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Acceptance of Virtual Reality in Trainees Using a Technology Acceptance Model: Survey Study.

Ellen Y Wang1, Daniel Qian2, Lijin Zhang3

  • 1Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States.

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|December 27, 2024
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Summary

Graduate medical education trainees show intention to use virtual reality (VR) for patient anxiety relief. Social influence and facilitating conditions are key factors for adopting VR technology in healthcare settings.

Keywords:
TAMTechnology Acceptance ModelUTAUTUnited Theory of Acceptance and Use of TechnologyVRfactor analysisgraduate medical education traineesmedical educationtechnology adoptiontechnology assessmentvirtual reality

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Area of Science:

  • Medical Education Technology
  • Digital Health Interventions
  • Patient Anxiety Management

Background:

  • Virtual reality (VR) shows therapeutic potential in healthcare, but trainee perspectives on its use are underexplored.
  • Behavioral intentions of graduate medical education (GME) trainees regarding VR for anxiety remain uncharacterized by technology adoption frameworks.
  • Limited data exists on GME trainee acceptance and usability of VR as a clinical tool.

Purpose of the Study:

  • To apply a hybrid Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) model to GME trainees.
  • To identify factors predicting GME trainees' behavioral intentions to use VR for patient anxiolysis.
  • To assess the reliability of the combined TAM-UTAUT model in this context.

Main Methods:

  • Surveyed 202 GME trainees after a VR experience designed to reduce perioperative anxiety.
  • Collected data on demographics, perceptions, attitudes, environmental factors, and behavioral intentions.
  • Utilized confirmatory factor analysis to evaluate the TAM-UTAUT model and its reliability.

Main Results:

  • Perceptions of usefulness, ease of use, enjoyment, social influence, and facilitating conditions predicted VR adoption intention.
  • Age, prior VR use, price sensitivity, and curiosity were weaker predictors.
  • The TAM-UTAUT model demonstrated good fit and acceptable reliability for all measurements.

Conclusions:

  • The TAM-UTAUT model is valid and reliable for predicting GME trainee intentions to use VR for patient anxiety.
  • Modifiable factors like social influence and facilitating conditions can promote VR adoption through training and exposure.
  • Future research should explore model reliability across different medical specialties and geographic locations.