Anthropometric Measures and Frailty Prediction in the Elderly: An Easy-to-Use Tool

Purpose. Anthropometry is a useful tool for assessing some risk factors for frailty. Thus, the aim of this study was to verify the discriminatory performance of anthropometric measures in identifying frailty in the elderly and to create an easy-to-use tool. Methods. Cross-sectional study: a subset f...

Full description

Saved in:
Bibliographic Details
Published in:Current gerontology and geriatrics research 2017, Vol.2017 (2017), p.1-8
Main Authors: Gomes, Irenio, Flores, João Henrique Ferreira, Ziegelmann, Patricia Klarmann, Closs, Vera Elizabeth, Schwanke, Carla Helena Augustin
Format: Article
Language:English
Subjects:
Abdomen
Aged
Anthropometry
Body measurements
Clinical medicine
Cross-sectional studies
Demographic aspects
Frailty
Genotype & phenotype
Geriatrics
Gerontology
Health planning
Health services
Movement disorders
Nutrition
Obesity
Older people
Physiological aspects
Predictions
Primary care
Risk factors
Weight control
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Purpose. Anthropometry is a useful tool for assessing some risk factors for frailty. Thus, the aim of this study was to verify the discriminatory performance of anthropometric measures in identifying frailty in the elderly and to create an easy-to-use tool. Methods. Cross-sectional study: a subset from the Multidimensional Study of the Elderly in the Family Health Strategy (EMI-SUS) evaluating 538 older adults. Individuals were classified using the Fried Phenotype criteria, and 26 anthropometric measures were obtained. The predictive ability of anthropometric measures in identifying frailty was identified through logistic regression and an artificial neural network. The accuracy of the final models was assessed with an ROC curve. Results. The final model comprised the following predictors: weight, waist circumference, bicipital skinfold, sagittal abdominal diameter, and age. The final neural network models presented a higher ROC curve of 0.78 (CI 95% 0.74–0.82) (P
ISSN:1687-7063
1687-7071
DOI:10.1155/2017/8703503