The Effects of Combining Videogame Dancing and Pelvic Floor Training to Improve Dual-Task Gait and Cognition in Women with Mixed-Urinary Incontinence
Bibliographic record
Abstract
OBJECTIVE: Many women over 65 years of age suffer from mixed urinary incontinence (MUI) and executive function (EF) deficits. Both incontinence and EF declines increase fall risk. The current study assessed EF and dual-task gait after a multicomponent intervention that combined pelvic floor muscle (PFM) training and videogame dancing (VGD). MATERIALS AND METHODS: Baseline (Pre1), pretraining (Pre2), and post-training (Post) neuropsychological and dual-task gait assessments were completed by 23 women (mean age, 70.4 years) with MUI. During the dual-task, participants walked and performed an auditory n-back task. From Pre2 to Post, all women completed 12 weeks of combined PFM and VGD training. RESULTS: After training (Pre2 to Post), the number of errors in the Inhibition/Switch Stroop condition decreased significantly, the Trail Making Test difference score improved marginally, and the number of n-back errors during dual-task gait significantly decreased. A subgroup analysis based on continence improvements (pad test) revealed that only those subjects who improved in the pad test had significantly reduced numbers of n-back errors during dual-task gait. CONCLUSIONS: The results of this study suggest that a multicomponent intervention can improve EFs and the dual-task gait of older women with MUI. Future research is needed to determine if the training-induced improvements in these factors reduce fall risk.
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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) | 0.001 | 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) | 0.002 | 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".