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
