Examination of an eHealth literacy scale and a health literacy scale in a population with moderate to high cardiovascular risk: Rasch analyses

Sarah S Richtering, Rebecca Morris, Sze-Ee Soh, Anna Barker, Fiona Bampi, Lis Neubeck, Genevieve Coorey, John Mulley, John Chalmers, Tim Usherwood, David Peiris, Clara K Chow, Julie Redfern, Sarah S Richtering, Rebecca Morris, Sze-Ee Soh, Anna Barker, Fiona Bampi, Lis Neubeck, Genevieve Coorey, John Mulley, John Chalmers, Tim Usherwood, David Peiris, Clara K Chow, Julie Redfern

Abstract

Introduction: 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.

Methods: 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.

Results: 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).

Conclusion: 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.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1. Targeting of the eHEALS as…
Fig 1. Targeting of the eHEALS as demonstrated by the person-item threshold distribution.
Fig 2
Fig 2
Category probability curve showing (a) disordered thresholds in HLQ subscale-9, Item 26 (b) ordered thresholds when response categories ‘very difficult’ and ‘quite difficult’ are collapsed into one.
Fig 3. Targeting of the HLQ subscale-4…
Fig 3. Targeting of the HLQ subscale-4 as demonstrated by the Person-item Threshold Distribution.

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