international journal of research and analysis review

INTERNATIONAL JOURNAL OF RESEARCH AND ANALYTICAL REVIEWS

Current Issue

Advertising influence on consumer intent to buy fmcg products, a systemic review of the relationship between asthma and cardiovascular disease, quick links.

Hard Copy Information

Submit Paper

• Editorial Board
• Aim & Scope
• Review Committee
• Author Guidelines
• Call for Paper
• Review Process
• Upload Your Article
• Join as Reviewer
• Open Access
• Association With
• Publication Ethics Policy

IJRAR is not there in UGC CARE List

ijrar.org is our newly developed website

Shifting Website of IJRAR

Please visit New site of IJRAR for New submission. Visit: http://ijrar.org/

International Journal of Research and Analytical Reviews(IJRAR) is an open-access journal publishing full-length research papers and review articles covering subjects that fall under the wide spectrum of Education, science and technology. The journal is dedicated towards dissemination of knowledge related to the advancement in scientific research. The prestigious interdisciplinary editorial board reflects the diversity of subjects covered in this journal. Under the realm of science and technology, the coverage includes environmental science, pure and applied mathematics, agricultural research and engineering, biology, biotechnology, bioinformatics, Healthcare sciences (including clinical medicine, public health), physics, biophysics, computer science, chemistry and bioengineering, to name a few.

International Journal of Research and Analytical Reviews (IJRAR) is published by Atman Research Centre co-published by Atman Publishing Academy, Gujarat. The journal now brings out Quarterly issues since its inception in 2014. It supports the open access policy for making scientific research accessible to one and all.

The journal is addressed to both practicing professionals and researchers in the field of education, science and technology, professionals in academia, former researchers, students and other specialists interested in the results of scientific research and related subjects covered by the journal.

Peer Review Process

Submissions to the International Journal of Research and Analytical Reviews passes through a double-blind peer-review process. The criteria for publication in Indian Journal of Science and Technology are as follows:

• The study presents the results of primary scholarly research.
• Results reported have not been published elsewhere.
• The research meets all applicable standards of ethics and research integrity.
• Experiments, statistics, and other analyses are performed to a high technical standard and are described in sufficient detail.
• Conclusions are presented in an appropriate fashion and are supported by the data.
• The article is presented in an intelligible fashion and is written in standard English.

Once the manuscripts have passed quality control, they are assigned to a member of the Editorial Board (or an expert who is not a member of the Board) for conducting the peer-review process and for making a decision to accept, invite revision of, or reject the article.

Editorial Publishing Policies

The following editorial and publishing policies apply to content the International Journal of Research and Analytical Reviews.

• Open Access Agreement
• Submission of related manuscripts
• Confidentiality
• Corrections and Additions
• Publication Ethics
• Publication Charges
• Open Access Agreement.

Upon submission of an article, the authors are asked to indicate their agreement to abide by an open access  Creative Commons license (CC-BY) . Under the terms of this license, authors retain ownership of the copyright of their articles. The license permits any user to download, print out, extract, reuse, archive, and distribute the article, so long as appropriate credit is given to the authors and source of the work. The license ensures that the authors' article will be available as widely as possible and that the article can be included in any scholarly archive.

During the submission of an article, all authors are asked to indicate that they do not have a related or duplicate manuscript under consideration (or accepted) for publication elsewhere. If related work has been submitted elsewhere, a copy must be included with the article submitted to  International Journal of Research and Analytical Reviews . Reviewers will be asked to comment on the overlap between related submissions.

Editors and reviewers are required to treat all submitted manuscripts in strict confidence. The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party.

Responsibility for the accuracy of content lies with the author(s) of the paper. Any corrections or updates to an article after publishing will be accepted at an additional charge of USD 15/INR 500.

Authors are expected to be aware of, and comply with, best practice in publication ethics specifically with regard to authorship (for example avoidance of ghost or guest authorship), dual submission, plagiarism, manipulation of figures, competing interests and compliance with policies on research ethics.

• Fees and discounts.  To provide open access, International Journal of Research and Analytical Reviews uses a business model in which expenses including those of peer review, journal production, and online hosting and archiving are recovered in part by charging a journal-specific  publication fee  to the authors for each article they publish. Authors who have registered as a member in the Indian Society for Education and Environment will receive subscription waiver for a period of 1 year. Publication charge covers part of the handling expenses and thus entirely non-refundable. Publication charge does not bind the journal to be listed by a professional body or agency/ or being indexed in a database.
• Fee waivers.   International Journal of Research and Analytical Reviews offers a complete or partial fee waiver for authors who do not have access to funds to cover publication fees. Editors and reviewers have no access to payment information, and hence ability to pay or lack thereof will not influence the decision to publish a paper.  

Reviewers and Editors are required to treat manuscripts fairly and in confidence.

International Journal of Research and Review

Current issue international journal of research and review journal's doi: https://doi.org/10.52403/ijrr volume 11; issue 8; august 2024 cover page of journal is available for download. click here note: articles will be added in this issue till 25.08.2024.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Exploring Research Methodology: Review Article

2019, https://www.ijrrjournal.com/IJRR_Vol.6_Issue.3_March2019/Abstract_IJRR0011.html

Research methodology is a way to systematically solve the research problem. It may be understood as a science of studying how research is done scientifically. In it we study the various steps that are generally adopted by a researcher in studying his research problem along with the logic behind them. It is necessary for the researcher to know not only the research methods/techniques but also the methodology. Researchers not only need to know how to develop certain indices or tests, how to calculate the mean, the mode, the median or the standard deviation or chi-square, how to apply particular research techniques, but they also need to know which of these methods or techniques, are relevant and which are not, and what would they mean and indicate and why. Researchers also need to understand the assumptions underlying various techniques and they need to know the criteria by which they can decide that certain techniques and procedures will be applicable to certain problems and others will not. All this means that it is necessary for the researcher to design his methodology for his problem as the same may differ from problem to problem.

Related Papers

Scholarly Communication and the Publish or Perish Pressures of Academia A volume in the Advances in Knowledge Acquisition, Transfer, and Management (AKATM) Book Series

Dr. Naresh A . Babariya , Alka V. Gohel

The most important of research methodology in research study it is necessary for a researcher to design a methodology for the problem chosen and systematically solves the problem. Formulation of the research problem is to decide on a broad subject area on which has thorough knowledge and second important responsibility in research is to compare findings, it is literature review plays an extremely important role. The literature review is part of the research process and makes a valuable contribution to almost every operational step. A good research design provides information concerning with the selection of the sample population treatments and controls to be imposed and research work cannot be undertaken without sampling. Collecting the data and create data structure as organizing the data, analyzing the data help of different statistical method, summarizing the analysis, and using these results for making judgments, decisions and predictions. Keywords: Research Problem, Economical Plan, Developing Ideas, Research Strategy, Sampling Design, Theoretical Procedures, Experimental Studies, Numerical Schemes, Statistical Techniques.

Xochitl Ortiz

The authors felt during their several years of teaching experience that students fail to understand the books written on Research Methodology because generally they are written in technical language. Since this course is not taught before the Master’s degree, the students are not familiar with its vocabulary, methodology and course contents. The authors have made an attempt to write it in very non- technical language. It has been attempted that students who try to understand the research methodology through self-learning may also find it easy. The chapters are written with that approach. Even those students who intend to attain high level of knowledge of the research methodology in social sciences will find this book very helpful in understanding the basic concepts before they read any book on research methodology. This book is useful those students who offer the Research Methodology at Post Graduation and M.Phil. Level. This book is also very useful for Ph.D. Course Work examinations.

Anil Jharotia

Research is an important activity of any nation and societies for generating the information to its developments. Robust collection of qualitative information helps in the development of the any nations. Research & Development is an important tool for acquiring new knowledge in any field of human survival. Various type of problems and questions need to use research methodology depend on the rationale of researchers. How to use the research for finding answers of any research questions/problems.

Scholars Bulletin

Wahied Khawar Balwan

Research is one of the means by which we seek to discover the truth. It is based upon the tacit assumption that the world is a cosmos whose happenings have causes and are controlled by forces and relationships that can be expressed as laws and principles. Discovery of these controls of nature provides us with a hunting license to search for ways of controlling our environment. To search for truth in a scientific way research methodology provides principles to refine our common beliefs through research activity that establishes rules of logical and appropriate reasoning. In order to apply the scientific research methodology properly in research work, the researcher must have a clear basic concept of research methodology & methods that will ensure to find potential research results. This paper deals with the conceptuality of the research methodology like the meaning of the research, objectives of research, motivation in research and types of research. The basic approaches to research,...

IJRASET Publication

The term Research means a systematic way to investigate new facts or analyse the existing information to update the knowledge. Research methodology refers to the Science of Understanding how the solution to research problem can be obtained systematically. It can also be termed as the specific methods used to conduct the research. This paper presents the detail overview of different research methods. The research methods and methodology differ from problem to problem. In order to conduct a research, it is important for a researcher to not only have a good knowledge on Research methods but also on the research methodology. Researchers need to develop a Research design which acts like a blue print for conducting the research. This paper provides the analysis of different research methods and how to choose the research method based on the application Index Terms-Methodology, Research Process, Pure Research, Qualitative methods, and Quantitative methods I. INTRODUCTION Research is very important in order to progress. The term research is a combination of two words "Re-again, Search-find out". It an art of finding solution to the problem. According to the Oxford Advanced American Dictionary research is defined as "A careful study of a subject, especially in order to discover new facts or information about it" [1]P.M. Cook referred as "Research is an honest, exhaustive, intelligent searching for facts and their meanings or implications with reference to a given problem. The product or findings of a given piece of research should be an authentic, verifiable contribution to knowledge in the field studied." [2] Methodology refers to the organized, theoretical investigation of the methods used in the research. It includes the analysis of the research methods along with the ideologies related to the area of investigation. Technically it includes paradigm, research model, and the research techniques. [3] Research Methodology is art of studying how research is done systematically. It aims to explain on how to conduct a research, what are the problems that need to be answered and what are the pitfalls while conducting a research.

Khamis S Moh'd

Yuanita Damayanti

Research is any original and systematic investigation undertaken to increase knowledge and understanding and to establish facts and principles. It comprises the creation of ideas and generation of new knowledge that lead to new and improved insights and the development of new material, devices, products and processes. The word " research " perhaps originates from the old French word recerchier that meant to 'search again'. It implicitly assumes that the earlier search was not exhaustive and complete and hence a repeated search is called for.

RESEARCH METHODOLOGY (Basis in the Management and Business Process)

Boyke Hatman

Research method is a method or scientific technique to obtain data with specific purposes and uses. The scientific means or techniques in question are where research activities are carried out based on scientific characteristics. This is a set of rules, activities, and procedures used by the perpetrators. The methodology is also a theoretical analysis of a method or method. Research is a systematic investigation to increase knowledge, as well as systematic and organized efforts to investigate certain problems that require answers. The nature of research can be understood by studying various aspects that encourage research to do it properly. Every person has a different motivation, including influenced by their goals and profession. Motivation and research objectives in general are basically the same, namely research is a reflection of the desire of people who always try to know something. The desire to acquire and develop knowledge is a basic human need which is generally a motivation to conduct research. The validity of research data can be obtained by using valid instruments, using appropriate and adequate amounts of data sources, as well as correct data collection and analysis methods. To obtain reliable data, the instrument must be reliable and the research carried out repeatedly. Furthermore, to obtain objective data, the number of sample data sources approaches the population.Each study has specific goals and uses. In general, there are three types of research objectives, namely the nature of discovery, verification and development. The finding means that the data obtained from research is truly new data that has not been previously known.

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Durga Prasad

collins wetiatia

Raphael Sena

Saeed Anwar

Thabit Alomari

Wafae Barkani

Vikalpa: The Journal for Decision Makers

Vivek Patkar

MD Ashikur Rahman

Ngoc Nguyen

Dr. Awais H. Gillani

caroline tobing

Anush Ramanujan

Amina Belabed

Abla BENBELLAL

Piumi Tillekerathne

Second Language Learning and Teaching

Magdalena Walenta

Dr.Larry Adams

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

• Submit Article
• Notifications   

International Journal of Research and Analysis is owned and managed by International Forum for Research and Analysis. The research journal provides a platform for the researchers, academicians and students to showcase their knowledge and research prowess. IJRA is an on-line, gratis access, multidisciplinary journal published bi monthly.

• Notifications

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Author Biographies
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Impacts of generative artificial intelligence in higher education: research trends and students’ perceptions.

1. Introduction

2. materials and methods.

• “Generative Artificial Intelligence” or “Generative AI” or “Gen AI”, AND;
• “Higher Education” or “University” or “College” or “Post-secondary”, AND;
• “Impact” or “Effect” or “Influence”.
• Q1— Does GenAI have more positive or negative effects on higher education? Options (to choose one): 1. It has more negative effects than positives; 2. It has more positive effects than negative; 3. There is a balance between positive and negative effects; 4. Don’t know.
• Q2— Identify the main positive effect of Gen AI in an academic context . Open-ended question.
• Q3— Identify the main negative effect of Gen AI in an academic context . Open-ended question.

3.1. Impacts of Gen AI in HE: Research Trends

3.1.1. he with gen ai, the key role that pedagogy must play, new ways to enhance the design and implementation of teaching and learning activities.

• Firstly, prompting in teaching should be prioritized as it plays a crucial role in developing students’ abilities. By providing appropriate prompts, educators can effectively guide students toward achieving their learning objectives.
• Secondly, configuring reverse prompting within the capabilities of Gen AI chatbots can greatly assist students in monitoring their learning progress. This feature empowers students to take ownership of their education and fosters a sense of responsibility.
• Furthermore, it is essential to embed digital literacy in all teaching and learning activities that aim to leverage the potential of the new Gen AI assistants. By equipping students with the necessary skills to navigate and critically evaluate digital resources, educators can ensure that they are prepared for the digital age.

The Student’s Role in the Learning Experience

The key teacher’s role in the teaching and learning experience, 3.1.2. assessment in gen ai/chatgpt times, the need for new assessment procedures, 3.1.3. new challenges to academic integrity policies, new meanings and frontiers of misconduct, personal data usurpation and cheating, 3.2. students’ perceptions about the impacts of gen ai in he.

• “It harms the learning process”: ▪ “What is generated by Gen AI has errors”; ▪ “Generates dependence and encourages laziness”; ▪ “Decreases active effort and involvement in the learning/critical thinking process”.

4. Discussion

• Training: providing training for both students and teachers on effectively using and integrating Gen AI technologies into teaching and learning practices.
• Ethical use and risk management: developing policies and guidelines for ethical use and risk management associated with Gen AI technologies.
• Incorporating AI without replacing humans: incorporating AI technologies as supplementary tools to assist teachers and students rather than replacements for human interaction.
• Continuously enhancing holistic competencies: encouraging the use of AI technologies to enhance specific skills, such as digital competence and time management, while ensuring that students continue to develop vital transferable skills.
• Fostering a transparent AI environment: promoting an environment in which students and teachers can openly discuss the benefits and concerns associated with using AI technologies.
• Data privacy and security: ensuring data privacy and security using AI technologies.
• The dynamics of technological support to align with the most suitable Gen AI resources;
• The training policy to ensure that teachers, students, and academic staff are properly trained to utilize the potential of Gen AI and its tools;
• Security and data protection policies;
• Quality and ethical action policies.

