Diabetes Management and mHealth Technology: The Importance of Healthcare Partnterships and Clinical Integration

Sunday, 24 July 2016: 3:35 PM

Sheridan Miyamoto, PhD, MSN, FNP, RN
College of Nursing, The Pennsylvania State University, University Park, PA, USA
Stuart Henderson, PhD
Evaluation, Clinical and Translational Science Center, UC Davis, Sacramento, CA, USA
Sarina Fazio, MS, BSN, RN
Betty Irene Moore School of Nursing, University of California Davis Health System, Sacramento, CA, USA
Madan Dharmar, PhD, MBBS
Betty Irene Moore School of Nursing and Department of Pediatrics, University of California Davis, Sacramento, CA, USA
Heather M. Young, PhD, RN, FAAN
Betty Irene Moore School of Nursing, University of California Davis, Sacramento, CA, USA

Purpose:  Diabetes affects more than 29 million people in the United States, and an estimated 86 million people have pre-diabetes. The World Health Organization estimates that 9% of people have diabetes globally, and that by 2030, diabetes will be the 7th leading cause of death. Diabetes type-2, the most common type of diabetes, is amenable to interventions that focus on behavior changes such as physical activity and diet. There is increasing evidence that person-centered models of care that target behavioral health are more successful in improving and addressing chronic illnesses such as diabetes. mHealth technologies are emerging as a promising approach to engage persons with diabetes in improving their management of the disease.  Smart phones apps and text messaging allow persons with diabetes to receive health information wherever they are. If this technology is developed to allow bi-directional, timely communication of data and tailored feedback, it has the potential to change an individual’s health behavior and prevent or mitigate the factors that lead to disease. Globally, over 4 billion people are using mobile phones, and almost half have smart phones. Given that 91% of adults in the United States own a mobile phone, 63% of adult cell phone owners report use of their phone to access the internet, and 62% of adults with two or more chronic conditions report tracking a health indicator, it appears the barriers to mHealth technology access are being quickly overcome and will assume a larger role in future health care leading to improved health outcomes in individuals with chronic diseases. Despite promising statistics of widespread mobile adoption and studies which detail preferences of potential mHealth users, little evidence exists about which users are likely to adopt and benefit from the technology being created.  The types of users most appropriate for mHealth as well as the barriers and drivers for this technology are still not well understood. As mHealth expands, better understanding of potential users is essential to ensure the right content and technology is offered to the right user at the right time in order to move people forward on a behavior change continuum.

Methods: This presentation will feature results of focus group interviews with stakeholders (persons with diabetes, providers and technology experts) regarding their experiences, expectations, and recommendations for design of integrated sensor and mobile health technologies into healthcare delivery for diabetes self-management support.  We explored users’ opinions and reactions to multiple mobile health technology devices, delivery approaches, and health care team interactions to gain a better understanding of the role this technology may have in sustaining individuals’ interest in improving their health.  Our team conducted 8 focus groups with stakeholders.  Prior to attending the group, participants completed a survey requesting demographic information, experience with mobile health technology, and self-rated wellness.  Focus group topics included participants’ experiences with mobile technology and with health behavior change, reactions to current mobile health technology, preferences for the type of health data to be collected, and views on privacy and data sharing.  A combination of deductive and emergent coding strategies were used to identify themes from the focus groups.

 Results: The eight focus groups of people living with chronic disease were comprised of 36 women and 14 men, with an average of 6 participants in each group.  Participants ranged in age from 18 to 86 years; 56% self-identified as white, 15% as African-American, 19% as Latino, and 8% as Pacific Islander, East Indian or American Indian. Most participants rated their health as fair, good, or very good, although 63% reported living with a chronic health problem.  There were 42 health care providers and technology experts involved in 5 focus groups to explore the views of these experts in envisioning the technology and systems that would best support improving the health of individuals using technology that interfaces with health care partners.

Despite variation of participants’ reactions to mobile health as a technology to support behavior change, some patterns emerged. At the extremes were potential users who were enthusiastic about using mobile health technology for behavior change and those who were skeptical it could add value to what they were already doing. General reactions could be placed on a high/low preparedness continuum regarding their desire for health data and their attention to their current health habits.  In addition, contextual factors such as trust, functionality, integration and customization play a role in moving people along the continuum of wanting to track personalized health data and/or focusing on their health behaviors.  Persons with diabetes described their experience of daily awareness of their condition and the need to monitor many aspects of their lives. They reported ways they coped with the physical and mental challenges of living with chronic illness and highlighted ways technology and coaching could support their health trajectory.  Providers emphasized the importance of integration of patient-generated data into both the electronic health record displays and into clinic workflow. They expressed enthusiasm for having access to synthesized and summarized patient-generated data that would provide insight into behavioral health efforts. Technology experts identified issues of scalability and application of this technology across multiple use cases.

Conclusion: Innovative health technologies have the potential to increase engagement of individuals with diabetes with personalized, targeted education, action plans or feedback wherever they may be.  Providers and technology experts endorsed the concept of technology enabled patient-generated data and advised on design features to optimize integration. Research and health programs that are person-centered and responsive to priorities of the person living with diabetes have the potential to promote healthier behaviors, motivate these individuals and improve care and outcomes.