research paper journal analysis

Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

Definition and Introduction

Journal article analysis assignments require you to summarize and critically assess the quality of an empirical research study published in a scholarly [a.k.a., academic, peer-reviewed] journal. The article may be assigned by the professor, chosen from course readings listed in the syllabus, or you must locate an article on your own, usually with the requirement that you search using a reputable library database, such as, JSTOR or ProQuest . The article chosen is expected to relate to the overall discipline of the course, specific course content, or key concepts discussed in class. In some cases, the purpose of the assignment is to analyze an article that is part of the literature review for a future research project.

Analysis of an article can be assigned to students individually or as part of a small group project. The final product is usually in the form of a short paper [typically 1- 6 double-spaced pages] that addresses key questions the professor uses to guide your analysis or that assesses specific parts of a scholarly research study [e.g., the research problem, methodology, discussion, conclusions or findings]. The analysis paper may be shared on a digital course management platform and/or presented to the class for the purpose of promoting a wider discussion about the topic of the study. Although assigned in any level of undergraduate and graduate coursework in the social and behavioral sciences, professors frequently include this assignment in upper division courses to help students learn how to effectively identify, read, and analyze empirical research within their major.

Franco, Josue. “Introducing the Analysis of Journal Articles.” Prepared for presentation at the American Political Science Association’s 2020 Teaching and Learning Conference, February 7-9, 2020, Albuquerque, New Mexico; Sego, Sandra A. and Anne E. Stuart. "Learning to Read Empirical Articles in General Psychology." Teaching of Psychology 43 (2016): 38-42; Kershaw, Trina C., Jordan P. Lippman, and Jennifer Fugate. "Practice Makes Proficient: Teaching Undergraduate Students to Understand Published Research." Instructional Science 46 (2018): 921-946; Woodward-Kron, Robyn. "Critical Analysis and the Journal Article Review Assignment." Prospect 18 (August 2003): 20-36; MacMillan, Margy and Allison MacKenzie. "Strategies for Integrating Information Literacy and Academic Literacy: Helping Undergraduate Students make the most of Scholarly Articles." Library Management 33 (2012): 525-535.

Benefits of Journal Article Analysis Assignments

Analyzing and synthesizing a scholarly journal article is intended to help students obtain the reading and critical thinking skills needed to develop and write their own research papers. This assignment also supports workplace skills where you could be asked to summarize a report or other type of document and report it, for example, during a staff meeting or for a presentation.

There are two broadly defined ways that analyzing a scholarly journal article supports student learning:

Improve Reading Skills

Conducting research requires an ability to review, evaluate, and synthesize prior research studies. Reading prior research requires an understanding of the academic writing style , the type of epistemological beliefs or practices underpinning the research design, and the specific vocabulary and technical terminology [i.e., jargon] used within a discipline. Reading scholarly articles is important because academic writing is unfamiliar to most students; they have had limited exposure to using peer-reviewed journal articles prior to entering college or students have yet to gain exposure to the specific academic writing style of their disciplinary major. Learning how to read scholarly articles also requires careful and deliberate concentration on how authors use specific language and phrasing to convey their research, the problem it addresses, its relationship to prior research, its significance, its limitations, and how authors connect methods of data gathering to the results so as to develop recommended solutions derived from the overall research process.

Improve Comprehension Skills

In addition to knowing how to read scholarly journals articles, students must learn how to effectively interpret what the scholar(s) are trying to convey. Academic writing can be dense, multi-layered, and non-linear in how information is presented. In addition, scholarly articles contain footnotes or endnotes, references to sources, multiple appendices, and, in some cases, non-textual elements [e.g., graphs, charts] that can break-up the reader’s experience with the narrative flow of the study. Analyzing articles helps students practice comprehending these elements of writing, critiquing the arguments being made, reflecting upon the significance of the research, and how it relates to building new knowledge and understanding or applying new approaches to practice. Comprehending scholarly writing also involves thinking critically about where you fit within the overall dialogue among scholars concerning the research problem, finding possible gaps in the research that require further analysis, or identifying where the author(s) has failed to examine fully any specific elements of the study.

In addition, journal article analysis assignments are used by professors to strengthen discipline-specific information literacy skills, either alone or in relation to other tasks, such as, giving a class presentation or participating in a group project. These benefits can include the ability to:

• Effectively paraphrase text, which leads to a more thorough understanding of the overall study;
• Identify and describe strengths and weaknesses of the study and their implications;
• Relate the article to other course readings and in relation to particular research concepts or ideas discussed during class;
• Think critically about the research and summarize complex ideas contained within;
• Plan, organize, and write an effective inquiry-based paper that investigates a research study, evaluates evidence, expounds on the author’s main ideas, and presents an argument concerning the significance and impact of the research in a clear and concise manner;
• Model the type of source summary and critique you should do for any college-level research paper; and,
• Increase interest and engagement with the research problem of the study as well as with the discipline.

Kershaw, Trina C., Jennifer Fugate, and Aminda J. O'Hare. "Teaching Undergraduates to Understand Published Research through Structured Practice in Identifying Key Research Concepts." Scholarship of Teaching and Learning in Psychology . Advance online publication, 2020; Franco, Josue. “Introducing the Analysis of Journal Articles.” Prepared for presentation at the American Political Science Association’s 2020 Teaching and Learning Conference, February 7-9, 2020, Albuquerque, New Mexico; Sego, Sandra A. and Anne E. Stuart. "Learning to Read Empirical Articles in General Psychology." Teaching of Psychology 43 (2016): 38-42; Woodward-Kron, Robyn. "Critical Analysis and the Journal Article Review Assignment." Prospect 18 (August 2003): 20-36; MacMillan, Margy and Allison MacKenzie. "Strategies for Integrating Information Literacy and Academic Literacy: Helping Undergraduate Students make the most of Scholarly Articles." Library Management 33 (2012): 525-535; Kershaw, Trina C., Jordan P. Lippman, and Jennifer Fugate. "Practice Makes Proficient: Teaching Undergraduate Students to Understand Published Research." Instructional Science 46 (2018): 921-946.

Structure and Organization

A journal article analysis paper should be written in paragraph format and include an instruction to the study, your analysis of the research, and a conclusion that provides an overall assessment of the author's work, along with an explanation of what you believe is the study's overall impact and significance. Unless the purpose of the assignment is to examine foundational studies published many years ago, you should select articles that have been published relatively recently [e.g., within the past few years].

Since the research has been completed, reference to the study in your paper should be written in the past tense, with your analysis stated in the present tense [e.g., “The author portrayed access to health care services in rural areas as primarily a problem of having reliable transportation. However, I believe the author is overgeneralizing this issue because...”].

Introduction Section

The first section of a journal analysis paper should describe the topic of the article and highlight the author’s main points. This includes describing the research problem and theoretical framework, the rationale for the research, the methods of data gathering and analysis, the key findings, and the author’s final conclusions and recommendations. The narrative should focus on the act of describing rather than analyzing. Think of the introduction as a more comprehensive and detailed descriptive abstract of the study.

Possible questions to help guide your writing of the introduction section may include:

• Who are the authors and what credentials do they hold that contributes to the validity of the study?
• What was the research problem being investigated?
• What type of research design was used to investigate the research problem?
• What theoretical idea(s) and/or research questions were used to address the problem?
• What was the source of the data or information used as evidence for analysis?
• What methods were applied to investigate this evidence?
• What were the author's overall conclusions and key findings?

Critical Analysis Section

The second section of a journal analysis paper should describe the strengths and weaknesses of the study and analyze its significance and impact. This section is where you shift the narrative from describing to analyzing. Think critically about the research in relation to other course readings, what has been discussed in class, or based on your own life experiences. If you are struggling to identify any weaknesses, explain why you believe this to be true. However, no study is perfect, regardless of how laudable its design may be. Given this, think about the repercussions of the choices made by the author(s) and how you might have conducted the study differently. Examples can include contemplating the choice of what sources were included or excluded in support of examining the research problem, the choice of the method used to analyze the data, or the choice to highlight specific recommended courses of action and/or implications for practice over others. Another strategy is to place yourself within the research study itself by thinking reflectively about what may be missing if you had been a participant in the study or if the recommended courses of action specifically targeted you or your community.

