Lyndsay A Nelson1, Shelagh A Mulvaney2, Tebeb Gebretsadik3, Yun-Xian Ho4, Kevin B Johnson5, Chandra Y Osborn6. 1. Department of Medicine, Vanderbilt University, Nashville, TN, USA Center for Health Behavior and Health Education, Vanderbilt University, Nashville, TN, USA. 2. School of Nursing, Vanderbilt University, Nashville, TN, USA Center for Diabetes Translational Research, Vanderbilt University, Nashville, TN, USA Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA Department of Pediatrics, Vanderbilt University, Nashville, TN, USA. 3. Department of Biostatistics, Vanderbilt University, Nashville, TN, USA. 4. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. 5. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA Department of Pediatrics, Vanderbilt University, Nashville, TN, USA. 6. Department of Medicine, Vanderbilt University, Nashville, TN, USA Center for Health Behavior and Health Education, Vanderbilt University, Nashville, TN, USA Center for Diabetes Translational Research, Vanderbilt University, Nashville, TN, USA Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA chandra.osborn@vanderbilt.edu.
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.
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.
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