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Validation of anthropometric-based weight prediction equations among Ugandan adults: A Cross-sectional study

2024Open accessMakerere University

Abstract

ABSTRACT Introduction Proper patient management often requires accurate weight estimation. However, the appropriate weight-measuring equipment is not always available in resource-limited settings, making clinicians resort to less reliable methods like the visual estimation of weight often with negative consequences. In this study, we assess the accuracy of anthropometric-based equations in predicting weight in Ugandan adults. Methods A cross-sectional study was conducted at Kira Health Center IV between December 2021 and February 2022. A sample of 240 adult patients, 18 years and above, was selected by quota sampling, stratified by sex and body mass index. Demographic information was obtained and anthropometric measurements including weight, height, knee height, subscapular skin fold thickness, and circumference measurements were taken. The predicted weight was computed using the proposed equations and the accuracy of the different equations was determined using Bland Altman analysis taking the equation with the best agreement as the most accurate. Results Out of 240 participants, 50% were females. The mean (standard deviation) age was 32.5(11.8) years; (34.1(12.9) years for males and 30.8(10.3) years for females). The mean (standard deviation) actual weight was 66.43 (16.33) kg while the mean height was 1.64 (0.09) meters. Using Bland-Altman analysis, Rabito equation (Weight = (0.5759xMAC) + (0.5263xAC) + (1.2452xCC) - (4.8689xS)-32.9241) was found to have the best agreement with the actual weight with a mean difference (standard deviation) of 2.55(6.99) kilograms overall. Conclusion Equation R3 was the most accurate equation for predicting weight among Ugandan adults. Therefore, in the absence of appropriate weighing scales to weigh patients, using this anthropometric-based weight estimation equation can act as a relatively accurate alternative.

Research topics

  • Body Composition Measurement Techniques

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DOI: 10.1101/2024.06.18.24309142

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