Latent class analysis of eHealth behaviors among adults with epilepsy

Abstract

Objective

The objective of this study was to determine the proportions of uptake and factors associated with electronic health (eHealth) behaviors among adults with epilepsy.

Methods

The 2013, 2015 and 2017 National Health Interview Surveys were analyzed. We assessed the proportions of use of five domains of eHealth in those with epilepsy: looked up health information on the internet, filled a prescription on the internet, scheduled a medical appointment on the internet, communicated with a healthcare provider via email, and used chat groups to learn about health topics. Multivariable logistic regressions were conducted to identify factors associated with any eHealth behaviors among those with active epilepsy. Latent class analysis (LCA) was performed to identify underlying patterns of eHealth activity. Survey participants were classified into 3 discrete classes – 1) frequent, 2) infrequent, and 3) non-users of eHealth. Multinomial logistic regression was performed to identify factors associated with frequency of eHealth use.

Results

There were 1,770 adults with epilepsy, of whom 65.87% had at least one eHealth behavior in the prior year. By domain, 62.61% looked up health information on the internet, 15.81% filled a prescription on the internet, 14.95% scheduled a medical appointment on the internet, 17.20% communicated with a healthcare provider via email, and 8.27% used chat groups to learn about health topics. Among those with active epilepsy, female sex, more frequent computer usage, and internet usage were associated with any eHealth behavior. Female sex and frequent computer use were associated with frequent eHealth use as compared to non-users.

Significance

A majority of persons with epilepsy were found to use at least one form of eHealth. Various technological and demographic factors were associated with eHealth behaviors. Individuals with lower eHealth behaviors should be provided with targeted interventions that address barriers to the adoption of these technologies.

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