Passive Smartphone-Based Digital Phenotyping for the Identification of PTSD Symptoms: Initial Validation of a Digital Behavioral Marker
Abstract
Background: Passive smartphone-based digital phenotyping offers a scalable approach for continuous mental health assessment. Continuous passive monitoring may be particularly valuable for posttraumatic stress disorder (PTSD), given fluctuating symptoms and the limitations of episodic clinical assessment. While previously validated for depression and anxiety, its application to PTSD remains limited. This study evaluated passive smartphone behavioral data for estimating PTSD symptom severity. Methods: Adults (N = 176) completed the PTSD Checklist for DSM-5 (PCL-5) and passively contributed smartphone behavioral metadata via the Behavidence app. A Random Forest classifier using 44 behavioral features was developed to classify none (PCL-5 <31) versus severe (PCL-5 >61) PTSD symptom severity. The final model generated participant-level PTSD-related scores, which were evaluated across the full cohort. Feature importance was examined using Gini impurity and SHAP. Results: In the holdout test set, the classifier achieved 80.3% accuracy, an AUC of 0.82, and 89.5% precision. When applied to the full cohort, participant-level MHSS values demonstrated a moderate positive correlation with continuous PCL-5 scores (r = 0.49, p < .001) and increased significantly across questionnaire-defined PTSD severity groups (F(3,172) = 24.06, p < .001, η² = 0.30). Feature importance analyses identified smartphone engagement, temporal usage patterns, and application-specific behaviors as the strongest contributors to model predictions. Conclusions: Passive smartphone-based digital phenotyping demonstrated promising performance for identifying PTSD symptom severity. Participant-level MHSS values were associated with continuous and categorical measures of PTSD severity, supporting passive behavioral monitoring as a scalable complement to traditional PTSD assessment and longitudinal symptom monitoring.
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BibTeX
@misc{levari2026passive,
title = {{Passive Smartphone-Based Digital Phenotyping for the Identification of PTSD Symptoms: Initial Validation of a Digital Behavioral Marker}},
author = {Rony Kapel Lev-ari and Christian A. Webb and Doron Amsalem and Annika C. Sweetland and Yuval Neria and Milton Wainberg},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/bpqg2_v1},
url = {https://osf.io/preprints/psyarxiv/bpqg2_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Passive Smartphone-Based Digital Phenotyping for the Identification of PTSD Symptoms: Initial Validation of a Digital Behavioral Marker
AU - Rony Kapel Lev-ari
AU - Christian A. Webb
AU - Doron Amsalem
AU - Annika C. Sweetland
AU - Yuval Neria
AU - Milton Wainberg
PY - 2026
DA - 2026/10/15
PB - PsyArXiv
DO - 10.31234/osf.io/bpqg2_v1
UR - https://osf.io/preprints/psyarxiv/bpqg2_v1/
AB - Background: Passive smartphone-based digital phenotyping offers a scalable approach for continuous mental health assessment. Continuous passive monitoring may be particularly valuable for posttraumatic stress disorder (PTSD), given fluctuating symptoms and the limitations of episodic clinical assessment. While previously validated for depression and anxiety, its application to PTSD remains limited. This study evaluated passive smartphone behavioral data for estimating PTSD symptom severity. Methods: Adults (N = 176) completed the PTSD Checklist for DSM-5 (PCL-5) and passively contributed smartphone behavioral metadata via the Behavidence app. A Random Forest classifier using 44 behavioral features was developed to classify none (PCL-5 <31) versus severe (PCL-5 >61) PTSD symptom severity. The final model generated participant-level PTSD-related scores, which were evaluated across the full cohort. Feature importance was examined using Gini impurity and SHAP. Results: In the holdout test set, the classifier achieved 80.3% accuracy, an AUC of 0.82, and 89.5% precision. When applied to the full cohort, participant-level MHSS values demonstrated a moderate positive correlation with continuous PCL-5 scores (r = 0.49, p < .001) and increased significantly across questionnaire-defined PTSD severity groups (F(3,172) = 24.06, p < .001, η² = 0.30). Feature importance analyses identified smartphone engagement, temporal usage patterns, and application-specific behaviors as the strongest contributors to model predictions. Conclusions: Passive smartphone-based digital phenotyping demonstrated promising performance for identifying PTSD symptom severity. Participant-level MHSS values were associated with continuous and categorical measures of PTSD severity, supporting passive behavioral monitoring as a scalable complement to traditional PTSD assessment and longitudinal symptom monitoring.
ER -