1Department of Sports Science, Sports Authority of India, Salt Lake City, Kolkata, India
2Sports Authority of India, New Delhi, India
3University of Calcutta, University Colleges of Science and Technology, Sports and Exercise Physiology Laboratory, Department of Physiology, Kolkata, India
4Department of Physiology, Rammohan College, Kolkata, India
5Sports Authority of India, Salt Lake City, Kolkata, India
Prediction of Athletic Performance through Nutrition Knowledge and Practice: A Cross-Sectional Study among Young Team Athletes
The present study was conducted to assess the nutrition knowledge, practice, and status and to identify the nutritional and body composition factors predicting athletes’ performance. Young team athletes including 40 footballers and 50 hockey players were recruited in this study (age 16.48±1.5) to assess the nutrition knowledge (NK), nutrition practice (NP), and 24-hour dietary recall using a semi-structured questionnaire. Physical characteristics, including height, weight and body mass index (BMI), along with static strength- handgrip and relative back strength, were recorded. Fat mass (FM), fat-free mass (FFM), muscle mass (MM), basal metabolic rate (BMR) and glycogen store was determined using a bioelectrical impedance analyser. Aerobic capacity (VO2max) was measured with a beep test. The majority of the athletes with good NK scores were found to have good NP scores as well and vice versa (χ2=23.861, p=0.000). Their mean recorded scores for NK and NP were found to be 11.13±3.6 and 7.30±2.0 out of a total of 20 and 12, respectively. Daily consumption of protein (β=0.336; p value=0.004), sodium (β=0.273; p value=0.006) and dietary fibre (β=0.220; p value=0.002) were found to be the best predictors for nutritional practice. Nutrition knowledge and practice had significant positive correlation with BMR (0.314***; 0.419***), calcium intake (0.248*; 0.482***), iron intake (0.303***; 0.221*) and VO2max (0.331***; 0.428***), respectively. Daily calorie consumption (β=0.144, p=0.029), BMR (β=0.304, p<0.001***), MM (β=0.213, p=0.020), calcium (β=0.275, p=0.001) and iron intake (β=0.240, p=.001) were the significant predictors of athletic performance. Therefore, good nutrition knowledge may improve the nutritional habits and dietary pattern of athletes. Body composition and nutrient intake can predict athletic performance. Intervention studies should emphasize nutrition education aiming for improved athletic performance.
basal metabolic rate, bioelectrical impedance analysis, body composition, dietary pattern, aerobic capacity
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