A Psychometric Analysis of the Italian Version of the eHealth Literacy Scale Using Item Response and Classical Test Theory Methods

Diviani, Nicola; Dima, Alexandra; Schulz, Peter

doi:10.2196/jmir.6749

Cited by 86 publications

(143 citation statements)

References 42 publications

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“…The factor structure and variance of eHEALS scores may differ as a function of age, which could influence results from eHEALS studies including younger versus older samples. Diviani et al [ 34 ] conducted a validation study of the Italian version of eHEALS administered among young to middle-aged adults (mean age 37.37 years, SD 13.78). Confirmatory factor analysis results showed suboptimal model fit among 2 rival models (1-factor structure vs 2-factor structure), yet parametric and nonparametric item response theory (IRT) analyses confirmed that the single-factor model best fit the data in the study sample.…”

Section: Introductionmentioning

confidence: 99%

Reliability and Validity of the Telephone-Based eHealth Literacy Scale Among Older Adults: Cross-Sectional Survey

et al. 2017

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BackgroundOnly 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.ObjectiveThe 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.MethodsRespondents (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.ResultsCompared 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.ConclusionsResults 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.

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Section: Introductionmentioning

confidence: 99%

Reliability and Validity of the Telephone-Based eHealth Literacy Scale Among Older Adults: Cross-Sectional Survey

et al. 2017

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“…IRT [27,46] is one of the most widely used approaches for item evaluation and test construction [29,30,47]. For example, the Patient Reported Outcomes Measurement Information System funded by the National Institutes of Health has used IRT to characterize item banks and to support computerized adaptive testing [28].…”

Section: Methodsmentioning

confidence: 99%

“…From these notes, a group of physicians and medical researchers generated questions using the Sentence Verification Technique (SVT) [24-26]. We obtained responses for these questions from the crowdsourcing platform Amazon Mechanical Turk (AMT) and analyzed the results using item response theory (IRT) [27-30] to select a subset of questions for a test of EHR note comprehension. To the best of our knowledge, the ComprehENotes question set is the first instrument to assess EHR note comprehension.…”

Section: Introductionmentioning

confidence: 99%

ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation

Lalor¹,

Wu²,

Chen³

et al. 2018

J Med Internet Res

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BackgroundPatient portals are widely adopted in the United States and allow millions of patients access to their electronic health records (EHRs), including their EHR clinical notes. A patient’s ability to understand the information in the EHR is dependent on their overall health literacy. Although many tests of health literacy exist, none specifically focuses on EHR note comprehension.ObjectiveThe aim of this paper was to develop an instrument to assess patients’ EHR note comprehension.MethodsWe identified 6 common diseases or conditions (heart failure, diabetes, cancer, hypertension, chronic obstructive pulmonary disease, and liver failure) and selected 5 representative EHR notes for each disease or condition. One note that did not contain natural language text was removed. Questions were generated from these notes using Sentence Verification Technique and were analyzed using item response theory (IRT) to identify a set of questions that represent a good test of ability for EHR note comprehension.ResultsUsing Sentence Verification Technique, 154 questions were generated from the 29 EHR notes initially obtained. Of these, 83 were manually selected for inclusion in the Amazon Mechanical Turk crowdsourcing tasks and 55 were ultimately retained following IRT analysis. A follow-up validation with a second Amazon Mechanical Turk task and IRT analysis confirmed that the 55 questions test a latent ability dimension for EHR note comprehension. A short test of 14 items was created along with the 55-item test.ConclusionsWe developed ComprehENotes, an instrument for assessing EHR note comprehension from existing EHR notes, gathered responses using crowdsourcing, and used IRT to analyze those responses, thus resulting in a set of questions to measure EHR note comprehension. Crowdsourced responses from Amazon Mechanical Turk can be used to estimate item parameters and select a subset of items for inclusion in the test set using IRT. The final set of questions is the first test of EHR note comprehension.

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“…These socio-demographic differences are consistent for mHealth use, health literacy, eHealth Literacy and Media Health Literacy, particularly with regard to education and age, and secondarily with regard to gender and ethnic background. Cultural background has also been considered to significantly influence eHealth Literacy and Media Health Literacy so much so that researchers in South Korea [27] and Italy [28], conducted several validation studies for the eHEALs model to assure that it is relevant to local culture.…”

Section: The Role Of Sociodemographicsmentioning

confidence: 99%

Media Health Literacy, eHealth Literacy, and the Role of the Social Environment in Context

Levin-Zamir¹,

Bertschi²

2018

Preprint

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Health literacy describes skills and competencies that enable people to gain access to, understand and apply health information to positively influence their own health and the health of those in the social environment. In an increasingly media saturated and digitized world, these skill sets are necessary for accessing and navigating sources of health information and tools, such as television, the Internet, and mobile apps. The concepts of Media Health Literacy (MHL) and eHealth Literacy (eHL) describe the specific competencies such tasks require. This article introduces the two concepts, and then reviews findings on the associations of MHL and eHL with several contextual variables in the social environment such as socio-demographics, social support, and system complexity, as a structural variable. As eHL and MHL are crucial for empowering people to actively engage in their own health, a growing body of literature reports of the potential and the effectiveness of intervention initiatives to positively influence these competencies. From an ethical standpoint, equity is emphasized, stressing the importance of accessible media environments for all -including those at risk of exclusion from (digital) media sources. Alignment of micro and macro contextual spheres will ultimately facilitate both non-digital and digital media to effectively support and promote public health.

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A Psychometric Analysis of the Italian Version of the eHealth Literacy Scale Using Item Response and Classical Test Theory Methods

Cited by 86 publications

References 42 publications

Reliability and Validity of the Telephone-Based eHealth Literacy Scale Among Older Adults: Cross-Sectional Survey

Reliability and Validity of the Telephone-Based eHealth Literacy Scale Among Older Adults: Cross-Sectional Survey

ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation

Media Health Literacy, eHealth Literacy, and the Role of the Social Environment in Context

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