Effective German and English Language mHealth Apps for Self-management of Bronchial Asthma in Children and Adolescents: Comparison Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) apps hold great potential for asthma self-management. Data on the suitability of asthma apps intended for children are insufficient, and the availability of German language apps is still inadequate compared with English language apps. OBJECTIVE: This study aims to identify functional asthma apps for children in German and to compare them with English language apps. In line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, the Google Play Store and Apple App Store are systematically searched to preselect the most efficient apps, which are then compared according to a self-compiled criteria catalog. METHODS: Both app stores were screened for the term asthma. Following a PRISMA preselection process, the apps that met the inclusion criteria (ie, available free of charge, German or English language, and suitable for children) were rated by 3 independent persons following a criteria catalog consisting of 9 categories, some conceived for this purpose (availability, child-friendly, learning factor, and range of functions) and some adopted from existing validated catalogs (functionality and design, ease of use, potential for improving asthma self-management, fun factor and incentives, and information management and medical accuracy). The highest rated apps in German and English were compared. RESULTS: A total of 403 apps were identified on the Google Play Store and the Apple App Store. Finally, 24 apps that met the inclusion criteria were analyzed. In the first step of the quality assessment, only 4 available German language asthma apps were compared with 20 English language asthma apps. The 4 German language apps were then compared with the 4 highest rated English language apps. All selected apps, independent of the language, were comparable in the following categories: availability, functionality and design, ease of use, and information management and medical accuracy. The English language apps scored significantly higher in the following categories: potential for improving self-management, child-friendly, fun factor, learning factor, and range of function. English language apps (mean total points 34.164, SD 1.09) performed significantly better than German language asthma apps (mean total points 22.91, SD 2.898; P=.003). The best rated English language app was Kiss my asthma (36/42 points), whereas the best rated German language app Kata achieved only 27.33 points. CONCLUSIONS: The recommended English language apps are Kiss my asthma, AsthmaXcel, AsthmaAustralia, and Ask Me, AsthMe!, whereas the only recommended German language app is Kata. The use of apps plays an increasingly important role in patients' lives and in the medical field, making mHealth a staple in the future of asthma treatment plans. Although validated recommendations on rating mHealth apps have been published, it remains a challenging task for physicians and patients to choose a suitable app for each case, especially in non-English-speaking countries.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".