Background

Digital therapeutics are evidence-based behavioral treatments delivered online that can increase accessibility and effectiveness of health care. However, few studies have examined long-term clinical outcomes of digital therapeutics.

Objective

The objective of this study was to conduct a 2-year follow-up on participants in the Internet-based Prevent diabetes prevention program pilot study, specifically examining the effects on body weight and A1c, which are risk factors for diabetes development.

Methods

A quasi-experimental research design was used, including a single-arm pre- and post-intervention assessment of outcomes. Participants underwent a 16-week weight loss intervention and an ongoing weight maintenance intervention. As part of the program, participants received a wireless scale, which was used to collect body weight data on an ongoing basis. Participants also received A1c test kits at baseline, 0.5 year, 1 year, and 2-year time points.

Results

Participants previously diagnosed with prediabetes (n=220) were originally enrolled in the pilot study. A subset of participants (n=187) met Centers for Disease Control and Prevention (CDC) criteria for starting the program (starters), and a further subset (n=155) met CDC criteria for completing the program (completers) and were both included in analyses. Program starters lost an average of 4.7% (SD 0.4) of baseline body weight after 1 year and 4.2% (SD 0.8) after 2 years, and reduced A1c by mean 0.38% (SD 0.07) after 1 year and 0.43% (SD 0.08) after 2 years. Program completers lost mean 4.9% (SD 0.5) of baseline body weight after 1 year and 4.3% (SD 0.8) after 2 years, and reduced A1c by 0.40% (SD 0.07) after 1 year and 0.46% (SD 0.08) after 2 years. For both groups, neither 2-year weight loss nor A1c results were significantly different from 1-year results.

Conclusions

Users of the Prevent program experienced significant reductions in body weight and A1c that are maintained after 2 years. Contrary to the expected progression from prediabetes to diabetes over time, average A1c levels continued to show an average regression from within the prediabetic range (5.7%-6.4%) initially to the normal range (<5.7%) after 2 years. Further investigation is warranted to test digital therapeutics as a scalable solution to address national diabetes and cardiovascular disease prevention efforts.

Purpose

The purpose of this study was to evaluate the efficacy of Prevent, an online social network-based translation of the Diabetes Prevention Program (DPP) lifestyle intervention, against the Centers for Disease Control and Prevention (CDC) Diabetes Prevention and Recognition Program (DPRP) outcome standards and weight loss outcomes of other DPP translations.

Methods

Two hundred twenty participants previously diagnosed with prediabetes were recruited online and enrolled in Prevent, a DPP-based group lifestyle intervention that integrates a private online social network, weekly lessons, health coaching, and a wireless scale and pedometer. Participants underwent a core 16-week intensive lifestyle change intervention and were then offered to continue with a post-core lifestyle change maintenance intervention, with the entire intervention (core plus post-core) totaling 12 months.

Results

One hundred eighty-seven participants met inclusion criteria for the core program and achieved an average of 5.0% and 4.8% weight loss at 16 weeks and 12 months, respectively. They also had a 0.37% reduction in their A1C level at final measurement. One hundred forty-four of these same participants also met inclusion criteria for the post-core program and achieved an average of 5.4% and 5.2% weight loss at 16 weeks and 12 months, respectively, and a 0.40% reduction in A1C at final measurement.

Conclusion

Results indicate that Prevent meets CDC DPRP outcome standards for diabetes prevention programs and performs favorably to other DPP translations. Considering national initiatives to address the obesity and diabetes epidemics, online delivery platforms like Prevent offer an effective and scalable solution.

Objective

Translations of the Diabetes Prevention Program (DPP) have proliferated in recent years, with increasing expansion to digital formats. Although these DPP translations have consistently shown favorable clinical outcomes, long-term data for digital formats are limited. This study's objective was to examine clinical outcomes up to 3 years post-baseline and the relationship between program engagement and clinical outcomes in a digital DPP.

Research design and methods

In a single-arm, non-randomized trial, 220 patients previously diagnosed with prediabetes were enrolled in the Omada Health Program, a commercially available, 16-week DPP-based weight loss intervention followed by an ongoing weight maintenance intervention. Changes in body weight and A1c were assessed annually. Relationships between program engagement during the first year and clinical outcomes across 3 years were examined.

Results

Participants were socioeconomically diverse (62% women, 50.2% non-Hispanic white, 51.7% college educated or higher). From baseline to 3 years, those participants who completed four or more lessons and nine or more lessons achieved significant sustained weight loss (-3.0% and -2.9%, respectively) and an absolute reduction in A1c (-0.31 and -0.33, respectively) with an average remission from the prediabetes range to the normal glycemic range. Factor analysis of engagement metrics during the first year revealed two underlying dimensions, one comprising lesson completion and health behavior tracking consistency, and the other comprising website logins and group participation. When these two factors were used to predict weight loss, only the logins and group participation factor was a significant predictor of weight loss at 16 weeks and 1 year.

