Senior Researcher, Solve ME/CFS Initiative, Inc.
1 active project
Descriptive understanding of ME/CFS cohort
Scientific Questions Being Studied
We are exploring the data before formalizing a research question. Initially, we seek to understand simply look at the number of patients and offer descriptive statistics akin to a Table 1 in a publication. Once we understand the characteristics and data available on ME/CFS patients included in AOU, we hope to in order to determine if some analyses of interest are compatible with these data.
Scientific questions we hope to answer depending on available data include:
Can comparing a cohort of ME/CFS patients to controls using wearables data offer insights into the question of whether differences in heart rate variability are present?
Does heart rate variability in ME/CFS correlate with fatigue?
Does available wearables data show patterns of post-exertional malaise in ME/CFS patients which are not observed in a control cohort?
- Other Purpose (To understand the characteristics and data available on ME/CFS patients included in AOU in order to determine analyses of interest that are compatible with these data. )
Scientific approach: Initially just descriptive statistics to understand what data are available. Later we will establish a cohort of controls for comparisons.
Overall health: to better understand reported fatigue
Wearables/ physical measurements: Fitbit HR and daily activity
EHR: Conditions to identify cases and controls
Research methods: descriptive statistics, age matching where possible, case/control comparisons using chi sqare tests, logistic regression / conditional logistic regression.
Feasibility for additional analyses; describing teh demographics of a large cohort of ME/CFS patients.
Clarity on questions about how ME/CFS patients experience heart rate variability, fatigue, and post-exertional malaise compared to a control group, that this analysis would use wearables data instead of self-report is a way of validating prior studies based on self-reported symptoms. We believe that ME/CFS is in need of subgrouping but few studies have the power to detect patterns that only impact a subset of the ME/CFS population. Our hope is that AOU data will be powered to find this and allow us to start to characterize the subset with, for example, lower HRV or more severe PEMS. Such sub-grouping will help not only with diagnosing ME/CFS but ultimately in customization of treatments.
Demographic Categories of Interest
- Race / Ethnicity
- Disability Status
- Education Level
Data Set UsedRegistered Tier
- Leslie Phillips - Senior Researcher, Solve ME/CFS Initiative, Inc.
- Sara Vlajic - Project Personnel, Solve ME/CFS Initiative, Inc.
- Julia Vogel - Project Personnel, Scripps Research
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