Will Nintendo “Wii Fit” Get You Fit? An Evaluation of the Energy Expenditure from Active-Play Videogames
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine whether Nintendo(®) (Redmond, WA) "Wii Fit™" games can help individuals meet physical activity recommendations. SUBJECTS AND METHODS: Thirty young healthy volunteers were recruited for this randomized crossover study to evaluate the energy expenditure associated with (1) a 30-minute "Wii Fit Free Run," (2) three 10-minute bouts of "Wii Fit" aerobic games ("Rhythm Boxing," "Super Hula Hoop," and "Advanced Steps"), and (3) 30-minute treadmill running/walking. Energy expenditure was measured by indirect calorimetry using breath-by-breath analyses of O2 consumption and CO2 production. RESULTS: The "Wii Fit" conditions produced a moderate exercise intensity (5.0, 4.1, 3.9, and 3.8 metabolic equivalents [METs] in "Free Run," "Rhythm Boxing," "Super Hula Hoop," and "Advanced Steps"), whereas the treadmill running/walking produced a vigorous exercise intensity (METs=8.0). Based on federal guidelines, an individual could achieve the minimum weekly goal of 500 MET-minutes by playing selected "Wii Fit" aerobics games for 20-26 minutes a day, 5 days a week. CONCLUSIONS: Although not as vigorous as the treadmill, active-play videogames such as "Wii Fit" may provide an alternative way to encourage exercise and increase adoption and adherence to the physical activity guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".