{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T01:48:34Z","timestamp":1775872114375,"version":"3.50.1"},"reference-count":43,"publisher":"Wiley","issue":"28-30","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"vor","delay-in-days":4,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["NS073671"],"award-info":[{"award-number":["NS073671"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["GM124104"],"award-info":[{"award-number":["GM124104"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["MH123487"],"award-info":[{"award-number":["MH123487"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>With the rapid advancements in wearable device technologies, there is a growing interest in learning useful digital biomarkers from wearable device data as objective, low\u2010cost, real\u2010time alternatives to use in healthcare settings. They have the potential to facilitate disease progression monitoring, medication tailoring, and supplementing clinical trial endpoints. For example, triaxial accelerometer sensor data is promising for monitoring symptoms of movement\u2010related diseases, such as tremors in Parkinson's disease (PD). However, existing methods for accelerometer studies based on hidden Markov models (HMM) often analyze each individual's activity data separately, leading to inefficiency and limited generalizability. This paper proposes a joint nonparametric Bayesian method that extends the hierarchical Dirichlet process autoregressive HMM (HDP\u2010AR\u2010HMM) to incorporate subject\u2010specific transition parameters. This approach allows for simultaneous estimation across multiple subjects and repeated measurements, accounts for between\u2010subject variability, and provides consistent hidden state estimation without pre\u2010specifying the number of states. We validate our method on simulated data and show that it can achieve higher accuracy in detecting the true hidden states compared to alternative methods. We apply the method to a free\u2010living study, the Biomarker &amp; Endpoint Assessment to Track Parkinson's disease (BEAT\u2010PD) DREAM Challenge CIS\u2010PD study, to demonstrate its utility in monitoring disease symptoms in PD patients.<\/jats:p>","DOI":"10.1002\/sim.70334","type":"journal-article","created":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T06:25:41Z","timestamp":1765002341000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint Bayesian Hidden Markov Model With Subject\u2010Specific Transitions for Wearable Sensor Data"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6377-3252","authenticated-orcid":false,"given":"Wenbo","family":"Fei","sequence":"first","affiliation":[{"name":"Department of Biostatistics Columbia University  New York New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Miao","sequence":"additional","affiliation":[{"name":"Microsoft  Redmond Washington USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianchen","family":"Xu","sequence":"additional","affiliation":[{"name":"Bristol Myers Squibb  Berkeley Heights New Jersey USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanjia","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Columbia University  New York New York USA"},{"name":"Department of Psychiatry Columbia University  New York New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278576.3278582"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.abd7865"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21062047"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1063\/5.0086935"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/bios14110560"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2499621"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2014.07.009"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2023.2225742"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1561\/2000000004"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-5971-6_45"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.2174\/138920209789177575"},{"key":"e_1_2_10_13_1","volume-title":"Advances in Neural Information Processing Systems","author":"Beal M.","year":"2001"},{"key":"e_1_2_10_14_1","article-title":"Sharing Clusters Among Related Groups: Hierarchical Dirichlet Processes","volume":"17","author":"Teh Y.","year":"2004","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390196"},{"key":"e_1_2_10_16_1","article-title":"Nonparametric Bayesian Learning of Switching Linear Dynamical Systems","volume":"21","author":"Fox E.","year":"2008","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22239454"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3037857"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001592"},{"key":"e_1_2_10_20_1","doi-asserted-by":"crossref","unstructured":"B.RenandI.Barnett \u201cCombining Mixed Effects Hidden Markov Models With Latent Alternating Recurrent Event Processes to Model Diurnal Active\u2010Rest Cycles \u201d2022. arXiv preprint arXiv:2201.09179.","DOI":"10.1111\/biom.13865"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.3390\/s18041215"},{"key":"e_1_2_10_22_1","unstructured":"M. B.Fiecas C.Coffman M.Xu et al. \u201cApproximate Hidden Semi\u2010Markov Models for Dynamic Connectivity Analysis in Resting\u2010State fMRI \u201dbioRxiv 20212021."},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-7932-2"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000208"},{"key":"e_1_2_10_25_1","first-page":"639","article-title":"A Constructive Definition of Dirichlet Priors","volume":"4","author":"Sethuraman J.","year":"1994","journal-title":"Statistica Sinica"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000000302"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1515\/9780691218632"},{"key":"e_1_2_10_28_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/978-3-642-51684-9_2","article-title":"The Markov\u2010Switching Vector Autoregressive Model","volume":"5","author":"Krolzig H. M.","year":"1997","journal-title":"Markov\u2010Switching Vector Autoregressions: Modelling, Statistical Inference, and Application to Business Cycle Analysis"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1214\/10-AOAS395"},{"key":"e_1_2_10_30_1","volume-title":"Bayesian Forecasting and Dynamic Models","author":"West M.","year":"2006"},{"key":"e_1_2_10_31_1","unstructured":"E. B.Fox \u201cBayesian Nonparametric Learning of Complex Dynamical Phenomena \u201d(PhD Thesis Massachusetts Institute of Technology Cambridge MA 2009)."},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1137\/0105003"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.1137\/070690274"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.1186\/1743-0003-10-70"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0270(97)02254-1"},{"key":"e_1_2_10_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/PL00007781"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/aws023"},{"key":"e_1_2_10_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-67658-2_35"},{"key":"e_1_2_10_39_1","first-page":"673","article-title":"Bayesian Nonparametric Hidden Semi\u2010Markov Models","volume":"14","author":"Johnson M. J.","year":"2013","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_10_40_1","unstructured":"S.Rojas\u2010Salazar E. M.Schliep C. K.Wikle E. H.Stanley S. R.Carpenter andN. R.Lottig \u201cA Bayesian Hidden Semi\u2010Markov Model With Covariate\u2010Dependent State Duration Parameters for High\u2010Frequency Environmental Data \u201d2021. arXiv preprint arXiv:2109.09949."},{"key":"e_1_2_10_41_1","doi-asserted-by":"publisher","DOI":"10.1214\/20-AOAS1375"},{"key":"e_1_2_10_42_1","first-page":"752","volume-title":"Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics JMLR Workshop and Conference Proceedings","author":"Wang C.","year":"2011"},{"key":"e_1_2_10_43_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2015.1"},{"key":"e_1_2_10_44_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.clinpsy.3.022806.091415"}],"container-title":["Statistics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/sim.70334","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/sim.70334","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/sim.70334","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T00:58:42Z","timestamp":1775869122000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/sim.70334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":43,"journal-issue":{"issue":"28-30","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["10.1002\/sim.70334"],"URL":"https:\/\/doi.org\/10.1002\/sim.70334","archive":["Portico"],"relation":{},"ISSN":["0277-6715","1097-0258"],"issn-type":[{"value":"0277-6715","type":"print"},{"value":"1097-0258","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"2024-11-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70334"}}