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CC BY 4.0 · ACI open 2020; 04(01): e9-e21
DOI: 10.1055/s-0039-1701022
Original Article
Georg Thieme Verlag KG Stuttgart · New York

Data-Driven Diabetes Education Guided by a Personalized Report for Patients on Insulin Pump Therapy

Danielle Groat
1   Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, United States
,
Krystal Corrette
2   Department of Biomedical Informatics, Arizona State University, Tempe, Arizona, United States
,
Adela Grando
2   Department of Biomedical Informatics, Arizona State University, Tempe, Arizona, United States
,
Vaishak Vellore
2   Department of Biomedical Informatics, Arizona State University, Tempe, Arizona, United States
,
Mike Bayuk
2   Department of Biomedical Informatics, Arizona State University, Tempe, Arizona, United States
,
George Karway
2   Department of Biomedical Informatics, Arizona State University, Tempe, Arizona, United States
,
Mary Boyle
3   Department of Endocrinology, Mayo Clinic Arizona, Scottsdale, Arizona, United States
,
Rozalina McCoy
4   Division of Community Internal Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Kevin Grimm
5   Department of Psychology, Arizona State University, Tempe, Arizona, United States
,
Bithika Thompson
3   Department of Endocrinology, Mayo Clinic Arizona, Scottsdale, Arizona, United States
› Author Affiliations
Further Information

Publication History

11 March 2019

16 December 2019

Publication Date:
06 February 2020 (online)

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Abstract

Objective It is difficult to assess self-management behaviors (SMBs) and incorporate them into a personalized self-care plan. We aimed to develop and apply SMB phenotyping algorithms from data collected by diabetes devices and a mobile health (mHealth) application to create patient-specific SMBs reports to guide individualized interventions. Follow-up interventions aimed to understand patient's reasoning behind discovered SMB choices.

Methods This study deals with adults on continuous subcutaneous insulin infusion using a continuous glucose monitor (CGM) who self-tracked SMBs with an mHealth application for 1 month. Patient-generated data were quantified and an SMB report was designed and populated for each participant. A diabetes educator used the report to conduct personalized, data-driven educational interventions. Thematic analysis of the intervention was conducted.

Results Twenty-two participants recorded 118 alcohol, 251 exercise, 2,661 meal events, and 1,900 photos. A patient-specific SMB report was created from this data and used to conduct the educational intervention. High variability of SMB was observed between patients. There was variability in the percentage of alcohol events accompanied by a blood glucose check, median 79% (38–100% range), and frequency of changing the bolus waveform, median 11 (7–95 range). Interventions confirmed variability of SMBs. Main emerging themes from thematic analysis were: challenges and barriers, motivators, current SMB techniques, and future plans to improve glycemic control.

Conclusion The ability to quantify SMBs and understand patients' rationale may help improve diabetes self-care and related outcomes. This study describes our first steps in piloting a patient-specific diabetes educational intervention, as opposed to the current “one size fits all” approach.

Keywords

type 1 diabetes - phenotype - smartphone - diabetes mellitus - self-care - mhealth - patients with chronic illness

Protection of Human and Animal Subjects

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


 

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