Possible questions to help guide your writing of the analysis section may include:

Introduction

• Did the author clearly state the problem being investigated?
• What was your reaction to and perspective on the research problem?
• Was the study’s objective clearly stated? Did the author clearly explain why the study was necessary?
• How well did the introduction frame the scope of the study?
• Did the introduction conclude with a clear purpose statement?

Literature Review

• Did the literature review lay a foundation for understanding the significance of the research problem?
• Did the literature review provide enough background information to understand the problem in relation to relevant contexts [e.g., historical, economic, social, cultural, etc.].
• Did literature review effectively place the study within the domain of prior research? Is anything missing?
• Was the literature review organized by conceptual categories or did the author simply list and describe sources?
• Did the author accurately explain how the data or information were collected?
• Was the data used sufficient in supporting the study of the research problem?
• Was there another methodological approach that could have been more illuminating?
• Give your overall evaluation of the methods used in this article. How much trust would you put in generating relevant findings?

Results and Discussion

• Were the results clearly presented?
• Did you feel that the results support the theoretical and interpretive claims of the author? Why?
• What did the author(s) do especially well in describing or analyzing their results?
• Was the author's evaluation of the findings clearly stated?
• How well did the discussion of the results relate to what is already known about the research problem?
• Was the discussion of the results free of repetition and redundancies?
• What interpretations did the authors make that you think are in incomplete, unwarranted, or overstated?
• Did the conclusion effectively capture the main points of study?
• Did the conclusion address the research questions posed? Do they seem reasonable?
• Were the author’s conclusions consistent with the evidence and arguments presented?
• Has the author explained how the research added new knowledge or understanding?

Overall Writing Style

• If the article included tables, figures, or other non-textual elements, did they contribute to understanding the study?
• Were ideas developed and related in a logical sequence?
• Were transitions between sections of the article smooth and easy to follow?

Overall Evaluation Section

The final section of a journal analysis paper should bring your thoughts together into a coherent assessment of the value of the research study . This section is where the narrative flow transitions from analyzing specific elements of the article to critically evaluating the overall study. Explain what you view as the significance of the research in relation to the overall course content and any relevant discussions that occurred during class. Think about how the article contributes to understanding the overall research problem, how it fits within existing literature on the topic, how it relates to the course, and what it means to you as a student researcher. In some cases, your professor will also ask you to describe your experiences writing the journal article analysis paper as part of a reflective learning exercise.

Possible questions to help guide your writing of the conclusion and evaluation section may include:

• Was the structure of the article clear and well organized?
• Was the topic of current or enduring interest to you?
• What were the main weaknesses of the article? [this does not refer to limitations stated by the author, but what you believe are potential flaws]
• Was any of the information in the article unclear or ambiguous?
• What did you learn from the research? If nothing stood out to you, explain why.
• Assess the originality of the research. Did you believe it contributed new understanding of the research problem?
• Were you persuaded by the author’s arguments?
• If the author made any final recommendations, will they be impactful if applied to practice?
• In what ways could future research build off of this study?
• What implications does the study have for daily life?
• Was the use of non-textual elements, footnotes or endnotes, and/or appendices helpful in understanding the research?
• What lingering questions do you have after analyzing the article?

NOTE: Avoid using quotes. One of the main purposes of writing an article analysis paper is to learn how to effectively paraphrase and use your own words to summarize a scholarly research study and to explain what the research means to you. Using and citing a direct quote from the article should only be done to help emphasize a key point or to underscore an important concept or idea.

Business: The Article Analysis . Fred Meijer Center for Writing, Grand Valley State University; Bachiochi, Peter et al. "Using Empirical Article Analysis to Assess Research Methods Courses." Teaching of Psychology 38 (2011): 5-9; Brosowsky, Nicholaus P. et al. “Teaching Undergraduate Students to Read Empirical Articles: An Evaluation and Revision of the QALMRI Method.” PsyArXi Preprints , 2020; Holster, Kristin. “Article Evaluation Assignment”. TRAILS: Teaching Resources and Innovations Library for Sociology . Washington DC: American Sociological Association, 2016; Kershaw, Trina C., Jennifer Fugate, and Aminda J. O'Hare. "Teaching Undergraduates to Understand Published Research through Structured Practice in Identifying Key Research Concepts." Scholarship of Teaching and Learning in Psychology . Advance online publication, 2020; Franco, Josue. “Introducing the Analysis of Journal Articles.” Prepared for presentation at the American Political Science Association’s 2020 Teaching and Learning Conference, February 7-9, 2020, Albuquerque, New Mexico; Reviewer's Guide . SAGE Reviewer Gateway, SAGE Journals; Sego, Sandra A. and Anne E. Stuart. "Learning to Read Empirical Articles in General Psychology." Teaching of Psychology 43 (2016): 38-42; Kershaw, Trina C., Jordan P. Lippman, and Jennifer Fugate. "Practice Makes Proficient: Teaching Undergraduate Students to Understand Published Research." Instructional Science 46 (2018): 921-946; Gyuris, Emma, and Laura Castell. "To Tell Them or Show Them? How to Improve Science Students’ Skills of Critical Reading." International Journal of Innovation in Science and Mathematics Education 21 (2013): 70-80; Woodward-Kron, Robyn. "Critical Analysis and the Journal Article Review Assignment." Prospect 18 (August 2003): 20-36; MacMillan, Margy and Allison MacKenzie. "Strategies for Integrating Information Literacy and Academic Literacy: Helping Undergraduate Students Make the Most of Scholarly Articles." Library Management 33 (2012): 525-535.

Writing Tip

Not All Scholarly Journal Articles Can Be Critically Analyzed

There are a variety of articles published in scholarly journals that do not fit within the guidelines of an article analysis assignment. This is because the work cannot be empirically examined or it does not generate new knowledge in a way which can be critically analyzed.

If you are required to locate a research study on your own, avoid selecting these types of journal articles:

• Theoretical essays which discuss concepts, assumptions, and propositions, but report no empirical research;
• Statistical or methodological papers that may analyze data, but the bulk of the work is devoted to refining a new measurement, statistical technique, or modeling procedure;
• Articles that review, analyze, critique, and synthesize prior research, but do not report any original research;
• Brief essays devoted to research methods and findings;
• Articles written by scholars in popular magazines or industry trade journals;
• Academic commentary that discusses research trends or emerging concepts and ideas, but does not contain citations to sources; and
• Pre-print articles that have been posted online, but may undergo further editing and revision by the journal's editorial staff before final publication. An indication that an article is a pre-print is that it has no volume, issue, or page numbers assigned to it.

Journal Analysis Assignment - Myers . Writing@CSU, Colorado State University; Franco, Josue. “Introducing the Analysis of Journal Articles.” Prepared for presentation at the American Political Science Association’s 2020 Teaching and Learning Conference, February 7-9, 2020, Albuquerque, New Mexico; Woodward-Kron, Robyn. "Critical Analysis and the Journal Article Review Assignment." Prospect 18 (August 2003): 20-36.

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Research Paper Analysis: How to Analyze a Research Article + Example

Why might you need to analyze research? First of all, when you analyze a research article, you begin to understand your assigned reading better. It is also the first step toward learning how to write your own research articles and literature reviews. However, if you have never written a research paper before, it may be difficult for you to analyze one. After all, you may not know what criteria to use to evaluate it. But don’t panic! We will help you figure it out!

In this article, our team has explained how to analyze research papers quickly and effectively. At the end, you will also find a research analysis paper example to see how everything works in practice.

• 🔤 Research Analysis Definition

📊 How to Analyze a Research Article

✍️ how to write a research analysis.

• 📝 Analysis Example
• 🔎 More Examples

🔗 References

🔤 research paper analysis: what is it.

A research paper analysis is an academic writing assignment in which you analyze a scholarly article’s methodology, data, and findings. In essence, “to analyze” means to break something down into components and assess each of them individually and in relation to each other. The goal of an analysis is to gain a deeper understanding of a subject. So, when you analyze a research article, you dissect it into elements like data sources , research methods, and results and evaluate how they contribute to the study’s strengths and weaknesses.

📋 Research Analysis Format

A research analysis paper has a pretty straightforward structure. Check it out below!

Research articles usually include the following sections: introduction, methods, results, and discussion. In the following paragraphs, we will discuss how to analyze a scientific article with a focus on each of its parts.

How to Analyze a Research Paper: Purpose

The purpose of the study is usually outlined in the introductory section of the article. Analyzing the research paper’s objectives is critical to establish the context for the rest of your analysis.

