Diabetes mellitus (DM) is a growing public health problem highly amenable to prevention and health promotion interventions. Over 26 million people are diagnosed with diabetes, and an estimated 79 million people have pre-diabetes (1). Physical inactivity, poor eating habits, obesity and smoking are common risk factors for multiple chronic diseases, including DM, and are associated with premature deaths in the United States (2). Traditional interventions for diabetes emphasize education and typically do not address patient-generated health data (3). Motivational interviewing (MI) is a counseling tool to improve self-efficacy and support behavioral changes that has been used successfully in treatment of addictions and chronic conditions including diabetes (4-6). MI focuses on enhancing goal-setting skills by encouraging focus on manageable steps to improve overall health, establishing reasonable goals, and managing barriers and obstacles to goal attainment. Mobile health (mHealth) technology can be used to allow bi-directional, timely communication of data and tailored feedback between the patient and health care team; thus mHealth has the potential to change an individual' health behavior and prevent factors that lead to disease (7). This proposal tests the effectiveness of nurse coaches and the mHealth platform as resources to patients. Specifically, the investigators will test a mobile technology-enabled nurse coaching intervention as a means to engage and empower patients to set their own personal goals to address lifestyle and behavioral changes to better manage their diabetes and comorbidities. The addition of a wireless sensing wearable device to monitor participant activities (physical activity, energy expenditure and sleep quality) will allow the investigators to perform meaningful analysis of patient-generated health data and provide tailored feedback to motivate the patient to reach personal goals. The investigators will achieve this project through the following Specific Aims:

Specific Aim 1: To evaluate the effectiveness of the P2E2T2 program on diabetes management as measured by the following outcomes: 1) quality of life (QOL); 2) self-efficacy; 3) readiness to change; and 4) clinically relevant outcomes.

This study is a randomized, controlled trial with two groups: 1) Usual care (Care Coordination); and 2) the Patient and Provider Engagement and Empowerment Through Technology (P2E2T2) program - nurse coaching paired with mobile sensor technology to provide targeted feedback of patient-generated, real-world community physical activity and sleep quality data to the nurse coach, participants, and primary care providers to improve self-management of diabetes. Participants will be recruited from the UC Davis Primary Care Clinics network. After enrollment in the study, those randomized to receive the intervention will interact with their nurse coach through mobile technologies by telephone, or by text/e-mail through the secure mHealth dashboard platform established for communication and transfer of data. The investigators expect to enroll at least 150 patients in each of the intervention and control arms. This sample size will be sufficient to detect differences between the two groups. Even under the conservative assumption that design effects and dropout rates may result in a reduced sample size of 100 per treatment group, the study will still have at least 80% power to detect the specified clinically important effect size. Based on a previous study of nurse coaching using MI to improve disease self-management (8), the investigators found improvement in self-efficacy scores significantly higher in the intervention group compared to the control group. Data will be collected from participants at three time points: 1) baseline data at time of recruitment; 2) 3 months (coinciding with the completion of the intervention); and 3) 9 months (selected to assess sustained effects of the intervention (6 months after intervention ended)). At baseline, in addition to outcomes, all participants will complete a demographic survey which will include age, gender, race/ethnicity, education level, income level and insurance type and health history using the Charlson Comorbidity Index which includes common chronic conditions. All data for the study will be collected by the research coordinator and entered into the REDCap (Research Electronic Data Capture) system housed in the servers managed by UC Davis Clinical and Translational Science Center (CTSC) (9).