{"doi":"10.1038/s41598-025-16640-y","title":"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 performed with excellent sensitivity to detect non-wear (0.96) and specificity to confirm wear (0.99), out-performing all univariable temperature cutoffs from 25 to 30 °C. The algorithm was then used to evaluate biosensor adherence in Study Two, a four-week field study where participants (N = 114) wore the Skyn and self-reported non-wear intervals each day. The algorithm detected 1.6 h of daily non-wear per participant and had more agreement with self-report compared with the temperature cutoff method. This non-wear algorithm can assess biosensor adherence in field studies and may also facilitate precise data imputation, resulting in more objective models of alcohol-related outcomes.","journal":"Scientific Reports","year":2025,"id":546339,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9565,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":696635,"name":"Rachel L. Gunn","orcid":"0000-0001-5244-1105","position":1,"is_corresponding":false},{"id":341494,"name":"Andrea C. King","orcid":"0000-0001-5943-7109","position":2,"is_corresponding":false},{"id":274803,"name":"Eric C. Polley","orcid":"0000-0002-5152-8740","position":3,"is_corresponding":false},{"id":634018,"name":"Jennifer E. Merrill","orcid":"0000-0002-8310-140X","position":4,"is_corresponding":false},{"id":658654,"name":"Nancy P. Barnett","orcid":"0000-0001-9634-8287","position":5,"is_corresponding":false},{"id":658652,"name":"Daniel J. Fridberg","orcid":"0000-0003-3451-1418","position":6,"is_corresponding":false},{"id":1080943,"name":"Nathan Didier","orcid":"0009-0002-0279-9314","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":null,"created_at":"2026-07-19T02:53:32.285899Z","pmid":"40851098","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}