JMIR Publications

ABSTRACT

Background:

Variations in individual’s eHealth literacy may influence the degree to which health consumers can draw benefits from eHealth. The eHealth Literacy Scale (eHEALS) is a common measure of eHealth literacy. However, there is an absence of guidelines for the standardised, accurate and clinically meaningful interpretation of eHEALS scores. Cut-points are often arbitrarily applied at the eHEALS item or global level, assuming a dichotomy of high and low eHealth literacy, disregarding scale constructs and resulting in inaccurate and inconsistent conclusions. Cluster analysis is an exploratory technique which can be used to overcome these issues, by identifying classes of patients reporting similar eHealth literacy, without imposing data cut-points. Such analysis is needed to improve our understanding of eHealth literacy within patient populations. Hence, informing the potential utility of eHealth in clinical settings, and development of targeted eHealth literacy improvement interventions.

Objective:

This cross-sectional study sought to identify classes of patients reporting similar eHealth literacy, and to assess characteristics associated with class membership.

Methods:

Medical imaging outpatients were recruited consecutively in the waiting room of one major public hospital in NSW, Australia. Participants completed a self-report questionnaire assessing their sociodemographic characteristics and eHealth literacy, using the eHEALS. Latent class analysis was used to explore eHealth literacy clusters identified by distance-based cluster analysis, and to identify characteristics associated with class membership.

Results:

Of 268 eligible and consenting participants, 256 (95.5%) completed the eHEALS. Consistent with distance-based findings, four latent classes were identified, which were labelled as low (21.1%), moderate (26.2%), high (32.8%) and very high (19.9%) eHealth literacy. Compared to the low class, participants who preferred to receive a lot of health information reported significantly higher odds of moderate eHealth literacy (OR = 16.67; 95% CI = 1.67 - 100.00; P = .02), and those who used the internet at least daily reported significantly higher odds of high eHealth literacy (OR = 4.76; 95% CI = 1.59 - 14.29; P = 0.007).

Conclusions:

The identification of multiple classes, using both distance-based and latent class analyses, highlights issues with using the eHEALS global score as a dichotomous measurement tool, and suggests that eHealth literacy support needs vary in this population. Particularly, low and moderate eHealth literacy classes indicate that eHealth literacy improvement interventions are needed, and these should be targeted based on individuals’ internet use frequency and health information amount preferences. However, minimal high-quality intervention research has been completed to determine the effectiveness of such interventions, and therefore, an evidence gap remains. Until this is addressed, the design and implementation of eHealth resources should be tailored to patients’ varying levels of eHealth literacy. This may promote eHealth engagement and receipt of benefits.