Richtering Sarah S, Morris Rebecca, Soh Sze-Ee, Barker Anna, Bampi Fiona, Neubeck Lis, Coorey Genevieve, Mulley John, Chalmers John, Usherwood Tim, Peiris David, Chow Clara K, Redfern Julie
The George Institute for Global Health, Sydney, NSW, Australia.
Hôpitaux Universitaires de Genève, Université de Genève, Geneva, Switzerland.
PLoS One. 2017 Apr 27;12(4):e0175372. doi: 10.1371/journal.pone.0175372. eCollection 2017.
Electronic health (eHealth) strategies are evolving making it important to have valid scales to assess eHealth and health literacy. Item response theory methods, such as the Rasch measurement model, are increasingly used for the psychometric evaluation of scales. This paper aims to examine the internal construct validity of an eHealth and health literacy scale using Rasch analysis in a population with moderate to high cardiovascular disease risk.
The first 397 participants of the CONNECT study completed the electronic health Literacy Scale (eHEALS) and the Health Literacy Questionnaire (HLQ). Overall Rasch model fit as well as five key psychometric properties were analysed: unidimensionality, response thresholds, targeting, differential item functioning and internal consistency.
The eHEALS had good overall model fit (χ2 = 54.8, p = 0.06), ordered response thresholds, reasonable targeting and good internal consistency (person separation index (PSI) 0.90). It did, however, appear to measure two constructs of eHealth literacy. The HLQ subscales (except subscale 5) did not fit the Rasch model (χ2: 18.18-60.60, p: 0.00-0.58) and had suboptimal targeting for most subscales. Subscales 6 to 9 displayed disordered thresholds indicating participants had difficulty distinguishing between response options. All subscales did, nonetheless, demonstrate moderate to good internal consistency (PSI: 0.62-0.82).
Rasch analyses demonstrated that the eHEALS has good measures of internal construct validity although it appears to capture different aspects of eHealth literacy (e.g. using eHealth and understanding eHealth). Whilst further studies are required to confirm this finding, it may be necessary for these constructs of the eHEALS to be scored separately. The nine HLQ subscales were shown to measure a single construct of health literacy. However, participants' scores may not represent their actual level of ability, as distinction between response categories was unclear for the last four subscales. Reducing the response categories of these subscales may improve the ability of the HLQ to distinguish between different levels of health literacy.
电子健康(eHealth)策略不断发展,因此拥有有效的量表来评估电子健康和健康素养变得至关重要。项目反应理论方法,如拉施测量模型,越来越多地用于量表的心理测量评估。本文旨在使用拉施分析,在心血管疾病风险为中度至高风险的人群中检验电子健康和健康素养量表的内部结构效度。
CONNECT研究的前397名参与者完成了电子健康素养量表(eHEALS)和健康素养问卷(HLQ)。分析了总体拉施模型拟合情况以及五个关键心理测量特性:单维性、反应阈值、目标定位、项目功能差异和内部一致性。
eHEALS具有良好的总体模型拟合度(χ2 = 54.8,p = 0.06),反应阈值有序,目标定位合理,内部一致性良好(个人分离指数(PSI)为0.90)。然而,它似乎测量了电子健康素养的两个结构。HLQ子量表(子量表5除外)不适合拉施模型(χ2:18.18 - 60.60,p:0.00 - 0.58),并且大多数子量表的目标定位欠佳。子量表6至9显示出无序的阈值,表明参与者难以区分反应选项差异。尽管如此,所有子量表都显示出中度至良好的内部一致性(PSI:0.62 - 0.82)。
拉施分析表明,eHEALS具有良好的内部结构效度测量指标,尽管它似乎捕捉到了电子健康素养的不同方面(例如使用电子健康和理解电子健康)。虽然需要进一步研究来证实这一发现,但可能有必要对eHEALS的这些结构分别计分。九个HLQ子量表被证明测量了健康素养的单一结构。然而,参与者的分数可能无法代表他们的实际能力水平,因为最后四个子量表的反应类别之间的区分不明确。减少这些子量表的反应类别可能会提高HLQ区分不同健康素养水平的能力。