Innovating Care for Postmenopausal Women Using a Digital Approach for Pelvic Floor Dysfunctions: Prospective Longitudinal Cohort Study
Bibliographic record
Abstract
Background: The menopause transition is a significant life milestone that impacts quality of life and work performance. Among menopause-related conditions, pelvic floor dysfunctions (PFDs) affect ∼40%-50% of postmenopausal women, including urinary or fecal incontinence, genito-pelvic pain, and pelvic organ prolapse. While pelvic floor muscle training (PFMT) is the primary treatment, access barriers leave many untreated, advocating for new care delivery models. Objective: This study aims to assess the outcomes of a digital pelvic program, combining PFMT and education, in postmenopausal women with PFDs. Methods: This prospective, longitudinal study evaluated engagement, safety, and clinical outcomes of a remote digital pelvic program among postmenopausal women (n=3051) with PFDs. Education and real-time biofeedback PFMT sessions were delivered through a mobile app. The intervention was asynchronously monitored and tailored by a physical therapist specializing in pelvic health. Clinical measures assessed pelvic floor symptoms and their impact on daily life (Pelvic Floor Impact Questionnaire-short form 7, Urinary Impact Questionnaire-short form 7, Colorectal-Anal Impact Questionnaire-short form 7, and Pelvic Organ Prolapse Impact Questionnaire-short form 7), mental health, and work productivity and activity impairment. Structural equation modeling and minimal clinically important change response rates were used for analysis. Results: The digital pelvic program had a high completion rate of 77.6% (2367/3051), as well as a high engagement and satisfaction level (8.6 out of 10). The safety of the intervention was supported by the low number of adverse events reported (21/3051, 0.69%). The overall impact of pelvic floor symptoms in participants' daily lives decreased significantly (-19.55 points, 95% CI -22.22 to -16.88; P<.001; response rate of 59.5%, 95% CI 54.9%-63.9%), regardless of condition. Notably, nonwork-related activities and productivity impairment were reduced by around half at the intervention-end (-18.09, 95% CI -19.99 to -16.20 and -15.08, 95% CI -17.52 to -12.64, respectively; P<.001). Mental health also improved, with 76.1% (95% CI 60.7%-84.9%; unadjusted: 97/149, 65.1%) and 54.1% (95% CI 39%-68.5%; unadjusted: 70/155, 45.2%) of participants with moderate to severe symptomatology achieving the minimal clinically important change for anxiety and depression, respectively. Recovery was generally not influenced by the higher baseline symptoms' burden in individuals with younger age, high BMI, social deprivation, and residence in urban areas, except for pelvic health symptoms where lower BMI levels (P=.02) and higher social deprivation (P=.04) were associated with a steeper recovery. Conclusions: This study demonstrates the feasibility, safety, and positive clinical outcomes of a fully remote digital pelvic program to significantly improve PFD symptoms, mental health, and work productivity in postmenopausal women while enhancing equitable access to personalized interventions that empower women to manage their condition and improve their quality of life.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".