participant information, measurement tools, etc.  

8. Citations  

Important Tips for Writing a Research Proposal  

Writing a research proposal begins much before the actual task of writing. Planning the research proposal structure and content is an important stage, which if done efficiently, can help you seamlessly transition into the writing stage. 3,5  

The Planning Stage  

• Manage your time efficiently. Plan to have the draft version ready at least two weeks before your deadline and the final version at least two to three days before the deadline.
• What is the primary objective of your research?  
• Will your research address any existing gap?  
• What is the impact of your proposed research?  
• Do people outside your field find your research applicable in other areas?  
• If your research is unsuccessful, would there still be other useful research outcomes?  

  The Writing Stage  

• Create an outline with main section headings that are typically used.  
• Focus only on writing and getting your points across without worrying about the format of the research proposal , grammar, punctuation, etc. These can be fixed during the subsequent passes. Add details to each section heading you created in the beginning.   
• Ensure your sentences are concise and use plain language. A research proposal usually contains about 2,000 to 4,000 words or four to seven pages.  
• Don’t use too many technical terms and abbreviations assuming that the readers would know them. Define the abbreviations and technical terms.  
• Ensure that the entire content is readable. Avoid using long paragraphs because they affect the continuity in reading. Break them into shorter paragraphs and introduce some white space for readability.  
• Focus on only the major research issues and cite sources accordingly. Don’t include generic information or their sources in the literature review.  
• Proofread your final document to ensure there are no grammatical errors so readers can enjoy a seamless, uninterrupted read.  
• Use academic, scholarly language because it brings formality into a document.  
• Ensure that your title is created using the keywords in the document and is neither too long and specific nor too short and general.  
• Cite all sources appropriately to avoid plagiarism.  
• Make sure that you follow guidelines, if provided. This includes rules as simple as using a specific font or a hyphen or en dash between numerical ranges.  
• Ensure that you’ve answered all questions requested by the evaluating authority.  

Key Takeaways   

Here’s a summary of the main points about research proposals discussed in the previous sections:  

• A research proposal is a document that outlines the details of a proposed study and is created by researchers to submit to evaluators who could be research institutions, universities, faculty, etc.  
• Research proposals are usually about 2,000-4,000 words long, but this depends on the evaluating authority’s guidelines.  
• A good research proposal ensures that you’ve done your background research and assessed the feasibility of the research.  
• Research proposals have the following main sections—introduction, literature review, objectives, methodology, ethical considerations, and budget.  

Frequently Asked Questions  

Q1. How is a research proposal evaluated?  

A1. In general, most evaluators, including universities, broadly use the following criteria to evaluate research proposals . 6  

• Significance —Does the research address any important subject or issue, which may or may not be specific to the evaluator or university?  
• Content and design —Is the proposed methodology appropriate to answer the research question? Are the objectives clear and well aligned with the proposed methodology?  
• Sample size and selection —Is the target population or cohort size clearly mentioned? Is the sampling process used to select participants randomized, appropriate, and free of bias?  
• Timing —Are the proposed data collection dates mentioned clearly? Is the project feasible given the specified resources and timeline?  
• Data management and dissemination —Who will have access to the data? What is the plan for data analysis?  

Q2. What is the difference between the Introduction and Literature Review sections in a research proposal ?  

A2. The Introduction or Background section in a research proposal sets the context of the study by describing the current scenario of the subject and identifying the gaps and need for the research. A Literature Review, on the other hand, provides references to all prior relevant literature to help corroborate the gaps identified and the research need.  

Q3. How long should a research proposal be?  

A3. Research proposal lengths vary with the evaluating authority like universities or committees and also the subject. Here’s a table that lists the typical research proposal lengths for a few universities.  

Q4. What are the common mistakes to avoid in a research proposal ?  

A4. Here are a few common mistakes that you must avoid while writing a research proposal . 7  

• No clear objectives: Objectives should be clear, specific, and measurable for the easy understanding among readers.  
• Incomplete or unconvincing background research: Background research usually includes a review of the current scenario of the particular industry and also a review of the previous literature on the subject. This helps readers understand your reasons for undertaking this research because you identified gaps in the existing research.  
• Overlooking project feasibility: The project scope and estimates should be realistic considering the resources and time available.   
• Neglecting the impact and significance of the study: In a research proposal , readers and evaluators look for the implications or significance of your research and how it contributes to the existing research. This information should always be included.  
• Unstructured format of a research proposal : A well-structured document gives confidence to evaluators that you have read the guidelines carefully and are well organized in your approach, consequently affirming that you will be able to undertake the research as mentioned in your proposal.  
• Ineffective writing style: The language used should be formal and grammatically correct. If required, editors could be consulted, including AI-based tools such as Paperpal , to refine the research proposal structure and language.  

Thus, a research proposal is an essential document that can help you promote your research and secure funds and grants for conducting your research. Consequently, it should be well written in clear language and include all essential details to convince the evaluators of your ability to conduct the research as proposed.  

This article has described all the important components of a research proposal and has also provided tips to improve your writing style. We hope all these tips will help you write a well-structured research proposal to ensure receipt of grants or any other purpose.  

References  

• Sudheesh K, Duggappa DR, Nethra SS. How to write a research proposal? Indian J Anaesth. 2016;60(9):631-634. Accessed July 15, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037942/  
• Writing research proposals. Harvard College Office of Undergraduate Research and Fellowships. Harvard University. Accessed July 14, 2024. https://uraf.harvard.edu/apply-opportunities/app-components/essays/research-proposals  
• What is a research proposal? Plus how to write one. Indeed website. Accessed July 17, 2024. https://www.indeed.com/career-advice/career-development/research-proposal  
• Research proposal template. University of Rochester Medical Center. Accessed July 16, 2024. https://www.urmc.rochester.edu/MediaLibraries/URMCMedia/pediatrics/research/documents/Research-proposal-Template.pdf  
• Tips for successful proposal writing. Johns Hopkins University. Accessed July 17, 2024. https://research.jhu.edu/wp-content/uploads/2018/09/Tips-for-Successful-Proposal-Writing.pdf  
• Formal review of research proposals. Cornell University. Accessed July 18, 2024. https://irp.dpb.cornell.edu/surveys/survey-assessment-review-group/research-proposals  
• 7 Mistakes you must avoid in your research proposal. Aveksana (via LinkedIn). Accessed July 17, 2024. https://www.linkedin.com/pulse/7-mistakes-you-must-avoid-your-research-proposal-aveksana-cmtwf/  

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Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

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

Published on 26.7.2024 in Vol 26 (2024)

Supportive eHealth Technologies and Their Effects on Physical Functioning and Quality of Life for People With Lung Cancer: Systematic Review

Authors of this article:

• Suriya Kirkpatrick, BA, MSc   ; 
• Zoe Davey * , PhD   ; 
• Peter Richard Wright * , PhD   ; 
• Catherine Henshall * , PhD  

School of Nursing, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford, United Kingdom

*these authors contributed equally

Corresponding Author:

Suriya Kirkpatrick, BA, MSc

School of Nursing

Faculty of Health and Life Sciences

Oxford Brookes University

Headington Rd, Headington

Oxford, OX3 0BP

United Kingdom

Phone: 44 07909921833

Email: [email protected]

Background: Despite advancements in treatment and early diagnosis, people with lung cancer are not living as long as those with other cancers. The more common symptoms of lung cancer, such as breathlessness, fatigue, and depression, can be alleviated by improving patients’ physical functioning. Therefore, good symptom management and improved health-related quality of life (HRQoL) are priorities in this patient group. However, current health care services have limited capacity to provide this support. One way to address this issue of health care resources is to empower patients to self-manage their condition using eHealth technologies.

