Mobile App-Based Interactive Care Plan for Migraine: Survey Study of Usability and Improvement Opportunities
Bibliographic record
Abstract
Background: We implemented a novel mobile app-based Migraine Interactive Care Plan (MICP) integrated with our electronic health records (EHRs). The MICP facilitates remote assessment of adult patients with migraine, educational content delivery, and care team communication. Feasibility of the MICP was demonstrated in a pilot implementation study. Objective: We aimed to assess the preferences and satisfaction of patients with migraine users of a mobile app-based care plan integrated with the EHR. Methods: An electronic survey was administered to a single cohort of MICP users between December 6, 2021, and December 30, 2021. The survey assessed patient preferences for which data to track, frequency of tracking, and satisfaction with the MICP. Survey responses were compared between subsets determined by patient-reported headache frequency and treatment with and without botulinum toxin and calcitonin gene-related peptide (CGRP) antagonist therapy. The Wilcoxon rank-sum test was used for continuous variables and the χ2 test or Fisher exact test for categorical variables. Results: The total sample size was 184 and the survey response rate was 30.4% (56/184). No significant differences in age (P=.26) or sex (P=.19) between respondents and nonrespondents were observed. Respondent median age was 42 (range 20-72) years and 94.6% (53/56) were female. Headache frequency was (1) 0 to 8 days (26/56, 46.4%), (2) 9 to 14 days (12/56, 21.4%), and (3) 15 or more days (18/56, 32.1%). No difference was observed in any survey responses based on headache frequency or treatment. The majority of respondents preferred to track headache days weekly (30/56, 53.6%) or daily (15/56, 26.8%) and preferred to change the frequency of headache tracking reminders (42/56, 75%). Respondents were somewhat or very interested in daily tracking personal observations in free text (41/52, 78.8%), medication treatment (43/52, 82.7%) and treatment response (39/56, 69.6%), class of medication treatment (36/52, 69.2%), severity of functional impairment (39/56, 69.6%), type of functional impairment (35/53, 66%), headache day (40/54, 74.1%), and headache pain level on a scale of 1 to 10 (38/53, 71.7%). Respondents agreed or strongly agreed that the education content was useful (31/51, 60.8%) but lacked personalization (25/51, 49%). Most respondents agreed or strongly agreed that they were satisfied with the MICP (38/50, 76%) and that it helped them communicate with their care team (38/53, 71.7%). Conclusions: Most MICP users were motivated to track headache frequency, medication treatment with response, functional impairment, and pain intensity. Opportunities to improve the MICP include (1) allowing patients to change the frequency of assessments and notifications; (2) recording personal observations or comments through free text, which may include headache triggers; (3) assessment of headache severity using a 1 to 10 pain scale; and (4) tailoring headache education based on frequency and severity (episodic vs chronic migraine). These observations may be useful to improve the usability of the MICP and similar EHR-integrated migraine care platforms that others may develop.
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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 | 0.007 | 0.016 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".