Predicting the effect of interactive video bikes on exercise adherence: An efficacy trial
Bibliographic record
Abstract
Exercise games that employ video game technology are increasing in the marketplace but have received scant research attention despite their popularity. The purpose of this study was to evaluate the effect of videobike gaming on the constructs of the theory of planned behavior (TPB) and adherence in comparison to a cycling condition where participants listen to self-selected music. Participants were 29 inactive young men assigned randomly to experimental (n = 16) or comparison (n = 13) conditions. The recommended training regime consisted of moderate intensity activity (60-75% heart rate reserve), 3 days/week for 30 min/day for 6 weeks. At the end of the first session, participants were asked to complete TPB measures and these were subsequently measured 6 weeks later. Attendance was used as the measure of adherence. Results showed that affective attitude and adherence across the 6 weeks significantly favored the videobike condition over the comparison condition. Regression analyses suggested partial mediation of the effect of the videobike condition on adherence via affective attitude. This is the first study to provide evidence that interactive videobikes may improve adherence over traditional cycling because the activity produces higher affective attitudes. The results are promising for expanding to community-based evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".