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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».