View in EDS

Precision prediction of posttraumatic stress disorder symptom surges: A pilot study integrating real‐time daily data with supervised learning.

Saved in:
Bibliographic Details
Title: Precision prediction of posttraumatic stress disorder symptom surges: A pilot study integrating real‐time daily data with supervised learning.
Authors: Davis, Jordan P. (AUTHOR), Prindle, John (AUTHOR), Pedersen, Eric R. (AUTHOR), Leightley, Daniel (AUTHOR), Dilkina, Bistra (AUTHOR), Dworkin, Emily (AUTHOR), Saba, Shaddy (AUTHOR), Thota, Praneeth (AUTHOR), Nuthi, Sriram (AUTHOR), Prince, Mark A. (AUTHOR), Sedano, Angeles (AUTHOR)
Source: Journal of Traumatic Stress. Apr2026, Vol. 39 Issue 2, p319-329. 11p.
Subjects: Post-traumatic stress disorder, Machine learning, Affect (Psychology), Ecological momentary assessments (Clinical psychology), Veterans, Psychological stress, Therapeutics, Prediction models
Abstract: The application of machine learning algorithms to daily diary data represents a valuable tool for improving dynamic prediction of posttraumatic stress disorder (PTSD) symptom escalations. This prospective, intensive longitudinal study aimed to evaluate whether combining baseline (static) and daily diary (dynamic) predictors with machine learning can help forecast clinically significant PTSD symptom increases among veterans. Participants were 74 recently discharged U.S. veterans (Mage = 33.5 years) who completed twice‐daily diary surveys for up to 87 days via a mobile app, yielding 4,307 diary days. The outcome was a binary indicator of clinically significant daily PTSD symptom increase (> 1.0 standard deviation above a participant's individual mean over the first 2 study weeks). Random forest models identified top predictors; LASSO regression estimated effect sizes among top predictors. Daily negative affect was the top predictive variable, OR = 1.33, retained in 100% of LASSO iterations. Daily depressed mood, OR = 1.35; anxious mood, OR = 1.15; and perceived stress, OR = 1.13, were also reliably retained. Variables involving alcohol, cannabis use, and baseline impulsivity were less robust but remained prominent predictors of PTSD symptom escalations. Post hoc interaction analyses showed that co‐occurring high negative affect and anxiety yielded a > 55% probability of PTSD symptom escalation. The findings show that daily affective states, especially negative mood and stress, strongly predict PTSD symptom increases in veterans. Using machine learning and high‐frequency tracking, advances in personalized, real‐time PTSD care are possible. Findings support just‐in‐time interventions for when veterans need help most: in the moment. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Traumatic Stress is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
FullText Text:
  Availability: 0
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 192727289
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Precision prediction of posttraumatic stress disorder symptom surges: A pilot study integrating real‐time daily data with supervised learning.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Davis%2C+Jordan+P%2E%22">Davis, Jordan P.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Prindle%2C+John%22">Prindle, John</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pedersen%2C+Eric+R%2E%22">Pedersen, Eric R.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Leightley%2C+Daniel%22">Leightley, Daniel</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dilkina%2C+Bistra%22">Dilkina, Bistra</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dworkin%2C+Emily%22">Dworkin, Emily</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Saba%2C+Shaddy%22">Saba, Shaddy</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Thota%2C+Praneeth%22">Thota, Praneeth</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nuthi%2C+Sriram%22">Nuthi, Sriram</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Prince%2C+Mark+A%2E%22">Prince, Mark A.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sedano%2C+Angeles%22">Sedano, Angeles</searchLink> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Traumatic+Stress%22">Journal of Traumatic Stress</searchLink>. Apr2026, Vol. 39 Issue 2, p319-329. 11p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Post-traumatic+stress+disorder%22">Post-traumatic stress disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Affect+%28Psychology%29%22">Affect (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Ecological+momentary+assessments+%28Clinical+psychology%29%22">Ecological momentary assessments (Clinical psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Veterans%22">Veterans</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+stress%22">Psychological stress</searchLink><br /><searchLink fieldCode="DE" term="%22Therapeutics%22">Therapeutics</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The application of machine learning algorithms to daily diary data represents a valuable tool for improving dynamic prediction of posttraumatic stress disorder (PTSD) symptom escalations. This prospective, intensive longitudinal study aimed to evaluate whether combining baseline (static) and daily diary (dynamic) predictors with machine learning can help forecast clinically significant PTSD symptom increases among veterans. Participants were 74 recently discharged U.S. veterans (Mage = 33.5 years) who completed twice‐daily diary surveys for up to 87 days via a mobile app, yielding 4,307 diary days. The outcome was a binary indicator of clinically significant daily PTSD symptom increase (> 1.0 standard deviation above a participant's individual mean over the first 2 study weeks). Random forest models identified top predictors; LASSO regression estimated effect sizes among top predictors. Daily negative affect was the top predictive variable, OR = 1.33, retained in 100% of LASSO iterations. Daily depressed mood, OR = 1.35; anxious mood, OR = 1.15; and perceived stress, OR = 1.13, were also reliably retained. Variables involving alcohol, cannabis use, and baseline impulsivity were less robust but remained prominent predictors of PTSD symptom escalations. Post hoc interaction analyses showed that co‐occurring high negative affect and anxiety yielded a > 55% probability of PTSD symptom escalation. The findings show that daily affective states, especially negative mood and stress, strongly predict PTSD symptom increases in veterans. Using machine learning and high‐frequency tracking, advances in personalized, real‐time PTSD care are possible. Findings support just‐in‐time interventions for when veterans need help most: in the moment. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Traumatic Stress is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=192727289
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/jts.70036
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 319
    Subjects:
      – SubjectFull: Post-traumatic stress disorder
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Affect (Psychology)
        Type: general
      – SubjectFull: Ecological momentary assessments (Clinical psychology)
        Type: general
      – SubjectFull: Veterans
        Type: general
      – SubjectFull: Psychological stress
        Type: general
      – SubjectFull: Therapeutics
        Type: general
      – SubjectFull: Prediction models
        Type: general
    Titles:
      – TitleFull: Precision prediction of posttraumatic stress disorder symptom surges: A pilot study integrating real‐time daily data with supervised learning.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Davis, Jordan P.
      – PersonEntity:
          Name:
            NameFull: Prindle, John
      – PersonEntity:
          Name:
            NameFull: Pedersen, Eric R.
      – PersonEntity:
          Name:
            NameFull: Leightley, Daniel
      – PersonEntity:
          Name:
            NameFull: Dilkina, Bistra
      – PersonEntity:
          Name:
            NameFull: Dworkin, Emily
      – PersonEntity:
          Name:
            NameFull: Saba, Shaddy
      – PersonEntity:
          Name:
            NameFull: Thota, Praneeth
      – PersonEntity:
          Name:
            NameFull: Nuthi, Sriram
      – PersonEntity:
          Name:
            NameFull: Prince, Mark A.
      – PersonEntity:
          Name:
            NameFull: Sedano, Angeles
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 08949867
          Numbering:
            – Type: volume
              Value: 39
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Journal of Traumatic Stress
              Type: main
ResultId 1