Turner Dana P, Patel Twinkle, Caplis Emily, Houle Timothy T
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School Boston, MA, USA.
medRxiv. 2025 Apr 25:2025.04.23.25325821. doi: 10.1101/2025.04.23.25325821.
Identifying migraine triggers is a common goal for most individuals with migraine but remains challenging due to the vast number of possible trigger candidates and their fluctuating nature. The of daily experiences, quantified through information-theoretic surprisal, may integrate many sources of variation and predict future migraine attacks.
The objective of this study was to evaluate the association between surprisal and future headache attacks.
In this prospective daily diary study, N = 109 individuals with migraine were enrolled, with N = 104 completing twice-daily electronic entries for up to 28 days, yielding 5,176 total diaries. Diary items captured exposure to potential migraine triggers across behavioral, emotional, and environmental domains. For each entry, total surprisal scores were calculated using within-person empirical probability distributions to reflect how atypical each day's experiences were.
Participants experienced a headache on 1,518/5,145 (29.5%) of days with complete diary information. Higher surprisal significantly predicted increased migraine risk within 12 hours (OR = 1.86 [95%CI: 1.12-3.08], p = 0.016) and 24 hours (OR = 2.15 [95%CI: 1.44-3.20], p < 0.001), with stronger effects observed at the longer interval. Notably, the association between current surprisal and migraine onset was moderated by recent surprisal history and exhibited nonlinear properties at 12 hours. Random effects revealed substantial between-person variability in surprisal sensitivity, and individuals with higher baseline headache risk showed attenuated associations.
Surprisal offers a novel, individualized measure of trigger unpredictability that is associated with short-term migraine risk. Incorporating surprisal into digital tools may improve personalized prediction and prevention strategies, moving beyond static trigger lists to a dynamic, context-aware model of migraine self-management.
识别偏头痛触发因素是大多数偏头痛患者的共同目标，但由于可能的触发因素众多且具有波动性，这一目标仍具有挑战性。通过信息论惊奇度量化的日常经历可能整合多种变异来源并预测未来的偏头痛发作。
本研究旨在评估惊奇度与未来头痛发作之间的关联。
在这项前瞻性每日日记研究中，招募了N = 109名偏头痛患者，其中N = 104名患者完成了长达28天的每日两次电子记录，共产生5176篇日记。日记条目记录了行为、情绪和环境领域中潜在偏头痛触发因素的暴露情况。对于每一条记录，使用个体内经验概率分布计算总惊奇度得分，以反映每天经历的非典型程度。
在有完整日记信息的日子里，参与者有1518/5145（29.5%）天经历了头痛。较高的惊奇度显著预测了12小时内（OR = 1.86 [95%CI：1.12 - 3.08]，p = 0.016）和24小时内（OR = 2.15 [95%CI：1.44 - 3.20]，p < 0.001）偏头痛风险增加，在较长时间间隔内观察到更强的效应。值得注意的是，当前惊奇度与偏头痛发作之间的关联受到近期惊奇度历史的调节，并且在12小时时表现出非线性特性。随机效应显示个体间惊奇度敏感性存在显著差异，基线头痛风险较高的个体关联减弱。
惊奇度提供了一种新颖的、个体化的触发因素不可预测性度量，与短期偏头痛风险相关。将惊奇度纳入数字工具可能会改善个性化预测和预防策略，从静态的触发因素列表转向动态的、情境感知的偏头痛自我管理模型。
