Assessing the Physiological Cost of Active Videogames (Xbox Kinect) Versus Sedentary Videogames in Young Healthy Males
Bibliographic record
Abstract
Abstract Objectives: The aims of this study were twofold: (1) to compare the physiological costs of active videogames (AVGs) and sedentary videogames (SVGs) and (2) to compare the exercise intensities attained during AVGs with the exercise intensity criteria for moderate and vigorous physical activity, as stated in current physical activity recommendations for improving public health. Materials and Methods: Nineteen young males participated in the study (age, 23 ± 3 years; height, 178 ± 6 cm; weight, 78 ± 15 kg). Participants completed a maximum oxygen uptake ( \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$\dot{{\rm V}}{\rm O}_{2\max}$$ \end{document} ) test and a gaming session, including AVGs (“Reflex Ridge,” “River Rush,” and “Boxing” for the Microsoft [Redmond, WA] Kinect™) and SVGs (“FIFA 14” [Electronic Arts, Burnaby, BC, Canada] and “Call of Duty” [Activision, Santa Monica, CA]). Heart rate (HR) and oxygen uptake \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$\dot{{\rm V}}{\rm O}_{2}$$ \end{document} ) were recorded continuously during all videogames. Rating of perceived exertion (RPE) was taken every 3 minutes during AVGs and SVGs. Energy expenditure (EE), expressed as metabolic equivalents (METs), was calculated. One MET was defined as the volume of oxygen consumed at rest in a seated position and is equal to 3.5 mL of O 2 /kg of body mass/minute. The exercise intensity for each game was expressed as a percentage of \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$\dot{{\rm V}}{\rm O}_{2\max}$$ \end{document} and percentage of age-predicted maximum HR (HR max ). Results: Exercise intensity (percentage HR max , percentage \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$\dot{{\rm V}}{\rm O}_{2\max}$$ \end{document} , and RPE) and EE (METs) were significantly higher during active gaming compared with sedentary gameplay ( P < 0.01). AVGs elicited moderate levels of exercise intensity (64–72 percent HR max ) in line with current recommended physical activity guidelines. Conclusions: Our results indicate AVGs provoke physiological responses equivalent to a moderate-intensity physical activity.
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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) | 0.003 | 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".