Virtual Exercise in Medicine: A Proof of Concept in a Healthy Population
Bibliographic record
Abstract
BACKGROUND: Science is beginning to establish the benefits of the use of virtual reality (VR) in health care. This therapeutic approach may be an appropriate complementary treatment for some mental illnesses. It could prevent high levels of morbidity and improve the physical health of patients. For many years, the literature has shown the health benefits of physical exercise. Physical exercise in a VR environment may improve the management of mild to moderate mental health conditions. In this context, we developed a virtual environment combined with an ergocycle (the augmented physical training for isolated and confined environments [APTICE] system). OBJECTIVE: This study aims to investigate the impact of physical exercise in a VR environment. METHODS: A total of 14 healthy participants (11 men and 3 women; mean age 43.28, SD 10.60 years) undertook 15 minutes of immersive physical exercise using the system. Measures included mindfulness and immersion disposition, subjective perceptions of sensory information, user experience, and VR experience (ie, psychological state, flow, and presence). RESULTS: First, the APTICE system appears to be a useful tool because the user experience is positive (subscales in the AttrakDiff questionnaire: pragmatic quality=0.99; hedonic quality-stimulation=1.90; hedonic quality-identification=0.67; attractiveness=1.58). Second, the system can induce a positive psychological state (negative emotion, P=.06) and an experience of flow and presence (P values ranging from <.001 to .04). Third, individual immersive and mindful disposition plays a role in the VR experience (P values ranging from <.02 to .04). Finally, our findings suggest that there is a link between the subjective perception of sensory information and the VR experience (P values ranging from <.02 to .04). CONCLUSIONS: These results indicate that the device is well accepted with positive psychological and exteroceptive outcomes. Overall, the APTICE system could be a proof of concept to explore the benefits of virtual physical exercise in clinical medicine.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".