Objective: The purpose of this review was to identify and assess available research on technologies that support persons with lung cancer to improve or maintain their physical functioning, HRQoL, or both.

Methods: Six databases—PubMed, Web of Science, CINAHL, MEDLINE, SPORTDiscus, and PsycINFO—were searched from January 1, 1990, to April 30, 2023. Studies were suitable for inclusion if the participants included people with lung cancer aged >18 years who had been exposed to a physical activity, exercise, or training intervention that was delivered via an electronic or web-based application with or without a comparator. Furthermore, the study had to report on the impact of the intervention on physical functioning and HRQoL. Studies that focused on telemedicine without a digital intervention were excluded. The Grading of Recommendations Assessment, Development, and Evaluation system was used to assess the quality of the included papers. Due to the heterogeneity of the studies, a narrative synthesis was undertaken.

Results: This review is reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 794 papers were initially identified through our search, of which, after screening, 8 (1%) were confirmed suitable for inclusion in the review. As 2 (25%) of the 8 papers reported on different stages of the same study, we included only 7 studies in our analysis. The studies were undertaken between 2010 and 2018 across multiple countries and aimed to develop a technology and test its feasibility or acceptance. The 7 technologies identified included web-based applications, mobile apps, and gaming consoles. The studies demonstrated impact on walking distance, muscle strength, balance, dyspnea symptoms, and cancer-related fatigue. HRQoL scores also showed improvement.

Conclusions: The findings indicate that eHealth technologies can positively impact physical functioning and well-being for people with lung cancer, but there are limited studies that demonstrate the impact of these digital interventions over longer periods. None of the studies reported on the implementation or adoption of a mobile health or eHealth intervention in routine clinical practice, highlighting the need for further research in this area.

Trial Registration: PROSPERO CRD42023414094; https://tinyurl.com/39hhbwyx

Introduction

Lung cancer is the most prevalent cancer globally, with 2.21 million new cases being diagnosed in 2020; this is anticipated to increase to 3.8 million by 2050 [ 1 ]. Lung cancer also accounts for the highest number of cancer-related deaths across all cancer types [ 2 ].

In the United Kingdom, approximately 48,500 new lung cancer cases are diagnosed per year [ 3 ]. The incidence of lung cancer strongly correlates with age, peaking among older individuals. In the United Kingdom, from 2016 to 2018, >44% of new cases annually were in those aged ≥75 years. Age-specific incidence rates rise sharply from around ages 45 to 49 years, reaching a peak in women aged 75 to 79 years and men aged 85 to 89 years, and then decline in older age groups. Women typically have lower incidence rates than men, particularly evident at age ≥90 years, where the rate in women is half that of men [ 3 ]. One-year survival rates have almost doubled since the 1970s due to early diagnosis and improved treatments. However, lung cancer survival rates at 5 and 10 years have not improved as much as those for other cancers [ 3 ].

For people living with lung cancer, it is imperative that supportive care needs, which are central to patient-centered care [ 4 ], are addressed promptly because their condition is associated with a high symptom burden and high levels of unmet needs throughout the disease trajectory [ 5 ]. In addition, approximately two-thirds of people with lung cancer have at least 1 other preexisting health condition, and up to half have ≥2 preexisting health conditions [ 6 ]. Addressing their supportive care needs will contribute to efficient use of health care resources and minimize hospital admissions. If not managed effectively, this could negatively impact patient outcomes, including physical functioning, psychological well-being, and health-related quality of life (HRQoL) [ 4 ].

Common symptoms of lung cancer include fatigue, breathlessness, and depression—all of which can be alleviated by exercise interventions [ 7 ]. More generally, other positive implications for people with cancer undertaking physical activity include improvements in HRQoL [ 8 - 11 ], lung function [ 8 , 10 ], sleep [ 8 , 12 ], immune function and markers [ 8 , 13 ], mood [ 8 , 9 ], bone strength [ 8 , 14 ], and muscle mass [ 8 , 15 ], as well as decreased cancer cell proliferation [ 8 , 13 ]. However, less than one-third of people with lung cancer meet recommended exercise guidelines to reduce time spent sedentary, increase strength- and balance-building activities, and undertake 150 minutes of aerobic activity per week [ 16 ].

Self-management practices, including those with an exercise component, can help patients with cancer to regain health and fitness, reduce side effects from treatment and symptoms of the disease, relax the mind and body, enhance HRQoL, and regain a sense of normality [ 17 ]. More recently, the National Institute for Health and Care Excellence has recognized exercise as a first-line treatment within health care and holistic disease management [ 18 ]. In the absence of a robust national rehabilitation system, there is pressure for self-management support to be integrated into routine cancer care [ 19 ]. However, patient adherence to rehabilitation programs delivered at hospital outpatient centers can be low due to the required travel and associated socioeconomic factors [ 20 ]. Furthermore, studies have demonstrated that home-based rehabilitation improves patient adherence and HRQoL [ 21 ].

Web-based interventions have grown in popularity in recent years. These interventions enable the user to independently navigate a recommended online program that is operated via a website with the aim to create positive changes in health and well-being [ 22 ]. Government organizations are actively trying to transition in-person activities to web-based platforms [ 23 ]. After the COVID-19–related lockdown, this gained renewed prominence because the advantages of digital technology became increasingly evident. Furthermore, earlier reviews have identified several mobile and electronic apps designed to support various stages of the cancer continuum ranging from prevention to survivorship [ 24 ]. The use of mobile health (mHealth) and eHealth technologies, such as wearables and activity trackers as well as apps and web-based programs that can be accessed via smartphones and tablets, provide new methods for educating, monitoring, and supporting patients with chronic conditions and cancer. The World Health Organization recognizes the potential of mHealth and eHealth interventions to support health care delivery [ 25 ]. These interventions can assist patients in self-managing their health behaviors and are considered feasible, acceptable, and effective approaches to providing supportive care [ 26 , 27 ].

There is a growing body of evidence to support technology interventions in health care, and this is supported within the UK National Health Service Long Term Plan [ 28 ]. Nevertheless, evidence-based mHealth and eHealth interventions to enhance exercise and physical activity for people with lung cancer remain uncommon. Furthermore, of the cancer-related apps that are available, a limited number adopt a personalized approach to physical activity and exercise that accommodates patients’ preferences.

This study aims to identify the mHealth and eHealth technologies that have been developed to support people with lung cancer to improve or maintain physical functioning and enhance their HRQoL.

The primary objective of the review was to determine whether any of the mHealth or eHealth technologies identified impacted the physical functioning and HRQoL of people with lung cancer.

The secondary objectives were to assess the demand on clinician time; evaluate the acceptability of the intervention to patients, carers, and health care professionals; investigate user satisfaction with the technology; identify security features (clinical safety, data protection, and technical security); and examine the cost impact of the mHealth or eHealth app or intervention.

This review was prospectively registered with PROSPERO (CRD42023414094) and is reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to improve the quality of the review and ensure transparency at all stages.

Inclusion Criteria

Types of studies.

We included all primary research studies, without study design or publication status limitations or language or geographic area restrictions. Case studies were also included. Reference lists of systematic reviews were cross-checked to identify any potential studies for inclusion. We limited the search to studies published after January 1, 1990, because internet interventions did not exist before this date [ 29 ].

