Sisitha Jayasinghe1, Manoja P Herath1, Jeffrey M Beckett1, Kiran D K Ahuja1, Nuala M Byrne1, Andrew P Hills2. 1. School of Health Sciences, College of Health and Medicine, University of Tasmania, Launceston, TAS, 7250, Australia. 2. School of Health Sciences, College of Health and Medicine, University of Tasmania, Launceston, TAS, 7250, Australia. andrew.hills@utas.edu.au.
Abstract
BACKGROUND/ OBJECTIVES: Prediction equations generated from anthropometric measures are frequently used to quantify paediatric body composition. We tested the agreeability and predictive power of select (Lingwood and Aris) fat mass prediction equations against body fat measured via ADP; and generated and evaluated new anthropometry-based models for use in the first 6 months of life. SUBJECTS/ METHODS: Data were obtained from 278 white European Australian infants at birth, 3 and 6 months. Prediction models (i.e. Baby-bod models) were generated for each time point via stepwise linear regression and compared for agreeability with ADP via limits of agreement, mean difference and total bias in Bland-Altman analyses. Predictive power of all equations in comparison to ADP were assessed using linear regression analysis. RESULTS: Overall, there was poor agreeability between percent body fat predicted via published equations and ADP. Proportional bias was detected for both methods (i.e. published equations and Baby-bod models) of body fat prediction. At birth, both Lingwood and BB0 equations overestimated percent body fat at the lower end of the FM spectrum. This trend was repeated at 3 months with all equations displaying a propensity to overestimate body fat at lower FM levels and underestimate at higher FM levels. CONCLUSIONS: The results indicate that anthropometry, although less costly and relatively easier to implement, does not always produce comparable results with objective measures such as ADP. Given the importance of the accurate assessment of physical growth, including body composition in early life, it is timely to recommend the increased utilisation of techniques such as ADP.
BACKGROUND/ OBJECTIVES: Prediction equations generated from anthropometric measures are frequently used to quantify paediatric body composition. We tested the agreeability and predictive power of select (Lingwood and Aris) fat mass prediction equations against body fat measured via ADP; and generated and evaluated new anthropometry-based models for use in the first 6 months of life. SUBJECTS/ METHODS: Data were obtained from 278 white European Australian infants at birth, 3 and 6 months. Prediction models (i.e. Baby-bod models) were generated for each time point via stepwise linear regression and compared for agreeability with ADP via limits of agreement, mean difference and total bias in Bland-Altman analyses. Predictive power of all equations in comparison to ADP were assessed using linear regression analysis. RESULTS: Overall, there was poor agreeability between percent body fat predicted via published equations and ADP. Proportional bias was detected for both methods (i.e. published equations and Baby-bod models) of body fat prediction. At birth, both Lingwood and BB0 equations overestimated percent body fat at the lower end of the FM spectrum. This trend was repeated at 3 months with all equations displaying a propensity to overestimate body fat at lower FM levels and underestimate at higher FM levels. CONCLUSIONS: The results indicate that anthropometry, although less costly and relatively easier to implement, does not always produce comparable results with objective measures such as ADP. Given the importance of the accurate assessment of physical growth, including body composition in early life, it is timely to recommend the increased utilisation of techniques such as ADP.
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