Effect Of Exergaming On Core Muscle Endurance And Enjoyment In Young Adults: A Pilot Study
Bibliographic record
Abstract
Background: The core provides a foundation for movement in the periphery and comprises muscles that stabilize central and peripheral major joints. Strength and endurance are both necessary for core stability, but poor endurance of core muscle is a major risk factor for low back pain in a healthy person. Despite traditional methods of improving core stability, poor adherence to exercise is a significant problem in young adults. In the 21st century, “exergame” or exercisebased video gaming is an attractive option for improving physical function. So, the present study aims to evaluate the effect of exergame on core muscle endurance and enjoyment of young adults.Methods: Quasi-experimental research was used. Male and female participants (n=30,15 in the Intervention group and 15 in the Control group) were recruited. The intervention group was given training with Nintendo® Ring Fit Adventure (RFA) exergame three times a week for six weeks. The control group received a general core endurance training program with the same duration and frequency. The McGill endurance test assessed core muscle endurance. Enjoyment of exergame was assessed using an Exergame Enjoyment Questionnaire.Results: The study showed that in both groups, there was a significant increase in endurance time (p<0.05). Betweengroup analysis showed that the intervention group had a highly significant difference in endurance time compared to the control group (p<0.01).Conclusion: RFA exergame can offer more enjoyment while playing and improve young adults' core muscle endurance. Exergames can be an exciting way of improving physical function in the technological era.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".