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
OBJECTIVE: To examine the association of premenopausal and postmenopausal breast cancer risk with dietary carbohydrate, fiber and sugar intake.
MATERIALS AND METHODS: This population based case-control study was conducted in Malaysia with 382 breast cancer patients and 382 controls. Food intake pattern was assessed via an interviewer-administered food frequency questionnaire. Logistic regression was used to compute odds ratios (OR) with 95% confidence intervals (CI) and a broad range of potential confounders were included in analysis.
RESULTS: A significant two fold increased risk of breast cancer among premenopausal (OR Q4 to Q1=1.93, 95%CI: 1.53-2.61, p-trend=0.001) and postmenopausal (OR Q4 to Q1=1.87, 95%CI: 1.03-2.61, p-trend=0.045) women was observed in the highest quartile of sugar. A higher intake of dietary fiber was associated with a significantly lower breast cancer risk among both premenopausal (OR Q4 to Q1=0.31, 95%CI: 0.12-0.79, p-trend=0.009) and postmenopausal (OR Q4 to Q1=0.23, 95%CI: 0.07-0.76, p-trend=0.031) women.
CONCLUSIONS: Sugar and dietary fiber intake were independently related to pre- and postmenopausal breast cancer risk. However, no association was observed for dietary carbohydrate intake.
METHODS: This cross-sectional study involved 1,404 school adolescents aged 12 years (46% boys and 54% girls). Socio-demographic, dietary and physical activity data were collected using questionnaires whilst body weight and height were measured and body mass index was classified based on WHO BMI-for-age Z-scores cut-off.
RESULTS: A multivariable linear regression model showed that BMI z-score was positively associated with parents' BMI (P<0.001), birth weight (P=0.003), and serving size of milk and dairy products (P=0.036) whilst inversely associated with household size (P=0.022). Overall, 13.1% of the variances in BMI Z-scores were explained by parents' BMI, birth weight, servings of milk and dairy products and household size.
CONCLUSION: This study found important determinants of body weight status among adolescents mainly associated with family and home environmental factor. This evidence could help to form the effective and tailored strategies at the earliest stage to prevent obesity in this population.
Aim: The objective of this study was to assess the school environment by interviewing the teachers and compare the school environment score between rural and urban schools in Terengganu, Malaysia.
Methods: Thirty-two teachers from 16 primary schools in Terengganu were interviewed using a set of validated Malay version "School Environmental Mapping" questionnaire. A total of 76 items consisting of four domains of school environment factor: physical (what is available) with 41 items; economic (what the costs are) with nine items; political (what the rules are) with nine items; and socio-cultural (what the attitudes and beliefs are) with 17 items. Every item was questioned using an initial closed question followed by an open question when the criteria were not met or need further information regarding those particular items.
Results: The present study revealed that the school environment of school in state of Terengganu is still low and not satisfied. Based on the schoolteacher's information and observation, there are significant barriers to promoting healthy eating and physical activity at school e.g. limited financial and budget allocation; lack of school facilities; lack of manpower to organise and monitor the programme; lack of participation and cooperation from parents; and no enforcement and serious action from authorized personnel on street hawkers near the school. This is reflected by the score achieved for 16 schools in Terengganu was only 63.05%. The political environment indicated the highest score among the domains, which was 77.78%, whereas, the lowest score was an economic environment (50.00%). Upon comparing between the urban and rural areas, the present study reported that there was a significant difference between school settings (p < 0.001) for an overall school environment, in which the rural areas had a significantly higher score than urban counterparts (64.86% vs 59.34%, p < 0.001). For each domain of the school environment, the findings showed that only two domains (physical and political environment) were significantly different between school settings.
Conclusion: This study revealed that the level of a healthy school environment among schools in both settings is still not satisfied. Addressing the obesogenic elements of school environments is one of the strategies in prevention since the school environments exert a great influence on children's behaviour.