Evaluation of Children Playing a New-Generation Motion-Sensitive Active Videogame by Accelerometry and Indirect Calorimetry
Bibliographic record
Abstract
OBJECTIVE: New-generation active videogames (AVGs) use motion-capture video cameras to connect a player's arm, leg, and body movements through three-dimensional space to on-screen activity. We sought to determine if the whole-body movements required to play the AVG elicited moderate-intensity physical activity (PA) in children. A secondary aim was to examine the utility of using accelerometry to measure the activity intensity of AVG play in this age group. SUBJECTS AND METHODS: The PA levels of boys (n=26) and girls (n=15) 5-12 years of age were measured by triaxial accelerometry (n=25) or accelerometry and indirect calorimetry (IC) (n=16) while playing the "Kinect Adventures!" videogame for the Xbox Kinect (Microsoft(®), Redmond, WA) gaming system. The experiment simulated a typical 20-minute in-home free-play gaming session. RESULTS: Using 10-second recording epochs, the average (mean±standard deviation) PA intensity over 20 minutes was 4.4±0.9, 3.2±0.7, and 3.3±0.6 metabolic equivalents (METs) when estimated by IC or vertical axis (Crouter et al. intermittent lifestyle equation for vertical axis counts/10 seconds [Cva2RM]) and vector magnitude (Crouter et al. intermittent lifestyle equation for vector magnitude counts/10 seconds [Cvm2RM]) accelerometry. In total, 16.9±3.2 (IC), 10.6±4.5 (Cva2RM), and 11.1±3.9 (Cvm2RM) minutes of game playing time were at a 3 MET intensity or higher. CONCLUSIONS: In this study, children played the Xbox Kinect AVG at moderate-intensity PA levels. The study also showed that current accelerometry-based methods underestimated the PA of AVG play compared with IC. With proper guidance and recommendations for use, video motion-capture AVG systems could reduce sedentary screen time and increase total daily moderate PA levels for children. Further study of these AVG systems is warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".