Utilizing machine learning algorithms for personalized workout recommendations and monitoring: A systematic review on smartwatch-assisted exercise prescription

Background Smartwatches, equipped with advanced sensors, have become increasingly prominent in health and fitness domains. Their integration with machine learning (ML) algorithms presents novel opportunities for personalized exercise prescription and physiological monitoring. Objective This systematic review aimed to evaluate the effectiveness, limitations, and practical applications of smartwatch-ML systems in delivering tailored fitness interventions and health tracking. Methods Following PRISMA guidelines, five databases (PubMed, Scopus, IEEE Xplore, Web of Science, and SPORTDiscus) were searched for studies published from January 2000 to December 2023. Inclusion criteria required empirical studies involving human participants, the use of smartwatches for exercise monitoring or prescription, and the application of ML algorithms. Forty-nine studies met the eligibility criteria and were synthesized narratively using thematic clustering. Results The majority of included studies demonstrated high algorithmic performance in activity recognition (>98% accuracy) and vital sign tracking. However, external validity was often limited due to lab-based testing, narrow demographic representation, and lack of standardized evaluation frameworks. Few studies incorporated explainable artificial intelligence, behavioral adaptation, or longitudinal validation. Ethical and regulatory considerations were rarely addressed. Conclusion Smartwatch-ML integration holds substantial promise for individualized, real-time health support, especially in fitness and rehabilitation. To ensure broader impact and clinical adoption, future research must address generalizability, ethical data governance, interpretability, and interdisciplinary system design.