Family Exergaming: Correlates and Preferences
Bibliographic record
Abstract
OBJECTIVES: The family home is an important environment for reducing sedentary behavior and increasing physical activity (PA). Exergames are gaining attention as a possible modality for increasing energy expenditure within the home setting. The purpose of this study was to explore the use, correlates, and preferences of family exergaming. METHODS: An online survey of exergame preferences, social cognition (theory of planned behavior), and behaviors that parents perceive as displaced during exergaming was conducted among a representative sample of 483 Canadian parents with a child between the ages of 6 and 14 years of age who own a videogame platform in their family home. RESULTS: Three quarters of parents indicated that they played exergames with their children but play time averaged a single bout every second week (mean = 29 minutes per bout). Parents overwhelmingly preferred to play sports and dance exergames with their children on weekends (80.1%) and during inclement weather (i.e., rainy = 51.3%; snowy = 45.5%). Family exergame playtime was associated with intention, affective and instrumental attitudes, and descriptive norm. TV watching was reported as being the most common activity that exergames would displace (64.1%). DISCUSSION: Findings suggest that exergames could be a potentially viable option for family PA, especially during bad weather and on weekends to help increase total PA. Intervention efforts could benefit from promoting the social aspects of family exergaming and attitudinal factors.
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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 | 0.002 |
| 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".