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Qi W, Zhu X, He D, Wang B, Cao S, Dong C, Li Y, Chen Y, Wang B, Shi Y, Jiang G, Liu F, Boots LMM, Li J, Lou X, Yao J, Lu X, Kang J. Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis. J Med Internet Res 2024; 26:e57830. [PMID: 39116438 PMCID: PMC11342017 DOI: 10.2196/57830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/04/2024] [Accepted: 06/25/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research. OBJECTIVE The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally. METHODS This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels. RESULTS To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers. CONCLUSIONS The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.
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Affiliation(s)
- Wenhao Qi
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiaohong Zhu
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Danni He
- School of Nursing, Hangzhou Normal University, Hangzhou, China
- Nursing Department, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Bin Wang
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Shihua Cao
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Chaoqun Dong
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Yunhua Li
- College of Education, Chengdu College of Arts and Sciences, Sichuan, China
| | - Yanfei Chen
- School of Nursing, Hangzhou Normal University, Hangzhou, China
- Nursing Department, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Bingsheng Wang
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Yankai Shi
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Guowei Jiang
- Department of Psychiatry and Neuropsychology and Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, Netherlands
| | - Fang Liu
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
| | - Lizzy M M Boots
- Department of Psychiatry and Neuropsychology and Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, Netherlands
| | - Jiaqi Li
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiajing Lou
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Jiani Yao
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiaodong Lu
- Department of Neurology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Junling Kang
- Department of Neurology, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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Wu M, Islam MM, Poly TN, Lin MC. Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis. Interact J Med Res 2024; 13:e54490. [PMID: 38621231 PMCID: PMC11058558 DOI: 10.2196/54490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/27/2024] [Accepted: 03/04/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. OBJECTIVE The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. METHODS We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. RESULTS A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). CONCLUSIONS This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.
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Affiliation(s)
- MeiJung Wu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department of Nursing, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Md Mohaimenul Islam
- Department of Outcomes and Translational Sciences, College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Ming-Chin Lin
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan
- Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan
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Jiang S, Ng JYY, Chong KH, Peng B, Ha AS. Effects of eHealth Interventions on 24-Hour Movement Behaviors Among Preschoolers: Systematic Review and Meta-Analysis. J Med Internet Res 2024; 26:e52905. [PMID: 38381514 PMCID: PMC10918543 DOI: 10.2196/52905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/04/2023] [Accepted: 01/18/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND The high prevalence of unhealthy movement behaviors among young children remains a global public health issue. eHealth is considered a cost-effective approach that holds great promise for enhancing health and related behaviors. However, previous research on eHealth interventions aimed at promoting behavior change has primarily focused on adolescents and adults, leaving a limited body of evidence specifically pertaining to preschoolers. OBJECTIVE This review aims to examine the effectiveness of eHealth interventions in promoting 24-hour movement behaviors, specifically focusing on improving physical activity (PA) and sleep duration and reducing sedentary behavior among preschoolers. In addition, we assessed the moderating effects of various study characteristics on intervention effectiveness. METHODS We searched 6 electronic databases (PubMed, Ovid, SPORTDiscus, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials) for experimental studies with a randomization procedure that examined the effectiveness of eHealth interventions on 24-hour movement behaviors among preschoolers aged 2 to 6 years in February 2023. The study outcomes included PA, sleep duration, and sedentary time. A meta-analysis was conducted to assess the pooled effect using a random-effects model, and subgroup analyses were conducted to explore the potential effects of moderating factors such as intervention duration, intervention type, and risk of bias (ROB). The included studies underwent a rigorous ROB assessment using the Cochrane ROB tool. Moreover, the certainty of evidence was evaluated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) assessment. RESULTS Of the 7191 identified records, 19 (0.26%) were included in the systematic review. The meta-analysis comprised a sample of 2971 preschoolers, which was derived from 13 included studies. Compared with the control group, eHealth interventions significantly increased moderate to vigorous PA (Hedges g=0.16, 95% CI 0.03-0.30; P=.02) and total PA (Hedges g=0.37, 95% CI 0.02-0.72; P=.04). In addition, eHealth interventions significantly reduced sedentary time (Hedges g=-0.15, 95% CI -0.27 to -0.02; P=.02) and increased sleep duration (Hedges g=0.47, 95% CI 0.18-0.75; P=.002) immediately after the intervention. However, no significant moderating effects were observed for any of the variables assessed (P>.05). The quality of evidence was rated as "moderate" for moderate to vigorous intensity PA and sedentary time outcomes and "low" for sleep outcomes. CONCLUSIONS eHealth interventions may be a promising strategy to increase PA, improve sleep, and reduce sedentary time among preschoolers. To effectively promote healthy behaviors in early childhood, it is imperative for future studies to prioritize the development of rigorous comparative trials with larger sample sizes. In addition, researchers should thoroughly examine the effects of potential moderators. There is also a pressing need to comprehensively explore the long-term effects resulting from these interventions. TRIAL REGISTRATION PROSPERO CRD42022365003; http://tinyurl.com/3nnfdwh3.
