Characterization of Exercise and Alcohol Self-Management Behaviors of Type 1 Diabetes Patients on Insulin Pump Therapy

Abstract

Diabetes mellitus is a complex, chronic disease affecting 29.1 million (9.3%) US residents. 1 Chronic hyperglycemia can result in potentially devastating microvascular and macrovascular complications, leading to major morbidity, mortality, and economic consequences in this patient population. Evidence demonstrates that these complications can be prevented by maintaining glycemic control to near normal levels. Achieving and maintaining adequate glycemic control requires consistent medical care, and most importantly, ongoing patient self-management. 2 While evidence shows that alcohol and exercise affect the absorption of insulin and increase the risk of hypoglycemia, there is a lack of evidence-based decision tools to allow for translation of this information into practice. 3-6 Patients with type 1 diabetes (T1D) must manage their disease by injecting insulin deliverable through syringes, insulin pens, or insulin pumps. Premeal insulin dosage compliance and accuracy is a key factor in achieving target postprandial glucose levels. In 2013 insulin pumps were being used by over 350 000 people in the United States, 7 incorporate proprietary mathematical algorithms called bolus calculators or bolus wizards to determine individualized premeal dosing. 8-10 The benefits achieved through the use of insulin pumps and continuous 663746D STXXX10.

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

“…10,28 Our previous work in T1DM patients on CSII therapy using both data from a survey in conjunction with self-tracked data collection using a mHealth app indicated a high intra-and interpatient variability of SMBs. 19,20,22,24 Findings on variability of SMBs from the phenotyping algorithms used were discussed in follow-up personalized interventions. Our interviews asked about SMBs and practices discovered in the created SMB reports to manage diabetes when eating, drinking alcohol, and exercising.…”

“…Accordant with previous studies, there were considerable variations in SMBs chosen by participants to compensate for alcohol and exercise. 5,19,20 During the personalized educational interventions, participants indicated interest in changing and/or improving SMBs related to fat content of meals. Participant-generated data showed either avoidance of changing the insulin bolus waveform from normal to dual/square, or doing so regularly.…”

“…Moreover, and partly as a result of these challenges, there are scarce data regarding adherence to CSII therapy and SMBs among adults with T1DM, as prior qualitative and quantitative studies have focused primarily on children or young adults. 5,6,[9][10][11][12][16][17][18] While recent evidence shows that there is considerable variability of SMBs within and between adult patients with T1DM on insulin pump therapy who use CSII, [19][20][21][22] current behavioral interventions are mostly based on the "one size fits all" approach of standardized diabetes self-management education. 1 This is unfortunate since individuals are more likely to follow self-management protocols that take their lifestyle preferences into account.…”

“…Previous research by the study team examined and characterized SMBs in adults of all ages with T1D. [19][20][21][22]24 Building on knowledge gleaned from that work, SMBs identified and quantified by the biomedical informatics team and a statistician were presented to a diabetes care team for review. An iterative process with feedback from all members of the research team was used to refine the report templates to present the behaviors in a meaningful format for health providers at the individual patient level.…”

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

“…6 Studies have found that most individuals with T1D resort to trial and error as they compensate for exercise, which can lead to considerable variability in self-care behaviors between and within participants. 11,12 Ideally, clinicians would be able to review patient-generated exercise logs and compensation techniques employed, along with glucose data, to provide personalized recommendations aimed at improving glycemic control. However, one of the challenges in individualizing recommendations for exercise is the difficult nature in establishing the individual impact of physical activity on glucose levels in real-world, noncontrolled environments.…”