| Sumario: | Introduction: Adolescent physical and mental health is a critical public health concern. This study investigates the potential of integrating wearable devices and artificial intelligence (AI) to enhance social adaptability and mental health through physical activity. Methods: First, we employed regression modeling and two‐stage least squares analysis using data from the China Education Panel Survey (CEPS) to examine the relationship between physical activity and social adaptability in adolescents. Subsequently, wearable devices were utilized to monitor exercise intensity, frequency, and peer interactions. A convolutional neural network (CNN) was then applied to classify behavioral data, enabling personalized behavioral profiling and the development of tailored exercise plans. Results: Our findings confirmed a significant positive association between physical activity and social adaptability in adolescents. Moreover, the study elucidated the mediating roles of family and friend network interactions, as well as self‐efficacy, in this relationship. Discussion: The integration of wearable devices and AI offers a promising approach to personalized health education. By leveraging behavioral clustering techniques, we propose a novel method to optimize health education strategies and promote adolescent well‐being. This study contributes to the advancement of public health interventions and the development of innovative solutions for improving adolescent health outcomes. Conclusions: The integration of wearable technology and AI‐driven personalized exercise recommendations presents a viable strategy for enhancing adolescent social adaptability and mental health, offering actionable guidance for the design of technology‐supported public health education programs.
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