Energy Expenditure And Mood States During Two Modes Of A Virtual Reality Fitness Game
Bibliographic record
Abstract
BACKGROUND: The effectiveness of virtual reality (VR) fitness games as a form of moderate-to-vigorous physical activity (MVPA) and their effect on mood has yet to be thoroughly quantified. VR fitness games’ energy expenditures are highly variable and their effectiveness as a form of MVPA should not be generalized. PURPOSE: To examine the effectiveness of two medium intensity modes (“Flow”, “Boxing”) of a VR Fitness game using indirect calorimetry and mood scales. METHODS: A metabolic cart was used to examine the relative and objective VO2, METs, and calories burned during two medium-intensity bouts of a VR fitness game in young (25.42 ± 3.25 years of age) active individuals (12 female, 11 male). The METs and calories burned were compared to a triaxial waist worn accelerometer, smart watch, and VR headset. Mood states were assessed pre- and post-VR fitness bout using the shortened Profile of Mood States. T-Tests were used to compare VR fitness game modes, sex, and pre-post exercise session changes. RESULTS: VO2 (absolute) averaged 1.93 ± 0.44 L/min and VO2 (relative) averaged 27.61 ± 5.60 mL/kg/min between modes. Given the recorded METs for each modality, both “Flow” (8.2 METs) and “Boxing” (7.6 METs) can be classified as high energy expenditure, vigorous activity. Regarding caloric expenditure, the values of the waist-worn accelerometer and VR headset differed from the metabolic cart, but the smart watch values were similar. Mood changes pre-to-post exercise were consistent with expected values for MVPA with participants reporting for both “Flow” and “Boxing” respectively, that they felt more “active” (p < 0.01; p < 0.01), more “full of pep” (p = 0.02; p = 0.05), and more “lively” (p = 0.019; p < 0.01). No sex differences were revealed for any metric. CONCLUSIONS: Both the “Flow” and “Boxing” modes of the VR fitness game modes can provide both mental and physical health benefits through mechanisms induced by engaging in physical activity and may be an effective exercise modality in a VO2 training program. Meta/Supernatural
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".