ArticleIEEE journal of biomedical and health informatics2021
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.
Article in IEEE journal of biomedical and health informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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Who cites it
17 citing papers in PubMed, 31 citations in OpenAlex.
- Predicting longitudinal post-traumatic stress disorder symptoms using psychological measures and heart rate variability: a machine-learning approach.European journal of psychotraumatology · 2026Article
- Detection of Posttraumatic Stress Disorder With Rest-Activity Data: Machine Learning Approach Using Wearable and Self-Report Data.JMIR formative research · 2026Article
- Circadian instability following mass trauma predicts PTSD risk.Translational psychiatry · 2026Article
- Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data.IEEE transactions on bio-medical engineering · 2026Article
- Multilevel and multimodal biobehavioral methods in the study of stress, trauma, and resilience: a systems approach.Journal of neural transmission (Vienna, Austria : 1996) · 2025Review
- ECG Signal Analysis for Detection and Diagnosis of Post-Traumatic Stress Disorder: Leveraging Deep Learning and Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2025Article
- Continuous-Time Hidden Markov Factor Model for Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae.IEEE journal of biomedical and health informatics · 2025Article
- pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis.Physiological measurement · 2024Article
- Multimodal Mental Health Digital Biomarker Analysis From Remote Interviews Using Facial, Vocal, Linguistic, and Cardiovascular Patterns.IEEE journal of biomedical and health informatics · 2024Article
- Associations of alcohol and cannabis use with change in posttraumatic stress disorder and depression symptoms over time in recently trauma-exposed individuals.Psychological medicine · 2024Article
- The 2023 wearable photoplethysmography roadmap.Physiological measurement · 2023Review
- Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review.Sensors (Basel, Switzerland) · 2023Review
- Feasibility of a wrist-worn wearable device for estimating mental health status in patients with mental illness.Frontiers in psychiatry · 2023Article
- Photoplethysmography Enabled Wearable Devices and Stress Detection: A Scoping Review.Journal of personalized medicine · 2022Article
- Time of trauma prospectively affects PTSD symptom severity: The impact of circadian rhythms and cortisol.Psychoneuroendocrinology · 2022Article
- Post-traumatic stress disorder: clinical and translational neuroscience from cells to circuits.Nature reviews. Neurology · 2022Review
- Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors.Chronic stress (Thousand Oaks, Calif.)Article
Corrections and comments
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Authors and funding
48 authors at 20 institutions in 2 countries.
Funding
Abstract
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. 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.
resultsThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.