Abstract
INTRODUCTION & OBJECTIVES: Nutrition knowledge of an athlete, as well as practice, is expected to influence athlete's performance. The study assessed the nutrition knowledge and practice as well as athletes' performance and identified the factors predicting the athletes' performance. METHODOLOGY: A cross-sectional survey, involved 110 purposively selected undergraduate athletes (47 females, 63 males) of University of Ibadan, Nigeria, between July 2013 and December 2013. A semi-structured, self-administered questionnaire assessed the nutrition knowledge and practice. 24-hr diet recall and food frequency questionnaire were done. Anthropometric measurements were taken; body composition was determined by bioelectrical impedance analysis method. Handgrip strength (HGS), as an indirect measure of athlete performance, was assessed with the hand dynamometer. Chi-square and t-test analysis were used for the bivariate analysis. Pearson correlation and simple linear regression were used to determine relationships and predict athletic performance. The level of statistical significance was p<0.05. RESULTS: More than half (58.2%) had good nutrition knowledge (NK), and 62.7% had good nutrition practices (NP). Majority (75.4%) had normal handgrip strength (HGS). More than 70.0% frequently do not consume cereals, roots and tubers, fruits and vegetables, legumes /nuts. About 30.0-40.0% frequently do not consume eggs/milk, meat/fish. Having good NK was significantly associated with good NP (χ2=15.520, p=0.000), but not with athlete's performance (HGS). There is no significant correlation between NK, NP, and HGS. There is a significant positive correlation between HGS and lean muscle mass (LMM) (r=.670, p=0.000), weight (r=.492, p=0.000), height (r=.521, p=0.000) and energy intake (r=.386, p=0.000). There is a significant negative correlation between HGS and percentage body fat (r=-.400, p=0.000). Athletes' performance was significantly predicted by the resting metabolic rate (beta=.454 C.I=0.011 to 0.045, p=0.003), Lean muscle mass (beta=.297 C.I=.059 to 0.562, p=0.024) and the weight (beta=.228, C.I=1.852 to .489, p=0.047). CONCLUSION: Having good nutrition knowledge or practice did not directly determine athletic performance. However, there is the need for nutrition education interventions, to improve athlete's performance by promoting adequate energy intake, lean muscle mass and appropriate weight gain in athletes.