Oliver, Jonathan M.

Abstract Askow, AT, Jennings, W, Jagim, AR, Fields, JB, Beaudoin, RG, Sanchez, GM, Weeks, JE, Oliver, JM, and Jones, MT. Athlete external load measures across a competitive season in high school basketball. J Strength Cond Res 37(11): 2206–2212, 2023—The purpose of this retrospective analysis was to quantify in-season external load and to determine if relationships existed between load metrics and basketball performance. Eleven male high school varsity basketball athletes (n = 11; mass 80.5 ± 9.6 kg, height 190.2 ± 9.4 cm, age 17.6 ± 0.7 years) were monitored across a season. PlayerLoad (PL), PL per minute (PL·min −1 ), total jumps, and explosive movements (EMs) were quantified using a commercially available local positioning unit. Basketball-specific performance metrics, including points scored, points allowed, point differentials, and shooting percentages for each quarter and game, were compiled. Data were analyzed using repeated-measure analysis of variance to evaluate differences in load by starting status, session type, game outcome, and game type. Pearson's correlation coefficients were used to assess relationships between load metrics and basketball performance. Statistical significance was set at p < 0.05. The mean values across 23 games for PL, PL·min −1 , total jumps, and EMs were 457 ± 104 AU, 10.9 ± 1.6 AU, 42.6 ± 9.6, and 46.7 ± 7.2, respectively. Relationships were observed ( p < 0.05) between PL and points scored ( r = 0.38) and free throw percentage ( r = 0.21). Further relationships were observed between PL·min −1 and free throw shooting percentage ( r = −0.27), and between points scored and total jumps ( r = 0.28), and EMs ( r = 0.26). Notable differences in game demands were observed for playing status. Meaningful differences in measures of external load were observed between each quarter of play, with the highest measures evident in quarters 1 and 3. Guards and forwards experienced minimal differences in external load during gameplay, and game outcome did not result in differences. Higher point totals corresponded with higher PL, total jumps, and EM.

Aim: To compare the efficacy of two popular weight loss approaches on weight loss, body composition, and markers of health in sedentary obese women. Methods: In total, 51 sedentary women (age 34.5 ± 7.7 yrs.; weight 90.0 ± 14.5 kg; BMI 34.0 ± 5.1 kg/m2; 46.5 ± 7.0% fat) were matched and randomized to participate in the Weight Watchers® Momentum™ (WW) or Curves® (CV) Fitness and Weight Management program for 16 weeks. Participants in the WW group (n = 27) were provided a point-based diet program, received weekly progress checks and counseling, and were encouraged to exercise. Participants in the CV group (n = 24) followed a menu-based higher protein/low-fat diet (1200 kcal/d) for 1 week; 1500 kcal/d diet for 3 weeks; and 2000–2500 kcals/d for 2 weeks that was repeated three times (except the last segment) while participating in a supervised circuit-style resistance training program (3 d/wk). A general linear model (GLM) with repeated measures was used to analyze data and are presented as mean changes from baseline (mean [UL, LL]). Results: Supervised CV training resulted in greater amounts of vigorous and total physical activity. After 16 weeks, both groups lost weight (WW −6.1 [−7.8, −4.6], CV −4.9 [−6.2, −3.2] kg, p = 0.264). Participants in the CV group observed greater reductions in fat mass (WW −2.9 [−6.7, −0.2], CV −6.4 [−9.2, −3.6] kg, p = 0.081) and increases in lean mass (WW −2.5 [−4.3, −0.7], CV 1.3 [−0.6, 3.2] kg, p = 0.005) resulting in more favorable changes in percent body fat (WW −1.4 [−4.1, 1.2], CV −4.7 [−7.5, −1.8]%, p = 0.098). Both groups observed improvements in peak aerobic capacity and muscular endurance, although bench press lifting volume was greater in the CV group. Those in the CV group experienced a greater increase in HDLc and reduction in the CHL–HDLc ratio and triglycerides. Conclusion: Both interventions promoted weight loss and improvements in fitness and markers of health. The CV program, which included supervised resistance training and higher protein diet menus, promoted greater fat loss, increases in lean mass, and improvements in percent body fat and blood lipids. Trial Registration: clinicaltrials.gov, #NCT04372771, registered retrospectively 1 May 2020.

Purpose: The purpose of this study was to determine whether session rating of perceived exertion-derived training load (sRPE-TL) correlates with GPS-derived measures of external load in National Collegiate Athletics Association (NCAA) Division I female soccer athletes. Methods: Twenty-one NCAA Division 1 collegiate women’s soccer athletes (11 starters, 10 non-starters; 65.1 ± 7.2 kg, 168.4 ± 7.9 cm, 20.3 ± 1.5 yrs) volunteered to take part in this study. Data for this study were collected over the course of 16 weeks during the 2018 NCAA women’s soccer season. External load and heart rate (HR) data were collected during each training session and match during the season. At least 30 min after the end of an activity (e.g., match or practice), athletes were prompted to complete a questionnaire reporting their perceived exertion for the session. sRPE-TL was calculated at the end of the season by multiplying perceived exertion by the respective session duration. Results: sRPE-TL was very strongly correlated with total distance, distance covered in velocity zones 1–3, the number of accelerations in zones 4 and 5, total PlayerLoad™, and PlayerLoad™. For internal load, sRPE-TL correlated very strongly (0.70 ≤ |r| < 0.90) with Edward’s and Bannister’s TRIMP and strongly (0.50 ≤ |r| < 0.70) with duration spent in in heart rate zones 5 and 6 (80–90% and 90–100% max HR, respectively) while correlations with maximum HR (bpm), mean HR (bpm), and mean HR (%) and sRPE-TL were moderate (0.30 ≤ |r| < 0.50). Conclusions: In NCAA Division I women soccer, sRPE-TL is strongly associated with external measures of workload. These relationships were stronger during match play, with acceleration load and total distance exhibiting the strongest relationship with sRPE-TL.

