Trial reportBMJ open2022
Prediction model study focusing on eHealth in the management of urinary incontinence: the Personalised Advantage Index as a decision-making aid.
Trial report in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Mobile App-Based Self-Management of Urinary Incontinence in Pregnant Women: Multicenter Pragmatic Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- Digital Health Interventions Incorporating Behavior Change Techniques and Gamification for Bladder Health in Adults Aged 50 Years and Older: Rapid Review.Interactive journal of medical research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop a prediction model and illustrate the practical potential of personalisation of treatment decisions between app-based treatment and care as usual for urinary incontinence (UI).
designA prediction model study using data from a pragmatic, randomised controlled, non-inferiority trial.
settingDutch primary care from 2015, with social media included from 2017. Enrolment ended on July 2018.
participantsAdult women were eligible if they had ≥2 episodes of UI per week, access to mobile apps and wanted treatment. Of the 350 screened women, 262 were eligible and randomised to app-based treatment or care as usual; 195 (74%) attended follow-up. PREDICTORS: Literature review and expert opinion identified 13 candidate predictors, categorised into two groups: Prognostic factors (independent of treatment type), such as UI severity, postmenopausal state, vaginal births, general physical health status, pelvic floor muscle function and body mass index; and modifiers (dependent on treatment type), such as age, UI type and duration, impact on quality of life, previous physical therapy, recruitment method and educational level.
main outcome measurePrimary outcome was symptom severity after a 4-month follow-up period, measured by the International Consultation on Incontinence Questionnaire the Urinary Incontinence Short Form. Prognostic factors and modifiers were combined into a final prediction model. For each participant, we then predicted treatment outcomes and calculated a Personalised Advantage Index (PAI).
resultsBaseline UI severity (prognostic) and age, educational level and impact on quality of life (modifiers) independently affected treatment effect of eHealth. The mean PAI was 0.99±0.79 points, being of clinical relevance in 21% of individuals. Applying the PAI also significantly improved treatment outcomes at the group level.
conclusionsPersonalising treatment choice can support treatment decision making between eHealth and care as usual through the practical application of prediction modelling. Concerning eHealth for UI, this could facilitate the choice between app-based treatment and care as usual. TRIAL REGISTRATION NUMBER: NL4948t.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.