Using Active Video Games For Nanotraining To Minimum Acsm Physical Activity Requirements
Bibliographic record
Abstract
The time counstraint is the major problem for inactive people to reach minimal physical activity ACSM requirements. Nanotraining, short high intensity training, can be a solution providing that the intensity is high enough. However, few tools are available to the public to access a NanoTraining program. PURPOSE: to measure if an active video game providing Nanotraining can prescribe the minimum intensity necessary to induce health benefits. METHODS: A total of 23 participants (11 women and 12 men, 33 ± 4 years) practicing less than 120 minutes per week of physical activity were recruited to test 4 mini-games (Shape-Up, Ubisoft Entertainment Inc). Each mini-game lasted around 1.5 mins where the participant needed to give their maximum during the games. The 4 mini-games selected were: Squat me to the moon (squat), Push them up (push-up), Snow ball (running), and Arctic punch (punch). During the mini-games, oxygen uptake (VO2), heart rate (HR) and energy expenditure (EE) were measured with a portable metabolic analyser (K4b2, Cosmed, It.) and these variables were reported, relatively and respectively, to peak VO2 obtained by a progressive maximal step test. RESULTS: The % VO2peak for the 4 mini-games were respectively 81±12%, 49±17%, 93±13%, 60±14%. The average VO2 was respectively: 23±3, 15±2, 26±4, 17±5 ml/kg/min. The average heart rate was 164 ± 13, 150 ± 11, 164 ±21, 138±17 bpm. CONCLUSIONS: Shape-up has the potential to be used as a NanoTraining modality providing that some of the mini-games selected are high intensity games (ex: Squat and running). Furthermore, the motivation and pleasure of using active video games can help to improve the exercise intensity deployed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".