Excellent Article on Mathematical Modeling of Athletic Training

I came across this wonderful article of and 
A number of professions rely on exercise prescription to improve health or athletic performance, including coaching, fitness/personal training, rehabilitation, and exercise physiology. It is therefore advisable that the professionals involved learn the various tools available for designing effective training programs. Mathematical modeling of athletic training and performance, which we henceforth call “performance modeling,” is one such tool. Two models, the critical power (CP) model and the Banister impulse-response (IR) model, offer complementary information. The CP model describes the relationship between work rates and the durations for which an individual can sustain them during constant-work-rate or intermittent exercise. The IR model describes the dynamics by which an individual’s performance capacity changes over time as a function of training. Both models elegantly abstract the underlying physiology, and both can accurately fit performance data, such that educating exercise practitioners in the science of performance modeling offers both pedagogical and practical benefits. In addition, performance modeling offers an avenue for introducing mathematical modeling skills to exercise physiology researchers. A principal limitation to the adoption of performance modeling is a lack of education. The goal of this report is therefore to encourage educators of exercise physiology practitioners and researchers to incorporate the science of performance modeling in their curricula and to serve as a resource to support this effort. The resources include a comprehensive review of the concepts associated with the development and use of the models, software to enable hands-on computer exercises, and strategies for teaching the models to different audiences.

This paper was published on Advances in Physiology Education which is a relatively new journal of the American Physiological Society.
Here is the full reference: 
Clarke DC, Skiba PF. Rationale and resources for teaching the mathematical modeling of athletic training and performance. Adv Physiol Educ. 37(2):134-52. June 2013.

If you want to read more about Dr. Skiba’s work you can go here.
Great paper and most of all great supplementary material, excellent job @DrPhilipSkiba!

Aspetar Journal Special Issue on Handball

This is great news I hope for all the handball readers of my blog. Aspetar Sports Medicine Journal, which you can receive for free in print and access online if you subscribe has just published a special issue on Handball. 

This issue was developed by the editor in chief Dr. Nebojsa Popovic, an excellent Sports Medicine specialist with a glorious past as a handball player (Olympic Champion with ex-Yugoslavia in 1972) and contains an excellent series of articles on various aspects of handball.
I wrote something about strength training available here.
So if you are working with handball players or you are a coach, make sure you don’t miss this special issue of Aspetar Sports Medicine Journal. 


New Article Published

This work was a collaboration with colleagues at Center of Excellence for Sport Science and Coach Education, in East Tennessee State University.

J Sports Med Phys Fitness. 2014 Apr 9. [Epub ahead of print]

Repeated change-of-direction test for collegiate male soccer players.

Author information

  • 1Center of Excellence for Sport Science and Coach Education, Department of Kinesiology, Leisure, and Sport Science, East Tennessee State University, Johnson City, TN, USA – harahara10@hotmail.com.

Abstract

AIM:

The aim of the study was to investigate the applicability of a repeated change-of-direction (RCoD) test for NCAA Division-I male soccer players.

METHODS:

The RCoD test consisted of 5 diagonal direction changes per repetition with a soccer ball to be struck at the end. Each player performed 15 repetitions with approximately 10 seconds to jog back between repetitions. Data were collected in two sessions. In the first session, 13 players were examined for heart rate responses and blood lactate concentrations. In the second session, 22 players were examined for the test’s ability to discriminate the primary from secondary players (78.0 ± 16.1 and 10.4 ± 13.3 minutes per match, respectively).

RESULTS:

Heart rate data were available only from 9 players due to artifacts. The peak heart rate (200.2 ± 6.6 beats∙min1: 99.9 ± 3.0% maximum) and blood lactate concentration (14.8 ± 2.4 mmol∙L1 immediately after) resulted in approximately 3.5 and 6.4fold increases from the resting values, respectively. These values appear comparable to those during intense periods of soccer matches. In addition, the average repetition time of the test was found to discriminate the primary (4.85 ± 0.23 s) from the secondary players (5.10 ± 0.24 s) (p = 0.02).

CONCLUSION:

The RCoD test appears to induce physiological responses similar to intense periods of soccer matches with respect to heart rate and blood lactate concentration. Players with better average repetition times tend to be those who play major minutes.