Background: Consumer wearable technologies have become ubiquitous, with clinical and non-clinical populations leveraging a variety of devices to quantify various aspects of health and wellness. However, the accuracy with which these devices measure biometric outcomes such as heart rate, sleep and physical activity remains unclear. Objective: To conduct a ‘living’ (i.e. ongoing) evaluation of the accuracy of consumer wearable technologies in measuring various physiological outcomes. Methods: A systematic search of the literature was conducted in the following scientific databases: MEDLINE via PubMed, Embase, Cinahl and SPORTDiscus via EBSCO. The inclusion criteria required systematic reviews or meta-analyses that evaluated the validation of consumer wearable devices against accepted reference standards. In addition to publication details, review protocol, device specifics and a summary of the authors’ results, we extracted data on mean absolute percentage error (MAPE), pooled absolute bias, intraclass correlation coefficients (ICCs) and mean absolute differences. Results: Of 904 identified studies through the initial search, 24 systematic reviews met our inclusion criteria; these systematic reviews included 249 non-duplicate validation studies of consumer wearable devices involving 430,465 participants (43% female). Of the commercially available wearable devices released to date, approximately 11% have been validated for at least one biometric outcome. However, because a typical device can measure a multitude of biometric outcomes, the number of validation studies conducted represents just 3.5% of the total needed for a comprehensive evaluation of these devices. For heart rate, wearables showed a mean bias of ± 3%. In arrhythmia detection, wearables exhibited a pooled sensitivity and specificity of 100% and 95%, respectively. For aerobic capacity, wearables significantly overestimated VO_2max by ± 15.24% during resting tests and ± 9.83% during exercise tests. Physical activity intensity measurements had a mean absolute error ranging from 29 to 80%, depending on the intensity of the activity being undertaken. Wearables mostly underestimated step counts (mean absolute percentage errors ranging from − 9 to 12%) and energy expenditure (mean bias = − 3 kcal per minute, or − 3%, with error ranging from − 21.27 to 14.76%). For blood oxygen saturation, wearables showed a mean absolute difference of up to 2.0%. Sleep measurement showed a tendency to overestimate total sleep time (mean absolute percentage error typically > 10%). Conclusions: While consumer wearables show promise in health monitoring, a conclusive assessment of their accuracy is impeded by pervasive heterogeneity in research outcomes and methodologies. There is a need for standardised validation protocols and collaborative industry partnerships to enhance the reliability and practical applicability of wearable technology assessments. Prospero ID: CRD42023402703.

The widespread use of wearable devices has enabled continuous monitoring of biometric data, including heart rate variability (HRV) and resting heart rate (RHR). However, the validity of these measurements, particularly from consumer devices like Apple Watch, remains underexplored. This study aimed to validate HRV measurements obtained from Apple Watch Series 9 and Ultra 2 against the Polar H10 chest strap paired with the Kubios HRV software, which together served as the reference standard. A prospective cohort of 39 healthy adults provided 316 HRV measurements over a 14-day period. Generalized Estimating Equations were used to assess the difference in HRV between devices, accounting for repeated measures. Apple Watch tended to underestimate HRV by an average of 8.31 ms compared to the Polar H10 (p = 0.025), with a mean absolute percentage error (MAPE) of 28.88% and a mean absolute error (MAE) of 20.46 ms. The study found no significant impact of RHR discrepancies on HRV differences (p = 0.156), with RHR showing a mean difference of −0.08 bpm, an MAPE of 5.91%, and an MAE of 3.73 bpm. Equivalence testing indicated that the HRV measurements from Apple Watch did not fall within the pre-specified equivalence margin of ±10 ms. Despite accurate RHR measurements, these findings underscore the need for improved HRV algorithms in consumer wearables and caution in interpreting HRV data for clinical or performance monitoring.

Introduction: Consumer wearables increasingly provide users with Composite Health Scores (CHS) – integrated biometric indices that claim to quantify readiness, recovery, stress, or overall well-being. Despite their growing adoption, the validity, transparency, and physiological relevance of these scores remain unclear. This study systematically evaluates CHS from leading wearable manufacturers to assess their underlying methodologies, contributors, and scientific basis. Content: Information was synthesised from publicly available company documentation, including technical white papers, user manuals, app interfaces, and research literature where available. We identified 14 CHS across 10 major wearable manufacturers, including Fitbit (Daily Readiness), Garmin (Body Battery™ and Training Readiness), Oura (Readiness and Resilience), WHOOP (Strain, Recovery, and Stress Monitor), Polar (Nightly Recharge™), Samsung (Energy Score), Suunto (Body Resources), Ultrahuman (Dynamic Recovery), Coros (Daily Stress), and Withings (Health Improvement Score). The most frequently incorporated biometric contributors in this catalogue of CHS were heart rate variability (86 %), resting heart rate (79 %), physical activity (71 %), and sleep duration (71 %). However, significant discrepancies were identified in data collection timeframes, metric weighting, and proprietary scoring methodologies. None of the manufacturers disclosed their exact algorithmic formulas, and few provided empirical validation or peer-reviewed evidence supporting the accuracy or clinical relevance of their scores. Summary and outlook: While the concept of CHS represent a promising innovation in digital health, their scientific validity, transparency, and clinical applicability remain uncertain. Future research should focus on establishing standardized sensor fusion frameworks, improving algorithmic transparency, and evaluating CHS across diverse populations. Greater collaboration between industry, researchers, and clinicians is essential to ensure these indices serve as meaningful health metrics rather than opaque consumer tools.