5. Conclusions

• Database constraints: the analysis is based on existing publications in SCOPUS and the Web of Science, potentially omitting relevant research from other sources.
• Inclusion criteria: due to the early stage of scientific production on this topic, all publications were included in the analysis, rather than focusing solely on articles from highly indexed journals and/or with a high number of citations as recommended by bibliometric and systematic review best practices.
• Dynamic landscape: the rate of publications on Gen AI has been rapidly increasing and diversifying in 2024, highlighting the need for ongoing analysis to track trends and changes in scientific thinking.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Akakpo, Martin Gameli. 2023. Skilled for the Future: Information Literacy for AI Use by University Students in Africa and the Role of Librarians. Internet Reference Services Quarterly 28: 19–26. [ Google Scholar ] [ CrossRef ]
• AlAfnan, Mohammad Awad, Samira Dishari, Marina Jovic, and Koba Lomidze. 2023. ChatGPT as an Educational Tool: Opportunities, Challenges, and Recommendations for Communication, Business Writing, and Composition Courses. Journal of Artificial Intelligence and Technology 3: 60–68. [ Google Scholar ] [ CrossRef ]
• Almaraz-López, Cristina, Fernando Almaraz-Menéndez, and Carmen López-Esteban. 2023. Comparative Study of the Attitudes and Perceptions of University Students in Business Administration and Management and in Education toward Artificial Intelligence. Education Sciences 13: 609. [ Google Scholar ] [ CrossRef ]
• Al-Zahrani, Abdulrahman. 2023. The impact of generative AI tools on researchers and research: Implications for academia in higher education. Innovations in Education and Teaching International , 1–15. [ Google Scholar ] [ CrossRef ]
• Athilingam, Ponrathi, and Hong-Gu He. 2023. ChatGPT in nursing education: Opportunities and challenges. Teaching and Learning in Nursing 19: 97–101. [ Google Scholar ] [ CrossRef ]
• Álvarez-Álvarez, Carmen, and Samuel Falcon. 2023. Students’ preferences with university teaching practices: Analysis of testimonials with artificial intelligence. Educational Technology Research and Development 71: 1709–24. [ Google Scholar ] [ CrossRef ]
• Bannister, Peter, Elena Alcalde Peñalver, and Alexandra Santamaría Urbieta. 2023. Transnational higher education cultures and generative AI: A nominal group study for policy development in English medium instruction. Journal for Multicultural Education . ahead-of-print . [ Google Scholar ] [ CrossRef ]
• Bearman, Margaret, and Rola Ajjawi. 2023. Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology 54: 1160–73. [ Google Scholar ] [ CrossRef ]
• Boháček, Matyas. 2023. The Unseen A+ Student: Evaluating the Performance and Detectability of Large Language Models in the Classroom. CEUR Workshop Proceedings 3487: 89–100. Available online: https://openreview.net/pdf?id=9ZKJLYg5EQ (accessed on 7 January 2024).
• Chan, Cecilia Ka Yuk. 2023. A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education 20: 38. [ Google Scholar ] [ CrossRef ]
• Chan, Cecilia Ka Yuk, and Wenjie Hu. 2023. Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education 20: 43. [ Google Scholar ] [ CrossRef ]
• Chan, Cecilia Ka Yuk, and Wenxin Zhou. 2023. An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI. Smart Learning Environments 10: 64. [ Google Scholar ] [ CrossRef ]
• Chang, Daniel H., Michael Pin-Chuan Lin, Shiva Hajian, and Quincy Q. Wang. 2023. Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization. Sustainability 15: 12921. [ Google Scholar ] [ CrossRef ]
• Chiu, Thomas. 2023. The impact of Generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments , 1–17. [ Google Scholar ] [ CrossRef ]
• Chun, John, and Katherine Elkins. 2023. The Crisis of Artificial Intelligence: A New Digital Humanities Curriculum for Human-Centred AI. International Journal of Humanities and Arts Computing 17: 147–67. [ Google Scholar ] [ CrossRef ]
• Cowling, Michael, Joseph Crawford, Kelly-Ann Allen, and Michael Wehmeyer. 2023. Using leadership to leverage ChatGPT and artificial intelligence for undergraduate and postgraduate research supervision. Australasian Journal of Educational Technology 39: 89–103. [ Google Scholar ] [ CrossRef ]
• Crawford, Joseph, Carmen Vallis, Jianhua Yang, Rachel Fitzgerald, Christine O’Dea, and Michael Cowling. 2023a. Editorial: Artificial Intelligence is Awesome, but Good Teaching Should Always Come First. Journal of University Teaching & Learning Practice 20: 01. [ Google Scholar ] [ CrossRef ]
• Crawford, Joseph, Michael Cowling, and Kelly-Ann Allen. 2023b. Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching & Learning Practice 20: 02. [ Google Scholar ] [ CrossRef ]
• Currie, Geoffrey. 2023a. A Conversation with ChatGPT. Journal of Nuclear Medicine Technology 51: 255–60. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Currie, Geoffrey. 2023b. GPT-4 in Nuclear Medicine Education: Does It Outperform GPT-3.5? Journal of Nuclear Medicine Technology 51: 314–17. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Currie, Geoffrey, and Kym Barry. 2023. ChatGPT in Nuclear Medicine Education. Journal of Nuclear Medicine Technology 51: 247–54. [ Google Scholar ] [ CrossRef ]
• Currie, Geoffrey, Clare Singh, Tarni Nelson, Caroline Nabasenja, Yazan Al-Hayek, and Kelly Spuur. 2023. ChatGPT in medical imaging higher education. Radiography 29: 792–99. [ Google Scholar ] [ CrossRef ]
• Dai, Yun, Ang Liu, and Cher P. Lim. 2023. Reconceptualizing Chatgpt and Generative AI as a Student-driven Innovation in Higher Education. Procedia CIRP Volume 119: 84–90. [ Google Scholar ] [ CrossRef ]
• Dogru, Tarik, Nathana Line, Lydia Hanks, Fulya Acikgoz, Je’Anna Abbott, Selim Bakir, Adiyukh Berbekova, Anil Bilgihan, Ali Iskender, Murat Kizildag, and et al. 2023. The implications of generative artificial intelligence in academic research and higher education in tourism and hospitality. Tourism Economics 30: 1083–94. [ Google Scholar ] [ CrossRef ]
• Duong, Cong Doanh, Trong Nghia Vu, and Thi Viet Nga Ngo. 2023. Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator. The International Journal of Management Education 21: 100883. [ Google Scholar ] [ CrossRef ]
• Eager, Bronwyn, and Ryan Brunton. 2023. Prompting Higher Education Towards AI-Augmented Teaching and Learning Practice. Journal of University Teaching & Learning Practice 20: 5. [ Google Scholar ] [ CrossRef ]
• Elkhodr, Mahmoud, Ergun Gide, Robert Wu, and Omar Darwish. 2023. ICT students’ perceptions towards ChatGPT: An experimental reflective lab analysis. STEM Education 3: 70–88. [ Google Scholar ] [ CrossRef ]
• Farrelly, Tom, and Nick Baker. 2023. Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice. Education Sciences 13: 1109. [ Google Scholar ] [ CrossRef ]
• Farrokhnia, Mohammadreza, Seyyed Banihashem, Seyyed Kazem Banihashem, Omid Noroozi, and Arjen Wals. 2023. A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International 61: 460–74. [ Google Scholar ] [ CrossRef ]
• Gong, Furong. 2023. The Impact of Generative AI like ChatGPT on Digital Literacy Education in University Libraries. Documentation, Information & Knowledge 40: 97–106, 156. [ Google Scholar ] [ CrossRef ]
• Han, Bingyi, Sadia Nawaz, George Buchanan, and Dana McKay. 2023. Ethical and Pedagogical Impacts of AI in Education. In Artificial Intelligence in Education . Edited by Ning Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga Santos and Vania Dimitrova. Lecture Notes in Computer Science. Cham: Springer, pp. 667–73. [ Google Scholar ] [ CrossRef ]
• Hassoulas, Athanasios, Ned Powell, Lindsay Roberts, Katja Umla-Runge, Laurence Gray, and Marcus J. Coffey. 2023. Investigating marker accuracy in differentiating between university scripts written by students and those produced using ChatGPT. Journal of Applied Learning and Teaching 6: 71–77. [ Google Scholar ] [ CrossRef ]
• Hernández-Leo, Davinia. 2023. ChatGPT and Generative AI in Higher Education: User-Centered Perspectives and Implications for Learning Analytics. CEUR Workshop Proceedings , 1–6. Available online: https://ceur-ws.org/Vol-3542/paper2.pdf (accessed on 7 January 2024).
• Hidayat-ur-Rehman, Imdadullah, and Yasser Ibrahim. 2023. Exploring factors influencing educators’ adoption of ChatGPT: A mixed method approach. Interactive Technology and Smart Education . ahead-of-print . [ Google Scholar ] [ CrossRef ]
• Ilieva, Galina, Tania Yankova, Stanislava Klisarova-Belcheva, Angel Dimitrov, Marin Bratkov, and Delian Angelov. 2023. Effects of Generative Chatbots in Higher Education. Information 14: 492. [ Google Scholar ] [ CrossRef ]
• Javaid, Mohd, Abid Haleem, Ravi Pratap Singh, Shahbaz Khan, and Haleem Ibrahim. 2023. Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system. Bench Council Transactions on Benchmarks, Standards and Evaluations 3: 100115. [ Google Scholar ] [ CrossRef ]
• Kaplan-Rakowski, Regina, Kimberly Grotewold, Peggy Hartwick, and Kevin Papin. 2023. Generative AI and Teachers’ Perspectives on Its Implementation in Education. Journal of Interactive Learning Research 34: 313–38. Available online: https://www.learntechlib.org/primary/p/222363/ (accessed on 7 January 2024).
• Karunaratne, Thashmee, and Adenike Adesina. 2023. Is it the new Google: Impact of ChatGPT on Students’ Information Search Habits. Paper presented at the European Conference on e-Learning (ECEL 2023), Pretoria, South Africa, October 26–27; pp. 147–55. [ Google Scholar ] [ CrossRef ]
• Kelly, Andrew, Miriam Sullivan, and Katrina Strampel. 2023. Generative artificial intelligence: University student awareness, experience, and confidence in use across disciplines. Journal of University Teaching & Learning Practice 20: 12. [ Google Scholar ] [ CrossRef ]
• Kohnke, Lucas, Benjamin Luke Moorhouse, and Di Zou. 2023. Exploring generative artificial intelligence preparedness among university language instructors: A case study. Computers and Education: Artificial Intelligence 5: 100156. [ Google Scholar ] [ CrossRef ]
• Laker, Lauren, and Mark Sena. 2023. Accuracy and detection of student use of ChatGPT in business analytics courses. Issues in Information Systems 24: 153–63. [ Google Scholar ] [ CrossRef ]
• Lemke, Claudia, Kathrin Kirchner, Liadan Anandarajah, and Florian Herfurth. 2023. Exploring the Student Perspective: Assessing Technology Readiness and Acceptance for Adopting Large Language Models in Higher Education. Paper presented at the European Conference on e-Learning, (ECEL 2023), Pretoria, South Africa, October 26–27; pp. 156–64. [ Google Scholar ] [ CrossRef ]
• Limna, Pongsakorn, Tanpat Kraiwanit, Kris Jangjarat, and Prapasiri Klayklung. 2023a. The use of ChatGPT in the digital era: Perspectives on chatbot implementation. Journal of Applied Learning and Teaching 6: 64–74. [ Google Scholar ] [ CrossRef ]
• Limna, Pongsakorn, Tanpat Kraiwanit, Kris Jangjarat, and Yarnaphat Shaengchart. 2023b. Applying ChatGPT as a new business strategy: A great power comes with great responsibility [Special issue]. Corporate & Business Strategy Review 4: 218–26. [ Google Scholar ] [ CrossRef ]
• Lopezosa, Carlos, Carles Lluís Codina, Carles Pont-Sorribes, and Mari Vállez. 2023. Use of Generative Artificial Intelligence in the Training of Journalists: Challenges, Uses and Training Proposal. Profesional De La información Information Professional 32: 1–12. [ Google Scholar ] [ CrossRef ]
• Martineau, Kim. 2023. What Is Generative AI? IBM Research Blog . April 20. Available online: https://research.ibm.com/blog/what-is-generative-AI (accessed on 7 January 2024).
• Mondal, Himel, Shaikat Mondal, and Indrashis Podder. 2023. Using ChatGPT for Writing Articles for Patients’ Education for Dermatological Diseases: A Pilot Study. Indian Dermatology Online Journal 14: 482–86. [ Google Scholar ] [ CrossRef ]
• Moorhouse, Benjamin, Marie Alina Wan, and Yuwei Wan. 2023. Generative AI tools and assessment: Guidelines of the world’s top-ranking universities. Computers and Education Open 5: 100151. [ Google Scholar ] [ CrossRef ]
• Overono, Acacia L., and Annie Ditta. 2023. The Rise of Artificial Intelligence: A Clarion Call for Higher Education to Redefine Learning and Reimagine Assessment. College Teaching , 1–4. [ Google Scholar ] [ CrossRef ]
• Page, Matthew J., Joanne E. McKenzie, Patrick M. Bossuyt, Isabelle Boutron, Tammy C. Hoffmann, Cynthia D. Mulrow, Larissa Shamseer, Jennifer M. Tetzlaff, Elie A. Akl, Sue E. Brennan, and et al. 2021. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 372: n71. [ Google Scholar ] [ CrossRef ]
• Pechenkina, Ekaterina. 2023. Artificial intelligence for good? Challenges and possibilities of AI in higher education from a data justice perspective. In Higher Education for Good: Teaching and Learning Futures . Edited by Laura Czerniewicz and Catherine Cronin. Cambridge, UK: Open Book Publishers, pp. 239–66. [ Google Scholar ] [ CrossRef ]
• Perkins, Mike, Jasper Roe, Darius Postma, James McGaughran, and Don Hickerson. 2023. Detection of GPT-4 Generated Text in Higher Education: Combining Academic Judgement and Software to Identify Generative AI Tool Misuse. Journal of Academic Ethics 22: 89–113. [ Google Scholar ] [ CrossRef ]
• Pitso, Teboho. 2023. Post-COVID-19 Higher Learning: Towards Telagogy, A Web-Based Learning Experience. IAFOR Journal of Education 11: 39–59. [ Google Scholar ] [ CrossRef ]
• Plata, Sterling, Maria Ana De Guzman, and Arthea Quesada. 2023. Emerging Research and Policy Themes on Academic Integrity in the Age of Chat GPT and Generative AI. Asian Journal of University Education 19: 743–58. [ Google Scholar ] [ CrossRef ]
• Rudolph, Jürgen, Samson Tan, and Shannon Tan. 2023a. War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. Journal of Applied Learning and Teaching 6: 364–89. [ Google Scholar ] [ CrossRef ]
• Rudolph, Jürgen, Samson Tan, and Shannon Tan. 2023b. ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching 6: 342–63. [ Google Scholar ] [ CrossRef ]
• Ryall, Adelle, and Stephen Abblitt. 2023. “A Co-Pilot for Learning Design?”: Perspectives from Learning Designers on the Uses, Challenges, and Risks of Generative Artificial Intelligence in Higher Education. In People, Partnerships and Pedagogies. Proceedings ASCILITE 2023 . Edited by Thomas Cochrane, Vickel Narayan, Cheryl Brown, MacCallum Kathryn, Elisa Bone, Christopher Deneen, Robert Vanderburg and Brad Hurren. Christchurch: Te Pae Conference Center, pp. 525–30. [ Google Scholar ] [ CrossRef ]
• Santiago, Cereneo S., Steve I. Embang, Ricky B. Acanto, Kem Warren P. Ambojia, Maico Demi B. Aperocho, Benedicto B. Balilo, Erwin L. Cahapin, Marjohn Thomas N. Conlu, Samson M. Lausa, Ester Y. Laput, and et al. 2023. Utilization of Writing Assistance Tools in Research in Selected Higher Learning Institutions in the Philippines: A Text Mining Analysis. International Journal of Learning, Teaching and Educational Research 22: 259–84. [ Google Scholar ] [ CrossRef ]
• Solopova, Veronika, Eiad Rostom, Fritz Cremer, Adrian Gruszczynski, Sascha Witte, Chengming Zhang, Fernando Ramos López, Lea Plößl, Florian Hofmann, Ralf Romeike, and et al. 2023. PapagAI: Automated Feedback for Reflective Essays. In KI 2023: Advances in Artificial Intelligence. KI 2023 . Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Cham: Springer, vol. 14236, pp. 198–206. [ Google Scholar ] [ CrossRef ]
• Sridhar, Pragnya, Aidan Doyle, Arav Agarwal, Christopher Bogart, Jaromir Savelka, and Majd Sakr. 2023. Harnessing LLMs in Curricular Design: Using GPT-4 to Support Authoring of Learning Objectives. CEUR Workshop Proceedings 3487: 139–50. [ Google Scholar ]
• Sullivan, Miriam, Andrew Kelly, and Paul McLaughlan. 2023. ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching 6: 31–40. [ Google Scholar ] [ CrossRef ]
• Tominc, Polona, and Maja Rožman. 2023. Artificial Intelligence and Business Studies: Study Cycle Differences Regarding the Perceptions of the Key Future Competences. Education Sciences 13: 580. [ Google Scholar ] [ CrossRef ]
• van den Berg, Geesje, and Elize du Plessis. 2023. ChatGPT and Generative AI: Possibilities for Its Contribution to Lesson Planning, Critical Thinking and Openness in Teacher Education. Education Sciences 13: 998. [ Google Scholar ] [ CrossRef ]
• Walczak, Krzysztof, and Wojciech Cellary. 2023. Challenges for higher education in the era of widespread access to Generative AI. Economics and Business Review 9: 71–100. [ Google Scholar ] [ CrossRef ]
• Wang, Ting, Brady D. Lund, Agostino Marengo, Alessandro Pagano, Nishith Reddy Mannuru, Zoë A. Teel, and Jenny Pange. 2023. Exploring the Potential Impact of Artificial Intelligence (AI) on International Students in Higher Education: Generative AI, Chatbots, Analytics, and International Student Success. Applied Sciences 13: 6716. [ Google Scholar ] [ CrossRef ]
• Watermeyer, Richard, Lawrie Phipps, Donna Lanclos, and Cathryn Knight. 2023. Generative AI and the Automating of Academia. Postdigital Science and Education 6: 446–66. [ Google Scholar ] [ CrossRef ]
• Wolf, Leigh, Tom Farrelly, Orna Farrell, and Fiona Concannon. 2023. Reflections on a Collective Creative Experiment with GenAI: Exploring the Boundaries of What is Possible. Irish Journal of Technology Enhanced Learning 7: 1–7. [ Google Scholar ] [ CrossRef ]
• Yilmaz, Ramazan, and Fatma Gizem Karaoglan Yilmaz. 2023. The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence 4: 100147. [ Google Scholar ] [ CrossRef ]
• Zawiah, Mohammed, Fahmi Y. Al-Ashwal, Lobna Gharaibeh, Rana Abu Farha, Karem H. Alzoubi, Khawla Abu Hammour, Qutaiba A. Qasim, and Fahd Abrah. 2023. ChatGPT and Clinical Training: Perception, Concerns, and Practice of Pharm-D Students. Journal of Multidisciplinary Healthcare 16: 4099–110. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Saúde, S.; Barros, J.P.; Almeida, I. Impacts of Generative Artificial Intelligence in Higher Education: Research Trends and Students’ Perceptions. Soc. Sci. 2024 , 13 , 410. https://doi.org/10.3390/socsci13080410

Saúde S, Barros JP, Almeida I. Impacts of Generative Artificial Intelligence in Higher Education: Research Trends and Students’ Perceptions. Social Sciences . 2024; 13(8):410. https://doi.org/10.3390/socsci13080410

Saúde, Sandra, João Paulo Barros, and Inês Almeida. 2024. "Impacts of Generative Artificial Intelligence in Higher Education: Research Trends and Students’ Perceptions" Social Sciences 13, no. 8: 410. https://doi.org/10.3390/socsci13080410

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Effects of Exercise Intervention on Physical and Mental Health of Children and Adolescents with Attention-Deficit/Hyperactivity Disorder: A Systematic Review and Meta-analysis Based on ICF-CY

• Review Article
• Published: 08 August 2024

Cite this article

• Lili Feng   ORCID: orcid.org/0000-0002-5311-3688 1   na1 ,
• Bowen Li 1   na1 ,
• Su Sean Yong 1 &
• Zhenjun Tian   ORCID: orcid.org/0000-0002-8747-6161 2  

Attention deficit/hyperactivity disorder (ADHD) affects the physical and mental health in children and adolescents. Evidence suggests that participation in exercise may benefit children and adolescents with ADHD and enhance current and future physical and mental health. This systematic review and meta-analysis investigated the effects of exercise interventions on the physical and mental health of children and adolescents with ADHD, based on the International Classification of Functioning, Disability and Health-Children and Youth Version (ICF-CY) framework.

This review systematically searched for studies published up to August 1, 2023, through PubMed, Web of Science, PsycINFO, and Scopus. A meta-analysis was performed on studies that reported physical and mental health outcomes more than 10 times. A semiquantitative analysis was performed on studies that reported those indicators less than 10 times. In addition, all physical and mental health outcome indicators were linked to ICF-CY codes.

A total of 43 studies were included in the systematic review, 13 of which were eligible for meta-analysis. Our meta-analysis results showed that levels of anxiety and depression significantly decreased after exercise intervention, with medium (Hedges’ g  = − 0.63, 95% CI [1.17, − 0.09], P  < 0.05) and large effect sizes (Hedges’ g  = − 1.03, 95% CI [− 1.94, − 0.12], P  < 0.05), respectively. The level of attention problem significantly decreased after exercise intervention, with a large effect size (Hedges’ g  = − 1.28, 95% CI [− 2.59, 0.04], P  = 0.06), but no statistical difference was observed. The level of motor skills significantly improved after exercise intervention with a large effect size (Hedges’ g  = 0.97, 95% CI [0.42, 1.51], P  < 0.01). The level of muscle strength significantly improved after exercise intervention, with a small effect size (Hedges’ g  = 0.37, 95% CI [0.05, 0.68], P  < 0.05). The included studies covered a total of 31 outcome indicators, which could be divided into 4 one-level classifications and 27 two-level classifications according to the ICF-CY framework. Among the outcome indicators, 21 (67.74%) were related to “physical functions”, 9 (29.03%) were related to “activities and participation”, and 1 (3.23%) was related to “body structures”.

This study confirmed that exercise could improve the physical and mental health in children and adolescents with ADHD. Regarding exercise intervention to improve the health of children and adolescents with ADHD, existing research has focused on verifying the immediate effect of intervention from the perspective of “physical functions”. However, there is a lack of in-depth exploration into changes in the dimensions of “body structures” and “activities and participation”, as well as the long-term intervention effects. Future studies should focus more on a holistic view of health that considers “body structures and functions” and “activities and participation”, which could ultimately favor comprehensive and long-term improvements in the health status of children and adolescents with ADHD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Abramovitch A, Goldzweig G, Schweiger A. Correlates of physical activity with intrusive thoughts, worry and impulsivity in adults with attention deficit/hyperactivity disorder: a cross-sectional pilot study. Isr J Psychiatry Relat Sci. 2013;50(1):47–54.

