Investigating the relationship between sleep and migraine in a global sample: a Bayesian cross-sectional approach

Abstract

Abstract Background There is a bidirectional link between sleep and migraine, however causality is difficult to determine. This study aimed to investigate this relationship using data collected from a smartphone application. Methods Self-reported data from 11,166 global users (aged 18–81 years, mean: 41.21, standard deviation: 11.49) were collected from the Migraine Buddy application (Healint Pte. Ltd.). Measures included: start and end times of sleep and migraine attacks, and pain intensity. Bayesian regression models were used to predict occurrence of a migraine attack the next day based on users’ deviations from average sleep, number of sleep interruptions, and hours slept the night before in those reporting ≥ 8 and < 25 migraine attacks on average per month. Conversely, we modelled whether attack occurrence and pain intensity predicted hours slept that night. Results There were 724 users (129 males, 412 females, 183 unknown, mean age = 41.88 years, SD = 11.63), with a mean monthly attack frequency of 9.94. More sleep interruptions (95% Highest Density Interval (95%HDI [0.11 – 0.21]) and deviation from a user’s mean sleep (95%HDI [0.04 – 0.08]) were significant predictors of a next day attack. Total hours slept was not a significant predictor (95%HDI [-0.04 – 0.04]). Pain intensity, but not attack occurrence was a positive predictor of hours slept. Conclusions Sleep fragmentation and deviation from typical sleep are the main drivers of the relationship between sleep and migraine. Having a migraine attack does not predict sleep duration, yet the pain associated with it does. This study highlights sleep as crucial in migraine management.

Keywords

• Migraine
• Headache
• Sleep
• Pain
• Bayesian
• Modelling