Jubair Hassan, Mehenaz Mithela
Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh.
Department of Electrical and Electronic Engineering, Varendra University, Rajshahi, Bangladesh.
Digit Health. 2025 Sep 4;11:20552076251355365. doi: 10.1177/20552076251355365. eCollection 2025 Jan-Dec.
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.
This systematic review aimed to evaluate the effectiveness, limitations, and practical applications of smartwatch-ML systems in delivering tailored fitness interventions and health tracking.
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.
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.
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.
配备先进传感器的智能手表在健康和健身领域日益突出。它们与机器学习（ML）算法的整合为个性化运动处方和生理监测带来了新机遇。
本系统评价旨在评估智能手表-ML系统在提供定制化健身干预和健康追踪方面的有效性、局限性及实际应用。
遵循PRISMA指南，检索了五个数据库（PubMed、Scopus、IEEE Xplore、Web of Science和SPORTDiscus）中2000年1月至2023年12月发表的研究。纳入标准要求进行涉及人类参与者的实证研究，使用智能手表进行运动监测或处方制定，以及应用ML算法。49项研究符合纳入标准，并采用主题聚类进行叙述性综合分析。
大多数纳入研究在活动识别（准确率>98%）和生命体征追踪方面表现出较高的算法性能。然而，由于基于实验室的测试、狭窄的人口统计学代表性以及缺乏标准化评估框架，外部效度往往受到限制。很少有研究纳入可解释人工智能、行为适应或纵向验证。伦理和监管考量很少被提及。
智能手表与ML的整合在个性化实时健康支持方面具有巨大潜力，尤其是在健身和康复领域。为确保更广泛的影响和临床应用，未来研究必须解决普遍性、伦理数据治理、可解释性和跨学科系统设计等问题。
