When Musical Accompaniment Allows the Preferred Spatio-Temporal Pattern of Movement
Bibliographic record
Abstract
A musical accompaniment is often used in movement coordination and stability exercise modalities, although considered obstructive for their fundament of preferred movement pace. This study examined if the rhythmic strength of musical excerpts used in movement coordination and exercise modalities allows the preferred spatio-temporal pattern of movement. Voluntary and spontaneous body sway (70 s) were tested (N=20 young women) in a non-musical (preferred) and two rhythmic strength (RS) musical conditions (Higher:HrRS, Lower:LrRS). The center of pressure trajectory was used for the body sway spatio-temporal characteristics (Kistler forceplate, 100 Hz). Statistics included paired t-tests between each musical condition and the non-musical one, as well as between musical conditions (p≤0.05). Results indicated no significant difference between the musical and the non-musical conditions (p>0.05). The HrRS differed significantly from LrRS only in the voluntary body sway, with increased sway duration (p=0.03), center of pressure path (p=0.04) and velocity (p=0.01). The findings provide evidence-based support for the rhythmic strength recommendations in movement coordination and stability exercise modalities. The HrRS to LrRS differences in voluntary body sway most possibly indicate that low-frequency musical features rather than just tempo and pulse clarity are also important.
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 | 0.000 | 0.002 |
| 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".