Pilot Study Evaluating the Usability and Acceptability of a Mobile App for Overactive Bladder Disease Management
Notice bibliographique
Résumé
Background: Overactive bladder (OAB), defined by urinary urgency with or without urge urinary incontinence (UI), usually with frequency and nocturia, can significantly impact patient’s quality of life. Tracking symptoms is an important part of OAB management and has been shown to assist in enhancing patient interaction with health care providers (HCP) when discussing solutions for symptom management. Objective: The primary goal of this study was to assess the usability and acceptability of an Android smartphone mobile app designed to help participants learn about OAB symptom management through tracking and self-management. Secondarily, we also assessed engagement with the app over the three-month study period. Methods: Eligible participants were experiencing OAB symptoms without an existing enlarged prostate or urinary tract infection (BPH/UTI), and enrolled through referrals from within the Partners Healthcare network. The mobile app was installed at the enrollment visit, and participants were instructed to complete monthly, 3-day symptom journals, as well as surveys and optional free-text notes for 12 weeks. Additionally, medication reminders, Kegel and bladder training exercises were available for use in the app. A visit with their HCP was scheduled between weeks 6 and 12 of the study for the HCP and participant to review collected symptom data via an app-linked portal. Qualitative input from the HCP, closeout participant interviews and app usage data (percent viewed and number of hits) were used to assess participant engagement. Closeout interviews (n=10) also assessed usability of the various app features. Demographic and usability satisfaction data were collected via questionnaires developed by investigators. Descriptive analyses were conducted to present the demographic and usability data. NVivo for Mac (version 11) was used to conduct a thematic analysis on qualitative data. Results: Of the total enrolled (n=33), 26 participants completed the study. Participant engagement with the app was 100% for months one and two of the study then dropped to 72% by month three. Most participants (80%) reported using the app as needed vs regularly. As a group, female participants >50 years demonstrated the highest engagement (75%) at closeout. The most used app feature was the free-text diary feature (100%; 5516 hits), followed by the “event log” (100%; 2105 hits). The majority of other app features were also rated as useful by participants (52-100%). Participant interviews found the app was a valuable OAB information source, simplifying symptom tracking and follow-through on clinician recommendations. Perceived usefulness of the portal varied between primary care providers and specialists. Participants indicated the app was “Easy to Learn” (96%), “Simple to Use” (92%), useful for understanding changes in symptoms (91%), enabled better symptom tracking (96%), and facilitated communication with their HCP (75%). Conclusions: A mobile app to increase awareness of OAB symptoms improved confidence in self-management for participants and increased access to data for decision making and participant communication for specialists. Participant-reported outcomes indicate that the tracking void frequency and urgency features were very useful, while other features such as medication reminders, pad usage, bladder and Kegel trainings were used less frequently among participants.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».