Artificial Intelligence in Exercise Prescription for Sub-optimal Health Status University Students: Current Applications, Challenges, and Future Perspectives

DOI: https://doi.org/10.62517/jhve.202616201

Author(s)

Chaoguang Chen

Affiliation(s)

Zhengzhou Normal University, Zhengzhou, Henan, China

Abstract

Sub-optimal health status has emerged as a pervasive public health challenge among university students, manifesting through complex physiological and psychological deficits. While exercise prescription is a core non-pharmacological intervention, traditional empirical models encounter significant bottlenecks in scalability and precision. This review explores the transformative potential of an Artificial Intelligence (AI)-integrated exercise prescription ecosystem in reshaping collegiate health management. We systematically delineate the evolutionary trajectory of AI in exercise science-from basic supportive tools to generative decision-support systems-and assess the current performance of Large Language Models in generating FITT-VP-compliant protocols. The review further analyzes AI-driven methodologies for multidimensional profiling, including the integration of Traditional Chinese Medicine constitutions and real-time wearable sensor data. Despite the clinical efficacy of intelligent interventions in enhancing physical fitness and mental resilience, significant challenges persist regarding algorithmic transparency, ethical data governance, and the "over-defensive" nature of AI recommendations. By synthesizing current evidence, this review provides a robust theoretical framework and a strategic roadmap for the institutionalization of AI-driven, individualized health interventions within the collegiate ecosystem.

Keywords

Sub-Optimal Health Status; Artificial Intelligence; Exercise Prescription; University Students; FITT-VP; Personalized Health Management

References

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