PubMed   Google Scholar  

Ahmed GM, Mohamed S. Effect of regular aerobic exercises on behavioral, cognitive and psychological response in patients with attention deficit-hyperactivity disorder. Life Sci J Acta Zhengzhou Univ Overseas Ed. 2011;8(2):392–7.

Google Scholar  

Ameri EA, Dehkhoda MR, Hemayattalab R. Bone mineral density changes after physical training and calcium intake in students with attention deficit and hyper activity disorders. Res Dev Disabil. 2012;33(2):594–9. https://doi.org/10.1016/j.ridd.2011.10.017 .

Article   Google Scholar  

American Psychiatric Association D, Association AP. Diagnostic and statistical manual of mental disorders: DSM-5. Washington, DC: American Psychiatric Association; 2013.

Book   Google Scholar  

Anand D, Colpo GD, Zeni G, Zeni CP, Teixeira AL. Attention-deficit/hyperactivity disorder and inflammation: what does current knowledge tell us? A systematic review. Front Psychiatry. 2017;8:287679. https://doi.org/10.3389/fpsyt.2017.00228 .

Ayuso-Mateos JL, Avila CC, Anaya C, Cieza A, Vieta E, Bipolar Disorders Core Sets E. Development of the International Classification of Functioning, Disability and Health core sets for bipolar disorders: results of an international consensus process. Disabil Rehabil. 2013;35(25):2138–46. https://doi.org/10.3109/09638288.2013.771708 .

Article   PubMed   Google Scholar  

Bauermeister JJ, Shrout PE, Chavez L, Rubio-Stipec M, Ramírez R, Padilla L, Anderson A, García P, Canino G. ADHD and gender: are risks and sequela of ADHD the same for boys and girls? J Child Psychol Psychiatry. 2007;48(8):831–9. https://doi.org/10.1111/j.1469-7610.2007.01750.x .

Benzing V, Schmidt M. Cognitively and physically demanding exergaming to improve executive functions of children with attention deficit hyperactivity disorder: a randomised clinical trial. BMC Pediatr. 2017;17:1–8. https://doi.org/10.1186/s12887-016-0757-9 .

Best JR. Effects of physical activity on children’s executive function: contributions of experimental research on aerobic exercise. Dev Rev. 2010;30(4):331–51. https://doi.org/10.1016/j.dr.2010.08.001 .

Article   PubMed   PubMed Central   Google Scholar  

Biederman J, Newcorn J, Sprich S. Comorbidity of attention-deficit hyperactivity disorder with conduct, depressive, anxiety, and other disorders. Am J Psychiatry. 1991;148(5):564–77.

Article   CAS   PubMed   Google Scholar  

Bigelow H, Gottlieb MD, Ogrodnik M, Graham JD, Fenesi B. The differential impact of acute exercise and mindfulness meditation on executive functioning and psycho-emotional well-being in children and youth with ADHD. Front Psychol. 2021;12:660845. https://doi.org/10.3389/fpsyg.2021.660845 .

Bolte S, Mahdi S, Coghill D, Gau SS, Granlund M, Holtmann M, Karande S, Levy F, Rohde LA, Segerer W, de Vries PJ, Selb M. Standardised assessment of functioning in ADHD: consensus on the ICF core sets for ADHD. Eur Child Adolesc Psychiatry. 2018;27(10):1261–81. https://doi.org/10.1007/s00787-018-1119-y .

Bolte S, Lawson WB, Marschik PB, Girdler S. Reconciling the seemingly irreconcilable: the WHO’s ICF system integrates biological and psychosocial environmental determinants of autism and ADHD The International Classification of Functioning (ICF) allows to model opposed biomedical and neurodiverse views of autism and ADHD within one framework. BioEssays. 2021;43(9):e2000254. https://doi.org/10.1002/bies.202000254 .

Bowling A, Slavet J, Miller DP, Haneuse S, Beardslee W, Davison K. Cybercycling effects on classroom behavior in children with behavioral health disorders: an RCT. Pediatrics. 2017;139(2):e20161985. https://doi.org/10.1542/peds.2016-1985 .

Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, Carty C, Chaput JP, Chastin S, Chou R, Dempsey PC, DiPietro L, Ekelund U, Firth J, Friedenreich CM, Garcia L, Gichu M, Jago R, Katzmarzyk PT, Lambert E, Leitzmann M, Milton K, Ortega FB, Ranasinghe C, Stamatakis E, Tiedemann A, Troiano RP, van der Ploeg HP, Wari V, Willumsen JF. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451–62. https://doi.org/10.1136/bjsports-2020-102955 .

Bustamante EE, Davis CL, Frazier SL, Rusch D, Fogg LF, Atkins MS, Marquez DX. Randomized controlled trial of exercise for ADHD and disruptive behavior disorders. Med Sci Sports Exerc. 2016;48(7):1397–407. https://doi.org/10.1249/mss.0000000000000891 .

Cerrillo-Urbina AJ, Garcia-Hermoso A, Sanchez-Lopez M, Pardo-Guijarro MJ, Santos Gomez JL, Martinez-Vizcaino V. The effects of physical exercise in children with attention deficit hyperactivity disorder: a systematic review and meta-analysis of randomized control trials. Child Care Health Dev. 2015;41(6):779–88. https://doi.org/10.1111/cch.12255 .

Chan Y-S, Jang J-T, Ho C-S. Effects of physical exercise on children with attention deficit hyperactivity disorder. Biomed J. 2022;45(2):265–70. https://doi.org/10.1016/j.bj.2021.11.011 .

Christiansen L, Beck MM, Bilenberg N, Wienecke J, Astrup A, Lundbye-Jensen J. Effects of exercise on cognitive performance in children and adolescents with ADHD: potential mechanisms and evidence-based recommendations. J Clin Med. 2019;8(6):841. https://doi.org/10.3390/jcm8060841 .

Cieza A, Chatterji S, Andersen C, Cantista P, Herceg M, Melvin J, Stucki G, de Bie R. ICF core sets for depression. J Rehabil Med. 2004;36:128–34. https://doi.org/10.1080/16501960410016055 .

Clark D, Seymour KE, Findling RL, Mostofsky SH. Subtle motor signs as a biomarker for mindful movement intervention in children with attention-deficit/hyperactivity disorder. J Dev Behav Pediatr. 2020;41(5):349–58. https://doi.org/10.1097/dbp.0000000000000795 .

Coghill DR, Seth S, Pedroso S, Usala T, Currie J, Gagliano A. Effects of methylphenidate on cognitive functions in children and adolescents with attention-deficit/hyperactivity disorder: evidence from a systematic review and a meta-analysis. Biol Psychiatry. 2014;76(8):603–15. https://doi.org/10.1016/j.biopsych.2013.10.005 .

Curatolo P, Paloscia C, D’Agati E, Moavero R, Pasini A. The neurobiology of attention deficit/hyperactivity disorder. Eur J Paediatr Neurol. 2009;13(4):299–304. https://doi.org/10.1016/j.ejpn.2008.06.003 .

Da Silva LA, Doyenart R, Salvan PH, Rodrigues W, Felipe Lopes J, Gomes K, Thirupathi A, Pinho RA, Silveira PC. Swimming training improves mental health parameters, cognition and motor coordination in children with Attention Deficit Hyperactivity Disorder. Int J Environ Health Res. 2020;30(5):584–92. https://doi.org/10.1080/09603123.2019.1612041 .

D’Agati E, Curatolo P, Mazzone L. Comorbidity between ADHD and anxiety disorders across the lifespan. Int J Psychiatry Clin Pract. 2019;23(4):238–44. https://doi.org/10.1080/13651501.2019.1628277 .

de Morton NA. The PEDro scale is a valid measure of the methodological quality of clinical trials: a demographic study. Aust J Physiother. 2009;55(2):129–33. https://doi.org/10.1016/s0004-9514(09)70043-1 .

de Schipper E, Lundequist A, Wilteus AL, Coghill D, de Vries PJ, Granlund M, Holtmann M, Jonsson U, Karande S, Levy F, Al-Modayfer O, Rohde L, Tannock R, Tonge B, Bölte S. A comprehensive scoping review of ability and disability in ADHD using the International Classification of Functioning, Disability and Health-Children and Youth Version (ICF-CY). Eur Child Adolesc Psychiatry. 2015;24(8):859–72. https://doi.org/10.1007/s00787-015-0727-z .

Diamond A. Attention-deficit disorder (attention-deficit/hyperactivity disorder without hyperactivity): a neurobiologic ally and behaviorally distinct disorder from attention-deficit/hyperactivity disorder (with hyperactivity). Dev Psychopathol. 2005;17(3):807–25. https://doi.org/10.1017/s0954579405050388 .

Dvorsky MR, Langberg JM. A review of factors that promote resilience in youth with ADHD and ADHD symptoms. Clin Child Fam Psychol Rev. 2016;19(4):368–91. https://doi.org/10.1007/s10567-016-0216-z .

Fietsam AC, Tucker JR, Kamath MS, Huang-Pollock C, Wang Z, Neely KA. Manual dexterity and strength and in young adults with and without Attention-Deficit/Hyperactivity Disorder (ADHD). Neurosci Lett. 2022;766:136349. https://doi.org/10.1016/j.neulet.2021.136349 .

Garcia-Gomez A, Rodriguez-Jimenez M, Guerrero-Barona E, Carlos Rubio-Jimenez J, Garcia-Pena I, Manuel M-M. Benefits of an experimental program of equestrian therapy for children with ADHD. Res Dev Disabil. 2016;59:176–85. https://doi.org/10.1016/j.ridd.2016.09.003 .

Gawrilow C, Stadler G, Langguth N, Naumann A, Boeck A. Physical activity, affect, and cognition in children with symptoms of ADHD. J Atten Disord. 2016;20(2):151–62. https://doi.org/10.1177/1087054713493318 .

Gelade K, Janssen TWP, Bink M, Twisk JWR, van Mourik R, Maras A, Oosterlaan J. A 6-month follow-up of an RCT on behavioral and neurocognitive effects of neurofeedback in children with ADHD. Eur Child Adolesc Psychiatry. 2018;27(5):581–93. https://doi.org/10.1007/s00787-017-1072-1 .

Goharinejad S, Goharinejad S, Hajesmaeel-Gohari S, Bahaadinbeigy K. The usefulness of virtual, augmented, and mixed reality technologies in the diagnosis and treatment of attention deficit hyperactivity disorder in children: an overview of relevant studies. BMC Psychiatry. 2022;22(1):4. https://doi.org/10.1186/s12888-021-03632-1 .

Golubchik P, Kodesh A, Weizman A. Attention-deficit/hyperactivity disorder and comorbid subsyndromal depression: what is the impact of methylphenidate on mood? Clin Neuropharmacol. 2013;36(5):141–5. https://doi.org/10.1097/WNF.0b013e31829eb204 .

Golubovic S, Milutinovic D, Golubovic B. Benefits of physical exercises in developing certain fitness levels in children with hyperactivity. J Psychiatr Ment Health Nurs. 2014;21(7):594–600. https://doi.org/10.1111/jpm.12091 .

Goulardins JB, Marques JCB, De Oliveira JA. Attention deficit hyperactivity disorder and motor impairment: a critical review. Percept Mot Skills. 2017;124(2):425–40. https://doi.org/10.1177/0031512517690607 .

Gu Q, Zou L, Loprinzi PD, Quan M, Huang T. Effects of open versus closed skill exercise on cognitive function: a systematic review. Front Psychol. 2019;10:467457. https://doi.org/10.3389/fpsyg.2019.01707 .

Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U, Lancet Physical Activity Series Working Group. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247–57. https://doi.org/10.1016/s0140-6736(12)60646-1 .

Halperin JM, Bedard A-CV, Curchack-Lichtin JT. Preventive interventions for ADHD: a neurodevelopmental perspective. Neurotherapeutics. 2012;9(3):531–41. https://doi.org/10.1007/s13311-012-0123-z .

Hattabi S, Forte P, Kukic F, Bouden A, Have M, Chtourou H, Sortwell A. A randomized trial of a swimming-based alternative treatment for children with attention deficit hyperactivity disorder. Int J Environ Res Public Health. 2022;19(23):16238. https://doi.org/10.3390/ijerph192316238 .

Hoogman M, Muetzel R, Guimaraes JP, Shumskaya E, Mennes M, Zwiers MP, et al. Brain imaging of the cortex in ADHD: a coordinated analysis of large-scale clinical and population-based samples. Am J Psychiatry. 2019;176(7):531–42. https://doi.org/10.1176/appi.ajp.2019.18091033 .

Hoza B, Smith AL, Shoulberg EK, Linnea KS, Dorsch TE, Blazo JA, Alerding CM, McCabe GP. A randomized trial examining the effects of aerobic physical activity on attention-deficit/hyperactivity disorder symptoms in young children. J Abnorm Child Psychol. 2015;43(4):655–67. https://doi.org/10.1007/s10802-014-9929-y .

Huang C-J, Huang C-W, Tsai Y-J, Tsai C-L, Chang Y-K, Hung T-M. A Preliminary examination of aerobic exercise effects on resting EEG in children with ADHD. J Atten Disord. 2017;21(11):898–903. https://doi.org/10.1177/1087054714554611 .

Huang H, Jin Z, He C, Guo S, Zhang Y, Quan M. Chronic exercise for core symptoms and executive functions in ADHD: a meta-analysis. Pediatrics. 2023;151(1):e2022057745. https://doi.org/10.1542/peds.2022-057745 .

Imai Y, Naruse H, Kurita R, Hiratani M, Tomori K. A hybrid intervention combining group-based physical activity and an individual goal-oriented approach for children with developmental disabilities: a pre-post intervention study. Int J Disabil Dev Educ. 2023;70(1):1–17. https://doi.org/10.1080/1034912x.2020.1856793 .

Inci SB, Ipci M, Ardic UA, Ercan ES. Psychiatric comorbidity and demographic characteristics of 1,000 children and adolescents with ADHD in Turkey. J Atten Disord. 2019;23(11):1356–67. https://doi.org/10.1177/1087054716666954 .

Jang B, Song J, Kim J, Kim S, Lee J, Shin HY, Kwon JY, Kim YH, Joung YS. Equine-assisted activities and therapy for treating children with attention-deficit/hyperactivity disorder. J Altern Complement Med. 2015;21(9):546–53. https://doi.org/10.1089/acm.2015.0067 .

Jarbin H, Hoglund K, Skarphedinsson G, Bremander A. Aerobic exercise for adolescent outpatients with persistent major depression: feasibility and acceptability of moderate to vigorous group exercise in a clinically referred sample. Clini Child Psychol Psychiatry. 2021;26(4):954–67. https://doi.org/10.1177/13591045211000782 .

Jeyanthi S, Arumugam N, Parasher RK. Effectiveness of structured exercises on motor skills, physical fitness and attention in children with ADHD compared to typically developing children—a pilot study. eNeurologicalSci. 2021;24:100357. https://doi.org/10.1016/j.ensci.2021.100357 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Jiang K, Xu Y, Li Y, Li L, Yang M, Xue P. How aerobic exercise improves executive function in ADHD children: a resting-state fMRI study. Int J Dev Neurosci. 2022;82(4):295–302. https://doi.org/10.1002/jdn.10177 .

Kamp CF, Sperlich B, Holmberg H-C. Exercise reduces the symptoms of attention-deficit/hyperactivity disorder and improves social behaviour, motor skills, strength and neuropsychological parameters. Acta Paediatr. 2014;103(7):709–14. https://doi.org/10.1111/apa.12628 .

Koyuncu A, Celebi F, Ertekin E, Kok BE, Tukel R. Attention deficit and hyperactivity in social anxiety disorder: relationship with trauma history and impulsivity. Attent Deficit Hyperact Disord. 2016;8(2):95–100. https://doi.org/10.1007/s12402-016-0189-2 .

Kulkarni M. Attention deficit hyperactivity disorder. Indian J Pediatr. 2015;82(3):267–71. https://doi.org/10.1007/s12098-014-1556-7 .

Larson K, Russ SA, Kahn RS, Halfon N. Patterns of comorbidity, functioning, and service use for US children With ADHD, 2007. Pediatrics. 2011;127(3):462–70. https://doi.org/10.1542/peds.2010-0165 .

Lessard SJ, MacDonald TL, Pathak P, Han MS, Coffey VG, Edge J, Rivas DA, Hirshman MF, Davis RJ, Goodyear LJ. JNK regulates muscle remodeling via myostatin/SMAD inhibition. Nat Commun. 2018;9:3030. https://doi.org/10.1038/s41467-018-05439-3 .

Li B, Ng K, Tong X, Zhou X, Ye J, Yu JJ. Physical activity and mental health in children and youth during COVID-19: a systematic review and meta-analysis. Child Adolesc Psychiatry Ment Health. 2023;17(1):92. https://doi.org/10.1186/s13034-023-00629-4 .

Liang X, Li R, Wong SHS, Sum RKW, Sit CHP. The impact of exercise interventions concerning executive functions of children and adolescents with attention-deficit/hyperactive disorder: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2021;18(1):68. https://doi.org/10.1186/s12966-021-01135-6 .

Liang X, Qiu H, Wang P, Sit CHP. The impacts of a combined exercise on executive function in children with ADHD: a randomized controlled trial. Scand J Med Sci Sports. 2022;32(8):1297–312. https://doi.org/10.1111/sms.14192 .

Liang X, Li R, Wong SHS, Sum RKW, Wang P, Yang BR, Sit CHP. Physical activity and executive function in children with ADHD: the mediating role of sleep. Front Pediatr. 2022;9:775589. https://doi.org/10.3389/fped.2021.775589 .

Lotfi Y, Rezazadeh N, Moossavi A, Haghgoo HA, Rostami R, Bakhshi E, Badfar F, Moghadam SF, Sadeghi-Firoozabadi V, Khodabandelou Y. Preliminary evidence of improved cognitive performance following vestibular rehabilitation in children with combined ADHD (cADHD) and concurrent vestibular impairment. Auris Nasus Larynx. 2017;44(6):700–7. https://doi.org/10.1016/j.anl.2017.01.011 .

Lubans D, Richards J, Hillman C, Faulkner G, Beauchamp M, Nilsson M, Kelly P, Smith J, Raine L, Biddle S. Physical activity for cognitive and mental health in youth: a systematic review of mechanisms. Pediatrics. 2016;138(3):e20161642. https://doi.org/10.1542/peds.2016-1642 .

Ludyga S, Gerber M, Mucke M, Brand S, Weber P, Brotzmann M, Puhse U. The acute effects of aerobic exercise on cognitive flexibility and task-related heart rate variability in children with ADHD and healthy controls. J Atten Disord. 2020;24(5):693–703. https://doi.org/10.1177/1087054718757647 .

Ludyga S, Mucke M, Leuenberger R, Bruggisser F, Pühse U, Gerber M, Capone-Mori A, Keutler C, Brotzmann M, Weber P. Behavioral and neurocognitive effects of judo training on working memory capacity in children with ADHD: a randomized controlled trial. Neuroimage Clin. 2022;36:103156. https://doi.org/10.1016/j.nicl.2022.103156 .

