ENhancing Outcomes Through Goal Assessment and Generating Engagement in Diabetes Mellitus (ENGAGE-DM): A 12-month Study of the Impact of Combined Shared-decision Making and Brief Negotiated Interviewing on Disease Control and Medication Adherence in Patients With Diabetes

The US is facing a growing epidemic of type 2 diabetes. Among patients with poorly controlled diabetes, it is often not clear whether the problem is attributable to failure to appropriately intensify therapy, poor adherence to prescribed medications, unwillingness to accept new treatments or a combination of these factors. Multi-component pharmacist-delivered interventions, particularly those rooted in patient engagement, have been shown to be some of the most effective at improving adherence to chronic disease medications. Even though shared decision making and brief negotiated interviewing are complementary patient engagement techniques, the effectiveness of combining these 2 intervention approaches, especially in the management of diabetes, is unknown.

In this study of patients using at least one oral hypoglycemic therapy with poorly controlled disease, the investigators examine the impact of a shared decision making and behavioral interviewing intervention delivered telephonically by pharmacists, compared with usual care. Briefly, all patients allocated to the intervention will be mailed a patient decision aid to prime them for encounters with pharmacists. After receiving the decision aid, these patients will be asked to engage in and provide informed consent for at least 4 telephonic discussions with pharmacists about their diabetes treatment options, goals, and preferences, medication adherence, strategies for reducing adherence barriers, and the benefits of maintaining blood glucose control. The study is being conducted within a large insurer and consists of 700 patients each allocated to the intervention group and the control group. Analyses will be performed on an intent-to-treat basis.

After study completion, the investigators will also use predictive analytics to examine whether treatment response could be predicted based on patient characteristics, such as sociodemographic, clinical, medication use, and other motivational characteristics, which will provide information about which patients are most likely to benefit from the intervention.