AI-Enhanced Strategies For Personalized And Accessible Physical Activity In Exercise Physiology and Health Promotion

The field of exercise physiology has traditionally focused on understanding the physiological responses and adaptations to physical activity, including exercise prescriptions, biomechanical analyses, and mind-body practices like Tai Chi and yoga—to enhance health outcomes across populations. However, these approaches often encounter challenges in personalization, scalability, real-time physiological feedback, and long-term adherence due to limitations in data integration and scalability. In parallel, rapid developments in artificial intelligence (AI) and digital health technologies offer transformative potential to address these gaps by enabling precise physiological modeling, adaptive exercise regimens, and integrated monitoring of biomarkers such as cardiovascular responses, metabolic changes, and neuromuscular adaptations. Recent studies reveal that AI can enable more tailored exercise recommendations, facilitate continuous health monitoring through smart devices, and enhance user engagement via immersive technologies such as virtual and augmented reality. Yet, large-scale integration, evidence-based evaluation, and the synthesis of multidimensional health data remain emerging challenges.This Research Topic aims to explore the integration of AI with core principles of exercise physiology to enhance the precision, efficacy, and accessibility of physical activity interventions. By focusing on the physiological mechanisms-such as energy metabolism, muscle physiology, and cardiorespiratory function-rather than public health strategies, it seeks to bridge human-centered physiological practices with AI-driven innovations. Key objectives include investigating how AI-driven solutions can optimize physiologically tailored exercise prescriptions, analyze multidimensional behavioral and physiological and biomechanical data, improve accessibility through digital tools while emphasizing physiological outcomes, and evaluate impacts on adherence and health via longitudinal physiological studies. Additionally, this topic will address ethical, equity, and regulatory considerations to ensure responsible advancements that prioritize physiological integrity and inclusivity.To advance insights into the fusion of AI with exercise physiology for physical activity promotion, we welcome articles addressing, but not limited to, the following themes:• AI-driven algorithms for physiologically personalized exercise planning and adaptation• Smart wearables and IoT for real-time physiological monitoring, feedback on exercise responses, and injury prevention• Virtual, augmented, and somatosensory technologies for enhancing motor learning, physiological skill acquisition, and immersive training• Integration of multidimensional physiological data integration for comprehensive exercise management• Theoretical and practical studies on AI-based mind-body exercise integrations (e.g., Tai Chi, yoga, Qigong) focusing on physiological benefits like stress reduction and autonomic nervous system regulation• Translational AI applications in exercise physiology for sports performance, fitness optimization, and rehabilitation of chronic conditions through physiological lenses • Evaluations, mechanistic studies, and longitudinal research on AI-integrated models emphasizing physiological adaptations and outcomes• Ethical, privacy, equity, and policy considerations in AI-enabled physical activity promotionSubmissions of Original Research, Review, Methods, Perspective/Opinion, and Case Report articles are welcome to capture diverse insights and approaches within the scope of this Research Topic. Topic editors: Zhixiong Zhou, Zan Gao, Yang Bai, Fan Yang Source: https://www.frontiersin.org/research-topics/79137/ai-enhanced-strategies-for-personalized-and-accessible-physical-activity-in-exercise-physiology-and-health-promotion