Body Mass Index Superior to Body Adiposity Index in Predicting Adiposity in Female Collegiate Athletes.

International Journal of Exercise Science 16(4): 1487-1498, 2023. Body mass index (BMI) is moderately correlated with %Fat and often used to assess obesity in athletes. Limited research assesses BMI as a surrogate for %Fat in female collegiate athletes. Body Adiposity Index (BAI) is an anthropometric measurement suggested to be superior to BMI at predicting adiposity but has not been well assessed within female athletic populations. This study aimed to determine if BAI is superior to other anthropometric indices to predict %Fat in female collegiate athletes and college-aged female non-athletes. Collegiate female athletes and female non-athletes were invited into the laboratory for anthropometrics and %Fat measurements via BOD POD. BAI was calculated as Hip Circumference/Height1.5 – 18. Eighty-eight female non-athletes and 72 female athletes from soccer (n = 27), softball (n = 28), and basketball (n = 17) completed the study. Using BMI, 19% of non-athletes had a false positive (FP). Sensitivity of BMI in non-athletes was 85.5%, while specificity was 73%. 16% of athletes had a FP. Sensitivity of BMI within athletes was 100%, specificity was 81%. BMI outperformed BAI in athletic (BMI: r = .725, p \u3c .001; BAI: r = .556, p \u3c .001) and nonathletic (BMI: r = .650, p \u3c .001; BAI: r = .499, p \u3c .001) groups. The strongest anthropometric predictor of %Fat within the non-athlete population was BMI (r2 = .42, p \u3c .001). Waist circumference was the strongest predictor in the athletic population (r2 = .62, p \u3c .001). BMI outperformed BAI in its ability to predict %Fat

Effect of Sustained COVID-19 Guidelines on Eating Behaviors and Weight Gain

PURPOSE: Prior research examining the impact of short-term self-quarantine indicates altered eating behaviors and weight change. Roughly ten months after the first lockdown was announced, the COVID-19 pandemic continues without abatement. The purpose of this study was to assess the impact of long-term COVID-19 guidelines on eating behaviors and weight change. METHODS: A research announcement was sent out via Facebook to 1200 possible participants. The Weight and Lifestyle Inventory (WALI) was used to assess possible changes in factors that contribute to eating. Participants were also asked to report weight before the pandemic and at the time of the study. Weight categories were created, and ordinal regression modeling was used to assess the impact of weight gain risk factors. RESULTS: One hundred and fifty-seven participants (40 male, 117 female) completed the surveys. Participants were 30.3 ± 13.5 years old with a BMI of 27.7 ± 7.4kg/m2. The average weight change was -1.9 ± 10.2 lbs. Thirty-three percent reported weight loss, while 27% reported weight gain. Besides, eating at breakfast and eating when tired, all eating behaviors were statistically related to weight gain (p \u3c .05). When all eating related factors were placed into a regression model, the only two still significant were ‘snacking after dinner’ (OR: 1.153, B: .142, p = .041, 95 CI: 1.006 – 1.321) and ‘eating too much food’ (OR: 1.333, B .288, p = .004, 95 CI: 1.095 – 1.623). CONCLUSION: The current study reported a mean weight loss of 1.9 pounds. However, the standard deviation was 10 pounds. Meaning, individual heterogeneity of the COVID bodyweight response is evident and appears to be partially explained by snacking after dinner and overeating

Anthropometric Predictors of Arterial Stiffness when Adjusting for Fitness in College-Aged Adults

PURPOSE: Cardiovascular Disease (CVD) is the largest cause of non-communicable disease death worldwide. Arterial stiffness is an independent predictor of CVD. Body mass index (BMI), waist circumference (WC), and waist-to-hip ratio predict arterial stiffness. However, there is inconstancy in the literature as to which is the best predictor of arterial stiffness. Measured cardiovascular fitness is also an independent predictor of arterial stiffness and is rarely controlled for in epidemiological studies. The purpose of this study was to identify the superior anthropometric predictor of arterial stiffness after controlling for measured fitness. METHODS: Healthy young adults were recruited from Grand Canyon University. Subjects came to the lab for one visit and had anthropometric measures of height, weight, WC, and hip circumference measured. Additionally, aortic blood pressure (BP), augmentation pressure (AP), augmentation index adjusted at a heartrate of 75 (Aix@75), carotid-femoral pulse wave velocity (cfPWV), and a VO2peak test were completed. RESULTS: 210 participants aged 20.8 ± 3.1 yr with a BMI of 25.3 ± 3.8 kg/m2 and a VO2peak of 36.2 ± 8.6 mL.kg.-1min-1 completed this study. Hierarchical regression analysis was run with age, gender, and VO2peak entered into the first block, and the anthropometric variables entered into the second block. The addition of BMI significantly explained 4.2% (p = 0.03) more variance in predicting central SBP and 6% (p = 0.001) more variance when explaining cfPWV. The addition of WC significantly explained 2.5% (p = 0.001) more variance in central systolic BP and 5% more variance in cfPWV (p = 0.002). The addition of waist-to-hip ratio explained 4.8% more variance in predicting cfPWV (p = 0.003). CONCLUSION: In conclusion, after accounting for measured fitness, it appears that BMI, WC, and waist to hip ratio all predict arterial stiffness. BMI explained the greatest amount of variance in predicting arterial stiffness