Allison A Lewinski1, Jacqueline Vaughn2, Anna Diane3, Angel Barnes4, Matthew J Crowley5, Dori Steinberg6, Janee Stevenson7, Qing Yang8, Allison A Vorderstrasse9, Daniel Hatch10, Meilin Jiang11, Ryan J Shaw12. 1. Research Health Scientist, Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC; Assistant Professor, School of Nursing, Duke University, Durham, NC, USA. 2. Clinical Instructor, School of Nursing, Duke University, Durham, NC; Postdoctoral Fellow, School of Nursing, University of North Carolina, Chapel Hill, NC, USA. 3. PhD student, School of Nursing, Duke University, Durham, NC, USA. 4. Clinical Research Coordinator, School of Nursing, Duke University, Durham, NC, USA. 5. Investigator, Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC; Associate Professor, Division of Endocrinology, Diabetes and Metabolism, Duke University School of Medicine, Durham, NC, USA. 6. Associate Professor, School of Nursing, Duke University, Durham, NC, USA. 7. Master of Nursing student, School of Nursing, Winston-Salem State University, Winston Salem, NC, USA. 8. Assistant Professor, School of Nursing, Duke University, Durham, NC, USA. 9. Professor and Dean, College of Nursing, University of Massachusetts Amherst, Amherst, MA, USA. 10. Biostatistician, School of Nursing, Duke University, Durham, NC, USA. 11. PhD student, University of Florida College of Public Health and Health Professions, University of Florida College of Medicine, Gainesville, FL, USA. 12. Associate Professor, School of Nursing, Duke University, Durham, NC; Center for Applied Genomics & Precision Medicine, School of Medicine, Duke University, Durham, NC, USA.
Abstract
PURPOSE: This study identified facilitators and barriers pertaining to the use of multiple mobile health (mHealth) devices (Fitbit Alta® fitness tracker, iHealth® glucometer, BodyTrace® scale) that support self-management behaviors in individuals with type 2 diabetes mellitus (T2DM). DESIGN: This qualitative descriptive study presents study participants' perceptions of using multiple mobile devices to support T2DM self-management. Additionally, this study assessed whether participants found visualizations, generated from each participant's health data as obtained from the three separate devices, useful and easy to interpret. METHODS: Semistructured interviews were completed with a convenience sample of participants (n = 20) from a larger randomized control trial on T2DM self-management. Interview questions focused on participants' use of three devices to support T2DM self-management. A study team member created data visualizations of each interview participant's health data using RStudio. RESULTS: We identified two themes from descriptions of study participants: feasibility and usability. We identified one theme about visualizations created from data obtained from the mobile devices. Despite some challenges, individuals with T2DM found it feasible to use multiple mobile devices to facilitate engagement in T2DM self-management behaviors. DISCUSSION: As mHealth devices become increasingly popular for diabetes self-management and are integrated into care delivery, we must address issues associated with the use of multiple mHealth devices and the use of aggregate data to support T2DM self-management. CLINICAL RELEVANCE: Real-time patient-generated health data that are easily accessible and readily available can assist T2DM self-management and catalyze conversations, leading to better self-management. Our findings lay an important groundwork for understanding how individuals with T2DM can use multiple mHealth devices simultaneously to support self-management.
PURPOSE: This study identified facilitators and barriers pertaining to the use of multiple mobile health (mHealth) devices (Fitbit Alta® fitness tracker, iHealth® glucometer, BodyTrace® scale) that support self-management behaviors in individuals with type 2 diabetes mellitus (T2DM). DESIGN: This qualitative descriptive study presents study participants' perceptions of using multiple mobile devices to support T2DM self-management. Additionally, this study assessed whether participants found visualizations, generated from each participant's health data as obtained from the three separate devices, useful and easy to interpret. METHODS: Semistructured interviews were completed with a convenience sample of participants (n = 20) from a larger randomized control trial on T2DM self-management. Interview questions focused on participants' use of three devices to support T2DM self-management. A study team member created data visualizations of each interview participant's health data using RStudio. RESULTS: We identified two themes from descriptions of study participants: feasibility and usability. We identified one theme about visualizations created from data obtained from the mobile devices. Despite some challenges, individuals with T2DM found it feasible to use multiple mobile devices to facilitate engagement in T2DM self-management behaviors. DISCUSSION: As mHealth devices become increasingly popular for diabetes self-management and are integrated into care delivery, we must address issues associated with the use of multiple mHealth devices and the use of aggregate data to support T2DM self-management. CLINICAL RELEVANCE: Real-time patient-generated health data that are easily accessible and readily available can assist T2DM self-management and catalyze conversations, leading to better self-management. Our findings lay an important groundwork for understanding how individuals with T2DM can use multiple mHealth devices simultaneously to support self-management.
Authors: Shivani Goyal; Plinio Morita; Gary F Lewis; Catherine Yu; Emily Seto; Joseph A Cafazzo Journal: Can J Diabetes Date: 2015-10-09 Impact factor: 4.190
Authors: William Martinez; Anthony L Threatt; S Trent Rosenbloom; Kenneth A Wallston; Gerald B Hickson; Tom A Elasy Journal: JMIR Hum Factors Date: 2018-09-24