Lufi D, Parish-Plass J. Sport-based group therapy program for boys with ADHD or with other behavioral disorders. Child Fam Behav Ther. 2011;33(3):217–30. https://doi.org/10.1080/07317107.2011.596000 .

Mahdi S, Viljoen M, Massuti R, Selb M, Almodayfer O, Karande S, de Vries PJ, Rohde L, Bolte S. An international qualitative study of ability and disability in ADHD using the WHO-ICF framework. Eur Child Adolesc Psychiatry. 2017;26(10):1219–31. https://doi.org/10.1007/s00787-017-0983-1 .

Mahdi S, Ronzano N, Knuppel A, Dias JC, Albdah A, Chien-Ho L, Almodayfer O, Bluschke A, Karande S, Huang HL, Christiansen H, Granlund M, de Vries PJ, Coghill D, Tannock R, Rohde L, Bölte S. An international clinical study of ability and disability in ADHD using the WHO-ICF framework. Eur Child Adolesc Psychiatry. 2018;27(10):1305–19. https://doi.org/10.1007/s00787-018-1124-1 .

Majorek M, Tuchelmann T, Heusser P. Therapeutic Eurythmy-movement therapy for children with attention deficit hyperactivity disorder (ADHD): a pilot study. Complement Ther Nurs Midwifery. 2004;10(1):46–53. https://doi.org/10.1016/s1353-6117(03)00087-8 .

Messler CF, Holmberg HC, Sperlich B. Multimodal therapy involving high-intensity interval training improves the physical fitness, motor skills, social behavior, and quality of life of boys with ADHD: a randomized controlled study. J Atten Disord. 2018;22(8):806–12. https://doi.org/10.1177/1087054716636936 .

Mohammadi MR, Zarafshan H, Khaleghi A, Ahmadi N, Hooshyari Z, Mostafavi SA, Ahmadi A, Alavi SS, Shakiba A, Salmanian M. Prevalence of ADHD and its comorbidities in a population-based sample. J Atten Disord. 2021;25(8):1058–67. https://doi.org/10.1177/1087054719886372 .

Neely KA, Chennavasin AP, Yoder A, Williams GKR, Loken E, Huang-Pollock CL. Memory-guided force output is associated with self-reported ADHD symptoms in young adults. Exp Brain Res. 2016;234(11):3203–12. https://doi.org/10.1007/s00221-016-4718-1 .

Neely KA, Wang P, Chennavasin AP, Samimy S, Tucker J, Merida A, Perez-Edgar K, Huang-Pollock C. Deficits in inhibitory force control in young adults with ADHD. Neuropsychologia. 2017;99:172–8. https://doi.org/10.1016/j.neuropsychologia.2017.03.012 .

O’Connor BC, Fabiano GA, Waschbusch DA, Belin PJ, Gnagy EM, Pelham WE, Greiner AR, Roemmich JN. Effects of a summer treatment program on functional sports outcomes in young children with ADHD. J Abnorm Child Psychol. 2014;42(6):1005–17. https://doi.org/10.1007/s10802-013-9830-0 .

Oh Y, Joung Y-S, Jang B, Yoo JH, Song J, Kim J, Kim K, Kim S, Lee J, Shin HY, Kwon JY, Kim YH, Jeong B. Efficacy of hippotherapy versus pharmacotherapy in attention-deficit/hyperactivity disorder: a randomized clinical trial. J Altern Complement Med. 2018;24(5):463–71. https://doi.org/10.1089/acm.2017.0358 .

Pan C-Y, Chu C-H, Tsai C-L, Lo S-Y, Cheng Y-W, Liu Y-J. A racket-sport intervention improves behavioral and cognitive performance in children with attention-deficit/hyperactivity disorder. Res Dev Disabil. 2016;57:1–10. https://doi.org/10.1016/j.ridd.2016.06.009 .

Pan C-Y, Chang Y-K, Tsai C-L, Chu C-H, Cheng Y-W, Sung M-C. Effects of physical activity intervention on motor proficiency and physical fitness in children with ADHD: an exploratory study. J Atten Disord. 2017;21(9):783–95. https://doi.org/10.1177/1087054714533192 .

Park M-S, Byun K-W, Park Y-K, Kim M-H, Jung S-H, Kim H. Effect of complex treatment using visual and auditory stimuli on the symptoms of attention deficit/hyperactivity disorder in children. J Exerc Rehabil. 2013;9(2):316–25. https://doi.org/10.12965/jer.130017 .

Patel H, Alkhawam H, Madanieh R, Shah N, Kosmas CE, Vittorio TJ. Aerobic vs anaerobic exercise training effects on the cardiovascular system. World J Cardiol. 2017;9(2):134–8. https://doi.org/10.4330/wjc.v9.i2.134 .

Posner J, Polanczyk GV, Sonuga-Barke E. Attention-deficit hyperactivity disorder. Lancet. 2020;395(10222):450–62. https://doi.org/10.1016/s0140-6736(19)33004-1 .

Qiu H, Liang X, Wang P, Zhang H, Shum DHK. Efficacy of non-pharmacological interventions on executive functions in children and adolescents with ADHD: a systematic review and meta-analysis. Asian J Psychiatry. 2023;87:103692. https://doi.org/10.1016/j.ajp.2023.103692 .

Rauf A, Badoni H, Abu-Izneid T, Olatunde A, Rahman MM, Painuli S, Semwal P, Wilairatana P, Mubarak MS. Neuroinflammatory markers: key indicators in the pathology of neurodegenerative diseases. Molecules. 2022;27(10):3194. https://doi.org/10.3390/molecules27103194 .

Rodrigues JMSM, Mestre MICP, Matos LC, Machado JP. Effects of taijiquan and qigong practice over behavioural disorders in school-age children: a pilot study. J Bodyw Mov Ther. 2019;23(1):11–5. https://doi.org/10.1016/j.jbmt.2018.01.019 .

Rodriguez-Ayllon M, Cadenas-Sánchez C, Estévez-López F, Muñoz NE, Mora-Gonzalez J, Migueles JH, Molina-García P, Henriksson H, Mena-Molina A, Martínez-Vizcaíno V, Catena A, Löf M, Erickson KI, Lubans DR, Ortega FB, Esteban-Cornejo I. Role of physical activity and sedentary behavior in the mental health of preschoolers, children and adolescents: a systematic review and meta-analysis. Sports Med. 2019;49(9):1383–410. https://doi.org/10.1007/s40279-019-01099-5 .

Sabzi AH, Dana A, Salehian MH, Yekta HS. The effect of water treadmill exercise on children with attention deficit hyperactivity disorder. Int J Pediatr-Mashhad. 2021;9(6):13671–81. https://doi.org/10.22038/ijp.2021.57015.4466 .

Sallis JF, Prochaska JJ, Taylor WC. A review of correlates of physical activity of children and adolescents. Med Sci Sports Exerc. 2000;32(5):963–75. https://doi.org/10.1097/00005768-200009000-00012 .

Seddio K, Pollack D, Crawford H, Lewis J. Attention-deficit/hyperactivity disorder (ADHD) and anxiety during the COVID-19 pandemic: implications for internalizing behaviors in college students. J Am Coll Health. 2022. https://doi.org/10.1080/07448481.2022.2069469 .

Shema-Shiratzky S, Brozgol M, Cornejo-Thumm P, Geva-Dayan K, Rotstein M, Leitner Y, Hausdorff JM, Mirelman A. Virtual reality training to enhance behavior and cognitive function among children with attention-deficit/hyperactivity disorder: brief report. Dev Neurorehabil. 2019;22(6):431–6. https://doi.org/10.1080/17518423.2018.1476602 .

Smith AL, Hoza B, Linnea K, McQuade JD, Tomb M, Vaughn AJ, Shoulberg EK, Hook H. Pilot physical activity intervention reduces severity of ADHD symptoms in young children. J Atten Disord. 2013;17(1):70–82. https://doi.org/10.1177/1087054711417395 .

Soori R, Goodarzvand F, Akbarnejad A, Effatpanah M, Ramezankhani A, Teixeira AL, Ghram A. Effect of high-intensity interval training on clinical and laboratory parameters of adolescents with attention deficit hyperactivity disorder. Sci Sports. 2020;35(4):207–15. https://doi.org/10.1016/j.scispo.2019.08.002 .

Sun W, Yu M, Zhou X. Effects of physical exercise on attention deficit and other major symptoms in children with ADHD: a meta-analysis. Psychiatry Res. 2022;311:114509. https://doi.org/10.1016/j.psychres.2022.114509 .

Sun F, Chow GC-C, Yu CC-W, Ho Y-F, Liu D, Wong SH-S, Siu PM-F, Cooper SB, Jenkins D. Effect of game-based high-intensity interval training program on the executive function of children with ADHD: protocol of a randomized controlled trial. PLoS ONE. 2022;17(7):e0272121. https://doi.org/10.1371/journal.pone.0272121 .

Taylor A, Kong C, Zhang Z, Herold F, Ludyga S, Healy S, Gerber M, Cheval B, Pontifex M, Kramer AF, Chen S, Zhang Y, Müller NG, Tremblay MS, Zou L. Associations of meeting 24-h movement behavior guidelines with cognitive difficulty and social relationships in children and adolescents with attention deficit/hyperactive disorder. Child Adolesc Psychiatry Ment Health. 2023;17(1):42. https://doi.org/10.1186/s13034-023-00588-w .

Verret C, Guay M-C, Berthiaume C, Gardiner P, Beliveau L. A physical activity program improves behavior and cognitive functions in children with ADHD: an exploratory study. J Atten Disord. 2012;16(1):71–80. https://doi.org/10.1177/1087054710379735 .

Wang S. Yoga for emotional control in children with ADHD Yoga no controle emocional em crianÇas com tdah Yoga para el control emocional en niÑos con tdah. Revista Brasileira de Medicina do Esporte. 2023;2023(29):e2022_0391. https://doi.org/10.1590/1517-8692202329012022_0391 .

Weerdmeester J, Cima M, Granic I, Hashemian Y, Gotsis M. A feasibility study on the effectiveness of a full-body videogame intervention for decreasing attention deficit hyperactivity disorder symptoms. Games Health J. 2016;5(4):258–69. https://doi.org/10.1089/g4h.2015.0103 .

Wigal SB, Nemet D, Swanson JM, Regino R, Trampush J, Ziegler MG, Cooper DM. Catecholamine response to exercise in children with attention deficit hyperactivity disorder. Pediatr Res. 2003;53(5):756–61. https://doi.org/10.1203/01.Pdr.0000061750.71168.23 .

World Health Organization. Constitution of the World Health Organization. 1948. https://www.afro.who.int/publications/constitution-world-health-organization/ . Accessed 27 Aug 2023.

World Health Organization. International classification of functioning, disability, and health: children & youth version: ICF-CY. 2007. https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health . Accessed 27 Aug 2023.

Wymbs FA, Wymbs B, Margherio S, Burd K. The effects of high intensity versus low intensity exercise on academic productivity, mood, and behavior among youth with and without ADHD. J Child Fam Stud. 2021;30(2):460–73. https://doi.org/10.1007/s10826-020-01880-5 .

Yen C-F, Chou W-J, Liu T-L, Yang P, Hu H-F. The association of Internet addiction symptoms with anxiety, depression and self-esteem among adolescents with attention-deficit/hyperactivity disorder. Compr Psychiatry. 2014;55(7):1601–8. https://doi.org/10.1016/j.comppsych.2014.05.025 .

Yu JJ, Burnett AF, Sit CH. Motor skill interventions in children with developmental coordination disorder: a systematic review and meta-analysis. Arch Phys Med Rehabil. 2018;99(10):2076–99. https://doi.org/10.1016/j.apmr.2017.12.009 .

Zang Y. Impact of physical exercise on children with attention deficit hyperactivity disorders: evidence through a meta-analysis. Medicine. 2019;98(46):e17980. https://doi.org/10.1097/md.0000000000017980 .

Zhu FL, Zhu XT, Bi XY, Kuang DQ, Liu BY, Zhou JY, Yang YM, Ren YC. Comparative effectiveness of various physical exercise interventions on executive functions and related symptoms in children and adolescents with attention deficit hyperactivity disorder: a systematic review and network meta-analysis. Front Public Health. 2023;11:1133727. https://doi.org/10.3389/fpubh.2023.1133727 .

Ziereis S, Jansen P. Effects of physical activity on executive function and motor performance in children with ADHD. Res Dev Disabil. 2015;38:181–91. https://doi.org/10.1016/j.ridd.2014.12.005 .

Download references

Acknowledgements

We thank the editors and reviewers of the Journal of Science in Sport and Exercise for supporting open science and hard work.

No financial or material support of any kind was received for the work described in this article.

Author information

Lili Feng and Bowen Li share the first authorship. Bowen Li and Zhenjun Tian are the co-corresponding authors.

Authors and Affiliations

College of Education, Department of Sports Science, Zhejiang University, Hangzhou, 310030, China

Lili Feng, Bowen Li & Su Sean Yong

Institute of Sports Biology, College of Physical Education, Shaanxi Normal University, Xi’an, 710119, China

Zhenjun Tian

You can also search for this author in PubMed   Google Scholar

Contributions

Lili Feng and Zhenjun Tian conceptualized this work; Lili Feng, Bowen Li, and Su Sean Yong conducted literature searches, evidence synthesis, and data analysis; Lili Feng, Bowen Li, and Su Sean Yong performed records screening and data extraction; Lili Feng wrote the manuscript; Su Sean Yong critically edited the manuscript. All authors have contributed to reading and approving the final manuscript. Lili Feng and Bowen Li share the first authorship. Bowen Li and Zhenjun Tian are the co-corresponding authors.

Corresponding authors

Correspondence to Bowen Li or Zhenjun Tian .

Ethics declarations

Conflict of interest.

The authors have no financial or proprietary interests in any material discussed in this article. No conflict different exists in the submission of this manuscript, and manuscript is approved by all authors for publication.We declare that the work described was original research that has not been published previously and is not under consideration for publication elsewhere, in whole or in part.

Ethics Approval and Data Availability

All data were obtained from publicly available websites and there are no ethical issues involved in the analysis or interpretation of the data. Since this study is a systematic review of published research, it did not require ethical approval.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 90 KB)

Supplementary file2 (docx 47 kb), rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Feng, L., Li, B., Yong, S.S. et al. Effects of Exercise Intervention on Physical and Mental Health of Children and Adolescents with Attention-Deficit/Hyperactivity Disorder: A Systematic Review and Meta-analysis Based on ICF-CY. J. of SCI. IN SPORT AND EXERCISE (2024). https://doi.org/10.1007/s42978-024-00295-8

Download citation

Received : 07 December 2023

Accepted : 24 May 2024

Published : 08 August 2024

DOI : https://doi.org/10.1007/s42978-024-00295-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Exercise intervention
• Physical health
• Mental health
• Find a journal
• Publish with us
• Track your research

IJRAR- International Journal of Research and Analytical Reviews

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations
• Deepak Kumar
• Ketan Mittal
• Suresh Kataria
• Seeone Sharma

• A-S. Alghamdi

• J CLEAN PROD
• Zhaojun Kou
• COMP MATER SCI
• P.S. Noori Banu

• SURF COAT TECH

• Chi-Ya Huang

• B.Sudheer Premkumar

• M.A. Gaffar

• D. John Basha
• K.C. Varaprasad
• J MECH BEHAV BIOMED
• Sertan Ozan

• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Research on the feasibility analysis of old renovation project model construction of sponge city integrating BP neural network and MIKE model

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, view options, recommendations, research on the sponge city smart platform of wenzhou high-speed railway new city.

Using the new technology of internet of things, big data and cloud computing, an intelligent sponge city platform with the functions of monitoring, data transmission, early warning and management is built. The hardware system based on NB-IoT and STM32 and ...

BP Neural Network-Based Research of University Science Research Capability Evaluation

Based on the substance of University Science Research Capability (USRC) and the highly self-organized, self-adapted and self-learned characteristics of Back Propagation (BP) Neural Network, the paper conducts a research on evaluation of USRC, in which ...

Soil Water Prediction of Moving Dune Based on BP Neural Network Model in Northwest Liaoning Sandy Land

With the moving dune in sandy land of Northwest Liaoning province as the research object, its water variation in soil was simulated and studied based on a BP Neural Network model. With principal meteorologic factors that affect soil water, such as ...

Information

Published in.

Inderscience Publishers

Geneva 15, Switzerland

Publication History

Author tags.

• urban waterlogging
• sponge city
• BP neural network
• MIKE flood model
• Research-article

Contributors

Other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View options

Login options.

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Share this publication link.

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

Log in using your username and password

• Search More Search for this keyword Advanced search
• Latest content
• Current issue
• For authors
• New editors
• BMJ Journals

You are here

• Volume 57, Issue 16
• Exercise as medicine for depressive symptoms? A systematic review and meta-analysis with meta-regression
• Article Text
• Article info
• Citation Tools
• Rapid Responses
• Article metrics

• http://orcid.org/0000-0001-9270-7027 Andreas Heissel 1 ,
• http://orcid.org/0000-0001-9172-9718 Darlene Heinen 1 ,
• http://orcid.org/0000-0003-4136-9704 Luisa Leonie Brokmeier 1 ,
• Nora Skarabis 1 ,
• http://orcid.org/0000-0001-8693-2949 Maria Kangas 2 ,
• http://orcid.org/0000-0002-4592-8625 Davy Vancampfort 3 ,
• http://orcid.org/0000-0001-7387-3791 Brendon Stubbs 4 ,
• http://orcid.org/0000-0002-0618-2752 Joseph Firth 5 ,
• http://orcid.org/0000-0002-5779-7722 Philip B Ward 6 ,
• http://orcid.org/0000-0002-8984-4941 Simon Rosenbaum 7 ,
• http://orcid.org/0000-0002-0599-2403 Mats Hallgren 8 ,
• http://orcid.org/0000-0002-5190-4515 Felipe Schuch 9 , 10 , 11
• 1 Social and Preventive Medicine, Department of Sports and Health Sciences, Intra faculty unit "Cognitive Sciences" , Faculty of Human Science and Faculty of Health Sciences Brandenburg, Research Area Services Research and e-Health , Potsdam , Brandenburg , Germany
• 2 School of Psychological Sciences, Centre for Emotional Health , Macquarie University , Sydney , New South Wales , Australia
• 3 Department of Rehabilitation Sciences , University of Leuven , Leuven , Belgium
• 4 Physiotherapy Department , South London and Maudsley NHS Foundation Trust; Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Kings College London , London , UK
• 5 Division of Psychology and Mental Health , University of Manchester, Manchester Academic Health Science Centre, Manchester, UK; NICM Health Research Institute, Western Sydney University, Westmead Australia; Greater Manchester Mental Health NHS Foundation Trust, Manchester Academic Health Science Centre , Manchester , UK
• 6 Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney New South Wales, Australia; Ingham Institute of Applied Medical Research , UNSW , Liverpool BC , New South Wales , Australia
• 7 School of Psychiatry , UNSW , Sydney , New South Wales , Australia
• 8 Epidemiology of Psychiatric Conditions, Substance use and Social Environment (EPiCSS) , Department of Public Health Sciences, Karolinska Institute Solna , Solna , Sverige , Sweden
• 9 Department of Sports Methods and Techniques , Federal University of Santa Maria , Santa Maria , Brazil
• 10 Institute of Psychiatry , Federal University of Rio de Janeiro , Rio de Janeiro , Brazil
• 11 Universidad Autónoma de Chile , Providencia , Chile
• Correspondence to Dr Andreas Heissel, Sport and Health Sciences, Social and Preventive Medicine, Faculty of Human Sciences, University of Potsdam, 14476 Potsdam, Germany; andreas.heissel{at}uni-potsdam.de

Objective To estimate the efficacy of exercise on depressive symptoms compared with non-active control groups and to determine the moderating effects of exercise on depression and the presence of publication bias.