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Affiliation(s)
- Shan Jiang
- Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Johan Y Y Ng
- Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Kar Hau Chong
- School of Health and Society and Early Start, Faculty of the Arts, Social Sciences and Humanities, University of Wollongong, Wollongong, Australia
| | - Bo Peng
- Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Amy S Ha
- Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
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Wu CC, Islam MM, Poly TN, Weng YC. Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research. Diagnostics (Basel) 2024; 14:397. [PMID: 38396436 PMCID: PMC10887584 DOI: 10.3390/diagnostics14040397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/03/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric analysis is essential. The purposes of this study are to systematically analyze and quantify the scientific output, research trends, and collaborative networks in the application of AI to kidney disease. This study collected AI-related articles published between 2012 and 20 November 2023 from the Web of Science. Descriptive analyses of research trends in the application of AI in kidney disease were used to determine the growth rate of publications by authors, journals, institutions, and countries. Visualization network maps of country collaborations and author-provided keyword co-occurrences were generated to show the hotspots and research trends in AI research on kidney disease. The initial search yielded 673 articles, of which 631 were included in the analyses. Our findings reveal a noteworthy exponential growth trend in the annual publications of AI applications in kidney disease. Nephrology Dialysis Transplantation emerged as the leading publisher, accounting for 4.12% (26 out of 631 papers), followed by the American Journal of Transplantation at 3.01% (19/631) and Scientific Reports at 2.69% (17/631). The primary contributors were predominantly from the United States (n = 164, 25.99%), followed by China (n = 156, 24.72%) and India (n = 62, 9.83%). In terms of institutions, Mayo Clinic led with 27 contributions (4.27%), while Harvard University (n = 19, 3.01%) and Sun Yat-Sen University (n = 16, 2.53%) secured the second and third positions, respectively. This study summarized AI research trends in the field of kidney disease through statistical analysis and network visualization. The findings show that the field of AI in kidney disease is dynamic and rapidly progressing and provides valuable information for recognizing emerging patterns, technological shifts, and interdisciplinary collaborations that contribute to the advancement of knowledge in this critical domain.
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Affiliation(s)
- Chieh-Chen Wu
- Department of Healthcare Information and Management, School of Health and Medical Engineering, Ming Chuan University, Taipei 111, Taiwan;
| | - Md. Mohaimenul Islam
- Outcomes and Translational Sciences, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA;
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan;
| | - Yung-Ching Weng
- Department of Healthcare Information and Management, School of Health and Medical Engineering, Ming Chuan University, Taipei 111, Taiwan;
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Chen L, Zhen W, Peng D. Research on digital tool in cognitive assessment: a bibliometric analysis. Front Psychiatry 2023; 14:1227261. [PMID: 37680449 PMCID: PMC10482043 DOI: 10.3389/fpsyt.2023.1227261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 07/31/2023] [Indexed: 09/09/2023] Open
Abstract
Objective The number of research into new cognitive assessment tools has increased rapidly in recent years, sparking great interest among professionals. However, there is still little literature revealing the current status and future trends of digital technology use in cognitive assessment. The aim of this study was to summarize the development of digital cognitive assessment tools through the bibliometric method. Methods We carried out a comprehensive search in the Web of Science Core Collection to identify relevant papers published in English between January 1, 2003, and April 3, 2023. We used the subjects such as "digital," "computer," and "cognitive," and finally 13,244 related publications were collected. Then we conducted the bibliometric analysis by Bibliometrix" R-package, VOSviewer and CiteSpace software, revealing the prominent countries, authors, institutions, and journals. Results 11,045 articles and 2,199 reviews were included in our analyzes. The number of annual publications in this field was rising rapidly. The results showed that the most productive countries, authors and institutions were primarily located in economically developed regions, especially the North American, European, and Australian countries. Research cooperation tended to occur in these areas as well. The application of digital technology in cognitive assessment appealed to growing attention during the outbreak of the COVID-19 epidemic. Conclusion Digital technology uses have had a great impact on cognitive assessment and health care. There have been substantial papers published in these areas in recent years. The findings of the study indicate the great potential of digital technology in cognitive assessment.
