Shao Yujiao, Xu Xuejun, Guo Hongyan, Duan Xiaocui, Zhang Zeyu, Zhao Shuang, Yang Xiumu
School of Nursing, Bengbu Medical University, Bengbu, Anhui, China.
Department of General Practice, First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China.
Front Public Health. 2025 Jul 18;13:1477314. doi: 10.3389/fpubh.2025.1477314. eCollection 2025.
To examine the heterogeneity and determinants of digital health literacy among older adult patients with chronic diseases and provide evidence for targeted interventions.
A convenience sample of 536 older adult patients with chronic diseases was recruited from three tertiary hospitals in Anhui Province between October 2023 and May 2024. Data were collected using structured questionnaires, including the Digital Health Literacy Assessment Scale, Social Support Rating Scale, General Self-Efficacy Scale, Brief Symptom Rating Scale, Brief Illness Perception Questionnaire, and the age-adjusted Charlson Comorbidity Index. Latent profile analysis (LPA) was conducted in Mplus 8.3. Univariate and multivariate logistic regression analyses were performed using SPSS 26.0 to identify literacy profiles and their associated factors.
The mean digital health literacy score was 41.36 (SD = 12.8), with an average item score of 2.76 (SD = 0.85). LPA identified three profiles: C1-Low Literacy, Passive Interaction ( = 142, 26.5%); C2-Moderate Literacy, Limited Interaction ( = 276, 51.5%); and C3-High Literacy, Active Interaction ( = 118, 22.0%). Multinomial logistic regression analysis showed that residence, participation in chronic disease health education, daily internet use, perceived ease of use and usefulness of digital health information, general self-efficacy, and social support were significant independent predictors of profile membership ( < 0.05). The model explained approximately 59.0% of the variance in profile classification (Nagelkerke = 0.590).
Digital health literacy among older adult patients with chronic diseases was generally low, particularly in interactive skills, with significant heterogeneity across subgroups. Tailored strategies that address the unique needs of each profile are essential to improve digital health literacy in this population.
探讨老年慢性病患者数字健康素养的异质性及其影响因素,为针对性干预提供依据。
2023年10月至2024年5月,从安徽省3家三级医院选取536例老年慢性病患者作为便利样本。采用结构化问卷收集数据,包括数字健康素养评估量表、社会支持评定量表、一般自我效能量表、简明症状量表、简易疾病感知问卷和年龄校正的Charlson合并症指数。在Mplus 8.3中进行潜在类别分析(LPA)。使用SPSS 26.0进行单因素和多因素逻辑回归分析,以确定素养类别及其相关因素。
数字健康素养平均得分为41.36(标准差=12.8),平均项目得分为2.76(标准差=0.85)。LPA确定了三个类别:C1-低素养、被动互动(n=142,26.5%);C2-中等素养、有限互动(n=276,51.5%);C3-高素养、积极互动(n=118,22.0%)。多项逻辑回归分析表明,居住地、参加慢性病健康教育、每日使用互联网、感知数字健康信息的易用性和有用性、一般自我效能和社会支持是类别归属的显著独立预测因素(P<0.05)。该模型解释了类别分类中约59.0%的变异(Nagelkerke R²=0.590)。
老年慢性病患者的数字健康素养普遍较低,尤其是在互动技能方面,各亚组之间存在显著异质性。针对每个类别的独特需求制定个性化策略对于提高该人群的数字健康素养至关重要。