ArticleScientific reports2021
Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Geolocation-derived mobility indices and the association with clinical symptoms and functioning in severe mental illness: A multivariate meta-analysis.Journal of psychopathology and clinical science · 2025Pooled it
- Innovative technologies to address neglected tropical diseases in African settings with persistent sociopolitical instability.Nature communications · 2024Pooled it
- H3-MOSAIC: multimodal generative AI for semantic place detection from high-frequency GPS on H3 grids in mental health geomatics.International journal of health geographics · 2025Article
- Multilevel and multimodal biobehavioral methods in the study of stress, trauma, and resilience: a systems approach.Journal of neural transmission (Vienna, Austria : 1996) · 2025Review
- The relation between passively collected data and PTSD: a systematic review and meta-analysis.NPJ digital medicine · 2025Article
- Passive Smartphone Sensors for Detecting Psychopathology.JAMA network open · 2025Article
- Digital Health Innovations for Screening and Mitigating Mental Health Impacts of Adverse Childhood Experiences: Narrative Review.JMIR pediatrics and parenting · 2024Review
- Aligning digital biomarker definitions in psychiatry with the National Institute of Mental Health Research Domain Criteria framework.NPP - digital psychiatry and neuroscience · 2024Review
- The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis.NPJ digital medicine · 2024Review
- War, emotions, mental health, and artificial intelligence.Frontiers in psychology · 2024Review
- Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.Clinical practice and epidemiology in mental health : CP & EMH · 2024Review
- Systematic review of machine learning in PTSD studies for automated diagnosis evaluation.Npj mental health research · 2023Review
- Analyzing Perceived Psychological and Social Stress of University Students: A Machine Learning Approach.Heliyon · 2023Article
- Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review.Sensors (Basel, Switzerland) · 2023Review
- Identifying Predictors of Psychological Problems Among Adolescents With Congenital Heart Disease for Referral to Psychological Care: A Pilot Study.CJC pediatric and congenital heart disease · 2023Article
- Digital Biomarkers in Living Labs for Vulnerable and Susceptible Individuals: An Integrative Literature Review.Yonsei medical journal · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Post-traumatic stress disorder (PTSD) is characterized by complex, heterogeneous symptomology, thus detection outside traditional clinical contexts is difficult. Fortunately, advances in mobile technology, passive sensing, and analytics offer promising avenues for research and development. The present study examined the ability to utilize Global Positioning System (GPS) data, derived passively from a smartphone across seven days, to detect PTSD diagnostic status among a cohort (N = 185) of high-risk, previously traumatized women. Using daily time spent away and maximum distance traveled from home as a basis for model feature engineering, the results suggested that diagnostic group status can be predicted out-of-fold with high performance (AUC = 0.816, balanced sensitivity = 0.743, balanced specificity = 0.8, balanced accuracy = 0.771). Results further implicate the potential utility of GPS information as a digital biomarker of the PTSD behavioral repertoire. Future PTSD research will benefit from application of GPS data within larger, more diverse populations.
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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.