Background: Consumer wearable devices collect physiological data from over one billion users worldwide, creating opportunities for population-level infectious disease surveillance. No review has mapped the use of aggregated wearable data for outbreak detection. Methods: This scoping review followed JBI methodology and was reported per PRISMA-ScR guidelines. Five databases (PubMed, Embase, Scopus, Web of Science, IEEE Xplore) were searched on 24 March 2026. Studies aggregating consumer wearable device data from ≥100 individuals for infectious disease surveillance, outbreak detection, or epidemic monitoring were eligible. AI-assisted title/abstract screening was validated by the reviewer against a stratified 20% human sample (n = 262; agreement 83.6%, Cohen's kappa 0.18), with all disagreements resolved in favour of the human decision. Data were extracted using a standardised 27-variable charting form. The protocol was registered on the Open Science Framework (https://doi.org/10.17605/OSF.IO/Y6827). Results: From 2,153 records, 108 studies were included after deduplication (843 removed) and two-stage screening. COVID-19 dominated (97 studies, 90%). Fitbit was the most used named device (27 studies, 25%), heart rate the most used signal (64, 59%). Twenty-three studies reported lead times of 1-75 days over traditional surveillance (median 7 days). Study scale ranged from 100 to 775,002 participants (median 2,519). Only 37% reported ethics approval, 19% discussed equity, and 22% shared code. Research gaps were concentrated in dengue, malaria, and RSV. Conclusions: Wearable-based population surveillance has demonstrated potential for early outbreak detection but remains overwhelmingly COVID-era, geographically concentrated, and deficient in performance reporting, equity, and reproducibility. Expansion to non-COVID infectious diseases and low- and middle-income country settings is the critical next step.
Hayden Luke Farquhar (Sat,) studied this question.