Research describing the match and specific positional demands during match play in women’s collegiate soccer is limited. The purpose of the study was to quantify the match demands of National Collegiate Athletic Association (NCAA) Division III soccer and assess position differences in movement kinematics, heart rate (HR), and energy expenditure. Twenty-five Division III women soccer players (height: 1.61 ± 0.3 m; body mass: 66.7 ± 7.5 kg; fat-free mass: 50.3 ± 6.5 kg; body fat%: 25.6 ± 5.1%) were equipped with a wearable global positioning system to assess the demands of 22 matches throughout a season. Players were categorized by position (goal keepers (GK), center defenders (CB), flank players (FP), forwards (F), and center midfielders (CM)). Players covered 9807 ± 2588 m and 1019 ± 552 m at high speeds (>249.6 m·m−1), with an overall average speed of 62.85 ± 14.7 m·m−1. This resulted in a mean HR of 74.2 ± 6% HR max and energy expenditure of 1259 ± 309 kcal. Significant and meaningful differences in movement kinematics were observed across position groups. CM covered the most distance resulting in the highest training load. FP covered the most distance at high speeds and mean HR values were highest in CM, CB, and FP positions.

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.

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.

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 XX(X): 000-000, 2018-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.

Objective To quantify the occurrence rate of abnormal ECG findings and symptoms following COVID-19 infection. Patients Adult patients (>18 years old) who were participating in collegiate athletics and previously tested positive for COVID-19 between August 2020 to November 2020. Methods In this retrospective study, we report findings of electrocardiogram (ECG) testing to screen athletes for cardiac abnormalities following COVID-19. Athletes underwent general examinations and ECG screening prior to being medically cleared for a return to sport following COVID-19. Predetermined predictors were grouped into categorical variables including: 1) Sex; 2) Symptom severity; and 3) BMI (normal vs. overweight = > 24 kg∙m -2 ). These were used to examine differences of abnormal rates occurred between different predictor categories. Results Of the 170 athletes screened, 6 (3.5%) presented with abnormal ECG criteria and were referred to cardiology. We found no evidence that symptom severity, sex and BMI category were associated with a higher rate of abnormal ECG ( p > 0.05). Greater severity of COVID-19 symptoms were associated with higher percentage of ST depression, T-wave inversion, ST-T changes and presence of fQRS. Loss of smell, loss of taste, headache and sore throat were the most prevalent symptoms with 32.9%, 38.8%, 36.5% and 25.3% of athletes reporting each symptom, respectively. Conclusions Preliminary findings indicate a low risk of myocardial injury secondary to COVID-19 infection with less than4% of patients presenting with abnormal ECG and 10% requiring referral to a cardiologist. While viral myocarditis was not demonstrated in any athlete referred for cardiology assessment, two patients developed effusative viral pericarditis.