The program times are listed in Central European Time (CEST)
TECHNOLOGY UTILIZATION IN AFRICAN AMERICAN YOUTH WITH TYPE 1 DIABETES: EXPLORING THE DECISION MAKING PROCESS
Abstract
Background and Aims
Significant disparities in diabetes device (DD) use exist for African American (AA) adolescents with type 1 diabetes (T1D), meriting further exploration. We sought to describe how AA adolescents with T1D and their guardians make decisions about using DDs and to understand personal, familial and cultural beliefs that may influence use.
Methods
Nineteen AA adolescents with T1D and 17 guardians participated in individual qualitative semi-structured interviews. Adolescents were purposively sampled for a range in socioeconomic and clinical demographics. Interview data were recorded, transcribed, and coded for thematic analysis, analyzed separately for guardians and adolescents, and then compared across groups. Data collection continued until thematic saturation was achieved.
Results
Adolescents and guardians reported similar themes related to (1) intersectionality of age, race and T1D; (2) decisions about DDs; (3) insight about use/nonuse of DDs; and (4) advice about enhancing success with DDs. Adolescents reported lacking peers with T1D “who look like me,” leading to stigmatization, exacerbated by device visibility and alarms. Cultural and familial traditions were described as both facilitators and barriers in decisions about DDs. TID self-management support included extended family, school personnel and clinic providers. Lack of familiarity with T1D, limited exposure to DDs, and mistrust were reasons for decreased uptake of DDs. Participants provided specific suggestions for clinical support for use of DDs.
Conclusions
Understanding the decision-making process surrounding DDs and preferences around methods of education, peer support and follow-up may help to ameliorate some disparities in DD use, leading to improved glycemic control and outcomes.
USE OF ADVANCED DIABETES TECHNOLOGIES IN PEOPLE WITH TYPE 1 DIABETES AND DYSFUNCTIONAL OR DISTURBED EATING
Abstract
Background and Aims
People with type 1 diabetes show an increased risk for dysfunctional eating behaviors and comorbid eating disorders. In this population, the use of continuous subcutaneous insulin infusion (CSII), continuous glucose monitoring (CGM) or automated insulin delivery systems (AID) may come with benefits, but also specific pitfalls. In this systematic review, we aimed to (1) identify and describe research investigating the use of advanced diabetes technologies (DT) in people with type 1 diabetes and dysfunctional/disordered eating and (2) to discuss potential advantages and disadvantages of DT use in this population, derived from previous research.
Methods
A systematic literature search was conducted in two databases for English language articles published between 2000 and 2020 (Prospero ID: CRD42020160244).
Results
From 70 publications initially identified, 17 met the inclusion criteria. Overall, evidence on potential benefits and pitfalls of DT use in people with type 1 diabetes and dysfunctional/disordered eating is scarce. Providing the greatest self-management flexibility, CSII may have beneficial effects on dysfunctional/disordered eating but may also facilitate manipulation of insulin dosage. CGM data may complement the diagnostic process of dysfunctional/disordered with a physiological indicator of insulin omission (i.e., time spent in hyperglycemia).
Conclusions
Evidence on potential (dis)advantages of DT use is scarce and mostly stems from cross-sectional data, small pilot trials in samples that predominantly consist of female adolescents/young adults, and anecdotical results from case reports. Prospective data from larger samples are needed to reliably determine the potential effects of DT on dysfunctional/disordered eating.
QUALITY OF LIFE OUTCOMES AND GLYCEMIC CONTROL FROM THE T:SLIM X2 PUMP WITH CONTROL-IQ TECHNOLOGY – REAL WORLD OBSERVATIONS FROM THE CLIO STUDY
Abstract
Background and Aims
To forward a holistic understanding of automated insulin dosing system efficacy, evaluation of both glycemic and psychosocial outcomes is necessary.
Methods
As part of the ongoing Control-IQ Observational (CLIO) Study, we evaluated glycemic and psychosocial outcomes in people with type 1 diabetes (PWT1D) using the Tandem Diabetes Care® t:slim X2™ insulin pump with Control-IQ® technology. Participants completed questionnaires at baseline (T1) and 3 months after study start (T2), uploaded at least 21 days of pump data to Tandem’s t:connect® web application, and had ≥75% CGM use during this time. Repeated measures ANOVA was used to assess differences in patient-reported outcomes from T1 to T2.
