Citations

This study examined the effects of different types of masks (no mask, surgical mask (SM), and N95-mask) on physiological and perceptual responses during 30-min of self-paced cycle ergometer exercise. This study was a prospective randomly assigned experimental design. Outcomes included workload (Watts), oxygen saturation (SpO2), end-tidal carbon dioxide (PetCO2), heart rate (HR), respiratory rate (RR), rating of perceived exertion (RPE), and rating of perceived dyspnea (RPD). Volunteers (54–83 years (n = 19)) completed two familiarization sessions and three testing sessions on an air braked cycle ergometer. No significant difference was found for condition x time for any of the dependent variables. RPE, RPD, and PetCO2 were significantly higher with an N95-mask vs. no mask (NM) ((p = 0.012), (p = 0.002), (p < 0.001)). HR was significantly higher with the SM compared to the NM condition (p = 0.027) (NM 107.18 ± 9.96) (SM 112.34 ± 10.28), but no significant difference was found when comparing the SM to the N95 condition or when comparing the N95condition to the NM condition. Watts increased across time in each condition (p = 0.003). Initially RR increased during the first 3 min of exercise (p < 0.001) with an overall gradual increase noted across time regardless of mask condition (p < 0.001). SpO2 significantly decreased across time but remained within normal limits (>95%). No significant difference was found in Watts, RR, or SpO2 regardless of mask condition. Overall, the N95mask was associated with increased RPE, RPD, and PetCO2 levels. This suggests trapping of CO2 inside the mask leading to increased RPE and RPD.

ABSTRACTPredictive resting metabolic rate (RMR) equations are widely used to determine total daily energy expenditure (TDEE). However, it remains unclear whether these predictive RMR equations accurately predict TDEE in the athletic populations. The purpose of this study was to examine the accuracy of 10 commonly used RMR prediction equations (Cunningham, De Lorenzo, Freire, Harris‐Benedict, Mifflin St. Jeor, Nelson, Owen, Tinsley, Watson, Schofield) in collegiate men and women athletes. One‐hundred eighty‐seven National Collegiate Athletic Association Division III men (n = 97) and women (n = 90) athletes were recruited to participate in one day of metabolic testing. RMR was measured using indirect calorimetry and body composition was analyzed using air displacement plethysmography. A repeated measures ANOVA with Bonferroni post hoc analyses was selected to determine mean differences between measured and predicted RMR. Linear regression analysis was used to assess the accuracy of each RMR prediction method (p<0.05). All prediction equations significantly underestimated RMR (p<0.001), although there was no difference between the De Lorenzo and Watson equations and measured RMR (p = 1.00) for women, only. In men, the Tinsley and Freire equations were the most agreeable formulas with the lowest root‐mean‐square prediction error value of 404 and 412 kcals, respectively. In women, the De Lorenzo and Watson equations were the most agreeable equations with the lowest root‐mean‐squared error value of 171 and 211 kcals, respectively. The results demonstrate that such RMR equations may underestimate actual energy requirements of athletes and thus, practitioners should interpret such values with caution.

ABSTRACT A scoping review methodology was used to assess player readiness via indicators of neuromuscular function in response to training load. Literature search included PubMed, Web of Science, SportDiscus, and CINAHL. Search strategy was based on Population-Concept-Context framework evaluating football code athletes with the focus on player readiness in the context of longitudinal monitoring across quantified training loads with an emphasis on neuromuscular function. Demographics, frequency of data collection, methods of monitoring readiness, neuromuscular function, duration of monitoring period, and quantification of training loads were reported. Authors independently reviewed articles and, in the case of disagreement, a vote determined inclusion. The sports of rugby ( n = 13) and soccer ( n = 11) were most frequently examined, followed by Australian Rules Football ( n = 6) and American Football ( n = 1). The most common methods assessed systemic readiness with the primary focus on jump and sprint testing. Findings identified limited evidence supporting structural and tissue-specific monitoring tools. Player readiness assessment strategies have evolved with advances in technology and analytics. It is recommended that future research focus on a variety of populations, including youth and women athletes. This review is the first to summarize methods of assessing player readiness using indicators of neuromuscular function in football code athletes.

Abstract Brandner, CF, Harty, PS, Luedke, JA, Erickson, JL, and Jagim, AR. Sport differences in fat-free mass index among a diverse sample of NCAA Division III collegiate athletes. J Strength Cond Res 36(8): 2212–2217, 2022—Fat-free mass index (FFMI) is becoming a popular metric to determine an athlete's potential for future fat-free mass accrual or to identify athletes who may be at risk for low fat-free mass (FFM). The aim of the current study was to examine sport-specific differences in FFMI among a cohort of collegiate athletes. NCAA Division III male ( n = 98; age: 20.1 ± 1.6 years, height: 1.82 ± 0.06 m, body mass: 92.7 ± 17.5 kg, %BF: 15.6 ± 8.8%) and female ( n = 92; age: 19.45 ± 1.1 years, height: 1.68 ± 0.06 m, body mass: 65.16 ± 11.04 kg, %BF: 22.71 ± 5.9%) athletes completed a body composition assessment using air displacement plethysmography. Fat-free mass index was calculated by dividing FFM by height squared. The mean ± SD FFMI was significantly higher ( p < 0.05) for males (23.37 ± 2.41 kg·m −2 ) compared with females (17.54 ± 1.8 kg·m −2 ). There was a significant main effect for sport category. Post hoc analysis indicated that throwers had a higher ( p < 0.001) FFMI (mean difference, 95% confidence interval) compared with sprinters and soccer athletes (4.17, 2.03–6.32 kg·m −2 ), endurance and weight-sensitive athletes (4.91, 2.67–7.14 kg·m −2 ), and court sport athletes (4.39, 1.97–6.81 kg·m −2 ), respectively, among female athletes. Post hoc analysis indicated that football players had a higher ( p < 0.01) FFMI than distance runners (3.89, 2.15, 5.62) and wrestlers (2.23, 0.78, 3.68), among male athletes. These findings indicate that sex differences in FFMI exist, with differences identified between sports. These findings can guide strength and nutritional programming decisions by providing sport-specific normative data profiles.

