Deep learning to promote health through sports and physical training

• Published in: Frontiers in public health Vol. 13; p. 1583581
• Main Author: Li, Xinyue
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 27.05.2025
• Subjects: Adult; artificial intelligence; Deep Learning; Exercise; Female; health improvement; Health Promotion - methods; Humans; Male; physical training; Public Health; Sports; sports science; time-series analysis; deep learning; sports science; health improvement; artificial intelligence; physical training; time-series analysis
• DOI: 10.3389/fpubh.2025.1583581
• ISSN: 2296-2565

Physical activity plays a crucial role in maintaining health and preventing chronic diseases. However, accurately assessing the impact of sports and physical training on health improvement remains a challenge. Recent advancements in deep learning and time-series analysis offer an opportunity to develop more personalized and accurate predictive models for assessing health improvement trends. This study proposes a Health Improvement Score (HIS) prediction model based on a sequence-to-sequence deep learning architecture with Long Short-Term Memory (LSTM) networks and an attention mechanism. The model integrates heterogeneous time-series data, including physiological parameters (heart rate, blood oxygen levels, respiration rate), activity metrics (steps, distance, calories burned), sleep patterns, and body measurements. A dataset comprising 384 participants over a 32-day period was used to train and evaluate the model. The experimental results demonstrate that the proposed HIS prediction model outperforms traditional and machine learning-based models. It achieves 22.8% lower Mean Absolute Error (MAE), 19.3% lower Root Mean Squared Error (RMSE), 6.5% higher , and 7.9% higher Explained Variance Score (EVS) compared to competitive models. The proposed HIS prediction model effectively captures complex temporal dependencies and improves the accuracy of health improvement predictions.