Predicting Efficacy of Virtual Reality-Based Stabilization for Individuals With Posttraumatic Stress Symptoms: A...

Area of Science:

• Mental Health
• Psychological Interventions
• Virtual Reality Applications

Background:

• Respiratory infectious diseases, like COVID-19, caused significant mental health challenges globally.
• There is a critical need for proactive psychological interventions to prepare for future pandemics.
• Virtual reality-based stabilization (VRS) was developed to mitigate posttraumatic stress symptoms (PTSS) and related comorbidities.

Purpose of the Study:

• To evaluate the effectiveness of VRS in COVID-19 survivors and healthcare workers.
• To predict the impact of VRS on PTSS and depression using machine learning.
• To identify factors influencing VRS effectiveness for personalized interventions.

Main Methods:

• A study involving 43 COVID-19 survivors and healthcare workers assessed VRS effectiveness over five sessions.
• Pre- and post-intervention psychological assessments measured PTSS, depression, anxiety, COVID-related fear, posttraumatic growth, and quality of life.
• A machine learning model predicted changes in PTSS and depression using baseline assessments and heart rate variability.

Main Results:

• VRS significantly improved all measured psychological outcomes post-intervention.
• The machine learning model showed good predictive accuracy (R2=0.414-0.723) for PTSS and depression changes.
• Higher baseline PTSS, elevated heart rate variability, and younger age correlated with greater improvement.

Conclusions:

• VRS is an effective intervention for PTSS and associated mental health conditions.
• Integrating clinical and demographic data enhances prediction models for personalized mental healthcare.
• VRS offers a promising approach for psychological support during and after pandemics.