Design Systematic review and meta-analysis with meta-regression.

Data sources The Cochrane Central Register of Controlled Trials, PubMed, MEDLINE, Embase, SPORTDiscus, PsycINFO, Scopus and Web of Science were searched without language restrictions from inception to 13 September2022 (PROSPERO registration no CRD42020210651).

Eligibility criteria for selecting studies Randomised controlled trials including participants aged 18 years or older with a diagnosis of major depressive disorder or those with depressive symptoms determined by validated screening measures scoring above the threshold value, investigating the effects of an exercise intervention (aerobic and/or resistance exercise) compared with a non-exercising control group.

Results Forty-one studies, comprising 2264 participants post intervention were included in the meta-analysis demonstrating large effects (standardised mean difference (SMD)=−0.946, 95% CI −1.18 to −0.71) favouring exercise interventions which corresponds to the number needed to treat (NNT)=2 (95% CI 1.68 to 2.59). Large effects were found in studies with individuals with major depressive disorder (SMD=−0.998, 95% CI −1.39 to −0.61, k=20), supervised exercise interventions (SMD=−1.026, 95% CI −1.28 to −0.77, k=40) and moderate effects when analyses were restricted to low risk of bias studies (SMD=−0.666, 95% CI −0.99 to −0.34, k=12, NNT=2.8 (95% CI 1.94 to 5.22)).

Conclusion Exercise is efficacious in treating depression and depressive symptoms and should be offered as an evidence-based treatment option focusing on supervised and group exercise with moderate intensity and aerobic exercise regimes. The small sample sizes of many trials and high heterogeneity in methods should be considered when interpreting the results.

• Public health

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

https://doi.org/10.1136/bjsports-2022-106282

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

WHAT IS ALREADY KNOWN?

Depression is the leading cause of disability worldwide with potentially increasing prevalence since the COVID-19 pandemic, yet more than two thirds of adults diagnosed with depression remain untreated.

Exercise is an efficacious treatment option for reducing depressive symptoms for individuals with depression.

However, evidence reported by meta-analyses reveals heterogeneous effects and is not up to date.

WHAT ARE THE NEW FINDINGS?

This methodologically sound systematic review and meta-analysis with meta-regression is the largest synthesis of the effect of exercise on major depressive disorder (MDD) and depressive symptoms covering 41 included studies, accounting for 2.264 adult participants postintervention.

Results show moderate to large effects of exercise on depressive symptoms even when limiting the analysis to low risk of bias studies or only MDD, although high heterogeneity among the studies was addressed with meta-regression.

Non-inferiority trials indicate that exercise is non-inferior to current first line treatments, and evidence that exercise is effective at long-term follow-ups are needed to clarify the identified evidence gaps.

Introduction

Depression is a prevalent and disabling disorder associated with reduced quality of life, medical comorbidity and mortality. 1 2 Over 300 million people live with depressive disorder, equating to approximately 4.4% of the world’s population. 3 The prevalence of depression has increased during the COVID-19 pandemic 4–7 by an estimated 27.6%, 7 highlighting the need for appropriate, accessible and cost-effective treatment options. 8

Currently, recommended treatments include psychotherapy and antidepressant medication (or a combination of both). 9 However, psychotherapy achieves remission rates of only 50% while typically being cost-intensive. 10 Side effects and relapses from antidepressant medication commonly occur 11 as can withdrawal symptoms. 12 Importantly, about two thirds of adults with depression do not receive adequate treatment. 13 Untreated depression often leads to intensification of the illness including the development of comorbidities resulting in even higher costs for society. 14 This attests to the need for rapid and readily available alternative treatment options.

Exercise has been recommended as an adjunct treatment for depression by both the WHO 15 and National Institute for Health and Care Excellence (NICE) guidelines. 16 Evidence for these recommendations included results from multiple meta-analyses investigating the antidepressant effect of exercise in people with depression. 17–20 However, some of these meta-analyses 18 21 found moderate, weak or no effects of exercise while others reported large effects. 17 19 20 These mixed results stem from methodological and conceptual differences regarding inclusion criteria and analytical approaches. For example, some studies focused on individuals with a diagnosis of major depressive disorder (MDD) while excluding studies that evaluated the presence of depression based on validated screening measures. 18 Others 17 investigated the effect of exercise alone or as a complementary treatment for depression to pharmacological therapy for studies published from 2003 to 2019. Further, some reviews included studies where patients also received exercise interventions 21 in the control groups. This creates the potential for bias 22 as even light intensity exercise can exert antidepressant effects. Importantly, a cause for concern has been raised in several reviews that exercise does not have a significant effect when restricted to ‘low risk of bias’ randomised controlled trials (RCTs) . 18 21 Therefore, extant meta-analyses have failed to provide convincing evidence to enable clinicians globally to implement exercise as an evidence-based effective treatment option for depression. One meta-analysis 20 addressed these methodological shortcomings by focusing on studies that included samples with depression using cut-offs on validated screening instruments and samples with MDD diagnosis assessed with diagnostic tools and including only studies that compared exercise versus non-active controls. The authors excluded trials comparing different exercise regimens. However, a large volume of studies has been published within the last 5 years, requiring an updated meta-analysis on the antidepressant effects of exercise, while addressing the shortcomings of previous reviews.

The objective of this meta‐analysis was to update the current evidence on the effects of exercise in reducing depressive symptoms in adults with clinically elevated levels of depression including MDD and dysthymia, comparing exercise with non-exercising control groups. Additionally, we aimed to investigate the potential moderators of the antidepressant effects of exercise, and the presence of publication bias.

Search strategy and selection criteria

This systematic review and meta-analysis was registered in the International Prospective Register of Systematic Reviews (PROSPERO) with the protocol number CRD42020210651. The PRISMA Statement was followed 23 in its updated version 24 additionally considering the PERSiST guidance (implementing PRISMA in Exercise, Rehabilitation, Sport medicine and SporTs science). 25

To structure the eligibility criteria, the PICOS (Patient/Population; Intervention; Comparison, Outcome; Study design) approach was used. 26 Eligible for this meta-analysis included studies that: (1) Investigated participants aged 18 years or older with a primary diagnosis of MDD or dysthymia defined by the Research Diagnostic Criteria, 24 Diagnostic and Statistical Manual of Mental Disorders (DSM-IV or DSM-5) 27 or International Classification of Diseases (ICD-10) 28 or adults with depressive symptoms determined by validated screening measures scoring above the threshold value (eg, Beck Depression Inventory (BDI) or Hamilton Rating Scale for Depression (HAM-D)). 29 30 If scales did not have validated cut-offs, the cut-off used by the author was accepted. (2) Investigated an exercise intervention in the treatment of depression, where exercise was defined as planned, structured, repetitive and purposive physical activity with the purpose to improve or maintain physical fitness. 31 Studies using yoga, tai chi or other mind-body activities were excluded, because the focus of such mind-body interventions are behavioural techniques that include, but not limited to, deep breathing, meditation/mindfulness and self-awareness. 32 (3) Included a non-exercising control group, such as usual-care, wait-list control conditions or placebo pills. Studies with any other exercise intervention (such as stretching or low-dose exercise) as a comparator were excluded as well as control and intervention groups commencing standardised interventions (eg, psychotherapy, medication) at the beginning of the intervention even if this was applied to both intervention and control groups (eg, starting medication treatment at the beginning of the intervention in both groups). However, ongoing treatments started at least 3 months before intervention initiation were included. (4) Examined the pre-post effects of exercise interventions on depressive symptoms using a validated depression scale. (5) Were RCTs and were published in peer-reviewed journals or as part of a dissertation. Conference proceedings were not included.

An electronic search of the following databases was conducted: Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, MEDLINE, Embase, SPORTDiscus and PsycINFO without any (eg, language or date) restrictions from inception to 13 September 2022. The search used a range of relevant terms to capture all potentially eligible results relating to exercise interventions for depressive symptoms (for the full list of search terms, see online supplemental text 1 ). Duplicate references were removed electronically and manually. To identify unpublished or ongoing studies, clinical trials.gov ( www.clinicaltrials.gov ) was searched. Additionally, reference lists of all eligible articles of recent reviews investigating the effectiveness of exercise versus control were screened to identify potentially eligible articles. All manuscripts were reviewed by at least two independent reviewers. Three reviewers (NS/LLB, DH) independently determined potentially eligible articles meeting the inclusion criteria using the titles and abstracts. Three independent reviewers (NS/LLB, DH) then applied the eligibility criteria after obtaining the full texts and generated a final list of included articles through consensus. If full-texts were not available, study authors were contacted to provide them. Five investigators (NS/ LLB, DH, FS, AH) judged article eligibility with any disagreements resolved through discussion.

Supplemental material

Data extraction.

Data extraction was done by three reviewers (NS/ LLB, DH) independently. A systematic extraction form was used for each article to collect the following data: (1) sample description (eg, sample size, mean age of participants); (2) intervention features (eg, type of exercise, length of trial); (3) methodological factors (eg, risk of bias, instruments used for diagnosis and symptom assessment); (4) effects on depressive symptoms (eg, changes in total depressive symptoms scored before and after intervention). For further information of extracted data see online supplemental tables 1, 4 and 5 .

Primary outcome

The primary outcome was the mean change in depressive symptoms in the exercise compared with the control group from baseline to postintervention. The primary outcome proposed by the authors was selected if two or more instruments were used.

Study quality assessment

Selected studies were assessed by three independent authors (NS/ LLB, DH) given an overall estimation of risk of bias (ie, low risk, some concerns or high risk) according to the revised Cochrane risk-of-bias tool for randomised trials (RoB2). 33 According to RoB2, the following domains were considered for the assessment of risk of bias: randomisation process, deviations from intended interventions, missing outcome data, measurement of the outcome and selection of the reported result (see online supplemental table 3 ).

Data-analysis

A random effects meta-analysis was calculated due to expected heterogeneity. The standardised mean difference (SMD) and 95% CIs were used as the effect size (ES) measure. The SMD was calculated using the difference from pre to post intervention 34 with correlations of 0.7 between the exercise and the control group. All results were calculated on an intention-to-treat basis. Heterogeneity was calculated using the I 2 . 35 Sensitivity analyses with pre-post-correlations of 0.6 and 0.8 were calculated to investigate changes in effect with less or more conservative values. Sensitivity analyses were further calculated excluding one study due to unequal distribution of psychotherapy among the intervention and control group and excluding studies with high risk for bias. Potential moderators (see table 1 ) of the antidepressant effects of exercise were investigated using linear meta-regression analyses for all studies and, separately, for studies including only patients with a diagnosis of MDD and/or dysthymia. Meta-regression assumptions were tested in JASP. Subgroup analyses were calculated to estimate the effect across depression classification, risk of bias, differing control conditions, intensity of exercise, exercise type, exercising in a group or individually, sample sizes and supervision (by different supervisors). We also calculated the mean difference (MD) on studies that assessed depressive symptoms using the Hamilton scale for depression or the BDI separately. Significance level was set at 0.05. 36 Publication bias was assessed with visual inspection of funnel plots and with the Begg-Mazumdar Kendall’s tau 37 and Egger bias test. 38 Whenever significant, the Duval and Tweedie Trim and Fill was applied. 39 Fail safe number of negative studies that would be required to nullify (ie, make p>0.05) the ES were calculated. 40 All analyses were performed using Comprehensive Meta-Analysis software, 38 and number needed to treat (NNT) 41 analyses were calculated using Lenhard and Lenhard 42 with the formula of converting Cohens’ d to NNT from Furukawa and Leucht. 43 Additionally, studies reporting (severe) adverse events and side effects were listed.

• View inline

Subgroup meta-analysis of studies included in the quantitative analyses

Search results

Searching of databases yielded 15 734 studies and an additional 84 studies were identified through other sources. Following removal of duplicates, 7100 potentially eligible studies remained for which abstracts were screened. At full text stage, 207 studies were reviewed and 166 removed because they failed to meet inclusion criteria (see online supplemental table 2 for references and exclusion reasons). The remaining 41 studies were included in the review and quantitative synthesis (see figure 1 ).

• Download figure
• Open in new tab
• Download powerpoint

Flowchart of study selection. Flowchart adapted from the PRISMA 2020 statement. 42 RCT, randomised controlled trial.

Study characteristics

In total, 2544 participants are included in the review and 2264 completed treatments (post-treatment n) and were included in the meta-analytical calculations, 1227 in intervention groups and 1037 in control groups. Twenty-one studies assessed depressive symptoms, 44–64 while MDD was diagnosed in 20 studies. 65–84 Percentage of females ranged from 26% to 100%, mean age from 18.8 to 87.9 years. Of the 41 included RCTs, studies originated from North and South America, Europe, Asia and Australia. See online supplemental table 1 for characteristics of selected studies (further characteristics are summarised in online supplemental tables 4 and 5 ).

Risk of bias

Risk of bias assessment revealed 12 studies to be rated of low risk of bias, 49 58 60 65 66 68–70 74 79 80 82 while 7 were rated with some concerns. 44 45 48 51 55 73 76 For 22 studies, RoB2 indicated high risk for bias. 46 47 50 52–54 56 57 59 61–64 67 71 72 75 77 78 81 83 84 For full details, see online supplemental table 3 .

Main analysis

The main analysis of pooled data from 41 studies showed a large effect favouring exercise for a pre-post-correlation of 0.7 (SMD=−0.946, 95% CI −1.18 to −0.71, p<0.001, I 2 =82.49, p<0.001; see figure 2 ). Publication bias was indicated by Begg-Mazumdar Kendall’s Tau 37 (=−0.379, p<0.001 and the Egger 38 tests (intercept=−2.706, p<0.001). However, Duval and Tweedies’ trim and fill method did not affect the effect. Fail-safe number of additional negative studies was 2789. The visual inspection of the funnel plot (see online supplemental figure 1 ) did not indicate risk of bias. Sensitivity analyses revealed a trivial change in the effect from −0.930 (95% CI −1.16 to −0.70, p<0.001, I 2 =82.032, p<0.001) for a 0.8 pre-post-correlation to −0.957 (95% CI −1.19 to −0.72, p<0.001, I 2 =82.820, p<0.001) for a 0.6 pre-post-correlation. Excluding one study 74 due to unequal distribution of psychotherapy treatments among the intervention and control group (20% vs 0%, respectively) revealed an effect of SMD=−0.938 (95% CI −1.17 to −0.70, p<0.001, I 2 =82.703, p<0.001). Excluding studies with high risk of bias (see online supplemental table 3 ) rendered a moderate effect favouring exercise intervention (SMD=−0.717, 95% CI −1.01 to −0.43, p<0.001, I 2 =82.372, p<0.001). Excluding studies with less than 6 weeks of intervention (see online supplemental table 1 ) showed a large effect favouring exercise intervention (SMD=−0.959, 95% CI −1.21 to −0.71, p<0.001, I 2 =84.132, p<0.001). I 2 is suggesting substantial heterogeneity for the analyses.

Meta-analysis of overall studies. N, preintervention n, postintervention, SMD, standardised difference.

Subgroup analyses

Subgroup analyses (summarised in table 1 ) showed that the beneficial effect of exercise on depression remained for all subgroups regarding depression classification, risk of bias, group exercise, the sample size of the trial and supervision by exercise professionals. Aerobic (SMD=−1.156) and resistance training (−1.042) as exercise types showed large effects whereas mixed aerobic and resistance training showed small effects (−0.455). Large effects were also found for studies including sample sizes in the intervention arm of less than 25 participants (SMD=−0.868 to −1.281) whereas larger samples of participants revealed moderate effects (SMD=−0.532). Subgroup analyses with health education, with light exercise interventions, or with unsupervised training only including small numbers of analysed studies showed comparable SMDs but no significant effects. Subgroup analyses with studies with low or moderate risk of bias confirm results by showing similar outcomes (see online supplemental table 6 ) as well as subgroup analyses for studies with the diagnosis of MDD and dysthymia (see online supplemental table 7 ).

Adverse events and side effects

In 10 studies, it was documented that no (serious) adverse events occurred. 52 62 63 69 73 74 80–82 Three of these studies reported minor adverse events like muscle or joint pain, headache and fatigue. 52 70 74 One study reported that adverse events occurred but did not provide further information. 79 Three studies reported few side effects like worsening of pre-existing orthopaedic injuries or admittance to psychiatric ward due to major depression. 46 66 71

Meta-regression

Meta-regression (see table 2 ) was calculated for the main analysis and MDD only. In the main analysis, duration of trial in weeks moderated the effect of exercise on depression, with shorter trials associated with larger effects (β=0.032, 95% CI 0.01 to 0.09, p=0.032, R²=0.06). For MDD only, higher antidepressant use by the control group was associated with smaller effects (β=−0.013, 95% CI −0.02 to −0.01, p=0.012, R²=0.28). A meta-regression with studies with low and moderate risk of bias (see online supplemental table 8 ) rendered a moderating effect of duration of trials overall (β=0.064, 95% CI 0.01 to 0.126, p=0.04, R²=0.12) as well as for MDD only (β=0.070, 95% CI 0.01 to 0.14, p=0.034, R²=0.26).

Meta-regression of moderators/correlates of effects of exercise on depression

Mean change and numbers needed to treat (NNT)

We found a mean change of −4.70 points (95% CI −6.25 to −3.15, p<0.001, n=685) on the HAM-D and for the BDI of −6.49 points (95% CI −8.55 to −4.42, p<0.001, n=275) as an additional improvement effect of exercise over control conditions. The calculated NNT was 2.0 (95% CI 1.68 to 2.59) for the main-analysis, and 2.8 (95% CI 1.94 to 5.22) for the low risk of bias studies. For MDD, only the NNT was 1.9 (95% CI 1.49 to 2.99) and 1.6 (95% CI 1.58 to 2.41) in supervision by other professionals/students.

This is the largest meta-analysis investigating the effects of exercise for depressive symptoms within samples with diagnosed or indicated depression. Among 41 RCTs, we found that exercise interventions had a large effect favouring exercise over control conditions. Publication bias tests indicate an underestimation of this effect. Subgroup analyses resolved several key questions that lacked clarity from previous reviews; 17–20 specifically, the positive effect of exercise remained significant regardless of risk of bias, depression classification, exercise type, group setting, type of supervision or sample size. Subgroup analyses with health education (k=3), with light exercise interventions (k=2) or with unsupervised training (k=6) showed comparable SMDs but no statistical significance, which can be attributable to the lack of power due to the small numbers of studies included in the subgroup analyses. Surprisingly, the combination of mixed aerobic and resistance training showed smaller effects than aerobic or resistance training as single interventions. We also found a decline in ES from large to moderate for studies with sample sizes in the intervention arm of 25 or more participants. Focusing on solely diagnosed MDD, significant effects of exercise were found for all subgroup analyses except for light and mixed exercise, unsupervised training and for studies with some concern for risk of bias which can again be attributable to a lack of power due to the small number of included studies in the analyses (k=2 to 3). Limiting analyses to studies with low risk of bias and some concerns according to ROB 2 reveal similar results but with ESs declining from high to moderate for most analyses (see online supplemental table 6 ). Meta-regressions indicated a moderating effect of trial duration favouring shorter interventions and remained robust in meta-regressions without studies of high risk of bias. Regarding the type of exercise, most trial arms (k=30) investigated aerobic exercise detecting large effects followed by resistance training with comparable outcomes. In terms of the exercise intensity, only two arms investigated light intensity exercise while 26 and 10 trials applied moderate and vigorous intensity respectively, with all trials evidencing large effects. Supervised exercise revealed large ESs compared with unsupervised exercise with small effects. Minimal differences were detected between group and non-group exercise interventions favouring group exercise, with both showing large effects. Intervention arms with samples sizes ≥25 revealed moderate effects (see table 1 for details).

