Real-World Evidence From a Digital Health Treatment Program for Female Urinary Incontinence: Observational Study of Outcomes Following User-Centered Product Design
Bibliographic record
Abstract
BACKGROUND: Urinary incontinence (UI) affects millions of women with substantial health and quality-of-life impacts. Supervised pelvic floor muscle training (PFMT) is the recommended first-line treatment. However, multiple individual and institutional barriers impede women's access to skilled care. Evidence suggests that digital health solutions are acceptable and may be effective in delivering first-line incontinence treatment, although these technologies have not yet been leveraged at scale. OBJECTIVE: The primary objective is to describe the effectiveness and safety of a prescribed digital health treatment program to guide PFMT for UI treatment among real-world users. The secondary objectives are to evaluate patient engagement following an updated user platform and identify the factors predictive of success. METHODS: This retrospective cohort study of women who initiated device use between January 1, 2022, and June 30, 2023, included users aged ≥18 years old with a diagnosis of stress, urgency, or mixed incontinence or a score of >33.3 points on the Urogenital Distress Inventory Short Form (UDI-6). Users are prescribed a 2.5-minute, twice-daily, training program guided by an intravaginal, motion-based device that pairs with a smartphone app. Data collected by the device or app include patient-reported demographics and outcomes, adherence to the twice-daily regimen, and pelvic floor muscle performance parameters, including angle change and hold time. Symptom improvement was assessed by the UDI-6 score change from baseline to the most recent score using paired 2-tailed t tests. Factors associated with meeting the UDI-6 minimum clinically important difference were evaluated by regression analysis. RESULTS: Of 1419 users, 947 met inclusion criteria and provided data for analysis. The mean baseline UDI-6 score was 46.8 (SD 19.3), and the mean UDI-6 score change was 11.3 (SD 19.9; P<.001). Improvement was reported by 74% (697/947) and was similar across age, BMI, and incontinence subtype. Mean adherence was 89% (mean 12.5, SD 2.1 of 14 possible weekly uses) over 12 weeks. Those who used the device ≥10 times per week were more likely to achieve symptom improvement. In multivariate logistic regression analysis, baseline incontinence symptom severity and maximum angle change during pelvic floor muscle contraction were significantly associated with meeting the UDI-6 minimum clinically important difference. Age, BMI, and UI subtype were not associated. CONCLUSIONS: This study provides real-world evidence to support the effectiveness and safety of a prescribed digital health treatment program for female UI. A digital PFMT program completed with visual guidance from a motion-based device yields significant results when executed ≥10 times per week over a period of 12 weeks. The program demonstrates high user engagement, with 92.9% (880/947) of users adhering to the prescribed training regimen. First-line incontinence treatment, when implemented using this digital program, leads to statistically and clinically substantial symptom improvements across age and BMI categories and incontinence subtypes.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".