Virtual reality lifeguarding scenarios as a potential training solution for pool lifeguards.

Drowning Lifeguards Training simulation Virtual reality Visual search Water safety

Journal

Applied ergonomics
ISSN: 1872-9126
Titre abrégé: Appl Ergon
Pays: England
ID NLM: 0261412

Informations de publication

Date de publication:
Apr 2023
Historique:
received: 07 07 2022
revised: 10 12 2022
accepted: 15 12 2022
pubmed: 26 12 2022
medline: 24 1 2023
entrez: 25 12 2022
Statut: ppublish

Résumé

Ensuring that pool lifeguards develop the skills necessary to detect drowning victims is challenging given that these situations are relatively rare, unpredictable and are difficult to simulate accurately and safely. Virtual reality potentially provides a safe and ecologically valid approach to training since it offers a near-to-real visual experience, together with the opportunity to practice task-related skills and receive feedback. As a prelude to the development of a training intervention, the aim of this research was to establish the construct validity of virtual reality drowning detection tasks. Using a repeated measures design, a total of 38 qualified lifeguards and 33 non-lifeguards completed 13 min and 23 min simulated drowning detection tasks that were intended to reflect different levels of sustained attention. During the simulated tasks, participants were asked to monitor a virtual pool and identify any drowning targets with accuracy, response latency, and dwell time recorded. During the simulated scenarios, pool lifeguards detected drowning targets more frequently and spent less time than non-lifeguards fixating on the drowning target prior to the drowning onset. No significant differences in response latency were evident between lifeguards and non-lifeguards nor for first fixations on the drowning target. The results provide support for the construct validity of virtual reality lifeguarding scenarios, thereby providing the basis for their development and introduction as a potential training approach for developing and maintaining performance in lifeguarding and drowning detection. This research provides support for the construct validity of virtual reality simulations as a potential training tool, enabling improvements in the fidelity of training solutions to improve pool lifeguard competency in drowning detection.

Sections du résumé

BACKGROUND BACKGROUND
Ensuring that pool lifeguards develop the skills necessary to detect drowning victims is challenging given that these situations are relatively rare, unpredictable and are difficult to simulate accurately and safely. Virtual reality potentially provides a safe and ecologically valid approach to training since it offers a near-to-real visual experience, together with the opportunity to practice task-related skills and receive feedback. As a prelude to the development of a training intervention, the aim of this research was to establish the construct validity of virtual reality drowning detection tasks.
METHOD METHODS
Using a repeated measures design, a total of 38 qualified lifeguards and 33 non-lifeguards completed 13 min and 23 min simulated drowning detection tasks that were intended to reflect different levels of sustained attention. During the simulated tasks, participants were asked to monitor a virtual pool and identify any drowning targets with accuracy, response latency, and dwell time recorded.
RESULTS RESULTS
During the simulated scenarios, pool lifeguards detected drowning targets more frequently and spent less time than non-lifeguards fixating on the drowning target prior to the drowning onset. No significant differences in response latency were evident between lifeguards and non-lifeguards nor for first fixations on the drowning target.
CONCLUSION CONCLUSIONS
The results provide support for the construct validity of virtual reality lifeguarding scenarios, thereby providing the basis for their development and introduction as a potential training approach for developing and maintaining performance in lifeguarding and drowning detection.
APPLICATION CONCLUSIONS
This research provides support for the construct validity of virtual reality simulations as a potential training tool, enabling improvements in the fidelity of training solutions to improve pool lifeguard competency in drowning detection.

Identifiants

pubmed: 36566527
pii: S0003-6870(22)00277-0
doi: 10.1016/j.apergo.2022.103954
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103954

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

David Lim (D)

School of Psychological Sciences, Macquarie University, Australia; The Centre for Performance, Expertise, and Training at Macquarie University, Australia.

Mark Wiggins (M)

School of Psychological Sciences, Macquarie University, Australia; The Centre for Performance, Expertise, and Training at Macquarie University, Australia. Electronic address: mark.wiggins@mq.edu.au.

Meredith Porte (M)

School of Computing Sciences, Macquarie University, Australia; The Centre for Performance, Expertise, and Training at Macquarie University, Australia.

Piers Bayl-Smith (P)

School of Psychological Sciences, Macquarie University, Australia; The Centre for Performance, Expertise, and Training at Macquarie University, Australia.

Kim M Curby (KM)

School of Psychological Sciences, Macquarie University, Australia; The Centre for Performance, Expertise, and Training at Macquarie University, Australia.

Kirk N Olsen (KN)

School of Psychological Sciences, Macquarie University, Australia; The Centre for Performance, Expertise, and Training at Macquarie University, Australia.

Melanie Taylor (M)

School of Psychological Sciences, Macquarie University, Australia; The Centre for Performance, Expertise, and Training at Macquarie University, Australia.

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Classifications MeSH