Diabetes self-management support using mHealth and enhanced informal caregiving

Abstract

Objective

To characterize diabetes patient engagement and clinician notifications for an mHealth interactive voice response (IVR) service.

Design

Observational study.

Methods

For three to six months, VA patients with diabetes received weekly IVR calls assessing health status and self-care along with tailored education. Patients could enroll with an informal caregiver who received suggestions on self-management support. Notifications were issued to clinicians when patients reported significant problems.

Results

Patients (n = 303) participated for a total of 5684 patient-weeks, during which 84% of calls were completed. The odds of call completion decreased over time (AOR = 0.96, p < 0.001), and were lower among unmarried patients (AOR = 0.67, p = 0.038) and those who had difficulties with health literacy (AOR = 0.67, p = 0.039), diabetes-related distress (AOR = 0.30, p = 0.018), or medication nonadherence (AOR = 0.57, p = 0.002). Twenty-one clinician notifications were triggered per 100 patient-weeks. The odds of notification were higher during the early weeks of the program (AOR = 0.95, p < 0.001) and among patients who were older (AOR = 1.03, p = 0.004) or more physically impaired (AOR = 0.97, p < 0.001).

Conclusions

By providing information that is reliable, valid, and actionable, IVR-based mHealth services may increase access to between-visit monitoring and diabetes self-management support. The system detects abnormal glycemia and blood pressure levels that might otherwise go unreported, although thresholds for clinician notifications might require adjustment to avoid overloading clinicians. Patient engagement might be enhanced by addressing health literacy and psychological distress.

Introduction

Inadequate self-management of blood glucose and blood pressure in type 2 diabetes is prospectively associated with chronic hyperglycemia, microvascular complications, and heart disease (Alberti & Zimmet, 1998). Although outcomes can improve with care management (Anderson, Funnell, Aikens, et al., 2009), comprehensive services are often unavailable due to limitations in the availability of personnel, appropriate technologies for between-visit monitoring, and reimbursement for telephone contacts (Piette, 1997). Mobile health (mHealth) refers to the use of mobile devices to support medical care and public health. It appears that mHealth services, including interactive voice response (IVR) calls (in which a person responds to re-recorded prompts from a calling computer using their telephone keypad), may help address these barriers to effective care (Piette, 2007, Schwartz, 1998).

Another strategy to improve outcomes is to enhance patients’ social support for self-management. In-home caregivers often lack the tools needed to systematically monitor changes in patients’ diabetes-related health status and support their self-care (Rosland, Heisler, Janevic, et al., 2013); and many caregivers are at risk for burnout (Armour, Norris, Jack, Zhang, & Fisher, 2005). Moreover, many patients live alone, with up to 7 million Americans receiving “long-distance” caregiving (Talley & Crews, 2007). In order to enable geographically-distant supportive individuals to be more involved and effective, we developed an mHealth service using IVR to provide patient monitoring and self-care support between clinician contacts.

In this report, we describe the implementation of this program in primary care settings. Patients with diabetes received weekly automated IVR monitoring and self-care support calls designed to assess self-monitoring of blood glucose (SMBG), medication and dietary adherence, blood glucose levels, blood pressure levels, foot inspection, and overall functioning. If the patient reported a difficulty in any of these key areas, the system provided the patient with pre-recorded self-management education corresponding to the area of difficulty. In addition, the system provided automated updates on patients’ status to an informal caregiver living outside the patient’s home, and notified the primary care team when the patient reported clinically significant problems. To better understand program implementation, we investigated sociodemographic indices, physical functioning, depressive symptoms, diabetes related distress, and functional health literacy as predictors of variation in: (a) patient engagement and the frequency, and (b) the types of clinical feedback generated by the service.