When analyzing the research aim, you should evaluate whether it was justified for the researchers to conduct the study. In other words, you should assess whether their research question was significant and whether it arose from existing literature on the topic.

Here are some questions that may help you analyze a research paper’s purpose:

• Why was the research carried out?
• What gaps does it try to fill, or what controversies to settle?
• How does the study contribute to its field?
• Do you agree with the author’s justification for approaching this particular question in this way?

How to Analyze a Paper: Methods

When analyzing the methodology section , you should indicate the study’s research design (qualitative, quantitative, or mixed) and methods used (for example, experiment, case study, correlational research, survey, etc.). After that, you should assess whether these methods suit the research purpose. In other words, do the chosen methods allow scholars to answer their research questions within the scope of their study?

For example, if scholars wanted to study US students’ average satisfaction with their higher education experience, they could conduct a quantitative survey . However, if they wanted to gain an in-depth understanding of the factors influencing US students’ satisfaction with higher education, qualitative interviews would be more appropriate.

When analyzing methods, you should also look at the research sample . Did the scholars use randomization to select study participants? Was the sample big enough for the results to be generalizable to a larger population?

You can also answer the following questions in your methodology analysis:

• Is the methodology valid? In other words, did the researchers use methods that accurately measure the variables of interest?
• Is the research methodology reliable? A research method is reliable if it can produce stable and consistent results under the same circumstances.
• Is the study biased in any way?
• What are the limitations of the chosen methodology?

How to Analyze Research Articles’ Results

You should start the analysis of the article results by carefully reading the tables, figures, and text. Check whether the findings correspond to the initial research purpose. See whether the results answered the author’s research questions or supported the hypotheses stated in the introduction.

To analyze the results section effectively, answer the following questions:

• What are the major findings of the study?
• Did the author present the results clearly and unambiguously?
• Are the findings statistically significant ?
• Does the author provide sufficient information on the validity and reliability of the results?
• Have you noticed any trends or patterns in the data that the author did not mention?

How to Analyze Research: Discussion

Finally, you should analyze the authors’ interpretation of results and its connection with research objectives. Examine what conclusions the authors drew from their study and whether these conclusions answer the original question.

You should also pay attention to how the authors used findings to support their conclusions. For example, you can reflect on why their findings support that particular inference and not another one. Moreover, more than one conclusion can sometimes be made based on the same set of results. If that’s the case with your article, you should analyze whether the authors addressed other interpretations of their findings .

Here are some useful questions you can use to analyze the discussion section:

• What findings did the authors use to support their conclusions?
• How do the researchers’ conclusions compare to other studies’ findings?
• How does this study contribute to its field?
• What future research directions do the authors suggest?
• What additional insights can you share regarding this article? For example, do you agree with the results? What other questions could the researchers have answered?

Now, you know how to analyze an article that presents research findings. However, it’s just a part of the work you have to do to complete your paper. So, it’s time to learn how to write research analysis! Check out the steps below!

1. Introduce the Article

As with most academic assignments, you should start your research article analysis with an introduction. Here’s what it should include:

• The article’s publication details . Specify the title of the scholarly work you are analyzing, its authors, and publication date. Remember to enclose the article’s title in quotation marks and write it in title case .
• The article’s main point . State what the paper is about. What did the authors study, and what was their major finding?
• Your thesis statement . End your introduction with a strong claim summarizing your evaluation of the article. Consider briefly outlining the research paper’s strengths, weaknesses, and significance in your thesis.

Keep your introduction brief. Save the word count for the “meat” of your paper — that is, for the analysis.

2. Summarize the Article

Now, you should write a brief and focused summary of the scientific article. It should be shorter than your analysis section and contain all the relevant details about the research paper.

Here’s what you should include in your summary:

• The research purpose . Briefly explain why the research was done. Identify the authors’ purpose and research questions or hypotheses .
• Methods and results . Summarize what happened in the study. State only facts, without the authors’ interpretations of them. Avoid using too many numbers and details; instead, include only the information that will help readers understand what happened.
• The authors’ conclusions . Outline what conclusions the researchers made from their study. In other words, describe how the authors explained the meaning of their findings.

If you need help summarizing an article, you can use our free summary generator .

3. Write Your Research Analysis

The analysis of the study is the most crucial part of this assignment type. Its key goal is to evaluate the article critically and demonstrate your understanding of it.

We’ve already covered how to analyze a research article in the section above. Here’s a quick recap:

• Analyze whether the study’s purpose is significant and relevant.
• Examine whether the chosen methodology allows for answering the research questions.
• Evaluate how the authors presented the results.
• Assess whether the authors’ conclusions are grounded in findings and answer the original research questions.

Although you should analyze the article critically, it doesn’t mean you only should criticize it. If the authors did a good job designing and conducting their study, be sure to explain why you think their work is well done. Also, it is a great idea to provide examples from the article to support your analysis.

4. Conclude Your Analysis of Research Paper

A conclusion is your chance to reflect on the study’s relevance and importance. Explain how the analyzed paper can contribute to the existing knowledge or lead to future research. Also, you need to summarize your thoughts on the article as a whole. Avoid making value judgments — saying that the paper is “good” or “bad.” Instead, use more descriptive words and phrases such as “This paper effectively showed…”

Need help writing a compelling conclusion? Try our free essay conclusion generator !

5. Revise and Proofread

Last but not least, you should carefully proofread your paper to find any punctuation, grammar, and spelling mistakes. Start by reading your work out loud to ensure that your sentences fit together and sound cohesive. Also, it can be helpful to ask your professor or peer to read your work and highlight possible weaknesses or typos.

📝 Research Paper Analysis Example

We have prepared an analysis of a research paper example to show how everything works in practice.

No Homework Policy: Research Article Analysis Example

This paper aims to analyze the research article entitled “No Assignment: A Boon or a Bane?” by Cordova, Pagtulon-an, and Tan (2019). This study examined the effects of having and not having assignments on weekends on high school students’ performance and transmuted mean scores. This article effectively shows the value of homework for students, but larger studies are needed to support its findings.

Cordova et al. (2019) conducted a descriptive quantitative study using a sample of 115 Grade 11 students of the Central Mindanao University Laboratory High School in the Philippines. The sample was divided into two groups: the first received homework on weekends, while the second didn’t. The researchers compared students’ performance records made by teachers and found that students who received assignments performed better than their counterparts without homework.

The purpose of this study is highly relevant and justified as this research was conducted in response to the debates about the “No Homework Policy” in the Philippines. Although the descriptive research design used by the authors allows to answer the research question, the study could benefit from an experimental design. This way, the authors would have firm control over variables. Additionally, the study’s sample size was not large enough for the findings to be generalized to a larger population.

The study results are presented clearly, logically, and comprehensively and correspond to the research objectives. The researchers found that students’ mean grades decreased in the group without homework and increased in the group with homework. Based on these findings, the authors concluded that homework positively affected students’ performance. This conclusion is logical and grounded in data.

This research effectively showed the importance of homework for students’ performance. Yet, since the sample size was relatively small, larger studies are needed to ensure the authors’ conclusions can be generalized to a larger population.

🔎 More Research Analysis Paper Examples

Do you want another research analysis example? Check out the best analysis research paper samples below:

• Gracious Leadership Principles for Nurses: Article Analysis
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• “Differential Effectiveness of Placebo Treatments”: Research Paper Analysis
• “Family-Based Childhood Obesity Prevention Interventions”: Analysis Research Paper Example
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• “Democracy and Collective Identity in the EU and the USA”: Article Analysis
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• Article Analysis: Fear of Missing Out
• Codependence, Narcissism, and Childhood Trauma: Analysis of the Article
• Relationship Between Work Intensity, Workaholism, Burnout, and MSC: Article Review

We hope that our article on research paper analysis has been helpful. If you liked it, please share this article with your friends!

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Introduction to Research Statistical Analysis: An Overview of the Basics

Christian vandever.

1 HCA Healthcare Graduate Medical Education

Description

This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted. Categorical and quantitative variable types are defined, as well as response and predictor variables. Statistical tests described include t-tests, ANOVA and chi-square tests. Multiple regression is also explored for both logistic and linear regression. Finally, the most common statistics produced by these methods are explored.

