Design and Testing of a Smartphone Application for Real-Time Self-Tracking Diabetes Self-Management Behaviors

Appl Clin Inform. 2018 Apr;9(2):440-449. doi: 10.1055/s-0038-1660438. Epub 2018 Jun 20.

Abstract

Background: Type 1 diabetes (T1D) care requires multiple daily self-management behaviors (SMBs). Preliminary studies on SMBs rely mainly on self-reported survey and interview data. There is little information on adult T1D SMBs, along with corresponding compensation techniques (CTs), gathered in real-time.

Objective: The article aims to use a patient-centered approach to design iDECIDE, a smartphone application that gathers daily diabetes SMBs and CTs related to meal and alcohol intake and exercise in real-time, and contrast patients' actual behaviors against those self-reported with the app.

Methods: Two usability studies were used to improve iDECIDE's functionality. These were followed by a 30-day pilot test of the redesigned app. A survey designed to capture diabetes SMBs and CTs was administered prior to the 30-day pilot test. Survey results were compared against iDECIDE logs.

Results: Usability studies revealed that participants desired advanced features for self-tracking meals and alcohol intake. Thirteen participants recorded over 1,200 CTs for carbohydrates during the 30-day study. Participants also recorded 76 alcohol and 166 exercise CTs. Comparisons of survey responses and iDECIDE logs showed mean% (standard deviation) concordance of 77% (25) for SMBs related to meals, where concordance of 100% indicates a perfect match. There was low concordance of 35% (35) and 46% (41) for alcohol and exercise events, respectively.

Conclusion: The high variability found in SMBs and CTs highlights the need for real-time diabetes self-tracking mechanisms to better understand SMBs and CTs. Future work will use the developed app to collect SMBs and CTs and identify patient-specific diabetes adherence barriers that could be addressed with individualized education interventions.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alcohol Drinking
  • Diabetes Mellitus, Type 1*
  • Diet
  • Exercise
  • Humans
  • Mobile Applications*
  • Self-Management / statistics & numerical data*
  • Smartphone*
  • Time Factors

Grants and funding

Funding This research was supported by the 2018 Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery and the Arizona State University Research Acceleration Grant.