Application of synchronous music reinforcement to increase walking speed: A novel approach for training intensity
Bibliographic record
Abstract
Walking is a common and preferred form of exercise. Although there are current recommendations for walking volume (e.g., steps per day), recent research has begun to distinguish volume from intensity (e.g., "brisk" walking) as an important dimension of exercise. Increasing intensity may confer health advantages beyond volume measures because it shifts cardiovascular performance to more vigorous training zones. Reinforcement-based approaches have been valuable in increasing volume measures of exercise, and the present study sought to develop a corresponding reinforcement approach to training walking intensity. For this study, we used a continuous reinforcement paradigm where music played only while walking met specified criteria; otherwise, music playback stopped. As a result, music was synchronized with walking performance. Seventeen participants walked on a nonmotorized treadmill at a self-selected pace. Across the session, different conditions arranged for music to play independent of walking speed or contingent on speed increases or decreases. An extinction component assessed performance when music was withdrawn completely. Walking speed was selectively increased and decreased by adjusting the contingencies that were arranged for music, and variability in speed increased during extinction, with both findings indicating that music was a reinforcer. Heart rate was also increased to moderate-vigorous intensities during reinforcement. The findings provide a compelling case that walking intensity can be modified by music reinforcement. We suggest that synchronous schedules may be an important foundation for future exercise technologies that are based on reinforcement.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".