Mobile App-Based Intervention for Pregnant Women With Stress Urinary Incontinence: Protocol for a Hybrid Effectiveness-Implementation Trial
Bibliographic record
Abstract
BACKGROUND: Stress urinary incontinence (SUI) is a common source of distress among women during and after pregnancy. It has a negative effect on quality of life but with poor care-seeking. Mobile health (mHealth) may be a promising solution with potential advantages. However, there is uncertainty whether a mobile app is effective for SUI symptom improvement during and after pregnancy. The implementation is also unclear. We developed an app named UIW (Urinary Incontinence for Women) aimed at improving perinatal incontinence. OBJECTIVE: The objective of this study is to evaluate the effectiveness of the UIW app-based intervention in improving SUI symptoms among pregnant women and explore the facilitators and barriers to using the UIW app to help refine and optimize the intervention. METHODS: This study is a hybrid effectiveness-implementation trial with a randomized controlled trial alongside a mixed-methods process evaluation according to the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Pregnant women with SUI (n=336) will be recruited from a university-affiliated hospital in China. They will be randomly allocated (1:1) to either the intervention group that receive usual care plus UIW app or control group that receive usual care alone. The intervention period will last 2 months. The 5 dimensions of the RE-AIM framework will be evaluated at recruitment (-T1), baseline (T0), immediately after intervention (T1), 42 days after delivery (T2), 3 months after delivery (T3), and 6 months after delivery (T4) through project documents, online questionnaires and a pelvic floor muscle training diary, surface electromyography, log data in the background management system, and qualitative interviews. Data analysis will follow the intention-to-treat principle. Descriptive statistics, t tests, chi-square tests, and a linear mixed model will be used to analyze the quantitative data. Deductive and inductive content analysis will be used to analyze the qualitative data. RESULTS: The effectiveness-implementation trial started in June 2020, trial recruitment was completed in October 2020, and the intervention will last for a 2-month period. Completion of the 6-month follow-up will be in July 2021, and we anticipate that the results of this study will be published in December 2021. CONCLUSIONS: This study will evaluate both effectiveness and implementation of the UIW app-based intervention among pregnant women. The hybrid effectiveness-implementation trial design according to the RE-AIM framework with a mixed-methods approach will give valuable insights into the effects as well as facilitators and barriers to the implementation that will influence the effects of the UIW app-based intervention. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1800016171; http://www.chictr.org.cn/showproj.aspx?proj=27455. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/22771.
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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 | 0.031 | 0.024 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.062 | 0.008 |
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".