Feasibility and Usability of a Mobile App–Based Interactive Care Plan for Migraine in a Community Neurology Practice: Development and Pilot Implementation Study
Bibliographic record
Abstract
BACKGROUND: Migraine is a common and major cause of disability, poor quality of life, and high health care use. Access to evidence-based migraine care is limited and projected to worsen. Novel mobile health app-based tools may effectively deliver migraine patient education to support self-management, facilitate remote monitoring and treatment, and improve access to care. The risk that such an intervention may increase the care team workload is a potential implementation barrier. OBJECTIVE: This study aims to describe a novel electronic health record-integrated mobile app-based Migraine Interactive Care Plan (MICP) and evaluate its feasibility, usability, and impact on care teams in a community neurology practice. METHODS: Consecutive enrollees between September 1, 2020, and February 16, 2022, were assessed in a single-arm observational study of usability, defined by 74.3% (127/171) completing ≥1 assigned task. Task response rates, rate and type of care team escalations, and patient-reported outcomes were summarized. Patients were prospectively recruited and randomly assigned to routine care with or without the MICP from September 1, 2020, to September 1, 2021. Feasibility was defined by equal to or fewer downstream face-to-face visits, telephone contacts, and electronic messages in the MICP cohort. The Wilcoxon rank-sum test was used to compare continuous variables, and the chi-square test was used for categorical variables for those with at least 3 months of follow-up. RESULTS: A total of 171 patients were enrolled, and of these, 127 (74.3%) patients completed ≥1 MICP-assigned task. Mean escalations per patient per month was 0.9 (SD 0.37; range 0-1.7). Patient-confirmed understanding of the educational materials ranged from 26.6% (45/169) to 56.2% (95/169). Initial mean headache days per week was 4.54 (SD 2.06) days and declined to 2.86 (SD 1.87) days at week 26. The percentage of patients reporting favorable satisfaction increased from a baseline of 35% (20/57) to 83% (15/18; response rate of 42/136, 30.9% to 28/68, 41%) over the first 6 months. A total of 121 patients with MICP were compared with 62 patients in the control group. No differences were observed in the rate of telephone contacts or electronic messages. Fewer face-to-face visits were observed in the MICP cohort (13/121, 10.7%) compared with controls (26/62, 42%; P<.001). CONCLUSIONS: We describe the successful implementation of an electronic health record-integrated mobile app-based care plan for migraine in a community neurology practice. We observed fewer downstream face-to-face visits without increasing telephone calls, medication refills, or electronic messages. Our findings suggest that the MICP has the potential to improve patient access without increasing care team workload and the need for patient input from diverse populations to improve and sustain patient engagement. Additional studies are needed to assess its impact in primary care.
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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.001 | 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".