Quantifying Migraine’s Cognitive Toll: Real-world Evidence from the MIND Cohort
Ali Ezzati1, Babak Khorsand1, Devin Teichrow1, Richard Lipton2
1University of California, Irvine, 2Albert Einstein College of Medicine
Objective:
To evaluate whether daily smartphone-based cognitive tests detect objective cognitive slowing on migraine headache days compared with non-headache days.
Background:
Patients with migraine often experience episodic cognitive difficulties, particularly in attention, processing speed, and working memory, that vary in severity across the course of an attack. Conventional in-clinic assessments cannot capture these day-to-day changes. To address this limitation, we implemented a smartphone-based ecological momentary assessment (EMA) design to measure cognition repeatedly across headache and non-headache days in the real-world setting.
Design/Methods:
We enrolled 129 adults with migraine via social media into the MIND (Migraine Impact on Neurocognitive Dynamics) cohort. For 30 consecutive days, participants recorded daily headache status and completed three brief smartphone tasks: Symbol Search (attention, processing speed), Color Dots (item–location binding in visual working memory), and Color Shapes (feature-binding change detection in visual working memory). We compared each participant’s performance on headache versus non-headache days, adjusting for age, sex, and test-occasion (to account for practice effects).
Results:
Participants had an average age of 38.1 ± 11.5 years, and 84.5% were female. On headache days, response times were slower by 6.2% on Symbol Search (β = 100.7, p < .001) and 4.0% on Color Dots (β = 67.1, p < .001), whereas the 0.8% slowing on Color Shapes was not significant (β = 12.8, p = 0.37). Accuracy rates were comparable between headache and non-headache days across all tasks.
Conclusions:
Daily digital cognitive testing revealed migraine-related, task-dependent cognitive slowing that scaled with task complexity while accuracy remained unchanged. These findings support the feasibility of real-world digital endpoints for quantifying migraine-associated cognitive burden.
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