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EDA-Based Adaptive Support in VR ALS Training

Effects of Electrodermal Activity-Based Adaptive Support Systems on Performance in VR-Based Advanced Life Support Training: A Randomized Controlled Study

Status
Not yet recruiting
Phases
Unknown
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT07650019
Enrollment
72
Registered
2026-06-16
Start date
2026-07-01
Completion date
2026-07-01
Last updated
2026-07-02

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Adaptive Support Condition, Standard Training Condition

Keywords

virtual reality, adaptive training, stress, Adaptive Life Support Training, electrodermal activity

Brief summary

This randomized controlled study aims to investigate the effects of an electrodermal activity (EDA)-based adaptive support system on performance during virtual reality (VR)-based advanced life support (ALS) training. Participants' stress levels will be continuously monitored through EDA during the simulation. When stress exceeds a predefined threshold, the adaptive system will automatically activate a support mode that provides additional guidance and time. The performance outcomes of participants receiving adaptive support will be compared with those receiving standard VR training. The study will also examine the role of presence, anxiety, social anxiety, and personality traits in the relationship between stress and performance.

Detailed description

This study is a randomized controlled trial designed to evaluate whether an electrodermal activity (EDA)-based adaptive support system can improve performance in VR-based advanced life support (ALS) training. The research focuses on integrating physiological stress monitoring into automated VR simulation systems to create a more responsive and individualized learning environment. Simulation-based education is widely used in medical training because it enables learners to practice high-risk clinical procedures in a safe and controlled environment. However, most automated VR training systems currently provide standardized instruction regardless of participants' stress levels or individual needs. Since acute stress may negatively influence cognitive and psychomotor performance during complex medical tasks, adaptive systems that dynamically respond to learners' stress may improve educational outcomes. The study will recruit 72 volunteer second-year anesthesia technician students from Acıbadem Mehmet Ali Aydınlar University. Participants will be randomly assigned to either an adaptive-support condition or a standard-training control condition. Before the experimental phase, all participants will complete a standardized ALS preparatory training and competency assessment to ensure comparable baseline knowledge levels across groups. During VR-based ALS training, participants' EDA levels will be continuously recorded using a non-invasive physiological monitoring system. Baseline EDA measurements will first be collected during a resting period. In the adaptive-support condition, when a participant's EDA exceeds a predefined threshold indicating elevated stress, the system will automatically activate a support mode. This mode will provide additional guidance, more frequent instructions, and extended response time during the simulation. Participants in the control group will continue the simulation under standard conditions without adaptive modifications. After completing the training, all participants will undergo the same VR-based performance examination without guidance. Performance will be automatically evaluated by the simulation system. In addition to performance outcomes, participants will complete self-report measures assessing presence, state and trait anxiety, social anxiety, and personality traits. These measures will allow the researchers to investigate how individual differences influence stress responses, presence experiences, and training outcomes in adaptive VR environments. The findings of this study are expected to contribute to the development of more flexible, personalized, and physiologically responsive VR-based medical training systems. The project may also provide empirical evidence for integrating real-time psychophysiological monitoring into automated simulation-based education.

Interventions

OTHEREDA-Based Adaptive Support System

Participants receive VR-based advanced life support training with an electrodermal activity (EDA)-based adaptive support system that automatically provides additional guidance, more frequent instructions, and extended response time when stress levels exceed a predefined threshold.

OTHERStandard VR-Based Training

Participants receive standard VR-based advanced life support training without adaptive support. Although electrodermal activity (EDA) is monitored during the simulation, the training flow remains unchanged regardless of stress levels.

Sponsors

Acibadem University
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to 25 Years
Healthy volunteers
Yes

Inclusion criteria

* Being older than 18

Exclusion criteria

* prior completion of ALS training * history of virtual reality (VR)-induced motion sickness * medical conditions such as vertigo or the use of medications known to cause vertigo-like symptoms

Design outcomes

Primary

MeasureTime frameDescription
Advanced Life Support Performance ScoreUp to 4 weeksParticipants' performance during the post-training VR-based advanced life support examination will be automatically evaluated by the simulation system. The score will reflect the accuracy and completeness of advanced life support procedures performed without guidance. Scores will range from 0 to 100 with higher scores indicating better success.

Secondary

MeasureTime frameDescription
Electrodermal Activity (EDA) LevelUp to 4 weeksDescription: Participants' physiological stress responses during the VR simulation will be continuously measured using electrodermal activity recordings. Baseline and training-phase EDA values will be analyzed. Higher EDA levels will suggest higher stress levels.
Presence ScoreUp to 4 weeksParticipants' sense of presence in the VR environment will be assessed using the Turkish adaptation of the Presence Questionnaire developed by Witmer and Singer (1998). Higher scores indicate a stronger sense of immersion and realism in the virtual environment.
State Anxiety ScoreUp to 4 weeksarticipants' situational anxiety levels following the VR training will be assessed using the State Anxiety subscale of the State-Trait Anxiety Inventory (STAI). Scores will range from 1 to 4 and higher scores will indicate higher anxiety levels.
Trait Anxiety ScoreUp to 4 weeksParticipants' general tendency toward anxiety will be assessed using the Trait Anxiety subscale of the State-Trait Anxiety Inventory (STAI). Scores will range from 1 to 4 and higher scores will indicate higher anxiety levels.
Social Anxiety ScoreUp to 4 weeksParticipants' social anxiety levels will be assessed using the Liebowitz Social Anxiety Scale, which evaluates anxiety and avoidance in social and performance situations. Scores will range from 1 to 4 and higher scores will indicate higher anxiety levels.
Personality TraitsUp to 4 weeksParticipants' personality characteristics will be assessed using the Ten-Item Personality Inventory, which measures extraversion, agreeableness, conscientiousness, emotional stability, and openness to experience. Scores will range from 1 to 7, with higher scoes indicating closeness to the assesed personality trait.

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Jul 3, 2026