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Stephen J. McGregor, Ph.D.

Graduate Program Coordinator; Professor

Stephen J. McGregor, Ph.D. 101-C Rackham Bldg
734.487.2820
smcgregor@emich.edu

Education

Ph.D., Applied Physiology - University of Toledo (2001)
M.S., Biology - University of Toledo (1994)
B.S., Biology/Chemistry - Tri-State University (1991)

Professional Summary

In larger terms, my research interests span a very broad range of topics including some of the most applied aspects of Exercise Physiology (e.g. interval training and athletic performance (6, 9, 12, 14)) and, at the same time, some of the most esoteric (e.g. entropy of physiological signals (1, 4, 7, 11)). That being said, there are two primary foci of research we have been pursuing at Eastern Michigan University over the past nine years. The first relates to the use of wireless high resolution accelerometers (HRA) networks and tools from "traditional" exercise physiology to quantify movement and, in particular, investigate what underlies inter-individual differences in running ability. This work is exciting as it is practically relevant and accessible for students, but also provides increased understanding of fundamental elements of physiology.

The second area of research we have been focusing on relates to the non-linear analysis of physiological signals obtained from various types of sensors and measuring devices (1, 2, 4, 7, 8, 11). The Applied Physiology-Dynamical Systems (AP-DS) Group is a collaborative effort between the School of Health Promotion and Human Performance at Eastern Michigan University (EMU) and Department of Mathematics and Computer Science at Clarkson University (CU). The EMU group has had extensive experience working with a range of human subjects, from untrained to elite athletes, in order to develop tools that objectively quantify workload and physiological responses during exercise, particularly in the field. The CU group is well accomplished in the field of non-linear dynamical systems and has developed approaches that have been specifically applied to the physiological signals and/or workload measures developed by the EMU group. Most notably of these non-linear tools is Control Entropy (CE), which can be used as a systems health monitoring tool under conditions that do not satisfy the criteria of stationarity (1).

Courses Taught

  • SPMD 642: Advanced Exercise Physiology I
  • SPMD 644: Advanced Exercise Physiology II
  • SPMD 518: Sports Supplements 
  • SPMD 300: Physiology of Exercise
  • SPMD 269: Anatomy and Physiology for Allied Health Professions 
  • SPMD 279: Applied Performance Physiology
  • SPMD 550: Applied Performance Physiology I
  • SPMD 552: Applied Performance Physiology II
  • SPMD 681: Techniques in Performance Analysis

Selected Presentations and/or Publications

Publications:

