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.

METHODS: A study was carried out in 2013, which involved a total of 40 secondary schools. They were randomly selected using a two-stage clustering sampling method. Subsequently, all upper secondary school students (aged 16 to 17 years) from each selected school were recruited into the study. Data was collected using a validated standardised questionnaire.

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.

METHODS: Data were obtained from the National Health and Morbidity (NHMS) 2018 survey on the health of older Malaysian adults and analyzed. This cross-sectional population-based study used a two-stage stratified random sampling design. Sociodemographic characteristics, smoking status, and social support data were collected from respondents aged 60 years and more. A validated Malay language interviewer-administered questionnaire of 11-items, the Duke Social Support Index, was utilized to assess the social support status. A multivariable logistic regression analysis was used to assess the association of social support and smoking status among the respondents.

RESULTS: The prevalence of good social support was significantly higher among the 60-69 years old (73.1%) compared to the ≥80 years old respondents (50%). Multivariate logistic regression analysis showed that respondents aged ≥80 years old were 1.7 times more likely to have poor social support compared to those aged 60-69 years. Respondents with no formal education were 1.93 times more likely to have poor social support compared to respondents who had tertiary education. Respondents with an income of MYR 3000. Former smokers had good social support compared to current smokers (73.6% vs. 78.7%). For current smokers, they had poor social support, which is almost 1.42 times higher than that for non-smokers.

METHODS: This study used data from the 2015 National Health and Morbidity Survey (NHMS), a nationwide cross-sectional survey that implemented a two-stage stratified random sampling design. Respondents aged 18 years and above (n = 17,261) were included in the analysis. The short version of International Physical Activity Questionnaire (IPAQ) was administered to assess the respondents' PA levels. The respondents' height and weight were objectively measured and body mass index (BMI) was calculated. The respondents were categorized according to BMI as either normal-weight (18.5-24.9 kg/m2) or overweight/obese (≥ 25 kg/m2). Descriptive and complex sample logistic regression analyses were employed as appropriate.

DESIGN: Data were derived from the Global Adult Tobacco Survey, Malaysia (GATS-M). GATS-M is a nationwide study that employed a multistage, proportionate-to-size sampling strategy to select a representative sample of 5112 Malaysian adults aged 15 years and above. Multiple logistic regression was used to identify factors associated with support for smoke-free policy in selected public domains that is, workplaces, restaurants, bars, hotels, casinos, karaoke centres, public transport terminals and shopping centres.

RESULTS: The level of support for enactment of a smoke-free policy at selected public domains varied from 37.8% to 94.4%, with the highest support was for gazetted smoke-free domains, namely, shopping centres (94.4%, 95% CI: 93.2% to 95.3%) and public transport terminals (85.2%, 95% CI: 83.3% to 86.9%). Multiple logistic regression revealed that non-smokers were more likely to support smoke-free policy at all domains. In addition, respondents who worked in workplaces with total or partial smoking restrictions were more likely to support a smoke-free policy ((total restriction adjusted OR (AOR): 14.94 (6.44 to 34.64); partial restriction AOR: 2.96 (1.138 to 6.35); non-restriction was applied as a reference).