Introduction

Statistical analysis is necessary for any research project seeking to make quantitative conclusions. The following is a primer for research-based statistical analysis. It is intended to be a high-level overview of appropriate statistical testing, while not diving too deep into any specific methodology. Some of the information is more applicable to retrospective projects, where analysis is performed on data that has already been collected, but most of it will be suitable to any type of research. This primer will help the reader understand research results in coordination with a statistician, not to perform the actual analysis. Analysis is commonly performed using statistical programming software such as R, SAS or SPSS. These allow for analysis to be replicated while minimizing the risk for an error. Resources are listed later for those working on analysis without a statistician.

After coming up with a hypothesis for a study, including any variables to be used, one of the first steps is to think about the patient population to apply the question. Results are only relevant to the population that the underlying data represents. Since it is impractical to include everyone with a certain condition, a subset of the population of interest should be taken. This subset should be large enough to have power, which means there is enough data to deliver significant results and accurately reflect the study’s population.

The first statistics of interest are related to significance level and power, alpha and beta. Alpha (α) is the significance level and probability of a type I error, the rejection of the null hypothesis when it is true. The null hypothesis is generally that there is no difference between the groups compared. A type I error is also known as a false positive. An example would be an analysis that finds one medication statistically better than another, when in reality there is no difference in efficacy between the two. Beta (β) is the probability of a type II error, the failure to reject the null hypothesis when it is actually false. A type II error is also known as a false negative. This occurs when the analysis finds there is no difference in two medications when in reality one works better than the other. Power is defined as 1-β and should be calculated prior to running any sort of statistical testing. Ideally, alpha should be as small as possible while power should be as large as possible. Power generally increases with a larger sample size, but so does cost and the effect of any bias in the study design. Additionally, as the sample size gets bigger, the chance for a statistically significant result goes up even though these results can be small differences that do not matter practically. Power calculators include the magnitude of the effect in order to combat the potential for exaggeration and only give significant results that have an actual impact. The calculators take inputs like the mean, effect size and desired power, and output the required minimum sample size for analysis. Effect size is calculated using statistical information on the variables of interest. If that information is not available, most tests have commonly used values for small, medium or large effect sizes.

When the desired patient population is decided, the next step is to define the variables previously chosen to be included. Variables come in different types that determine which statistical methods are appropriate and useful. One way variables can be split is into categorical and quantitative variables. ( Table 1 ) Categorical variables place patients into groups, such as gender, race and smoking status. Quantitative variables measure or count some quantity of interest. Common quantitative variables in research include age and weight. An important note is that there can often be a choice for whether to treat a variable as quantitative or categorical. For example, in a study looking at body mass index (BMI), BMI could be defined as a quantitative variable or as a categorical variable, with each patient’s BMI listed as a category (underweight, normal, overweight, and obese) rather than the discrete value. The decision whether a variable is quantitative or categorical will affect what conclusions can be made when interpreting results from statistical tests. Keep in mind that since quantitative variables are treated on a continuous scale it would be inappropriate to transform a variable like which medication was given into a quantitative variable with values 1, 2 and 3.

Categorical vs. Quantitative Variables

Both of these types of variables can also be split into response and predictor variables. ( Table 2 ) Predictor variables are explanatory, or independent, variables that help explain changes in a response variable. Conversely, response variables are outcome, or dependent, variables whose changes can be partially explained by the predictor variables.

Response vs. Predictor Variables

Choosing the correct statistical test depends on the types of variables defined and the question being answered. The appropriate test is determined by the variables being compared. Some common statistical tests include t-tests, ANOVA and chi-square tests.

T-tests compare whether there are differences in a quantitative variable between two values of a categorical variable. For example, a t-test could be useful to compare the length of stay for knee replacement surgery patients between those that took apixaban and those that took rivaroxaban. A t-test could examine whether there is a statistically significant difference in the length of stay between the two groups. The t-test will output a p-value, a number between zero and one, which represents the probability that the two groups could be as different as they are in the data, if they were actually the same. A value closer to zero suggests that the difference, in this case for length of stay, is more statistically significant than a number closer to one. Prior to collecting the data, set a significance level, the previously defined alpha. Alpha is typically set at 0.05, but is commonly reduced in order to limit the chance of a type I error, or false positive. Going back to the example above, if alpha is set at 0.05 and the analysis gives a p-value of 0.039, then a statistically significant difference in length of stay is observed between apixaban and rivaroxaban patients. If the analysis gives a p-value of 0.91, then there was no statistical evidence of a difference in length of stay between the two medications. Other statistical summaries or methods examine how big of a difference that might be. These other summaries are known as post-hoc analysis since they are performed after the original test to provide additional context to the results.

Analysis of variance, or ANOVA, tests can observe mean differences in a quantitative variable between values of a categorical variable, typically with three or more values to distinguish from a t-test. ANOVA could add patients given dabigatran to the previous population and evaluate whether the length of stay was significantly different across the three medications. If the p-value is lower than the designated significance level then the hypothesis that length of stay was the same across the three medications is rejected. Summaries and post-hoc tests also could be performed to look at the differences between length of stay and which individual medications may have observed statistically significant differences in length of stay from the other medications. A chi-square test examines the association between two categorical variables. An example would be to consider whether the rate of having a post-operative bleed is the same across patients provided with apixaban, rivaroxaban and dabigatran. A chi-square test can compute a p-value determining whether the bleeding rates were significantly different or not. Post-hoc tests could then give the bleeding rate for each medication, as well as a breakdown as to which specific medications may have a significantly different bleeding rate from each other.

A slightly more advanced way of examining a question can come through multiple regression. Regression allows more predictor variables to be analyzed and can act as a control when looking at associations between variables. Common control variables are age, sex and any comorbidities likely to affect the outcome variable that are not closely related to the other explanatory variables. Control variables can be especially important in reducing the effect of bias in a retrospective population. Since retrospective data was not built with the research question in mind, it is important to eliminate threats to the validity of the analysis. Testing that controls for confounding variables, such as regression, is often more valuable with retrospective data because it can ease these concerns. The two main types of regression are linear and logistic. Linear regression is used to predict differences in a quantitative, continuous response variable, such as length of stay. Logistic regression predicts differences in a dichotomous, categorical response variable, such as 90-day readmission. So whether the outcome variable is categorical or quantitative, regression can be appropriate. An example for each of these types could be found in two similar cases. For both examples define the predictor variables as age, gender and anticoagulant usage. In the first, use the predictor variables in a linear regression to evaluate their individual effects on length of stay, a quantitative variable. For the second, use the same predictor variables in a logistic regression to evaluate their individual effects on whether the patient had a 90-day readmission, a dichotomous categorical variable. Analysis can compute a p-value for each included predictor variable to determine whether they are significantly associated. The statistical tests in this article generate an associated test statistic which determines the probability the results could be acquired given that there is no association between the compared variables. These results often come with coefficients which can give the degree of the association and the degree to which one variable changes with another. Most tests, including all listed in this article, also have confidence intervals, which give a range for the correlation with a specified level of confidence. Even if these tests do not give statistically significant results, the results are still important. Not reporting statistically insignificant findings creates a bias in research. Ideas can be repeated enough times that eventually statistically significant results are reached, even though there is no true significance. In some cases with very large sample sizes, p-values will almost always be significant. In this case the effect size is critical as even the smallest, meaningless differences can be found to be statistically significant.

These variables and tests are just some things to keep in mind before, during and after the analysis process in order to make sure that the statistical reports are supporting the questions being answered. The patient population, types of variables and statistical tests are all important things to consider in the process of statistical analysis. Any results are only as useful as the process used to obtain them. This primer can be used as a reference to help ensure appropriate statistical analysis.

Funding Statement

This research was supported (in whole or in part) by HCA Healthcare and/or an HCA Healthcare affiliated entity.

Conflicts of Interest

The author declares he has no conflicts of interest.

Christian Vandever is an employee of HCA Healthcare Graduate Medical Education, an organization affiliated with the journal’s publisher.

This research was supported (in whole or in part) by HCA Healthcare and/or an HCA Healthcare affiliated entity. The views expressed in this publication represent those of the author(s) and do not necessarily represent the official views of HCA Healthcare or any of its affiliated entities.

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Behavior Analysis: Research and Practice

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Behavior Analysis: Research and Practice is a multidisciplinary journal committed to increasing the communication between the subdisciplines within behavior analysis and psychology, and bringing up-to-date information on current developments within the field.

