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  1. Article ; Online: Evaluating machine learning-enabled and multimodal data-driven exercise prescriptions for mental health

    Miaoqing Tan / Yanning Xiao / Fengshi Jing / Yewei Xie / Sanmei Lu / Mingqiang Xiang / Hao Ren

    Frontiers in Psychiatry, Vol

    a randomized controlled trial protocol

    2024  Volume 15

    Abstract: BackgroundMental 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 ... ...

    Abstract BackgroundMental 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.ObjectiveThis 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.MethodsThe 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 outcomesThe 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 approvalThis is approved by Human Experimental Ethics Inspection of Guangzhou Sport University, and the registration is under ...
    Keywords mental health ; exercise prescription ; artificial intelligence ; personalized medicine ; randomized controlled trial ; Psychiatry ; RC435-571
    Subject code 796
    Language English
    Publishing date 2024-01-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Effects of Acute Exercise and Learning Strategy Implementation on Memory Function

    Paul D. Loprinzi / Faith Harris / Kyle McRaney / Morgan Chism / Raymond Deming / Timothy Jones / Liye Zou / Miaoqing Tan

    Medicina, Vol 55, Iss 9, p

    2019  Volume 568

    Abstract: Background and Objectives : Long-term potentiation (LTP), the functional connectivity among neurons, is considered a mechanism of episodic memory. Both acute exercise and learning are thought to influence memory via an LTP-related mechanism. Limited ... ...

    Abstract Background and Objectives : Long-term potentiation (LTP), the functional connectivity among neurons, is considered a mechanism of episodic memory. Both acute exercise and learning are thought to influence memory via an LTP-related mechanism. Limited research has evaluated the individual and combined effects of acute exercise and learning strategy implementation (e.g., 3-R technique, cue-integration) on memory, which was the purpose of this study. Materials and Methods : For Experiment 1, participants ( n = 80; M age = 20.9 years) were randomized into one of four experimental groups, including Exercise + Learning (E + L), Learning Only (L), Exercise Only (E), and Control Group (C; no exercise and no learning strategy implementation). The exercise stimulus involved an acute 15-min bout of lower-intensity (60% of heart rate max) walking exercise and the learning strategy involved the implementation of the 3-R technique. Experiment 2 ( n = 77; M age = 21.1 years) replicated Experiment 1 but addressed limitations (e.g., exposure level of the memory task) from Experiment 1 and employed a higher-intensity bout of exercise (77% of heart rate max). Experiment 3 ( n = 80; M age = 21.0 years) evaluated these same four experimental conditions but employed a cue-integration learning strategy and a moderate-intensity bout of acute exercise (64% of heart rate max). Results : These three experiments demonstrate that both learning techniques were effective in enhancing memory and we also provided evidence of a main effect for acute exercise (Experiment 3). However, we did not observe consistent evidence of a learning by exercise interaction effect. Conclusions: We demonstrate that both acute exercise and different learning techniques are effective in enhancing long-term memory function.
    Keywords cognition ; episodic memory ; exercise ; learning ; memory ; Medicine (General) ; R5-920
    Subject code 796
    Language English
    Publishing date 2019-09-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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