{"doi":"10.1016/j.sleh.2024.10.003","title":"Performance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting","abstract":"GOAL AND AIMS: One challenge using wearable sensors is nonwear time. Without a nonwear (e.g., capacitive) sensor, actigraphy data quality can be biased by subjective determinations confounding sleep/wake classification. We developed and evaluated a machine learning algorithm supplemented by dynamic features to discern wear/nonwear episodes. FOCUS TECHNOLOGY: Actigraphy data from wrist actigraph (Spectrum, Philips-Respironics). REFERENCE TECHNOLOGY: The built-in nonwear sensor as \"ground truth\" to classify nonwear periods using other data, mimicking features of Actiwatch 2. SAMPLE: Data were collected over 1week from employed adults (n = 853). DESIGN: Extreme gradient boosting (XGBoost), a tree-based classifier algorithm, was used to classify wear/nonwear, supplemented by dynamic features calculated over various time windows. CORE ANALYTICS: The performance of the proposed algorithm was tested over 30-second epochs. Additional analytics and exploratory analyses: Evaluation of the SHapley Additive exPlanations (SHAP) values to find the effectiveness of the dynamic features. CORE OUTCOMES: The XGBoost classifier yielded substantial improvements in balanced accuracy, sensitivity, and specificity, including dynamic features and comparison to default actiwatch classification algorithms. IMPORTANT SUPPLEMENTAL OUTCOMES: The proposed classifier effectively distinguished between valid and invalid days, and the duration of contiguous periods of nonwear correctly identified. CORE CONCLUSION: Our findings highlight the potential of XGBoost using dynamic features of varying activity levels across the time series to provide insights on wear/nonwear classification using a large dataset. The methodology provides an alternative to laborious manual benchmarking of the data for similar devices that do not have a nonwear sensor.","journal":"Sleep Health","year":2025,"id":540040,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9546,"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":366786,"name":"Linying Ji","orcid":"0000-0003-1908-3718","position":1,"is_corresponding":false},{"id":1427490,"name":"Yuqi Shen","orcid":"0009-0008-9114-8743","position":2,"is_corresponding":false},{"id":1427491,"name":"Soundar Kumara","orcid":"0000-0002-7941-8818","position":3,"is_corresponding":false},{"id":257582,"name":"Orfeu M. Buxton","orcid":"0000-0001-5057-633X","position":4,"is_corresponding":false},{"id":255669,"name":"Sy‐Miin Chow","orcid":"0000-0003-1938-027X","position":5,"is_corresponding":false},{"id":1427489,"name":"Jyotirmoy Nirupam Das","orcid":"0000-0003-4068-3988","position":0,"is_corresponding":true}],"reference_count":26,"raw_metadata":null,"created_at":"2026-07-19T02:52:34.520788Z","pmid":"39788836","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":[]}