Functional capacity in the oldest old: cross-sectional analysis based on a decision model

Geriatrics, Gerontology and Aging

Endereço:
Avenida Nossa Senhora de Copacabana,500 - 609 - Copacabana
Rio de Janeiro / RJ
22020001
Site: http://ggaging.com
Telefone: (21) 2285-8115
ISSN: 2447-2123
Editor Chefe: Roberto Alves Lourenço
Início Publicação: 10/10/2007
Periodicidade: Trimestral
Área de Estudo: Educação física, Área de Estudo: Enfermagem, Área de Estudo: Farmácia, Área de Estudo: Fisioterapia e terapia ocupacional, Área de Estudo: Fonoaudiologia, Área de Estudo: Medicina, Área de Estudo: Nutrição, Área de Estudo: Odontologia, Área de Estudo: Saúde coletiva, Área de Estudo: Serviço social, Área de Estudo: Multidisciplinar

Functional capacity in the oldest old: cross-sectional analysis based on a decision model

Ano: 2020 | Volume: 14 | Número: 1
Autores: Sarah de Souza Mendonçaa; Ana Paula de Oliveira Marquesa,b; Marília Gabrielle Santos Nunesa; Edson Rios D’Angelob; Márcia Carrera Campos Leala,b
Autor Correspondente: S.S. Mendonça | [email protected]

Palavras-chave: the oldest old; activities of daily living; geriatric assessment; primary health care; decisions trees.

Resumos Cadastrados

Resumo Inglês:

INTRODUCTION: The oldest old adults, aged 80 years and above, is the fastest growing age group in the world. In this section of the population, functional disability (FD) is more prevalent compared to other age groups.
OBJECTIVE: To characterize functional capacity and analyze potential associations in the oldest old from a Brazilian capital city, based on a decision model.
METHODS: Cross-sectional study of 100 non-institutionalized oldest old participants assisted at the Family Health Strategy of Recife, in the Brazilian northeastern state of Pernambuco, selected by probabilistic sample. Sociodemographic, economic, and clinical data were collected by means of home interviews, anthropometric measurements, and medical records. For bivariate analysis, Pearson’s chi-square test was used, establishing significance at p < 0.05. For the multivariate analysis, a decision tree was built from the Exhaustive CHAID algorithm.
RESULTS: The prevalence of FD in the sample corresponded to 67.0%. In the bivariate analysis, the following data contributed to this outcome: income (p = 0.032), social security status (p < 0.01), nutritional status (p = 0.010), neurological diseases (p < 0.01), neoplasms (p < 0.01), self-perceived health (p = 0.025) and social support network (p = 0.032), remaining in the multivariate analysis: income (p = 0.003), social support network (p = 0.032), and nutritional status (p = 0.040). The decision tree allowed the identification of the variables most strongly associated with the outcome, being able to adequately predict moderate dependence, with 72.1% assertiveness.
CONCLUSION: The decision model proved to be a timely tool in deducing the most relevant determinants of FD. Its use potentially contributes to increase the accuracy of the diagnosis and to identify populations at risk.