User Design and Experience Preferences in a Novel Smartphone Application for Migraine Management: A Think Aloud Study of the RELAXaHEAD Application
Bibliographic record
Abstract
OBJECTIVE: Scalable nonpharmacologic treatment options are needed for chronic pain conditions. Migraine is an ideal condition to test smartphone-based mind-body interventions (MBIs) because it is a very prevalent, costly, disabling condition. Progressive muscle relaxation (PMR) is a standardized, evidence-based MBI previously adapted for smartphone applications for other conditions. We sought to examine the usability of the RELAXaHEAD application (app), which has a headache diary and PMR capability. METHODS: Using the "Think Aloud" approach, we iteratively beta-tested RELAXaHEAD in people with migraine. Individual interviews were conducted, audio-recorded, and transcribed. Using Grounded Theory, we conducted thematic analysis. Participants also were asked Likert scale questions about satisfaction with the app and the PMR. RESULTS: Twelve subjects participated in the study. The mean duration of the interviews (SD, range) was 36 (11, 19-53) minutes. From the interviews, four main themes emerged. People were most interested in app utility/practicality, user interface, app functionality, and the potential utility of the PMR. Participants reported that the daily diary was easy to use (75%), was relevant for tracking headaches (75%), maintained their interest and attention (75%), and was easy to understand (83%). Ninety-two percent of the participants would be happy to use the app again. Participants reported that PMR maintained their interest and attention (75%) and improved their stress and low mood (75%). CONCLUSIONS: The RELAXaHEAD app may be acceptable and useful to migraine participants. Future studies will examine the use of the RELAXaHEAD app to deliver PMR to people with migraine in a low-cost, scalable manner.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".