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Predictive 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 (

Background: The purpose of this study was to examine the relationship between training load and next-day recovery in collegiate American football (AF) players during pre-season.Methods: Seventeen a...

Early morning training sessions may affect sleep quantity in athletes. The purpose of this study is to examine differences in total sleep time of collegiate athletes on nights prior to early morning training sessions relative to non-training nights. Wristwatch monitors equipped with photoplethysmography and accelerometer technology were worn by 18 NCAA Division III collegiate athletes (Age: 20.1 ± 1.6 years, Height: 1.81 ± 0.02 m, Body Mass: 91.2 ± 6.5 kg, Body Fat %: 20.8 ± 1.6%) during a two-week period of training to monitor total sleep times. Athletes recorded time in and out of bed using a sleep diary, anxiety levels due to having to wake up in the morning, and perceived recovery status (PRS) upon waking the next day. The data were divided into: nights before non-training days (NT) and Training days (TD). Data were analyzed using univariate analysis. All athletes obtained significantly less total sleep on nights before TD relative to NT (NT: 8:15 ± 1:03 vs. TD: 6:08 ± 0:59 hh:mm; p < 0.05). There was a positive relationship between total sleep time and recovery status (p < 0.01). Anxiety scores were inversely related to total sleep time (p < 0.01). Next-day recovery status was inversely related to anxiety scores (p < 0.001). College athletes obtained significantly less total sleep time on nights before early morning training sessions (< 0700) during the off-season, regardless of sex and sport. Coaches should consider later training sessions or promote optimal sleep quantity in order to minimize the risks associated to early morning training sessions.

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; p < 0.001), but when adjusted for BM (M: 25.6 ± 8.3 vs. F: 25.9 ± 2.5 kcals·kg BM per day; p = 0.82) and FFM (M: 31.1 ± 10.6 vs. F: 33.6 ± 3.8 kcals·kg 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.

Sport nutrition knowledge has been shown to influence dietary habits of athletes. The purpose of the current study was to examine relationships between sport nutrition knowledge and body composition and examine potential predictors of body weight goals in collegiate athletes. Participants included National Collegiate Athletic Association Division III women (n = 42, height: 169.9 ± 6.9 cm, body mass: 67.1 ± 8.6 kg, fat-free mass: 51.3 ± 6.6 kg, body fat per cent: 24.2 ± 5.3%) and men (n = 25, height: 180.8 ± 7.2 cm, body mass: 89.2 ± 20.5 kg, fat-free mass: 75.9 ± 12.2 kg, body fat per cent: 13.5 ± 8.9%) athletes. Body composition was assessed via air displacement plethysmography. Athletes completed a validated questionnaire designed to assess sport nutrition knowledge and were asked questions about their perceived dietary energy and macronutrient requirements, as well as their body weight goal (i.e., lose, maintain, gain weight). Athletes answered 47.98 ± 11.29% of questions correctly on the nutrition questionnaire with no differences observed between sexes (men: 49.52 ± 11.76% vs. women: 47.03 ± 11.04%, p = 0.40). An inverse relationship between sport nutrition knowledge scores and body fat percentage (BF%) (r = –0.330, p = 0.008), and fat mass (r = –.268, p = 0.032) was observed for all athletes. Fat mass (β = 0.224), BF% (β = 0.217), and body mass index (BMI) (β = 0.421) were all significant (p &lt, 0.05) predictors of body weight goal in women. All athletes significantly (p &lt, 0.001) underestimated daily energy (–1,360 ± 610.2 kcal/day), carbohydrate (–301.6 ± 149.2 grams/day [g/day]), and fat (–41.4 ± 34.5 g/day) requirements. Division III collegiate athletes have a low level of sport nutrition knowledge, which was associated with a higher BF%. Women athletes with a higher body weight, BF% and BMI were more likely to select weight loss as a body weight goal. Athletes also significantly underestimated their energy and carbohydrate requirements based upon the demands of their sport, independent of sex.

