Evidence map›Paper›PMID 41232100›Full record

ArticleJMIR mental health2025

Using Digital Phenotypes to Identify Individuals With Alexithymia in Posttraumatic Stress Disorder: Cross-Sectional Study.

Tomas Meaney, Vijay Yadav, Isaac Galatzer-Levy, Richard Bryant

Abstract read
In one paragraph

Article in JMIR mental health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tomas MeaneySchool of Psychology, Faculty of Science, University of New South Wales, Sydney, Australia.ORCID 0009-0005-5674-7589
Vijay YadavDepartment of Psychiatry, Grossman School of Medicine, New York University, New York City, NY, United States.ORCID 0000-0003-2801-9005
Isaac Galatzer-LevyDepartment of Psychiatry, Grossman School of Medicine, New York University, New York City, NY, United States.ORCID 0000-0003-1864-064X
Richard BryantSchool of Psychology, Faculty of Science, University of New South Wales, Sydney, Australia.ORCID 0000-0002-9607-819X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlexithymia, defined as difficulty identifying and describing one's emotions, has been identified as a transdiagnostic emotional process that impacts the course, severity, and treatment outcomes of psychiatric conditions such as posttraumatic stress disorder (PTSD). As such, alexithymia is an important process to accurately measure and identify in clinical contexts. However, research identifying the association between the experience of alexithymia and psychopathology has been limited by an overreliance on self-report scales, which have restricted use for measuring constructs that involve deficits in self-awareness, such as alexithymia. Hence, more suitable and effective methods of measuring and identifying those experiencing alexithymia in clinical samples are needed.

objectiveIn this cross-sectional study, we aimed to determine if facial, vocal, and language phenotypes extracted from 1-minute recordings of war veterans with PTSD describing a traumatic event could be used to identify those experiencing alexithymia.

methodsA total of 96 participants were included in this cross-sectional study. Specialized software was used to extract facial, vocal, and language features from the recordings. These features were then integrated into machine learning (extreme gradient boosting [XGBoost]) classification models that were trained and tested within a 5-fold nested cross-validation pipeline for their capacity to classify veterans scoring above the cutoff for alexithymia on the Toronto Alexithymia Scale-20.

resultsThe best performing XGBoost classification model trained in the nested cross-validation pipeline was able to classify those experiencing alexithymia with a good level of accuracy (average F

conclusionsThese findings indicate that facial, vocal, and language phenotypes incorporated in machine learning models could represent a promising alternative to identifying individuals with PTSD who are experiencing alexithymia. The further validation and use of this approach could facilitate more tailored and effective allocation of treatment resources to individuals experiencing alexithymia in clinical settings.

Indexed as

Affective SymptomsStress Disorders, Post-TraumaticVeteransAdultCross-Sectional StudiesFemaleHumansMachine LearningMaleMiddle AgedPhenotypealexithymiadigital healthdigital phenotypingmachine learningmental healthposttraumatic stress disorderveterans

Identifiers

PMID41232100
PMCPMC12661231

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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.