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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".