The impact of Wii Fit yoga training on flexibility and heart rate : original research article
Bibliographic record
Abstract
Background : Flexibility training is an often overlooked, but nevertheless important component of overall fitness, health and athletic performance and is positively impacted by flexibility training. Emerging technologies, including the Wii Fit, offer an alternative to traditional flexibility training. To date however, no research has examined the impact of the flexibility component of the Wii Fit on upper and lower body flexibility. Research question : The purpose of the study was to determine the impact of a four week Wii Fit yoga training session on flexibility and heart rate. Type of study : A pre-test post-test control group design was used. Methods : Healthy, moderately active participants (N = 32) were randomly assigned to either a training (N=14) or control (N=18) group, where the training group completed four weeks of Wii flexibility training and flexibility test practice, for three sessions a week. Flexibility was assessed for both the control group and training groups prior to and after the training sessions, using the sit and reach and shoulder flexibility tests. Results : Significant improvements in both upper and lower body flexibility were noted for the Wii Fit users, while the control group showed no changes in flexibility over the testing period. Conclusions : Data suggest that the flexibility training provided by the Wii Fit yoga exercises have a significant impact on upper and lower body flexibility, suggesting the Wii Fit can be effectively utilised as part of overall flexibility training.
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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) | 0.004 | 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".