The Use of Virtual Reality to Influence Motivation, Affect, Enjoyment, and Engagement During Exercise: A Scoping Review
Bibliographic record
Abstract
Many adults are physically inactive. While the reasons are complex, inactivity is, in part, influenced by the presence of negative feelings and low enjoyment during exercise. While virtual reality (VR) has been proposed as a way to improve engagement with exercise (e.g., choosing to undertake exercise), how VR is currently used to influence experiences during exercise is largely unknown. Here we aimed to summarize the existing literature evaluating the use of VR to influence motivation, affect, enjoyment, and engagement during exercise. A Population (clinical, and healthy), Concept (the extent and nature of research about VR in exercise, including underpinning theories), and Context (any setting, demographic, social context) framework was used. A systematic search of Medline, Scopus, Embase, PsycINFO, and Google Scholar was completed by two independent reviewers. Of 970 studies identified, 25 unique studies were included ( n = 994 participants), with most (68%) evaluating VR influences on motivation, affect, enjoyment, and engagement during exercise in healthy populations ( n = 8 studies evaluating clinical populations). Two VR strategies were prominent – the use of immersion and the use of virtual avatars and agents/trainers. All studies but one used virtual agents/trainers, suggesting that we know little about the influence of virtual avatars on experiences during exercise. Generally, highly immersive VR had more beneficial effects than low immersive VR or exercise without VR. The interaction between VR strategy and the specific exercise outcome appeared important (e.g., virtual avatars/agents were more influential in positively changing motivation and engagement during exercise, whereas immersion more positively influenced enjoyment during exercise). Presently, the knowledge base is insufficient to provide definitive recommendations for use of specific VR strategies to target specific exercise outcomes, particularly given the numerous null findings. Regardless, these preliminary findings support the idea that VR may influence experiences during exercise via multiple mechanistic pathways. Understanding these underlying mechanisms may be important to heighten effects targeted to specific exercise outcomes during exercise. Future research requires purposeful integration of exercise-relevant theories into VR investigation, and careful consideration of VR definitions (including delineation between virtual avatars and virtual agents), software possibilities, and nuanced extension to clinical populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".