ArticleJMIR human factors2018
Three-Factor Structure of the eHealth Literacy Scale Among Magnetic Resonance Imaging and Computed Tomography Outpatients: A Confirmatory Factor Analysis.
Article in JMIR human factors, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Systematic review of the measurement properties of patient-reported outcome measures (PROMs) of eHealth literacy in adult populations.Systematic reviews · 2026Pooled it
- Measuring health literacy: A systematic review and bibliometric analysis of instruments from 1993 to 2021.PloS one · 2022Pooled it
- eHealth Literacy Instruments: Systematic Review of Measurement Properties.Journal of medical Internet research · 2021Pooled it
- Psychometric Analysis of the eHealth Literacy Scale in Portuguese Older Adults (eHEALS-PT24): Instrument Development and Validation.Journal of medical Internet research · 2025Article
- Electronic Health Literacy Scale-Web3.0 for Older Adults with Noncommunicable Diseases: Validation Study.Journal of medical Internet research · 2024Article
- Structural Validation and Measurement Invariance Testing of the Chinese Version of the eHealth Literacy Scale Among Undergraduates: Cross-Sectional Study.Journal of medical Internet research · 2023Article
- Professionals Evaluating Clients' Suitability for Digital Health and Social Care: Scoping Review of Assessment Instruments.Journal of medical Internet research · 2023Article
- Validation of the Short Food Literacy Questionnaire in the Representative Sample of Polish Internet Users.International journal of environmental research and public health · 2022Article
- Evaluating the Psychometric Properties of the eHealth Literacy Scale (eHEALS) among Polish Social Media Users.International journal of environmental research and public health · 2022Article
- Measuring Electronic Health Literacy: Development, Validation, and Test of Measurement Invariance of a Revised German Version of the eHealth Literacy Scale.Journal of medical Internet research · 2022Article
- Validity Evidence Based on Relations to Other Variables of the eHealth Literacy Questionnaire (eHLQ): Bayesian Approach to Test for Known-Groups Validity.Journal of medical Internet research · 2021Article
- An Instrument for Measuring Social Participation to Examine Older Adults' Use of the Internet as a Social Platform: Development and Validation Study.JMIR aging · 2021Article
- The relationship among social capital, eHealth literacy and health behaviours in Chinese elderly people: a cross-sectional study.BMC public health · 2021Article
- Psychometric Properties of the Norwegian Version of the Electronic Health Literacy Scale (eHEALS) Among Patients After Percutaneous Coronary Intervention: Cross-Sectional Validation Study.Journal of medical Internet research · 2020Article
- Internet Use, eHealth Literacy, and Dietary Supplement Use Among Young Adults in Pakistan: Cross-Sectional Study.Journal of medical Internet research · 2020Article
- Electronic Health Literacy in Swiss-German Parents: Cross-Sectional Study of eHealth Literacy Scale Unidimensionality.Journal of medical Internet research · 2020Article
- Validation of the Ethiopian Version of eHealth Literacy Scale (ET-eHEALS) in a Population with Chronic Disease.Risk management and healthcare policy · 2020Article
- Adapting the eHealth Literacy Scale for Carers of People With Chronic Diseases (eHeals-Carer) in a Sample of Greek and Cypriot Carers of People With Dementia: Reliability and Validation Study.Journal of medical Internet research · 2019Article
- The Psychometric Properties of the Chinese eHealth Literacy Scale (C-eHEALS) in a Chinese Rural Population: Cross-Sectional Validation Study.Journal of medical Internet research · 2019Article
- The Reliability and Validity of the Telephone-Based and Online Polish eHealth Literacy Scale Based on Two Nationally Representative Samples.International journal of environmental research and public health · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundElectronic health (eHealth) literacy is needed to effectively engage with Web-based health resources. The 8-item eHealth literacy scale (eHEALS) is a commonly used self-report measure of eHealth literacy. Accumulated evidence has suggested that the eHEALS is unidimensional. However, a recent study by Sudbury-Riley and colleagues suggested that a theoretically-informed three-factor model fit better than a one-factor model. The 3 factors identified were awareness (2 items), skills (3 items), and evaluate (3 items). It is important to determine whether these findings can be replicated in other populations.
objectiveThe aim of this cross-sectional study was to verify the three-factor eHEALS structure among magnetic resonance imaging (MRI) and computed tomography (CT) medical imaging outpatients.
methodsMRI and CT outpatients were recruited consecutively in the waiting room of one major public hospital. Participants self-completed a touchscreen computer survey, assessing their sociodemographic, scan, and internet use characteristics. The eHEALS was administered to internet users, and the three-factor structure was tested using structural equation modeling.
resultsOf 405 invited patients, 87.4% (354/405) were interested in participating in the study, and of these, 75.7% (268/354) were eligible. Of the eligible participants, 95.5% (256/268) completed all eHEALS items. Factor loadings were 0.80 to 0.94 and statistically significant (P<.001). All reliability measures were acceptable (indicator reliability: awareness=.71-.89, skills=.78-.80, evaluate=.64-.79; composite reliability: awareness=.89, skills=.92, evaluate=.89; variance extracted estimates: awareness=.80, skills=.79, evaluate=.72). Two out of three goodness-of-fit indices were adequate (standardized root mean square residual (SRMR)=.038; comparative fit index (CFI)=.944; root mean square error of approximation (RMSEA)=.156). Item 3 was removed because of its significant correlation with item 2 (Lagrange multiplier [LM] estimate 104.02; P<.001) and high loading on 2 factors (LM estimate 91.11; P<.001). All 3 indices of the resulting 7-item model indicated goodness of fit (χ
conclusionsThe three-factor eHEALS structure was supported in this sample of MRI and CT medical imaging outpatients. Although further factorial validation studies are needed, these 3 scale factors may be used to identify individuals who could benefit from interventions to improve eHealth literacy awareness, skill, and evaluation competencies.
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