Examining weight bias attitudes and obesity beliefs among undergraduate students.

Objective: This cross-sectional study examined the weight bias attitudes and obesity beliefs of health science (HS), nursing, and pre-medicine undergraduate students. Methods: Using snowballing and convenience sampling strategies, students (N = 139) completed an online survey, including a 24-item Antifat Attitudes Scale (AFAS) and eight-item Belief About Obese Persons (BAOP) scale. Results: HS students have higher weight bias than nursing and pre-medicine students combined (M = 43.45, SD = 10.75), t(137) = -2.45, p = .016). A negative correlation exists between AFAS and BAOP suggesting high weight bias influences a belief that obesity is controllable. Gender positively predicted weight bias attitudes (ß = -11.43, t = -4.33, p < .001) and obesity beliefs (ß = 3.75, t = 3.01, p = . 003). Conclusions: Findings confirm that HS students have weight bias attitudes. This supports undergraduate curricular changes on obesity etiology that may improve treatment plans of individuals who are obese.

Physiological Performance Measures as Indicators of CrossFit® Performance.

CrossFit® began as another exercise program to improve physical fitness and has rapidly grown into the "sport of fitness". However, little is understood as to the physiological indicators that determine CrossFit® sport performance. The purpose of this study was to determine which physiological performance measure was the greatest indicator of CrossFit® workout performance. Male (n = 12) and female (n = 5) participants successfully completed a treadmill graded exercise test to measure maximal oxygen uptake (VO2max), a 3-minute all-out running test (3MT) to determine critical speed (CS) and the finite capacity for running speeds above CS (D'), a Wingate anaerobic test (WAnT) to assess anaerobic peak and mean power, the CrossFit® total to measure total body strength, as well as the CrossFit® benchmark workouts: Fran, Grace, and Nancy. It was hypothesized that CS and total body strength would be the greatest indicators of CrossFit® performance. Pearson's r correlations were used to determine the relationship of benchmark performance data and the physiological performance measures. For each benchmark-dependent variable, a stepwise linear regression was created using significant correlative data. For the workout Fran, back squat strength explained 42% of the variance. VO2max explained 68% of the variance for the workout Nancy. Lastly, anaerobic peak power explained 57% of the variance for performance on the CrossFit® total. In conclusion, results demonstrated select physiological performance variables may be used to predict CrossFit® workout performance.