Last update:
11-Nov-2026

Wearable sensors offer non-invasive, continuous health monitoring and are rapidly transforming clinical exercise physiology. Sleep, a cornerstone of physiological recovery, remains under-assessed in outpatient care, despite its impact on adaptation, cardiovascular function, metabolic regulation, and immune restoration. This review explores the role of wearable technologies −such as smartwatches, rings, headbands, chest straps, and smart textiles− in tracking sleep architecture, heart rate variability (HRV), respiratory metrics, and circadian rhythms in real-world conditions. Devices integrating photoplethysmography, accelerometry, electrocardiography (ECG), and electroencephalogram (EEG) increasingly provide accurate and ecologically valid insights into recovery dynamics. Artificial intelligence (AI) enhances the clinical potential of these wearables by enabling real-time, individualized feedback and predictive modeling. Applications include recovery monitoring in cardiac and pulmonary rehabilitation, early detection of sleep-disordered breathing, assessment of post-COVID fatigue, and identification of neurodegenerative risk through changes in sleep patterns. In older adults and those with metabolic disorders, sleep-derived metrics such as reduced deep sleep or altered HRV provide actionable data for tailoring exercise prescriptions and detecting maladaptive responses. However, limitations still remain. Many devices lack validation against gold-standard polysomnography, and proprietary algorithms limit clinical transparency. Integration into electronic health records and routine workflows is still uncommon. Moreover, issues of digital access, data privacy, and algorithmic bias pose ethical and practical challenges, particularly in underserved populations. Despite these barriers, wearable sleep monitoring presents a promising frontier in recovery science. By combining biosignal analysis with AI-driven interpretation, these technologies enable a shift from passive data collection to active, personalized health management. Future research should prioritize standardized validation across diverse populations, open-source algorithm development, and equitable deployment. Ultimately, sleep metrics −when integrated thoughtfully− can serve as critical biomarkers of readiness and recovery, empowering clinicians and patients alike in optimizing health outcomes.

Key words: Artificial intelligence, Recovery of function, Sleep monitoring.