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.

Relative Energy Deficiency in sport is experiencing remarkable popularity of late, particularly among female athletes. This condition is underpinned by low energy availability, which is a byproduct of high energy expenditure, inadequate energy intake, or a combination of the two. Several contributing factors exist that may predispose an athlete to low energy availability, and therefore a holistic and comprehensive assessment may be required to identify the root causes. The focus of the current narrative review is to discuss the primary contributing factors as well as known risk factors for low energy availability among female athletes to help practitioners increase awareness on the topic and identify future areas of focus.

Research quantifying the unique workload demands of starters and reserves in training and match settings throughout a season in collegiate soccer is limited. Purpose: The purpose of the current study is to compare accumulated workloads between starters and reserves in collegiate soccer. Methods: Twenty-two NCAA Division III female soccer athletes (height: 1.67 ± 0.05 m; body mass: 65.42 ± 6.33 kg; fat-free mass: 48.99 ± 3.81 kg; body fat %: 25.22 ± 4.78%) were equipped with wearable global positioning systems with on-board inertial sensors, which assessed a proprietary training load metric and distance covered for each practice and 22 matches throughout an entire season. Nine players were classified as starters (S), defined as those playing >50% of playing time throughout the entire season. The remaining 17 were reserves (R). Goalkeepers were excluded. A one-way ANOVA was used to determine the extent of differences in accumulated training load throughout the season by player status. Results: Accumulated training load and total distance covered for starters were greater than reserves ((S: 9431 ± 1471 vs. R: 6310 ± 2263 AU; p < 0.001) and (S: 401.7 ± 31.9 vs. R: 272.9 ± 51.4 km; p < 0.001), respectively) throughout the season. Conclusions: Starters covered a much greater distance throughout the season, resulting in almost double the training load compared to reserves. It is unknown if the high workloads experienced by starters or the low workloads of the reserves is more problematic. Managing player workloads in soccer may require attention to address potential imbalances that emerge between starters and reserves throughout a season.

Background: Energy drinks are one of the most popular packaged beverage products consumed within the United States (US). Energy drinks are considered a functional beverage, a category that also includes sports drinks and nutraceutical beverages. Purpose: The focus of the current study was to examine the nutrition fact panels of the top selling commercially available energy drink and energy shot products within the US to characterize common ingredient profiles to help establish a standard definition and ingredient profile of energy drinks and energy shots for consumers, health care practitioners, and researchers. Methods: The top 75 commercially available energy drinks and shots were identified and compiled from multiple commercial retail websites as of September 2021. For the purpose of this study, an energy drink must have met the following criteria: (A) marketed as an energy drink; (B) purported to improve energy, focus, or alertness; (C) not sold as a dietary supplement (no supplement fact panels); (D) manufactured as a pre-packaged and ready-to-drink beverage; and (E) contains at least three of (1) caffeine, (2) B-vitamins, (3) sugar, (4) taurine, (5) creatine, (6) quercetin, (7) guarana, (8) ginseng, (9) coenzyme Q10, or (10) branched chain amino acids. Energy shots must have met similar criteria to be included: (A) marketed as an energy shot; (B) purported to improve energy, focus, or alertness; (C) sold as a dietary supplement; (D) manufactured as a pre-packaged beverage with a small volume (<3.5 mL); and (E) contains at least three of the ingredients stated above. Results: Twenty energy shots and fifty-five energy drinks were included in this analysis. The number of ingredients per product (mean ± SD) was 18.2 ± 5.7, with 15 products containing proprietary blends with undisclosed ingredient amounts. The relative prevalence and average amounts of the top ingredients were as follows: caffeine (100%; 174.4 ± 81.1 mg), vitamin B6 (72%; 366.9 ± 648.1 percent daily value (%DV)), vitamin B3 (67%; 121.44 ± 69.9% DV), vitamin B12 (67%; 5244.5 ± 10,474.6% DV), vitamin B5 (37.3%; 113.6 ± 76.6% DV), and taurine (37.3%; amounts undisclosed). Conclusions: Our findings suggest a high prevalence of caffeine and B-vitamins in these energy products, with many of the formulations containing well above the recommended daily value of B-vitamins.