Author Affiliations: Faculty of Medicine, Department of Nursing Science (Drs Lee and Tang, Ms Lim, Ms Yahya), and Faculty of Computer Science & Information Technology, Department of Information System (Dr Varathan), University of Malaya, Kuala Lumpur; and Kulliyyah of Nursing, International Islamic University Malaysia (Dr Ludin), Kuantan, Malaysia.
Comput Inform Nurs. 2021 Nov 2;40(4):244-250. doi: 10.1097/CIN.0000000000000854.
The COVID-19 pandemic has rerouted the healthcare ecosystem by accelerating digital health, and rapid adoption of eHealth is partly influenced by eHealth literacy (eHL). This study aims to examine patients' eHL in relation to their "technology readiness"-an innate attitude that is underexplored in clinical research. A total of 276 adult inpatients with hypertension, diabetes mellitus, and coronary heart disease were surveyed cross-sectionally in 2019 using self-reported questionnaires: eHealth Literacy Scale and Technology Readiness Index (2.0). The study found moderate eHL (mean, 27.38) and moderate technology readiness (mean, 3.03) among patients. The hierarchical regression model shows that lower eHL scores were associated with patients of minor ethnicity (Malaysian Chinese), with an unemployed status, and having >1 cardiovascular risk (β = -0.136 to -0.215, R2 = 0.283, Ps < .005). Technology readiness is a strong determinant of eHL (ΔR2 = 0.295, P < .001) with its subdomains (optimism, innovativeness, and discomfort) significantly influencing eHL (|β| = 0.28-0.40, Ps < .001), except for the insecurity subdomain. Deployment of eHealth interventions that incorporate assessment of patients' eHL and technology readiness will enable targeted strategies, especially in resource-limited settings hit hard by the pandemic crisis.
新冠疫情通过加速数字医疗改变了医疗保健生态系统，电子健康的快速采用在一定程度上受到电子健康素养 (eHL) 的影响。本研究旨在探讨患者的 eHL 与其“技术准备就绪”之间的关系，而后者是临床研究中尚未充分探索的内在态度。2019 年，共对 276 名患有高血压、糖尿病和冠心病的成年住院患者进行了横断面调查，使用自我报告的问卷：电子健康素养量表和技术准备指数 (2.0)。研究发现患者的 eHL 处于中等水平（平均值 27.38），技术准备处于中等水平（平均值 3.03）。分层回归模型显示，较低的 eHL 评分与少数民族（马来西亚华人）、失业状态和患有 >1 种心血管风险的患者相关（β=-0.136 至-0.215，R2=0.283，P<.005）。技术准备是 eHL 的一个重要决定因素（ΔR2=0.295，P<.001），其子域（乐观、创新和不适）显著影响 eHL（|β|=0.28-0.40，P<.001），除了不安全子域。实施电子健康干预措施，评估患者的 eHL 和技术准备就绪程度，将能够制定有针对性的策略，特别是在受到疫情危机严重打击的资源有限的环境中。
