Characterizing fall risk factors in Belgian older adults through machine learning: a data-driven approach.

ABSTRACT:

Background

Falls are a major problem associated with ageing. Yet, fall-risk classification models identifying older adults at risk are lacking. Current screening tools show limited predictive validity to differentiate between a low- and high-risk of falling.

Objective

This study aims at identifying risk factors associated with higher risk of falling by means of a quality-of-life questionnaire incorporating biological, behavioural, environmental and socio-economic factors. These insights can aid the development of a fall-risk classification algorithm identifying community-dwelling older adults at risk of falling.

Methods

The questionnaire was developed by the Belgian Ageing Studies research group of the Vrije Universiteit Brussel and administered to 82,580 older adults

SUBMITTER: Lathouwers E 

PROVIDER: S-EPMC9707258 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature