Disparities in the use of a mHealth medication adherence promotion intervention for low-income adults with type 2 diabetes

Abstract

Objective: Mobile health (mHealth) interventions may improve diabetes outcomes, but require engagement. Little is known about what factors impede engagement, so the authors examined the relationship between patient factors and engagement in an mHealth medication adherence promotion intervention for low-income adults with type 2 diabetes (T2DM).

Materials and methods: Eighty patients with T2DM participated in a 3-month mHealth intervention called MEssaging for Diabetes that leveraged a mobile communications platform. Participants received daily text messages addressing and assessing medication adherence, and weekly interactive automated calls with adherence feedback and questions for problem solving. Longitudinal repeated measures analyses assessed the relationship between participants' baseline characteristics and the probability of engaging with texts and calls.

Results: On average, participants responded to 84.0% of texts and participated in 57.1% of calls. Compared to Whites, non-Whites had a 63% decreased relative odds (adjusted odds ratio [AOR] = 0.37, 95% confidence interval [CI], 0.19-0.73) of participating in calls. In addition, lower health literacy was associated with a decreased odds of participating in calls (AOR = 0.67, 95% CI, 0.46-0.99, P = .04), whereas older age (Pnonlinear = .01) and more depressive symptoms (AOR = 0.62, 95% CI, 0.38-1.02, P = .059) trended toward a decreased odds of responding to texts.

Conclusions: Racial/ethnic minorities, older adults, and persons with lower health literacy or more depressive symptoms appeared to be the least engaged in a mHealth intervention. To facilitate equitable intervention impact, future research should identify and address factors interfering with mHealth engagement.

Keywords: disparities; mHealth; medication adherence; patient engagement; type 2 diabetes mellitus.

© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Figures

Figure 1:

Flow chart of patient recruitment and enrollment.

Figure 2:

The association between participants age and the probability of responding to two-way text messages Note: We used repeated measures logistic regression to assess the association between age and probability of text response with adjustment for covariates and clustering. Age was included as a non-linear term using restricted cubic splines.

Figure 3:

The association between time exposed to the intervention (in weeks) and the probability of participating in IVR calls Note: We used repeated measures logistic regression to assess the association between time and probability of call participation with adjustment for covariates and clustering.