{"doi":"10.1098/rsif.2023.0030","title":"Efficient assessment of real-world dynamics of circadian rhythms in heart rate and body temperature from wearable data","abstract":"Laboratory studies have made unprecedented progress in understanding circadian physiology. Quantifying circadian rhythms outside of laboratory settings is necessary to translate these findings into real-world clinical practice. Wearables have been considered promising way to measure these rhythms. However, their limited validation remains an open problem. One major barrier to implementing large-scale validation studies is the lack of reliable and efficient methods for circadian assessment from wearable data. Here, we propose an approximation-based least-squares method to extract underlying circadian rhythms from wearable measurements. Its computational cost is ∼ 300-fold lower than that of previous work, enabling its implementation in smartphones with low computing power. We test it on two large-scale real-world wearable datasets: [Formula: see text] of body temperature data from cancer patients and ∼ 184 000 days of heart rate and activity data collected from the 'Social Rhythms' mobile application. This shows successful extraction of real-world dynamics of circadian rhythms. We also identify a reasonable harmonic model to analyse wearable data. Lastly, we show our method has broad applicability in circadian studies by embedding it into a Kalman filter that infers the state space of the molecular clocks in tissues. Our approach facilitates the translation of scientific advances in circadian fields into actual improvements in health.","journal":"Journal of The Royal Society Interface","year":2023,"id":325515,"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":31,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9492,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":290034,"name":"Caleb Mayer","orcid":"0000-0002-8286-2186","position":1,"is_corresponding":false},{"id":1043186,"name":"Minki P. Lee","orcid":"0009-0007-0976-8285","position":2,"is_corresponding":false},{"id":289202,"name":"Sung Won Choi","orcid":"0000-0002-6321-3834","position":3,"is_corresponding":false},{"id":289203,"name":"Muneesh Tewari","orcid":"0000-0002-7781-3152","position":4,"is_corresponding":false},{"id":290037,"name":"Daniel B. Forger","orcid":"0000-0001-7581-4031","position":5,"is_corresponding":false},{"id":904994,"name":"Dae Wook Kim","orcid":"0000-0003-1060-891X","position":0,"is_corresponding":true}],"reference_count":53,"raw_metadata":null,"created_at":"2026-07-19T01:08:22.919783Z","pmid":"37608712","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":[]}