Results
In all, 700 PWT1D (59% female, 87% White, mean age=39 (SD=17), mean diabetes duration=21 years (SD=15)) were included in the analysis. 81% reported baseline HbA1c <8.5%, 80% had been using an insulin pump, and 89% were using CGM prior to using Control-IQ technology. At T2, sensor time in range for the overall sample using Control-IQ technology was 72.5% (median, IQR=71-73%) and sensor time <70mg/dl=1.1%. Participants reported greater satisfaction with their insulin delivery device and its impact on their diabetes management (7.06 vs. 8.77) (p<0.001). Majority (96%) reported improved sleep quality at T2. A significant reduction in the perceived negative impact of diabetes (4.79 vs 4.41) (p<0.001) (i.e., improved quality of life) across life dimensions (e.g., freedom to eat, emotional well-being) was noted.
Conclusions
Control-IQ technology users reported substantial psychosocial benefits including improved QoL, satisfaction, and sleep quality within three months of using the system.
DIABETES TECHNOLOGY: AWARENESS, CURRENT USE, AND SATISFACTION AMONG PEOPLE WITH TYPE 1 DIABETES IN SINGAPORE.
Abstract
Background and Aims
Diabetes technology has significantly advanced over the past decade. Appropriate use of diabetes technology reduces the burden and improves outcomes in people with type 1 diabetes. We conducted an online survey to study the awareness, current use, and satisfaction of diabetes technology among people with type 1 diabetes (T1D) in Singapore.
Methods
An anonymous online survey was developed and advertised on social media and at the T1D clinic at Singapore General Hospital. Sections included demographics, diabetes profile, technology awareness, current technology use, and technology satisfaction. Descriptive data are presented as count with percentages [n(%)] or mean and standard deviation (M+SD).
Results
n=104, 72(69%) women participated. Age at diagnosis was <= 20 years(y) for 49(47%), mean duration of diabetes was 16.9y+12.4y and 81(78%) reported most recent HbA1c< 8% (64mmol/mol). Awareness about continuous glucose monitors (CGM) was the highest [81(78%)], only 46(44%) knew about government-subsidized insulin pump therapy and only 25(24%) knew about smartphone bolus calculator applications. Only 57(55%) had ever used a smartphone diabetes application and 26(25%) were unaware of them. Majority [79(76%)] used capillary glucose meters; while 32(31%) used CGM. 75(72%) used multiple daily injections(MDI) and 29(28%) used insulin pumps. 63(60%) felt satisfied with their glucose monitoring devices. 84(81%) felt that their insulin delivery system worked well, helped them have good glucose [71(68%)], and made them feel in control [85(82%)].
Conclusions
A third of people with T1D used CGM and/or insulin pumps. Awareness about bolus calculators and subsidized insulin pump therapy was low. Satisfaction rates were lower for glucose monitoring devices compared to insulin delivery devices.
TIME TREND OF DIABETOLOGISTS' ATTITUDES TOWARDS DIGITALIZATION AND NEW TECHNOLOGISTS AND THEIR ADVANTAGES AND DISADVANTAGES
Abstract
Background and Aims
Despite many new innovations, little is known to date about diabetologists' attitudes and expectations regarding digitization and new technologies in diabetology and how they are changing over time.
For the "Digitization and Technology Report Diabetes 2021", diabetologists nationwide in Germany were asked from August to October 2020 about their attitudes toward diabetes technologies and digitization in diabetology, among other things, and compared with a similar survey in 2018/2019.
Methods
337 diabetologists (2018: 422; 2019: 324) participated in the survey (43% female, average age 53.2 years). On average, 324 people with diabetes using Flash-Glucose-Monitoring, 100 using real-time continuous glucose monitoring (CGM), and 94 using insulin pump therapy (CSII) were treated per diabetes institution in 2020.
