{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T08:15:58Z","timestamp":1788250558281,"version":"build-2803163510"},"reference-count":36,"publisher":"Royal College of Psychiatrists","issue":"5","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["BJPsych open"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:sec id=\"S2056472426120559_as1\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Post-traumatic stress disorder (PTSD) symptoms can fluctuate substantially over short periods, yet routine screening typically relies on infrequent self-report. Wearable sensors provide continuous behavioural and physiological signals that may help identify periods of elevated risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S2056472426120559_as2\">\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>This study aimed to evaluate whether combining wearable sensor features with daily self-report data could identify short-term PTSD symptom increases among recently discharged veterans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S2056472426120559_as3\">\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>Seventy-four veterans wore commercial activity trackers and completed brief daily questionnaires over 87 days. For each participant, we defined an individual baseline by using the first 14 days of PTSD scores. Wearable variables were transformed into baseline-referenced deviation features to capture departures from personal norms. Missing data were addressed with multiple imputation by chained equations. Candidate predictors were prioritised with least absolute shrinkage and selection operator regression, and a set of machine-learning classifiers was evaluated. Primary performance was assessed by using the area under the precision\u2013recall curve (PR AUC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S2056472426120559_as4\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Across feature set sizes (\n                      <jats:italic>k<\/jats:italic>\n                      = 1\u201325), performance peaked at\n                      <jats:italic>k<\/jats:italic>\n                      = 17. At this iteration, LightGBM achieved the strongest discrimination (PR AUC 0.86 (s.d. 0.07); area under the receiver-operating characteristic curve 0.89 (s.d. 0.04)) with a precision of 0.67 (s.d. 0.08), recall of 0.64 (s.d. 0.08) and F1 of 0.65 (s.d. 0.07). Key predictors reflected a multimodal profile, combining self-reported affect and perceived stress with wearable indicators of sleep continuity, activity variability and autonomic regulation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S2056472426120559_as5\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Baseline-referenced wearable features combined with daily self-report may help identify near-term PTSD symptom increases among recently discharged veterans with elevated PTSD symptoms and problematic cannabis use. Future work should validate performance in broader PTSD populations, including samples without problematic cannabis use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1192\/bjo.2026.12055","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T07:40:09Z","timestamp":1788248409000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":0,"title":["Detecting short-term post-traumatic stress disorder symptom increases among veterans: machine-learning analysis integrating wearable sensor and daily self-report data"],"prefix":"10.1192","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9512-752X","authenticated-orcid":false,"given":"Daniel","family":"Leightley","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0220mzb33","id-type":"ROR","asserted-by":"publisher"}],"name":"King\u2019s College London"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Prindle","sequence":"additional","affiliation":[{"name":"University of Southern California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaddy","family":"Saba","sequence":"additional","affiliation":[{"name":"New York University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bistra","family":"Dilkina","sequence":"additional","affiliation":[{"name":"University of Southern California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric R.","family":"Pedersen","sequence":"additional","affiliation":[{"name":"University of Southern California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily","family":"Dworkin","sequence":"additional","affiliation":[{"name":"University of Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esther","family":"Howe","sequence":"additional","affiliation":[{"name":"University of California Berkeley"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Praneeth","family":"Thota","sequence":"additional","affiliation":[{"name":"University of Southern California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sriram","family":"Nuthi","sequence":"additional","affiliation":[{"name":"University of Southern California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angeles","family":"Sedano","sequence":"additional","affiliation":[{"name":"University of Southern California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jordan P.","family":"Davis","sequence":"additional","affiliation":[{"name":"University