{"doi":"10.2147/nss.s373107","title":"Actigraphy-Based Sleep Detection: Validation with Polysomnography and Comparison of Performance for Nighttime and Daytime Sleep During Simulated Shift Work","abstract":"Purpose: Actigraphy-based sleep detection algorithms were mostly validated using nighttime sleep, and their performance in detecting daytime sleep is unclear. We evaluated and compared the performance of Actiware and the Cole-Kripke algorithm (C-K) – two commonly used actigraphy-based algorithms – in detecting daytime and nighttime sleep. Participants and Methods: Twenty-five healthy young adults were monitored by polysomnography and actigraphy during two in-lab protocols with scheduled nighttime and/or daytime sleep (within-subject design). Mixed-effect models were conducted to compare the sensitivity, specificity, and F1 score (a less-biased measure of accuracy) of Actiware (with low/medium/high threshold setting, separately) and C-K in detecting sleep epochs from actigraphy recordings during nighttime/daytime. t -tests and intraclass correlation coefficients were used to assess the agreement between actigraphy-based algorithms and polysomnography in scoring total sleep time (TST). Results: Sensitivity was similar between nighttime (Actiware: 0.93– 0.99 across threshold settings; C-K: 0.61) and daytime sleep (Actiware: 0.93– 0.99; C-K: 0.66) for both the C-K and Actiware (daytime/nighttime×algorithm interaction: p > 0.1). Specificity for daytime sleep was lower (Actiware: 0.35– 0.54; C-K: 0.91) than that for nighttime sleep (Actiware: 0.37– 0.62; C-K: 0.93; p = 0.001). Specificity was also higher for C-K than Actiware (p < 0.001), with no daytime/nighttime×algorithm interaction (p > 0.1). C-K had lower F1 (nighttime = 0.74; daytime = 0.77) than Actiware (nighttime = 0.95– 0.98; daytime = 0.90– 0.91) for both nighttime and daytime sleep (all p < 0.05). The daytime-nighttime difference in F1 was opposite for Actiware (daytime: 0.90– 0.91; nighttime: 0.95– 0.98) and C-K (daytime: 0.77; nighttime: 0.74; interaction p = 0.003). Bias in TST was lowest in Actiware (with medium-threshold) for nighttime sleep (underestimation of 5.99 min/8h) and in Actiware (with low-threshold) for daytime sleep (overestimation of 17.75 min/8h). Conclusion: Daytime/nighttime sleep affected specificity and F1 but not sensitivity of actigraphy-based sleep scoring. Overall, Actiware performed better than the C-K algorithm. Actiware with medium-threshold was the least biased in estimating nighttime TST, and Actiware with low-threshold was the least biased in estimating daytime TST. Keywords: Actiware, Cole-Kripke algorithm, sleep scoring, shift worker, circadian rhythms","journal":"Nature and Science of Sleep","year":2022,"id":243885,"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":32,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9227,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":396850,"name":"Peng Li","orcid":"0000-0002-4684-4909","position":1,"is_corresponding":false},{"id":285052,"name":"Christopher J. Morris","orcid":"0000-0002-7703-4474","position":2,"is_corresponding":false},{"id":694274,"name":"Xi Zheng","orcid":"0009-0001-9847-7063","position":3,"is_corresponding":false},{"id":667066,"name":"Ma Cherrysse Ulsa","orcid":null,"position":4,"is_corresponding":false},{"id":582193,"name":"Lei Gao","orcid":"0000-0003-1476-1460","position":5,"is_corresponding":false},{"id":249994,"name":"Frank A. J. L. Scheer","orcid":"0000-0002-2014-7582","position":6,"is_corresponding":false},{"id":396849,"name":"Kun Hu","orcid":"0000-0003-0350-3132","position":7,"is_corresponding":false},{"id":490630,"name":"Chenlu Gao","orcid":"0000-0001-7162-8303","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-19T00:23:20.901576Z","pmid":"36275180","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":[]}