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Record W3165064618 · doi:10.2196/26212

A Smartphone App for Supporting the Self-management of Daytime Urinary Incontinence in Adolescents: Development and Formative Evaluation Study of URApp

2021· article· en· W3165064618 on OpenAlexvenueno aff
Katie Whale, Lucy Beasant, Anne Wright, Lucy Yardley, Louise Wallace, Louise Moody, Carol Joinson

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2021
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsnot available
FundersMedical Research CouncilElizabeth Blackwell Institute for Health Research, University of BristolUniversity of BristolNational Institute for Health Research Health Protection Research UnitNational Institute for Health and Care ResearchNational Institute for Health Research Southampton Biomedical Research CentreWellcome Trust
KeywordsConcordanceToiletMobile appsSmartphone appUrinary incontinenceFormative assessmentMedicinemHealthIntervention (counseling)PsychologyApplied psychologyPhysical therapyNursingPsychological interventionComputer scienceInternet privacyWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Daytime urinary incontinence (UI) is common in childhood and often persists into adolescence. UI in adolescence is associated with a range of adverse outcomes, including depressive symptoms, peer victimization, poor self-image, and problems with peer relationships. The first-line conservative treatment for UI is bladder training (standard urotherapy) that aims to establish a regular fluid intake and a timed schedule for toilet visits. The success of bladder training is strongly dependent on good concordance, which can be challenging for young people. OBJECTIVE: This paper aims to describe the development of a smartphone app (URApp) that aims to improve concordance with bladder training in young people aged 11 to 19 years. METHODS: URApp was designed by using participatory co-design methods and was guided by the person-based approach to intervention design. The core app functions were based on clinical guidance and included setting a daily drinking goal that records fluid intake and toilet visits, setting reminders to drink fluids and go to the toilet, and recording progress toward drinking goals. The development of URApp comprised the following four stages: a review of current smartphone apps for UI, participatory co-design workshops with young people with UI for gathering user requirements and developing wireframes, the development of a URApp prototype, and the user testing of the prototype through qualitative interviews with 23 young people with UI or urgency aged 10 to 19 years and 8 clinicians. The app functions and additional functionalities for supporting concordance and behavior change were iteratively optimized throughout the app development process. RESULTS: Young people who tested URApp judged it to be a helpful way of supporting their concordance with a timed schedule for toilet visits and drinking. They reported high levels of acceptability and engagement. Preliminary findings indicated that some young people experienced improvements in their bladder symptoms, including a reduction in UI. Clinicians reported that URApp was clinically appropriate and aligned with the best practice guidelines for bladder training. URApp was deemed age appropriate, with all clinicians reporting that they would use it within their own clinics. Clinicians felt URApp would be of particular benefit to patients whose symptoms were not improving or those who were not engaging with their treatment plans. CONCLUSIONS: The next stage is to evaluate URApp in a range of settings, including pediatric continence clinics, primary care, and schools. This research is needed to test whether URApp is an effective (and cost-effective) solution for improving concordance with bladder training, reducing bladder symptoms, and improving the quality of life.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.308
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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