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Articles

Virtual Reality Interventions for Personal Development: A Meta-Analysis of Hardware and Software

 

Abstract

Virtual reality (VR) has been repeatedly applied for personal development purposes, ranging from learning and training (cognitive outcomes), to psychological therapies (emotional outcomes), to physical rehabilitation (physical outcomes). Several factors lead to a successful VR intervention, most notably the hardware and software. In the current article, a meta-analysis is performed to test the effect of specialized input hardware (e.g motion sensors, floor pads, etc.), advanced output hardware (i.e., head-mounted displays, surround-screen displays, etc.), and game elements (i.e., score, competition, etc.) across and within the three noted applications of VR intervention. When analyzing the overall effects, only game elements had a significant impact on outcomes. When analyzing specific applications, input hardware did not have a notable impact on outcomes for any application; output hardware had a notable impact on cognitive and emotional outcomes but not physical; and game elements had a notable impact on cognitive outcomes but not emotional or physical. From these results, the current article provides direct suggestions for future research and practice. Particularly, certain mediating mechanisms are suggested to explain the impact of output hardware and game elements on VR intervention outcomes, sparking possible new directions for research and practice.

Notes

1 The current study is multidisciplinary in nature and relevant to all applications of VR technology for personal development. For this reason, the current study uses the term “intervention” to refer to any effort to alter individuals’ characteristics, whether it is a training to develop knowledge and skills, a therapy to alter emotions, a rehabilitation program to improve physical abilities, or any other application of VR for personal development.

2 The current literature search coincided with Howard (Citation2017b). The reported meta-analytic effect sizes do not overlap with those reported in Howard (Citation2017b).

Additional information

Notes on contributors

Matt C. Howard

Matt C. Howard ([email protected], MattCHoward.com) is a researcher with an interest in statistics, workplace technologies, personal development, and personality; he is an Assistant Professor in the Mitchell College of Business at the University of South Alabama.

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