We included studies that were undertaken with adults (aged >18 y) who were diagnosed with lung cancer, regardless of the stage of their disease, treatment allocation, sex, or where they received care.

Intervention

Study participants in the included studies must have been exposed to a physical activity, exercise, or training intervention that was delivered via an electronic or web-based application with or without a comparator.

Outcome Measures

Studies were included if they reported on the impact of the digital intervention on physical function or HRQoL or both physical functioning and HRQoL using any validated measure. We included studies that measured impact at ≥1 time points.

Exclusion Criteria

The following studies were excluded: (1) cancer studies in which the total number of participants with lung cancer accounted for <50% of the study population, (2) studies that focused on telehealth care only and did not include an electronic or web-based intervention (eg, studies that evaluated remote sessions with a clinician via video link), and (3) studies in which apps that were used to track activity could be linked to a wearable device but did not provide any other support.

Search Strategy

A search of 6 databases—PubMed, Web of Science, CINAHL, MEDLINE, SPORTDiscus, and PsycINFO—was carried out on April 17, 2023, via EBSCO, using a list of key terms focusing on 3 distinct categories: the intervention characteristics (eg, web-based, internet, app or application, remote, and digital), physical functioning (eg, activity, exercise, training, movement, and athletics), and the population of interest (eg, patients with lung cancer or survivors of lung cancer and patients with cancer). These were amalgamated using Boolean operators to formulate a comprehensive search string. The full list of search terms is presented in Textbox 1 . Other sources, such as references of included records, were also searched.

Search terms

lung cancer patient * or lung cancer surviv * or lung cancer or lung neoplasm * or lung tumor * or lung tumour * or lung adenocarcinoma AND physical activity or exercise or training or physical function * or mobility or rehabilitation or prehabilitation or physical fitness training or physical rehabilitation or physical recovery or mobility training AND mobile applications or mobile apps or mobile phone apps or phone apps or smartphone apps or smartphone applications or web apps or web applications or mhealth or m-health or ehealth or e-health or online support system or web-based technology or app or apps or software app or cell phone apps or cellular phone apps or mobile technologies or mobile devices or smartphones or technology-enabled care services or interactive apps or telemedicine or virtual medicine or interactive consultative services or Web based tool or activity tracker or fitness tracker or physical fitness tracker or technology enabled care services

Data Collection and Analysis

Selection of studies.

Of the 794 papers identified through the database search, 213 (26.8%) duplicates were removed, leaving 581 (73.2%) papers. Two reviewers (SK, PRW, CH, or ZD) screened the titles and abstracts of these 581 papers, excluding 544 (93.6%) in the process and retaining 37 (6.4%). Of these 37 papers, a further 7 (23%) were excluded because they were conference abstracts. A full-text screening of the remaining 30 papers was then conducted, and 22 (73%) were excluded, leaving 8 (27%) for final inclusion. Any discrepancies identified by the reviewers during the screening process were resolved by discussion with a third member of the review team. The screening process is outlined in the PRISMA diagram ( Multimedia Appendix 1 ).

Data Extraction

Data were independently extracted from the included papers by the lead author (SK) using a data extraction template developed for the purpose of this review. The extracted data were reviewed by the coauthors (CH, ZD, and PRW), and discrepancies were resolved. In case of missing study data, we attempted to contact the corresponding authors to obtain the required information. Three authors were contacted [ 30 - 32 ], but none replied. However, these papers were not excluded because they still met the inclusion criteria and reported on some of our objectives. We extracted the data shown in Textbox 2 .

Study characteristics

Authors, year of publication, title, country of study, year of study, study objective, overall study design, recruitment method, sample size, participant age range, sex, and study duration

Intervention characteristics

Technology product used, setting, intervention details, and exercise details (type, frequency, intensity, and duration)

Outcome measures of interest

Impact on physical functioning, impact on HRQoL, and user acceptability

Other outcomes of interest

Impact on clinician time, user acceptability or satisfaction, safety features, and cost impact

Critical Appraisal

Risk of bias.

Two reviewers (SK and ZD) independently assessed the risk of bias of each included study and confirmed agreement. The JBI checklists [ 33 ] were used to assess the methodological and reporting quality of the included studies.

Quality Appraisal

The Grading of Recommendations Assessment, Development, and Evaluation approach was adopted to assess the quality of the evidence used to support the synthesized findings [ 34 , 35 ].

Data Synthesis and Analysis

Due to the methodological heterogeneity of the included studies, we were unable to undertake a meta-analysis. Instead, the Synthesis Without Meta-Analysis reporting items checklist was used to aid transparency in the reporting process [ 36 ]. This enabled us to report on the key features of the included studies, group the studies, and explain our findings. A narrative synthesis [ 37 ] was also undertaken in accordance with the study objectives stated earlier. This allowed us to provide a comprehensive summary of the impact and effectiveness of the interventions identified in the included studies.

Eight papers were identified as suitable for inclusion. However, 2 (25%) of these 8 papers [ 30 , 38 ] reported on different stages of the same study; therefore, only 7 studies were included in the review. The screening process is outlined in the PRISMA diagram presented in Figure 1 .

Study Characteristics

The included studies were published between 2013 and 2022 across international settings, including the United States [ 30 , 38 ], the Netherlands [ 39 ], South Korea [ 32 , 40 , 41 ], the United Kingdom [ 31 ], and Canada [ 42 ].

The studies were undertaken between 2010 [ 30 ] and 2018 [ 40 ]. The aim of the included studies was to develop a technology [ 31 ] and test its usability [ 39 ], feasibility [ 31 , 39 , 41 , 42 ], acceptance [ 30 , 31 ], and efficacy [ 30 , 32 , 38 , 41 ]. The studies were primarily quasi-experimental and nonrandomized experimental studies [ 30 , 32 , 39 , 41 , 42 ]. Only 1 randomized controlled trial [ 40 ] and 1 cohort study [ 31 ] were included. The research methods used were predominantly quantitative (5/7, 71%) [ 30 , 32 , 40 - 42 ], with the remaining studies using mixed methods (2/7, 29%) [ 31 , 39 ].

Participants were identified from secondary care settings. The number of study participants ranged from 5 [ 42 ] to 100 [ 41 ], and their mean ages ranged from 55.1 (SD 8.7) years [ 41 ] to 64.6 (SD 6.5) years [ 30 , 38 ]. Overall, there were more female participants (195/340, 57.4%) than male participants (145/340, 42.6%) across 4 (57%) of the 7 studies [ 30 , 39 - 41 ]; the study by Kadiri et al [ 31 ] did not report the sex breakdown of the participants. The intervention duration ranged from 6 to 12 weeks; the study by Kadiri et al [ 31 ] did not report the intervention duration, but it was clear that the intervention was delivered pre- and postoperatively with a study duration of ≤18 months. The characteristics of the included studies are detailed in Table 1 .

a NSCLC: non–small cell lung cancer.

b F: female.

d Not applicable.

e QoL: quality of life.

f NR: not reported.

The risk of bias for all studies was low, and quality appraisal scores were moderate ( Table 1 ).

Intervention Characteristics

The review identified 7 technologies that had been studied in people with lung cancer: 4 (57%) mobile apps [ 31 , 32 , 40 , 41 ] and 3 (43%) web applications [ 30 , 38 , 39 , 42 ]. Of the 3 web applications, 2 (67%) used a gaming console to deliver part of the exercise prescription [ 30 , 38 , 42 ].

The interventions were delivered or accessed from various settings: web based [ 31 , 32 , 39 , 40 ], home based [ 30 , 38 , 42 ], or a combination of outpatient department and home based [ 41 ].

