Fitness level and body composition indices: cross-sectional study among Malaysian adolescent

Abstract

Background: The importance of fitness level on the well-being of children and adolescent has long been recognised. The aim of this study was to investigate the fitness level of school-going Malaysian adolescent, and its association with body composition indices. Methods: 1071 healthy secondary school students participated in the fitness assessment for the Malaysian Health and Adolescents Longitudinal Research Team (MyHEART) study. Body composition indices such as body mass index for age, waist circumference and waist height ratio were measured. Fitness level was assessed with Modified Harvard Step Test. Physical Fitness Score was calculated using total time of step test exercise and resting heart rates. Fitness levels were divided into 3 categories -unacceptable, marginally acceptable, and acceptable. Partial correlation analysis was used to determine the association between fitness score and body composition, by controlling age, gender, locality, ethnicity, smoking status and sexual maturation. Multiple regression analysis was conducted to determine which body composition was the strongest predictor for fitness. Results: 43.3% of the participants were categorised into the unacceptable fitness group, 47.1% were considered marginally acceptable, and 9.6% were acceptable. There was a significant moderate inverse association (p < 0.001) between body composition with fitness score (r = -0.360, -0.413 and -0.403 for body mass index for age, waist circumference and waist height ratio, respectively). Waist circumference was the strongest and significant predictor for fitness (ß = -0.318, p = 0.002). Conclusion: Only 9.6% of the students were fit. There was also an inverse association between body composition and fitness score among apparently healthy adolescents, with waist circumference indicated as the strongest predictor. The low fitness level among the Malaysian adolescent should necessitate the value of healthy lifestyle starting at a young age.

Prevalence and lifestyle determinants of central obesity in children

“…This fact could be attributed to the beneficial effect of increased meal frequency on appetite regulation, on postprandial metabolic and endocrine responses, as well as on non-excise physical activity, although results of the scientific literature on this topic remain largely controversial and are not so far supported by interventional studies [59][60][61]. Our findings also confirm previous results that physical activity level, cardiorespiratory fitness level and time spent in sedentary activities are associated with the presence of childhood obesity and central obesity [25][26][27][28][29][30][31]. It is noteworthy that in our study, sedentariness was a stronger predictor of childhood central obesity compared to physical activity level, suggesting that besides a lack of physical activity it could also reflect other unhealthy habits.…”

“…television watching, computer use, etc.) have been inversely associated with the likelihood of central obesity in children and adolescents [25][26][27][28][29][30][31]. However, the association between lifestyle habits and the presence of central obesity in childhood remains vague, given that most of the available data come from cross-sectional studies confined to particular geographical areas and with relatively small sample sizes [17-19, 21, 24-26, 29].…”

Physical activity, body composition and lipids changes in adolescents: analysis from the MyHeART Study

“…In addition, reverse correlations between PA level and indicators of obesity (e.g., BMI, waist circumference [WC], and body-fat percentage [BF %]) among Malaysian adolescents suggested that the risk of obesity was greater among those who are physically inactive 7 . Therefore, immediate assessment of trends in PA level, BC indices, and lipid profiles seems vital in preventing the future negative health consequences 9 .…”

Evidence-Based Optimal Cutoff Values with the Validation of Criterion-Referenced Standards for Sarcopenic Elderly Fitness Improvement

“…Hooten et al used multiple variable linear regression for the relation between knee joint kinetics and pressure pain threshold [20]. Multiple regression was used for predictive factors such as physical composition to physical fitness standards [21]. Gorman et al used logistic regression [22], while Duncan et al randomly divided groups into the following quantile ranks according to cardiorespiratory fitness: lower, middle, and high groups, representing the lower twentieth, twentieth to fifty-ninth, and over sixtieth, respectively [23].…”