Attitudes Towards Physical Activity and Perceived Exertion in Three Different Multitask Cybercycle Navigational Environments
Bibliographic record
Abstract
Physical activity and positive health behaviors are not usually associated with playing video games. Participating in exergames, video games that combine exercise and virtual environments may encourage physical activity by making it more enjoyable. The investigation aimed to study attitudes toward physical activity and perceived exertion in three different multitask cybercycle navigational environments. A sample of 56 adults participated in one of three navigation tasks while riding a stationary bicycle with an interactive computer-based simulation program displayed on the built-in screen. Subjects were randomly assigned one of the three navigation groups: Gauges Monitoring (n=18), Touring (n=19) and Gaming (n=19). After completing the ride and concurrent multitask tests, an attitude survey questionnaire was administered concerning individuals’ perceptions of the experience and toward exercise in general. Post-ride participants were also asked to rate their perceived exertion during the ride using the Borg Scale of Perceived Exertion. Analysis of variance tests were used to compare the results among the three groups and between genders on each factor and on the Borg Scale of Perceived Exertion. Significant differences were found for interaction between environment and gender for the Physical Activity factor (P = 0.020), a gender effect for the Walk Skills factor (P = 0.007), and for the Borg Scale (P = 0.004). Subsequent post hoc Tukey tests indicated that the perceived exertion was higher in the Gaming Group when compared with Gauges Monitoring and Touring Groups (P = 0.006; 0.014, respectively). Overall, participants enjoyed the activity irrespective of environment. Results support the proposition that exergaming in light-to-moderate exercise conditions is perceived as being physically active.
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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".