Paige Samantha R, Krieger Janice L, Stellefson Michael, Alber Julia M
University of Florida, Department of Health Education and Behavior, PO Box 118210, Gainesville, FL 32611, USA.
University of Florida, STEM Translational Communication Center, 2024 Weimer Hall, Gainesville, FL 32611, USA.
Patient Educ Couns. 2017 Feb;100(2):320-326. doi: 10.1016/j.pec.2016.09.008. Epub 2016 Sep 16.
Chronic disease patients are affected by low computer and health literacy, which negatively affects their ability to benefit from access to online health information.
To estimate reliability and confirm model specifications for eHealth Literacy Scale (eHEALS) scores among chronic disease patients using Classical Test (CTT) and Item Response Theory techniques.
A stratified sample of Black/African American (N=341) and Caucasian (N=343) adults with chronic disease completed an online survey including the eHEALS. Item discrimination was explored using bi-variate correlations and Cronbach's alpha for internal consistency. A categorical confirmatory factor analysis tested a one-factor structure of eHEALS scores. Item characteristic curves, in-fit/outfit statistics, omega coefficient, and item reliability and separation estimates were computed.
A 1-factor structure of eHEALS was confirmed by statistically significant standardized item loadings, acceptable model fit indices (CFI/TLI>0.90), and 70% variance explained by the model. Item response categories increased with higher theta levels, and there was evidence of acceptable reliability (ω=0.94; item reliability=89; item separation=8.54).
eHEALS scores are a valid and reliable measure of self-reported eHealth literacy among Internet-using chronic disease patients.
Providers can use eHEALS to help identify patients' eHealth literacy skills.
慢性病患者受计算机和健康素养水平低的影响,这对他们从获取在线健康信息中受益的能力产生负面影响。
使用经典测试(CTT)和项目反应理论技术估计慢性病患者电子健康素养量表(eHEALS)得分的信度并确认模型规格。
对患有慢性病的黑人/非裔美国人(N = 341)和白种人(N = 343)成年人进行分层抽样,完成一项包括eHEALS的在线调查。使用双变量相关性和Cronbach's α系数探索项目区分度以评估内部一致性。进行分类验证性因素分析以检验eHEALS得分的单因素结构。计算项目特征曲线、内拟合/外拟合统计量、ω系数以及项目信度和区分度估计值。
eHEALS的单因素结构通过具有统计学意义的标准化项目负荷、可接受的模型拟合指数(CFI/TLI > 0.90)以及模型解释70%的方差得到确认。项目反应类别随θ水平升高而增加,且有证据表明信度可接受(ω = 0.94;项目信度 = 89;项目区分度 = 8.54)。
eHEALS得分是衡量使用互联网的慢性病患者自我报告的电子健康素养的有效且可靠的指标。
医疗服务提供者可以使用eHEALS来帮助识别患者的电子健康素养技能。