Comparative Impacts Of Traditional Yoga And Cued, High-Speed Yoga On Older Adults' Cognitive Performance
Bibliographic record
Abstract
Age is the greatest risk factor for cognitive decline. Hatha Yoga (HG) is a feasible practice for helping older adults to maintain or improve cognitive functions. YogaCue (YC) integrates visual and auditory cues into traditional Hatha practice to improve cognitive performance in cognitively healthy older adults. PURPOSE: This study compared improvements in the cognitive performance of older adults following 24 weeks of HG or YC training. METHODS: 21 healthy older adults (YC = 11, HG = 10; 73.7 ± 4.6 y; Montreal Cognitive Assessment (MoCA) ≥ 24) participated in the study. Subjects completed a battery of neuropsychological measures, including the computerized National Institute of Health (NIH) Toolbox (Cognitive Module), the Hopkins Verbal Learning Test (HVLT), and the Trail Making Test parts A and B (TMT-A, TMT-B). Following pre-testing, subjects were stratified according to their score on the Montreal Cognitive Assessment (MOCA) and age, and randomly assigned to YC or the HG. Subjects attended three 1-hour sessions per week for 24 weeks. RESULTS: Significant time effects were seen for the List Sorting Test (LST; p = 0.049, ηp2 = 0.189) and the Fluid Cognition Composite Score (FCS; p = 0.005, ηp2 = 0.352). Pairwise comparison revealed a significant increase in LST (Mdiff ± SE = 7.0 ± 3.4; p = .049; g = .61) and FCS (7.891 ± 2.46; p = 0.005; g = 0.59) following training. No significant main effects or interactions were seen for any other tests in the NIH Cognitive Toolbox. For the HVLT, there were significant time effects for Total Recall (HTR: p = 0.007, ηp2 = 0.328) and Delayed Recall (HDR: p = 0.027, ηp2 = 0.231) and a trend towards a significant time effect for Percent Retained (HPR: p = 0.051, ηp2 = 0.186). All changes were driven by improvements by YC (HTR 3.82 ± 1.32; p = 0.009; g = 0.77; HDR: 2.00 ± 0.69; p = 0.009; d = 0.49; HPR: 15.02 ± 5.96%; p = 0.021; d = 0.31). No significant main effects or interactions were seen for any other components of the HVLT or TMT-A or TMT-B. CONCLUSION: Both YC and HG are valuable tools for improving memory and mitigating age-related cognitive decline. However, HVLT tests reveal that YC may have a greater impact on retaining verbal information than HG. Supported by: Funded by a McKnight Brain Research Foundation Inter-Institutional Cognitive Aging and Memory Interventional (CAMI) Core Grant
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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) | 0.001 | 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) | 0.001 | 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".