A Mobile Health App for Facilitating Disease Management in Children With Atopic Dermatitis: Feasibility and Impact Study
Bibliographic record
Abstract
BACKGROUND: Inadequate control of atopic dermatitis (AD) increases the frequency of exacerbations and reduces the quality of life. Mobile health apps provide information and communication technology and may increase treatment adherence and facilitate disease management at home. The mobile health app, Atopic App, designed for patients and their caregivers, and the associated web-based patient education program, Atopic School, provide an opportunity for improving patients' and caregivers' engagement and adherence to the management of AD. OBJECTIVE: This noninterventional, observational study aimed to explore the feasibility and potential impact on the management of AD in children by caregivers using the Atopic App mobile health app. METHODS: The patient-oriented eczema measure (POEM) and numerical rating scale for the grading of pruritus were used as severity scores (scale range: 0-28). The artificial intelligence model of the app was used to assess the severity of AD based on the eczema area and severity index approach. The deidentified data enabled the analysis of the severity of AD, treatment plan history, potential triggers of flare-ups, usage of available features of the app, and the impact of patient education. RESULTS: During a 12-month period, of the 1223 users who installed the app, 910 (74.4%) registered users were caregivers of children with AD. The web-based Atopic School course was accessed by 266 (29.2%) caregivers of children with AD, 134 (50.4%) of whom completed the course. Usage of the app was significantly more frequent among those who completed the Atopic School program than among those who did not access or did not complete the course (P<.001). Users who completed a second POEM 21 to 27 days apart exhibited a significant improvement of AD severity based on the POEM score (P<.001), with an average improvement of 3.86 (SD 6.85) points. The artificial intelligence severity score and itching score were highly correlated with the POEM score (r=0.35 and r=0.52, respectively). CONCLUSIONS: The Atopic App provides valuable real-world data on the epidemiology, severity dynamics, treatment patterns, and exacerbation-trigger correlations in patients with AD. The significant reduction in the POEM score among users of the Atopic App indicates a potential impact of this tool on health care engagement by caregivers of children with AD.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".