A recent meta-analysis of Cuijpers et al 85 found a moderate ES for psychotherapy treatment for depression across all age groups (g=0.75), and also when solely including studies with low risk of bias (g=0.51); while in terms of antidepressant efficacy, Cipriani et al 86 found medication to be more effective in comparison to placebo with Odds ratio of 2.13 indicating a small ES of d=0.417. This is notable as the presented results suggest exercise to qualify as an efficacious treatment option for depressive symptoms among individuals with depression.

These results extend the findings from an earlier meta-analysis of Schuch et al 20 (based on 25 studies including 1487 participants, revealing high heterogeneity of I 2 82.10%). Notably, the present findings are based on an additional 17 studies 44 45 48 52 53 55 59 62 67–69 71 74 78 79 84 since Schuch et al ’s 20 review and 4 45 55 68 74 studies following the most recent meta-analysis by Carneiro et al , 17 comprising only 15 studies focusing on different inclusion criteria including medication in treatment and control arm conditions.

In contrast to Krogh et al , 18 the analyses including only low risk of bias studies resulted in moderate effects with wide 95% CIs ranging from −0.99 to −0.34. Of note, we used the current risk of bias tool (RoB2) and included a greater number of low risk of bias trials compared with Krogh et al ’s 18 meta-analysis (11 vs 4). To reduce risk of bias, we compared exercise treatment groups with non-exercising control groups only. From the included 35 trials in the Cochrane Meta-Analysis by Cooney et al 21 consisting of 1356 participants, they reported 63% heterogeneity for the main analyses, the current review excluded 13 of these studies as groups were labelled as either ‘controls’ (ie, they received psychotherapy or pharmacotherapy) or groups labelled as ‘exercise’ groups (ie, they received a combination of exercise and another form of therapy or no therapy at all) or participants did not meet criteria for depression (see Ref. 22 for a critical appraisal). Krogh et al 18 also included 35 trials comprising 2498 participants with high heterogeneity (I 2 =81%) of which the current review excluded 17 studies with control groups that received stretching, relaxation or compared exercise to psychotherapy, medication or combined exercise with psychotherapy. Morres et al 19 included 11 trials involving 455 patients revealing low and non-statistically significant heterogeneity (I²=21%) but focused on aerobic exercise only; however, five of these studies were excluded from the current review because they included medication, active control conditions or cognitive or counselling therapy as comparator conditions. Carneiro et al 17 included 15 studies in their meta-analysis with a total sample size of 1532 individuals focusing on pharmacological treatment, exercise treatment and combined exercise with psychotherapy, of which the current review excluded 7 studies due to the inclusion of pharmacological therapy as a comparator condition either alone or in combination with psychotherapy. A further study was also excluded because participants were offered internet guided text modules on how to become more physically active but no actual exercise intervention was administered. Carneiro et al . 17 overall reported moderate heterogeneity (I²= 33%).

This summary reveals that a notable methodological limitation based on the former published meta-analyses in this field, included a proportion of trials with questionable intervention or control group conditions, which resulted in the inability to detect the effect of exercise per se (while excluding other forms of interventions). Hence, this notable shortcoming was addressed in this current meta-analytic review. Although we explored heterogeneity with sub-analyses and meta regression, we also found similar large heterogeneity comparable to previous larger meta-analyses 18 20 21 which guarantees comparability, yet needs to be considered when interpreting the results.

Our meta-regressions indicated that shorter trials are associated with larger effects than longer trials. A possible explanation is that larger trials had more dropouts, and higher dropout rates can reduce the effect in intention to treat analyses. 87 Alternatively, it is possible that the effect wanes with the time. However, all but three studies had interventions lasting 16 weeks or less and further studies with longer follow-ups should confirm this effect. 88 Also, we have found that studies in which control groups had a higher percentage of participants taking antidepressants identified smaller effects of exercise. This is expected as the difference on the magnitude of the improvement on depressive symptoms is smaller when exercise is compared with effective treatments, such as the use of pharmacological antidepressants, or when compared with controls without any treatment. 87

Clinical implications included that if 100 people were each in the control and the exercise group, 20 participants in the control and 54 in the exercise group for the main analysis and 43 in the exercise group for the low risk of bias studies, analyses can be expected to have favourable outcomes. 89 The NNT for the main-analysis was 2, while it was 2.8 in the low risk of bias studies, 1.9 in MDD only and 1.6 in supervision by other professionals/students. This effect is comparable to recent meta-analyses with psychotherapy revealing a NNT of 2.5 for the main analyses and 3.5 in the low risk of bias studies and for medication of 4.3. 85 86 Based on a NNT of 2 for the main analyses this means that for every two people treated with exercise, it is expected at least one to have a large magnitude reduction in depressive symptoms. 43 Furthermore, exercise showed an additional declining effect over control conditions of −4.70 points in the HAM-D as a diagnostic clinician measure in 16 studies and −6.49 in the BDI in eight studies indicating a clinically meaningful reduction of depressive symptoms from moderate to mild depression. According to the NICE guidelines, a three-point change is indicated as clinically meaningful for both measures. 16

Limitations

We acknowledge that limitations lie in the high heterogeneity of the included studies that can stem from different control group conditions, cultural backgrounds, gender distribution, variable forms of assessments and diagnosis of depression severity or MDD. Notwithstanding, we have performed several subgroup analyses and meta-regressions to explore the sources of this heterogeneity. Additionally, most of the included studies comprised small sample sizes for example, 13 studies with intervention arms of ≤10 participants in each group postintervention which we addressed with subanalyses. However, studies with larger samples sizes showed smaller but still moderate effects. Some subanalyses showed non-significant results as they lacked power due to the small number of studies included. In principle, the overreliance of significance testing should be avoided and interpretation of results based on SMD and 95% CI along with p values. Mostly seen wider ranges in CIs within the analyses can stem to a large extent from smaller studies (eg, 10 studies with n<10) and small number of studies in the subanalyses (especially less than k=10) which brings some uncertainty pertaining to the true effect. However, for the main analyses, 95% CIs were documented for exercise conditions comprising moderate intensity, aerobic exercise, group exercise and supervised exercise (ranging between 26 and 41 included studies), thus indicating moderate to large effects even for the lower limits. These outcomes provide adequate evidence to support the recommendation that exercise has utility in treating depression based on the aforementioned conditions. Long-term effects could not be investigated due to missing follow-up data for most studies. Moreover, it was not possible to control for placebo effects due to the nature of the interventions. Furthermore, 6 out of the 41 included studies were published prior to 2001 and can therefore be assigned to the pre-CONSORT era. This means that these earlier trials might not reflect the current standards and/or feature incomplete reporting of methodological details that was introduced with the CONSORT guidelines and checklist, therefore increasing scope in biased risk assessments and heterogeneity. 22 90

Further steps need to be undertaken to consider exercise as a first-line treatment for depression alongside psychotherapy and medication, including conducting non-inferiority trials to demonstrate that exercise is non-inferior to current first line treatments, and evidence that exercise is effective at long-term follow-ups. Future large-scale research studies should also investigate which patients benefit most from which exercise condition and identify any groups for whom exercise might not be the optimal treatment choice. It is noteworthy that the studies included in the current and former reviews consisted of samples which met the trial inclusion criteria comprising individuals that were willing, motivated and physically able to take part in the exercise regimen (eg, assessed by the Physical Activity Readiness Questionnaire 91 ) and excluded individuals with diagnoses that exercise may pose a risk (for example, individuals with cardiovascular diseases that require physician guidance to undertake exercise). Further, adverse events and outcomes due to exercise may occur in rare instances (nevertheless, they should be reported which was not documented for the majority of studies in this review), and not everyone has access to any form of exercise or exercise with the needed quality (eg, with a former sport medical examination). It is also noteworthy that the included studies were mainly conducted in high-income and upper-middle income countries, for example, no study was identified from the African continent. Future study designs should consider these relevant points including motivational aspects of attendance and samples from developing countries or rural areas to increase the generalisability of the results for healthcare.

Further strengthening the evidence base for exercise also has utility as it may be a less stigmatising treatment option for depressed individuals who may be reluctant to seek and adhere to psychotherapy and/or medication.

The findings from this review represent the most up to date and comprehensive meta-analysis of the available evidence and further supports the use of exercise focusing specifically on supervised and group exercise with moderate intensity and aerobic exercise regimes. This offers a further evidence-based treatment option for the large amount of untreated individuals with depression, including individuals who refuse or cannot tolerate medication and/or psychotherapy. However, given the high heterogeneity and mainly small and selected samples of the included studies, this requires individual decisions involving the treating physician to determine if and which conditions of exercise are the optimal treatment of choice while also recognising the potential synergistic effects of exercise in managing both physical and mental well-being. Updated guidelines as well as routine clinical decisions regarding interventions for treating depression should consider the current findings. This is particularly timely, following the post COVID-19 pandemic, given that rates of depression have continued to increase worldwide.

Ethics statements

Patient consent for publication.

Not applicable.

• Andrade LH ,
• Hwang I , et al
• World Health Organization
• Stevenson J ,
• Lazuras L , et al
• González-Sanguino C ,
• Castellanos MA , et al
• Veldhuis CB ,
• Nesoff ED ,
• McKowen ALW , et al
• Santomauro DF ,
• Mantilla Herrera AM ,
• Pfefferbaum B ,
• Bassett D , et al
• Cuijpers P ,
• Karyotaki E , et al
• Jakobsen JC ,
• Katakam KK ,
• Schou A , et al
• Thornicroft G ,
• Chatterji S ,
• Evans-Lacko S , et al
• National Institute for Health and Care Excellence
• Carneiro L ,
• Vasoncelos-Raposo J , et al
• Hjorthøj C ,
• Speyer H , et al
• Morres ID ,
• Hatzigeorgiadis A ,
• Stathi A , et al
• Schuch FB ,
• Vancampfort D ,
• Richards J , et al
• Cooney GM ,
• Greig CA , et al
• Ekkekakis P
• Liberati A ,
• Tetzlaff J , et al
• McKenzie JE ,
• Bossuyt PM , et al
• Ardern CL ,
• Büttner F ,
• Andrade R , et al
• Spitzer RL ,
• Endicott J ,
• American Psychiatric Association
• Mendelson M , et al
• Caspersen CJ ,
• Powell KE ,
• Christenson GM
• Etnier J , et al
• Sterne JAC ,
• Savović J ,
• Page MJ , et al
• Higgins JPT ,
• Thompson SG ,
• Deeks JJ , et al
• Wetterslev J ,
• Winkel P , et al
• Davey Smith G ,
• Schneider M , et al
• Rosenthal R ,
• Borenstein M ,
• Higgins J , et al
• Lenhard W ,
• Furukawa TA ,
• Mather AS ,
• Rodriguez C ,
• Guthrie MF , et al
• Abdelbasset WK ,
• Alqahtani BA ,
• Elshehawy AA , et al
• Brenes GA ,
• Williamson JD ,
• Messier SP , et al
• Ossip-Klein DJ ,
• Bowman ED , et al
• Qi H , et al
• Hallgren M ,
• Kraepelien M ,
• Öjehagen A , et al
• Hemat-Far A ,
• Shahsavari A ,
• Tsai YH , et al
• McNeil JK ,
• LeBlanc EM ,
• Makizako H ,
• Tsutsumimoto K ,
• Doi T , et al
• Mutrie N , et al
• Nabkasorn C ,
• Sootmongkol A , et al
• Prakhinkit S ,
• Suppapitiporn S ,
• Tanaka H , et al
• Shahidi M ,
• Mojtahed A ,
• Modabbernia A , et al
• Davidson S , et al
• Taylor N , et al
• Williams CL ,
• Alfonso H ,
• Newton RU , et al
• Blumenthal JA ,
• Babyak MA ,
• Doraiswamy PM , et al
• Carneiro LSF ,
• Fonseca AM ,
• Vieira-Coelho MA , et al
• Wong EYW , et al
• Cheung LK ,
• Danielsson L ,
• Papoulias I ,
• Petersson EL , et al
• Ziegenbein M ,
• Hoos O , et al
• Dunbar SB ,
• Higgins MK , et al
• La Rocque CL ,
• Mazurka R ,
• Stuckless TJR , et al
• Mota-Pereira J ,
• Silverio J ,
• Carvalho S , et al
• Oertel-Knöchel V ,
• Thiel C , et al
• Hardoy MC , et al
• Govindan R ,
• Muralidharan K
• Schneider KL ,
• Handschin B , et al
• Vasconcelos-Moreno MP ,
• Borowsky C , et al
• Clements KM ,
• Fiatarone MA
• Stavrinos TM ,
• Scarbek Y , et al
• Le Fevre K ,
• Pantelis C , et al
• Vieira JLL ,
• Rocha PGM da
• Karyotaki E ,
• Eckshtain D , et al
• Cipriani A ,
• Salanti G , et al
• Rosenbaum S , et al
• Magnusson K
• Lepage L , et al
• Marshall AL ,
• Sjöström M , et al

Supplementary materials

Supplementary data.

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

• Data supplement 1

Twitter @AndreasHeissel, @LuisaBrokmeier, @MariaKangas88, @davyvancampfort, @PhilWardAu, @simon_rosenbaum, @SchuchFelipe

Correction notice This article has been corrected since it published Online First. The article type has been changed to systematic review.

Contributors AH and FS conceived and designed the study. AH, LLB and FS had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. NS/LLB and DH did the literature search. AH, LLB, NS, DH and FS conducted the analyses, interpreted the data and wrote the first draft of the manuscript. All authors contributed to critical revision of the report for important intellectual content.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests AH is founder and CEO of the Centre for Emotional Health Germany GmbH supported by the Potsdam Transfer Centre from the University of Potsdam. BS has an NIHR Advanced fellowship (NIHR-301206, 2021–2026) and is coinvestigator on an NIHR program grant: Supporting Physical and Activity through Co-production in people with Severe Mental Illness (SPACES). BS is on the Editorial board of Mental Health and Physical Activity and The Brazilian Journal of Psychiatry. BS has received honorarium from a coedited book on exercise and mental illness and advisory work from ASICS for unrelated work. MK is on the Editorial boards for Behavior Therapy (Associate Editor), Stress and Health (Sections Editor), Psychological Bulletin, and Behaviour Research and Therapy. JF is supported by a UK Research and Innovation Future Leaders Fellowship (MR/T021780/1) and has received honoraria / consultancy fees from Atheneum, Informa, Gillian Kenny Associates, Big Health, Wood For Trees, Nutritional Medicine Institute, Angelini, ParachuteBH, Richmond Foundation and Nirakara, independent of this work. FS is on the Editorial board of Mental Health and Physical Activity, The Brazilian Journal of Psychiatry and Journal Brasileiro de Psiquiatria. FS has received honorarium from a co-edited book on lifestyle and mental illness. The other authors declare no funding, editorial or potential competing interests.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

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

Read the full text or download the PDF:

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

Published on 8.8.2024 in Vol 13 (2024)

The Effectiveness of Collaborative Care Interventions for the Management of Patients With Multimorbidity: Protocol for a Systematic Review, Meta-Analysis, and Meta-Regression Analysis

Authors of this article:

• Anne-Maj Knudsen 1, 2 , MSc   ; 
• Ann-Cathrine Dalgård Dunvald 3, 4 , PhD   ; 
• Stine Hangaard 1, 2 , PhD   ; 
• Ole Hejlesen 1 , PhD   ; 
• Thomas Kronborg 1, 2 , PhD  

1 Department of Health Science and Technology, Aalborg University, Gistrup, Denmark

2 Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark

3 Department of Clinical Pharmacology, Odense University Hospital, Odense, Denmark

4 Department of Clinical Research, University of Southern Denmark, Odense, Denmark

Corresponding Author:

Anne-Maj Knudsen, MSc

Department of Health Science and Technology

Aalborg University

Selma Lagerløfs Vej 249

Gistrup, 9260

Phone: 45 99409940

Email: [email protected]

Background: Collaborative care interventions have been proposed as a promising strategy to support patients with multimorbidity. Despite this, the effectiveness of collaborative care interventions requires further evaluation. Existing systematic reviews describing the effectiveness of collaborative care interventions in multimorbidity management tend to focus on specific interventions, patient subgroups, and settings. This necessitates a comprehensive review that will provide an overview of the effectiveness of collaborative care interventions for adult patients with multimorbidity.

Objective: This systematic review aims to systematically assess the effectiveness of collaborative care interventions in comparison to usual care concerning health-related quality of life (HRQoL), mental health, and mortality among adult patients with multimorbidity.

Methods: Randomized controlled trials evaluating collaborative care interventions designed for adult patients (18 years and older) with multimorbidity compared with usual care will be considered for inclusion in this review. HRQoL will be the primary outcome. Mortality and mental health outcomes such as rating scales for anxiety and depression will serve as secondary outcomes. The systematic search will be conducted in the CENTRAL, PubMed, CINAHL, and Embase databases. Additional reference and citation searches will be performed in Google Scholar, Web of Science, and Scopus. Data extraction will be comprehensive and include information about participant characteristics, study design, intervention details, and main outcomes. Included studies will be assessed for limitations according to the Cochrane Risk of Bias tool. Meta-analysis will be conducted to estimate the pooled effect size. Meta-regression or subgroup analysis will be undertaken to explore if certain factors can explain the variation in effect between studies, if feasible. The certainty of evidence will be evaluated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach.

Results: The preliminary literature search was performed on February 16, 2024, and yielded 5255 unique records. A follow-up search will be performed across all databases before submission. The findings will be presented in forest plots, a summary of findings table, and in narrative format. This systematic review is expected to be completed by late 2024.

Conclusions: This review will provide an overview of pooled estimates of treatment effects across HRQoL, mental health, and mortality from randomized controlled trials evaluating collaborative care interventions for adults with multimorbidity. Furthermore, the intention is to clarify the participant, intervention, or study characteristics that may influence the effect of the interventions. This review is expected to provide valuable insights for researchers, clinicians, and other decision-makers about the effectiveness of collaborative care interventions targeting adult patients with multimorbidity.

Trial Registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42024512554; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=512554

International Registered Report Identifier (IRRID): DERR1-10.2196/58296

Introduction

Multimorbidity lacks a universally accepted standard definition but is most commonly defined as the presence of 2 or more coexisting chronic conditions in one individual [ 1 ]. Multimorbidity is distinguished from the related concept of comorbidity by treating all conditions equally as opposed to prioritizing or designating an index condition in the case of comorbidity [ 1 ].

