Predicting VO2max in Collegiate American-Style Football Athletes

Introduction: Maximal oxygen uptake (VO2max) is an important measurement for athletic performance. A common method of VO2max prediction is the Foster equation (MSSE, 1996). This equation produces accurate predictions in a normal population, however, significant difference has been noted between predicted and measured VO2max values when testing athletes. While other studies have produced new equations for athletes in general or even for soccer players, to our knowledge none have made one specifically for American-style football players. Purpose: The aim of this study is to develop an accurate VO2max prediction equation for collegiate American-style football athletes for testing on the treadmill with the standard Bruce protocol. Methods: Over 13 years, a total of 413 collegiate American football players (age: 18.5±1.15 yrs, height: 186.8±7.0 cm, weight 102.1±20.8 kg) were assessed for VO2max (Medical Graphics, Corp® Metabolic Cart) using the standard Bruce treadmill protocol. Linear regression analysis (JMP v. 12) determined which factor out of height, weight, or time spent on the test had a greater impact on VO2max. The linear regression analysis of the most significant factor against VO2max produced a prediction equation. Predicted VO2max was calculated using these data in both the Foster equation and this novel equation. Predicted values were compared to actual measured values with a t-test. α=0.05 for all statistical tests. Results: Of all the factors, time had the strongest relationship (p\u3c0.0001; r2=0.6464). The linear regression between VO2max and time produced a prediction equation: VO2max= -3.546 + 3.904(time in minutes). Both the Foster equation and this new equation were significantly and positively correlated with the actual VO2max values (Foster=0.805, New r=0.804). However, t-tests indicate that the Foster equation results were significantly different from the measured values (p=0.0007), and the new model’s results were not significantly different (p=1.0). Conclusion: The Foster equation is not a reliable predictor of VO2max as assessed on a treadmill in collegiate American-style football athletes. This new equation is more accurate to predict VO2max in this population

DEXA Body Composition and Cardiovascular Risk Factors Weakly Related in Police Officers

There is currently little research on whether fat mass and distribution is a predictive factor of cardiovascular risk. PURPOSE: To determine if obesity measures, such as fat mass and distribution (android vs gynoid), could be used to predict cardiovascular risk, particularly lipid levels, systolic blood pressure (SBP) and blood glucose. Our hypothesis was that fat mass is not an accurate predictor of these cardiovascular risk factors. METHODS: 182 police officers (166 males, 16 females; age 37.6±8.1 yrs; ht 1.7±0.1 m; wt 92.2±17.8 kg; BMI 28.9±4.8) were part of an annual cardiovascular risk profile testing group. We measured resting heart rate and blood pressure, and body composition via DEXA scan (SBP 127.16±10.33 mmHg; fat mass 26.85±9.99 kg; lean mass 62.01±9.90 kg; percent android fat 35.54±10.07; percent gynoid fat 29.65±6.91). Fasting blood samples were drawn and analyzed by a clinically certified lab to determine total blood cholesterol (TC) (191.79±37.31 mg/dL), LDL (119.23±34.74 mg/dL), HDL (46.39±10.48 mg/dL), triglycerides (128.94±99.25 mg/dL), and glucose (86.67±18.65 mg/dL). Correlations were determined by using a bivariate Pearson correlation matrix, significance was set at and p\u3c0.01**. RESULTS: As fat mass increased, total cholesterol and LDL increased and HDL decreased. Triglycerides, glucose, and SBP also increased as fat mass increased. There were also significant increases in total cholesterol, LDL, triglycerides, glucose and SBP as android fat percentage increased. HDL decreased significantly as android fat percentage increased. CONCLUSION: Fat mass weakly correlates with blood cholesterol levels. We suggest that factors other than fat mass affect cholesterol, such as genetics and lifestyle. More research is needed to see if this correlation holds or is stronger in similar and different populations

Cross-validation of a Prediction Equation for Energy Expenditure of an Acute Resistance Exercise Bout

Previously, our laboratory introduced a regression equation for predicting net kcal consumption of a resistance exercise (RE) bout: Total net kcal = 0.874(height, cm) - 0.596(age, years) - 1.016(fat mass, kg) + 1.638(lean mass, kg) + 2.461(total volume x 10-3) - 110.742 (R2 = 0.773, SEE=28.5 kcal). PURPOSE: The purpose of this study was to validate this regression equation using the same variables as predictors. METHODS: Forty-seven healthy, active subjects (23 men, 24 women, 20-58 yrs, 173.5 ± 10.5 cm, 85.5 ± 19.0 kg, VO2max 36.0 ± 8.4 ml/kg/min) were randomly divided into validation and cross-validation groups (nv = 24, ncv = 23). The validation group’s data was used to develop an equation to predict net kcal consumption, which was applied to the cross-validation group’s data to estimate net kcal consumption. Similarly, a prediction equation was derived from the cross-validation group’s raw data and applied to that of the validation group. The strength of the relationship between each group’s measured and estimated net kcal consumption was assessed via correlational analysis. RESULTS: Multiple linear regression yielded the following estimates of net kcal consumption: validation net kcal = 1.125(height, cm) – 0.662(age, years) – 0.800(fat mass, kg) + 1.344(lean mass, kg) + 2.278(total volume x 10-3) – 144.846 (R2 = 0.751, p \u3c 0.0001, SEE=29.7 kcal); cross-validation net kcal = 0.515(height, cm) - 0.520(age, years) - 1.220(fat mass, kg) + 1.995(lean mass, kg) + 2.620(total volume x 10-3) – 59.988 (R2 = 0.823, p \u3c 0.0001, SEE=29.2 kcal). These equations had a cross-validation coefficient of 0.902 and a double cross-validation coefficient of 0.863. CONCLUSION: The strong relationship between the measured and estimated net kcal consumption of both the cross-validation and validation group lead us to conclude that the regression equation derived by this laboratory is valid for estimating net energy expenditure for a total RE bout

