Digital Health Interventions Incorporating Behaviour Change Techniques and Gamification for Bladder Health in Adults Aged 50 and over: A Rapid Review (Preprint)
Notice bibliographique
Résumé
Background: Urinary incontinence (UI) is the most prevalent pelvic floor dysfunction, with incidence increasing with age. Numerous studies have demonstrated the effectiveness of pelvic floor exercises in improving UI. However, access to pelvic floor treatment remains limited due to lengthy waiting lists and poor adherence to prescribed exercises. Digital solutions incorporating behavior change techniques (BCTs), including gamification elements, may support self-management of bladder health for individuals aged 50 years and older who may face challenges in accessing conventional treatments. Objective: This study aimed to identify mobile apps and websites integrating BCTs, including gamification elements, to facilitate bladder health self-management among adults aged 50 years and older. Methods: The search was initiated in July 2024 and updated in January 2025 across three sources: (1) mobile app stores (Google Play Store and Apple App Store available in Spain, Lithuania, and the United Kingdom) using the keywords "pelvic floor," "urinary incontinence," and "bladder"; (2) websites; and (3) academic databases (PubMed, Scopus, CINAHL, and Google Scholar) for journal articles, book chapters, and conference papers published in English, Spanish, or Lithuanian. Inclusion criteria required that solutions be independently usable without health care supervision, incorporate at least one BCT (eg, training, education), be evidence-informed (ie, include participants aged ≥50 years in their design or piloting phase or explicitly target this demographic), and be available in Spain, the United Kingdom, or Lithuania. The Mobile App Rating Scale (MARS) was used to assess app quality, and the taxonomy of 93 BCTs was used for BCT classification. All review and data extraction processes were conducted in duplicate. Results: Twenty-one studies met the inclusion criteria, identifying 8 eligible mobile apps and 1 website. Among the apps, only 2 were available on either Google Play or the Apple App Store. No apps were identified in Lithuania, whereas 1 app was found in Spain and 3 apps in the United Kingdom. Of these, 1 UK app was accessible on both Google Play and the Apple App Store, whereas the others were limited to a single platform. BCT extraction showed that the apps included between 9 and 16 BCTs (mean 12, SD 3.27). Regarding quality, all assessed apps obtained MARS scores ranging from 3 to 4 out of 5. Website searches did not identify any scientifically validated platforms across the 3 countries, except for 1 website cited in a scientific publication. UI reduction on the International Consultation on Incontinence Questionnaire ranged from -3.9 to -2.1 points, while perceived improvement reached 91.7% in the Tät app. Conclusions: Evidence-based digital interventions for individuals aged 50 years and older remain limited. Existing apps suggest potential benefits in UI reduction and quality of life improvement; however, further research and development are needed to enhance accessibility and efficacy.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».