Mobile Health App as an Auxiliary Tool in Management of Atopic Dermatitis in Children: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Mobile health apps can boost treatment adherence and support disease management at home. The Atopic App and web-based Atopic School patient education program offer a chance to enhance adherence to atopic dermatitis (AD) management. Objective: We aim to evaluate the feasibility, acceptability, and preliminary efficacy of the Atopic App mobile health intervention in the managing of AD in children. Methods: A randomized controlled study in children with AD divided participants into 3 groups: a control group (no app), an observational group with the app, and an interventional group with investigator supervision. Patients were examined at screening and follow-up visits 1 and 2 at 3-month intervals. Outcome measures included SCORAD (Scoring Atopic Dermatitis) for objective severity and Patient-Oriented Eczema Measure (POEM) for subjective effectiveness. Statistical analysis used paired t tests (2-tailed), the Mann-Whitney U test, and multiple regression. Results: Fifty-eight participants entered this study (38 boys and 20 girls): group 1 (control) comprised 17 patients, while experimental groups 2 and 3 consisted of 20 and 21 patients, respectively. The rates of missed appointments were similar and statistically insignificant across the groups. All groups showed a significant decrease in SCORAD and POEM scores (P<.05). Usage of the app for ≥8 days showed a more significant decrease in severity scores compared to those who used it for ≤7 days, or did not use it at all. Participants who used the app for ≥8 days had a median SCORAD of 6.25 (95% CI 4.6-14.1; IQR 4-16.3) at visit 1, significantly lower than nonusers (17.9, 95% CI 13.9-24.0; IQR 13.9-24; P=.03) and those using it ≤7 days (13, 95% CI 9.35-27; IQR 7.2-27; P=.04). Their median POEM of 2 (95% CI 1.0-4.5; IQR 1-5.3) was also significantly lower than those using the app ≤7 days (9, 95% CI 2-12; IQR 2-12; P=.04) and lower, though not significantly, than nonusers (7, 95% CI 1-9; IQR 1-9; P=.14). Additionally, using the Atopic App for ≥8 days after the screening visit strongly predicted a decrease in both SCORAD and POEM scores (P=.01 and P=.04, respectively). The time since the screening visit significantly predicted increased outcome scores, while prescriptions of topical calcineurin inhibitors, oral antihistamines, and oral antibiotics were weak and insignificant predictors of score changes. Conclusions: Our findings indicate that the Atopic App is helpful tool in managing AD in children, and they underscore the potential of mobile health interventions in the disease management.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".