Rhodes, Gregory2017-07-182017-07-182016-03https://hdl.handle.net/11299/188900University of Minnesota Ph.D. dissertation.March 2016. Major: Kinesiology. Advisors: Beth Lewis, Stacy Ingraham. 1 computer file (PDF); viii, 119 pages.Technology advances provide exercise and sport physiologist real-time data allowing for precision in assessing the interaction between external stimuli and physiological responses. The results of these advances provide valuable data points specific to clinical disease as well as performance measures. This increase in data points as well as the scientific and medical interpretation has provided improved insight specific to the interactions of training interventions and their influence on an individual’s physiology. The aim of this study was to utilize the method of variability analysis to assess the effect of a 16-week endurance running training program has on resting metabolic rate, substrate utilization, and heart rate variability in college-aged runners preparing for their first marathon. A sample population of 17 of the total 106 recreational marathoners who were enrolled in a Marathon Training course at the University of Minnesota volunteered to participate in this specific research. All participants completed two lab testing sessions that consisted of three visits over the course of 5 months. These visits consisted of a 2-mile time trial on an indoor track, assessment of body composition and aerobic capacity, and a resting metabolic test. The first period of testing was completed in early December prior to the start of the training program. The second period of testing was during the final two weeks of training before all participants ran the same marathon. Data collected from all visits was examined for changes pre- to post-training. Time series data (heart rate and respiratory exchange ratio) was analyzed using the non-linear variability analysis method of sample entropy. Participants in this study showed increases in fat oxidation during submaximal steady-state running (p<0.001) as well as increased running economy (p<0.001) . However, metabolic and heart rate changes were not observed at rest (p=0.915). Analysis of metabolic and heart rate data using both linear and non-linear methods provided insight into the effect that training for a running marathon has on human physiology. Additionally, more in-depth methods of analysis have increased the level in which individual variations in adaptations can be identified.enHeart RateMetabolismRespiratory Exchange RatioVariabilityThesis or Dissertation