METHODS: Fifty-two females (21.43 ± 4.8 years) were divided into "normal" (BMI = 18-24.9 kg/m2) and "high" (BMI ≥ 25 kg/m2) BMI groups. Participants wore pedometers throughout the day for nine weeks. Pre-post intervention tests performed on anthropometric, biochemical, and nutrient intake variables were tested at p ≤ 0.05.
RESULTS: Participants walked 7056 ± 1570 footsteps/day without a significant difference between normal (7488.49 ± 1098) and high (6739.18 ± 1793) BMI groups. After week 9, the normal BMI group improved significantly in BMI, body fat mass (BFM), and waist-hip ratio (WHR). Additionally, percent body fat, waist circumference (WC), and visceral fat area also reduced significantly in the high BMI group. A significant decrease in triglycerides (TG) (71.62 ± 29.22 vs. 62.50 ± 29.16 mg/dl, p=0.003) and insulin (21.7 ± 8.33 µU/l vs. 18.64 ± 8.25 µU/l, p=0.046) and increase in HMW-Adip (3.77 ± 0.46 vs. 3.80 ± 0.44 μg/ml, p=0.034) were recorded in the high BMI group. All participants exhibited significant inverse correlations between daily footsteps and BMI (r=-0.33, p=0.017), BFM (r=-0.29, p=0.037), WHR (r=-0.401, p=0.003), and MetS score (r=-0.49, p < 0.001) and positive correlation with HMW-Adip (r=0.331, p=0.017). A positive correlation with systolic (r=0.46, p=0.011) and diastolic (r=0.39, p=0.031) blood pressures and inverse correlation with the MetS score (r=-0.5, p=0.005) were evident in the high BMI group.
CONCLUSION: Counting footsteps using a pedometer is effective in improving MetS components (obesity, TG) and increasing HMW-Adip levels.
Methods: a cross-sectional study was conducted, using the Kedah audit samples data extracted from the National Diabetes Registry (NDR) from the year 2014 to 2018. A total of 25,062 registered type 2 diabetes mellitus patients were selected using the inclusion and exclusion criteria from the registry. Only patients with complete data on their HbA1C, lipid profile, waist circumference and BMI were analysed using SPSS version 21.
Results: the means for the age, BMI and waist circumference of the samples were 61.5 (±10.85) years, 27.3 (±5.05) kg/m2 and 89.46 (±13.58) cm, respectively. Poor glycaemic control (HbA1c>6.5%) was observed in 72.7% of the patients, with females having poorer glycaemic control. The BMI and waist circumference were found to be significantly associated with glycaemic control (P<0.001). The total cholesterol, triglycerides and low-density lipoproteins values showed positive correlation with glycaemic control (r = 0.178, 0.157, 0.145, p<0.001), while high-density lipoproteins values are negatively correlated (r = -0.019, p<0.001).
Conclusion: implementing lifestyle changes such as physical activity and dietary modifications are important in the management of BMI, waist circumference and body lipids, which in turn results in improved glycaemic control.
OBJECTIVES: To examine: (i) the relationships between sleep characteristics, including social jetlag, and obesity-related outcomes during childhood, and (ii) whether these relationships are moderated by sex.
METHODS: This cross-sectional study included 381 children aged 9-11 years (49.6% female). Average sleep duration, social jetlag, and physical activity were assessed via wrist-worn accelerometry. Sleep disturbances were quantified from the Children's Sleep Habits Questionnaire. Obesity-related outcomes included age-specific body mass index Z-scores (zBMI) and waist-to-height ratio. Additionally % fat, total fat mass, and fat mass index were assessed via bioelectrical impedance analysis. Linear mixed models that nested children within schools were used to identify relationships among sleep characteristics and obesity-related outcomes.
RESULTS: Positive associations between social jetlag with zBMI, % fat, and fat mass index were seen in univariable and unadjusted multivariable analyses. Following adjustments for known confounders, social jetlag remained significantly associated with zBMI (β = 0.12, p = 0.013). Simple slopes suggested a positive association in girls (β = 0.19, p = 0.006) but not in boys (β = 0.03, p = 0.703).
CONCLUSIONS: Obesity prevention efforts, particularly in girls, may benefit from targeted approaches to improving the consistency of sleep timing in youth.
METHODS: Two cross-sectional studies were conducted in urban and rural areas of Yangon Region in 2013 and 2014 respectively, using the WHO STEPwise approach to surveillance of risk factors of NCDs. Through a multi-stage cluster sampling method, 1486 participants were recruited.
RESULTS: Age-standardized prevalence of the behavioral risk factors tended to be higher in the rural than urban areas for all included factors and significantly higher for alcohol drinking (19.9% vs. 13.9%; p = 0.040) and low fruit & vegetable consumption (96.7% vs. 85.1%; p = 0.001). For the metabolic risk factors, the tendency was opposite, with higher age-standardized prevalence estimates in urban than rural areas, significantly for overweight and obesity combined (40.9% vs. 31.2%; p = 0.023), obesity (12.3% vs.7.7%; p = 0.019) and diabetes (17.2% vs. 9.2%; p = 0.024). In sub-group analysis by gender, the prevalence of hypercholesterolemia and hypertriglyceridemia were significantly higher in urban than rural areas among males, 61.8% vs. 40.4%; p = 0.002 and 31.4% vs. 20.7%; p = 0.009, respectively. Mean values of age-standardized metabolic parameters showed higher values in urban than rural areas for both male and female. Based on WHO age-standardized Framingham risk scores, 33.0% (95% CI = 31.7-34.4) of urban dwellers and 27.0% (95% CI = 23.5-30.8) of rural dwellers had a moderate to high risk of developing CHD in the next 10 years.
CONCLUSION: The metabolic risk factors, as well as a moderate or high ten-year risk of CHD were more common among urban residents whereas behavioral risk factors levels were higher in among the rural people of Yangon Region. The high prevalences of NCD risk factors in both urban and rural areas call for preventive measures to reduce the future risk of NCDs in Myanmar.