Effects of Virtual Reality and Non–Virtual Reality Exercises on the Exercise Capacity and Concentration of Users in a Ski Exergame: Comparative Study
Bibliographic record
Abstract
BACKGROUND: Recently, ski exergames have been gaining popularity due to the growing interest in health improvement. Conventional studies evaluating the effects of ski exergames only considered exercise capacity and overlooked concentration. Ski exergames consist of a motion platform for exercise and virtual reality (VR) content in the game. The VR content enhances the exercise capacity and concentration of the user by providing a challenging goal. OBJECTIVE: The aim of this study is to evaluate the effects of VR and non-VR exercises on the exercise capacity and concentration of users in a ski exergame. METHODS: To examine the effects of the VR content in ski exergames, we performed 2 experiments, non-VR exercise and VR exercise, where participants exercised on the motion platform. If a user performs an exercise without using any VR content, it is a non-VR exercise. Contrastingly, in the case of VR exercise, a user exercises according to the VR content (a downhill scenario). In addition to the range of motion (ROM) of the ankle and rated perceived exertion (RPE) to assess exercise capacity, we used electroencephalography (EEG) to assess users' concentration. RESULTS: We evaluated the effects of the VR content by comparing the results obtained from VR and non-VR exercises. The ROM of the ankle with VR exercise was wider than that with non-VR exercise. Specifically, ROM of the ankle was 115.71° (SD 17.71°) and 78.50° (SD 20.43°) in VR exercise and non-VR exercise, respectively. The RPE difference between the 2 exercises was not statistically significant. The result of the sensorimotor rhythm waves (which are concentration-related EEG signals) was more favorable for VR exercise than non-VR exercise. The ratios of sensorimotor rhythm wave in EEG were 3.08% and 2.70% in the VR exercise and non-VR exercise, respectively. CONCLUSIONS: According to the results of this experiment, higher exercise capability and concentration were achieved with the VR exercise compared with non-VR exercise. The observations confirm that VR content can enhance both exercise capability and concentration of the user. Thus, the ski exergames can be used effectively by those who, in general, do not like exercise but enjoy games.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".