Scoping review of wearable device data evaluated in clinical studies

Background Wearable device data are increasingly used to provide physiological and behavioural measures in clinical studies. Aims This scoping review maps out the data collected by wearable devices from participants in clinical studies, including the approaches used to interpret this data. Methods Scopus, Web of Science, the Cochrane Library and PubMed, along with ClinicalTrials.gov, were searched using relevant keywords. Clinical studies published or registered from January 2020 to July 2026 inclusive, in which wearable devices were used to monitor participants, were sought. Data on disease demographics, sample size, gender, ethnicity, study design, wearable type, application and data interpretation approaches were extracted using a data extraction form within Covidence software and then exported and compiled for analysis. Results Out of 13 744 identified records, 147 original articles were included. Cardiovascular diseases, followed closely by neurological diseases, were the most frequently studied clinical indications. The median sample size was 85 (range 1–455 699). Overall, the most measured wearable data were physical activity (106 (72%)), heart rate (48 (33%)) and sleep (34 (22%)). 98 studies used wearable data to measure outcomes (52 primary and 46 secondary), and 49 studies used wearable data for disease monitoring. 27 studies applied advanced computational or machine learning methods, with 9 studies integrating multiple wearable-derived variables to develop composite or disease-specific digital biomarkers. Conclusions The studies included in this review report the use of wearables to provide continuous, real-world data on patients’ physiological and behavioural metrics to assess health outcomes and disease progression outside traditional clinical settings. Advanced computational approaches remain a minority, with most studies relying on device-derived measures and conventional analytical methods.