The frequency, intensity, time, and type formula [ 43 ] was used to extract key components of the exercise prescription of each study. Only 5 (62%) of the 8 papers reported the full details of the exercise prescription according to the frequency, intensity, time, and type formula [ 22 , 30 , 31 , 38 , 41 ].

The papers that did not provide a detailed exercise prescription provided more general information and recommendations regarding physical activity [ 32 , 39 ]; alternatively, the interactive app would support the participant to edit the frequency, intensity, and duration of the exercise [ 40 ]. Where the technology included an interactive patient portal, it was noted that the physical activity support program that was incorporated in the portal was only used by one-third of the participants [ 39 ].

The intervention details are summarized in Table 2 .

a MET: metabolic equivalent of task.

b Not applicable.

c HR: heart rate.

d SpO 2 : peripheral oxygen saturation.

e VO 2peak : peak oxygen uptake.

Primary Outcome Measures

Physical functioning.

All included studies demonstrated some improvement in physical functioning, but the methods of assessment varied. The most common assessment was walking time and walking distance. In the studies by Ji et al [ 40 ] and Coats et al [ 42 ], participants demonstrated an improvement in the 6-minute walk test and the 6-minute walk distance test, while Yang et al [ 32 ] noted an improvement in the 2-minute walk distance test. An increase in walking duration and step count was also observed by Hoffman et al [ 30 ], while Kadiri et al [ 31 ] noted an increase in the distance covered in the shuttle walk test.

No improvement in muscle strength was noted by Coats et al [ 42 ], while Yang et al [ 32 ] noted an improvement in lower extremity muscle strength but not in upper extremity muscle strength. Only the study by Hoffman et al [ 30 , 38 ] showed an improvement in balance and cancer-related fatigue, while the study by Ji et al [ 40 ] showed an improvement in dyspnea grade.

The study by Groen et al [ 39 ] identified no significant improvement in physical activity, but an improvement in vigorous activity over time was noted. Finally, the study by Park et al [ 41 ] found that although exercise capacity improved in stable patients, this was not the case in patients with progressive disease.

Impact on HRQoL

Almost all studies (6/7, 86%) included in our review reported the impact of the digital health intervention on HRQoL [ 31 , 32 , 39 - 42 ].

The tool most commonly used to assess HRQoL was the European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire-30. Improvements in symptom scales were observed by Kadiri et al [ 31 ] and Park et al [ 41 ], improvements in functional scales were observed by Yang et al [ 32 ] and Park et al [ 41 ], while Coats et al [ 42 ] did not observe any significant changes.

Ji et al [ 40 ] used the EuroQol visual analog scale score to demonstrate a significant improvement between visit 1 and visit 3, but the EQ-5D scores did not differ between the same time points. The Short Form Health Survey-36 was used in the study by Groen et al [ 39 ], but no significant changes in the scores were noted.

Secondary Outcome Measures

Demand on clinician time.

The technologies varied with regard to the level of clinician involvement required for implementation and ongoing patient management. The authors attempted to assess the impact on clinician time across the included studies ( Table 3 ). Only the study by Kadiri et al [ 31 ] explicitly reported the impact of adopting the intervention on health care professional time: 60 minutes.

a HRQoL: health-related quality of life.

b MET: metabolic equivalent of task.

c SF-36: Short Form Health Survey-36.

d 6MWD: 6-minute walk distance.

e EQ-VAS: EuroQol visual analog scale.

f SpO 2 : peripheral oxygen saturation.

g 6MWT: 6-minute walk test.

h PGA: Patient Global Assessment.

i EORTC QLQ-C30: European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire-30.

j ECOG-PS: Eastern Cooperative Oncology Group-Performance Status.

k NR: not reported.

l TST: timed stair test.

m EORTC QLQ-LC13: EORTC QLQ–Lung Cancer 13.

Acceptability and User Satisfaction

All included papers reported either user satisfaction [ 40 - 42 ] or both acceptability and user satisfaction [ 30 - 32 , 39 ], as summarized in Table 3 , and demonstrated high levels of acceptability and user satisfaction.

Safety Features

The studies reported primarily on clinical safety features of the technology (eg, access to a mobile phone while exercising to contact the study nurse [ 30 , 38 ], heart rate monitoring [ 30 , 31 , 38 , 40 ], and monitoring of peripheral oxygen saturation levels [ 31 ], as noted in Table 3 ). Of the 8 studies, 2 (25%) also reported on the technology’s security features to ensure limited authorized access to the app [ 40 ] or database [ 39 ].

Cost-Effectiveness

None of the studies evaluated the cost-effectiveness of the digital health interventions in terms of clinical outcomes within a health economic context. Only the single-center cohort study [ 31 ] reported on the cost of implementing the technology, which was estimated to be between £16 (US $20.8) and £34 (US $44.2) per patient.

eHealth: Benefits and Concerns

eHealth, which encompasses information and communication technology, facilitates remote care delivery and health information transmission, promoting patient involvement, improving care quality, and enhancing accessibility, particularly in remote areas. Despite benefits such as convenience and reduced travel, concerns exist, such as privacy issues, fears of diminished human interaction, and lack of access to technology and internet infrastructure [ 44 ]. eHealth holds promise in supporting patient-centered care and empowers patients by enhancing their involvement in their health care [ 45 ].

Principal Findings

To our knowledge, this is the first review that has looked specifically at digital technologies developed for people with lung cancer and their impact on physical functioning and HRQoL. Despite searching across a wide range of databases, we were only able to identify 7 studies for inclusion in our review.

The findings of this review suggest that digital health interventions with an exercise component are acceptable to people with lung cancer because the interventions help them play a more active role in their health care and can positively impact their physical functioning and HRQoL. Various tools were used in the studies to assess HRQoL. The EQ-5D, a questionnaire conventionally used for economic cost analysis, was used in the study by Coats et al [ 42 ] to assess HRQoL, and the authors noted that, despite an observed increase in the EuroQol visual analog scale score, the EQ-5D score did not improve. The evidence to support the use of digital exercise interventions remains sparse, and we have drawn on the findings of only a few studies that aimed primarily to develop and assess the feasibility of the intervention used.

Nevertheless, the quality of the studies was high, thereby strengthening the credibility of the review findings. Further research is required to observe the effects of the interventions over a longer period and to explore the potential cost savings associated with using a remote health management mobile app, including a reduction in clinician time or the number of consultations, improved disease management, and the costs of implementing the technologies in routine care, as well as the safety and security risks of the technologies.

Results in Context

In recent years, there has been a growth in the number of digital health technologies, including mHealth and eHealth, as well as wearable devices that are becoming an integral part of modern health care [ 46 ]. The promotion of patient self-management practices facilitated by digital technology has gained attention because it can improve patient engagement and health care delivery [ 47 ]. This has become particularly relevant and has intensified in the wake of the pressures placed on the health service during the COVID-19 pandemic [ 22 ]. It is anticipated that strategies to encourage patients to self-manage their health behaviors will continue to be integrated into care pathways in the future [ 48 ].

In the context of lung cancer, approximately two-thirds of the people with lung cancer have at least 1 other preexisting health condition, and up to half have ≥2 preexisting health conditions [ 6 ], making this group more complex to treat because they require a more tailored approach to rehabilitation [ 49 ]. We note that the technologies identified in our review either produced an exercise prescription relevant to the individual participant’s capability or gave the patient guidance on exercises they might be able to undertake. As the participants’ exercise tolerance improved, the prescription was updated, affording a bespoke approach to rehabilitation that might be more relevant to people with lung cancer.

