Development of an urban HDSS is possible and is beneficial in providing data complementary to the existing demographic and health information system at local, national and global levels.

Background: The improvement in socioeconomic status has led to sedentary life style and more fast food consumption. Whether fast food consumption contributes to obesity in Indonesian adolescents remains unclear.Objective: The study was conducted to estimate the prevalence of obesity and to assess the association between fast food consumption and obesity in junior high school students.Methods: A cross-sectional survey was conducted in 2003 to estimate the prevalence of obesity in adolescent students of Yogyakarta Special Province. Subjects of this survey were junior high school students from urban (n= 4747) and rural areas (n=4602) were included in this survey. To further analyze the association between food consumption and obesity, a sample of 140 obese (of 460 obese students) and 140 non obese students (of 8889 non obese students) was randomly selected. Data on fast food consumption were collected using Food Frequency Questionnaire (FFQ) method from the selected obese and non-obese sample.Results: The prevalence of obesity among adolescent students was 7.9% in urban and 2% in rural areas. There was a significant difference in variety, quantity, and frequency of fast food consumption per month and hereby energy intake from fast food between obese and non-obese adolescent students (p<0.05).Conclusions: The prevalence of obesity in urban area was much higher than that in rural area. Obese students consumed fast food more frequently and more variably than non-obese students.

Background

Rosella-stevia tea consumption can lower the FBG level but not the 2-hour postprandial BG level in prediabetic women.

Background/objectivesMonitoring food intake of patients during hospitalization using simple methods and minimal training is an ongoing problem in hospitals. Therefore, there is a need to develop and validate a simple, easy to use, and quick tool that enables staff to estimate dietary intake. Thus, this study aimed to develop and validate the Pictorial Dietary Assessment Tool (PDAT).Subjects and methodsA total of 37 health care staff members consisting of dietitians, nurses, and serving assistants estimated 130 breakfast and lunch meals consumed by 67 patients using PDAT. PDAT was developed based on the hospital menu that consists of staple food (rice or porridge), animal source protein (chicken, meat, eggs, and fish), and non-animal source protein (tau fu and tempeh), with a total of six pictorials of food at each meal time. Weighed food intake was used as a gold standard to validate PDAT. Agreement between methods was analyzed using correlations, paired t-test, Bland–Altman plots, kappa statistics, and McNemar’s test. Sensitivity, specificity, and area under the curve of receiver operating characteristic were calculated to identify whether patients who had an inadequate food intake were categorized as at risk by the PDAT, based on the food weighing method. Agreement between different backgrounds of health care staff was calculated by intraclass correlation coefficient and analysis of variance test.ResultsThere was a significant correlation between the weighing food method and PDAT for energy (r=0.919, P<0.05), protein (r=0.843, P<0.05), carbohydrate (r=0.912, P<0.05), and fat (r=0.952; P<0.05). Nutrient intakes as assessed using PDAT and food weighing were rather similar (295±163 vs 292±158 kcal for energy; 13.9±7.8 vs 14.1±8.0 g for protein; 46.1±21.4 vs 46.7±22.3 g for carbohydrate; 7.4±3.1 vs 7.4±3.1 g for fat; P>0.05). The PDAT and food weighing method showed a satisfactory agreement beyond chance (k) (0.81 for staple food and animal source protein; 0.735 for non-animal source protein). Intraclass correlation coefficient ranged between 0.91 and 0.96 among respondents. There were no differences in energy, protein, carbohydrate, and fat intake estimated among health care staff (P=0.967; P=0.951; P=0.888; P=0.847, respectively).ConclusionIn conclusion, PDAT provides a valid estimation of macronutrient consumption among hospitalized adult patients.