Dance keeps us young: Older adults who participate in dance class do not differ from young adults in timing
Bibliographic record
Abstract
Aging is associated with vast neuromuscular and sensorimotor changes that may manifest in motor performance deficits (i.e., slowness and an increased variability in movement). The current study used a newly developed Hand Selection Complexity Task (HSCT) to compare timing and accuracy in older adults (OAs: n = 12, Mage = 77.2, 11F) from a retirement home (R-OAs: n = 6, Mage = 79.3, 5F) and dance class (D-OAs: n = 6, Mage = 75.0, 6F) to young adults (YAs: n = 20, Mage = 22.65, 12F) from the university community. HSCT gradients were displayed on a table in front of the participant in ipsilateral and contralateral space. Starting with hands at the midline, participants performed 10 reciprocal tapping movements between two targets as fast and accurately as possible. The gradient in contralateral space was completed first, where participants were free to select whichever hand felt most comfortable. Three conditions enabled manipulation of: (1) target amplitude, (2) target width, and (3) both target amplitude and width simultaneously. Within each condition, six levels of difficulty, determined using Fitts' Law, were randomly presented. Timing and accuracy were recorded. A main effect of age revealed that OAs took significantly longer to complete the task; however no differences in the number of errors emerged. Interestingly, when separated into two groups based on recruitment location, D-OAs did not differ from YAs; however, R-OAs were significantly slower than both YAs and D-OAs. Previous work has demonstrated that physical activity can help prevent cognitive decline. The current study provides preliminary evidence to extend the benefits of physical activity, such as dance, to preserve motor functioning.Acknowledgments: The authors would like to acknowledge the Natural Sciences and Engineering Research Council (PJB) for funding.
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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 | 0.001 |
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".