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Examination of an eHealth literacy scale and a health literacy scale in a population with moderate to high cardiovascular risk: Rasch analyses

INTRODUCTION: Electronic health (eHealth) strategies are evolving making it important to have valid scales to assess eHealth and health literacy. Item response theory methods, such as the Rasch measurement model, are increasingly used for the psychometric evaluation of scales. This paper aims to exa...

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Autores principales: Richtering, Sarah S., Morris, Rebecca, Soh, Sze-Ee, Barker, Anna, Bampi, Fiona, Neubeck, Lis, Coorey, Genevieve, Mulley, John, Chalmers, John, Usherwood, Tim, Peiris, David, Chow, Clara K., Redfern, Julie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5407817/
https://www.ncbi.nlm.nih.gov/pubmed/28448497
http://dx.doi.org/10.1371/journal.pone.0175372
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author Richtering, Sarah S.
Morris, Rebecca
Soh, Sze-Ee
Barker, Anna
Bampi, Fiona
Neubeck, Lis
Coorey, Genevieve
Mulley, John
Chalmers, John
Usherwood, Tim
Peiris, David
Chow, Clara K.
Redfern, Julie
author_facet Richtering, Sarah S.
Morris, Rebecca
Soh, Sze-Ee
Barker, Anna
Bampi, Fiona
Neubeck, Lis
Coorey, Genevieve
Mulley, John
Chalmers, John
Usherwood, Tim
Peiris, David
Chow, Clara K.
Redfern, Julie
author_sort Richtering, Sarah S.
collection PubMed
description INTRODUCTION: Electronic health (eHealth) strategies are evolving making it important to have valid scales to assess eHealth and health literacy. Item response theory methods, such as the Rasch measurement model, are increasingly used for the psychometric evaluation of scales. This paper aims to examine the internal construct validity of an eHealth and health literacy scale using Rasch analysis in a population with moderate to high cardiovascular disease risk. METHODS: The first 397 participants of the CONNECT study completed the electronic health Literacy Scale (eHEALS) and the Health Literacy Questionnaire (HLQ). Overall Rasch model fit as well as five key psychometric properties were analysed: unidimensionality, response thresholds, targeting, differential item functioning and internal consistency. RESULTS: The eHEALS had good overall model fit (χ(2) = 54.8, p = 0.06), ordered response thresholds, reasonable targeting and good internal consistency (person separation index (PSI) 0.90). It did, however, appear to measure two constructs of eHealth literacy. The HLQ subscales (except subscale 5) did not fit the Rasch model (χ(2): 18.18–60.60, p: 0.00–0.58) and had suboptimal targeting for most subscales. Subscales 6 to 9 displayed disordered thresholds indicating participants had difficulty distinguishing between response options. All subscales did, nonetheless, demonstrate moderate to good internal consistency (PSI: 0.62–0.82). CONCLUSION: Rasch analyses demonstrated that the eHEALS has good measures of internal construct validity although it appears to capture different aspects of eHealth literacy (e.g. using eHealth and understanding eHealth). Whilst further studies are required to confirm this finding, it may be necessary for these constructs of the eHEALS to be scored separately. The nine HLQ subscales were shown to measure a single construct of health literacy. However, participants’ scores may not represent their actual level of ability, as distinction between response categories was unclear for the last four subscales. Reducing the response categories of these subscales may improve the ability of the HLQ to distinguish between different levels of health literacy.
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spelling pubmed-54078172017-05-14 Examination of an eHealth literacy scale and a health literacy scale in a population with moderate to high cardiovascular risk: Rasch analyses Richtering, Sarah S. Morris, Rebecca Soh, Sze-Ee Barker, Anna Bampi, Fiona Neubeck, Lis Coorey, Genevieve Mulley, John Chalmers, John Usherwood, Tim Peiris, David Chow, Clara K. Redfern, Julie PLoS One Research Article INTRODUCTION: Electronic health (eHealth) strategies are evolving making it important to have valid scales to assess eHealth and health literacy. Item response theory methods, such as the Rasch measurement model, are increasingly used for the psychometric evaluation of scales. This paper aims to examine the internal construct validity of an eHealth and health literacy scale using Rasch analysis in a population with moderate to high cardiovascular disease risk. METHODS: The first 397 participants of the CONNECT study completed the electronic health Literacy Scale (eHEALS) and the Health Literacy Questionnaire (HLQ). Overall Rasch model fit as well as five key psychometric properties were analysed: unidimensionality, response thresholds, targeting, differential item functioning and internal consistency. RESULTS: The eHEALS had good overall model fit (χ(2) = 54.8, p = 0.06), ordered response thresholds, reasonable targeting and good internal consistency (person separation index (PSI) 0.90). It did, however, appear to measure two constructs of eHealth literacy. The HLQ subscales (except subscale 5) did not fit the Rasch model (χ(2): 18.18–60.60, p: 0.00–0.58) and had suboptimal targeting for most subscales. Subscales 6 to 9 displayed disordered thresholds indicating participants had difficulty distinguishing between response options. All subscales did, nonetheless, demonstrate moderate to good internal consistency (PSI: 0.62–0.82). CONCLUSION: Rasch analyses demonstrated that the eHEALS has good measures of internal construct validity although it appears to capture different aspects of eHealth literacy (e.g. using eHealth and understanding eHealth). Whilst further studies are required to confirm this finding, it may be necessary for these constructs of the eHEALS to be scored separately. The nine HLQ subscales were shown to measure a single construct of health literacy. However, participants’ scores may not represent their actual level of ability, as distinction between response categories was unclear for the last four subscales. Reducing the response categories of these subscales may improve the ability of the HLQ to distinguish between different levels of health literacy. Public Library of Science 2017-04-27 /pmc/articles/PMC5407817/ /pubmed/28448497 http://dx.doi.org/10.1371/journal.pone.0175372 Text en © 2017 Richtering et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Richtering, Sarah S.
Morris, Rebecca
Soh, Sze-Ee
Barker, Anna
Bampi, Fiona
Neubeck, Lis
Coorey, Genevieve
Mulley, John
Chalmers, John
Usherwood, Tim
Peiris, David
Chow, Clara K.
Redfern, Julie
Examination of an eHealth literacy scale and a health literacy scale in a population with moderate to high cardiovascular risk: Rasch analyses
title Examination of an eHealth literacy scale and a health literacy scale in a population with moderate to high cardiovascular risk: Rasch analyses
title_full Examination of an eHealth literacy scale and a health literacy scale in a population with moderate to high cardiovascular risk: Rasch analyses
title_fullStr Examination of an eHealth literacy scale and a health literacy scale in a population with moderate to high cardiovascular risk: Rasch analyses
title_full_unstemmed Examination of an eHealth literacy scale and a health literacy scale in a population with moderate to high cardiovascular risk: Rasch analyses
title_short Examination of an eHealth literacy scale and a health literacy scale in a population with moderate to high cardiovascular risk: Rasch analyses
title_sort examination of an ehealth literacy scale and a health literacy scale in a population with moderate to high cardiovascular risk: rasch analyses
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5407817/
https://www.ncbi.nlm.nih.gov/pubmed/28448497
http://dx.doi.org/10.1371/journal.pone.0175372
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