Gender Differences in Body Measurement Index in Athletic Trainers

Background: Athletic Trainers are healthcare workers that care for a niche population, the physically active. It could be hypothesized that since these sports medicine professionals work with the physically active, the provider is more likely to be physically healthy due to the population they work with. Though it is ideal to maintain a healthy weight, many factors can influence these variables, including athletic trainers. Innate factors involve genetics, gender, age, or developmental and lack voluntary control. Behavioral factors such as activity level, stress, medication, and diet can significantly impact weight and adipose tissue mass as well, but unlike the innate factors, can potentially be altered. Additionally, any of these behavioral factors can cause fluctuations in weight. Body Mass Index (BMI) is the most common and easiest objective form of measurement for determining obesity levels. Increased BMI can lead to morbidities such as type 2 diabetes, hypertension, heart disease, etc. The effects of obesity do not discriminate. Though there is considerable research on gender and BMI, and BMI in general, but there are few studies relating athletic trainer gender and BMI.

Methods: Cross-sectional design with certified athletic trainers (ATs). (males=96, females=165). An “e-blast” email was sent to 2500 members of the National Athletic Training Association (NATA) across the US. This study was part of a larger study identifying health behaviors in ATs. The survey included multiple sections: nutrition, eating disorder, physical activity assessment, and a demographics section that contained questions on height (ft) and weight (lbs) to calculate BMI. A one-way ANOVA was calculated for gender and BMI with an alpha of 0.05.

Results: The ANOVA demonstrated significant results. Because normality and homogeneity of variance were violated for all ANOVAs, most likely due to unequal sample sizes, Kruskal-Wallis tests, represented by a chi-square statistic (X2), were used as modifications. X2(3) = 12.06, < 0.05. A Games-Howell post hoc test demonstrated males were more likely to have a greater BMI, MD = 1.64, < 0.05.

Conclusions: Our data suggests that males ATs are more likely to have a greater BMI than female ATs. This may be a result of male stereotypes such as having a well-built and muscular physique. Additionally, males lack the negative views that females have toward obesity. Because of this negative view, females will spend more time, effort, and money on the becoming or staying thin, and therefore having a lower BMI.