Group-Based Pelvic Floor Telerehabilitation to Treat Urinary Incontinence in Older Women: A Feasibility Study
Bibliographic record
Abstract
Less than half of women with urinary incontinence (UI) receive treatment, despite the high prevalence and negative impact of UI and the evidence supporting the efficacy of pelvic floor muscle training (PFMT). A non-inferiority randomized controlled trial aiming to support healthcare systems in delivering continence care showed that group-based PFMT was non-inferior and more cost-effective than individual PFMT to treat UI in older women. Recently, the COVID-19 pandemic highlighted the importance of providing online treatment options. Therefore, this pilot study aimed to assess the feasibility of an online group-based PFMT program for UI in older women. Thirty-four older women took part in the program. Feasibility was assessed from both participant and clinician perspectives. One woman dropped out. Participants attended 95.2% of all scheduled sessions, and the majority (32/33, 97.0%) completed their home exercises 4 to 5 times per week. Most women (71.9%) were completely satisfied with the program's effects on their UI symptoms after completion. Only 3 women (9.1%) reported that they would like to receive additional treatment. Physiotherapists reported high acceptability. The fidelity to the original program guidelines was also good. An online group-based PFMT program appears feasible for the treatment of UI in older women, from both participant and clinician perspectives.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".