Correlations between Physical Activity Level, Quality of Life, and Cognitive Performance in Elderly Individuals Engaging in Multi-Year Dance Activities
Bibliographic record
Abstract
INTRODUCTION: It is well-acknowledged that cognitive and physical decline associated with aging can be prevented or reduced with the engagement in regular physical activity (PA). Dance activities combine cardiovascular, cognitive, and coordinative demands, providing a popular leisure PA among elderly. This study examined the correlations between quality of life (QoL), cognitive and physical performance, and PA level in older adults who participated in at least 10 years of amateur ballroom dancing. Methods: The study was designed as an observational study. A sample of 20 (10 men; age range: 65 to 80 years; BMI: 26.3 ± 3.0 kg/m²) amateur senior dancers were compared with a sample of 18 (8 men; age range: 65 to 75 years; BMI: 25.5 ± 2.4 kg/m²) non-sedentary individuals (control group) following an adapted PA program. Quality of life and cognitive functioning assessment tools were administered: 36 Health Status Survey (SF-36v2), Montreal Cognitive Assessment (MoCA), and Cognitive Reserve Index questionnaire (CRIq). Physical performance was measured with their preferred walking speed (PWS), and level of moderate to vigorous physical activity (MVPA) was evaluated using a multi-sensor activity monitor. Results: Participants presented a good SF-36v2 physical component and a very good mental com- ponent summary, a total MoCA score within the limits, and an average total CRIq score. Their PWS and daily MVPA were high. Differences neither in the three questionnaires nor in PWS and PA level were observed between groups. A significant, moderate, and positive correlation was found between PWS and SF-36v2 physical component summary score. Conclusion: Ballroom dancing seems to allow elderly individuals to maintain good cognitive and physical abilities, QoL, an acceptable normal cognitive reserve, notable physical performance, and PA level to the same extent as an adapted PA program. Both types of PA seem to be able to contrast the mental and physical decline associated with aging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".