METHODS: Using a cross-sectional study design, body weight and height were measured, and BMI was calculated and classified using WHO BMI-for-age Z-score. Data was obtained using the National Fitness Standard (SEGAK) assessment, which was uploaded in a specific Health Monitoring System (HEMS).
RESULTS: From a total of 62,567 school adolescents, 50.7% were boys and 49.3% were girls. Girls had significantly higher BMI than boys in age groups of 13 to 15 and 16 to 17 years old. Among boys and girls, there were significant differences in mean BMI of school adolescents between rural and urban school locations in all age groups (p
METHODS: Out of the 7247 students in the ten selected schools studied, a total of 6248 students (2928 males, 3320 females) took part. A validated self-administered questionnaire was used. Data was analysed using SPSS version 22. Multivariable logistic regression was used to determine the adjusted odd ratio.
RESULTS: The prevalence of overweight and obesity was 16.0% and 11.5% respectively. Obesity/overweight was significantly (p<0.05) associated with gender, age, ethnicity, education level of father, education level of mother, physical activity, disordered eating, smoking status, body size perception and body part satisfaction. The multivariable analysis results showed that the odds of being overweight/obesity were higher in males compared to females (OR 1.56, 95%CI: 1.37, 1.77). The results also showed that the odds of being overweight/obesity were highest among those in age group 12 and 13 years and among Malay ethnicity. The odds of overweight/obesity were higher in those who was dissatisfied with their body parts, (OR 1.96, 95%CI: 1.71, 2.25), dissatisfied with their body size (OR: 4.25, 95%CI: 3.60, 5.02), low physical activity (OR 1.23, 95%CI: 1.06, 1.44), current smokers (OR 1.38, 95%CI: 1.07, 1.78) and at risk of having eating disorder (OR: 1.39, 95%CI 1.22, 1.59).
CONCLUSION: The overall prevalence of overweight and obesity is high. The findings from this study can be used by policy makers to plan an integrated intervention program in schools.
METHODS AND ANALYSIS: The target participants are infants born from January to June 2015 in the South East Asia Community Observatory (SEACO) platform. The SEACO is a Health and Demographic Surveillance System (HDSS) that is established in the District of Segamat in the state of Johor, Malaysia. For the quantitative strand, the sociodemographic data, feeding practices, anthropometry measurement and total nutrient intake will be assessed. The assessment will occur around the time complementary feeding is expected to start (7 Months) and again at 12 months. A 24-hour diet recall and a 2-day food diary will be used to assess the food intake. For the qualitative strand, selected mothers will be interviewed to explore their infant feeding practices and factors that influence their practices and food choices in detail.
ETHICS AND DISSEMINATION: Ethical clearance for this study was sought through the Monash University Human Research and Ethics Committee (application number CF14/3850-2014002010). Subsequently, the findings of this study will be disseminated through peer-reviewed journals, national and international conferences.
DESIGN: Data on sociodemographic background were obtained from parents. Height and weight were measured and BMI-for-age was determined. Adolescents were interviewed on their habitual dietary intakes using a semi-quantitative FFQ. Cognitive ability was assessed using the Wechsler Nonverbal Scale of Ability in a one-to-one manner. Dietary patterns were constructed using principal component analysis based on thirty-eight food groups of the semi-quantitative FFQ.
SETTING: Urban secondary public schools in the district of Gombak in Selangor, Malaysia.
SUBJECTS: Malay adolescents aged 12 to 13 years (n 416).
RESULTS: The mean general cognitive ability score was 101·8 (sd 12·4). Four major dietary patterns were identified and labelled as 'refined-grain pattern', 'snack-food pattern', 'plant-based food pattern' and 'high-energy food pattern'. These dietary patterns explained 39·1 % of the variance in the habitual dietary intakes of the adolescents. The refined-grain pattern was negatively associated with processing speed, which is a construct of general cognitive ability. The high-energy food pattern was negatively associated with general cognitive ability, perceptual reasoning and processing speed. Monthly household income and parents' educational attainment were positively associated with all of the cognitive measures. In multivariate analysis, only the high-energy food pattern was found to contribute significantly towards general cognitive ability after controlling for socio-economic status.
CONCLUSIONS: Consumption of foods in the high-energy food pattern contributed towards general cognitive ability after controlling for socio-economic status. However, the contribution was small.
