OBJECTIVE: To develop international WC percentile cutoffs for children and adolescents with normal weight based on data from 8 countries in different global regions and to examine the relation with cardiovascular risk.
DESIGN AND SETTING: We used pooled data on WC in 113,453 children and adolescents (males 50.2%) aged 4 to 20 years from 8 countries in different regions (Bulgaria, China, Iran, Korea, Malaysia, Poland, Seychelles, and Switzerland). We calculated WC percentile cutoffs in samples including or excluding children with obesity, overweight, or underweight. WC percentiles were generated using the general additive model for location, scale, and shape (GAMLSS). We also estimated the predictive power of the WC 90th percentile cutoffs to predict cardiovascular risk using receiver operator characteristics curve analysis based on data from 3 countries that had available data (China, Iran, and Korea). We also examined which WC percentiles linked with WC cutoffs for central obesity in adults (at age of 18 years).
MAIN OUTCOME MEASURE: WC measured based on recommendation by the World Health Organization.
RESULTS: We validated the performance of the age- and sex-specific 90th percentile WC cutoffs calculated in children and adolescents (6-18 years of age) with normal weight (excluding youth with obesity, overweight, or underweight) by linking the percentile with cardiovascular risk (area under the curve [AUC]: 0.69 for boys; 0.63 for girls). In addition, WC percentile among normal weight children linked relatively well with established WC cutoffs for central obesity in adults (eg, AUC in US adolescents: 0.71 for boys; 0.68 for girls).
CONCLUSION: The international WC cutoffs developed in this study could be useful to screen central obesity in children and adolescents aged 6 to 18 years and allow direct comparison of WC distributions between populations and over time.
METHODS: This cross-sectional study was conducted among 219 primary school children (105 boys; 114 girls) aged 7 years old-10 years old in Kuala Lumpur, Malaysia in 2016-2017. Children from three main ethnicities, namely Malay, Chinese and Indian, were recruited. Weight, height and waist circumference were measured; body composition was assessed by deuterium dilution technique. CAPA and level of PA were obtained through self-administered questionnaires and reported as CAPA and PA scores.
RESULTS: Median CAPA and PA scores were 3.40 (Q1 = 3.00, Q3 = 3.80) and 2.31 (Q1 = 1.95, Q3 = 2.74), respectively. Significant gender differences were found in CAPA and PA scores, with boys being more attracted to PA (3.16 [Q1 = 2.90, Q3 = 3.44]; P = 0.001) and more physically active compared with girls (2.47 [Q1 = 2.07, Q3 = 3.07]; P = 0.001). CAPA and PA scores correlated positively in both sexes. Boys scored higher than girls in 'liking of games and sports' (ρ = 0.301, P = 0.002) and 'liking of vigorous PA' (ρ = 0.227, P = 0.02) CAPA subscales, which also correlated positively with PA scores. Girls' PA scores correlated with 'peer acceptance in games and sports' (ρ = 0.329, P < 0.001).
CONCLUSION: Boys are more physically active and have higher attraction to PA compared with girls. Differences in PA scores between the sexes were related to gender differences in CAPA scores. Thus, attention should be given to gender differences in CAPA related psychosocial factors when planning interventions to promote PA among children.
DESIGN: A cross-sectional study was conducted in the four countries, between May 2019 and April 2021. Data collected can be categorized into four categories: (1) Growth - anthropometry, body composition, development disorder, (2) Nutrient intake and dietary habits - 24-hour dietary recall, child food habits, breast feeding and complementary feeding, (3) Socio-economic status - food insecurity and child health status/environmental, and (4) Lifestyle behaviours - physical activity patterns, fitness, sunlight exposure, sleep patterns, body image and behavioural problems. Blood samples were also collected for biochemical and metabolomic analyses. With the pandemic emerging during the study, a COVID-19 questionnaire was developed and implemented.
SETTING: Both rural and urban areas in Malaysia, Indonesia, Thailand, and Vietnam.
PARTICIPANTS: Children who were well, with no physical disability or serious infections/injuries and between the age of 0.5-12 years old were recruited.
RESULTS: The South East Asian Nutrition Surveys II recruited 13,933 children. Depending on the country, data collection from children were conducted in schools and commune health centres, or temples, or sub-district administrative organizations.
CONCLUSIONS: The results will provide up-to-date insights into nutritional status and lifestyle behaviours of children in the four countries. Subsequently, these data will facilitate exploration of potential gaps in dietary intake among Southeast Asian children and enable local authorities to plan future nutrition and lifestyle intervention strategies.
DESIGN: This was a single-center prospective observational study that compared resting energy expenditure estimated by 15 commonly used predictive equations against resting energy expenditure measured by indirect calorimetry at different phases. Degree of agreement between resting energy expenditure calculated by predictive equations and resting energy expenditure measured by indirect calorimetry was analyzed using intraclass correlation coefficient and Bland-Altman analyses. Resting energy expenditure values calculated from predictive equations differing by ± 10% from resting energy expenditure measured by indirect calorimetry was used to assess accuracy. A score ranking method was developed to determine the best predictive equations.
