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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".