Evaluating machine learning-enabled and multimodal data-driven exercise prescriptions for mental health : a randomized controlled trial protocol
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Article number | 1352420 |
| Journal / Publication | Frontiers in Psychiatry |
| Volume | 15 |
| Online published | 15 Jan 2024 |
| Publication status | Published - 2024 |
Link(s)
| DOI | DOI |
|---|---|
| Attachment(s) | Documents
Publisher's Copyright Statement
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| Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85183879046&origin=recordpage |
| Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(9aaa96bd-7562-4395-ba41-acc8518990cc).html |
Abstract
Background: Mental illnesses represent a significant global health challenge, affecting millions with far-reaching social and economic impacts. Traditional exercise prescriptions for mental health often adopt a one-size-fits-all approach, which overlooks individual variations in mental and physical health. Recent advancements in artificial intelligence (AI) offer an opportunity to tailor these interventions more effectively.
Objective: This study aims to develop and evaluate a multimodal data-driven AI system for personalized exercise prescriptions, targeting individuals with mental illnesses. By leveraging AI, the study seeks to overcome the limitations of conventional exercise regimens and improve adherence and mental health outcomes.
Methods: The study is conducted in two phases. Initially, 1,000 participants will be recruited for AI model training and testing, with 800 forming the training set, augmented by 9,200 simulated samples generated by ChatGPT, and 200 as the testing set. Data annotation will be performed by experienced physicians from the Department of Mental Health at Guangdong Second Provincial General Hospital. Subsequently, a randomized controlled trial (RCT) with 40 participants will be conducted to compare the AI-driven exercise prescriptions against standard care. Assessments will be scheduled at 6, 12, and 18 months to evaluate cognitive, physical, and psychological outcomes.
Expected outcomes: The AI-driven system is expected to demonstrate greater effectiveness in improving mental health outcomes compared to standard exercise prescriptions. Personalized exercise regimens, informed by comprehensive data analysis, are anticipated to enhance participant adherence and overall mental well-being. These outcomes could signify a paradigm shift in exercise prescription for mental health, paving the way for more personalized and effective treatment modalities.
Registration and ethical approval: This is approved by Human Experimental Ethics Inspection of Guangzhou Sport University, and the registration is under review by ChiCTR.
© 2024 Tan, Xiao, Jing, Xie, Lu, Xiang and Ren.
Objective: This study aims to develop and evaluate a multimodal data-driven AI system for personalized exercise prescriptions, targeting individuals with mental illnesses. By leveraging AI, the study seeks to overcome the limitations of conventional exercise regimens and improve adherence and mental health outcomes.
Methods: The study is conducted in two phases. Initially, 1,000 participants will be recruited for AI model training and testing, with 800 forming the training set, augmented by 9,200 simulated samples generated by ChatGPT, and 200 as the testing set. Data annotation will be performed by experienced physicians from the Department of Mental Health at Guangdong Second Provincial General Hospital. Subsequently, a randomized controlled trial (RCT) with 40 participants will be conducted to compare the AI-driven exercise prescriptions against standard care. Assessments will be scheduled at 6, 12, and 18 months to evaluate cognitive, physical, and psychological outcomes.
Expected outcomes: The AI-driven system is expected to demonstrate greater effectiveness in improving mental health outcomes compared to standard exercise prescriptions. Personalized exercise regimens, informed by comprehensive data analysis, are anticipated to enhance participant adherence and overall mental well-being. These outcomes could signify a paradigm shift in exercise prescription for mental health, paving the way for more personalized and effective treatment modalities.
Registration and ethical approval: This is approved by Human Experimental Ethics Inspection of Guangzhou Sport University, and the registration is under review by ChiCTR.
© 2024 Tan, Xiao, Jing, Xie, Lu, Xiang and Ren.
Research Area(s)
- artificial intelligence, exercise prescription, mental health, personalized medicine, randomized controlled trial
Citation Format(s)
Evaluating machine learning-enabled and multimodal data-driven exercise prescriptions for mental health: a randomized controlled trial protocol. / Tan, Miaoqing; Xiao, Yanning; Jing, Fengshi et al.
In: Frontiers in Psychiatry, Vol. 15, 1352420, 2024.
In: Frontiers in Psychiatry, Vol. 15, 1352420, 2024.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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