Results
In 2020, 81.9% (2019: 75.8%; 2018: 63.7%) of the physicians surveyed had a positive or very positive attitude toward digitization, with only 3.6% having a negative attitude toward it (2019: 4.3%, 2018: 8.0%). The benefits of digitization for diabetology rated as greatest were "better metabolic control of patients" (75.9%), "better quality of own work" (74.1%) "more personalized treatment of patients in terms of personalized diabetes therapy" (71.9%), and "specialization of own institution" (65.9%). The "unclear and insufficient reimbursement of digital services" (89.6%) is stated as the greatest disadvantage.
Conclusions
The potential of digitization is considered to be very high by diabetologists. The comparison of the previous 3 surveys (2019,2020, 2021) indicates that the positive attitude towards digitization has increased even more in recent years and that a very positive basic mood now prevails among most respondents.
RAPID INCREASE IN THE PROPORTION OF CGM AND INSULIN PUMPS AMONG PEOPLE WITH TYPE 1 AND TYPE 2 DIABETES
Abstract
Background and Aims
To quantify the increase of new technologies in diabetology, diabetologists in Germany were surveyed annually about the use of continuous glucose monitoring and insulin pumps.
Methods
In 2018, 2019, and 2020, diabetologists participated in a survey ("Digitization and Technology Report Diabetes (D.U.T)") to determine how many of their patients use new technologies. 337 diabetologists (2018: 422; 2019: 324) participated in the survey (43% female, average age 53.2 years). On average, 324 patients are currently treated per practice with flash glucose monitoring (iscCGM), 100 with continuous glucose monitoring (rtCGM), 94 patients with an insulin pump (CSII), 8 with a hyprid closed-loop system, and 2 with a DIY-closed-loop system (DIY).
Results
By comparing 2018 - 2020, we can see the sharp increase in modern technologies - which, overall, have roughly doubled over this time period. The increase of iscCGM is 83%, of CGM 114%, of CSII 53%, of hyprid closed-loop systems 167% and DIY of 100%. In diabetes outpatient institutions 75.2% people with type 1 diabetes (TD1) and 31.1% with type 2 diabetes (T2D) are now using some form of continuous glucose monitoring, 32.9% TD1 and 7.5% an insulin pump.
Conclusions
Over a 3-year period, there is a clear and steady increase in the number of people with diabetes in Germany using modern technologies. Approximately 3/4 of all people with type 1 diabetes now use them, and the trend for type 2 diabetes is also growing strongly.
BEHAVIORAL MODEL OF POST-MEAL INSULIN CORRECTION BOLUS INJECTIONS IN TYPE 1 DIABETES INDIVIDUALS UNDER FREE-LIVING CONDITIONS
Abstract
Background and Aims
Besides describing the physiology of patients with type 1 diabetes (T1D), realistic simulation tools for in-silico trials should also mimic the behavioral aspects of patients’ lifestyle, which can notably affect glucose control. In a previous work (Camerlingo et al., J. Diabetes Sci. Technol., 2020) we modelled meal amount and timing variability, here we focus on the timing of post-meal insulin correction bolus (CB) injections.
Methods
A multicenter study involving 30 patients with T1D, monitored in free-living conditions for 1-month (Kovatchev et al., Diabetes Technol. Ther., 2017) was used to extract 539 CBs for 1,963 meals. 7-hour post-prandial windows were divided in 30-min portions, labelled as “1” or “0”, based on the occurrence of a CB injection. Three different binary classification techniques were implemented to predict the labels: support vector machine (SVM), decision tree (DT) and logistic regression (LOG), based on 13 features extracted from continuous glucose monitoring (CGM), insulin, and meal data as well as from patient’s characteristics. Average area under the receiver operating characteristic curve (AUROC) over 10-fold cross validation was used to select the best model.
Results
SVM provided an AUROC of 0.76±0.04 (mean±std), performing slightly better than LOG (0.75±0.04) and DT (0.73±0.04). The 8 most representative predictors were: time from last bolus and from last meal, current CGM reading and rate-of-change, daytime, patient’s age, body weight, and correction factor.
Conclusions
Once refined using larger datasets, the new model can be incorporated in T1D simulators. By mimicking patient behavior in self-administering CBs, the model will allow more realistic in-silico trials.