Identifying and Evaluating Patient-Centered Mobile and Web Apps for Patients With Chronic Spontaneous Urticaria: Systematic Search and Content Analysis
Bibliographic record
Abstract
Background: Chronic spontaneous urticaria (CSU) is characterized by recurrent wheals or angioedema lasting for more than 6 weeks and substantially affecting the quality of life. Given its fluctuating course, accurate symptom monitoring is essential. Mobile health apps (MHAs) offer promising tools for real-time symptom tracking, patient education, and communication. Systematic evaluation of existing MHAs for CSU is critical to inform the development of effective, patient-centered digital solutions. Objective: This study aimed to identify and evaluate publicly available MHAs for patients with CSU, assessing their quality, usability, and alignment with the needs of both patients and physicians to guide the development of future patient-centered apps. Methods: A systematic search of app stores and the internet was conducted to identify MHAs for CSU. Inclusion required German or English language support and patient-centered content. Apps were excluded if they contained advertisements; lacked patient-centered content, designed to assist patients in the self-management and care of their condition; or were focused on clinical trials or health care professional use. After screening, 1 app, CRUSE Control, met all criteria and was evaluated by 23 physicians and 16 patients with CSU using the German versions of the Mobile Application Rating Scale (MARS and end-user version of MARS) and the mHealth App Usability Questionnaire. Participants' technical affinity was assessed using the affinity for technology interaction scale and the Mobile Device Proficiency Questionnaire. Additionally, they completed a custom questionnaire on their personal needs and expectations for CSU-specific MHAs. Results: Fifteen MHAs were identified, with 12 available on both platforms. Eleven apps were excluded due to lack of specificity to CSU (n=10) or not being patient-centered (n=1). One app, CRUSE Control, met all inclusion criteria and was selected for final evaluation. CRUSE Control received similar mean (SD) quality ratings from physicians (MARS 4.03, SD 0.45) and patients (end-user version of MARS: 4.06, SD 0.40; P=.83). Among the MARS subcategories, functionality was rated significantly higher by patients than by physicians (4.75, SD 0.41 vs 4.47, SD 0.55; P=.04). Usability, measured using the German mHealth App Usability Questionnaire (assessing effectiveness, efficiency, and satisfaction), showed no significant difference between physicians (5.85, SD 0.71) and patients (5.76, SD 0.41; P=.64). Technology affinity was comparable between groups, with physicians scoring 3.50 (SD 0.66) and patients 4.00 (SD 0.88) on the affinity for technology interaction (P=.05). Proficiency with mobile devices, assessed via the Mobile Device Proficiency Questionnaire, also showed similar results (physicians: 4.81, SD 0.26; patients: 4.74, SD 0.45; P=.60). Conclusions: Few high-quality MHAs for CSU are currently available, and only 1 met the inclusion criteria. Patients and physicians rated the app highly, though patients placed greater emphasis on functionality. High technology affinity in both groups supports adoption. Patients prioritized features that facilitate disease management. Although limited to a single app, these findings suggest that MHAs may support CSU care.
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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 | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".