Children’s and Caregivers’ Review of a Guided Imagery Therapy Mobile App Designed to Treat Children With Functional Abdominal Pain Disorders: Leveraging a Mixed Methods Approach With User-Centered Design
Notice bibliographique
Résumé
BACKGROUND: Functional abdominal pain disorders (FAPDs) are highly prevalent and associated with substantial morbidity. Guided imagery therapy (GIT) is efficacious; however, barriers often impede patient access. Therefore, we developed a GIT mobile app as a novel delivery platform. OBJECTIVE: Guided by user-centered design, this study captured the critiques of our GIT app from children with FAPDs and their caregivers. METHODS: Children aged 7 to 12 years with Rome IV-defined FAPDs and their caregivers were enrolled. The participants completed a software evaluation, which assessed how well they executed specific app tasks: opening the app, logging in, initiating a session, setting the reminder notification time, and exiting the app. Difficulties in completing these tasks were tallied. After this evaluation, the participants independently completed a System Usability Scale survey. Finally, the children and caregivers were separately interviewed to capture their thoughts about the app. Using a hybrid thematic analysis approach, 2 independent coders coded the interview transcripts using a shared codebook. Data integration occurred after the qualitative and quantitative data were analyzed, and the collective results were summarized. RESULTS: We enrolled 16 child-caregiver dyads. The average age of the children was 9.0 (SD 1.6) years, and 69% (11/16) were female. The System Usability Scale average scores were above average at 78.2 (SD 12.6) and 78.0 (SD 13.5) for the children and caregivers, respectively. The software evaluation revealed favorable usability for most tasks, but 75% (12/16) of children and 69% (11/16) of caregivers had difficulty setting the reminder notification. The children's interviews confirmed the app's usability as favorable but noted difficulty in locating the reminder notification. The children recommended adding exciting scenery and animations to the session screen. Their preferred topics were animals, beaches, swimming, and forests. They also recommended adding soft sounds related to the session topic. Finally, they suggested that adding app gamification enhancements using tangible and intangible rewards for listening to the sessions would promote regular use. The caregivers also assessed the app's usability as favorable but verified the difficulty in locating the reminder notification. They preferred a beach setting, and theme-related music and nature sounds were recommended to augment the session narration. App interface suggestions included increasing the font and image sizes. They also thought that the app's ability to relieve gastrointestinal symptoms and gamification enhancements using tangible and intangible incentives would positively influence the children's motivation to use the app regularly. Data integration revealed that the GIT app had above-average usability. Usability challenges included locating the reminder notification feature and esthetics affecting navigation. CONCLUSIONS: Children and caregivers rated our GIT app's usability favorably, offered suggestions to improve its appearance and session content, and recommended rewards to promote its regular use. Their feedback will inform future app refinements.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,029 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».