Barriers and Facilitators Associated With App-Based Treatment for Female Urinary Incontinence: Mixed Methods Evaluation
Bibliographic record
Abstract
BACKGROUND: App-based treatment for urinary incontinence is a proven effective and cost-effective alternative to care as usual, but successful implementation requires that we identify and address the barriers and facilitators associated with app use. OBJECTIVE: The goal of the research was to explore the factors influencing app-based treatment for urinary incontinence and identify which barriers or facilitators are associated with treatment success or failure. METHODS: We used a sequential explanatory mixed methods design to connect the results of a randomized controlled trial with data from semistructured interviews. This previous RCT had shown the noninferiority of app-based treatment compared with care as usual for urinary incontinence over 4 months. Participants who reported success or failure with app-based treatment, as measured by the change in International Consultation on Incontinence Modular Questionnaire Urinary Incontinence Short Form symptom score, were selected for telephone interview by purposive sampling (n=17). This study reports mainly on the qualitative component of our mixed methods study. Qualitative analyses were conducted in two ways. First, we analyzed the qualitative data of all interviewed participants and discussed the relationships between the main themes. Second, the experiences between the success (n=9) and failure group (n=8) were compared and contrasted to explore factors that were positively or negatively associated with the quantitative effect of app-based treatment. These factors were then interpreted as barriers to and facilitators of successful app-based treatment. RESULTS: Four interrelated themes were identified as affecting the app based treatment effect: adherence, personal factors, app factors, and awareness. Qualitative analyses of the relationships between the themes showed that adherence-related factors directly influenced treatment effect in both a positive and negative matter. In turn, adherence was also positively and negatively influenced by the other 3 themes. Additionally, awareness was positively influenced by the treatment effect. Within these themes, several factors were identified that acted as barriers (eg, unrealistic expectation of time investment and interfering personal circumstances), facilitators (eg, strict integration of exercises and prior pelvic floor muscle therapy), or both (eg, personality traits and increased awareness of symptoms). CONCLUSIONS: This study shows that the effect of app-based treatment for urinary incontinence is mainly influenced by adherence, which in turn is affected by personal factors, app-based factors, and awareness. The identified factors could function as both facilitators and barriers depending on the user and interaction with other themes. Insight into these facilitators and barriers could lead to improved implementation and increased treatment effectiveness by targeting women most likely to benefit and through further development of the app. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1002/nau.23507.
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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 | 0.069 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".