Innovative, Technology-Driven, Digital Tools for Managing Pediatric Urinary Incontinence: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Urinary incontinence affects approximately 7% to 10% of children during the day and 9% to 12% of children during the night. Treatment mainly involves lifestyle advice and behavioral methods, but motivation and adherence are low. Traditional tools such as pen-and-paper solutions may feel outdated and no longer meet the needs of today's "digital native" children. Meanwhile, digital interventions have already shown effectiveness in other pediatric health care areas. OBJECTIVE: This scoping review aimed to identify and map innovative, technology-driven, digital tools for managing pediatric urinary incontinence. METHODS: PubMed, Web of Science, and the Cochrane Library were searched in March 2022 without date restrictions, complemented by cross-referencing. Studies were eligible if they focused on pediatric patients (aged ≤18 years) with bladder and bowel dysfunctions and explored noninvasive, technology-based interventions such as digital health, remote monitoring, and gamification. Studies on adults, invasive treatments, and conventional methods without tangible tools were excluded. Gray literature was considered, but non-English-language, inaccessible, or result-lacking articles were excluded. A formal critical appraisal was not conducted as the focus was on mapping existing tools rather than evaluating effectiveness. Data analysis combined descriptive statistics and qualitative content analysis, categorizing tools through iterative coding and team discussions. RESULTS: In total, 66 articles were included, with nearly one-third (21/66, 32%) focusing on nocturnal enuresis. Our analysis led to the identification of six main categories of tools: (1) digital self-management (7/66, 11%); (2) serious games (7/66, 11%); (3) reminder technology (6/66, 9%); (4) educational media (12/66, 18%), further divided into video (5/12, 42%) and other media (7/12, 58%); (5) telehealth and remote patient monitoring (13/66, 20%), with subcategories of communication (5/13, 38%) and technological advances (8/13, 62%); and (6) enuresis alarm innovations (21/66, 32%), further divided into novel configurations (8/21, 38%) and prevoid alarms (13/21, 62%). CONCLUSIONS: The field of pediatric urinary incontinence demonstrates a considerable level of innovation, as evidenced by the inclusion of 66 studies. Many tools identified in this review were described as promising and feasible alternatives to traditional methods. These tools were reported to enhance engagement, improve compliance, and increase patient satisfaction and preference while also having the potential to save time for health care providers. However, this review also identified gaps in research, highlighting the need for more rigorous research to better assess the tools' effectiveness and address the complex, multifaceted challenges of pediatric urinary incontinence management. Limitations of this review include restricting the search to 3 databases, excluding non-English-language articles, the broad scope, and single-reviewer screening, although frequent team discussions ensured rigor. We propose that future tools should integrate connected, adaptive, and personalized approaches that align with stakeholder needs, guided by a multidisciplinary, human-centered framework combining both qualitative and quantitative insights.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".