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: Parents of children aged 3-5 years, from 14 countries (8 low- and middle-income countries, LMICs) completed surveys to assess changes in movement behaviours and how these changes were associated with the COVID-19 pandemic. Surveys were completed in the 12 months up to March 2020 and again between May and June 2020 (at the height of restrictions). Physical activity (PA), sedentary screen time (SST) and sleep were assessed via parent survey. At Time 2, COVID-19 factors including level of restriction, environmental conditions, and parental stress were measured. Compliance with the World Health Organizations (WHO) Global guidelines for PA (180 min/day [≥60 min moderate- vigorous PA]), SST (≤1 h/day) and sleep (10-13 h/day) for children under 5 years of age, was determined.
RESULTS: Nine hundred- forty-eight parents completed the survey at both time points. Children from LMICs were more likely to meet the PA (Adjusted Odds Ratio [AdjOR] = 2.0, 95%Confidence Interval [CI] 1.0,3.8) and SST (AdjOR = 2.2, 95%CI 1.2,3.9) guidelines than their high-income country (HIC) counterparts. Children who could go outside during COVID-19 were more likely to meet all WHO Global guidelines (AdjOR = 3.3, 95%CI 1.1,9.8) than those who were not. Children of parents with higher compared to lower stress were less likely to meet all three guidelines (AdjOR = 0.5, 95%CI 0.3,0.9).
CONCLUSION: PA and SST levels of children from LMICs have been less impacted by COVID-19 than in HICs. Ensuring children can access an outdoor space, and supporting parents' mental health are important prerequisites for enabling pre-schoolers to practice healthy movement behaviours and meet the Global guidelines.
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: This is a cross-sectional study on 9553 adolescents (aged 12-15 years) from 8 Asian metropolitan cities (Tokyo, Hong Kong, Shanghai, Taipei, Bangkok, Kuala Lumpur, Seoul, and Singapore). Cardiorespiratory fitness was assessed by using a 15-m progressive aerobic capacity endurance run (PACER) test. The time spent on MVPA and watching television was assessed using the International Physical Activity Questionnaire-Short Form.
RESULTS: MVPA was more closely associated with the PACER score than the duration of watching television. Compared with the reference group (i.e. those with the lowest levels of MVPA [