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Investigating the relationship between sleep and migraine in a global sample: a Bayesian cross-sectional approach
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Milan
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486047/ https://www.ncbi.nlm.nih.gov/pubmed/37679693 http://dx.doi.org/10.1186/s10194-023-01638-6 |
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author | Stanyer, Emily C. Brookes, Jack Pang, Jia Rong Urani, Alexandre Holland, Philip R. Hoffmann, Jan |
author_facet | Stanyer, Emily C. Brookes, Jack Pang, Jia Rong Urani, Alexandre Holland, Philip R. Hoffmann, Jan |
author_sort | Stanyer, Emily C. |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s10194-023-01638-6. |
format | Online Article Text |
id | pubmed-10486047 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Milan |
record_format | MEDLINE/PubMed |
spelling | pubmed-104860472023-09-09 Investigating the relationship between sleep and migraine in a global sample: a Bayesian cross-sectional approach Stanyer, Emily C. Brookes, Jack Pang, Jia Rong Urani, Alexandre Holland, Philip R. Hoffmann, Jan J Headache Pain Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s10194-023-01638-6. Springer Milan 2023-09-08 /pmc/articles/PMC10486047/ /pubmed/37679693 http://dx.doi.org/10.1186/s10194-023-01638-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Stanyer, Emily C. Brookes, Jack Pang, Jia Rong Urani, Alexandre Holland, Philip R. Hoffmann, Jan Investigating the relationship between sleep and migraine in a global sample: a Bayesian cross-sectional approach |
title | Investigating the relationship between sleep and migraine in a global sample: a Bayesian cross-sectional approach |
title_full | Investigating the relationship between sleep and migraine in a global sample: a Bayesian cross-sectional approach |
title_fullStr | Investigating the relationship between sleep and migraine in a global sample: a Bayesian cross-sectional approach |
title_full_unstemmed | Investigating the relationship between sleep and migraine in a global sample: a Bayesian cross-sectional approach |
title_short | Investigating the relationship between sleep and migraine in a global sample: a Bayesian cross-sectional approach |
title_sort | investigating the relationship between sleep and migraine in a global sample: a bayesian cross-sectional approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486047/ https://www.ncbi.nlm.nih.gov/pubmed/37679693 http://dx.doi.org/10.1186/s10194-023-01638-6 |
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