Measuring partner synchrony during salsa dancing and its relationship to changes in psychosocial domains
Bibliographic record
Abstract
Abstract Background Dance is a promising rehabilitation adjunct but understanding the mechanisms through which dance improves physical and psychosocial well-being is necessary for implementation. This study investigated the feasibility of a measure of one possible mechanism: partner synchrony. Secondary objectives were to examine the relationships between partner synchrony, instructor ratings, and changes in psychosocial variables. Methods Participants wore an Inertial Measurement Unit (IMU) sensor during a salsa class that was video recorded. Partner synchrony was quantified with correlations of x, y, and z-axis acceleration between dancing pairs. Psychosocial variables were measured pre- and post-class. Salsa instructors rated partner synchrony from video recordings. Feasibility parameters included percentage of participants with data collected and sensor comfort. Pre-post changes were analyzed with Wilcoxon ranked tests and relationships were analyzed with Spearman correlations. Results Data was collected for 23/24 (96%) participants and 17/24 (81%) reported the sensor was comfortable. All psychosocial variables improved from pre- to post-class. Partner synchrony was significantly associated with instructor ratings and change in positive and negative affect. Conclusions An IMU-based measure of dance partner synchrony is feasible, associated with ratings by dance instructors and related to changes in mood. The partner synchrony measure can be used to advance our understanding of dance as a therapeutic tool after its reliability is investigated.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".