A Prospective Real-World Study Exploring Associations Between Passively Collected Tracker Data and Headache Burden Among Individuals with Tension-Type Headache and Migraine

Christian J. Cerrada, Jae S. Min, Luminita Constantin, Simon Hitier, Iva Igracki Turudic, Caroline Amand-Bourdon, Andrew Stewart, Caty Ebel-Bitoun, Peter J. Goadsby*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Introduction: Prevalence and burden of headache disorders in real-world settings is relatively unstudied. We explored the associations between passively collected activity data, headache burden, and quality of life in headache sufferers. Methods: Data from wearable activity tracking devices and daily short questionnaires were collected over 12 weeks to assess occurrence of headache, activity, quality of life and self-rated health. Variables were analyzed using a series of mixed-effects models and stratified based on headache type. Multiple linear and logistic regressions were used to analyze treatment preferences. Results: Behaviors inferred from activity tracker data suggested that individuals slept more, had reduced physical activity, and had lower maximum heart rate on days with headache. As headache-specific impact on quality of life increased, activity and maximum heart rate decreased and sleep increased. Headache days with higher self-rated health were associated with less napping, higher step count and maximum heart rate, correlating with increased activity. Migraineurs experienced greater burden in everyday life compared with tension-type headache sufferers. Conclusion: This study adds to existing evidence that activity trackers can be used to quantify headache burden in real-world settings and aid in understanding symptom management.

Original languageEnglish
JournalPain and Therapy
Volume11
Issue number1
Early online date7 Dec 2021
DOIs
Publication statusE-pub ahead of print - 7 Dec 2021

Keywords

  • Headache
  • Quality of life
  • Real-world evidence
  • Tracker-based behavioral features

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