Effects of an exercise program with audiovisual stimuli on affective mechanisms and future physical activity: a randomized controlled trial
Bibliographic record
Abstract
This study examined whether audiovisual stimuli influence affective valence (pleasure vs. displeasure), affective mechanisms (e.g., automatic affective valuation, exercise enjoyment), and future physical activity (PA) as a function of a 4-week exercise intervention. Sixty-one participants (55 females and 6 males) were randomly assigned to an audiovisual (watched and listened to self-selected audiovisual stimuli during exercise) or control group (without audiovisual stimuli) and completed 20-minute aerobic exercise sessions three times a week for four weeks. Affective valence and affective mechanisms were assessed at baseline, mid-intervention, end-of-intervention, and future PA engagement was evaluated one month post-intervention. Linear mixed models revealed that the audiovisual group had improved affective valence (p < 0.01) and exercise enjoyment (p = 0.02) during the 4-week exercise intervention compared to the control group (p < 0.01). A parallel-process latent growth curve model indicated that the initial level of automatic affective valuation was a predictor of the initial level of exercise enjoyment (p < 0.01), and positive changes in automatic affective valuation predicted better exercise enjoyment (p < 0.05) independent of the group. Affective valence directly predicted future PA (β = 0.44) and indirectly predicted future PA through automatic affective valuation and exercise enjoyment, respectively (β = 0.53). Results indicate that affective valence predicts future PA. Our findings, therefore, contribute to the growing body of evidence that hedonic responses evoked by audiovisual stimuli can positively influence lifestyle behaviours (i.e., PA) that are important for maintaining and improving health.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 0.001 |
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".