Stellefson Michael, Paige Samantha R, Tennant Bethany, Alber Julia M, Chaney Beth H, Chaney Don, Grossman Suzanne
Department of Health Education and Promotion, East Carolina University, Greenville, NC, United States.
Department of Health Education & Behavior, University of Florida, Gainesville, FL, United States.
J Med Internet Res. 2017 Oct 26;19(10):e362. doi: 10.2196/jmir.8481.
Only a handful of studies have examined reliability and validity evidence of scores produced by the 8-item eHealth literacy Scale (eHEALS) among older adults. Older adults are generally more comfortable responding to survey items when asked by a real person rather than by completing self-administered paper-and-pencil or online questionnaires. However, no studies have explored the psychometrics of this scale when administered to older adults over the telephone.
The objective of our study was to examine the reliability and internal structure of eHEALS data collected from older adults aged 50 years or older responding to items over the telephone.
Respondents (N=283) completed eHEALS as part of a cross-sectional landline telephone survey. Exploratory structural equation modeling (E-SEM) analyses examined model fit of eHEALS scores with 1-, 2-, and 3-factor structures. Subsequent analyses based on the partial credit model explored the internal structure of eHEALS data.
Compared with 1- and 2-factor models, the 3-factor eHEALS structure showed the best global E-SEM model fit indices (root mean square error of approximation=.07; comparative fit index=1.0; Tucker-Lewis index=1.0). Nonetheless, the 3 factors were highly correlated (r range .36 to .65). Item analyses revealed that eHEALS items 2 through 5 were overfit to a minor degree (mean square infit/outfit values <1.0; t statistics less than -2.0), but the internal structure of Likert scale response options functioned as expected. Overfitting eHEALS items (2-5) displayed a similar degree of information for respondents at similar points on the latent continuum. Test information curves suggested that eHEALS may capture more information about older adults at the higher end of the latent continuum (ie, those with high eHealth literacy) than at the lower end of the continuum (ie, those with low eHealth literacy). Item reliability (value=.92) and item separation (value=11.31) estimates indicated that eHEALS responses were reliable and stable.
Results support administering eHEALS over the telephone when surveying older adults regarding their use of the Internet for health information. eHEALS scores best captured 3 factors (or subscales) to measure eHealth literacy in older adults; however, statistically significant correlations between these 3 factors suggest an overarching unidimensional structure with 3 underlying dimensions. As older adults continue to use the Internet more frequently to find and evaluate health information, it will be important to consider modifying the original eHEALS to adequately measure societal shifts in online health information seeking among aging populations.
仅有少数研究检验了8项电子健康素养量表(eHEALS)在老年人中所产生分数的信效度证据。一般而言,老年人在被真人询问调查项目时比通过填写纸质或在线问卷进行自我管理时更愿意做出回应。然而,尚无研究探讨通过电话向老年人施测该量表时的心理测量学特性。
我们研究的目的是检验从50岁及以上通过电话回答项目的老年人中收集的eHEALS数据的信度和内部结构。
受访者(N = 283)完成eHEALS作为横断面固定电话调查的一部分。探索性结构方程模型(E-SEM)分析检验了eHEALS分数与单因素、双因素和三因素结构的模型拟合情况。随后基于部分计分模型的分析探索了eHEALS数据的内部结构。
与单因素和双因素模型相比,三因素eHEALS结构显示出最佳的整体E-SEM模型拟合指数(近似均方根误差 = 0.07;比较拟合指数 = 1.0;塔克-刘易斯指数 = 1.0)。尽管如此,这三个因素高度相关(r范围为0.36至0.65)。项目分析表明,eHEALS的第2至5项存在一定程度的过度拟合(均方内拟合/外拟合值 < 1.0;t统计量小于 -2.0),但李克特量表反应选项的内部结构按预期发挥作用。过度拟合的eHEALS项目(2 - 5)在潜在连续体上的相似点为受访者显示出相似程度的信息。测试信息曲线表明,eHEALS在潜在连续体较高端(即电子健康素养高的老年人)比在连续体较低端(即电子健康素养低的老年人)可能捕获更多关于老年人的信息。项目信度(值 = 0.92)和项目区分度(值 = 11.31)估计表明eHEALS的回答是可靠且稳定的。
结果支持在就老年人使用互联网获取健康信息进行调查时通过电话施测eHEALS。eHEALS分数能最好地捕捉测量老年人电子健康素养的三个因素(或子量表);然而,这三个因素之间具有统计学意义的相关性表明存在一个具有三个潜在维度的总体单维结构。随着老年人越来越频繁地使用互联网查找和评估健康信息,考虑修改原始的eHEALS以充分测量老年人群体在线健康信息搜索方面的社会转变将很重要。