Robert Buresh

The Effect of Resistive Exercise Rest Interval on Hormonal Response, Strength and Hypertrophy with Training

Buresh, R, Berg, K, and French, J. The effect of resistive exercise rest interval on hormonal response, strength, and hypertrophy with training. J Strength Cond Res 23(1): 62-71, 2009- The purpose of this study was to compare the effects of different between-set rest periods (1 and 2.5 minutes) on changes in hormone response, strength, arm cross-sectional area (CSA), thigh muscular cross-sectional area (MCSA), and body composition during a 10-week training period. Twelve untrained males (24.8 ± 5.9 years) engaged in resistance training using either 1 minute (short rest [SR], n = 6) or 2.5 minutes (long rest [LR], n = 6) of rest between sets, with a load that elicited failure on the third set of each exercise. Body composition, thigh MCSA, arm CSA, and five-repetition maximum (RM) squat and bench press were assessed before and after training. Blood samples were collected after exercise in weeks 1, 5, and 10. In week 1, postexercise plasma testosterone levels were greater in SR (0.41 ± 0.17 mmol·L−1) than in LR (0.24 ± 0.06 mmol·L−1, p < 0.05), and postexercise cortisol levels were greater in SR (963 ± 313 mmol·L−1) than in LR (629 ± 127 mmol·L−1, p < 0.05). Week 1 postexercise GH levels were not different (p = 0.28). The differences between hormone levels in weeks 5 and 10 were not significant. Arm CSA increased more with LR (12.3 ± 7.2%) than with SR (5.1 ± 2.9%, p < 0.05). There were no differences in strength increases. These results show that in healthy, recently untrained males, strength training with 1 minute of rest between sets elicits a greater hormonal response than 2.5-minute rest intervals in the first week of training, but these differences diminish by week 5 and disappear by week 10 of training. Furthermore, the hormonal response is highly variable and may not necessarily be predictive of strength and lean tissue gains in a 10-week training program.

Sit-and-reach flexibility and running economy of men and women collegiate distance runners.

Trehearn, TL and Buresh, RJ. Sit-and-reach flexibility and running economy of men and women collegiate distance runners. J Strength Cond Res 23(1): 158-162, 2009-Flexibility has been controversially suggested as one of the biomechanical factors contributing to the variability observed in running economy among distance runners. The purpose of this investigation was to determine the magnitude of the relationship between sit-and-reach flexibility and running economy in men and women. Eight collegiate distance runners (4 men and 4 women) served as subjects for this correlational study (age = 19.9 ± 1.25 years; &OV0312;o2max = 63.2 ± 3.4 ml·kg−1·min−1). Each subjects flexibility was measured using the standard sit-and-reach test, and running economy was recorded during an incremental maximal treadmill test at both absolute (men = 241.2 m·min−1; women = 198.32 m·min−1) and relative (10-km pace) velocities. Statistical analyses indicated a significant relationship between sit-and-reach scores and running economy at an absolute velocity (r = 0.826, p ≤ 0.05), as well as a significant sex difference in sit-and-reach scores (p ≤ 0.05). The significant relationship demonstrates that the less flexible distance runners tended to be more economical, possibly as a result of the energy-efficient function of the elastic components in the muscles and tendons during the stretch-shortening cycle.

A tutorial on oxidative stress and redox signaling with application to exercise and sedentariness.

Oxidative stress has been shown to play a role in the etiology of several chronic diseases, including cardiovascular disease, diabetes mellitus, and cancer. Free radicals and, most prominently, the superoxide radical, result from oxidative metabolism and several enzyme-catalyzed reactions, and endogenous cellular antioxidants dismutate many reactive oxygen species (ROS). Under certain conditions, ROS production can outpace dismutation (e.g., long-term sedentariness and positive energy balance) and the result is oxidative stress, with proteins, lipids, and DNA the most common targets of radicals. However, the molecules that contribute to oxidative stress also appear to participate in vital cell signaling activity that supports health and stimulates favorable adaptations to exercise training, such that inhibiting ROS formation prevents common adaptations to training. Furthermore, researchers have recently suggested that some proteins are not as readily formed when the redox state of the cell is insufficiently oxidative. Exercise training appears to optimize the redox environment by dramatically enhancing the capacity of the cell to neutralize ROS while regularly creating oxidative environments in which membrane and secretory proteins can be synthesized. The role that exercise plays in enhancing management of ROS likely explains many of the associated health benefits.

Comparison of an in-helmet temperature monitor system to rectal temperature during exercise.

Wickwire, PJ, Buresh, RJ, Tis, LL, Collins, MA, Jacobs, RD, and Bell, MM. Comparison of an in-helmet temperature monitor system to rectal temperature during exercise. J Strength Cond Res 26(1): 1–8, 2012—Body temperature monitoring is crucial in helping to decrease the amount and severity of heat illnesses; however, a practical method of monitoring temperature is lacking. In response to the lack of a practical method of monitoring the temperature of athletes, Hothead Technologies developed a device (HOT), which continuously monitors an athletes fluctuations in body temperature. HOT measures forehead temperature inside helmets. The purpose of this study was to compare HOT against rectal temperature (Trec). Male volunteers (n = 29, age = 23.5 ± 4.5 years, weight = 83.8 ± 10.4 kg, height = 180.1 ± 5.8 cm, body fat = 12.3 ± 4.5%) exercised on a treadmill at an intensity of 60–75% heart rate reserve (HRR) (wet bulb globe temperature [WBGT] = 28.7° C) until Trec reached 38.7° C. The correlation between Trec and HOT was 0.801 (R2 = 0.64, standard error of the estimate (SEE) = 0.25, p = 0.00). One reason for this relatively high correlation is the microclimate that HOT is monitoring. HOT is not affected by the external climate greatly because of its location in the helmet. Therefore, factors such as evaporation do not alter HOT temperature to a great degree. HOT was compared with Trec in a controlled setting, and the exercise used in this study was moderate aerobic exercise, very unlike that used in football. In a controlled laboratory setting, the relationship between HOT and Trec showed favorable correlations. However, in applied settings, helmets are repeatedly removed and replaced forcing HOT to equilibrate to forehead temperature every time the helmet is replaced. Therefore, future studies are needed to mimic how HOT will be used in field situations.

Associations between measures of health-related physical fitness and cardiometabolic risk factors in college students

abstract Objective: To determine the influence of health-related fitness on cardiometabolic risk factors in college students. Participants: 75 traditional students (33 men and 42 women, 21.8±1.8 years old) at a university in southeastern U.S. Methods: Height, weight, waist circumference, body composition, blood pressure, lipids, glucose, insulin, c-reactive protein, and glucose tolerance were measured. Indices of insulin sensitivity were calculated. Aerobic and muscular fitness were measured. Regression and correlation analyses, and comparisons of cardiometabolic markers in low- vs high-fit participants were performed. Results: Men and women with low muscular fitness exhibited higher fasting insulin, and poorer insulin sensitivity index scores than those with high muscular fitness. In addition, women with high body fat percentage exhibited higher fasting and 2-hour insulin levels and lower insulin sensitivity index scores than those with low body fat percentages. Conclusions: College students possessing low levels of health-related physical fitness exhibited less favorable cardiometabolic risk profiles.