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Appl Clin Inform 2018; 09(04): 919-926
DOI: 10.1055/s-0038-1676458
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Comparing Real-Time Self-Tracking and Device-Recorded Exercise Data in Subjects with Type 1 Diabetes

Danielle Groat
1   Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
2   Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, United States
,
Hyo Jung Kwon
2   Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, United States
,
Maria Adela Grando
2   Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, United States
3   Division of Endocrinology, Mayo Clinic Arizona, Scottsdale, Arizona, United States
,
Curtiss B. Cook
2   Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, United States
3   Division of Endocrinology, Mayo Clinic Arizona, Scottsdale, Arizona, United States
,
Bithika Thompson
3   Division of Endocrinology, Mayo Clinic Arizona, Scottsdale, Arizona, United States
› Institutsangaben Funding This research was supported by the 2018 ASU-Mayo Research Accelerator Award: Data-Driven Behavioral-Change Individualized Interventions to Improve Type 1 Diabetes.
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Publikationsverlauf

06. Juli 2018

27. Oktober 2018

Publikationsdatum:
26. Dezember 2018 (online)

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Abstract

Background Insulin therapy, medical nutrition therapy, and physical activity are required for the treatment of type 1 diabetes (T1D). There is a lack of studies in real-life environments that characterize patient-reported data from logs, activity trackers, and medical devices (e.g., glucose sensors) in the context of exercise.

Objective The objective of this study was to compare data from continuous glucose monitor (CGM), wristband heart rate monitor (WHRM), and self-tracking with a smartphone application (app), iDECIDE, with regards to exercise behaviors and rate of change in glucose levels.

Methods Participants with T1D on insulin pump therapy tracked exercise for 1 month with the smartphone app while WHRM and CGM recorded data in real time. Exercise behaviors tracked with the app were compared against WHRM. The rate of change in glucose levels, as recorded by CGM, resulting from exercise was compared between exercise events documented with the app and recorded by the WHRM.

Results Twelve participants generated 277 exercise events. Tracking with the app aligned well with WHRM with respect to frequency, 3.0 (2.1) and 2.5 (1.8) days per week, respectively (p = 0.60). Duration had very high agreement, the mean duration from the app was 65.6 (55.2) and 64.8 (54.9) minutes from WHRM (p = 0.45). Intensity had a low concordance between the data sources (Cohen's kappa = 0.2). The mean rate of change of glucose during exercise was –0.27 mg/(dL*min) and was not significantly different between data sources or intensity (p = 0.21).

Conclusion We collated and analyzed data from three heterogeneous sources from free-living participants. Patients' perceived intensity of exercise can serve as a surrogate for exercise tracked by a WHRM when considering the glycemic impact of exercise on self-care regimens.

Keywords

exercise - smartphone app - wearables - type 1 diabetes - patient-generated data

Protection of Human and Animal Subjects

This study was reviewed by the Arizona State University and Mayo Clinic Institutional Review Boards.


 

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