{"doi":"10.1093/sleep/zsaa180","title":"Predicting circadian misalignment with wearable technology: validation of wrist-worn actigraphy and photometry in night shift workers","abstract":"STUDY OBJECTIVES: A critical barrier to successful treatment of circadian misalignment in shift workers is determining circadian phase in a clinical or field setting. Light and movement data collected passively from wrist actigraphy can generate predictions of circadian phase via mathematical models; however, these models have largely been tested in non-shift working adults. This study tested the feasibility and accuracy of actigraphy in predicting dim light melatonin onset (DLMO) in fixed night shift workers. METHODS: A sample of 45 night shift workers wore wrist actigraphs before completing DLMO in the laboratory (17.0 days ± 10.3 SD). DLMO was assessed via 24 hourly saliva samples in dim light (<10 lux). Data from actigraphy were provided as input to a mathematical model to generate predictions of circadian phase. Agreement was assessed and compared to average sleep timing on non-workdays as a proxy of DLMO. Model code and an open-source prototype assessment tool are available (www.predictDLMO.com). RESULTS: Model predictions of DLMO showed good concordance with in-lab DLMO, with Lin's concordance coefficient of 0.70, which was twice as high as agreement using average sleep timing as a proxy of DLMO. The absolute mean error of the predictions was 2.88 h, with 76% and 91% of the predictions falling with 2 and 4 h, respectively. CONCLUSION: This study is the first to demonstrate the use of wrist actigraphy-based estimates of circadian phase as a clinically useful and valid alternative to in-lab measurement of DLMO in fixed night shift workers. Future research should explore how additional predictors may impact accuracy.","journal":"SLEEP","year":2020,"id":55973,"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":89,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9563,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":290032,"name":"Olivia Walch","orcid":"0000-0002-7881-6910","position":1,"is_corresponding":false},{"id":290033,"name":"Yitong Huang","orcid":"0000-0002-5200-8077","position":2,"is_corresponding":false},{"id":290034,"name":"Caleb Mayer","orcid":"0000-0002-8286-2186","position":3,"is_corresponding":false},{"id":290670,"name":"Chaewon Sagong","orcid":null,"position":4,"is_corresponding":false},{"id":290671,"name":"Andrea Cuamatzi Castelan","orcid":null,"position":5,"is_corresponding":false},{"id":290035,"name":"Helen J. Burgess","orcid":"0000-0003-3816-8194","position":6,"is_corresponding":false},{"id":290036,"name":"Thomas Roth","orcid":"0000-0001-8309-4134","position":7,"is_corresponding":false},{"id":290037,"name":"Daniel B. Forger","orcid":"0000-0001-7581-4031","position":8,"is_corresponding":false},{"id":290038,"name":"Christopher L. Drake","orcid":"0000-0002-5486-3587","position":9,"is_corresponding":false},{"id":290031,"name":"Philip Cheng","orcid":"0000-0002-2874-658X","position":0,"is_corresponding":true}],"reference_count":46,"raw_metadata":null,"created_at":"2026-07-18T21:05:29.672878Z","pmid":"32918087","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":[]}