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dc.contributor.authorCakmak, Ayse Selin
dc.contributor.authorHaran, John P
dc.contributor.authorClifford, Gari D.
dc.date2022-08-11T08:08:26.000
dc.date.accessioned2022-08-23T15:55:13Z
dc.date.available2022-08-23T15:55:13Z
dc.date.issued2021-01-22
dc.date.submitted2021-03-10
dc.identifier.citation<p>Cakmak AS, Perez Alday EA, Da Poian G, Bahrami Rad A, Metzler TJ, Neylan TC, House SL, Beaudoin FL, An X, Stevens J, Zeng D, Linnstaedt SD, Jovanovic T, Germine LT, Bollen KA, Rauch SL, Lewandowski C, Hendry PL, Sheikh S, Storrow AB, Musey PI, Haran JP, Jones CW, Punches BE, Swor RA, Gentile NT, Mcgrath ME, Seamon MJ, Mohiuddin K, Chang AM, Pearson C, Domeier RM, Bruce SE, O'Neil BJ, Rathlev NK, Sanchez LD, Pietrzak RH, Joormann J, Barch DM, Pizzagalli D, Harte SE, Elliott JM, Koenen KC, Ressler KJ, Kessler R, Li Q, Mclean SA, Clifford GD. Classification and prediction of post-trauma outcomes related to PTSD using circadian rhythm changes measured via wrist-worn research watch in a large longitudinal cohort. IEEE J Biomed Health Inform. 2021 Jan 22;PP. doi: 10.1109/JBHI.2021.3053909. Epub ahead of print. PMID: 33481725. <a href="https://doi.org/10.1109/JBHI.2021.3053909">Link to article on publisher's site</a></p>
dc.identifier.issn2168-2194 (Linking)
dc.identifier.doi10.1109/JBHI.2021.3053909
dc.identifier.pmid33481725
dc.identifier.urihttp://hdl.handle.net/20.500.14038/29704
dc.description<p>Full author list omitted for brevity. For the full list of authors, see article.</p>
dc.description.abstractPost-Traumatic Stress Disorder (PTSD) is a psychiatric condition resulting from threatening or horrifying events. We hypothesized that circadian rhythm changes, measured by a wrist-worn research watch are predictive of post-trauma outcomes. APPROACH: 1618 post-trauma patients were enrolled after admission to emergency departments (ED). Three standardized questionnaires were administered at week eight to measure post-trauma outcomes related to PTSD, sleep disturbance, and pain interference with daily life. Pulse activity and movement data were captured from a research watch for eight weeks. Standard and novel movement and cardiovascular metrics that reflect circadian rhythms were derived using this data. These features were used to train different classifiers to predict the three outcomes derived from week-eight surveys. Clinical surveys administered at ED were also used as features in the baseline models. RESULTS: The highest cross-validated performance of research watch-based features was achieved for classifying participants with pain interference by a logistic regression model, with an area under the receiver operating characteristic curve (AUC) of 0.70. The ED survey-based model achieved an AUC of 0.77, and the fusion of research watch and ED survey metrics improved the AUC to 0.79. SIGNIFICANCE: This work represents the first attempt to predict and classify post-trauma symptoms from passive wearable data using machine learning approaches that leverage the circadian desynchrony in a potential PTSD population.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=33481725&dopt=Abstract">Link to Article in PubMed</a></p>
dc.relation.urlhttps://doi.org/10.1109/jbhi.2021.3053909
dc.subjectActigraphy
dc.subjectCircadian rhythms
dc.subjectmHealth
dc.subjectPhotoplethysmography
dc.subjectPost-traumatic stress disorder
dc.subjectWearables
dc.subjectBiomedical Devices and Instrumentation
dc.subjectEmergency Medicine
dc.subjectMental Disorders
dc.subjectMusculoskeletal, Neural, and Ocular Physiology
dc.subjectNeuroscience and Neurobiology
dc.subjectTelemedicine
dc.titleClassification and prediction of post-trauma outcomes related to PTSD using circadian rhythm changes measured via wrist-worn research watch in a large longitudinal cohort
dc.typeJournal Article
dc.source.journaltitleIEEE journal of biomedical and health informatics
dc.source.volumePP
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/faculty_pubs/1918
dc.identifier.contextkey22011057
html.description.abstract<p>Post-Traumatic Stress Disorder (PTSD) is a psychiatric condition resulting from threatening or horrifying events. We hypothesized that circadian rhythm changes, measured by a wrist-worn research watch are predictive of post-trauma outcomes.</p> <p>APPROACH: 1618 post-trauma patients were enrolled after admission to emergency departments (ED). Three standardized questionnaires were administered at week eight to measure post-trauma outcomes related to PTSD, sleep disturbance, and pain interference with daily life. Pulse activity and movement data were captured from a research watch for eight weeks. Standard and novel movement and cardiovascular metrics that reflect circadian rhythms were derived using this data. These features were used to train different classifiers to predict the three outcomes derived from week-eight surveys. Clinical surveys administered at ED were also used as features in the baseline models.</p> <p>RESULTS: The highest cross-validated performance of research watch-based features was achieved for classifying participants with pain interference by a logistic regression model, with an area under the receiver operating characteristic curve (AUC) of 0.70. The ED survey-based model achieved an AUC of 0.77, and the fusion of research watch and ED survey metrics improved the AUC to 0.79.</p> <p>SIGNIFICANCE: This work represents the first attempt to predict and classify post-trauma symptoms from passive wearable data using machine learning approaches that leverage the circadian desynchrony in a potential PTSD population.</p>
dc.identifier.submissionpathfaculty_pubs/1918
dc.contributor.departmentDepartment of Emergency Medicine


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