老年心力衰竭患者电子健康素养与治疗负担的异质性：一项多维潜在剖面分析

摘要(中文译文)

心力衰竭（HF）管理给老年人带来了沉重的多维治疗负担（BoT）。尽管数字健康干预提供了潜在的解决方案，但其成功在很大程度上取决于患者的电子健康素养（eHL）。以往基于变量为中心、评估总量表得分的研究系统性地掩盖了eHL与BoT之间的个体异质性及维度交互作用。旨在识别慢性HF老年患者中电子健康素养与治疗负担的多维潜在剖面，并探讨这些剖面的独立社会人口学及临床预测因素。开展了一项横断面研究，纳入中国425名老年HF患者。使用人口学/临床资料提取表、中文电子健康素养量表（C-eHEALS）以及治疗与自我管理患者体验量表（PETS）收集数据。采用上述量表中的14个具体连续维度指标进行潜在剖面分析（LPA）。运用多项logistic回归识别影响剖面归属的因素。共识别出三个不同的潜在剖面。标准化Z分数模式直观支持了以下结果：“脆弱型”剖面（28.9%）呈现出截然相反的临床状态，其特征为所有eHL维度均存在严重缺陷，且系统性BoT极高。“胜任型”剖面（22.1%）则表现出相反的最佳模式（高eHL、极低BoT）。“过渡型”剖面（48.9%）得分居中，但维度间不一致性较高（例如，信息获取能力尚可但评价能力较差，同时伴随较高的饮食/运动负担）。多项logistic回归显示，高龄、小学教育水平、独居以及较高的Charlson合并症指数是脆弱型剖面归属的显著独立预测因素。老年HF患者对数字健康需求和自我照护工作量的体验并不一致。识别这些高度分化、维度特异性的类型学特征，凸显了从“一刀切”式数字部署转向旨在减轻特定脆弱性的精准化、分层照护模式的潜在价值。

Abstract

Heart failure (HF) management imposes a substantial multidimensional treatment burden (BoT) on older adults. While digital health interventions offer potential solutions, their success heavily depends on patients' eHealth literacy (eHL). Previous variable-centered research evaluating total scale scores has systematically masked the individual heterogeneity and dimensional interplay between eHL and BoT. To identify the multidimensional latent profiles of eHealth literacy and treatment burden among older adults with chronic HF, and to explore the independent sociodemographic and clinical predictors of these profiles. A cross-sectional study was conducted involving 425 older adults with HF in China. Data were collected using demographic/clinical abstraction forms, the Chinese eHealth Literacy Scale (C-eHEALS), and the Patient Experience with Treatment and Self-Management (PETS). Latent profile analysis (LPA) was executed using 14 specific continuous dimensional indicators from these scales. Multinomial logistic regression was utilized to identify factors influencing profile membership. Three distinct latent profiles were identified. As visually supported by the standardized Z-score patterns, the "Vulnerable" profile (28.9%) exhibited a starkly inverse clinical state characterized by profound deficits across all eHL dimensions and overwhelmingly high systemic BoT. The "Capable" profile (22.1%) demonstrated the opposite, optimal pattern (high eHL, minimal BoT). The "Transitional" profile (48.9%) displayed intermediate scores but high dimensional discordance (e.g., adequate information acquisition but poor evaluation, paired with high diet/exercise burdens). Multinomial logistic regression revealed that older age, primary-level education, living alone, and a higher Charlson Comorbidity Index were significant independent predictors of membership in the Vulnerable profile. Older adults with HF do not experience digital health demands and self-care workloads uniformly. Identifying these highly distinct, dimension-specific typologies highlights the potential value of moving away from "one-size-fits-all" digital deployments toward precision-based, stratified care models designed to mitigate specific vulnerabilities.

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