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Purpose: The purpose of this investigation was to examine the effects of a submaximal running warm-up on running performance in male endurance athletes (n = 16, M[subscript age] = 21 ± 2 years, M[subscript VO2max] = 69.3 ± 5.1 mL/kg/min). Method: Endurance performance was determined by a 30-min distance trial after control and submaximal running warm-up conditions in a randomized crossover fashion. The warm-up began with 5 min of quiet sitting, followed by 6 min of submaximal running split into 2-min intervals at speeds corresponding to 45%, 55%, and 65% maximal oxygen consumption (VO[subscript 2]max). A 2-min walk at 3.2 km/hr concluded the 13-min warm-up protocol. For the control condition, participants sat quietly for 13 min. VO[subscript 2] and heart rate (HR) were determined at Minutes 0, 5, and 13 of the pre-exercise protocol in each condition. Results: At the end of 13 min prior to the distance trial, mean VO[subscript 2] (warm-up = 14.1 ± 2.2 mL/kg/min vs. control = 5.5 ± 1.7 mL/kg/min) and mean HR (warm-up = 105 ± 11 bpm vs. control = 67 ± 11 bpm) were statistically greater (p < 0.001) in the warm-up condition compared with the control condition. The distance run did not statistically differ (p = 0.37) between the warm-up (7.8 ± 0.5 km) and control (7.7 ± 0.6 km) conditions; however, effect size calculation revealed a small effect (d = 0.2) in favor of the warm-up condition. Thus, the warm-up employed may have important and practical implications to determine placing among high-level athletes in close races. Conclusions: These findings suggest a submaximal running warm-up may have a small but critical effect on a 30-min distance trial in competitive endurance athletes. Further, the warm-up elicited increases in physiological variables VO[subscript 2] and HR prior to performance; thus, a submaximal specific warm-up should warrant consideration.

Purpose: Excess postexercise oxygen consumption (EPOC) is dependent on intensity, duration, and mode of exercise. The purpose of this study was to compare the effect of both exercise mode and intensity on EPOC while controlling for caloric expenditure and duration. Method: Ten low to moderately physically active men (22 ± 2 yrs) performed 3 nonrandomized isocaloric bouts of exercise separated by 7 days. The 1st session was resistance training (RT), followed by moderate-intensity steady-state (SS) aerobic exercise, and concluding with a high-intensity intermittent (IT) aerobic session. Results: Total energy expenditure, rate of energy expenditure, and duration did not differ among trials (p>0.05). Respiratory exchange ratio was greater during the RT trial than the SS trial (p < 0.05). At 12 hr postexercise, resting metabolic rate (RMR) was higher after the RT trial (4.7 ± 0.67 mL/kg/min) and IT trial (4.6 ± 0.62 mL/kg/min) compared with their respective baseline measurements (p < 0.008) and the SS trial (4.3 ± 0.58 mL/kg/min; p < 0.008). At 21 hr postexercise, RMR was higher after the RT trial (3.7 ± 0.51 mL/kg/min) and IT trial (3.5 ± 0.39 mL/kg/min) compared with the SS trial (3.2 ± 0.38 mL/kg/min; p < 0.008). The SS trial did not influence RMR at either 12 hr or 21 hr postexercise. Conclusion: Both RT and IT aerobic work increased EPOC to a greater degree than did SS work, indicating that either mode may be more effective at increasing total daily caloric expenditure than SS aerobic exercise.

Wilson, JM, Hornbuckle, LM, Kim, J.-S, Ugrinowitsch, C, Lee, S.-R, Zourdos, MC, Sommer, B, and Panton, LB. Effects of static stretching on energy cost and running endurance performance. J Strength Cond Res 24(9): 2274-2279, 2010-Stretching before anaerobic events has resulted in declines in performance; however, the immediate effects of stretching on endurance performance have not been investigated. This study investigated the effects of static stretching on energy cost and endurance performance in trained male runners. Ten trained male distance runners aged 25 [+ or -] 7 years with an average [[??]o.sub.2]max of 63.8 [+ or -] 2.8 ml/kg/min were recruited. Participants reported to the laboratory on 3 separate days. On day 1, anthropometrics and [[??]o.sub.2] max were measured. On days 2 and 3, participants performed a 60-minute treadmill run randomly under stretching or nonstretching conditions separated by at least 1 week. Stretching consisted of 16 minutes of static stretching using 5 exercises for the major lower body muscle groups, whereas nonstretching consisted of 16 minutes of quiet sitting. The run consisted of a 30-minute 65% [[??]o.sub.2]max preload followed by a 30-minute performance run where participants ran as far as possible without viewing distance or speed. Total calories expended were determined for the 30minute preload run, whereas performance was measured as distance covered in the performance run. Performance was significantly greater in the nonstretching (6.0 [+ or -] 1.1 km) vs. the stretching (5.8 [+ or -] 1.0 km) condition (p < 0.05), with significantly greater energy expenditure during the stretching compared with the nonstretching condition (425 [+ or -] 50 vs. 405 [+ or -] 50 kcals). Our findings suggest that stretching before an endurance event may lower endurance performance and increase the energy cost of running. KEY WORDS muscle stiffness, flexibility, economy, sit-and-reach, rating of perceived exertion

Earlier studies associated the first year of college with a dramatic increase in body weight, termed the 'freshman 15'. However, recent studies showed that weight gain might be smaller. The purpose of this review was to evaluate the extent of observed weight/body composition changes, including factors associated with them, among students entering university. Searches were conducted for studies examining weight/body composition changes during freshman semesters. Most studies were not comprehensive in assessing numerous potential causative factors for weight gain. Methods for assessing diet, physical activity, and behavioral factors varied among studies. Weight changes were often not quantified by measures of body composition (lean/fat) to ascertain that changes were limited just to gains in fat mass. Overall, weight changes ranged from 0.7-3.1 kg, but among individuals who gained weight, the range was narrower, 3.1-3.4 kg. There may be specific groups of students with a greater predisposition for weight gain and future research should focus on identifying those groups.

This study evaluated the effects of a self-reported pedometer-walking program on gait, lower extremity function, and Body Mass Index for 33 obese women, ages 31-65 years (M = 48.0, SD = 8.0) and whose initial average Body Mass Index was 40.30 [+ or -] 9.60 kg/[m.sup.2]. During the 12-mo. intervention participants wore pedometers and reported the number of daffy steps walked. Body Mass Index, three gait parameters, steps/day, and lower extremity function were taken over 3-mo. intervals. All participants increased in gait velocity, % single-leg support, and lower extremity function, and decreased in Body Mass Index over time. Those whose steps/day increased by 2,000 over 6 mo. had significantly faster velocities and longer step lengths than those whose steps/day did not increase. The 1-yr. walking program stimulated changes in gait, Body Mass Index, and lower extremity function for these obese women. Ultimately, these changes may reduce the risk of falls for this group of women.

The purpose of the study was to determine the accuracy of 11 prediction equations in estimating the 1 repetition maximum (1RM) bench press from repetitions completed by collegiate football players (N = 69) using 225 lb. The demographic variables race, age, height, weight, fat-free weight, and percent body fat were measured to determine whether these variables increased the accuracy of the prediction equations; race was the most frequently selected variable in the regression analyses. The validity of the prediction equations was dependent upon the number of repetitions performed, i.e., validity was higher when fewer repetitions were completed. Explained variability of 1RM was slightly higher for all 11 equations when demographic variables were included. A new prediction equation was also developed using the number of repetitions performed and the demographic variables height and fat-free weight. Key Words: muscular strength, strength prediction, percentages, muscular endurance