This review highlights the benefits of a digital health intervention that included an exercise component and demonstrated improvements in cancer-related fatigue and balance [ 30 , 38 ], walking duration or distance or step count [ 30 - 32 , 38 , 42 ], general activity levels, dyspnea [ 40 ], and muscle strength for people living with lung cancer [ 32 ]. Nevertheless, there are still areas for development; for example, the study by Yang et al [ 32 ] found that while there was an increase in lower extremity muscle strength, no change in upper extremity muscle strength was noted, and, in another study [ 41 ], for participants with progressive disease, no improvement was observed in exercise capacity or dyspnea scale score. Furthermore, none of the included studies provided a bespoke exercise prescription and demonstrated a positive impact on exercise endurance, muscle endurance, or dyspnea symptoms. This reinforces the need to produce an exercise prescription that is relevant to the participants’ disease status and individual exercise goals. More consideration is required to develop technologies with intelligent algorithms or artificial intelligence that can support persons with lung cancer to input data on their current condition and capability before a safe, bespoke exercise prescription is recommended. This needs to be factored into app development and further research.

Improving HRQoL is a priority goal in supporting people with lung cancer [ 49 ]. All studies that assessed the impact of HRQoL reported a positive impact. The direction of the association between HRQoL and improved physical functioning was consistent across most of the studies (n=5, 71%) that reported on both outcomes [ 31 , 32 , 40 - 42 ]. Improvements were primarily noted in the global health score as well as the symptom and functional scales, but it is unclear whether these improvements are a direct result of the exercise intervention or improved disease management and control.

This review highlighted the high levels of acceptability and participant satisfaction with the digital intervention. Having a home-based exercise intervention or web-based intervention, such as the technologies highlighted in this review, is advantageous because participants can continue to exercise during chemotherapy treatment when they are usually advised to limit their contact with other people to reduce the risk of infection. Furthermore, the studies that used a game console as part of their exercise program incentivized their participants to use the technology, work out more often, and increase the intensity of the exercise [ 30 , 38 , 42 ]. Other useful features of the technologies that have been highlighted include the use of a virtual environment for exercising [ 30 , 38 ], dance-based workouts [ 42 ], dietary advice [ 32 ], and the value of incentivizing participants [ 30 , 38 , 42 ]. Hence, it is important for future app developers to consider what incentives can be built into the intervention to improve adherence, reduce clinician workload, and empower patients to be more independent regarding their disease management.

It is anticipated that health technologies, such as the ones identified in this review, will enable improved disease management, reduce hospital admissions, and save the UK National Health Service money [ 31 ]. However, limited cost analyses have been conducted to date to demonstrate the cost impact on health care services, the health care workforce, and people living with lung cancer. Our review identified only 1 cohort study [ 31 ] undertaken at a single research center that reported the cost of the intervention to the health care provider and the estimated time impact on health care professionals.

Limitations

We reported on evidence available within the published papers. We faced challenges in gathering details regarding missing study data. We made attempts to reach out to the corresponding authors to obtain the necessary information, but we did not receive any response. Furthermore, it is unclear whether any development information not included in the reports of the studies was excluded [ 30 - 32 ]; in other words, important details could have been left out of the study reports, and the reasons for their possible omission are not known.

The studies included in our review were primarily quasi-experimental and nonrandomized studies [ 30 , 32 , 39 , 41 , 42 ]. Only 1 randomized controlled trial [ 40 ] and 1 cohort study [ 31 ] were included. The studies focused on technology development and feasibility assessment, most frequently for patients undergoing surgery [ 30 - 32 , 39 , 40 ]. In addition, the small cell subtype of lung cancer has poorer outcomes than non–small cell subtypes, with a primary treatment focus being to improve HRQoL; this population was not well represented in the included studies. Furthermore, various tools were used to assess physical functioning and HRQoL, making it difficult to draw comparisons across the studies due to their heterogeneity.

Conclusions

There seems to be a consistent relationship between the use of digital technologies by people with lung cancer and a positive impact on physical functioning and HRQoL. The technologies reported in this review demonstrated high levels of acceptability or user satisfaction and have the potential to support people with lung cancer to manage their health more independently. However, most of the studies (n=5,71%) identified here report on the development and feasibility of the technology. Further multicenter, large-scale research studies using a randomized controlled trial design over an extended period and including a cost-effectiveness outcome measure are required to fully assess the true benefit of adopting eHealth technologies in standard care services for health care providers and people with lung cancer.

Acknowledgments

The authors would like to thank the academic librarian at Oxford Brookes University, Richard Persaud, for his support with conducting the searches across the scientific databases. CH acknowledges the support of the National Institute for Health and Care Research Oxford Health Clinical Research Facility. The views expressed are those of the authors and not necessarily those of the National Institute for Health and Care Research, the UK National Health Service, or the UK Department of Health and Social Care.

Authors' Contributions

SK drafted the manuscript. CH, ZD, and PRW supported the development of the concept for the review. SK and 1 other author (CH, ZD, or PRW) screened all abstracts and full texts. All data were extracted by SK and subsequently reviewed by CH, ZD, and PRW. SK conducted the quality assessment in collaboration with ZD and CH. All authors contributed to the methods for the review, multiple versions of the manuscript, and approved the final version of the manuscript.

Conflicts of Interest

None declared.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

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Abbreviations

Edited by K Williams; submitted 22.09.23; peer-reviewed by S Harding, C Holländer-Mieritz; comments to author 08.03.24; revised version received 01.05.24; accepted 18.05.24; published 26.07.24.

©Suriya Kirkpatrick, Zoe Davey, Peter Richard Wright, Catherine Henshall. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.07.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Of the 54 pairs with discordant sample sizes and primary end point results, 13 also had discordant primary end points.

eAppendix. Supplemental Methods and Results

eTable 1. Observed Reasons for Discordance of Preprint-Journal Article Pairs With Discordant Results for the Primary Endpoint(s), N = 101

eTable 2. Observed Reasons for Discordance for Preprint-Journal Article Pairs With Discordant Interpretations, N = 21

eTable 3. Concordance Characteristics of COVID-19 vs Non-COVID-19 Preprint-Journal Article Pairs, N = 547

eTable 4. Concordance Characteristics for All Preprint-Journal Pairs Across Study Designs, N = 547

eTable 5. Concordance Characteristics for Preprint-Journal Article Pairs With Multiple Versions, N = 87

eTable 6. Concordance Characteristics for All Preprint-Journal Article Pairs Using the First Posted Version of the Preprint, N = 547

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Janda G , Khetpal V , Shi X , Ross JS , Wallach JD. Comparison of Clinical Study Results Reported in medRxiv Preprints vs Peer-reviewed Journal Articles. JAMA Netw Open. 2022;5(12):e2245847. doi:10.1001/jamanetworkopen.2022.45847

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Comparison of Clinical Study Results Reported in medRxiv Preprints vs Peer-reviewed Journal Articles

• 1 Yale School of Medicine, New Haven, Connecticut
• 2 Department of Medicine, Warren Alpert Medical School of Brown University, Providence, Rhode Island
• 3 Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut
• 4 Section of General Medicine and the National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
• 5 Center for Outcomes Research and Evaluation, Yale–New Haven Health System, New Haven, Connecticut
• 6 Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut
• 7 Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia

Question   What is the concordance among sample size, primary end points, results for primary end points, and interpretations described in preprints of clinical studies posted on medRxiv that are subsequently published in peer-reviewed journals (preprint-journal article pairs)?

