Clinical Research Studies

Proudly presenting two research studies carried out in
partnership with major universities

Clinical Evidence
of VR Therapy

ESEARCH VOLUME
Over 200,000 articles are listed in Google Scholar since 1995 (link)

ANXIETY REDUCTION
VR exposure therapy can reduce anxiety symptoms by 50-60% (link)

BOOSTED ADHERENCE
VR exposure therapy boosts patient engagement by 40% compared to traditional therapy (link)

416 publications at ResearchGate (link)
30,000 citations (link)
h-index of 86, and an i10-index of 274

PsyTechVR’s solutions, developed in collaboration
with Dr. Rizzo, reduce anxiety,
and phobia symptoms by up to 45%,
with 90% preferring VR therapy.

PsyTech VR Study #1

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PROBLEM

Traditional exposure therapy, often used to treat phobias (like fear of heights), is highly effective but has limitations. These include high costs, limited accessibility, and the discomfort of facing real-life triggers
OPPORTUNITY

Virtual reality (VR) offers a safer and more controlled environment to expose patients to their fears, which may address these barriers.
OBJECTIVE

This study explored whether VR therapy is as effective as traditional methods in reducing fear and anxiety while making therapy more flexible and accessible

KEY RESEARCH GOALS

Evaluate the effectiveness of XR-based exposure therapy for phobias (acrophobia and agoraphobia)
Examine the role of 'presence' (immersion, fluidity, authenticity) in enhancing therapy outcomes.

METHODOLOGIES USED

PARTICIPANT GROUPS

45 participants (18-25 years), divided into three groups: XR exposure, traditional therapy, and time-delay control.
PHYSIOLOGICAL MONITORING

Monitored physiological markers (heart rate variability, electrodermal activity, skin temperature).
DATA COLLECTION PROCESS

Data collected pre- and post exposure using FDA-approved Empatica E4 sensors.

KEY RESULT AND FINDINGS

CLINICAL EFFICACY

XR therapy showed parity with traditional exposure therapy in reducing phobic responses.
USER PREFERENCE

90% of participants preferred XR therapy over traditional methods, especially for phobias like fear of spiders.
QUANTIFIABLE RESULTS
- Significant improvements in physiological measures:
^{Heart Rate Variability (HRV): Improved by 25%.}
^{Electrodermal Activity (EDA): Reduced by 18%.}
- Measured autonomic responses (HRV, EDA) showed strong links with the impact of XR environments.

CONCLUSIONS AND IMPLICATIONS

WHAT WE FOUND (WITH NUMBERS):
- VR therapy was as effective as traditional therapy in reducing fear responses, with no significant difference between the two.
- 90% of participants with arachnophobia (fear of spiders) preferred VR therapy over traditional methods.
- Physiological measures showed significant improvement:
- Heart rate variability: Improved by an average of 25% post treatment.
- Electrodermal activity (stress-related sweat response): Reduced by 18% during VR sessions
WHY IT MATTERS:
- VR therapy offers cost-effective and scalable treatments, potentially reducing therapy costs by 30%-40% due to fewer in-person sessions needed.
- With customizable environments, therapists can tailor treatments to individual needs, increasing the success rate (86% for exposure therapy in general).

UNIVERSITY OF GEORGIA

PsyTech VR Study #2

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PROBLEM

Understanding and measuring fear during therapy is challenging. Traditional methods rely on self-reported feelings, which may not be accurate or real-time.
OPPORTUNITY

Advanced brain activity tracking (via EEG) combined with VR creates a new way to monitor fear responses objectively and in real-time.
OBJECTIVE

This study tested whether brainwave data (EEG) could be used with machine learning to classify and manage fear levels during VR therapy sessions.

KEY RESEARCH GOALS

Use EEG signals to assess acrophobia severity in real-time during VR exposure.
Evaluate machine learning and deep learning classifiers for classifying fear levels.

METHODOLOGIES USED

PARTICIPANTS AND SETUP

18 participants (20-35 years), exposed to VR environments like bridges and skyscrapers.
DATA COLLECTION

EEG signals collected using Emotiv Epoc+ headset.
ANALYSIS AND TECHNOLOGY

Analyzed using classifiers: CNN, ANN, Random Forest, and KNN.

KEY RESULT AND FINDINGS

CNN AND ANN PERFORMANCE

CNN (Convolutional Neural Network)achieved exceptional accuracy with 99% on testing, while ANN (Artificial Neural Network) followed closely with 97%, showcasing the power of deep learning in real-time fear detection.
EEG SIGNAL INSIGHTS

High-beta and gamma brainwave activity showed a strong correlation with acrophobic responses, providing critical biomarkers for fear detection.
KNN SUCCESS IN TRADITIONAL METHODS

Among traditional machine learning models, KNN (K Nearest Neighbors) stood out with 86% accuracy, highlighting its reliability in classifying fear levels.

CONCLUSIONS AND IMPLICATIONS

WHAT WE FOUND (WITH NUMBERS):
- Machine learning classifiers used EEG brain data to predict fear levels with high accuracy:
- Convolutional Neural Network (CNN): 99% accuracy.
- Artificial Neural Network (ANN): 97% accuracy.
- K-Nearest Neighbors (KNN): 86% accuracy (best among traditional machine learning methods).
- High-beta and gamma brainwave activity strongly correlated with fear responses, with correlations as high as r = 0.85.
- Participants reported a 45% reduction in fear symptoms after just six VR therapy sessions.
WHY IT MATTERS:
- PsyTechVR's use of EEG-based fear monitoring ensures real time precision, helping therapists adjust therapy dynamically.
- The technology can classify fear levels into four categories (relaxation, mild fear, moderate fear, and intense fear) with nearly perfect accuracy, making it ideal for highly tailored therapy

SAPIENZA UNIVERSITY

OUR NEXT STUDIES

These ongoing studies are actively underway, with results expected to be unveiled in 2025

RESULTS IN Q2 2025

Ruhr University of Bochum (Germany)

Exploring the concept of animal-assisted therapy using PsyTechVR's AI powered virtual animal for emotional support

RESULTS IN Q3 2025

Clalit Health Services (Israel's Largest Healthcare Provider)

Assessing PsyTechVR’s software for relaxation, exposure, and EMDR therapy to integrate into Israel's largest health system

RESULTS IN Q3 2025

Galilee Medical Center, Bar-Ilan University (Israel)

Testing PsyTechVR’s software to gain AMAR certification, a key milestone for entry into Israel's public health sector

RESULTS IN Q4 2025

Stanford University Psychiatry Department (USA)

Studying the effectiveness of VR exposure therapy with biofeedback for autistic individuals using Imaginal Exposure

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