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Episodic Migraine Pain Curves: Real-Time Smartphone-Based Analysis and Clinical Implications
Ana Beatriz Gago-Veiga1, Alicia Gonzalez-Martinez1, Sonia Quintas1
1Neurology Department, Hospital Universitario de La Princesa & Instituto de Investigación Sanitaria Princesa (IIS-Princesa), Madrid, Spain.
Journal of Pain Research
|December 18, 2025
Summary
This study identified four distinct migraine pain patterns, revealing significant differences in patient characteristics and treatment responses. Understanding these migraine subtypes can lead to more personalized treatment strategies.
Area of Science:
- Neurology
- Data Science in Medicine
- Patient-Reported Outcomes
Background:
- Migraine is a disabling neurological condition with complex pain characteristics.
- Current classification systems like the International Classification of Headache Disorders, 3rd edition (ICHD-3), provide a framework but may benefit from deeper analysis of pain dynamics.
- Understanding variations in migraine pain onset, duration, and intensity is crucial for developing personalized treatment strategies.
Purpose of the Study:
- To analyze socio-demographic and clinical characteristics across previously identified episodic migraine subgroups based on pain curve dynamics.
- To identify differences in migraine parameters, symptoms, triggers, and treatment responses among these subgroups.
- To explore the utility of pain curve-based classification for advancing personalized migraine management.
Main Methods:
- Utilized data from 51 episodic migraine patients meeting ICHD-3 criteria.
- Employed a smartphone app for real-time collection of migraine parameters: onset, pain duration, intensity, symptoms, triggers, and treatment responses.
- Categorized patients into four distinct migraine patterns (Type 1-4) based on pain curve dynamics.
Main Results:
- Identified four migraine patterns: Type 1 (High intensity), Type 2 (Acute onset and intense), Type 3 (Prolonged and intense), and Type 4 (Low intensity).
- Found significant associations between migraine curve types and demographic factors (sex, aura presence) and cardiovascular risk.
- Observed differences in associated symptoms (nausea, phonophobia) and treatment response profiles across the identified subgroups.
Conclusions:
- Pain curve-based classification identifies clinically relevant migraine subtypes.
- These subtypes exhibit distinct characteristics in terms of demographics, symptoms, and treatment responsiveness.
- Real-time data collection and pain dynamics analysis offer a promising approach for personalized migraine management.