Findings   In this cross-sectional study of 547 clinical studies that were initially posted to medRxiv and later published in peer-reviewed journals, 86.4% of preprint-journal article pairs were concordant in terms of sample size, 97.6% in terms of primary end points, 81.1% in terms of results of primary end points, and 96.2% in terms of study interpretations.

Meaning   This study suggests that most clinical studies posted as preprints on medRxiv and subsequently published in peer-reviewed journals had concordant study characteristics, results, and final interpretations.

Importance   Preprints have been widely adopted to enhance the timely dissemination of research across many scientific fields. Concerns remain that early, public access to preliminary medical research has the potential to propagate misleading or faulty research that has been conducted or interpreted in error.

Objective   To evaluate the concordance among study characteristics, results, and interpretations described in preprints of clinical studies posted to medRxiv that are subsequently published in peer-reviewed journals (preprint-journal article pairs).

Design, Setting, and Participants   This cross-sectional study assessed all preprints describing clinical studies that were initially posted to medRxiv in September 2020 and subsequently published in a peer-reviewed journal as of September 15, 2022.

Main Outcomes and Measures   For preprint-journal article pairs describing clinical trials, observational studies, and meta-analyses that measured health-related outcomes, the sample size, primary end points, corresponding results, and overarching conclusions were abstracted and compared. Sample size and results from primary end points were considered concordant if they had exact numerical equivalence.

Results   Among 1399 preprints first posted on medRxiv in September 2020, a total of 1077 (77.0%) had been published as of September 15, 2022, a median of 6 months (IQR, 3-8 months) after preprint posting. Of the 547 preprint-journal article pairs describing clinical trials, observational studies, or meta-analyses, 293 (53.6%) were related to COVID-19. Of the 535 pairs reporting sample sizes in both sources, 462 (86.4%) were concordant; 43 (58.9%) of the 73 pairs with discordant sample sizes had larger samples in the journal publication. There were 534 pairs (97.6%) with concordant and 13 pairs (2.4%) with discordant primary end points. Of the 535 pairs with numerical results for the primary end points, 434 (81.1%) had concordant primary end point results; 66 of the 101 discordant pairs (65.3%) had effect estimates that were in the same direction and were statistically consistent. Overall, 526 pairs (96.2%) had concordant study interpretations, including 82 of the 101 pairs (81.2%) with discordant primary end point results.

Conclusions and Relevance   Most clinical studies posted as preprints on medRxiv and subsequently published in peer-reviewed journals had concordant study characteristics, results, and final interpretations. With more than three-fourths of preprints published in journals within 24 months, these results may suggest that many preprints report findings that are consistent with the final peer-reviewed publications.

Preprints, which are preliminary research reports that have not yet undergone peer review, have been widely adopted to enhance the timely dissemination of research across many scientific fields. 1 , 2 The launch of the preprint server medRxiv has recently led to the increasing use of preprints in the clinical and health science research community. 3 Although the COVID-19 pandemic has highlighted several benefits of preprints, including the rapid and open evaluation of research findings, 4 - 6 concerns remain that early, public access to preliminary medical research has the potential to harm patients or public health practices by propagating misleading or faulty research that has been conducted or interpreted in error. 7

Although not all preprints will be published in peer-reviewed journals, among those that are (preprint-journal article pairs), it is possible to examine the extent to which the studies’ design, results, and conclusions are changed as of publication. Although previous evaluations have provided some reassurance of the consistency between preprints and subsequent publications, these efforts have often focused on specific fields and journal types. For instance, evidence suggests that COVID-19–related preprint-journal article pairs are largely similar in terms of their abstracts, figures, and interpretations. 8 , 9 Moreover, preprints of clinical studies posted on medRxiv and subsequently published in clinical journals with the highest impact factors (impact factor >10) were found to have high levels of agreement with respect to sample size, primary end points, results, and overall interpretations. 10 However, few preprints are eventually published in journals with the highest impact factors, 10 and those published by these journals could represent the highest-quality preprints and may be less likely to require major changes during the peer review process. To further inform discourse about the use of preprints in clinical and health science research, we conducted a cross-sectional evaluation examining the agreement between clinical studies posted as preprints to medRxiv in September 2020 and subsequently published in any peer-reviewed journal, including sample size, primary end points, results of primary end points, and overall interpretations.

We did not involve members of the public or patients when we designed our study, interpreted the results, or wrote the manuscript. However, we asked members of the public to read our manuscript after submission to ensure it was understandable. The study did not require institutional review board approval because it was based on publicly available information, in accordance with 45 CFR §46. Informed consent was not needed because no patient data were used. This cross-sectional study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

To identify a representative sample of clinical and health science preprints, we used medRxiv’s built-in application programming interface (API) to locate all manuscripts posted to medRxiv in September 2020 (eAppendix in Supplement 1 ). The digital object identifier, preprint title, authors, and medRxiv-assigned study category were automatically collected through the API. Because medRxiv allows authors to post updated versions of their manuscripts, we limited our sample to preprints for which the first posting was in September 2020. For manuscripts that were updated after their initial September 2020 posting, we used medRxiv’s built-in version tracker to locate and select the most recent manuscript version.

Four investigators (G.J., V.K., X.S., and J.D.W.) manually reviewed each preprint and characterized the study design of each into 1 of the following categories: clinical trials, observational studies, meta-analyses with or without systematic reviews, modeling studies, or other (eAppendix in Supplement 1 ). One author (J.D.W.) checked 25% of the overall sample for consistency and accuracy. Any disagreements were resolved by consensus.

To identify a corresponding peer-reviewed journal article for each preprint (ie, preprint-journal article pairs), we first used the medRxiv API, which matches preprints to journal articles based on title and author(s) (eAppendix in Supplement 1 ). For preprints that did not have a publication linked with the medRxiv API, we conducted Google searches using the preprint title, key terms, and first and last author(s) names to find corresponding publications. The cutoff date for our publication assessment was September 15, 2022. To measure the time from first posting of a preprint to publication in a peer-reviewed journal, we identified each preprint’s initial date of posting on medRxiv and corresponding e-publication date in a peer-reviewed journal. If a preprint was updated after the initial posting, the date of the most recent version was used for the concordance evaluation, which allowed preprint authors to update their preprint based on internal quality control, community feedback, or any other reason. However, to help ensure that these changes were not conflated with changes made during peer review, we excluded preprints that were updated after the date of journal acceptance. We then used InCites Journal Citation Reports to collect the 2021 journal impact factor for each journal.

To evaluate the agreement between preprint-journal article pairs, we narrowed our sample to clinical trials, observational studies, and meta-analyses that measured health-related outcomes. Four investigators (G.J., V.K., X.S., and J.D.W.) reviewed the preprint-journal article pairs and collected the following information: abstract-reported sample size, primary end point(s), results for each primary end point, and overall interpretations or conclusions. For preprints or journal articles with unclear information at the abstract level, we reviewed the full text. For clinical trials and observational studies, sample size was defined as the number of the individuals in the cohort or database used for the primary analysis. For meta-analyses, sample size was defined as the number of discrete studies included in the primary analysis. For each preprint-journal article pair, we identified the measurement scale (eg, odds ratio, mortality rate, or other estimate) and ascertainment time for the primary end points. For clinical trials or prospective observational studies, ascertainment times were defined as the follow-up times for the cohorts of interest. For all other observational study designs and meta-analyses, ascertainment times were defined as the cutoff dates for data collection or database searches. Last, we recorded the Altmetric score for each preprint and journal article. 11

Using previously developed methods, 10 we assessed the concordance between the preprint-journal article pairs in terms of sample size, primary end point(s), results of each primary end point, and interpretation. For sample size, the preprint-journal article pairs were classified as concordant if they had numerical equivalence; if a pair was discordant, we conducted further investigation to characterize the type of discordance (eAppendix in Supplement 1 ).