of Southern California"},{"name":"RAND Corporation"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"170","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"S2056472426120559_ref21","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0332239"},{"key":"S2056472426120559_ref13","doi-asserted-by":"publisher","DOI":"10.30773\/pi.2019.0112"},{"key":"S2056472426120559_ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6226-1"},{"key":"S2056472426120559_ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s40614-022-00339-w"},{"key":"S2056472426120559_ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2025.116626"},{"key":"S2056472426120559_ref29","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"S2056472426120559_ref28","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022627411411"},{"key":"S2056472426120559_ref11","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00548-8"},{"key":"S2056472426120559_ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-91916-x"},{"key":"S2056472426120559_ref20","doi-asserted-by":"publisher","DOI":"10.1080\/08897077.2022.2074595"},{"key":"S2056472426120559_ref2","unstructured":"2 National Center for PTSD. 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U.S. Department of Veterans Affairs, 2025 (https:\/\/www.ptsd.va.gov\/professional\/treat\/essentials\/dsm5_ptsd.asp)."},{"key":"S2056472426120559_ref34","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"S2056472426120559_ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2015.05.057"},{"key":"S2056472426120559_ref4","first-page":"205520761984298","article-title":"Efficacy of mobile application interventions for the treatment of post-traumatic stress disorder: a systematic review","volume":"5","author":"Wickersham","year":"2019","journal-title":"Digit Health"},{"key":"S2056472426120559_ref36","doi-asserted-by":"publisher","DOI":"10.1586\/14737167.4.5.581"},{"key":"S2056472426120559_ref27","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"S2056472426120559_ref31","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"S2056472426120559_ref5","doi-asserted-by":"publisher","DOI":"10.4088\/JCP.20m14029"},{"key":"S2056472426120559_ref1","volume-title":"Diagnostic and Statistical Manual of Mental Disorders","year":"2013"},{"key":"S2056472426120559_ref3","first-page":"827","article-title":"Identifying and managing posttraumatic stress disorder","volume":"88","author":"Warner","year":"2013","journal-title":"Am Fam Phys"},{"key":"S2056472426120559_ref22","volume-title":"Navigating the Software Development Landscape: Introducing the Term Research Viable Product (RVP)","author":"Leightley","year":"2024"},{"key":"S2056472426120559_ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2022.104850"},{"key":"S2056472426120559_ref23","doi-asserted-by":"publisher","DOI":"10.1002\/mhs2.75"},{"key":"S2056472426120559_ref18","doi-asserted-by":"publisher","DOI":"10.1002\/jts.70036"},{"key":"S2056472426120559_ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.drugalcdep.2025.112661"},{"key":"S2056472426120559_ref12","doi-asserted-by":"publisher","DOI":"10.1001\/jamapsychiatry.2022.4533"},{"key":"S2056472426120559_ref25","unstructured":"25 Weathers, FW , Litz, BT , Keane, TM , Palmieri, PA , Marx, BP , Schnurr, PP . The PTSD Checklist for DSM-5 (PCL-5). U.S. Department of Veterans Affairs, National Center for PTSD, 2013 (https:\/\/www.ptsd.va.gov\/professional\/assessment\/adult-sr\/ptsd-checklist.asp)."},{"key":"S2056472426120559_ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychires.2025.12.032"},{"key":"S2056472426120559_ref6","doi-asserted-by":"publisher","DOI":"10.1093\/epirev\/mxu012"},{"key":"S2056472426120559_ref26","first-page":"30","article-title":"Multiple imputation with multivariate imputation by chained equation (MICE) package","volume":"4","author":"Zhang","year":"2016","journal-title":"Ann Transl Med"},{"key":"S2056472426120559_ref33","author":"vonLuxburg"},{"key":"S2056472426120559_ref15","doi-asserted-by":"publisher","DOI":"10.1080\/09638237.2018.1521946"},{"key":"S2056472426120559_ref19","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01825-6"},{"key":"S2056472426120559_ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s11606-016-3703-5"},{"key":"S2056472426120559_ref32","author":"Chen","year":"2016"},{"key":"S2056472426120559_ref35","doi-asserted-by":"publisher","DOI":"10.1177\/2327857917061055"}],"container-title":["BJPsych Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S2056472426120559","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T07:40:11Z","timestamp":1788248411000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S2056472426120559\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":36,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["S2056472426120559"],"URL":"https:\/\/doi.org\/10.1192\/bjo.2026.12055","relation":{},"ISSN":["2056-4724"],"issn-type":[{"value":"2056-4724","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"\u00a9 The Author(s), 2026. Published by Cambridge University Press on behalf of Royal College of Psychiatrists","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https:\/\/creativecommons.org\/licenses\/by\/4.0\/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}],"article-number":"e223"}}