Time series analysis of cardiometabolic parameters: reliability and energy drink response

Abstract

Cardiometabolic data is currently analyzed primarily by the use of averages. While this method can provide some data, further analysis by time series (variability) methods can provide more physiologic insights. Historically, time series analysis has been performed primarily using heart rate data in the form of heart rate variability (HRV) analysis. This was done to determine the status of the autonomic nervous system via changes in parasympathetic and sympathetic output. Researchers have used different methods of analysis, but a lack of reproducibility studies raises questions about the validity of these methods when applied to heart rate (HR) data. Currently in the literature, these methods have not applied to metabolic data such as the respiratory exchange ratio (RER). This dissertation will investigate the reliability of time series assessments of caridiometaoblic parameters. We hypothesize that in healthy individuals, HRV analysis performed on the same RR intervals but by two different measurement systems, are indeed interchangeable. We further hypothesize that the time series analysis of metabolic data such as the RER will be stable and repeatable over two trials conducted under the same conditions. Lastly, we hypothesize that under conditions of physical stress (e.g. ride time-to-exhaustion) and biochemical stress (e.g. energy drink), resting HR and HR variability preexercise will be altered and the ride time-to-exhaustion will be increased after subjects consume an energy drink (standardized to 2.0mg/kg caffeine) compared to a taste-matched placebo. The results of this dissertation will provide further insight into the repeatability of these time series analyses, which could be utilized for future research to determine metabolic flexibility.