Medicine
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This repository contains the published and unpublished research of the Faculty of Medicine by the staff members of the faculty
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Item Prediction equation for physical activity energy expenditure in 11-13-year-old Sri Lankan children(MDPI Publishing, 2023) Dabare, P.; Wickramasinghe, P.; Waidyatilaka, I.; Devi, S.; Kurpad, A.V.; Samaranayake, D.; de Lanerolle-Dias, M.; Wickremasinghe, R.; Hills, A.P.; Lanerolle, P.This study aimed to develop a regression equation to predict physical activity energy expenditure (PAEE) using accelerometry. Children aged 11-13 years were recruited and randomly assigned to validation (n = 54) and cross-validation (n = 25) groups. The doubly labelled water (DLW) technique was used to assess energy expenditure and accelerometers were worn by participants across the same period. A preliminary equation was developed using stepwise multiple regression analysis with sex, height, weight, body mass index, fat-free mass, fat mass and counts per minute (CPM) as independent variables. Goodness-of-fit statistics were used to select the best prediction variables. The PRESS (predicted residual error sum of squares) statistical method was used to validate the final prediction equation. The preliminary equation was cross-validated on an independent group and no significant (p > 0.05) difference was observed in the PAEE estimated from the two methods. Independent variables of the final prediction equation (PAEE = [0.001CPM] - 0.112) accounted for 70.6% of the variance. The new equation developed to predict PAEE from accelerometry was found to be valid for use in Sri Lankan children.Item Validation of accelerometer-based energy expenditure equations using doubly-labelled water technique in 11-13 year-old Sri Lankan children(Sri Lanka College of Paediatricians, 2021) Dabare, P.M.; Wickramasinghe, P.; Waidyatilaka, I.; Devi, S.; Kurpad, A.V.; Samaranayake, D.; de Lanerolle-Dias, M.; Wickremasinghe, R.; Hills, A.P.; Lanerolle, P.INTRODUCTION: Accelerometer based prediction equations are used to calculate physical activity energy expenditure (PAEE) among children. Currently, accelerometer-derived PAEE prediction equations validated against a criterion method do not exist for Sri Lankan children. Objective: To assess the validity of published prediction equations to estimate PAEE in Sri Lankan children against the doubly labelled water (DLW) technique. Method: Ninety-six children aged 11-13 years from an urban area of Sri Lanka were included in the study. Energy expenditure was assessed using the DLW technique over 10 days and participants wore ActiGraph accelerometers during the same period. Correlation between the measured and predicted PAEE was assessed by the Pearson correlation coefficient. Validity of equations was assessed by the paired t-test and the level of agreement using the Bland Altman analysis. Results: Predicted PAEE values were significantly (p<0.05) correlated with the measured PAEE except for the equations of Treuth and Schmitz. Prediction equations of Ekelund, Freedson, Mattock and Zhu significantly overestimated measured PAEE (p<0.05) whereas, Trost and Puyau equations significantly underestimated PAEE. A wide limit of agreement with a large mean bias was observed in all estimated PAEE, except for the equation of Zhu. Conclusions: Existing accelerometer-based PAEE equations have low accuracy in predicting PAEE in Sri Lankan children.Item Lifestyle patterns and dysglycaemic risk in urban Sri Lankan women(Cambridge University Press, 2014) Waidyatilaka, I.; de Silva, A.; de Lanerolle-Dias, M.; Wickremasinghe, R.; Atukorala, S.; Somasundaram, N.; Lanerolle, P.Specific dietary patterns are associated with the risk of chronic disease. An in-depth understanding more reflective of lifestyle would be possible when assessing the synergistic effects of both diet and physical activity in pattern analysis. In the present study, we examined the biochemical markers of dysglycaemia and cardiometabolic risk in relation to lifestyle patterns using principal component analysis (PCA). Urban women (n 2800) aged 30-45 years were screened for dysglycaemia using cluster sampling from the Colombo Municipal Council area. All the 272 dysglycaemic women detected through screening and 345 randomly selected normoglycaemic women were enrolled. The International Physical Activity Questionnaire and a quantitative FFQ were used to assess physical activity and diet, respectively. Anthropometric measurements, bioelectrical impedance analysis and biochemical estimations were carried out. Lifestyle patterns were identified based on dietary and physical activity data using exploratory factor analysis. PCA was used for the extraction of factors. A total of three lifestyle patterns were identified. Women who were predominantly physically inactive and consumed snacks and dairy products had the greatest cardiometabolic risk, with a higher likelihood of having unfavourable obesity indices (increased waist circumference, fat mass percentage and BMI and decreased fat-free mass percentage), glycaemic indices (increased glycosylated Hb (HbA1c) and fasting blood sugar concentrations) and lipid profile (increased total cholesterol/TAG and decreased HDL-cholesterol concentrations) and increased high-sensitivity C-reactive protein concentrations. For the first time, we report lifestyle patterns and demonstrate the synergistic effects of physical activity/inactivity and diet and their relative association with cardiometabolic risk in urban women. Lifestyle pattern analysis greatly increases our understanding of high-risk behaviours occurring within real-life complexities. © The Authors 2014