Beneficial Features of a mHealth Asthma App for Children and Caregivers: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: mHealth and uHealth apps are available for children with asthma and their caregivers. However, previous studies on mHealth apps for children older than 7 years old with asthma are limited, and most studies on asthma apps do not consider interactions involving communication between children and caregivers. Therefore, a prototype mHealth child asthma app was developed for children and their caregivers, with features of tailored feedback messages in continuing self-management and interactions between children and caregivers. OBJECTIVE: The aim of this study was to identify the beneficial features of a prototype mHealth app developed for children with asthma and their caregivers. METHODS: Children diagnosed with persistent asthma by allergy specialists at the National Center for Child Health and Development were recruited. The features of a prototype mHealth app for children with asthma and their caregivers were investigated using semistructured interviews after they tried the app. Data were analyzed using thematic analysis. Content-characteristic words were named and grouped together as categories to explore themes. RESULTS: We recruited 27 children with asthma aged 2 to 12 years and 26 their caregivers. Findings on the good aspects of the app for children older than 7 years old and caregivers suggested 4 themes (confirmation of asthma knowledge, child-caregiver interaction, design of the app, and child's interest), and 6 categories were identified. Findings on the good aspects of app for children 7 to 12 years old and caregivers suggested 5 themes (new knowledge, manga as a Japanese-style comic, child's interest, trigger of self-management, and design and operability), and 11 categories were identified. Findings on the beneficial features of app suggested 6 themes (asthma knowledge, elements for continuous, universal design, notification, monitoring, and functions), and 12 categories were identified. CONCLUSIONS: Children with asthma and their caregivers perceived that the good aspects of the app were learning asthma knowledge with fun, including manga; interaction between child and caregiver; and easy-to-read design, such as colors. They wanted not only the asthma knowledge but also the universal design and enhanced elements, monitoring, and notification functions of the app.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".