JMIR Publications

ABSTRACT

Background:

Self-management is integral for control of type 2 diabetes mellitus (T2DM). Patient self-management is improved when they receive real-time information on their health status and behaviors and ongoing facilitation from health professionals. Yet, timely information for these behaviors is notably absent in the healthcare system. Providing real-time data could help improve patient understanding of the dynamics of their illness and assist clinicians in developing targeted approaches to improve health outcomes and in delivering personalized care when and where it is most needed. Mobile technologies (e.g., wearables, apps, connected scales) have the potential to make these patient-provider interactions a reality. To date, there are no studies on the application of these devices for real-time care and tracking data related to T2DM. What strategies might best help patients overcome self-management challenges using self-generated diabetes-related data? How might clinicians effectively guide patient self-management with the advantage of real-time data?

Objective:

We describe the protocol for an ongoing study (June 2016 – May 2019) that examines trajectories of symptoms, health behaviors, and associated challenges among individuals with T2DM utilizing multiple mobile technologies, including a wireless body scale, wireless glucometer, and a wrist-worn accelerometer over a six-month period.

Methods:

We are conducting an explanatory sequential mixed-methods study of 60 patients with T2DM recruited from a primary care clinic. Patients were asked to track relevant clinical data for six months using a wireless body scale, wireless glucometer, a wrist-worn accelerometer, and a medication adherence text message survey. Data generated from the devices were then analyzed and visualized. A subset of patients are currently being interviewed to discuss their challenges and successes in diabetes self-management, and they are being shown visualizations of their own data. Following the data collection period, we will conduct interviews with study clinicians to explore ways in which they might collaborate with patients.

Results:

This study has received regulatory approval. Enrollment is ongoing with a sample size of 60 patients and up to 20 clinicians. At the patient level, data collection is complete but data analysis is pending. At the clinician level, data collection is currently ongoing.

Conclusions:

This study seeks to expand the use of mobile technologies to generate real-time data to enhance self-management strategies. It also seeks to obtain both patient and provider perspectives on using real-time data to develop algorithms for software that will facilitate real-time self-management strategies. We expect that the findings of this study will offer important insight into how to support patients and providers using real-time data to manage a complex chronic illness. Clinical Trial: NCT03012074