Multimorbidity is highly prevalent on a global scale [ 2 , 3 ]; however, the prevalence shows substantial variation across studies conducted in different countries and age groups [ 4 ]. Approximately 37% of adults worldwide are estimated to have multimorbidity [ 3 ]. The number of patients with multimorbidity has been rising worldwide and is expected to continue to increase in the future [ 3 , 5 ]. This is partly due to changing lifestyle risk factors and the aging population [ 3 ]. A range of lifestyle factors are associated with a higher risk of developing multimorbidity, such as smoking, excessive alcohol intake, lack of physical activity, obesity, and poor diet quality [ 6 - 8 ]. The prevalence of multimorbidity also commonly increases with age [ 9 ] and the majority of patients with multimorbidity are typically 65 years and older [ 2 , 10 ]. However, multimorbidity is also associated with gender [ 9 , 10 ], socioeconomic status [ 9 ], and educational level [ 10 , 11 ].

Multimorbidity leads to increased resource use due to a higher health care demand and treatment complexities. Patients with multimorbidity face a higher likelihood of hospital admissions, extended hospital stays, and premature mortality compared to those with a single condition [ 12 , 13 ]. The economic burden of managing patients with multimorbidity is substantial and increases in proportion to the number of additional conditions [ 14 - 17 ]. Multimorbidity is also associated with an increased risk of mental disorder, psychological distress [ 18 ], impaired quality of life [ 19 - 21 ], and poorer physical function [ 22 ]. Depression is especially common among patients with multimorbidity, being 2-3 times more common than among people with no or only one chronic condition [ 23 ]. Thus, multimorbidity negatively impacts quality of life [ 21 ].

Collaboration between sectors, specialties, and professions has been proposed as a promising approach to improve health care for patients with multimorbidity. Typically, the health care teams involved in these interventions are composed of a range of different types of professionals (eg, nurses, physicians, or social workers) or professionals from different disciplines or specialties (eg, general practice or endocrinology) [ 24 ]. Despite previous systematic reviews exploring the effects of using collaborative care interventions in the management of patients with multimorbidity [ 25 - 33 ], these reviews have generally been limited to a certain type of intervention, subpopulation, or setting. The general findings in these systematic reviews are further subject to uncertainties regarding the effectiveness of collaborative care interventions for managing multimorbidity due to mixed results, insufficient data, and evidence gaps [ 25 - 28 , 31 ]. Multiple explanations have been suggested for these findings. The current definitions of multimorbidity result in the inclusion of a substantial number of diverse patients, some of whom may not require collaborative care interventions [ 34 ]. One plausible explanation is that the effect of collaborative care interventions depends on the target subpopulation, implying that studies involving patients with limited room for improvement demonstrate less effectiveness [ 25 , 35 ]. This emphasizes the importance of identifying the patients who would generally benefit the most from collaborative care interventions.

Collectively, this situation leads to a fragmented overview of the collaborative care interventions used for patients with multimorbidity, necessitating a comprehensive review of these interventions and their corresponding effects. This calls for a new systematic review with a meta-analysis incorporating specific analyses such as meta-regression or subgroup analysis that is capable of handling differences between studies with regard to participant and intervention characteristics. This need is underlined by the rapid development in research within the field of collaborative care interventions for patients with multimorbidity.

Therefore, this systematic review aims to assess the effectiveness of collaborative care interventions compared with usual care on health-related quality of life (HRQoL), mental health, and mortality among adult patients with multimorbidity.

The main research question is: What is the effectiveness of being assigned to collaborative care interventions compared to usual care for adult patients with multimorbidity?

The secondary research question is: Can differences in effectiveness between studies be explained by participant, intervention, or study characteristics?

Study Design

The development of this protocol is based on the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) checklist [ 36 ]. The proceeding systematic review will be conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [ 37 ]. This study was registered on the International Prospective Register of Systematic Reviews (PROSPERO; CRD42024512554) on February 15, 2024.

Eligibility Criteria

Participants.

Participants must be adults, defined as being at minimum 18 years of age. The included participants must have multimorbidity, defined as at least 2 chronic conditions in one individual [ 38 ]; chronic diseases will be considered according to the World Health Organization definition of “health problems that require ongoing management over a period of years or decades” [ 39 ]. Studies with subgroups of patients with multimorbidity, presented in separate subgroup analyses, will be considered for inclusion.

Intervention

The definition of collaborative care is based on the systematic review by Gunn et al [ 40 ]. The intervention must involve any type of collaborative care activity in addition to the delivery of patient care. The intervention should include at least one health care professional (HCP) and the collaboration must involve at least 2 different health or social professions (eg, nurses, social workers, or pharmacologists) or similar HCPs from at least 2 different specialties (eg, collaboration between 2 physicians specialized in cardiology and endocrinology). Alternative professions such as “health coordinators” or “clinical care managers” will be considered when these professionals actively contribute as independent participants in the intervention, providing coordination or insight from social or health care perspectives. Furthermore, the intervention must encompass a structured management plan and scheduled patient follow-ups [ 40 ].

The comparison must be usual care. For this review, usual care is defined as the level of collaboration expected as part of normal practice, without explicitly mentioning a higher degree of collaboration, inspired by the study of Pascucci et al [ 41 ].

Prioritization of outcomes in this systematic review is based on the core outcome set for multimorbidity proposed by Smith et al [ 42 ]. The primary outcome is HRQoL, given its highest rank in the core outcome set [ 42 ]. The secondary outcomes will cover various elements such as mental health outcomes (encompassing general mental health, depression, and anxiety scales) and mortality due to their status as essential outcomes in the core outcome set [ 42 ].

Randomized controlled trials (RCTs) with a parallel design will be considered in this review. Feasibility studies with an RCT design presenting results on effectiveness will also be considered for inclusion. Other criteria encompass inclusion of articles written in English, Danish, Swedish, and Norwegian. The articles must be available in full text. All studies available up to the submission date will be considered for inclusion. Only peer-reviewed articles will be included.

Information Sources

The systematic research will be conducted in the following scientific databases: CENTRAL, PubMed (including the MEDLINE database), CINAHL, and Embase. All included articles will undergo reference screening and a citation search to ensure relevant papers are captured. Citation searches of relevant articles will be carried out in Scopus, Web of Science, and Google Scholar.

Search Strategy

The search strategy was developed by conducting an initial search in CENTRAL and PubMed to identify the relevant literature. The retrieved literature from the initial search was then used to identify relevant keywords that were integrated into the search strategy. The search will be performed through a block search structured according to the Population, Intervention, Comparator, and Outcome (PICO) framework, encompassing terms related to the population and intervention. The search strategy will be tailored to the specific database used. Synonyms, similar terms, and variations in spelling will be considered in the searches. The databases’ built-in search functions will also be used, such as the inherent thesaurus, Boolean operators, and filtering options. The search strategies are planned in collaboration with a research librarian. The complete search strategy used in all databases is provided in Multimedia Appendix 1 . Prior to submission, a follow-up search in all databases will be performed to ensure inclusion of all newly published studies.

Study Records

Data management.

Following the search, all references will be uploaded into Zotero version 6.0.30 [ 43 ]. The function merge duplicates in the duplet section will initially be used. Subsequently, the references will be exported to the web-based software Covidence [ 44 ], facilitating collaborative inclusion and exclusion of studies.

Selection Process

Potential studies will be evaluated for inclusion in this review based on the presented eligibility criteria. Two authors will independently assess all studies from the block search. Initially, the studies will be screened by title and abstract, and those not aligning with the eligibility criteria will be excluded. The remaining studies will undergo a full-text assessment, and only those meeting the eligibility criteria will be included. In case of disagreement, a third author will be involved in the decision-making process. Additional citation and reference searches will be performed by the first author. A PRISMA flow diagram will be provided to ensure transparency in the selection of articles (see Multimedia Appendix 2 ).

Data Collection Process

Data extraction will be performed using a customized Microsoft Excel form. In cases where usable data extraction is hindered due to incomplete representation of study details or results in some articles, the authors of these studies will be contacted for clarification.

The information relevant for this review includes elements of the study design, participant characteristics, intervention details, and main outcomes. The data extracted from the studies will encompass various elements, including author details, publication year, country, study design, baseline characteristics, total sample size, intervention specifics, intervention team, setting, comparator, and study outcomes. A table summarizing the key characteristics of each study will be provided. Concerning the type of participants in the study, a distinction will be made between comorbidity studies, focusing on patients with specific combinations of conditions, and multimorbidity studies, encompassing participants with diverse combinations of conditions [ 45 ]. Baseline characteristics will include elements such as age, gender, BMI, or body weight. Details about the interventions will encompass aspects such as the setting, intervention duration and frequency, and the types of professionals and specialties represented in the collaborative care teams. Furthermore, it will be noted whether the intervention incorporates a digital element. The collected data items pertaining to the study outcomes will include details such as outcome scores or values, statistical precision metrics, assessment tool, sample size in each group at the time of assessment, and time of assessment. In the meta-analysis, results related to the latest time of assessment will be collected. The authors of the included studies will be contacted to provide additional information if characteristics or results at the study level are not available.

Risk of Bias Assessment

The included studies will be assessed for limitations according to the Cochrane Risk of Bias tool version 2 [ 46 ], with independent assessments conducted by 2 authors. In cases where differences in assessment occur, a discussion involving a third author will be initiated to reach agreement. All eligible studies will be included in this review, regardless of their quality. Only studies with an overall risk of bias judged to be “low risk of bias” or “some concerns” will be included in the meta-analysis. If missing information makes risk of bias assessment impossible, corresponding authors will be contacted to obtain the necessary information. The results of the risk of bias assessment will be presented in a risk of bias chart.

Data Synthesis

Meta-analysis.

A meta-analysis will be performed to analyze the available data. The analysis will be performed in RStudio using the Meta-Analysis Package for R (Metafor package) enabling the creation of a data frame containing various measures for the meta-analysis model (eg, effect size, t statistics, or P values). However, if the measures differ due to discrepancies in the data provided by some studies, supplementary calculations to standardize the data for analysis will be performed. These adjustments will adhere to the equations outlined in the Cochrane Handbook [ 47 ]. Outcome scores or values given as the absolute mean difference from baseline or absolute mean at follow-up, along with statistical precision metrics (eg, SD, CI, or SE), will be used to estimate the overall effect size for continuous variables. For studies where these data are not reported, the between-group estimates that quantify the effect of the intervention on the outcome and their precision will be used. If results are presented as medians and IQRs, the means and SDs will be estimated [ 48 ]. The article with the largest sample will be used for the meta-analysis if 2 or more articles present data from the same study for a particular outcome.

The meta-analysis will be based on the standardized mean difference for continuous variables sharing the same construct but different outcome measures. There is no rule as to when certain outcomes measures can be combined, which is generally determined by clinical judgment [ 49 ]. The decision to combine different outcome measures will involve at least 2 authors. Should disagreement arise, a third author will be consulted to reach agreement. Conversely, the weighted mean difference will be applied for continuous variables sharing the same outcome measure.

As it is difficult to make assumptions about the underlying distribution of effect sizes, the meta-analysis will follow the pragmatic approach proposed in the Cochrane Handbook regarding the choice of model [ 50 ]. This means that both the random- and fixed-effects models will be used in this meta-analysis because the included studies are expected to vary in multiple parameters [ 51 ]. One parameter is the samples in the studies, which do not originate from the same population. Although the overall population is patients with multimorbidity, the selected samples in the studies are expected to vary considerably. Consequently, the effects estimated across the studies may not be identical but rather follow a similar distribution. The findings from both models will be presented using forest plots and tables, accompanied by a sensitivity analysis. The effect estimate of continuous variables will be presented as a pooled mean difference or pooled standardized mean difference with 95% CIs. Odds ratios will be given for dichotomous variables with 95% CIs. Two-sided P values <.05 will be considered statistically significant. Interpretation of the pooled effect size will be guided by the principles outlined in the Cochrane Handbook [ 52 ]. To test for heterogeneity, the Higgins I 2 statistic will be calculated. The interpretation of I 2 will also be made according to the Cochrane Handbook [ 50 ].

Sensitivity Analysis

Sensitivity analysis will be performed, including variations in statistical models (random vs fixed effects) as well as assessments of risk of bias. If relevant, sensitivity analysis will also be performed by excluding studies that only report medians and IQRs. Other potential sensitivity analyses may consider a range of parameters such as sample size or follow-up length, with the purpose of evaluating the potential impact of these factors on the overall findings. A summary table will showcase the results of any sensitivity analyses performed [ 50 ].

Meta-Regression and Subgroup Analysis

Meta-regression or subgroup analysis will be performed to explore if certain factors can explain the variation in effect between studies. To explore associations between the primary outcome and various factors in the meta-analysis, a meta-regression analysis will be performed. In the analysis, participant characteristics will be examined by distinguishing between comorbidity and multimorbidity studies. Other participant-related factors to be explored include demographics, socioeconomic status, health indicators, lifestyle factors, and treatment variables. Interventions will be examined across multiple dimensions, including setting, duration, and frequency. Additionally, the types of professionals and specialties represented in the collaborative care teams will be evaluated to understand their influence on intervention outcomes. Furthermore, the presence of digital components will be considered to assess if they contribute to variations in effectiveness across studies.

Subgroup analysis may be performed if the meta-regression is impossible due to limitations related to the data. This may be due to the limited availability of continuous variables if studies predominantly report variables categorically (eg, in case of age groups instead of age as a continuous variable). Additionally, meta-regression analysis will not be considered if there are fewer than 10 studies in a meta-analysis [ 50 ].

Narrative Format

A table containing a narrative description of the key characteristics and findings of all studies will be provided. Due to heterogeneity or high risk of bias, meta-analysis may not be feasible for certain outcomes. In these cases, a narrative description will be provided. The results of the quantitative synthesis will be presented in a table with a summary of findings.

Assessment of Meta-Biases

To assess potential publication biases, a funnel plot with a regression line will be created. The funnel plot will be complemented by a statistical test for funnel plot asymmetry, such as the Egger test. The funnel plot will only be constructed for evaluation if the specific meta-analysis contains results from at least 10 studies [ 53 ].

Confidence in Cumulative Evidence

The GRADE (The Grading of Recommendations Assessment, Development, and Evaluation) system will be used for grading the certainty of evidence [ 54 ]. The GRADE evaluation will be performed independently by 2 authors. In case of disagreement, a third author will perform an additional GRADE evaluation. The web-based GRADEpro Guideline Development Tool software (Evidence Prime) will be used to perform the evaluation [ 55 ]. The results from the GRADE evaluation will be presented in a summary of findings table. This grading will be based on risk of bias, directness, heterogeneity, precision, and risk of publication bias.

This systematic review and meta-analysis is ongoing. Results from the preliminary search, performed on February 16, 2024, can be found in Multimedia Appendices 1 and 2 . A total of 6730 records were retrieved, of which 1475 were duplicates. This resulted in 5255 unique records remaining for initial title and abstract screening. A follow-up search across all databases will be conducted prior to submission. The findings will be visually presented through forest plots and in a summary of findings table where meta-analysis is feasible, and in cases where it is not, through narrative comparisons. This systematic review and meta-analysis is expected to be completed in late 2024. Following completion, the results will undergo peer review before being submitted for publication.

This systematic review aims to assess the treatment effect on HRQoL, mental health, and mortality in collaborative care interventions compared to usual care in adult patients with multimorbidity. The presented systematic review will have both strengths and limitations. The strengths are rooted in the comprehensive exploration and broad scope undertaken. This review aims to investigate the impact of heterogeneity in participant and intervention characteristics on the pooled effect size. Such exploration holds the potential to provide valuable insights into the effectiveness of collaborative care interventions and to identify the factors that influence this effect. This may contribute to determining the optimal selection of patients with multimorbidity expected to derive the greatest benefit from collaborative care interventions. Additionally, this review may reveal intervention characteristics that prove meaningful to investigate further. Conducting a subsequent qualitative analysis may offer a deeper understanding of aspects that quantitative data alone may not capture. Triangulating these findings therefore ensures a thorough investigation of the complexities inherent in collaborative care interventions for patients with multimorbidity.

Nevertheless, limitations are expected due to the high degree of clinical diversity. This variability extends across the population, encompassing diverse conditions and diagnoses, as well as within the intervention characteristics, where differences stem from factors such as the composition of the collaborative care teams and the settings where the interventions are implemented. The planned comparison is therefore expected to be complicated due to the lack of homogeneity within the field. Hence, the forthcoming discussion will also encompass the consideration of confounders and methodological limitations to ensure meaningful conclusions. Further limitations will be examined and elucidated in the discussion section of the review to contribute valuable insights for guiding future research efforts.

Acknowledgments

This systematic review is funded by the Novo Nordisk Foundation and Steno Diabetes Center North Denmark. The research librarian Louise Thomsen, Aalborg University Library, is acknowledged for assisting in planning the search strategy of this systematic review.

Authors' Contributions

AMK wrote the initial protocol draft, while the other authors provided substantial revisions, ensuring a comprehensive and informed final version of this protocol. Each author contributed significantly to the design of this study, defining the aim and objectives, formulating the eligibility criteria, and planning the data extraction. AMK and ACDD developed the search strategy in collaboration with a research librarian. The final version of this protocol was collectively reviewed and approved by all authors.

Conflicts of Interest

None declared.

Search strategy.

Study selection flow diagram.

• Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining comorbidity: implications for understanding health and health services. Ann Fam Med. 2009;7(4):357-363. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Nguyen H, Manolova G, Daskalopoulou C, Vitoratou S, Prince M, Prina AM. Prevalence of multimorbidity in community settings: a systematic review and meta-analysis of observational studies. J Comorb. 2019;9:2235042X19870934. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chowdhury SR, Chandra Das D, Sunna TC, Beyene J, Hossain A. Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis. EClinicalMedicine. Mar 2023;57:101860. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Salive ME. Multimorbidity in older adults. Epidemiol Rev. 2013;35(1):75-83. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Uijen AA, van de Lisdonk EH. Multimorbidity in primary care: prevalence and trend over the last 20 years. Eur J Gen Pract. Jul 11, 2008;14 Suppl 1(1):28-32. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Katikireddi SV, Skivington K, Leyland AH, Hunt K, Mercer SW. The contribution of risk factors to socioeconomic inequalities in multimorbidity across the lifecourse: a longitudinal analysis of the Twenty-07 cohort. BMC Med. Aug 24, 2017;15(1):152. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Freisling H, Viallon V, Lennon H, Bagnardi V, Ricci C, Butterworth AS, et al. Lifestyle factors and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study. BMC Med. Jan 10, 2020;18(1):5. [ CrossRef ] [ Medline ]
• Fortin M, Haggerty J, Almirall J, Bouhali T, Sasseville M, Lemieux M. Lifestyle factors and multimorbidity: a cross sectional study. BMC Public Health. Jul 05, 2014;14:686. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Violan C, Foguet-Boreu Q, Flores-Mateo G, Salisbury C, Blom J, Freitag M, et al. Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies. PLoS One. Jul 21, 2014;9(7):e102149. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Frølich A, Ghith N, Schiøtz M, Jacobsen R, Stockmarr A. Multimorbidity, healthcare utilization and socioeconomic status: a register-based study in Denmark. PLoS One. Aug 01, 2019;14(8):e0214183. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Pathirana TI, Jackson CA. Socioeconomic status and multimorbidity: a systematic review and meta-analysis. Aust N Z J Public Health. Apr 2018;42(2):186-194. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Menotti A, Mulder I, Nissinen A, Giampaoli S, Feskens EJ, Kromhout D. Prevalence of morbidity and multimorbidity in elderly male populations and their impact on 10-year all-cause mortality: The FINE study (Finland, Italy, Netherlands, Elderly). J Clin Epidemiol. Jul 2001;54(7):680-686. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Vogeli C, Shields AE, Lee TA, Gibson TB, Marder WD, Weiss KB, et al. Multiple chronic conditions: prevalence, health consequences, and implications for quality, care management, and costs. J Gen Intern Med. Dec 2007;22(Suppl 3):391-395. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Picco L, Achilla E, Abdin E, Chong SA, Vaingankar JA, McCrone P, et al. Economic burden of multimorbidity among older adults: impact on healthcare and societal costs. BMC Health Serv Res. May 10, 2016;16:173. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wang L, Si L, Cocker F, Palmer AJ, Sanderson K. A systematic review of cost-of-illness studies of multimorbidity. Appl Health Econ Health Policy. Feb 2018;16(1):15-29. [ CrossRef ] [ Medline ]
• Tran PB, Kazibwe J, Nikolaidis GF, Linnosmaa I, Rijken M, van Olmen J. Costs of multimorbidity: a systematic review and meta-analyses. BMC Med. Jul 19, 2022;20(1):234. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lehnert T, Heider D, Leicht H, Heinrich S, Corrieri S, Luppa M, et al. Review: Health care utilization and costs of elderly persons with multiple chronic conditions. Med Care Res Rev. Aug 2011;68(4):387-420. [ CrossRef ] [ Medline ]
• Fortin M, Bravo G, Hudon C, Lapointe L, Dubois M, Almirall J. Psychological distress and multimorbidity in primary care. Ann Fam Med. 2006;4(5):417-422. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Fortin M, Lapointe L, Hudon C, Vanasse A, Ntetu AL, Maltais D. Multimorbidity and quality of life in primary care: a systematic review. Health Qual Life Outcomes. Sep 20, 2004;2:51. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Fortin M, Bravo G, Hudon C, Lapointe L, Almirall J, Dubois M, et al. Relationship between multimorbidity and health-related quality of life of patients in primary care. Qual Life Res. Feb 2006;15(1):83-91. [ CrossRef ] [ Medline ]
• Makovski TT, Schmitz S, Zeegers MP, Stranges S, van den Akker M. Multimorbidity and quality of life: systematic literature review and meta-analysis. Ageing Res Rev. Aug 2019;53:100903. [ CrossRef ] [ Medline ]
• Kadam UT, Croft PR, North Staffordshire GP Consortium Group. Clinical multimorbidity and physical function in older adults: a record and health status linkage study in general practice. Fam Pract. Oct 2007;24(5):412-419. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Read JR, Sharpe L, Modini M, Dear BF. Multimorbidity and depression: a systematic review and meta-analysis. J Affect Disord. Oct 15, 2017;221:36-46. [ CrossRef ] [ Medline ]
• Chamberlain-Salaun J, Mills J, Usher K. Terminology used to describe health care teams: an integrative review of the literature. J Multidiscip Healthc. 2013;6:65-74. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Smith SM, Wallace E, Clyne B, Boland F, Fortin M. Interventions for improving outcomes in patients with multimorbidity in primary care and community setting: a systematic review. Syst Rev. Oct 20, 2021;10(1):271. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Li M, Tang H, Liu X. Primary care team and its association with quality of care for people with multimorbidity: a systematic review. BMC Prim Care. Jan 19, 2023;24(1):20. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lammila-Escalera E, Greenfield G, Barber S, Nicholls D, Majeed A, Hayhoe BWJ. A systematic review of Interventions that use multidisciplinary team meetings to manage multimorbidity in primary care. Int J Integr Care. 2022;22(4):6. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kappelin C, Carlsson AC, Wachtler C. Specific content for collaborative care: a systematic review of collaborative care interventions for patients with multimorbidity involving depression and/or anxiety in primary care. Fam Pract. Jul 19, 2022;39(4):725-734. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Musuuza J, Sutherland BL, Kurter S, Balasubramanian P, Bartels CM, Brennan MB. A systematic review of multidisciplinary teams to reduce major amputations for patients with diabetic foot ulcers. J Vasc Surg. Apr 2020;71(4):1433-1446. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Huang Y, Wei X, Wu T, Chen R, Guo A. Collaborative care for patients with depression and diabetes mellitus: a systematic review and meta-analysis. BMC Psychiatry. Oct 14, 2013;13:260. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Helou N, Dwyer A, Shaha M, Zanchi A. Multidisciplinary management of diabetic kidney disease: a systematic review and meta-analysis. JBI Database System Rev Implement Rep. Jul 2016;14(7):169-207. [ CrossRef ] [ Medline ]
• Di Serafino F, Pascucci D, Sassano M, Di Pilla A, Carini E, Specchia M, et al. Systematic review on multidisciplinarity and management of multimorbid chronic patients in hospital. Eur J Public Health. Sep 2020;30(5):ckaa166.501. [ FREE Full text ] [ CrossRef ]
• Acosta-García H, Alfaro-Lara ER, Sánchez-Fidalgo S, Sevilla-Sánchez D, Delgado-Silveira E, Juanes-Borrego A, et al. Intervention effectiveness by pharmacists integrated within an interdisciplinary health team on chronic complex patients. Eur J Public Health. Oct 01, 2020;30(5):886-899. [ CrossRef ] [ Medline ]
• Wallace E, Salisbury C, Guthrie B, Lewis C, Fahey T, Smith SM. Managing patients with multimorbidity in primary care. BMJ. Jan 20, 2015;350:h176. [ CrossRef ] [ Medline ]
• Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, et al. Multimorbidity. Nat Rev Dis Primers. Jul 14, 2022;8(1):48. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. PRISMA-P Group. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. Jan 02, 2015;350:g7647. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Mar 29, 2021;372:n71. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Integrated Health Services (IHS) WHO Team. Multimorbidity: technical series on safer primary care. World Health Organization. Dec 13, 2016. URL: https://www.who.int/publications-detail-redirect/9789241511650 [accessed 2024-02-12]
• Management-Screening, Diagnosis and Treatment (MND) WHO Team. Innovative care for chronic conditions: building blocks for actions. World Health Organization. Oct 31, 2002. URL: https:/​/www.​who.int/​publications-detail-redirect/​innovative-care-for-chronic-conditions-building-blocks-for-actions [accessed 2023-12-12]
• Gunn J, Diggens J, Hegarty K, Blashki G. A systematic review of complex system interventions designed to increase recovery from depression in primary care. BMC Health Serv Res. Jul 16, 2006;6:88. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Pascucci D, Sassano M, Nurchis MC, Cicconi M, Acampora A, Park D, et al. Impact of interprofessional collaboration on chronic disease management: findings from a systematic review of clinical trial and meta-analysis. Health Policy. Feb 2021;125(2):191-202. [ CrossRef ] [ Medline ]
• Smith SM, Wallace E, Salisbury C, Sasseville M, Bayliss E, Fortin M. A core outcome set for multimorbidity research (COSmm). Ann Fam Med. Mar 2018;16(2):132-138. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zotero. URL: https://www.zotero.org/download/ [accessed 2023-12-27]
• Covidence. URL: https://www.covidence.org/ [accessed 2024-01-12]
• Smith SM, Wallace E, O'Dowd T, Fortin M. Interventions for improving outcomes in patients with multimorbidity in primary care and community settings. Cochrane Database Syst Rev. Mar 14, 2016;3(3):CD006560. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Higgins J, Savovic J, Page M, Elbers R, Sterne J. Chapter 8: Assessing risk of bias in a randomized trial. Cochrane Handbook for Systematic Reviews of Interventions version 6.4. 2023. URL: https://training.cochrane.org/handbook/current/chapter-08 [accessed 2024-06-25]
• Higgins JPT, Li T, Deeks JJ. Chapter 6: Choosing effect measures and computing estimates of effect. Cochrane Handbook for Systematic Reviews of Interventions version 6.4. 2023. URL: https://training.cochrane.org/handbook/current/chapter-06 [accessed 2024-02-07]
• Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. Dec 19, 2014;14:135. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Higgins J, Green S. 9.1.4 When not to use meta-analysis in a review. Cochrane Handbook for Systematic Reviews of Interventions version 5.1.0. 2011. URL: https://handbook-5-1.cochrane.org/chapter_9/9_1_4_when_not_to_use_meta_analysis_in_a_review.htm [accessed 2024-01-15]
• Deeks JJ, Higgins JPT, Altman DG. Chapter 10: Analysing data and undertaking meta-analyses. Cochrane Handbook for Systematic Reviews of Interventions version 6.4. 2023. URL: https://training.cochrane.org/handbook/current/chapter-10 [accessed 2023-11-08]
• Spineli LM, Pandis N. Meta-analysis: random-effects model. Am J Orthod Dentofacial Orthop. Feb 2020;157(2):280-282. [ CrossRef ] [ Medline ]
• Schünemann H, Vist G, Higgins J, Santesso N, Deeks J, Glasziou P, et al. Chapter 15: Interpreting results and drawing conclusions. Cochrane Handbook for Systematic Reviews of Interventions version 6.4. 2023. URL: https://training.cochrane.org/handbook/current/chapter-15 [accessed 2023-11-08]
• Page M, Higgins J, Sterne J. Chapter 13: Assessing risk of bias due to missing results in a synthesis. Cochrane Handbook for Systematic Reviews of Interventions version 6.4. 2023. URL: https://training.cochrane.org/handbook/current/chapter-13 [accessed 2024-02-07]
• Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, et al. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol. Apr 2011;64(4):383-394. [ CrossRef ] [ Medline ]
• McMaster University, Evidence Prime Inc. GRADEpro. 2021. URL: https://www.gradepro.org/ [accessed 2024-01-02]

Abbreviations

Edited by A Mavragani; submitted 12.03.24; peer-reviewed by C Kappelin, Y Li; comments to author 11.06.24; revised version received 02.07.24; accepted 09.07.24; published 08.08.24.

©Anne-Maj Knudsen, Ann-Cathrine Dalgård Dunvald, Stine Hangaard, Ole Hejlesen, Thomas Kronborg. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 08.08.2024.

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 JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.

Aug. 8, 2024

New international journal ‘fills gap’ for imaging data analysis research, rice statistics professor marina vannucci to serve as editor-in-chief of statistics and data science in imaging..

A new international journal will soon give the statistical community an outlet for discussing advancements in processing, modeling and analysis of imaging data.

Marina Vannucci , Rice University’s Noah Harding Professor of Statistics, is a founding editor of Statistics and Data Science in Imaging (SDSI) and will serve as editor-in-chief when the publication launches on August 30.  

SDSI will provide a new platform for statisticians, data scientists and field investigators to discuss methodological advancements and applications related to the statistical analysis of imaging data. It also serves as a collaborative environment for interdisciplinary research in data analytics in imaging, filling a significant gap in current outlets.

According to a statement from the founding editors, “the need for this new journal comes from the fact that the ever growing community of researchers working at the interface of statistics, data science, and imaging currently lack a dedicated forum to present, compare, and address statistical work related to imaging data analysis.”

The journal welcomes discussion papers, in-depth reviews and case studies to promote engagement and learning among the statistical community. Contributors can also submit short communications to highlight emerging issues of interest and best practice papers.    SDSI will be published by the Taylor & Francis Group on behalf of the American Statistical Association. 

Vannucci’s co-editors include Michele Guindani, professor of biostatistics at UCLA; Martin Lindquist, professor of biostatistics at Johns Hopkins University; and Hernando Ombao, professor of statistics at King Abdullah University of Science and Technology. 

• Election 2024
• Entertainment
• Newsletters
• Photography
• AP Buyline Personal Finance
• AP Buyline Shopping
• Press Releases
• Israel-Hamas War
• Russia-Ukraine War
• Global elections
• Asia Pacific
• Latin America
• Middle East
• Delegate Tracker
• AP & Elections
• 2024 Paris Olympic Games
• Auto Racing
• Movie reviews
• Book reviews
• Financial Markets
• Business Highlights
• Financial wellness
• Artificial Intelligence
• Social Media

Paris’ Olympics opening was wacky and wonderful — and upset bishops. Here’s why

Paris Olympics organizers apologized to anyone who was offended by a tableau that evoked Leonardo da Vinci’s “The Last Supper” during the glamorous opening ceremony, but defended the concept behind it Sunday. Da Vinci’s painting depicts the moment when Jesus Christ declared that an apostle would betray him. The scene during Friday’s ceremony featured DJ and producer Barbara Butch — an LGBTQ+ icon — flanked by drag artists and dancers.

Delegations arrive at the Trocadero as spectators watch French singer Philippe Katerine performing on a giant screen, in Paris, during the opening ceremony of the 2024 Summer Olympics, Friday, July 26, 2024 in Paris. (Ludovic Marin/Pool Photo via AP)

• Copy Link copied

Catch up on the latest from Day 13 of the 2024 Paris Olympics:

• Basketball: LeBron James, Kevin Durant and Steph Curry vs. Nikola Jokic is quite the matchup in the U.S. v. Serbia men’s basketball semifinals Thursday.
• Track and field: Noah Lyles won the 100 by five thousandths of a second on Sunday night, and is a big favorite heading into the 200.
• Keep up : Follow along with our Olympics medal tracker and list of winners. Check out the Olympic schedule of events.

PARIS (AP) — Paris: the Olympic gold medalist of naughtiness.

Revolution ran like a high-voltage wire through the wacky, wonderful and rule-breaking Olympic opening ceremony that the French capital used to astound, bemuse and, at times, poke a finger in the eye of global audiences on Friday night.

That Paris put on the most flamboyant, diversity-celebrating, LGBTQ+-visible of opening ceremonies wasn’t a surprise. Anything less would have seemed a betrayal of the pride the French capital takes in being a home to humanity in all its richness.

But still. Wow. Paris didn’t just push the envelope. It did away with it entirely as it hammered home a message that freedom must know no bounds.

A practically naked singer painted blue made thinly veiled references to his body parts. Blonde-bearded drag queen Piche crawled on all fours to the thumping beat of “Freed From Desire” by singer-songwriter Gala, who has long been a potent voice against homophobia . There were the beginnings of a menage à trois — the door was slammed on the camera before things got really steamy — and the tail end of an intimate embrace between two men who danced away, hugging and holding hands.

“In France, we have the right to love each other, as we want and with who we want. In France, we have the right to believe or to not believe. In France, we have a lot of rights. Voila,” said the audacious show’s artistic director, Thomas Jolly.

Jolly, who is gay, says being bullied as a child for supposedly being effeminate drove home early on how unjust discrimination is.

The amorous vibe and impudence were too much for some.

“Know that it is not France that is speaking but a left-wing minority ready for any provocation,” posted far-right French politician Marion Maréchal, adding a hashtagged “notinmyname.”

Here’s a closer look at how Paris both awed and shocked.

A 21st-century update of Leonardo da Vinci’s ‘Last Supper’

DJ and producer Barbara Butch, an LGBTQ+ icon who calls herself a “love activist,” wore a silver headdress that looked like a halo as she got a party going on a footbridge across the Seine, above parading athletes — including those from countries that criminalize LGBTQ+ people. Drag artists, dancers and others flanked Butch on both sides.

The tableau brought to mind Leonardo da Vinci’s “Last Supper,” which depicts the moment when Jesus Christ declared that an apostle would betray him.

Jolly says that wasn’t his intention. He saw the moment as a celebration of diversity, and the table on which Butch spun her tunes as a tribute to feasting and French gastronomy.

“My wish isn’t to be subversive, nor to mock or to shock,” Jolly said. “Most of all, I wanted to send a message of love, a message of inclusion and not at all to divide.”

Still, critics couldn’t unsee what they saw.

“One of the main performances of the Olympics was an LGBT mockery of a sacred Christian story - the Last Supper - the last supper of Christ. The apostles were portrayed by transvestites,” the spokesperson for Russia’s Foreign Ministry, Maria Zakharova, posted on Telegram.

“Apparently, in Paris they decided that since the Olympic rings are multi-colored, they can turn everything into one big gay parade,” she added.

The French Catholic Church’s conference of bishops deplored what it described as “scenes of derision and mockery of Christianity” and said “our thoughts are with all the Christians from all continents who were hurt by the outrage and provocation of certain scenes.”

LGBTQ+ athletes, though, seemed to have a whale of a time. British diver Tom Daley posted a photo of himself recreating the standout Kate Winslet-Leonardo DiCaprio scene from “Titanic,” only with the roles reversed: He was at the boat’s prow with arms outstretched, as rower Helen Glover held him from behind.

Is that a revolver in your pocket?

When a giant silver dome lifted to reveal singer Philippe Katerine reclining on a crown of fruit and flowers, practically naked and painted blue, audiences who didn’t think he was Papa Smurf may have guessed that he represented Dionysus, the Greek god of wine and ecstasy.

But unless they speak French, they may not have caught the cheekiness of his lyrics.

“Where to hide a revolver when you’re completely naked?” he sang, pointing down to his groin. “I know where you’re thinking. But that’s not a good idea.”

“No more rich and poor when you go back to being naked. Yes,” Katerine continued.

Decades after Brigitte Bardot sang “Naked in the Sun,” this was Paris’ reminder that everyone starts life in their birthday suit, so where’s the shame?

Paris museums are full of paintings that celebrate the human form. Gustave Courbet’s “Origin of the World” hangs in the Musée d’Orsay. The 16th-century “Gabrielle d’Estrées and one of her sisters,” showing one bare-breasted woman pinching the nipple of another, hangs in the Louvre.

France sends a message

Clad in a golden costume, French-Malian pop star Aya Nakamura strode confidently out of the hallowed doors of the Institut de France, a prestigious stronghold of French language, culture and commitment to freedom of thought. Even without a note being sung, the message of diversity, inclusion and Black pride was loud.

The most listened-to French-speaking artist in the world was a target of fierce attacks from extreme-right activists when her name emerged earlier this year as a possible performer at the show. Paris prosecutors opened an investigation of alleged racism targeting the singer.

Nakamura performed with musicians of the French military’s Republican Guard, who danced around her.

Au revoir, closed minds and stuffy traditions.

Off with their head!

When London hosted the Summer Games in 2012, it paid homage to the British monarchy by giving Queen Elizabeth II a starring role in the opening ceremony. Actor Daniel Craig, in character as James Bond, was shown visiting the head of state at Buckingham Palace before the pair appeared to parachute out of a helicopter over the stadium.

The French love to joyfully tease their neighbors across the English Channel and, perhaps not incidentally, took a totally different, utterly irreverent tack.

A freshly guillotined Marie Antoinette, France’s last queen before the French Revolution of 1789, was shown clutching her severed head, singing: “The aristocrats, we’ll hang them.” Then, heavy metal band Gojira tore the Paris evening with screeching electric guitar.

Freedom: Does anyone do it better than the French?

AP journalists Sylvie Corbet in Paris and Jim Heintz in Tallinn, Estonia, contributed.

For more coverage of the Paris Olympics, visit https://apnews.com/hub/2024-paris-olympic-games .