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Areas of interest include, but are not limited to, clinical behavior analysis, applied and translational behavior analysis, behavior therapy, behavioral consultation, organizational behavior management, and human performance technology.

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Joel E. Ringdahl University of Georgia, United States

Associate editors

Jonathan C. Baker, PhD, BCBA-D Western Michigan University, United States

Andrew R. Craig, PhD SUNY Upstate Medical University, United States

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Maria G. Valdovinos, PhD, BCBA-D Drake University, United States

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Keith D. Allen, PhD, BCBA-D Munroe-Meyer Institute for Genetics and Rehabilitation, United States

Cynthia M. Anderson, PhD, BCBA-D May Institute, United States

Scott P. Ardoin, PhD, BCBA-D University of Georgia, United States

Jennifer Austin, PhD, BCBA-D Georgia State University, United Kingdom

Judah Axe, PhD, BCBA-D, LABA Simmons University, United States

Jessica  Becraft, PhD Kennedy Krieger Institute, United States

Kevin Michael Ayres, PhD, BCBA-D The University of Georgia, United States

Jordan Belisle, PhD, BCBA, LBA Missouri State University, United States

Carrie S.W. Borrero, PhD, BCBA-D, LBA Kennedy Krieger Institute, United States

Rachel R. Cagliani, PhD, BCBA-D University of Georgia, United States

Regina Carroll, PhD University of Nebraska Medical Center, United States

Joseph D. Cautilli, PhD Behavior Analysis and Therapy Partners, United States

Linda J. Cooper-Brown, PhD University of Iowa, United States

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Odessa Luna, BCBA-D, PhD St. Cloud State University, United States

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Raymond G. Miltenberger, PhD University of South Florida, United States

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Matthew Normand, Ph.D., BCBA-D University of the Pacific, United States

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Yaniz Padilla Dalmau, PhD, BCBA-D Seattle Children’s Hospital, United States

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Sacha T. Pence, PhD Western Michigan University, United States

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Shawn Quigley, PhD, BCBA-D, CDE Melmark, United States

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Mark P. Reilly, PhD Central Michigan University, United States

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Julie M. Slowiak, PhD, BCBA-D University of Minnesota Duluth, United States

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Karla A. Zabala-Snow, PhD, BCBA-D Emory University/Marcus Autism Center, United States

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Abstracting and indexing services providing coverage of Behavior Analysis: Research and Practice

Special issue of APA’s journal Behavior Analysis: Research and Practice, Vol. 21, No. 3, August 2021. This special issue highlights works that offer new or innovative perspectives on the role behavior analysis plays in growing this area of research and practice via (a) informing health and fitness behavior change; (b) designing and evaluating interventions to support health-behavior change or improve fitness and sport performance; and (c) identifying opportunities and recommendations to advance research and inform practice in the areas of health, sport, and fitness.

Special issue of the APA journal Behavior Analysis: Research and Practice, Vol. 18, No. 1, February 2018. Themes of the articles include addressing difficulties associated with neurocognitive disorders such as Alzheimer's disease and the use of stimulus preference assessment procedures.

Special issue of the APA journal Behavior Analysis: Research and Practice, Vol. 16, No. 4, November 2016. Articles discuss behavioral pharmacology's contributions to understanding the behavioral effects of drugs of abuse and other substances, the variables that modulate those effects, and the mechanisms through which they are produced, and offer novel and important suggestions for advancing the discipline.

Special issue of the APA journal Behavior Analysis: Research and Practice, Vol. 17, No. 3, August 2017. The articles in this issue address behavior analysis in education in three domains: replicating procedures established in controlled evaluations in classrooms, expanding access to behavioral intervention, and evaluating variations of procedures designed for school use.

Special issue of the APA journal Behavioral Analysis: Research and Practice, Vol. 15, No. 1, February 2015. Includes articles about operant discrimination learning, class size effects, game research, and behavior research using animals.

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Global Research Trends in Tricuspid Regurgitation from 2010 to 2023: A Bibliometric Analysis

22 Pages Posted: 24 Jul 2024 Publication Status: Under Review

Shandong Academy of Medical Sciences

Weichen Yuan

Objectives To analyzes global research trends in tricuspid regurgitation (TR) from 2010 to 2023 and explore hot topics in this field. Methods We reviewed the literature on TR from January 1, 2010 to December 31, 2023 using the Web of Science Core Collection (WoSCC) database, and the main type of literature was articles. Data on countries, organizations, authors, journals, etc., from which the literature originated were collected, processed and visualized using "bibliometrics" and VOSviewer software. Results From 2010 to 2023, we retrieved a total of 4,662 publications from the WOSCC database, and we found that a total of 100 countries, 4,572 affiliates, and 23,283 researchers were involved in this field of research. The countries with the most publications in this field is the United States, the most active institution is the Mayo Clinic, and the author who has made the most contributions is Bax JJ. TR related studies were most often published in the Journal of Thoracic and Cardiovascular Surgery. The keywords formed four clusters: (1) etiology of TR, (2) pathogenesis of secondary TR, (3) grading of TR severity, and (4) treatment of TR. Conclusions Using bibliometric analysis, this study summarized information on scientific publications on TR research from 2010 to 2023, providing global research trends in TR research.

Note: Funding declaration: This work was supported by the Natural Science Foundation of Shandong Province (ZR2022MH142). Conflict of Interests: The authors have no conflicts to disclose.

Keywords: valve disease, Bibliometric, Valve regurgitation, Tricuspid regurgitation, visualization

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5:2 MR indicates an intermittent fasting plan consisting of 2 nonconsecutive fasting days and 5 days of habitual intake per week and meal replacement diet.

A, Changes in hemoglobin A 1c (HbA 1c ) concentration from baseline to 16 weeks. The adjusted least-squares mean (LSM) changes were intermittent fasting plan consisting of 2 nonconsecutive fasting days and 5 days of habitual intake per week and meal replacement diet (5:2 MR), −1.9% (SE, 0.2%); metformin, −1.6% (SE, 0.2%); and empagliflozin, −1.5% (SE, 0.2%) (to convert to proportion of total hemoglobin, multiply by 0.01). The adjusted LSM difference between 5:2 MR and metformin was −0.3% (95% CI, −0.5% to 0.1%), and the adjusted LSM difference between 5:2 MR and empagliflozin was −0.4% (95% CI, −0.6% to 0.2%). B, Changes in HbA 1c concentration from baseline to 16 weeks in patients with overweight or obesity. C, Percentage of patients with HbA 1c concentrations of less than 7.0% or of less than 6.5% at week 16. D, Changes in fasting plasma glucose (FPG) concentrations from baseline to 16 weeks (to convert glucose to millimoles per liter, multiply by 0.0555). Error bars display SEs.

Patients were randomized to receive intermittent fasting plan consisting of 2 nonconsecutive fasting days and 5 days of habitual intake per week and meal replacement diet (5:2 MR) (n = 135), metformin (n = 134), or empagliflozin (n = 136). BMI indicates body mass index (calculated as weight in kilograms divided by height in meters squared); and LSM, least-squares mean.

Trial Protocol

eMethods. Multiple Imputation

eTable 1. Study Centers, Location, Ethics Committee Approvals

eTable 2. The Contents of Dietary and Exercise Guidance, and General Diabetes Education for All Participants

eTable 3. Nutrition Facts of Kang Zhijun™

eTable 4. Changes in Primary and Secondary Outcomes From Baseline to Week 16 (Intention-to-Treat Analysis)

eTable 5. Unadjusted Analyses of Outcomes From Baseline to Week 16 in the Three Groups

eTable 6. Primary and Secondary Outcomes From Baseline to Week 16 in Three Groups (Completers Analysis)

eTable 7. Primary and Secondary Outcomes From Baseline to Week 24 in Three Groups (Completers Analysis)

eFigure 1. Study Design

eFigure 2. The 5:2 MR Approach

eFigure 3. Changes in Body Weight

eFigure 4. Post-Hoc Subgroup Analysis of Weight Loss at Week 16 by Intention-to-Treat Analysis

eReference.