Peer-Reviewed Journal Articles since 2005

  • McGregor, S.J., Bollt EM. Control Entropy: What is it, and what does it tell us?. Clinical Kinesiology. 2012; In Press.
  • McGregor, S.J., The record power profile to assess performance in elite cyclists. Int J Sports Med. 2012 May;33(5):415
  • McGregor, S.J., W.J. Armstrong, J.A. Yaggie, E.M. Bollt, R. Parshad, J.J. Bailey, S.M. Johnson, A.M. Goin, and S.R. Kelly, Lower extremity fatigue increases complexity of postural control during a single-legged stance. J Neuroeng Rehabil, 2011. 8: p. 43.
  • McGregor, S.J., M.A. Busa, J.A. Yaggie, R. Parshad, and E.M. Bollt, Control entropy of gait: Does running fitness affect complexity of walking? . Clinical Kinesiology, 2011. 65(1): p. 9-17.
  • Parshad, R., S.J. McGregor, M.A. Busa, J. Skufca, and E.M. Bollt, A Novel Statistical Approach to the Use of Control Entropy Identifies Differences in Constraints of Gait in Highly Trained Versus Untrained Runners Mathematical Biosciences and Engineering, 2011. In Press.
  • Armstrong, W.J., S.J. McGregor, J.A. Yaggie, J.J. Bailey, S.M. Johnson, A.M. Goin, and S.R. Kelly, Reliability of mechanomyography and triaxial accelerometry in the assessment of balance. J Electromyogr Kinesiol, 2010. 20(4): p. 726-31.
  • McGregor, S.J., P.M. Johnson, D. Madrigal, S. Levine, and M. Rubenfire, Power changes with treatment of coronary stenosis in a highly trained cyclist. Clin J Sport Med, 2010. 20(4): p. 325-6.
  • Bollt, E.M., J.D. Skufca, and S.J. McGregor, Control Entropy: A Complexity Measure for Nonstationary Signals. Mathematical Biosciences and Engineering, 2009. 6(1): p. 1-25.
  • McGregor, S.J., M.A. Busa, J. Skufca, J.A. Yaggie, and E.M. Bollt, Control entropy identifies differential changes in complexity of walking and running gait patterns with increasing speed in highly trained runners. Virtual Journal of Biological Physics Research, 2009. 18(1).
  • McGregor, S.J., M.A. Busa, J. Skufca, J.A. Yaggie, and E.M. Bollt, Control entropy identifies differential changes in complexity of walking and running gait patterns with increasing speed in highly trained runners. Chaos, 2009. 19(2): p. 026109.
  • McGregor, S.J., M.A. Busa, J.A. Yaggie, and E.M. Bollt, High resolution MEMS accelerometers to estimate VO2 and compare running mechanics between highly trained inter-collegiate and untrained runners. PLoS One, 2009. 4(10): p. e7355.
  • McGregor, S.J., R.K. Weese, and I.K. Ratz, Performance Modeling in an Olympic 1500 m finalist: A practical approach. Journal of Strength and Conditioning Research, 2009. 23(9): p. 2515-23.
  • Busa, M.A. and S.J. McGregor, The use of accelerometers to assess human locomotion. Clinical Kinesiology, 2008. 62(4): p. 21-25.

Peer-Reviewed Journal Articles Currently in Review

  • Busa, M.A., T. Muth, J.E. Hornyak, C.W. Herman, E.M. Bollt, and S.J. McGregor, High-Resolution MEMS Accelerometers Identify Changes in Center of Mass Mechanics in Highly Trained Runners. European Journal of Applied Physiology, In Review.
  • McGregor, S.J., E.M. Bollt, J. Skufca, and M. Rubenfire, Control Entropy demonstrates changes in complexity of heart rate signal can be transient or sustained Journal of Electrocardiology, In Review.
  • McGregor, S.J., Z. Maino, A. Workman, A. Daoud, J. Gordon, and F.J. Fedel, Lower Anterior-Posterior Accelerations Contribute to Improved Metabolic Running Economy in Trained Runners versus Triathletes. European Journal of Applied Physiology, In Review.
  • Lindsay, T., Yaggie, J.A., McGregor, S.J., High Resolution Accelerometer Measurement of Running Gait is Repeatable and Agrees with Optical Motion Capture. Journal of Sports Sciences. In Review
  • Lindsay T, Noakes TR, and McGregor, S.J. The entropy of stride timing dynamics is higher for high intensity running intervals than slower running and is not affected by fatigue. Journal of Applied Physiology, In Review, 2012.

Additional Information

  • Inventor of NGP/rTSS system
  • Faculty: USA Cycling Science and Education
    • Instructor USAC Level II Coaching Certification: Sports Sciences
    • Instructor USAC Power Training Certification Course
    • Instructor USAC Level I (Elite) Coaching Certification Interval Training
  • Instructor South African Cycling Coaching and Power Training Certification
  • Physiological Advisor to 2008 US Olympic BMX Team
  • Physiological Advisor to Eastern Michigan Men's Cross Country/Distance Track Team
  • Advisor to numerous cyclists, triathletes and runners at the national and international level
  • Co-author of the book from Human Kinetics titled "The Runner's Edge"

The Exercise Science Program is part of the School of Health Promotion & Human Performance, 318 Porter Building, 734.487.2824