Mixed reality training for medical first responders: system evaluation and recommendations
This study assesses the integration of mixed reality (MR) technologies in medical first responder (MFR) training, focusing on identifying key factors influencing behavioral intention to use MR systems and practical implications for technology acceptance and enhanced realism through haptic feedback....
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
24 April 2025
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| In: |
Virtual reality
Year: 2025, Volume: 29, Pages: 1-25 |
| ISSN: | 1434-9957 |
| DOI: | 10.1007/s10055-025-01144-x |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s10055-025-01144-x Verlag, kostenfrei, Volltext: https://link.springer.com/journal/10055 
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| Author Notes: | Jakob Carl Uhl, Olivia Zechner, Anke Baetzner, Tanja Birrenbach, Sebastian Egger-Lampl, Helmut Schrom-Feiertag, Manfred Tscheligi |
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| 520 | |a This study assesses the integration of mixed reality (MR) technologies in medical first responder (MFR) training, focusing on identifying key factors influencing behavioral intention to use MR systems and practical implications for technology acceptance and enhanced realism through haptic feedback. Through a user-centered design approach, involving co-creation workshops, iterative development, and evaluations in pilot and field trials across six countries, this study evaluated technology acceptance, presence, user experience, and workload among MFRs. Both quantitative measures and qualitative feedback were collected to analyze the determinants of technology acceptance and user engagement. The MED1stMR training system, developed as a result, demonstrates that performance expectancy, effort expectancy, and social presence are significant predictors of behavioral intention to use MR training systems among MFRs. High technology acceptance and positive user experience were reported, with specific emphasis on the educational value of haptic feedback in skill training. Trainer feedback highlighted the importance of real-time performance metrics and openness to AI-driven training assistance for enhancing training outcomes. The study underscores the critical role of realistic patient interaction and the importance of aligning training challenges with users’ skills to create engaging MR training environments for MFRs. Identifying factors influencing behavioral intention offers valuable insights for the development of MR training systems, suggesting a focus on social presence and interactive capabilities to improve realism and educational value. The findings advocate for the integration of adaptive training features and further exploration of AI support in scenario optimization and performance enhancement. | ||
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