Objective: To quantify the occurrence rate of abnormal electrocardiographic (ECG) findings and symptoms following coronavirus disease 2019 (COVID-19) infection. Patients and Methods: In this retrospective analysis, we studied adult patients (>18 years old) who were participating in collegiate athletics and previously tested positive for COVID-19 between August 1, 2020, and December 30, 2020. The athletes underwent general examinations and ECG screening prior to being medically cleared for a return to sports following their COVID-19 diagnosis. Predetermined predictors were grouped into categorical variables including (1) sex, (2) symptom severity, and (3) body mass index (normal vs overweight [≥24 kg/m2]). These variables were used to examine differences of abnormal rates that occurred between different predictor categories. Results: Of the 170 athletes screened, 6 (3.5%) presented with abnormal ECG findings and were referred to cardiologists. We found no evidence that sex, symptom severity, and body mass index category were associated with a higher rate of abnormal ECG findings (all P>.05). Greater severity of COVID-19 symptoms was associated with a higher percentage of ST depression, T-wave inversion, ST-T changes, and the presence of fragmented QRS complex. Loss of smell, loss of taste, headache, and fatigue were the most prevalent symptoms, with 38.8% (66), 36.5% (62), 32.9% (56), and 25.3% (43), respectively, of the 170 athletes reporting each symptom. Conclusion: Preliminary findings indicate a low risk of myocardial injury secondary to COVID-19 infection, with less than 4% of the 170 patients in our study presenting with abnormal ECG findings and a total of 16 patients (9.4%) requiring referral to a cardiologist. Although viral myocarditis was not detected in any athlete referred for cardiological assessment, 2 patients experienced effusive viral pericarditis.

The purpose of the current study was to examine the impact of COVID-19 government-enforced shutdown measures on the training habits and perceptions of athletes. A web-based electronic survey was developed and distributed online to athletes. The survey contained questions regarding currently available resources, changes in weekly training habits, and perceptions of training such as intensity, motivation, and enjoyment. A total of 105 (males: n = 31; females: n = 74) athletes completed the survey (mean ± SD age = 19.86 ± 2.13 years). Ninety-nine (94.3%) athletes continued to receive guidance from their primary sport coach or strength training staff. There was a significant (p < 0.001) decrease (mean ± SD) in self-reported participation time for strength training (−1.65 ± 4.32 h. week−1), endurance (−1.47 ± 3.93 h. week−1), and mobility (−1.09 ± 2.24 h. week−1), with the largest reduction coming from participation time in sport-specific activities (−6.44 ± 6.28 h. week−1) pre- to post-shutdown. When asked to rate their current state of emotional well-being using a visual analog scale of 0–100, with 100 being exceptional, the mean score was 51.6 ± 19.6 AU. Athletes experienced notable reductions in training frequency and time spent completing various training related activities. In the future, practitioners should have preparations in place in the event of another lockdown period or future pandemic to avoid or minimize significant disruptions in training. Special considerations may be needed when athletes are allowed to return to sport in the event of significant levels of detraining that may have occurred.

(1) Background: Limited information exists on the prevalence of low energy availability (LEA) in collegiate team sports. The purpose of this study was to examine the prevalence of LEA in collegiate women soccer players. (2) Methods: Collegiate women soccer athletes (n = 18, height: 1.67 &plusmn, 0.05 m, body mass: 65.3 &plusmn, 7.9 kg, body fat %: 24.9 &plusmn, 5.6%) had their body composition and sport nutrition knowledge assessed in the pre-season. Energy availability was assessed mid-season using a 4-day dietary log and activity energy expenditure values from a team-based monitoring system. A validated screening tool was used to screen for LEA. (3) Results: The screening tool classified 56.3% of athletes as at risk of LEA (&lt, 30 kcal/kg of FFM), however, the actual dietary intake identified 67% as LEA. Athletes identified as non-LEA consumed significantly more absolute (p = 0.040) and relative (p = 0.004) energy than LEA athletes. (4) Conclusions: There was a high prevalence of LEA among collegiate women soccer athletes. Although previously validated in women endurance athletes, the LEA screening tool was not effective in identifying those at risk of LEA in this sample of athletes.

Background The estimation of resting energy expenditure (REE) can be a valuable tool in developing programs for weight loss interventions and body composition management. REE prediction equations are a low-cost alternative to assess REE versus directly measuring REE, which typically requires expensive laboratory equipment. Though often used to estimate REE in active populations, the majority of REE equations have been developed in overweight or sedentary populations. This study sought to examine the accuracy of three commonly used REE estimation equations in a recreationally active population.