Strengthening the study of migraine headache triggers through mobile health tools and n-of-1 methods
Abstract/Contents
- Abstract
- Episodic migraine headache attacks are thought to be triggered, or proximately caused, by a wide range of transient factors encountered in daily life. Even after a great deal of published research on migraine triggers, scientific understanding is limited. In order to reduce the societal burden of migraine, we need rigorous data collection and statistical analysis to understand trigger factors from causal and predictive perspectives and at individual and population levels. In this dissertation, we applied state-of-the-art mobile app methodology and careful statistical analysis to achieve two main objectives: (1) evaluate the predictability of migraine attacks among persons with episodic migraine based on commonly cited trigger factors and physiological data, and (2) evaluate the precision and power associated with Cox proportional hazards regression for estimating the effects of potential migraine trigger factors at the individual patient (n-of-1) level. We found that despite statistically significant associations between several known trigger factors and short-term risk of migraine, migraine attacks were not predictable based on the self-reported data or physiological factors collected. In order to predict migraines in a clinically relevant way, greater accuracy or breadth of data collection are required. In addition, we found that in an n-of-1 observational study of triggers of recurrent events (such as episodic migraine attacks) analyzed with Cox regression, at least 24 events were needed to reliably detect moderately sized statistical associations between potential trigger factors and event risk. In order to avoid wasted efforts in n-of-1 studies of episodic migraines and other recurrent events, the study design must include sufficient event numbers to yield informative results. These findings and the ensuing discussion highlight key current challenges and opportunities for strengthening the study of migraine triggers through mobile health data collection and n-of-1 methodology
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Holsteen, Katherine Kroeger |
|---|---|
| Degree supervisor | Nelson, Lorene M |
| Thesis advisor | Nelson, Lorene M |
| Thesis advisor | Baiocchi, Michael |
| Thesis advisor | Hastie, Trevor |
| Thesis advisor | King, Abby C |
| Degree committee member | Baiocchi, Michael |
| Degree committee member | Hastie, Trevor |
| Degree committee member | King, Abby C |
| Associated with | Stanford University, Department of Epidemiology and Population Health. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Katherine Kroeger Holsteen |
|---|---|
| Note | Submitted to the Department of Epidemiology and Population Health |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Katherine Kroeger Holsteen
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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