Self-monitoring diabetes with multiple mobile health devices

Abstract

Objective The purpose of this study was to examine the use of multiple mobile health technologies to generate and transmit data from diverse patients with type 2 diabetes mellitus (T2DM) in between clinic visits. We examined the data to identify patterns that describe characteristics of patients for clinical insights. Methods We enrolled 60 adults with T2DM from a US healthcare system to participate in a 6-month longitudinal feasibility trial. Patient weight, physical activity, and blood glucose were self-monitored via devices provided at baseline. Patients also responded to biweekly medication adherence text message surveys. Data were aggregated in near real-time. Measures of feasibility assessing total engagement in device submissions and survey completion over the 6 months of observation were calculated. Results It was feasible for participants from different socioeconomic, educational, and racial backgrounds to use and track relevant diabetes-related data from multiple mobile health devices for at least 6 months. Both the transmission and engagement of the data revealed notable patterns and varied by patient characteristics. Discussion Using multiple mobile health tools allowed us to derive clinical insights from diverse patients with diabetes. The ubiquitous adoption of smartphones across racial, educational, and socioeconomic populations and the integration of data from mobile health devices into electronic health records present an opportunity to develop new models of care delivery for patients with T2DM that may promote equity as well.

Testing the Feasibility of Sensor-Based Home Health Monitoring (TEC4Home) to Support the Convalescence of Patients With Heart Failure: Pre–Post Study

“…Our participants submitted data on nearly 95% of days, which compares favorably to similar studies. A recent telemonitoring feasibility study with patients with type 2 diabetes mellitus found lower adherence rates compared to our trial [18]. We further found the success rate of presurvey completion to be 67% (47/70), postsurvey completion to be 70% (49/70), and pre-and postsurvey completion by the same participants to be 50% (35/70).…”

Perceptions of Using Multiple Mobile Health Devices to Support Self‐Management Among Adults With Type 2 Diabetes: A Qualitative Descriptive Study

“…We chose a qualitative descriptive study design to examine qualitative data collected as part of the Diabetes Mobile Care study; protocol details and findings (Shaw et al, 2019; Shaw et al, 2020) were published elsewhere. All research occurred at an academic medical center in the southeastern United States.…”

“…referenced the participant's use of the three mobile devices and their perceptions of the data visualizations. Questions regarding use of the devices focused on overall experience, challenges and problems, problem solving, (Burford et al, 2019;Choe et al, 2014;Lewinski et al, 2019;Lewinski et al, 2018;Shaw et al, 2019;Shaw et al, 2020;Zarghom et al, 2013) perceptions of support, and discussions with others (i.e., family, friends, clinicians). Questions about the visualizations concerned overall impressions, perception of missing data and displays, and suggestions for improvements.…”

“…mHealth devices enable the in situ collection of large quantities of longitudinal patient‐generated behavioral and clinical data, which facilitate evaluation of the individual’s health status and/or responses to T2DM medical care (Sieverdes et al, 2013). Although in a recent study by Shaw et al (2020) adults with T2DM found it feasible to use multiple mobile devices to support their self‐management of T2DM, individuals with T2DM may feel challenged or overwhelmed by behavioral and clinical data collected from multiple mHealth devices (Caban & Gotz, 2015). Data overload and fatigue, and lack of understanding of the data collected, can lead overwhelmed individuals to discontinue their use of devices altogether (Caban & Gotz, 2015).…”

Quantification of Smoking Characteristics Using Smartwatch Technology: Pilot Feasibility Study of New Technology

“…In recent years, there have been several reports of utilizing commercially available smartwatches in studying human activities. These include generic activities such as step counts, sleep detection, and rest periods, while others include more specific activities such as eating [4], drinking [5], managing diabetes [6,7], or smoking [8]. Previous work has established the use of wristworn devices in observing and interpreting smoking behavior in laboratory settings [9][10][11][12][13] and in situ [8,14].…”