Can smartwatches predict migraines? Using machine learning (ML) with wearable-derived nocturnal autonomic nervous
Lewis E Tomalin, Benjamin R Kummer, Maya C Campbell1
1Icahn School of Medicine at Mount Sinai.
Objective:
To investigate whether nocturnal autonomic nervous system (ANS) activity and sleep metrics, as measured by a wearable device, can predict the occurrence of next-day migraine in patients with episodic and chronic migraine.
Background:
The unpredictable nature of migraine episodes contributes to disease burden and limits effective application of tailored preventive strategies. Small-molecule calcitonin-gene-related peptide (CGRP) receptor antagonists, available in limited monthly quantities, are now used for both acute and preventative treatment of migraine. Consequently, improving the ability to identify days with heightened migraine risk could significantly improve migraine management and treatment outcomes.
Methods:
In this prospective and observational study, adults with migraine (N = 10; 5 with chronic migraine and 5 with episodic migraine) wore the Empatica EmbracePlus®, smartwatch during sleep for a target duration of four weeks. Participants kept a headache diary recording days with no headache, non-migraine headache only, or migraine. First, group level analysis was performed using linear mixed-effects models (LMM). Next, personalized machine learning (ML) models were trained using nocturnal electrodermal activity (EDA), pulse rate variability (PRV), respiratory rate (RR), sleep duration, sleep interruptions, and awakenings to predict: (1) next-day migraine, and (2) next-day headache (both migraine and non-migraine). Performance was summarized using area under the receiver-operating and precision-recall curves (AUROC, AUPRC), sensitivity, specificity, accuracy, and precision. SHapley Additive exPlanation (SHAP) analyses identified the most influential predictors in highest performing next-day migraine and next-day headache models. Generalized Additive Models (GAM) explored nocturnal temporal dynamics of PRV and EDA.
Results:
Group level predictive performance assessed with LMMs did not reveal significant differences between ANS and sleep metrics on nights prior to no headache days, days with migraine, and days with non-migraine headache. However, individualized models using elastic-net regression, random forests, and gradient boosting machines showed modestly better-than-random AUROCs for next-day migraine prediction in 5/10 participants and next-day headache in 3/10 participants. For next-day migraine prediction models, four of five patients with episodic migraine showed better-than-random AUROCs; no patients with chronic migraine had better-than-random AUROCs. The highest-performing individualized models achieved moderate-to-good performance (AUROC 0.68 for next-day migraine and 0.81 for next-day headache). In highest performing models, SHAP analyses demonstrated sleep duration and a higher minimum PRV influenced next-day migraine and next-day headache probability, while EDA influenced next-day migraine, but not next-day headache. GAM analyses demonstrated that the first three hours after sleep onset and prior to awakening were time periods when PRV and EDA differed prior to a day with migraine or headache in these high performing models.
Conclusions:
Our findings indicate that applying individualized ML models to wearable-derived autonomic and sleep data may assist in the identification of heightened migraine risk and identified EDA, PRV, and sleep duration as important forecasting features. Our results provide a rationale for future studies that investigate how targeted medication and behavioral interventions on high-risk days may enhance therapeutic precision of migraine treatment and emphasize the importance of defining mechanistic subgroups of patients with migraine most likely to benefit from predictive modeling.
