Evaluating machine learning-enabled and multimodal data-driven exercise prescriptions for mental health: a randomized controlled trial protocol

Front Psychiatry. 2024 Jan 15:15:1352420. doi: 10.3389/fpsyt.2024.1352420. eCollection 2024.

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.

Keywords: artificial intelligence; exercise prescription; mental health; personalized medicine; randomized controlled trial.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Guangdong Provincial Education Science Planning Project titled “Research on Promoting the Physical and Mental Development of Children and Adolescents under the Guidance of Xi Jinping’s Education Concepts” (Grant No. 2021GXJK009). Additionally, funding was provided by the Guangdong Provincial Department of Education’s Special Innovation Project for General Colleges and Universities (Humanities and Social Sciences) for the project “The Relationship between Physical Fitness and Academic Achievement in Primary School Students in Guangdong Province under the Healthy China Initiative: A Large-Scale Longitudinal Study” (Grant No. 2020WTSCX047). Besides, this work was supported by 2023 Discipline Co construction Project of Guangdong Planning Office of Philosophy and Social Science “Research on Pulmonary Function Intervention of Aquatic Exercise for People after COVID-19 Recovery” (Grant No. GD23XTY25). This work was also supported by Guangzhou Science and Technology Project (SL2023A03J01238).