Evaluation of active video games intensity: Comparison between accelerometer-based predictions and indirect calorimetric measurements
Bibliographic record
Abstract
BACKGROUND: Several active video game (AVG) intervention studies failed in showing an increase in physical activity by using accelerometry measurements. OBJECTIVE: To test the validity of accelerometry for monitoring AVG playing intensity. METHOD: Twenty-two adults performed 80 activities included in the Wii Sports and Wii Fit Plus series. The energy expenditure (EE) and subsequent MET values were measured by indirect calorimetry using metabolic chambers. Subjects wore an accelerometer-based monitor displaying MET values. For each activity, METs values obtained from indirect calorimetry and accelerometry were compared. Each activity was classified as light or moderate to vigorous physical activity (LPA: < 3METs or MVPA: ⩾ 3METs) for the two methods. RESULTS: AVG intensities have been slightly but significantly underestimated by the acceleromater-based monitor compared to the indirect calorimetry (2.5 ± 1.0 instead of 2.7 ± 0.9 METs). Fourty percent of activities have been significantly misestimated, and 20% have been misclassified. CONCLUSION: Those results point out the potential bias of accelerometry measurements for evaluating AVG intensities. Because average AVG intensity lays at the boundary between LPA and MVPA classes, misclassifications can frequently occur. Accelerometry data should be interpreted with caution in intervention studies using AVG.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".