Effects of Full Body Exergaming in Virtual Reality on Cardiovascular and Muscular Parameters: Cross-Sectional Experiment
Bibliographic record
Abstract
BACKGROUND: In recent years, many studies have associated sedentary behavior in front of screens with health problems in infants, children, and adolescents. Yet options for exergaming-playing video games that require rigorous physical exercise-seem to fall short of the physical activity levels recommended by the World Health Organization. OBJECTIVE: The purpose of this study was to investigate the effect of a fully immersive virtual reality (VR)-based training system on cardiovascular and muscular parameters of young adults. METHODS: A cross-sectional experiment design was used to analyze muscle activity (surface electromyography), heart rate, perceived exertion (RPE), cybersickness symptoms, perceived workload, and physical activity enjoyment (PACES) in 33 participants performing two 5-minute flights on a new training device. RESULTS: Participants' performance of the planking position required to play the game resulted in moderate aerobic intensity (108 [SD 18.69] bpm). Due to the mainly isometric contraction of the dorsal muscle chain (with a mean activation between 20.6% [SD 10.57] and 26.7% [SD 17.39] maximum voluntary isometric contraction), participants described the exercise as a moderate to vigorous activity (RPE 14.6 [SD 1.82]). The majority reported that they enjoyed the exercise (PACES 3.74 [SD 0.16]). However, six participants had to drop out because of cybersickness symptoms and two because of muscle pain due to prior injuries. CONCLUSIONS: Our findings suggest that fully immersive VR training systems can contribute to muscle-strengthening activities for healthy users. However, the dropout rate highlights the need for technological improvements in both software and hardware. In prevention and therapy, movement quality is a fundamental part of providing effective resistance training that benefits health. Exergaming on a regular basis has the potential to develop strong muscles and a healthy back. It is essential that future VR-based training systems take into account the recommendations of sport and exercise science.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".