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Smartphone-Based Muscle Relaxation for Migraine in the Emergency Department

2025· article· en· W4415261219 on OpenAlexfundno aff
Mia T. Minen, Elizabeth K. Seng, Benjamin W. Friedman, Alexis George, Kristina M. Fanning, Ryan Bostic, Scott W. Powers, Richard B. Lipton

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsnot available
FundersNational Center for Complementary and Integrative HealthNational Center for Advancing Translational SciencesAllerganNational Institutes of HealthGrifolsH. Lundbeck A/SEli Lilly and CompanyYork UniversityTeva Pharmaceutical IndustriesU.S. Food and Drug AdministrationGlaxoSmithKlineAmgenPfizerAlzheimer's Association
KeywordsEmergency departmentMigraineRelaxation (psychology)Muscle relaxation

Abstract

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Importance: The emergency department (ED) is a critical point of contact within the health care system and an opportunity to initiate nonpharmacologic migraine treatment. Objective: To examine whether progressive muscle relaxation (PMR) smartphone-based migraine self-management improved patient-reported outcomes for migraine compared with enhanced usual care. Design, Setting, and Participants: A randomized clinical trial of the smartphone application RELAXaHEAD with and without PMR. Patients aged 18 to 65 years visiting New York University Langone Health EDs for headache who met migraine criteria and self reported 4 or more migraine days per month were recruited from June 2019 to October 2021 with follow-up at 3 months. Data were analyzed from June 2022 to June 2025. Intervention: Participants in the intervention group were asked to listen to the app-based PMR for 60 days. Participants in the control group were asked to use the app as a symptom diary. Main Outcomes and Measures: Primary outcome was change in migraine-related disability (MIDAS). Secondary outcomes were change in migraine-specific quality of life (MSQv2) and monthly headache days (MHDs). Adherence (number of days of diary use, PMR use and total minutes of PMR use over 90-day period) was measured using back-end analytics. Results: Of the 94 patients (median [IQR] age, 33 [26-45] years; 57 [82.6%] female) randomized (48 control patients and 46 PMR patients), 69 of 94 (73%) had 1 or more follow-up MIDAS scores and constituted the modified intent-to-treat population (35 control patients and 34 PMR patients). The mean (SD) change in MIDAS scores from baseline to 3 months (last observation carried forward [LOCF] used if missing 3-month follow-up data) differed between groups (PMR, 25.09 [29.64] vs control, 6.86 [59.61]; P = .01). PMR had nearly double the number of respondents improving by 5 or more MIDAS points (28 of 34 [82.4%] vs 16 of 35 [45.7%] respondents; P = .002). There was no difference in MSQv2 domains from baseline to LOCF between PMR and control (mean [SD] role function preventive domain for PMR, 16.9 [24.5] vs control, 11.3 [25.9]); emotional function domain (mean [SD] for PMR, 26.5 [26.9] vs control, 19.8 [38.5]); and role function restrictive domain (mean [SD] for PMR, 18.1 [22.7] vs control, 18.7 [26.8]). Mean (SD) change in MHDs (baseline to 3 months) did not differ between groups (PMR, 2.9 [8.0]; 23 days vs control, -1.6 [6.5]; 25 days). Conclusion and Relevance: A PMR-based self-management program offered to patients with migraine after ED discharge yielded clinically significant reductions in migraine-related disability. Trial Registration: ClinicalTrials.gov Identifier: NCT04281030.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.346
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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