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Affiliation(s)
- Leian Chen
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
| | - Weizhe Zhen
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Dantao Peng
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
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Wu CC, Su CH, Islam MM, Liao MH. Artificial Intelligence in Dementia: A Bibliometric Study. Diagnostics (Basel) 2023; 13:2109. [PMID: 37371004 DOI: 10.3390/diagnostics13122109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/10/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
The applications of artificial intelligence (AI) in dementia research have garnered significant attention, prompting the planning of various research endeavors in current and future studies. The objective of this study is to provide a comprehensive overview of the research landscape regarding AI and dementia within scholarly publications and to suggest further studies for this emerging research field. A search was conducted in the Web of Science database to collect all relevant and highly cited articles on AI-related dementia research published in English until 16 May 2023. Utilizing bibliometric indicators, a search strategy was developed to assess the eligibility of titles, utilizing abstracts and full texts as necessary. The Bibliometrix tool, a statistical package in R, was used to produce and visualize networks depicting the co-occurrence of authors, research institutions, countries, citations, and keywords. We obtained a total of 1094 relevant articles published between 1997 and 2023. The number of annual publications demonstrated an increasing trend over the past 27 years. Journal of Alzheimer's Disease (39/1094, 3.56%), Frontiers in Aging Neuroscience (38/1094, 3.47%), and Scientific Reports (26/1094, 2.37%) were the most common journals for this domain. The United States (283/1094, 25.86%), China (222/1094, 20.29%), India (150/1094, 13.71%), and England (96/1094, 8.77%) were the most productive countries of origin. In terms of institutions, Boston University, Columbia University, and the University of Granada demonstrated the highest productivity. As for author contributions, Gorriz JM, Ramirez J, and Salas-Gonzalez D were the most active researchers. While the initial period saw a relatively low number of articles focusing on AI applications for dementia, there has been a noticeable upsurge in research within this domain in recent years (2018-2023). The present analysis sheds light on the key contributors in terms of researchers, institutions, countries, and trending topics that have propelled the advancement of AI in dementia research. These findings collectively underscore that the integration of AI with conventional treatment approaches enhances the effectiveness of dementia diagnosis, prediction, classification, and monitoring of treatment progress.
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Affiliation(s)
- Chieh-Chen Wu
- Department of Healthcare Information and Management, School of Health Technology, Ming Chuan University, Taipei 333, Taiwan
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei 111369, Taiwan
| | - Chun-Hsien Su
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei 111369, Taiwan
- Graduate Institute of Sports Coaching Science, College of Kinesiology and Health, Chinese Culture University, Taipei 11114, Taiwan
| | | | - Mao-Hung Liao
- Superintendent Office, Yonghe Cardinal Tien Hospital, New Taipei City 23148, Taiwan
- Department of Healthcare Administration, Asia Eastern University of Science and Technology, Banciao District, New Taipei City 220303, Taiwan
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Yang Z, Chen S, Bao R, Li R, Bao K, Feng R, Zhong Z, Wang X. Public Health Concern on Sedentary Behavior and Cardiovascular Disease: A Bibliometric Analysis of Literature from 1990 to 2022. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121764. [PMID: 36556966 PMCID: PMC9780999 DOI: 10.3390/medicina58121764] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/26/2022] [Accepted: 10/04/2022] [Indexed: 12/02/2022]
Abstract
Background and Objectives: Cardiovascular disease is a long-term threat to global public health security, while sedentary behavior is a modifiable behavior among cardiovascular risk factors. This study aimed to analyze the peer-reviewed literature published globally on sedentary behavior and cardiovascular disease (SB-CVD) and identify the hotspots and frontiers within this research area. Materials and Methods: Publications on SB-CVD from 1990 to 2022 were retrieved from the Web of Science Core Collection. CiteSpace and VOSviewer were applied to perform bibliometric and knowledge mapping visualization analyses. Results: A total of 2071 publications were retrieved, presenting a gradual growing trend. Authors from the USA topped the list with 748 (36.12%), followed by authors from England (373, 18.01%) and Australia (354, 17.09%). The University of Queensland, Australia, led with 95 (4.5%) publications. The top five active authors were all from Australia, while Dunstan D and Owen N published the most documents (56, 2.7%). A total of 71.27% of the publications received funding, and the United States Department of Health and Human Services provided 363 (17.53%) grants. Public Environmental Occupational Health (498, 24.05%), Sport Sciences (237, 11.44%), and Cardiac Cardiovascular Systems (212, 10.24%) were the three most popular disciplines, while PLOS One (96, 4.64%) and BMC Public Health (88, 4.25%) were the two most popular journals. Investigations within the SB-CVD research area addressed the entire lifespan, the most popular type of research was the epidemiological study, and the accelerometer was the primary instrument for measuring sedentary behavior. In terms of variables, physical activity and sedentary behavior were the dominant lifestyle behaviors, while obesity and hypertension were common health problems. Occupational physical activity and guidelines are at the frontier and are currently in the burst stage. Conclusions: The last three decades have witnessed the rapid development of the SB-CVD research area, and this study provided further research ideas for subsequent investigations.
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Affiliation(s)
- Zhen Yang
- Department of Movement Sciences, KU Leuven, 3001 Leuven, Belgium
| | - Sitong Chen
- Institute for Health and Sport, Victoria University, Melbourne, VIC 3011, Australia
| | - Ran Bao
- Priority Research Centre for Physical Activity and Nutrition, School of Education, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Ruizhe Li
- Department of Rehabilitation Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Kaiming Bao
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA 90033, USA
| | - Renzhi Feng
- School of Sports Medicine and Rehabilitation, Beijing Sport University, Beijing 100084, China
| | - Ziyi Zhong
- School of Biomedical Sciences, University of Leeds, Leeds LS2 9JT, UK
| | - Xuebin Wang
- Department of Physical Education, Shanghai Jiao Tong University, Shanghai 200240, China
- Correspondence:
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