Data Sharing Statement

• Fasting, Meal Substitute Improved Blood Sugar More Than Diabetes Drugs JAMA Medical News in Brief July 12, 2024 Emily Harris

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Guo L , Xi Y , Jin W, et al. A 5:2 Intermittent Fasting Meal Replacement Diet and Glycemic Control for Adults With Diabetes : The EARLY Randomized Clinical Trial . JAMA Netw Open. 2024;7(6):e2416786. doi:10.1001/jamanetworkopen.2024.16786

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A 5:2 Intermittent Fasting Meal Replacement Diet and Glycemic Control for Adults With Diabetes : The EARLY Randomized Clinical Trial

• 1 Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
• 2 Department of Endocrinology, The Third Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China
• 3 Department of Endocrinology, Nanyang Central Hospital, Nanyang, Henan, China
• 4 Department of Endocrinology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
• 5 Department of Endocrinology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
• 6 Department of Endocrinology, Hebei Provincial People’s Hospital, Shijiazhuang, Hebei, China
• 7 Department of Endocrinology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
• 8 Department of Endocrinology, Sir Run Run Hospital, Nanjing Medical University, Nanjing, China
• 9 Department of Endocrinology, The First Affiliated Hospital of Soochow University, Soochow, Jiangsu, China
• 10 Department of Clinical Nutrition, Beijing Tongren Hospital, Capital Medical University, Beijing, China
• Medical News in Brief Fasting, Meal Substitute Improved Blood Sugar More Than Diabetes Drugs Emily Harris JAMA

Question   What is the effect of a 16-week intermittent fasting plan consisting of 2 nonconsecutive fasting days and 5 days of habitual intake per week and meal replacement diet (5:2 MR) on the changes in hemoglobin A 1c level in Chinese adults with early type 2 diabetes?

Findings   In this randomized clinical trial of 405 adults, the 5:2 MR approach achieved better glycemic control at 16 weeks compared with metformin and empagliflozin.

Meaning   The 5:2 MR approach may serve as an effective initial lifestyle intervention instead of antidiabetic drugs for patients with type 2 diabetes.

Importance   An intermittent fasting plan consisting of 2 nonconsecutive fasting days and 5 days of habitual intake per week and meal replacement diet (5:2 MR) could provide additional benefits to patients with type 2 diabetes.

Objective   To evaluate the effect of the 5:2 MR on glycemic control among patients with early type 2 diabetes compared with metformin and empagliflozin.

Design, Setting, and Participants   The EARLY (Exploration of Treatment of Newly Diagnosed Overweight/Obese Type 2 Diabetes Mellitus) study is a randomized, open-label, active parallel-controlled clinical trial conducted between November 13, 2020, and December 29, 2022, in 9 centers across China. A total of 509 eligible patients underwent screening, out of which 405 were randomly assigned to 3 groups and included in the intention-to-treat analysis.

Interventions   Patients were randomly allocated in a 1:1:1 ratio to receive either metformin, empagliflozin, or 5:2 MR. The treatment was 16 weeks, with an 8-week follow-up.

Main Outcomes and Measures   The primary end point was the change in hemoglobin A 1c (HbA 1c ) level from baseline to 16 weeks. Secondary end points included changes in body weight, anthropometric measurements, and biochemical parameters.

Results   Of the 405 randomized participants (265 men [65.4%]; mean [SD] age, 45.5 [11.0] years; mean [SD] body mass index, 29.5 [4.1]; and mean [SD] HbA 1c level, 7.9% [0.6%]), 332 completed the 16-week treatment. From baseline to week 16, participants in the 5:2 MR group showed the greatest reduction in HbA 1c (least-squares mean [LSM], −1.9% [SE, 0.2%]), significantly greater than patients receiving metformin (LSM, −1.6% [SE, 0.2%]; adjusted LSM difference, −0.3% [95% CI, −0.4% to −0.1%]) and empagliflozin (LSM, −1.5% [SE, 0.2%]; adjusted LSM difference, −0.4% [95% CI, −0.6% to −0.2%]). At week 16, the mean weight loss in the 5:2 MR group (LSM, −9.7 kg [SE, 2.2 kg]) was greater than that in the metformin group (LSM, −5.5 kg [SE, 2.3 kg]) and empagliflozin group (LSM, −5.8 kg [SE, 2.3 kg]).

Conclusions and Relevance   This randomized clinical trial of Chinese adults with overweight or obesity and with early type 2 diabetes found that 5:2 MR could improve glycemic outcomes and weight loss in the short term compared with metformin or empagliflozin, making it a promising initial intervention and early management for type 2 diabetes.

Trial Registration   Chinese Clinical Trial Registry Identifier: ChiCTR2000040656

The latest data from the International Diabetes Federation in 2021 reveal that there are 537 million adults with diabetes globally, affecting approximately 1 in 10 adults. 1 China has the highest number of adults with diabetes in the world; from 2011 to 2021, the number increased from 90 million to 140.9 million, a 56.6% increase. 1 The prevalence of diabetes among Chinese adults is 12.4%. 2 According to China standards, 3 about half the population is either overweight (body mass index [BMI; calculated as weight in kilograms divided by height in meters squared] 24-27.9) or obese (BMI ≥28). 4

Overweight and obesity are significant risk factors for the development of type 2 diabetes. 5 - 7 Appropriate weight loss can improve glycemic control and reduce the dosage of antidiabetic drugs among patients with type 2 diabetes. 8 However, achieving weight loss is often challenging, necessitating the implementation of strategies such as meal replacement (MR) or dietary restriction.

Meal replacement is a prepackaged food or beverage that is substituted for 1 or more meals and provides energy. 9 The Look AHEAD study has demonstrated that, as part of a comprehensive lifestyle intervention, at 1 year MR effectively reduced hemoglobin A 1c (HbA 1c ) levels by 0.7% (to convert to proportion of total hemoglobin, multiply by 0.01) and achieved initial weight loss of 8.6% to 9.0% among patients with overweight or obesity and type 2 diabetes. 10 , 11 A systematic review including 23 studies and 7884 adults found that MR was associated with more weight loss (mean, −1.4 kg [95% CI −2.5 to −0.4 kg]) compared with other diets. 12 Important randomized clinical trials in White European indviduals (DiRECT), 13 Middle Eastern indviduals (DIADEM-I) 14 and South Asian individuals (STANDby) 15 have proved that MR can alleviate diabetes by lowering body weight.

As a dietary therapy, the 5:2 intermittent fasting diet involves 2 nonconsecutive fasting days (one-fourth the energy intake of habitual diet) and 5 days of habitual intake per week. 16 Individuals with obesity have successfully lost weight with this diet through both short-term and long-term interventions. 17 - 19 A single-center randomized clinical trial with a small sample size of 137 participants found that a 12-month 5:2 intermittent fasting diet significantly decreased HbA 1c levels among patients with overweight or obesity and type 2 diabetes, compared with a continuous energy restriction diet. 20

Combining the 5:2 intermittent fasting diet with MR (5:2 MR) could provide additional benefits to patients and is worthy of investigation. We aimed to investigate the efficacy of 16 weeks of 5:2 MR on HbA 1c changes among Chinese adults with overweight or obesity and early-stage type 2 diabetes.

The EARLY (Exploration of Treatment of Newly Diagnosed Overweight/Obese Type 2 Diabetes Mellitus) study is a randomized, open-label, active parallel-controlled clinical trial. The study protocol ( Supplement 1 ) was approved by the ethics committees of all participating centers (Beijing Hospital; the Third Affiliated Hospital of Jinzhou Medical University; Nanyang Central Hospital; Henan Provincial People’s Hospital; the First Affiliated Hospital of Zhengzhou University; Hebei Provincial People’s Hospital; the Second Hospital of Hebei Medical University; Sir Run Run Hospital, Nanjing Medical University; and the First Affiliated Hospital of Soochow University). The trial followed the International Conference on Harmonization Guidelines for Good Clinical Practice and the Declaration of Helsinki. 21 All patients provided written informed consent. This report adhered to the Consolidated Standards of Reporting Trials ( CONSORT ) reporting guideline.

We recruited adults with newly diagnosed (within 1 year) type 2 diabetes who had not used antidiabetic agents in the past 3 months, aged 18 to 65 years, with a BMI of 24 or more and an HbA 1c level of 7% to 9%. The recruitment was conducted concurrently at 9 hospitals across China (eTable 1 in Supplement 2 ) from November 13, 2020, to December 29, 2022. We excluded participants who had used weight-loss drugs or products within the past 3 months before enrollment, as well as pregnant or breastfeeding women ( Supplement 1 ).

