Machine learning models for identifying pre-frailty in community dwelling older adults.

ABSTRACT:

Background

There is increasing evidence that pre-frailty manifests as early as middle age. Understanding the factors contributing to an early trajectory from good health to pre-frailty in middle aged and older adults is needed to inform timely preventive primary care interventions to mitigate early decline and future frailty.

Methods

A cohort of 656 independent community dwelling adults, aged 40-75 years, living in South Australia, undertook a comprehensive health assessment as part of the Inspiring Health cross-sectional observational study. Secondary analysis was completed using machine learning models to identify factors common amongst participants identified as not frail or pre-frail using the Clinical Frailty Scale (CFS) and Fried Frailty Phenotype (FFP). A correlation-

SUBMITTER: Sajeev S 

PROVIDER: S-EPMC9554971 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature