Familiarity with music increases walking speed in rhythmic auditory cuing
Bibliographic record
Abstract
Rhythmic auditory stimulation (RAS) is a gait rehabilitation method in which patients synchronize footsteps to a metronome or musical beats. Although RAS with music can ameliorate gait abnormalities, outcomes vary, possibly because music properties, such as groove or familiarity, differ across interventions. To optimize future interventions, we assessed how initially familiar and unfamiliar low-groove and high-groove music affected synchronization accuracy and gait in healthy individuals. We also experimentally increased music familiarity using repeated exposure to initially unfamiliar songs. Overall, familiar music elicited faster stride velocity and less variable strides, as well as better synchronization performance (matching of step tempo to beat tempo). High-groove music, as reported previously, led to faster stride velocity than low-groove music. We propose two mechanisms for familiarity's effects. First, familiarity with the beat structure reduces cognitive demands of synchronizing, leading to better synchronization performance and faster, less variable gait. Second, familiarity might have elicited faster gait by increasing enjoyment of the music, as enjoyment was higher after repeated exposure to initially low-enjoyment songs. Future studies are necessary to dissociate the contribution of these mechanisms to the observed RAS effects of familiar music on gait.
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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) | 0.003 | 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".