Citations

The purpose of the present study was to examine characteristics of multi-ingredient pre-workout supplement (MIPS) users, their common patterns/habits of MIPS ingestion, and their associated feelings about the effectiveness and safety of this class of supplements. An online electronic survey was distributed through social media to assess self-reported supplementation practices and preferences among adult males and females who reported regular MIPS use. A total of 1045 individuals responded, with 872 of these individuals (males: n = 636, 72.9%; females: n = 233, 26.7%; mean ± SD; age = 27.7 ± 7.9 years; training age = 8.2 ± 7.3 years) completing the survey. The majority of respondents reported the length of current or past MIPS consumption as greater than one year (n = 630, 72.2%), with ingestion frequencies primarily of four (n = 210, 24.1%) or five (n = 212, 24.3%) days per week of training. In addition, the three most popular goals for ingesting MIPS were increased energy and focus (n = 776, 89.0%), muscular endurance (n = 325, 37.3%), and blood flow or “pump” (n = 322, 37.0%). Although most users reported ingesting one serving size with each use, 14% reported ingesting two or more, and 18% indicated they ingest MIPS more than once per day. Importantly, over half (54%) of the respondents reported experiencing side-effects following MIPS use, including skin reactions, heart abnormalities, and nausea. Females were more likely than males to experience these side effects, despite being less likely to consume two or more serving sizes per dose. Our findings also indicated that MIPS users should consume no more than the recommended serving size of a given supplement, as the potentially significant variability in the caffeine content of MIPS products is compounded as more doses are consumed. Furthermore, MIPS users should minimize the ingestion of other supplements which contain high levels of niacin and caffeine, as the concurrent consumption of such products may put users above the tolerable upper limits for these substances.

Abstract Currier, BS, Harty, PS, Zabriskie, HA, Stecker, RA, Moon, JM, Jagim, AR, and Kerksick, CM. Fat-free mass index in a diverse sample of male collegiate athletes. J Strength Cond Res 33(6): 1474–1479, 2019—Fat-free mass index (FFMI) is a body composition metric that has been used to assess relative muscularity in athletes. Fat-free mass index is calculated by dividing FFM by height squared, although further height corrections through linear regression may be needed in taller individuals. This study reported height-adjusted FFMI (FFMI Adj ) data in 209 male collegiate athletes from 10 sports (baseball, cross country, football, golf, ice hockey, weightlifting, rugby, swimming, track and field, and water polo) and the FFMI Adj natural upper limit for sports with sufficient sample size. The body composition of all subjects (mean ± SD ; age: 20.7 ± 1.9 years, height: 182.9 ± 6.7 cm, body mass: 90.8 ± 16.8 kg, and percent body fat: 15.6 ± 5.3) was measured using dual-energy x-ray absorptiometry. Linear regression was used to adjust for height, and the FFMI Adj natural upper limit was determined by calculating the 97.5th percentile of all values. One-way analyses of variance with Games-Howell post hoc comparisons were used to determine between-sport differences. A paired-samples t -test revealed a significant difference ( p < 0.001) between unadjusted and adjusted mean FFMI values. The overall mean FFMI Adj was 22.8 ± 2.8 kg·m −2 . Significant between-sport differences ( p < 0.001) in FFMI Adj were identified. Average FFMI Adj was highest in football athletes (24.28 ± 2.39 kg·m −2 ) and lowest in water polo athletes (20.68 ± 3.56 kg·m −2 ). The FFMI Adj upper limit was calculated for all athletes (28.32 kg·m −2 ), rugby (29.1 kg·m −2 ), and baseball (25.5 kg·m −2 ). This study reported FFMI Adj values in a diverse cohort of male collegiate athletes, providing data for the first time in several sports. These values can be used to guide nutritional and exercise interventions, predict athletic performance, and provide coaches with standardized information regarding the potential for further FFM accretion in male athletes.

Little data is available regarding the energy and nutritional status of female collegiate team sport athletes. Twenty female NCAA Division II lacrosse athletes (mean ± SD: 20.4 ± 1.8 years; 68.8 ± 8.9 kg; 168.4 ± 6.6 cm; 27.9 ± 3% body fat) recorded dietary intake and wore a physical activity monitor over four consecutive days at five different time points (20 days total) during one academic year. Body composition, bone health, and resting metabolic rate were assessed in conjunction with wearing the monitor during off-season, pre-season, and season-play. Body fat percentage decreased slightly during the course of this study (p = 0.037). Total daily energy expenditure (TDEE) (p < 0.001) and activity energy expenditure (AEE) (p = 0.001) energy were found to change significantly over the course of the year, with pre-season training resulting in the highest energy expenditures (TDEE: 2789 ± 391 kcal/day; AEE: 1001 ± 267 kcal/day). Caloric (2124 ± 448 kcal/day), carbohydrate (3.6 ± 1.1 g/kg), and protein (1.2 ± 0.3 g/kg) intake did not change over the course of the year (p > 0.05). Athletes self-reported a moderate negative energy balance (366–719 kcal/day) and low energy availability (22.9–30.4 kcal/kg FFM) at each measurement period throughout the study. Reported caloric and macronutrient intake was low given the recorded energy expenditure and macronutrient intake recommendations for athletes. Athletic support staff should provide athletes with appropriate fueling strategies, particularly during pre-season training, to adequately meet energy demands.

Multi-ingredient pre-workout supplements are a popular class of dietary supplements which are purported to improve exercise performance. However, the composition of these products varies substantially between formulations, thus making comparisons challenging. Therefore, the purpose of this study was to identify a common ingredient profile of top-selling pre-workout supplements and to compare ingredient dosages to established efficacious values. The top 100 commercially available pre-workout products were analyzed for listed ingredients and amounts, if available, from the supplement facts panel. The mean ± SD number of ingredients per supplement (n = 100) was 18.4 ± 9.7 with 8.1 ± 9.9 of these ingredients included in a proprietary blend at undisclosed quantities. Relative prevalence and average amounts of the top ingredients amounted to: Beta-alanine (87%; 2.0 ± 0.8 g), Caffeine (86%; 254.0 ± 79.5 mg), Citrulline (71%; 4.0 ± 2.5 g), Tyrosine (63%; 348.0 ± 305.7 mg), Taurine (51%; 1.3 ± 0.6 g), and Creatine (49%; 2.1 ± 1.0 g). Nearly half (44.3%) of all ingredients were included as part of a proprietary blend with undisclosed amounts of each ingredient. The average amount of beta-alanine per serving size was below the recommended efficacious dose. The average caffeine content was near the low end for an effective relative dose for a 70 kg individual (3–6 mg·kg−1 of bodyweight).

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.