Engagement, Retention, and Acceptability in a Digital Health Program for Atopic Dermatitis: Prospective Interventional Study
Bibliographic record
Abstract
BACKGROUND: Patients with atopic dermatitis can experience chronic eczema with pruritus, skin pain, sleep problems, anxiety, and other problems that reduce their quality of life (QoL). Current treatments aim to improve these symptoms and reduce inflammation, but poor treatment adherence and disease understanding are key concerns in the long-term management of atopic dermatitis. Digital therapeutics can help with these and support patients toward a healthier lifestyle to improve their overall QoL. OBJECTIVE: The aim of the study is to test the feasibility of a digital health program tailored for atopic dermatitis through program engagement, retention, and acceptability. METHODS: Adults with atopic dermatitis were recruited in Iceland for a 6-week digital health program delivered through a smartphone app. Key components of the digital program were disease and trigger education; medication reminders; patient-reported outcomes (PROs) on energy levels, stress levels, and quality of sleep (referred to as QoL PROs); atopic dermatitis symptom PROs; guided meditation; and healthy lifestyle coaching. The primary outcome was program feasibility, as assessed by in-app retention and engagement. User satisfaction was assessed by the mHealth (ie, mobile health) App Usability Questionnaire (MAUQ). RESULTS: A total of 21 patients were recruited (17 female, mean age 31 years), 20 (95%) completed the program. On average, users were active in the app 6.5 days per week and completed 8.2 missions per day. The education content, medication reminders, and PROs had high user engagement and retention; all users who were exposed to the QoL PROs (n=17) interacted with these, and 20/21 (95%) users were continuously engaged with the education missions, medication missions, and symptom PROs. Continued engagement with the step counter and mind missions among exposed users was lower (17/21 and 13/20 participants, respectively). Medication reminder and education task completion remained high over time (at least 18/20, 90%), but weekly interactions declined. All assigned users completed atopic dermatitis symptom PROs on weeks 1-5 and only one did not do so on week 6; the reported number and total severity of atopic dermatitis symptoms reduced during the program. Regarding the QoL PROs, 16/17 (94%) and 14/17 (82%) users interacted with these at least 3 times in the first and last week of the program, respectively, and all reported improvements over time. User satisfaction was high with a total score of 6.2/7. CONCLUSIONS: We found high overall engagement and retention in a targeted digital health program among patients with atopic dermatitis, as well as high compliance with missions relating to medication reminders, patient education, and PROs. Symptom number and severity were reduced, and QoL PROs improved over time. We conclude that a digital health program is feasible and may provide added benefits for patients with atopic dermatitis, including the tracking and improvement of atopic dermatitis symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".