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

Abstract

Objectives This article aims to evaluate adult type 1 diabetes mellitus (T1DM) self-management behaviors (SMBs) related to exercise and alcohol on a survey versus a smartphone app to compare self-reported and self-tracked SMBs, and examine inter- and 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.

Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications

“…Data interoperability is one of the main drawbacks of deployed solutions/systems where users are "constrained" within one application for a task and move between the applications to view/review the patient information. 19,[39][40][41] Further improvements are required such as adding diet information, ethnicity, location, socioeconomic status, living status, and other social factors that could have a significant influence on the healthy and active lifestyle for managing T2DM and delaying the prediabetes. [2][3][4]27,28 Early Detection of Prediabetes and T2DM Baig et al 7…”

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.…”

“…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.…”

Characteristics of smartphone-based dietary assessment tools: A systematic review

“…The most frequently used assessment method was taking a photo of the food, used in out of dietary assessment tools (48.8%). Participants were usually asked to take a picture immediately before eating (21,25,34,35,, but taking a photo was not always mandatory (63,64). In two studies, participants were specifically asked to fit all foods and drinks in one photo (65,66), while one study specifically reported that participants were also able to capture additional foods and drinks while eating (67).…”