Term of Award

Spring 2014

Degree Name

Master of Science in Kinesiology (M.S.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Health and Kinesiology

Committee Chair

John Dobson

Committee Member 1

Kristina Kendall

Committee Member 2

Stephen Rossi

Committee Member 3

Jim McMillan

Committee Member 3 Email



It is important for elite endurance athletes to have practical and reliable means of measuring fatigue throughout their training. Variations in Autonomic Nervous System activity (ANS) may provide an effective marker of fatigue and of recovery. ANS control of heart rate is well known to be affected by exercise training, and those adaptations can be determined using measures of heart rate variability (HRV). Previous research has examined the effect of training on HRV and ANS control of heart rate in males, there is a lack of any comprehensive studies that address adaptations in female athletes. Therefore, the purpose of this study was to investigate the changes in HRV and ANS fluctuations in female swimmer athletes throughout an entire collegiate swim season. 9 Division I female swimmers (Age: 20.6±1.01) were used to determine HRV at three different points in their competitive training: pre-season, mid-season, and post-season. During each testing session, HRV was measured both at rest and during a maximal 400 yd freestyle swim. Heart rate values were determined using Polar™ heart rate monitors, and the HRV analyses was conducted using Kubios 2.0 HRV analysis software. Global ANS balance was shown to significantly shift towards Sympathetic (SNS) predominance during the mid-season testing and significantly shift towards parasympathetic (PNS) predominance during post-season testing. HRV analysis appears to be an appropriate tool to monitor the effects of physical training loads on performance and fitness in female athletes, and it can be used to help identify and prevent overtraining states.