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Updated: Dec 5, 2025
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Anthropometry-based prediction of body fat in infants from birth to 6 months: the Baby-bod study
Sisitha Jayasinghe1, Manoja P Herath1, Jeffrey M Beckett1
1School of Health Sciences, College of Health and Medicine, University of Tasmania, Launceston, TAS, 7250, Australia.
Insights
Anthropometric prediction equations show poor agreement with air displacement plethysmography (ADP) for infant body composition. Objective measures like ADP are recommended for accurate assessment of infant fat mass.
Area of Science:
- Paediatric Endocrinology
- Human Physiology
- Nutritional Science
Background:
- Anthropometry-based prediction equations are commonly used for paediatric body composition assessment.
- Accurate body composition measurement is crucial for monitoring infant growth and health.
Purpose of the Study:
- To evaluate the accuracy of existing anthropometric fat mass prediction equations against air displacement plethysmography (ADP).
- To develop and assess new anthropometry-based models for infant body composition in the first six months of life.
Main Methods:
- Data collected from 278 European Australian infants at birth, 3, and 6 months.
- Stepwise linear regression used to generate new 'Baby-bod' models.
- Bland-Altman analyses and linear regression assessed agreement and predictive power against ADP.
Main Results:
- Poor agreement observed between predicted and ADP-measured percent body fat.
- Proportional bias detected in both published equations and new Baby-bod models.
- Equations tended to overestimate body fat at lower levels and underestimate at higher levels.
Conclusions:
- Anthropometric measures, while accessible, do not consistently yield results comparable to objective methods like ADP.
- Accurate assessment of early life body composition is vital.
- Increased utilization of objective techniques such as ADP is recommended for precise infant body composition analysis.
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.
