Performance of the Global Diet Quality Score (GDQS) App in Predicting Nutrient Adequacy and Metabolic Risk Factors among Thai Adults.

BACKGROUND: The Global Diet Quality Score (GDQS) was developed for monitoring nutrient adequacy and diet-related noncommunicable disease risk in diverse populations. A software application (GDQS app) was recently developed for the standardized collection of GDQS data. The application involves a simplified 24-h dietary recall (24HR) where foods are matched to GDQS-food groups using an onboard database, portion sizes are estimated at the food group level using cubic models, and the GDQS is computed. OBJECTIVES: The study aimed to estimate associations between GDQS scores collected using the GDQS app and nutrient adequacy and metabolic risks. METHODS: In this cross-sectional study of 600 Thai males and nonpregnant/nonlactating females (40-60 y), we collected 2 d of GDQS app and paper-based 24HR, food-frequency questionnaires (FFQs), anthropometry, body composition, blood pressure, and biomarkers. Associations between application scores and outcomes were estimated using multiple regression, and application performance was compared with that of metrics scored using 24HR and FFQ data: GDQS, Minimum Dietary Diversity-Women, Alternative Healthy Eating Index-2010, and Global Dietary Recommendations score. RESULTS: In covariate-adjusted models, application scores were significantly (P < 0.05) associated with higher energy-adjusted mean micronutrient adequacy computed using 24HR (range in estimated mean adequacy between score quintiles 1 and 5: 36.3%-44.5%) and FFQ (Q1-Q5: 40.6%-44.2%), and probability of protein adequacy from 24HR (Q1-Q5: 63%-72.5%). Application scores were inversely associated with BMI kg/m2 (Q1-Q5: 26.3-24.9), body fat percentage (Q1-Q5: 31.7%-29.1%), diastolic blood pressure (Q1-Q5: 84-81 mm Hg), and a locally-developed sodium intake score (Q1-Q5: 27.5-24.0 points out of 100); positively associated with high-density lipoprotein cholesterol (Q1-Q5: 49-53 mg/dL) and 24-h urinary potassium (Q1-Q5: 1385-1646 mg); and inversely associated with high midupper arm circumference (Q5/Q1 odds ratio: 0.52) and abdominal obesity (Q5/Q1 odds ratio: 0.51). Significant associations for the application outnumbered those for metrics computed using 24HR or FFQ. CONCLUSIONS: The GDQS app effectively assesses nutrient adequacy and metabolic risk in population surveys.

Low Sleeping Time, High TV Viewing Time, and Physical Inactivity in School Are Risk Factors for Obesity in Pre-Adolescent Thai Children.

OBJECTIVE: Explore the association between physically active behavior and obesity in 7- to 12-years-old Thai children. MATERIAL AND METHOD: As part of SEANUTS Thailand, information on anthropometry, physical activity, and sociodemographic variables were collected in 7- to 12-years-old urban and rural Thai children. Multi-stage sampling technique was used and 1,345 children (32% urban, and 50.3% boys) participated in the study. Anthropometric measurements included weight, height, and BMI-for-age Z-scores (BAZ) using World Health Organization Growth Reference. Obesity was defined as BAZ > 2SD. Physical activity was assessed using a validated physical activity questionnaire (PAQ). The PAQ provided an activity score, activity time in school, sleeping hours, and TV watching time as categorical variable, low, moderate, and high. Chi-square by likelihood ratio test and logistic regression were used to compare obese and non-obese groups. RESULTS: The overall prevalence of overweight and obesity was 10.2 and 10.8% respectively, whereas 8.2% was classified as thin. Maternal education and religion did not differ between obese and non-obese children. However, obese children's family income was higher. After controlling for family income, maternal education, and religion, obese children were significantly less active during break times in school, slept less, and watched more TV than non-obese. However, there was no difference in the activity score of obese and non-obese children. CONCLUSION: The study showed that physical activity during break time in school, sleep duration, and hours of TV viewing were associated with obesity in pre-adolescent Thai children. It is important to note that activity score was not associated with obesity. One of the most important benefits to be physically active in childhood is the potential to maintain this behavior into adulthood. Therefore, programs that encourage healthy behaviors and address these modifiable risk factors should be incorporated in the school curriculum.

Multicompartment model comparison of body fat assessment in Thai adolescents.

OBJECTIVE: To examine the limits of agreement of percent body fat (%BF) assessed by different compartment models (2C and 3C compared to 4C). MATERIAL AND METHOD: Fifty-one healthy Thai adolescents (25 males and 26 females) aged 16 to 19 years volunteered in the present study. Underwater weighing (UWW) and dual energy X-ray absorptiometry (DEXA) were used for measurement of %BF for 2C and 3C models. UWW was also used for body density, DEXA for bone mineral content and protein content, and deuterium oxide dilution method for total body water (TBW), used in Lohman's equation for 4C model. RESULTS: Body density total body water; bone mineral density, and fat free mass were significantly higher in males than in females, whereas females had significantly higher fat than males (p < 0.001). Compared to %BF4C, %BF(UWW) did not show significant deviations from the line of identity in males and females (R2 = 0.85 and 0.75 respectively), whereas %BF(DEXA) showed significant deviations from the line of identity in females (R2 = 0.59), but not in males (R2 = 0.60). Bland & Altman analysis demonstrated that UWW and DEXA tended to underestimate %BF in leaner adolescents and overestimate %BF in fatter adolescents. CONCLUSION: Percent body fat using underwater weighing (2C model) may be used interchangeable with the 4C model in both genders. However; DEXA (3C model) can only be used in males and not in females, which require further research. A regression equation to relate 2C and 3C models to 4C was developed to enable a better estimation of percent body fat in Thai adolescents.

Study on different sites of waist circumference and its relationship to weight-for-height index in Thai adolescents.

OBJECTIVE: To compare waist circumference (WC) measured at different sites of trunk region and to determine predictive WC values that were corresponding to weight-for-height index in Thai adolescents. MATERIAL AND METHOD: The authors conducted the cross-sectional study in 509 adolescents, aged 10-18 years old WC was measured at four different sites of trunk region, WC1; at midway between the lowest rib and the iliac crest, WC2; at the narrowest waist, WC3; at immediately above the iliac crest and WC4; at the umbilicus level. Receiver operating characteristic analysis was also performed to determine WC cutoffs to maximize the sensitivity and specificity. RESULTS: WC measured at all four sites provided small different powerful value in prediction of trunk fat and total body fat (TBF) in adolescents and that WC4 provided slightly better predictive value than other WC. In boys, WC provided better prediction of trunk fat than the prediction of TBF, whereas in girls, the prediction of trunk fat and TBF from WC were of similar magnitude. By receiver operating characteristic analysis, WC risk threshold for predicting the overweight adolescents using Thai weight-for-height Z score > or = 1.5 SD as reference was 73.5 cm for boys (sensitivity 96.8%, specificity 85.7%) and 72.3 cm for girls (sensitivity 96.1%, sensitivity 80.5%). WC threshold was increased to 75.8 cm. (sensitivity 96.3.%, specificity 86.4%) for boys and 74.6 cm for girls (sensitivity 95.1%, specificity 85.7%) in order to detect the obese children. CONCLUSION: Waist circumference has been proposed as the simple tool for screening the overweight adolescents and when measured at the umbilicus level, it is considered the feasible site for self-evaluation. Further study is needed to investigate the relationship between the increased WC and metabolic risk factors for obesity in adolescents.