Significant Predictors of Nonalcoholic Fatty Liver Disease in Texas Firefighters

Risk factors for Nonalcoholic Fatty Liver Disease (NAFLD) include obesity, hypertension, dyslipidemia, and diabetes mellitus. Not only are these prevalent in the general US population, but they are also present at high rates in a specific subset responsible for public safety – firefighters. PURPOSE: The aim of the present study is to use logistic regression to predict the likelihood of occurrence of NAFLD in firefighters using a subset of health-related factors associated with common cardiometabolic risk factors. METHODS: Data were collected on 136 firefighters (128 males, 8 females; 36.3 ± 9.0 yrs; 95.7 ± 17.0 kg; 178.9 ± 7.4 cm; 29.8 ± 4.2 kg/m2) participating in FITLIFE, a university-based fitness program at Texas A&M University. Nominal logistic regression with stepwise removal was used to estimate the best model to predict fatty liver disease. Stepwise removal identified resting systolic blood pressure (RSBP, mm HG), Body Mass Index (BMI, kg/m2), visceral adipose tissue (VAT, cm2), whether or not has hypertension or is on medication (HTNMED; 0=No,1=Yes), and plasma triglyceride concentrations (TG, mg/dL) as independent predictors (p\u3c0.05). Odds ratios (OR) were calculated to determine the change in the odds of NAFLD per unit increase in each predictor. RESULTS: Logistic regression yielded the following equation to predict the probability of developing NAFLD: Logit = -22.5176 + 0.0918(RSBP) + 0.2154(BMI) + 0.0065(TG) + 0.0161(VAT) + 1.830(HTNMED) (R2 = 0.4655, p \u3c 0.001). Of the predictors, the ORs from largest to smallest were 6.235, 1.240, 1.096, 1.016, and 1.002 for HTNMED, BMI, RSBP, VAT, and TG, respectively. CONCLUSION: Using RSBP, BMI, VAT, TG, and HTNMED as predictors, this study demonstrates that the probability of developing NAFLD in Texas firefighters can be reasonably predicted. This regression model and individual predictors may be used by health practitioners as a cost-effective screening tool to identify those at higher risk for NAFLD

Effects of an Acute Strength and Conditioning Training Session on Dual Energy X-ray Absorptiometry Results

In the use of dual x-ray absorptiometry (DXA) scans to obtain reliable measures of body composition, athletic staff must be aware of acute factors that may alter scan estimates. Although factors such as hydration status and food intake have been shown to alter DXA results (Tinsley, MSSE 2016), it is unknown whether an acute strength and conditioning (S&C) session will alter DXA scan estimates. PURPOSE: To determine if a strength and conditioning (S&C) training session, based upon what athletes regularly engage in, will alter body composition estimates (lean mass, fat mass, and bone mineral content) of a DXA scan. METHODS: The S&C session lasted ~ 90 minutes and consisted of upper and lower body resistance exercises and interval running. Twenty-two strength-trained subjects (15 men, 7 women, age 24 ± 2 yrs, height 174.2 ± 8.5 cm, weight 83.5 ± 15.0 kg) volunteered to participate in the study. A food log was distributed during the informed consent process, which participants maintained for 24 hours prior to the DXA scans. Each subject completed two standard DXA scans on the same day, before and within 45 minutes of completing the S&C session. Participants were instructed to consume a normal, free-living breakfast prior to the first scan, and to then avoid all food intake until completing the second scan. Throughout the S&C session, subjects were encouraged to drink water ad libitum. RESULTS: No significant difference was found on any of the total body measures between pre and post DXA body composition measurements except for total mass, which was found to be lower after the S&C session (pre to post: 83.8-83.5 kg). Compartmental results showed significant differences between pre and post scans in the arms, legs, and trunk. Arm and leg % fat were found to be lower (pre to post: arm % fat 20.5-19.9, leg % fat 23.2-22.6); arm total and lean mass, and leg lean mass were found to be higher (pre to post: arm total mass 10.8-11.0 kg, arm lean mass 8.3-8.5 kg, leg lean mass 21.5-21.8 kg); and trunk lean mass was found to be lower (pre to post: 28.7-28.2 kg) after the S&C session. CONCLUSION: Based on the results of the present study, the acute physiological effects of a S&C session alter body composition measures obtained by DXA scan. Thus, athletic staff should consider the timing of DXA scans in relation to S&C sessions