Metabolic responses of upper-body accelerometer-controlled video games in adults
Bibliographic record
Abstract
Historically, video games required little physical exertion, but new systems utilize handheld accelerometers that require upper-body movement. It is not fully understood if the metabolic workload while playing these games is sufficient to replace routine physical activity. The purpose of this study was to quantify metabolic workloads and estimate caloric expenditure while playing upper-body accelerometer-controlled and classic seated video games. Nineteen adults completed a peak oxygen consumption treadmill test followed by an experimental session where exercising metabolism and ventilation were measured while playing 3 video games: control (CON), low activity (LOW) and high activity (HI). Resting metabolic measures (REST) were also acquired. Caloric expenditure was estimated using the Weir equation. Mean oxygen consumption normalized to body weight for HI condition was greater than LOW, CON, and REST. Mean oxygen consumption normalized to body weight for LOW condition was also greater than CON and REST. Mean exercise intensities of oxygen consumption reserve for HI, LOW, and CON were 25.8% ± 5.1%, 6.4% ± 4.8%, and 0.8% ± 2.4%, respectively. Estimated caloric expenditure during the HI was significantly related to aerobic fitness, but not during other conditions. An active video game significantly elevated oxygen consumption and heart rate, but the increase was dependent on the type of game. The mean oxygen consumption reserve during the HI video game was below recommended international standards for moderate and vigorous activity. Although upper-body accelerometer-controlled video games provided a greater exercising stimulus than classic seated video games, these data suggest they should not replace routine moderate or vigorous exercise.
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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) | 0.001 | 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".