Transforming physical fitness and exercise behaviors in adolescent health using a life log sharing model

These findings demonstrate the efficacy of integrating multimodal life log data with deep learning for accurate physical activity classification. The high accuracy of the TS-CNN-BiLSTM model supports its potential for developing personalized health promotion strategies, including tailored interventions, behavioral incentives, and social support mechanisms, to enhance adolescent engagement in physical activities and advance public health education and personalized health management.