SETTING: General Intensive Care Unit, University of Malaya Medical Centre.
PATIENTS: Mechanically ventilated critically ill patients.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Indirect calorimetry was measured thrice during acute, late, and chronic phases among 305, 180, and 91 ICU patients, respectively. There were significant differences (F= 3.447; p = 0.034) in mean resting energy expenditure measured by indirect calorimetry among the three phases. Pairwise comparison showed mean resting energy expenditure measured by indirect calorimetry in late phase (1,878 ± 517 kcal) was significantly higher than during acute phase (1,765 ± 456 kcal) (p = 0.037). The predictive equations with the best agreement and accuracy for acute phase was Swinamer (1990), for late phase was Brandi (1999) and Swinamer (1990), and for chronic phase was Swinamer (1990). None of the resting energy expenditure calculated from predictive equations showed very good agreement or accuracy.
CONCLUSIONS: Predictive equations tend to either over- or underestimate resting energy expenditure at different phases. Predictive equations with "dynamic" variables and respiratory data had better agreement with resting energy expenditure measured by indirect calorimetry compared with predictive equations developed for healthy adults or predictive equations based on "static" variables. Although none of the resting energy expenditure calculated from predictive equations had very good agreement, Swinamer (1990) appears to provide relatively good agreement across three phases and could be used to predict resting energy expenditure when indirect calorimetry is not available.
METHODS: Using indirect calorimetry, REE was measured at acute (≤5 days; n = 294) and late (≥6 days; n = 180) phases of intensive care unit admission. PEs were developed by multiple linear regression. A multi-fold cross-validation approach was used to validate the PEs. The best PEs were selected based on the highest coefficient of determination (R2), the lowest root mean square error (RMSE) and the lowest standard error of estimate (SEE). Two PEs developed from paired 168-patient data were compared with measured REE using mean absolute percentage difference.
RESULTS: Mean absolute percentage difference between predicted and measured REE was <20%, which is not clinically significant. Thus, a single PE was developed and validated from data of the larger sample size measured in the acute phase. The best PE for REE (kcal/day) was 891.6(Height) + 9.0(Weight) + 39.7(Minute Ventilation)-5.6(Age) - 354, with R2 = 0.442, RMSE = 348.3, SEE = 325.6 and mean absolute percentage difference with measured REE was: 15.1 ± 14.2% [acute], 15.0 ± 13.1% [late].
CONCLUSIONS: Separate PEs for acute and late phases may not be necessary. Thus, we have developed and validated a PE from acute phase data and demonstrated that it can provide optimal estimates of REE for patients in both acute and late phases.
TRIAL REGISTRATION: ClinicalTrials.gov NCT03319329.
METHODS: The development process follows the systematic steps recommended by the Active Healthy Kids Global Alliance was used. Nationally representative data from 2016 to 2021, government reports and unpublished data were reviewed and consolidated by a panel of experts. Letter grades were assigned based on predefined benchmarks to 12 indicators including 10 core physical activity indicators that are common to Global Matrix 4.0 and two additional indicators (Diet and Weight Status). The current grading was then compared against those obtained in 2016.
RESULTS: Four of six indicators in the Daily Behaviors category received D- or C grades [Overall Physical Activity, Active Transportation and Diet (D-); Sedentary Behaviors (C)], which remains poor, similar to the 2016 report card. School indicator was graded for the Settings and Sources of Influence category, which showed an improvement from grade B (2016) to A- (2022). As for the Strategies and Investments category, B was again assigned to the Government indicator. Two new indicators were added after the 2016 Report Card, and they were graded B (Physical Fitness) and B- (Weight Status). Four indicators (Organized Sports and Physical Activity, Active Play, Family and Peers, and Community and Environment) were again graded Incomplete due to a lack of nationally representative data.
CONCLUSION: The 2022 Report Card revealed that Malaysian children and adolescents are still caught in the "inactivity epidemic". This warrants more engagement from all stakeholders, public health actions, and timely research, to comprehensively evaluate all indicators and drive a cultural shift to see Malaysian children and adolescents moving more every day.
DESIGN: Body weight and length/height were measured. The LMS method was used for calculating smoothened body-weight- and BMI-for-age percentile values. The standardized site effect (SSE) values were used for identifying large differences (i.e. $\left| {{\rm SSE}} \right|$ >0·5) between the pooled SEANUTS sample and the remaining pooled SEANUTS samples after excluding one single country each time, as well as with WHO growth references.
SETTING: Malaysia, Thailand, Vietnam and Indonesia.
SUBJECTS: Data from 14 202 eligible children.
RESULTS: The SSE derived from the comparisons of the percentile values between the pooled and the remaining pooled SEANUTS samples were indicative of small/acceptable (i.e. $\left| {{\rm SSE}} \right|$ ≤0·5) differences. In contrast, the comparisons of the pooled SEANUTS sample with WHO revealed large differences in certain percentiles.
CONCLUSIONS: The findings of the present study support the use of percentile values derived from the pooled SEANUTS sample for evaluating the weight status of children in each SEANUTS country. Nevertheless, large differences were observed in certain percentiles values when SEANUTS and WHO reference values were compared.