Metabolic Requirements of Interactive Video Game Cycling
Bibliographic record
Abstract
BACKGROUND: Interactive video game exercise leads to improved exercise adherence and health-related physical fitness in comparison to traditional stationary cycling. It has been postulated that interactive video game exercise has greater metabolic requirements than traditional cycling; however, this has not been tested to date. PURPOSE: To examine the metabolic requirements of interactive video game exercise in comparison to traditional stationary cycling at matched incremental workloads. METHODS: Fourteen participants (seven males and seven females) were examined during three separate sessions: 1) incremental cycle ergometer exercise for the assessment of maximal aerobic power and peak workload; 2) traditional cycling on a cycle ergometer using 5-min constant workloads of 25%, 50%, and 75% of maximal power output; and 3) cycling using identical constant, relative workloads while playing interactive video games. Measurements of oxygen consumption, heart rate, and rating of perceived exertion were assessed throughout. RESULTS: During interactive video game exercise, steady-state heart rate (26% +/- 18% and 14% +/- 13%), energy expenditure (61% +/- 41% and 25% +/- 21%), and oxygen consumption (34% +/- 17% and 18% +/- 12%) were significantly higher at the constant submaximal workloads of 25% and 50%, respectively. There was no significant difference in rating of perceived exertion between conditions at any workload. CONCLUSIONS: Interactive video game cycling results in greater metabolic requirements (despite similar perceptions of exertion) at submaximal constant workloads than traditional cycling. This form of training may be a novel and an attractive intervention in the battle against physical inactivity and associated health complications.
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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) | 0.002 | 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".