Enhancing diabetes self-management through collection and visualization of data from multiple mobile health technologies: Protocol for a development and feasibility trial

Ryan J. Shaw, Angel Barnes, Dori Steinberg, Jacqueline Vaughn, Anna Diane, Erica Levine, Allison Vorderstrasse, Matthew J. Crowley, Eleanor Wood, Daniel Hatch, Allison Lewinski, Meilin Jiang, Janee Stevenson, Qing Yang

Research output: Contribution to journalArticle

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. However, timely information for these behaviors is notably absent in the health care 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 (eg, wearables, apps, and connected scales) have the potential to make these patient-provider interactions a reality. 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: This study aims to 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 6-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 6 months using a wireless body scale, wireless glucometer, a wrist-worn accelerometer, and a medication adherence text message (short message service, SMS) survey. Data generated from the devices were then analyzed and visualized. A subset of patients is 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. Patient enrollment ongoing with a sample size of 60 patients is complete, and up to 20 clinicians will be enrolled. 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.

Original languageEnglish (US)
Article numbere13517
JournalJournal of medical Internet research
Volume21
Issue number6
DOIs
StatePublished - Jan 1 2019

Fingerprint

Biomedical Technology
Telemedicine
Self Care
Text Messaging
Type 2 Diabetes Mellitus
Technology
Wrist
Time Management
Medication Adherence
Health Behavior
Health
Sample Size
Health Status

Keywords

  • Self-management
  • Technology
  • Type 2 diabetes

ASJC Scopus subject areas

  • Health Informatics

Cite this

Enhancing diabetes self-management through collection and visualization of data from multiple mobile health technologies : Protocol for a development and feasibility trial. / Shaw, Ryan J.; Barnes, Angel; Steinberg, Dori; Vaughn, Jacqueline; Diane, Anna; Levine, Erica; Vorderstrasse, Allison; Crowley, Matthew J.; Wood, Eleanor; Hatch, Daniel; Lewinski, Allison; Jiang, Meilin; Stevenson, Janee; Yang, Qing.

In: Journal of medical Internet research, Vol. 21, No. 6, e13517, 01.01.2019.

Research output: Contribution to journalArticle

Shaw, RJ, Barnes, A, Steinberg, D, Vaughn, J, Diane, A, Levine, E, Vorderstrasse, A, Crowley, MJ, Wood, E, Hatch, D, Lewinski, A, Jiang, M, Stevenson, J & Yang, Q 2019, 'Enhancing diabetes self-management through collection and visualization of data from multiple mobile health technologies: Protocol for a development and feasibility trial', Journal of medical Internet research, vol. 21, no. 6, e13517. https://doi.org/10.2196/13517
Shaw, Ryan J. ; Barnes, Angel ; Steinberg, Dori ; Vaughn, Jacqueline ; Diane, Anna ; Levine, Erica ; Vorderstrasse, Allison ; Crowley, Matthew J. ; Wood, Eleanor ; Hatch, Daniel ; Lewinski, Allison ; Jiang, Meilin ; Stevenson, Janee ; Yang, Qing. / Enhancing diabetes self-management through collection and visualization of data from multiple mobile health technologies : Protocol for a development and feasibility trial. In: Journal of medical Internet research. 2019 ; Vol. 21, No. 6.
@article{2ed15f5ca15b433789949cc20d2c0896,
title = "Enhancing diabetes self-management through collection and visualization of data from multiple mobile health technologies: Protocol for a development and feasibility trial",
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. However, timely information for these behaviors is notably absent in the health care 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 (eg, wearables, apps, and connected scales) have the potential to make these patient-provider interactions a reality. 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: This study aims to 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 6-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 6 months using a wireless body scale, wireless glucometer, a wrist-worn accelerometer, and a medication adherence text message (short message service, SMS) survey. Data generated from the devices were then analyzed and visualized. A subset of patients is 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. Patient enrollment ongoing with a sample size of 60 patients is complete, and up to 20 clinicians will be enrolled. 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.",
keywords = "Self-management, Technology, Type 2 diabetes",
author = "Shaw, {Ryan J.} and Angel Barnes and Dori Steinberg and Jacqueline Vaughn and Anna Diane and Erica Levine and Allison Vorderstrasse and Crowley, {Matthew J.} and Eleanor Wood and Daniel Hatch and Allison Lewinski and Meilin Jiang and Janee Stevenson and Qing Yang",
year = "2019",
month = "1",
day = "1",
doi = "10.2196/13517",
language = "English (US)",
volume = "21",
journal = "Journal of Medical Internet Research",
issn = "1439-4456",
publisher = "Journal of medical Internet Research",
number = "6",

}

TY - JOUR

T1 - Enhancing diabetes self-management through collection and visualization of data from multiple mobile health technologies

T2 - Protocol for a development and feasibility trial

AU - Shaw, Ryan J.

AU - Barnes, Angel

AU - Steinberg, Dori

AU - Vaughn, Jacqueline

AU - Diane, Anna

AU - Levine, Erica

AU - Vorderstrasse, Allison

AU - Crowley, Matthew J.

AU - Wood, Eleanor

AU - Hatch, Daniel

AU - Lewinski, Allison

AU - Jiang, Meilin

AU - Stevenson, Janee

AU - Yang, Qing

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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. However, timely information for these behaviors is notably absent in the health care 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 (eg, wearables, apps, and connected scales) have the potential to make these patient-provider interactions a reality. 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: This study aims to 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 6-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 6 months using a wireless body scale, wireless glucometer, a wrist-worn accelerometer, and a medication adherence text message (short message service, SMS) survey. Data generated from the devices were then analyzed and visualized. A subset of patients is 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. Patient enrollment ongoing with a sample size of 60 patients is complete, and up to 20 clinicians will be enrolled. 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.

AB - 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. However, timely information for these behaviors is notably absent in the health care 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 (eg, wearables, apps, and connected scales) have the potential to make these patient-provider interactions a reality. 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: This study aims to 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 6-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 6 months using a wireless body scale, wireless glucometer, a wrist-worn accelerometer, and a medication adherence text message (short message service, SMS) survey. Data generated from the devices were then analyzed and visualized. A subset of patients is 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. Patient enrollment ongoing with a sample size of 60 patients is complete, and up to 20 clinicians will be enrolled. 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.

KW - Self-management

KW - Technology

KW - Type 2 diabetes

UR - http://www.scopus.com/inward/record.url?scp=85071311020&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85071311020&partnerID=8YFLogxK

U2 - 10.2196/13517

DO - 10.2196/13517

M3 - Article

VL - 21

JO - Journal of Medical Internet Research

JF - Journal of Medical Internet Research

SN - 1439-4456

IS - 6

M1 - e13517

ER -