For primary end point(s), preprint-journal article pairs were classified as concordant if all primary end points identified in the preprint were identified as primary end points in the publication, and no additional primary end points were specified in the publication. For each preprint-journal article pair, results of primary end points were classified as concordant if all effect estimates and/or CIs or P values were the same (ie, numerical equivalence). For any results of primary end points classified as discordant, we reviewed the Methods and Results sections of the preprint-journal article pairs to assess the type and potential reasons for the observed discordance (eAppendix in Supplement 1 ). For the study interpretations, we marked preprint-journal article pairs as concordant if the authors made the same or similar statements about the findings of the study and their implications for health science.

We conducted descriptive analyses to quantify the preprint characteristics and concordance rates between preprint-journal article pairs. Analyses of concordance rates were repeated across study designs (observational studies, clinical trials, or meta-analyses), journal impact factor (≥10 vs <10), topic (COVID-19–related vs non–COVID-19–related manuscripts), and use of the original posted preprint for the concordance assessment between preprint-journal article pairs with multiple versions. We also abstracted median (IQR) time to publication (in months) from the original post. The χ 2 test and the Fisher exact test were used to compare concordance rates across COVID-19 relation and journal impact factor. A 2-sided P  < .05 was considered to be statistically significant. Analyses were conducted using JMP Pro software, version 15.0.0 (SAS Institute Inc), and figures were generated using Python software, version 3.7 (BioVenn package; Python Software Foundation). 12

We identified 1853 preprints posted on medRxiv in September 2020, of which 1399 (75.5%) were new manuscripts rather than updated versions of previously posted preprints ( Figure 1 ). Of the 1399 preprints, 623 (44.5%) were observational studies, 280 (20.0%) were modeling studies, 62 (4.4%) were meta-analyses with or without systematic reviews, 42 (3.0%) were clinical trials, and 392 (28.0%) were other study designs ( Table 1 ). The most common subject areas were infectious disease (343 [24.5%]), epidemiology (282 [20.2%]), and public and global health (130 [9.3%]); overall, 840 preprints (60.0%) were COVID-19 related.

Of the 1399 preprints, 1077 (77.0%) had been subsequently published in a peer-reviewed journal as of September 15, 2022 ( Table 1 ), including 489 of 623 observational studies (78.5%), 210 of 280 modeling studies (75.0%), 47 of 62 meta-analyses (75.8%), and 35 of 42 clinical trials (83.3%). The most common subject areas were infectious disease (263 [24.4%]), epidemiology (191 [17.7%]), and public and global health (104 [9.7%]); overall, 625 (58.0%) were COVID-19 related.

The overall median time from first preprint posting to journal publication was 6.0 months (IQR, 3.0-8.0 months), which was consistent across study designs (observational studies: median time to publication, 5.0 months [IQR, 3.0-8.0 months]; modeling studies: median time to publication, 6.0 months [IQR, 3.0-8.0 months]; clinical trials: median time to publication, 6.0 months [IQR, 2.8-9.3 months]; meta-analyses: median time to publication, 6.0 months [IQR, 2.5-9.0 months]; and other designs: median time to publication, 6.0 months [IQR, 3.0-8.0 months]).

A total of 547 preprint-journal article pairs that measured health-related outcomes were included in our concordance analyses (450 observational studies [82.3%], 32 clinical trials [5.9%], and 46 meta-analyses [8.4%]) ( Table 1 ). The most common subject areas were infectious disease (113 [20.7%]), epidemiology (76 [13.9%]), and public and global health (62 [11.3%]). A total of 293 pairs (53.6%) were related to COVID-19. The median Altmetric scores were 3 (IQR, 1-13) for preprints and 7 (IQR, 1-26) for journal articles. Journal impact factors were obtained for 504 pairs (92.1%), and the median was 5.0 (IQR, 3.7-8.4); 400 of 504 articles (79.4%) were published in journals with an impact factor less than 10.

Of the 535 pairs (97.8%) reporting sample sizes in both sources, 462 (86.4%) were concordant ( Table 2 ); sample sizes were larger in the journal articles for 43 (58.9%) of the 73 pairs with discordant sample sizes. Of the 547 pairs reporting primary end points in both sources, 534 pairs (97.6%) had concordant primary end points. Of the 13 pairs (2.4%) with discordant primary end points, 6 had end points that completely changed between the preprint and journal article, 4 had an addition of at least 1 end point to the journal article, and 3 had removal of at least 1 end point from the journal article.

Of the 535 pairs with numerical results for the primary end points, 434 (81.1%) were concordant with respect to numerical effect estimates, direction of effect, and statistical significance. Of the 101 pairs with discordant results, 66 (65.3%) had effect estimates that were in same direction and were statistically consistent, 5 (5.0%) had results in which the direction of the effect estimates or statistical significance were discordant, and 17 (16.8%) had results in which the number of outcomes or number of reported outcomes were discordant.

Of the 101 of 535 pairs (18.9%) with discordant results for the primary end points, the most common observed reason for discordance was discordant sample size (46 [45.5%]) ( Figure 2 ; eTable 1 in Supplement 1 ). A total of 18 pairs (17.8%) had discordant results that were owing to a different number of outcomes or a different number of reported outcome components (ie, association studies), 12 pairs (11.9%) had numerically discordant results for primary end points that were likely owing to minor statistical or methodological changes, and 12 additional pairs (11.9%) had unknown reasons for the discordance of results.

Of the 547 pairs with available study interpretations, 526 (96.2%) had concordant study interpretations, including 82 of 101 pairs (81.2%) with discordant primary end point results ( Table 2 and Figure 2 ). Of the 21 pairs with discordant interpretations, 10 (47.6%) had discordant primary end points (eTable 2 in Supplement 1 ). Seven additional pairs (33.3%) of the the 21 with discordant interpretations had discordant results for the primary end points. Four pairs (19.0%) had results for primary end points that were concordant, but additional conclusions and interpretations were included in the final journal articles. Overall, 406 of 547 pairs (74.2%) were concordant across all study characteristics: sample size, primary end point, results, and interpretation.

Among pairs with articles published in journals with high impact factors (≥10), the concordance rates were lower than for pairs with articles published in journals with low impact factors for sample size (78 of 103 [75.7%] vs 345 of 391 [88.2%]; P  = .002) and for primary end point results (66 of 100 [66.0%] vs 331 of 393 [84.2%]; P  < .001) ( Table 3 ). The concordance rates for primary end points (102 of 104 [98.1%] vs 390 of 400 [97.5%]; P  ≥ .99) and interpretations (97 of 104 [93.3%] vs 386 of 400 [96.5%]; P  = .17) were not significantly different between journals with high impact factors and those with low impact factors.

The concordance rates were not significantly different between the 293 pairs reporting results from COVID-19–related studies and the 254 pairs reporting results from non–COVID-19–related studies (eTable 3 in Supplement 1 ). Concordance rates stratified across study design are available in eTable 4 in Supplement 1 .

