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Estimating weight in elderly residents in nursing homes: which equation to use?
Marcos Felipe Silva de Lima1, Natália Louise de Araújo Cabral1, Larissa Praça de Oliveira1
1Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil.
Introduction:
When it is impossible to measure weight in the elderly, estimation methods can be employed. However, such methods are not always applicable and can lead to estimation errors that compromise both the clinical practice and the results in epidemiological studies.
Objective:
To compare and validate weight estimation methods in the elderly living in nursing homes in Natal, Rio Grande do Norte, Brazil.
Methods:
The equations were evaluated qualitatively, by the reproducibility of the measurements that comprised them, and quantitatively, by the mean difference between the measured and the estimated weights, using Student's t -test for paired samples or ANOVA, the coefficient of determination (R²), root mean square error (RMSE), intra-class correlation coefficient (ICC) and graphical analysis of residuals. The significance value adopted was of p < 0.05. It was considered applicable when the equation presented R² > 0.7; lowest RMSE among the evaluated equations; ICC > 0.7; and respective 95% confidence interval with less distance between the upper and lower limits.
Results:
We evaluated 315 elderly from 10 nursing homes in Natal. The mean body weight was higher in the younger elderly and those without mobility restriction. The qualitative analysis showed the equation 5 as having the best reproducibility, since it does not use skinfold measurements. The quantitative analysis revealed the equation 5 as the one with the best applicability across the studied population and in the different genders, age groups (60 - 69 years, 70 - 79 years, and 80 years or more), and mobility restriction conditions.
Conclusion:
The equation 5 was applicable for estimating weight in the evaluated population and in the different strata analyzed.
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What are Estimates?
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...