Given the relationship between explosive-type training and power adaptation, tracking movement velocity has become popular. However, unlike previous variables, tracking velocity necessitates the use of a valid and reliable tool to monitor adaptation over time. Therefore, the primary purpose of this research was to assess the validity and reliability of a commercially-available linear position transducer (LPT). Nine resistance-trained men completed four sessions consisting of a single set of barbell back squat to volitional failure at 75% or 90% one-repetition maximum. Kinetic and kinematic data were captured for each repetition by the LPT and a 3-dimensional motion capture system and bipedal force platforms. In total, 357 instances of data from both systems were analyzed using intraclass correlations (ICC), effect size estimates, and standard error of measurement. Overall, the LPT yielded excellent ICCs (all ≥0.94) and small/trivial differences (d < 0.60). When categorized by median values, ICCs remained high (all ≥0.89) and differences remained small or trivial with the exception of high peak velocities (d = −1.46). Together, these data indicate that the commercially-available LPT is a valid and reliable measure for kinetic and kinematic variables of interest with the exception of high peak velocities.

Abstract Jagim, AR, Camic, CL, Askow, A, Luedke, J, Erickson, J, Kerksick, CM, Jones, MT, and Oliver, JM. Sex differences in resting metabolic rate among athletes. J Strength Cond Res 33(11): 3008–3014, 2019—The purpose of this study was to compare differences in resting metabolic rate (RMR) between sexes in Division III National Collegiate Athletic Association (NCAA) collegiate athletes and to identify predictors of RMR. Sixty-eight male (M) (age: 20.1 ± 1.5 years; height: 181.8 ± 5.9 cm; body mass (BM): 93.7 ± 16.3 kg; and body fat%: 16.3 ± 8.6%) and 48 female (F) athletes (age: 19.4 ± 1.3 years; height: 166.5 ± 6.0 cm; BM: 63.4 ± 12.7 kg; and body fat%: 21.5 ± 6.3%) participated in a single day of testing, which included determination of RMR using indirect calorimetry and air displacement plethysmography to measure fat mass and fat-free mass (FFM). An independent-samples t -test was used to compare differences in body composition and RMR between sexes, and regression analysis was used to identify predictors of RMR. Men had a significantly higher absolute RMR (M: 2,481 ± 209 vs. F: 1,553 ± 193 kcals·d −1 ; p < 0.001), but when adjusted for BM (M: 25.6 ± 8.3 vs. F: 25.9 ± 2.5 kcals·kg −1 BM per day; p = 0.82) and FFM (M: 31.1 ± 10.6 vs. F: 33.6 ± 3.8 kcals·kg −1 FFM per day; p = 0.12), these differences became nonsignificant. Regression analysis indicated that BM in both men (β = 0.73) and women (β = 0.88) was the strongest predictor of RMR. The results of the current study indicate minimal differences in RMR between sexes among athletic populations when adjusted for BM and FFM. In the current group of athletes, BM seems to account for the largest variability in RMR.

Abstract Jagim, AR, Camic, CL, Kisiolek, J, Luedke, J, Erickson, J, Jones, MT, and Oliver, JM. Accuracy of resting metabolic rate prediction equations in athletes. J Strength Cond Res 32(7): 1875–1881, 2018—The purpose of this study was to determine the accuracy of 5 different resting metabolic rate (RMR) prediction equations in male and female athletes. Twenty-two female (19.7 ± 1.4 years; 166.2 ± 5.5 cm; 63.5 ± 7.3 kg; 49.2 ± 4.3 kg of fat-free mass (FFM); 23.4 ± 4.4 body fat (BF) percent) and 28 male (20.2 ± 1.6 years; 181.9 ± 6.1 cm; 94.5 ± 16.2 kg; 79.1 ± 7.2 kg of FFM; 15.1 ± 8.5% BF) athletes were recruited to participate in 1 day of metabolic testing. Assessments comprised RMR measurements using indirect calorimetry, and body composition analyses using air displacement plethysmography. One-way repeated-measures analysis of variance with follow-up paired t tests were selected to determine differences between indirect calorimetry and 5 RMR prediction equations. Linear regression analysis was used to assess the accuracy of each RMR prediction method. An alpha level of p ≤ 0.05 was used to determine statistical significance. All the prediction equations significantly underestimated RMR while the Cunningham equation had the smallest mean difference (−165 kcals). In men, the Harris-Benedict equation was found to be the best prediction formula with the lowest root-mean-square prediction error value of 284 kcals. In women, the Cunningham equation was found to be the best prediction equation with the lowest root-mean-squared error value of 110 kcals. Resting metabolic rate prediction equations consistently seem to underestimate RMR in male and female athletes. The Harris-Benedict equation seems to be most accurate for male athletes, whereas the Cunningham equation may be better suited for female athletes.