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.
DESIGN AND METHODS: This cross-sectional study was carried out among 254 primary and secondary school adolescents aged 10 to 16 years. Anthropometric measurements and blood pressure were determined through standardized protocols, while participants' birth weight was obtained from birth certificate. Body mass index (BMI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR) and a body shape index (ABSI) were calculated.
RESULTS: Boys had significantly higher weight, height, WC, WHtR and systolic blood pressure (SBP) than girls (p +1SD had higher odds of being prehypertensive or hypertensive (aOR 8.97; 95% CI 3.16, 25.48), followed by participants with WC ≥ 90th percentile (aOR 6.31; 95% CI 2.48, 16.01) and participants with WHtR > 0.5 (aOR 5.10; 95% CI 2.05, 12.69). Multiple linear regression showed BMI was positively associated with both SBP and DBP. No significant association was found between birth weight and BP.
CONCLUSION: BMI had the best predictive ability for SBP and DBP. These findings strongly emphasize the importance of primary prevention of hypertension in adolescents, especially among those with high BMI.
METHODS: Data from 939 preschoolers aged 3-6 years (mean age = 4.83 ± 0.04 years, 53.7% boys) from the Second South East Asian Nutrition Surveys (SEANUTS II) Malaysia study was analyzed. Socio-demography, physical activity, sedentary behaviors, and sleep were parent-reported via questionnaire. Associations between adherence of 24-hMG and sociodemographic factors were analyzed using complex samples logistic regression.
RESULTS: Only 12.1% of preschoolers adhered to the overall 24-hMG, and 67.1%, 54.7%, and 42.7% of preschoolers adhered to physical activity, sleep, and sedentary behavior guidelines, respectively; while 6.8% did not meet any guidelines. Compared to 3-4-year olds, preschoolers aged 5-6 years had higher odds of adhering to physical activity guidelines, sedentary behavior guidelines, and overall 24-hMG, but lower odds of adhering to sleep guidelines. Chinese and Indian preschoolers were more likely to adhere to sedentary behavior guidelines than Malay preschoolers; however, Chinese preschoolers had lower odds of adhering to physical activity guidelines. Paternal tertiary education was associated with a higher likelihood of adherence to sleep guidelines.
CONCLUSION: Our findings suggest that adherence to 24-hMG among Malaysian preschoolers is associated with age, ethnicity, and paternal education level. This underscores the importance of targeted interventions and health awareness program to promote healthy movement behaviors, particularly among children under 5, ethnic minorities, and educationally disadvantaged families.
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.
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.
METHODS: We developed, piloted and implemented multiple cultural adaptations and two methodological innovations to the commonly used GMB process in Fang Cheng Gang city, China. We included formal, ceremonial and policy maker engagement events before and between GMB workshops, and incorporated culturally tailored arrangements during participant recruitment (officials of the same seniority level joined the same workshop) and workshop activities (e.g., use of individual scoring activities and hand boards). We made changes to the commonly used GMB activities which enabled mapping of shared drivers of multiple health issues (in our case MIAIF) in a single causal loop diagram. We developed and used a 'hybrid' GMB format combining online and in person facilitation to reduce travel and associated climate impact.
RESULTS: Our innovative GMB process led to high engagement and support from decision-makers representing diverse governmental departments across the whole food systems. We co-identified and prioritised systemic drivers and intervention themes of MIAIF. The city government established an official Local Action Group for long-term, inter-departmental implementation, monitoring and evaluation of the co-developed interventions. The 'hybrid' GMB format enabled great interactions while reducing international travel and mitigating limitations of fully online GMB process.
CONCLUSIONS: Cultural and methodological adaptations to the common GMB process for an Asian LMIC setting were successful. The 'hybrid' GMB format is feasible, cost-effective, and more environmentally friendly. These cultural adaptations could be considered for other Asian settings and beyond to address inter-related, complex issues such as MIAIF.