Self-Reported Triggers Evaluation of High-Risk Dietary and Environmental Factors Preceding Migraine Onset by Using a Mobile Tracking App (Migraine Insight): Comparative Analysis Study
Bibliographic record
Abstract
BACKGROUND: Migraines are a significant health concern affecting millions of individuals, often requiring habitual tracking of potential triggers to mitigate or predict episodes. Digital health tools such as mobile apps offer a scalable solution for personalized tracking and pattern recognition. Migraine Insight is one such app that facilitates daily logging to quantitatively assess individualized patterns of events preceding migraine onset. However, while various triggers have been reported in migraine literature, there is limited large-scale electronic user-driven data on the frequency and relative impact of specific triggers. OBJECTIVE: This study aims to address this gap by analyzing user-reported data from Migraine Insight to identify the most frequently reported triggers and evaluate their potential associations with migraine onset. METHODS: Food-associated migraine triggers were identified by performing a noninterventional, retrospective analysis of self-reported data obtained via the Migraine Insight app. A collaboration was made with the representatives of the Migraine Insight app to extrapolate the data needed for the study. A preliminary keyword list was assessed from a raw data set of 2605 data entry values, extracted from a 30-day period of September 19 to October 18, 2021, to identify high ranking self-reported events, classified by dietary habits, environmental conditions, body physiology, and medications. The variables examined included the following: dietary items, environmental conditions, body physiology, and electronics. Food items were combined into similar groups, considering variable spelling and descriptions of self-reported events. The association of migraine onset after consumption of top 5 dietary products was evaluated to establish the frequency of migraine episodes for all users who reported the items. RESULTS: Collectively, food (n=353) and beverage (n=252) totaled the highest reported entries, with chocolate, wine, tea, coffee, and cheese as the highest ranked foods for prevalence of reporting across all users and for frequency of migraine onset for users who consumed the items within 48 hours. The 4 highest nonfood entries were altered sleep patterns (n=245), stress or anxiety (n=199), rain or storm conditions (n=192), and bright light or brightness (n=191). Statistical analysis showed that chocolate was the only food trigger significantly associated with migraines (P=.003 vs 50%; P=.04 vs average). Consumption of tea approached significance (P=.051), while consumption of coffee, cheese, and wine were not significant. These findings suggest that chocolate is the most consistent dietary trigger. CONCLUSIONS: High-risk foods, environmental conditions, stress, and lighting with highest prevalence reporting have previously been reported in medical literature, implicating that a migraine tracking app is a valid alternative to paper-based diaries.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".