Randomization was conducted using an interactive web response system. The randomization list of participants was generated by the stratified blocked randomization method using SAS software, version 9.4 (SAS Institute Inc), in which stratification was based on the center (block size of 9). Within each stratum, participants were randomized using a block randomization method, with a block size of 9, in a ratio of 1:1:1 to receive either metformin, empagliflozin, or 5:2 MR. Both the lists for participant and treatment allocation were inputted into the interactive web response system. At the study site, participants were administered treatment based on the randomization code and the corresponding treatment group obtained from the interactive web response system. Due to the nature of the intervention, blinding of participants and investigators was not feasible in this study. However, during the data analysis, the statisticians remained blinded to the study groupings.

The treatment period lasted for 16 weeks, followed by an 8-week follow-up (eFigure 1 in Supplement 2 ). All participants received dietary and exercise guidance as well as general diabetes education from nutritionists and research physicians in accordance with China Guideline 22 (eTable 2 in Supplement 2 ) every 4 weeks.

Patients in the 5:2 MR group consumed low-energy MR product A (Kang zhijun, Beijing MetabolicControl Technology Co Ltd; eTable 3 in Supplement 2 ). The 5:2 MR approach (eFigure 2 in Supplement 2 ) means that, within 1 week, there were 2 nonconsecutive days on which meals are replaced. On these 2 days, participants were required to consume 1 serving of Kang zhijun A instead of all 3 regular meals, with a daily energy intake of 500 kcal for women and 600 kcal for men. On the remaining 5 days, participants chose their own breakfast and lunch but had 1 serving of Kang zhijun B for dinner and were encouraged to monitor their calorie intake. Throughout the 16 weeks, dietary intake was recorded in a diary.

Patients took metformin (Shanghai Bristol-Myers Squibb), 0.5 g, twice a day. If the initial drug dosage was well tolerated, it was escalated to 2 g per day. Empagliflozin (Shanghai Boehringer Ingelheim), 10 mg, was administered once a day. During the study, patients were instructed to promptly contact the research center’s physician in case of severe hypoglycemia.

The primary outcome was the change in HbA 1c level from baseline to 16 weeks. Secondary outcomes included changes in weight (measured by InBody 770 [InBody]), BMI, waist circumference, hip circumference, waist to hip ratio, systolic and diastolic blood pressure, fasting plasma glucose (FPG) level, fasting insulin level, fasting C-peptide level, homeostasis model assessment of insulin resistance (HOMA-IR = FPG [mmol/L] × fasting insulin [μU/mL]/22.5), lipid profiles (total cholesterol, triglycerides, high-density lipoprotein cholesterol [HDL-C], and low-density lipoprotein cholesterollevels), and uric acid levels. The primary and secondary outcomes were reevaluated at the end of 8-week follow-up (week 24).

Adverse events were assessed throughout the study. Adverse events of particular interest included gastrointestinal reactions, urinary tract and reproductive system infections, hypoglycemia, and hyperglycemia. Laboratory testing was conducted at a central laboratory.

The sample size calculation was based on the SD of the change in HbA 1c level from a previous study, 23 with a 2-sided α of .05, β of 0.2, a minimum detectable between-group difference of 0.1%, and an anticipated SD of 0.2% based on pilot data analysis and a multiple pairwise comparison test using the Tukey-Kramer test. It was computed that each group required 108 participants using PASS 15 software (NCSS). Accounting for an expected 20% dropout rate, each group required 135 patients.

The primary outcome was analyzed following the intention-to-treat principle in the full analysis set, which included all randomized participants who received at least 1 dose of drugs or 5:2 MR. The safety outcome was analyzed in the safety analysis set, defined as participants randomized who received at least 1 dose of drugs or 5:2 MR and had safety assessment data collected at least once after the baseline.

The primary outcome was analyzed using the analysis of covariance model, which calculated the least-squares mean (LSM) and 95% CI to compare changes in HbA 1c level and key secondary outcomes among the 3 groups. The model adjusted for sex, age, height, weight, family history of diabetes and hypertension, physical activity, smoking, alcohol consumption, and baseline HbA 1c . Multiple imputation was used for missing values in the primary and key secondary outcomes (eMethods in Supplement 2 ). Post hoc subgroup analyses were conducted to explore the potential effect of baseline differences on HbA 1c and weight loss. Statistical analyses were performed using SPSS, version 24.0 software (SPSS Inc). All P values were from 2-sided tests and results were deemed statistically significant at P  < .05.

Of the 509 participants screened, 405 adults with type 2 diabetes (265 men [65.4%] and 140 women [34.6%]; mean [SD] age, 45.5 [11.0] years; mean [SD] BMI, 29.5 [4.1]; mean [SD] HbA 1c level, 7.9% [0.6%]) were randomly allocated. The patients’ baseline characteristics are presented in Table 1 . Of these 405 participants, 134 were randomized to the metformin group, 136 to the empagliflozin group, and 135 to the 5:2 MR group, all included in the intention-to-treat analysis ( Figure 1 ). Finally, 332 patients completed the 16-week treatment, for a completion rate of 82.0%.

At weeks 8 and 12, no patients in the 5:2 MR group required additional metformin for FPG level of 180.2 mg/dL or more and 2-hour plasma glucose of 250.5 mg/dL or more (to convert glucose to millimoles per liter, multiply by 0.0555). Only 1 patient in the metformin group had a FPG level of 218.0 mg/dL and consequently received additional empagliflozin.

At week 16, patients in the 5:2 MR group showed the greatest reduction in HbA 1c level (LSM, −1.9% [SE, 0.2%]), significantly greater than patients receiving metformin (−1.6% [SE, 0.2%]; adjusted LSM difference, −0.3% [95% CI, −0.5% to −0.1%]) and empagliflozin (−1.5% [SE, 0.2%]; adjusted LSM difference, −0.4% [95% CI, −0.6% to −0.2%]) ( Figure 2 A; eTable 4 in Supplement 2 ). However, there was no difference between the 2 drug groups (adjusted LSM difference, –0.2% [95% CI, –0.4% to 0.01%]; P  = .06). Post hoc subgroup analysis revealed that, apart from individuals aged 60 years or older, 5:2 MR mirrored the trend of HbA 1c reduction seen in the primary analysis ( Figure 3 ). The unadjusted baseline characteristics of patients supported these findings (eTable 5 in Supplement 2 ). Similarly, analyses of patients who completed the 16-week treatment also yielded consistent results (eTable 6 in Supplement 2 ). Among individuals with obesity, 5:2 MR significantly reduced HbA 1c compared with metformin (LSM difference, −0.4% [95% CI, –0.6% to –0.1%]) and empagliflozin (LSM difference, −0.4% [95% CI, –0.7% to –0.1%]) ( Figure 2 B). More patients in the 5:2 MR group (88.9% [120 of 135]) achieved an HbA 1c level less than 7% compared with the metformin (73.9% [99 of 34]; P  = .002) and empagliflozin (70.6% [96 of 136]; P  < .001) groups ( Figure 2 C). Similarly, in the 5:2 MR group, 80.0% of patients (108 of 135) achieved an HbA 1c level of less than 6.5%, surpassing metformin (60.4% [81 of 134]; P  < .001) and empagliflozin (55.1% [75 of 136]; P  < .001). Fasting plasma glucose levels in the 5:2 MR group decreased by −30.3 mg/dL (95% CI, −46.7 to −13.7 mg/dL) ( Figure 2 D). At the end of 8-week follow-up, 72 of 94 participants (76.6%) in the 5:2 MR group maintained an HbA 1c less than 6.5% (eTable 7 in Supplement 2 ).

At week 16, patients in the 5:2 MR group showed greater weight loss (LSM, −9.7 kg [SE, 2.2 kg]) than those in the metformin group (−5.5 kg [SE, 2.3 kg]; adjusted LSM difference, −4.2 kg [95% CI, −6.2 to −2.2 kg]) and empagliflozin group (−5.8 kg [SE, 2.3 kg]; adjusted LSM difference, −3.9 kg [95% CI, −5.9 to −1.9 kg]; eFigure 3A and eTable 4 in Supplement 2 ), with a greater proportion of those in the 5:2 MR group achieving weight loss (eFigure 3B in Supplement 2 ). Subgroup analyses confirmed this trend (eFigure 4 in Supplement 2 ). In addition, patients in the 5:2 MR group had significant reduction in waist and hip circumference and systolic and diastolic blood pressure, but showed no notable differences in most metabolic markers, except for triglyceride and HDL-C, compared with patients receiving antidiabetic drugs (eTable 4 in Supplement 2 ).

