ArticlePsychiatry investigation2025
Predicting Efficacy of Virtual Reality-Based Stabilization for Individuals With Posttraumatic Stress Symptoms: A Machine Learning Approach.
Article in Psychiatry investigation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence-Integrated Virtual Reality in Mental Health Care: A Scoping Review of Evidence, Clinical Applications, and Future Directions.Journal of clinical medicine · 2026Review
- The Impact of Maternal Phubbing on Toddlers' Language Development and Subsequent Social Development: A Three-Month Time-Lagged Analysis.Behavioral sciences (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveThe global impact of respiratory infectious diseases led to significant mental health challenges, highlighting the need for proactive psychological interventions to prepare for future pandemics. In response, virtual reality-based stabilization (VRS) was developed to mitigate posttraumatic stress symptoms (PTSS) and related comorbidities.
methodsThis study evaluated and predicted the effectiveness of VRS in 43 coronavirus disease-2019 (COVID-19) survivors and healthcare workers from COVID-19 treatment units. The effectiveness of VRS, conducted over five sessions, was measured using preand post-intervention psychological assessments for PTSS, depression, anxiety, COVID-related fear, posttraumatic growth, and quality of life. Additionally, a machine learning model was used to predict the impact of the intervention on PTSS and depression based on preintervention psychological assessments and heart rate variability tests.
resultsThe post-intervention results showed significant improvements in all psychological outcomes. The machine learning-based model demonstrated good predictive accuracy for changes in PTSS and depression (R2=0.414-0.723). Notably, individuals with higher pre-intervention scores for PTSS and related comorbidities, as well as elevated heart rate variability and younger age, exhibited more significant improvements.
conclusionThese findings suggest that VRS is effective in addressing PTSS and related conditions, and incorporating clinical and demographic data can enhance prediction models, enabling more personalized intervention strategies.
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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 Socratic graph.