Real-World Evidence From a Digital Health Treatment Program for Female Urinary Incontinence: Observational Study of Outcomes Following User-Centered Product Design
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».