In the 5:2 MR group (n = 135), 1 patient experienced constipation, and 8 individuals (5.9%) had hypoglycemic symptoms, likely related to the low-energy diet ( Table 2 ). In the metformin group (n = 134), 26 individuals (19.4%) had mild gastrointestinal symptoms, and 8 individuals (6.0%) had hypoglycemia. In the empagliflozin group (n = 136), 3 patients (2.2%) experienced urinary symptoms, 5 patients (3.7%) experienced hypoglycemia, and 1 patient reported thirst. Two patients in the empagliflozin group experienced serious adverse events, including severe rash and hospitalization due to increased blood ketones, which resolved with treatment.

We found that among Chinese adults with overweight or obesity and newly diagnosed type 2 diabetes, the 5:2 MR approach achieved significant improvements in glycemic control and weight loss within a 16-week period, while also improving blood pressure and triglyceride and HDL-C levels. Therefore, 5:2 MR may potentially serve as an effective initial lifestyle intervention instead of antidiabetic drugs for early-stage type 2 diabetes.

Effective lifestyle interventions for patients with overweight and obesity and type 2 diabetes are crucial for achieving glycemic control and weight loss. Two single-center, small sample randomized clinical trials have confirmed that intermittent fasting can effectively reduce HbA 1c levels in these patients. 20 , 24 The 5:2 intermittent fasting diet for 12 months resulted in a reduction of 0.5% in HbA 1c level compared with a continuous energy restriction diet, with no difference in weight loss. 20 For patients with type 2 diabetes treated with insulin therapy, a 12-week 3:4 intermittent fasting intervention (3 days consuming 25% of recommended calories and 4 days without calorie restriction) led to a mean (SD) decrease of HbA 1c by 7.3 (12.0) mmol/mol (0.6% [1.1%]) and a mean (SD) weight loss of 4.8 (5.0) kg, with a daily total mean (SD) insulin dose reduction of 9 (10) IU. 24 A recent systematic review reported that the changes in HbA 1c after intermittent fasting intervention ranged from −1.5% to −0.3%. 25 Moreover, a meta-analysis of 2112 studies showed that partial or complete MR significantly reduced HbA 1c levels compared with conventional diabetes diets (−0.7% to −0.3%). 26 Our results found that after a 16-week intervention with the 5:2 MR, the mean HbA 1c reduction was 1.9%, greater than those achieved with metformin (0.3%) and empagliflozin (0.4%). According to American Diabetes Association recommendations, individuals with an HbA 1c of less than 6.5% for at least 6 months after the initiation of lifestyle interventions are considered to achieve diabetes remission. 27 In this study, 80.0% of patients reached this target with a 16-week 5:2 MR intervention. We acknowledge that the duration of our intervention was less than the recommended minimum of 6 months. Furthermore, at the end of the 8-week follow-up, 72 of 94 participants in the 5:2 MR group (76.6%) maintained an HbA 1c level of less than 6.5%, indicating that the 5:2 MR approach significantly and sustainably improves HbA 1c levels in patients with early type 2 diabetes.

In addition, our findings demonstrated that 5:2 MR reduced FPG levels, fasting insulin levels, C-peptide levels, and HOMA-IR. However, when compared with metformin and empagliflozin, the differences in fasting insulin levels, C-peptide levels, and HOMA-IR were not statistically significant. Animal studies have shown that fasting in diabetic mice can downregulate the expression of inflammatory factors, thereby alleviating inflammation. 28 A 5:2 MR plan may reshape the gut microbiota, promote white adipose tissue browning, and consequently reduce insulin resistance and the occurrence of obesity. 29 , 30 The MR used in this study contained omega-3 fatty acids and medium-chain fatty acids. Omega-3 fatty acids regulate leptin, inhibit fat synthesis, and promote fat breakdown. 31 Medium-chain fatty acids reduce heterotopic fat, enhance brown fat thermogenesis, and increase insulin sensitivity. 32

Compared with 2 antidiabetic drugs, 5:2 MR showed more significant and sustained benefits in weight loss and waist circumference reduction. Metformin exerts its effects by suppressing appetite, reducing insulin secretion, and improving gut microbiota. 33 Sodium-glucose cotransporter-2 inhibitors directly reduce body weight by increasing glucose excretion in the kidneys. 34 The DiRECT study confirmed that diabetes can be partially reversed through weight loss and proposed the “double cycle hypothesis,” suggesting that type 2 diabetes results from fat infiltration into the liver, pancreas, and muscle tissue, leading to the destruction of pancreatic β cells and tissue insulin resistance. Weight loss educes liver fat and significantly improves insulin resistance, and maintaining ideal body weight assists in β-cell function recovery, thus slowing down or even reversing the development of diabetes. 13 , 35 Our study cannot conclusively determine whether the glycemic improvement in patients with type 2 diabetes is due to weight loss or the 5:2 MR approach itself, requiring further investigation. The 5:2 MR reduced blood pressure and total cholesterol and increased HDL-C levels, consistent with previous studies indicating improved metabolic parameters with intermittent fasting and MR, 26 , 32 , 36 , 37 suggesting a potential cardiovascular protective effect.

The incidence of hypoglycemia was comparable across all 3 groups. When implementing a 5:2 MR intervention, it is essential to prevent hypoglycemia associated with low-energy diet. However, compared with medications, the 5:2 MR demonstrates favorable safety.

The 2020 China Guidelines emphasize lifestyle intervention as the foundational treatment for type 2 diabetes, with medication initiated only if lifestyle intervention fails to achieve glycemic control. 38 The EARLY study, for the first time to our knowledge, directly compared 5:2 MR with 2 widely used antidiabetic medications, providing evidence for the 5:2 MR approach as an effective initial lifestyle intervention for Chinese patients with early-stage type 2 diabetes.

This study has some limitations. First, it enrolled only patients not taking antidiabetic medication with a baseline HbA 1c level of less than 9%, so the efficacy of 5:2 MR for those taking medication or with a greater baseline HbA 1c needs further validation. Second, the 3-month washout period for eligibility regarding antidiabetic agents, including insulin, was short. A longer period without medication use (6 or 12 months) could offer more insights into prior medications’ effects. Third, the 5:2 MR intervention’s short duration means its long-term efficacy, especially for newly diagnosed patients with type 2 diabetes and overweight or obesity remains to be confirmed. Long-term follow-up studies are under way to assess the durability of 5:2 MR.

This randomized clinical study found that, for patients with newly diagnosed type 2 diabetes, a 16-week intervention with 5:2 MR could improve glycemic control and weight loss while also improving blood pressure, triglyceride levels, and HDL-C levels. Therefore, 5:2 MR may serve as an initial lifestyle intervention for patients with type 2 diabetes, providing an alternative to the use of metformin and empagliflozin medications.

Accepted for Publication: April 11, 2024.

Published: June 21, 2024. doi:10.1001/jamanetworkopen.2024.16786

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

Corresponding Author: Lixin Guo, MD, Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 Da Hua Rd, DongDan, Beijing 100730, China ( [email protected] ).

Author Contributions: Dr Guo 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. Drs Guo, Xi, and Jin are co–first authors.

Concept and design: Guo, Yu.

Acquisition, analysis, or interpretation of data: Guo, Xi, Jin, Yuan, Qin, Chen, Zhang, Y. Liu, Cheng, W. Liu.

Drafting of the manuscript: Guo, Xi, Jin, Yuan, Qin, Chen, Zhang, Y. Liu, Cheng, W. Liu.

Critical review of the manuscript for important intellectual content: Guo, W. Liu, Yu.

Statistical analysis: Guo, W. Liu.

Obtained funding: Guo, Qin.

Administrative, technical, or material support: Guo, Xi, Jin, Yuan, Chen, Zhang, Y. Liu, Cheng, Yu.

Supervision: Guo.

Conflict of Interest Disclosures: None reported.

Funding/Support: This trial was funded by Beijing MetabolicControl Technology Co Ltd.

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 3 .

Additional Contributions: We would like to thank the participants for their contributions to this trial. We also thank Bin Qiao, PhD, for providing medical writing support for the manuscript; he was not compensated for his contribution.

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