STUDY DESIGN: We assessed data from 6414 children aged 6-18 years, collected by the South East Asia Community Observatory. Child underweight, overweight, and obesity were expressed according to 3 internationally used BMI references: World Health Organization 2007, International Obesity Task Force 2012, and Centers for Disease Control and Prevention 2000. We assessed agreement in classification of anthropometric status among the references using Cohen's kappa statistic and estimated underweight, overweight, and obesity prevalence according to each reference using mixed effects Poisson regression.
RESULTS: There was poor to moderate agreement between references when classifying underweight, but generally good agreement when classifying overweight and obesity. Underweight, overweight, and obesity prevalence estimates generated using the 3 references were notably inconsistent. Overweight and obesity prevalence estimates were higher using the World Health Organization reference vs the other 2, and underweight prevalence was up to 8.5% higher and obesity prevalence was about 4% lower when using the International Obesity Task Force reference.
CONCLUSIONS: The choice of reference to express BMI may influence conclusions about child anthropometric status and malnutrition prevalence. This has implications regarding strategies for clinical management and public health interventions.
METHODS: We used data from health and demographic surveillance conducted by the South East Asia Community Observatory in Segamat, Malaysia. Analyses included 9207 individuals (4806 children, 2570 mothers and 1831 fathers). Child obesity was defined based on the World Health Organization 2007 reference. We assessed the relation between parental anthropometric (overweight, obesity and central obesity) and cardiometabolic (systolic hypertension, diastolic hypertension and hyperglycaemia) risk factors and child obesity, using mixed effects Poisson regression models with robust standard errors.
RESULTS: We found a high burden of overweight and obesity among children in this population (30% overweight or obese). Children of one or more obese parents had a 2-fold greater risk of being obese compared with children of non-obese parents. Sequential adjustment for parental and child characteristics did not materially affect estimates (fully adjusted relative risk for obesity in both parents: 2.39, 95% confidence interval: 1.82, 3.10, P
METHODS: Data were obtained from the 2012 Malaysia Global School-based Student Health Survey. Generalized ordered logit regression analysis was conducted on 24 339 adolescents by PA status.
RESULTS: Early- (ages 11-13) and middle-stage (ages 14-16) adolescents were associated with higher overweight and obesity risks than their older peers (ages 17-18). Male adolescents faced higher underweight and obesity likelihoods than females. Hunger due to food shortage at home was associated with higher likelihoods of underweight and normal weight BMI categories. Smokers were more likely to be underweight or normal weight than non-smokers. Segmented-sample analysis by PA status indicated that, while the direction of associations was parallel across PA status, the magnitudes of association between age, hunger and smoking status with BMI status were greater among active than inactive adolescents.
CONCLUSIONS: Male adolescents faced a dual burden of underweight and obesity. Other sociodemographic and dietary-lifestyle factors were associated with adolescent BMI categories. Segmented-sample analysis by PA status uncovered varying associations between factors that would otherwise be masked in pooled sample analysis. Public health authorities should take these factors into consideration when deliberating programs to ensure healthy adolescent body weight.
METHODS: This is a cross-sectional comparison study whereby 225 overweight/obese children matched for age, sex, and ethnicity with 225 normal weight children participated in this study. Body image dissatisfaction, disordered eating, and depressive symptoms were assessed through a self-administered questionnaire. Blood pressure was measured, whereas blood was drawn to determine insulin, high-sensitivity C-reactive protein (hs-CRP), glucose, and lipid profiles. Homeostasis model assessment-estimated insulin resistance (HOMA-IR) was calculated using glucose and insulin levels. Wechsler's Intelligence Scale for Children-Fourth Edition (WISC-IV) was used to assess cognitive function in children. Ordinary least square regression analysis was conducted to determine the direct and indirect relationships between weight status and cognitive function.
RESULTS: A negative relationship was found between overweight/obesity with cognitive function. Overweight/obese children were on average 4.075 units lower in cognitive function scores compared to normal weight children. Such difference was found through mediators, such as body image dissatisfaction, disordered eating, depression, systolic blood pressure, triglycerides, HOMA-IR, and hs-CRP, contributing 22.2% of the variances in cognitive function in children.
CONCLUSION: Results highlight the important mediators of the relationship between overweight/obesity and cognitive function. Consequently, future interventions should target to improve psychological well-being and reduce cardiovascular disease risk for the prevention of poorer cognitive performance in overweight/obese children.