Version 1
: Received: 6 April 2024 / Approved: 7 April 2024 / Online: 8 April 2024 (10:24:54 CEST)
How to cite:
Voll, S.; Muniz-Terrera, G. Five Factor Frailty Measure. Psychometrically Robust Research Tool for the English Longitudinal Study on Ageing.. Preprints2024, 2024040489. https://doi.org/10.20944/preprints202404.0489.v1
Voll, S.; Muniz-Terrera, G. Five Factor Frailty Measure. Psychometrically Robust Research Tool for the English Longitudinal Study on Ageing.. Preprints 2024, 2024040489. https://doi.org/10.20944/preprints202404.0489.v1
Voll, S.; Muniz-Terrera, G. Five Factor Frailty Measure. Psychometrically Robust Research Tool for the English Longitudinal Study on Ageing.. Preprints2024, 2024040489. https://doi.org/10.20944/preprints202404.0489.v1
APA Style
Voll, S., & Muniz-Terrera, G. (2024). Five Factor Frailty Measure. Psychometrically Robust Research Tool for the English Longitudinal Study on Ageing.. Preprints. https://doi.org/10.20944/preprints202404.0489.v1
Chicago/Turabian Style
Voll, S. and Graciela Muniz-Terrera. 2024 "Five Factor Frailty Measure. Psychometrically Robust Research Tool for the English Longitudinal Study on Ageing." Preprints. https://doi.org/10.20944/preprints202404.0489.v1
Abstract
Background: Availability of large longitudinal population health and aging studies, such as the English Longitudinal Study on Ageing, hold many opportunities for the examination of the development of Frailty over time. Lacking are psychometrically robust longitudinal measurement of the concepts of frailty required for accurate and reliable estimates and identification of frail individuals. Objectives. Development of a psychometrically robust longitudinal research tool of Frailty. The tool is to be easy to replicate for future use in health and aging research. Participants/Measurements. Data comes from four waves of ELSA (2014-2017); N=39,528. Six domains of health status deficits that may measure frailty were used: Mobility, Daily Function, Self-rated general health, Self-rated pain, Depression, Self-report doctor diagnoses of health. Design. Exploratory factor analysis (EFA) of common-self-reported deficits available across four waves of longitudinal data were compared to determine which deficits were a best fit for a research-based frailty index. Standardized regression factor scores were calculated to represent individual’s placement in factors identified from EFA. Cross-sectional validation and reliability are reported. Results. Five-Factors of Frailty found were consistent across ELSA waves. Mobility/Severe Pain, Planning/Self Care, Depression, Moderate Pain/Movement/Arthritis and Cardio-Metabolic. The variable make-up of each factor differed between males and females. Psychometric properties of the five factor frailty model were taken into account at each stage of development (factorability/sampling adequacy, cross-sectional replicability, internal consistency, low interdeterminacy, relations with age).Conclusions. Five distinct factors of an accumulation of deficits approach to frailty were found. Use and development of the five factors of frailty research tool are outlined. This research tool will aid researchers in determining the complex risk factors and outcomes of empirically derived components of frailty in ELSA.
Keywords
Frailty; Longitudinal; Psychometrics; ELSA
Subject
Social Sciences, Psychology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
