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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Journal of Advanced ...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Journal of Advanced Nursing
Article . 2024 . Peer-reviewed
License: Wiley Online Library User Agreement
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E‐health literacy in stroke patients: Latent profile analysis and influencing factors

Authors: Menghan Xue; Qian Wang; Jiajia Wang; Song Ge; Zhenxiang Zhang; Yongxia Mei;

E‐health literacy in stroke patients: Latent profile analysis and influencing factors

Abstract

Abstract Aims This study sought to explore latent categories of electronic health (e‐health) literacy among stroke patients and analyse its influencing factors. Design A cross‐sectional, descriptive exploratory design with the STROBE reporting checklist was applied. Methods Between July and October 2020, 558 stroke participants from three tertiary care hospitals in Henan Province, China, were recruited using a convenience sampling method. A general information questionnaire and the Electronic Health Literacy Scale were used to collect their socio‐demographic information and e‐health literacy. Latent profile analysis was used to analyse latent categories of e‐health literacy in stroke patients. Multiple logistic regression was used to analyse factors influencing latent categories of e‐health literacy in stroke patients. Results Three latent categories of e‐health literacy existed, including the low e‐health literacy group, the low application‐high decision‐making group and the high literacy‐low decision‐making group. Multiple logistic regression showed that education level, presence of comorbidities, willingness to interact with people with mental illness, health information sources, frequency of Internet access, frequency of health information inquiry and willingness to receive remote care were predictors of the participants' latent categories of e‐health literacy. Conclusion Three latent categories of e‐health literacy in stroke patients exist, and each latent category's characteristics should be considered while developing health education programmes. It is imperative that healthcare providers understand the requirement of creating tailored and efficient health education programmes for various categories of stroke patients to enhance their e‐health literacy. Impact It is imperative to improve Chinese stroke patients' overall e‐health literacy. We categorized stroke patients' e‐health literacy using advanced LPA. These findings hold implications for healthcare approaches, contributing to the enhancement of stroke patients' e‐health literacy, enabling them to apply the acquired e‐health information to manage and solve their own health issues. Patient or Public Contribution No patient or public contribution.

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Keywords

Male, Adult, Aged, 80 and over, China, Middle Aged, Health Literacy, Stroke, Cross-Sectional Studies, Surveys and Questionnaires, Humans, Female, Aged

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Average
Top 10%
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