Key Predictors of Adherence to a Mobile Health App for Managing Chronic Spontaneous Urticaria
Bibliographic record
Abstract
BACKGROUND: Mobile health technologies may improve the management of chronic diseases, such as chronic spontaneous urticaria. However, effectiveness of mHealth tools largely depends on patient adherence, which can be influenced by various demographic, clinical, behavioural, psychosocial factors, and apps characteristics (appealing and simplicity of use). Understanding these adherence patterns is crucial for optimizing mHealth interventions. In this study, we aimed to assess adherence patterns associated to the use of CRUSE, a mHealth app designed for patients with CSU. METHODS: We assessed users of the CRUSE app with self-reported CSU or suggested by a physician. For each user, we evaluated the number of days they completed the CRUSE daily monitoring questionnaire (app adherence) within the first 3 months after installation. We constructed univariable and multivariable ordered beta regression models to identify predictors of 3-month adherence to the app. RESULTS: We analysed data from 2085 patients (66,114 days). Median adherence to the CRUSE app was of 22 days (24.4% of 90 days). In multivariable regression models, the variables more strongly associated with increased adherence to CRUSE included age (average increase = 0.16 percent points [pp] per additional year; 95% credible interval [CrI] = 0.08; 0.23 pp), male sex (average difference = 4.24 pp; 95% CrI = 1.77; 6.39 pp), being from a European country (average difference = 2.66 pp; 95% CrI = 0.59; 5.19 pp), and using monoclonal antibodies (average difference = 4.60 pp; 95% CrI = 2.26; 6.65 pp). CONCLUSIONS: Our findings suggest that age, male sex, residence in Europe, and the use of monoclonal antibodies are significant factors associated with increased adherence to the CRUSE app. These insights may help identify patient subgroups who would benefit most from mHealth support in managing CSU.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".