Finally, we repeated our analysis of concordance using the original versions of preprints. Of the 547 pairs, 87 (15.9%) had preprints with multiple versions. Overall, the concordance rates were relatively consistent when using the original posted preprint instead of the most recent preprint (excluding those updated after journal acceptance) (eTables 5 and 6 in Supplement 1 ).

In this cross-sectional study of medRxiv preprints of clinical studies subsequently published in peer-reviewed journals, we found that 74.2% were fully concordant with respect to sample size, primary end points, results, and interpretations. When preprint-journal article pairs had discordant results, the discrepancies were often owing to minor sample size changes that were unlikely to affect the study interpretation. Although approximately 25% of preprints are not subsequently published in journals, these findings suggest that among the majority of preprints that are published in journals, changes to studies’ design, results, and conclusions are uncommon. This concordance provides some additional reassurance about the consistency of findings between preprints and subsequently published journal articles.

We found that 81.1% of preprint-journal article pairs were concordant in terms of numerical results of primary end points and that 96.2% were concordant in their interpretations. These findings are in general agreement with a previous study 10 that focused on the concordance between medRxiv preprints of clinical studies subsequently published in journals with the highest impact factors (>10), in which 68% of results of primary end points and 98% of interpretations were concordant. The current study extends this previous work by examining pairs in all journals, predominantly those with an impact factor less than 10. Our study also mirrors a previous evaluation of preprints posted to bioRxiv and medRxiv, which found that most abstracts did not change significantly after publication. 9 Similar to our evaluation, these studies also suggest that preprint-journal article pairs with numerical or statistical changes often have consistent conclusions. 9 , 10 High levels of concordance have also been observed across other fields, including preprints posted on arXiv and bioRxiv, which are preprint servers for the life and physical sciences, respectively. 13

Among preprint-journal article pairs with discordant results, we found that most were owing to minor numerical changes or adjustments, likely resulting from updates that increased the study sample size, which did not change the overall interpretations. According to a previous study 14 examining different versions of preprints and journal articles reporting on COVID-19 interventions, approximately one-third of the pairs with changes in study results had changes in sample size. Although this finding is smaller than our finding, it may not be surprising, given that the previous study examined changes across any of the results, not just the primary end points. Although we found only a few studies with discordant results owing to different primary outcomes or primary outcome components, evidence suggests that outcome switching may be more prevalent among COVID-19–related preprint-journal article pairs. 8 , 14 With only 21 pairs with discordant interpretations in our sample, we are unable to draw strong inferences as to why interpretations of preprints change after publication as journal articles. Previous work suggests that preprints within the medical sciences undergo more extensive reframing 15 ; however, further research on the potential reasons that preprints change on publication will be necessary.

In the health sciences, the benefits of preprints—namely, rapid dissemination and increased transparency—are typically weighed against concerns that preprint findings are preliminary and subject to change after peer review and editorial oversight. 16 , 17 Among our sample of preprints posted 24 months ago (September 2020), less than one-fourth were not published. Although this finding may suggest that there is a sizable fraction of research articles that would not have been publicly available without the opportunity to post as a preprint, the quality and utility of these studies were not assessed. Among preprints that were subsequently published, differences between the published article and the preprint were minor and often owing to changes in sample size, which could be requested by reviewers or editors during peer review. Another study noted that for COVID-19–related clinical trials initially posted as preprints, publication in a peer-reviewed journal did not significantly improve reporting transparency or quality. 18 Together, these studies may suggest that editors and peer reviewers primarily act as gatekeepers, preventing certain studies from being published and identifying those that merit publication; given the consistency between preprints and publications, perhaps those that merit publication are of higher quality and do not require major changes. However, given that we do not know the final fate of those preprints that have not yet been published, including whether they were ever submitted for publication and/or were rejected after peer review, future evaluations are needed to elucidate the exact role of peer review in preprint evaluation.

This study has several limitations. First, we were able to evaluate only preprints that were published in a peer-reviewed journal. Therefore, we are unable to draw conclusions about preprints that do not make it through the peer review process, whether it be because they were never submitted or were rejected. Future studies could survey authors of preprints without corresponding publications to learn more about the fate of preprints and the role of peer review. 14 Second, our study was limited to preprints posted in September 2020 and published within 24 months, and additional preprints may eventually be published in subsequent years. However, studies suggest that many manuscripts are published within 7 months of initial submission. 9 , 14 , 19 - 21 Third, given that we captured only preprints that were initially posted to medRxiv in September 2020, it is possible that the types of preprints that are posted may change over time, especially as the COVID-19 pandemic wanes. However, approximately half our sample was of non–COVID-19–related preprints, and we were able to capture a cross-section of preprints posted at a time when the influx of preprints related to COVID-19 leveled off. Fourth, we relied on subjective text review to assess reasons for discordance because not all manuscripts divulge reasons for changes between preprints and published versions of the manuscript. Although automated or algorithmic approaches may be more objective, they would capture only numerical or word changes and not the meanings or interpretations of manuscripts.

In this cross-sectional study of clinical studies posted as preprints on medRxiv in September 2020 and subsequently published in peer-reviewed journals, most had concordant study characteristics, results, and final interpretations. For preprint-journal article pairs with discordant results, most changes were minor numerical changes, often owing to sample size differences. More than three-fourths of the preprints were published in journals within 24 months, and the results of this study may suggest that most of these preprints report findings that are consistent with the final journal publication.

Accepted for Publication: October 14, 2022.

Published: December 9, 2022. doi:10.1001/jamanetworkopen.2022.45847

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Janda G et al. JAMA Network Open .

Corresponding Author: Joshua D. Wallach, PhD, MS, Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, 3rd Floor, Room 3028 ( [email protected] ).

Author Contributions: Dr Wallach had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Janda, Shi, Ross, Wallach.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Janda, Wallach.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Janda, Wallach.

Administrative, technical, or material support: Janda, Shi.

Supervision: Wallach.

Conflict of Interest Disclosures: Mr Janda reported receiving support from the Yale Center for Excellence in Regulatory Science and Innovation (CERSI) Scholars Program, outside the submitted work, and a Yale Center of Clinical Investigation scholarship. Ms Shi reported receiving support from a China Scholarship Council scholarship, the CERSI Scholars Program, and a Yale Graduate School of Arts and Science scholarship outside the submitted work. Dr Ross reported serving as cofounder of medRxiv, former Associate Editor of JAMA Internal Medicine , and current Research Editor at BMJ ; receiving research support through Yale University from Johnson & Johnson to develop methods of clinical trial data sharing, from the Medical Device Innovation Consortium as part of the National Evaluation System for Health Technology, from the US Food and Drug Administration (FDA) for the Yale-Mayo Clinic CERSI program, from the Agency for Healthcare Research and Quality, from the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH), and from the Laura and John Arnold Foundation to establish the Good Pharma Scorecard at Bioethics International; serving as an expert witness at the request of relators’ attorneys, the Greene Law Firm, in a qui tam suit alleging violations of the False Claims Act and Anti-Kickback Statute against Biogen Inc. Dr Wallach reported receiving grant support from the FDA, Arnold Ventures, Johnson & Johnson through Yale University, and the National Institute on Alcohol Abuse and Alcoholism of the NIH under award 1K01AA028258; serving as a consultant for Hagens Berman Sobol Shapiro LLP and Dugan Law Firm APLC; and serving as a medRxiv affiliate. No other disclosures were reported.

Meeting Presentation: This study was presented at the Ninth International Congress on Peer Review and Scientific Publication; September 10, 2022; Chicago, Illinois.

Data Sharing Statement: See Supplement 2 .

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