Development of an alcohol biosensor non-wear algorithm: laboratory-based machine learning and field-based deployment.

ABSTRACT: Wrist-worn alcohol biosensors can continuously track alcohol consumption, but their measurements are disrupted when the device is removed. Left unaddressed, non-wear data compromises observations of alcohol use and subsequent predictions of intoxication. To advance beyond commonly used temperature cutoffs and enable more precise detection of non-wear, we trained a random forest algorithm using laboratory ground truth data. Participants in Study One (N = 36) wore a wrist-worn alcohol biosensor (BACtrack Skyn) across 61 five-hour laboratory sessions, generating ground truth non-wear by removing and re-applying the device at specified times. Algorithm features included temperature, motion, and their time-series quadratic coefficients. According to device-based cross-validation, the algorithm

SUBMITTER: Didier NA 

PROVIDER: S-EPMC12375727 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature