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Development of a frailty index from the Dutch public health monitor 2016 and investigation of its psychometric properties: a cross-sectional study

BACKGROUND: Frailty in older adults is an increasing challenge for individuals, health care organizations and public health, both globally and in The Netherlands. To focus on frailty prevention from a public health perspective, understanding of frailty status is needed. To enable measurement of frai...

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Autores principales: Kleinenberg-Talsma, Nanda, van der Lucht, Fons, Jager-Wittenaar, Harriët, Krijnen, Wim, Finnema, Evelyn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142448/
https://www.ncbi.nlm.nih.gov/pubmed/37118785
http://dx.doi.org/10.1186/s13690-023-01093-4
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author Kleinenberg-Talsma, Nanda
van der Lucht, Fons
Jager-Wittenaar, Harriët
Krijnen, Wim
Finnema, Evelyn
author_facet Kleinenberg-Talsma, Nanda
van der Lucht, Fons
Jager-Wittenaar, Harriët
Krijnen, Wim
Finnema, Evelyn
author_sort Kleinenberg-Talsma, Nanda
collection PubMed
description BACKGROUND: Frailty in older adults is an increasing challenge for individuals, health care organizations and public health, both globally and in The Netherlands. To focus on frailty prevention from a public health perspective, understanding of frailty status is needed. To enable measurement of frailty within a health survey that currently does not contain an established frailty instrument, we aimed to construct a frailty index (FI) and investigate its psychometric properties. METHODS: We conducted a cross-sectional study using data from the Dutch Public Health Monitor (DPHM), including respondents aged ≥ 65 years (n = 233,498). Forty-two health deficits were selected based on literature, previously constructed FIs, face validity and standard criteria for FI construction. Deficits were first explored by calculating Cronbach’s alpha, point-polyserial correlations, and factor loadings. Thereafter, we used the Graded Response Model (GRM) to assess item difficulty, item discrimination, and category thresholds. RESULTS: Cronbach’s alpha for the 42 items was 0.91. Thirty-seven deficits showed strong psychometric properties: they scored above the cutoff values for point-polyserial correlations (0.3) or factor loadings (0.4) and had moderate to very high discrimination parameters (≥ 0.65). These deficits were retained in the scale. Retaining the deficits with favorable measurement properties and removing the remaining deficits resulted in the FI-HM37. CONCLUSION: The FI-HM37 was developed, an FI with 37 deficits indicative of frailty, both statistically and conceptually. Our results indicate that health monitors can be used to measure frailty, even though they were not directly designed to do so. The GRM is a suitable approach for deficit selection, resulting in a psychometrically strong scale, that facilitates assessment of frailty levels using the DPHM.
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spelling pubmed-101424482023-04-29 Development of a frailty index from the Dutch public health monitor 2016 and investigation of its psychometric properties: a cross-sectional study Kleinenberg-Talsma, Nanda van der Lucht, Fons Jager-Wittenaar, Harriët Krijnen, Wim Finnema, Evelyn Arch Public Health Research BACKGROUND: Frailty in older adults is an increasing challenge for individuals, health care organizations and public health, both globally and in The Netherlands. To focus on frailty prevention from a public health perspective, understanding of frailty status is needed. To enable measurement of frailty within a health survey that currently does not contain an established frailty instrument, we aimed to construct a frailty index (FI) and investigate its psychometric properties. METHODS: We conducted a cross-sectional study using data from the Dutch Public Health Monitor (DPHM), including respondents aged ≥ 65 years (n = 233,498). Forty-two health deficits were selected based on literature, previously constructed FIs, face validity and standard criteria for FI construction. Deficits were first explored by calculating Cronbach’s alpha, point-polyserial correlations, and factor loadings. Thereafter, we used the Graded Response Model (GRM) to assess item difficulty, item discrimination, and category thresholds. RESULTS: Cronbach’s alpha for the 42 items was 0.91. Thirty-seven deficits showed strong psychometric properties: they scored above the cutoff values for point-polyserial correlations (0.3) or factor loadings (0.4) and had moderate to very high discrimination parameters (≥ 0.65). These deficits were retained in the scale. Retaining the deficits with favorable measurement properties and removing the remaining deficits resulted in the FI-HM37. CONCLUSION: The FI-HM37 was developed, an FI with 37 deficits indicative of frailty, both statistically and conceptually. Our results indicate that health monitors can be used to measure frailty, even though they were not directly designed to do so. The GRM is a suitable approach for deficit selection, resulting in a psychometrically strong scale, that facilitates assessment of frailty levels using the DPHM. BioMed Central 2023-04-28 /pmc/articles/PMC10142448/ /pubmed/37118785 http://dx.doi.org/10.1186/s13690-023-01093-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Kleinenberg-Talsma, Nanda
van der Lucht, Fons
Jager-Wittenaar, Harriët
Krijnen, Wim
Finnema, Evelyn
Development of a frailty index from the Dutch public health monitor 2016 and investigation of its psychometric properties: a cross-sectional study
title Development of a frailty index from the Dutch public health monitor 2016 and investigation of its psychometric properties: a cross-sectional study
title_full Development of a frailty index from the Dutch public health monitor 2016 and investigation of its psychometric properties: a cross-sectional study
title_fullStr Development of a frailty index from the Dutch public health monitor 2016 and investigation of its psychometric properties: a cross-sectional study
title_full_unstemmed Development of a frailty index from the Dutch public health monitor 2016 and investigation of its psychometric properties: a cross-sectional study
title_short Development of a frailty index from the Dutch public health monitor 2016 and investigation of its psychometric properties: a cross-sectional study
title_sort development of a frailty index from the dutch public health monitor 2016 and investigation of its psychometric properties: a cross-sectional study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142448/
https://www.ncbi.nlm.nih.gov/pubmed/37118785
http://dx.doi.org/10.1186/s13690-023-01093-4
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