Self-Management Behaviors of Patients with Type 1 Diabetes: Comparing Two Sources of Patient-Generated Dat

George Karway, Maria Adela Grando, Kevin Grimm, Danielle Groat, Curtiss Cook, Bithika Thompson

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives This article aims to evaluate adult type 1 diabetes mellitus (T1DM) selfmanagement behaviors (SMBs) related to exercise and alcohol on a survey versus a smartphone app to compare self-reported and self-tracked SMBs, and examine interand intrapatient variability. Methods Adults with T1DM on insulin pump therapy were surveyed about their alcohol, meal, and exercise SMBs. For 4 weeks, participants self-tracked their alcohol, meal, and exercise events, and their SMBs corresponding with these events via an investigator-developed app. Descriptive statistics and generalized linear mixed-effect models were used to analyze the data Results Thirty-five participants self-tracked over 5,000 interactions using the app. Variability in how participants perceived the effects of exercise and alcohol on their blood glucose was observed. The congruity between SMBs self-reported on the survey and those self-tracked with the app was measured as mean (SD). The lowest congruity was for alcohol and exercise with 61.9% (22.7) and 66.4% (20.2), respectively. Congruity was higher for meals with 80.9% (21.0). There was significant daily intra-and interpatient variability in SMBs related to preprandial bolusing: recommended bolus, p < 0.05; own bolus choice, p < 0.01; and recommended basal adjustment, p < 0.01. Conclusion This study highlights the variability in intra-and interpatient SMBs obtained through the use of a survey and app. The outcomes of this study indicate that clinicians could use both one-time and every-day assessment tools to assess SMBs related to meals. For alcohol and exercise, further research is needed to understand the best assessment method for SMBs. Given this degree of patient variability, there is a need for an educational intervention that goes beyond the traditional "one-size-fits-all" approach of diabetes management to target individualized treatment barriers.

Original languageEnglish (US)
Pages (from-to)70-78
Number of pages9
JournalApplied Clinical Informatics
Volume11
Issue number1
DOIs
StatePublished - Jan 1 2020

Keywords

  • diabetes mellitus
  • patient-generated data
  • self-care
  • smartphone
  • surveys or interviews

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Health Information Management

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