Xue Menghan, Wang Qian, Wang Jiajia, Ge Song, Zhang Zhenxiang, Mei Yongxia
School of Nursing and Health, Zhengzhou University, Zhengzhou, People's Republic of China.
Department of Vaccination Clinic, Zhengzhou Yihe Hospital, Zhengzhou, People's Republic of China.
J Adv Nurs. 2025 Mar;81(3):1388-1398. doi: 10.1111/jan.16351. Epub 2024 Jul 26.
This study sought to explore latent categories of electronic health (e-health) literacy among stroke patients and analyse its influencing factors.
A cross-sectional, descriptive exploratory design with the STROBE reporting checklist was applied.
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.
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.
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.
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.
No patient or public contribution.
本研究旨在探索中风患者电子健康素养的潜在类别，并分析其影响因素。
采用横断面描述性探索性设计，并遵循STROBE报告清单。
2020年7月至10月期间，采用便利抽样法，从中国河南省的三家三级医院招募了558名中风参与者。使用一般信息问卷和电子健康素养量表收集他们的社会人口学信息和电子健康素养。采用潜在剖面分析来分析中风患者电子健康素养的潜在类别。使用多因素logistic回归分析影响中风患者电子健康素养潜在类别的因素。
存在三种电子健康素养潜在类别，包括低电子健康素养组、低应用-高决策组和高素养-低决策组。多因素logistic回归显示，教育水平、合并症的存在、与精神疾病患者互动的意愿、健康信息来源、上网频率、健康信息查询频率以及接受远程护理的意愿是参与者电子健康素养潜在类别的预测因素。
中风患者存在三种电子健康素养潜在类别，在制定健康教育计划时应考虑每个潜在类别的特征。医疗保健提供者必须了解为各类中风患者制定量身定制且高效的健康教育计划的要求，以提高他们的电子健康素养。
提高中国中风患者的整体电子健康素养势在必行。我们使用先进的潜在剖面分析对中风患者的电子健康素养进行了分类。这些发现对医疗保健方法具有启示意义，有助于提高中风患者的电子健康素养，使他们能够应用所获得的电子健康信息来管理和解决自己的健康问题。
无患者或公众贡献。