Conclusions

This study demonstrates significant long-term reductions in body weight and A1c in a digital DPP and identifies patterns of program engagement that predict weight loss.

Aims

To identify the unique sources of diabetes distress (DD) for adults with type 1 diabetes (T1D).

Methods

Sources of DD were developed from qualitative interviews with 25 T1D adults and 10 diabetes health care providers. Survey items were then developed and analyzed using both exploratory (EFA) and confirmatory CFA) analyses on two patient samples. Construct validity was assessed by correlations with depressive symptoms (PHQ8), complications, HbA1C, BMI, and hypoglycemia worry scale (HWS). Scale cut-points were created using multiple regression.

Results

An EFA with 305 U.S. participants yielded 7 coherent, reliable sources of distress that were replicated by a CFA with 109 Canadian participants: Powerlessness, Negative Social Perceptions, Physician Distress, Friend/Family Distress, Hypoglycemia Distress, Management Distress, Eating Distress. Prevalence of DD was high with 41.6% reporting at least moderate DD. Higher DD was reported for women, those with complications, poor glycemic control, younger age, without a partner, and non-White patients.

Conclusions

We identified a profile of seven major sources of DD among T1D using a newly developed assessment instrument. The prevalence of DD is high and is related to glycemic control and several patient demographic and disease-related patient characteristics, arguing for a need to address DD in clinical care.

Objective

To compare the effectiveness of two interventions to reduce diabetes distress (DD) and improve glycemic control among adults with type 1 diabetes (T1D).

Research design and methods

Individuals with T1D (n = 301) with elevated DD and HbA_1c were recruited from multiple settings and randomly assigned to OnTrack, an emotion-focused intervention, or to KnowIt, an educational/behavioral intervention. Each group attended a full-day workshop plus four online meetings over 3 months. Assessments occurred at baseline and 3 and 9 months. Primary and secondary outcomes were change in DD and change in HbA_1c, respectively.

Results

With 12% attrition, both groups demonstrated dramatic reductions in DD (effect size d = 1.06; 78.4% demonstrated a reduction of at least one minimal clinically important difference). There were, however, no significant differences in DD reduction between OnTrack and KnowIt. Moderator analyses indicated that OnTrack provided greater DD reduction to those with initially poorer cognitive or emotion regulation skills, higher baseline DD, or greater initial diabetes knowledge than those in KnowIt. Significant but modest reductions in HbA_1c occurred with no between-group differences. Change in DD was modestly associated with change in HbA_1c (r = 0.14, P = 0.01), with no significant between-group differences.

Conclusions

DD can be successfully reduced among distressed individuals with T1D with elevated HbA_1c using both education/behavioral and emotion-focused approaches. Reductions in DD are only modestly associated with reductions in HbA_1c. These findings point to the importance of tailoring interventions to address affective, knowledge, and cognitive skills when intervening to reduce DD and improve glycemic control.

Objective

To determine whether the use of continuous glucose monitoring (CGM) without confirmatory blood glucose monitoring (BGM) measurements is as safe and effective as using CGM adjunctive to BGM in adults with well-controlled type 1 diabetes (T1D).

Research design and methods

A randomized noninferiority clinical trial was conducted at 14 sites in the T1D Exchange Clinic Network. Participants were ≥18 years of age (mean 44 ± 14 years), had T1D for ≥1 year (mean duration 24 ± 12 years), used an insulin pump, and had an HbA_1c ≤9.0% (≤75 mmol/mL) (mean 7.0 ± 0.7% [53 ± 7.7 mmol/mol]); prestudy, 47% were CGM users. Participants were randomly assigned 2:1 to the CGM-only (n = 149) or CGM+BGM (n = 77) group. The primary outcome was time in range (70-180 mg/dL) over the 26-week trial, with a prespecified noninferiority limit of 7.5%.

Results

CGM use averaged 6.7 ± 0.5 and 6.8 ± 0.4 days/week in the CGM-only and CGM+BGM groups, respectively, over the 26-week trial. BGM tests per day (including the two required daily for CGM calibration) averaged 2.8 ± 0.9 and 5.4 ± 1.4 in the two groups, respectively (P < 0.001). Mean time in 70-180 mg/dL was 63 ± 13% at both baseline and 26 weeks in the CGM-only group and 65 ± 13% and 65 ± 11% in the CGM+BGM group (adjusted difference 0%; one-sided 95% CI -2%). No severe hypoglycemic events occurred in the CGM-only group, and one occurred in the CGM+BGM group.

Conclusions

Use of CGM without regular use of confirmatory BGM is as safe and effective as using CGM with BGM in adults with well-controlled T1D at low risk for severe hypoglycemia.

Objective

To assess the frequency of continuous glucose monitoring (CGM) device use, factors associated with its use, and the relationship of CGM with diabetes outcomes (HbA1c, severe hypoglycemia [SH], and diabetic ketoacidosis [DKA]).

Research design and methods

Survey questions related to CGM device use 1 year after enrollment in the T1D Exchange clinic registry were completed by 17,317 participants. Participants were defined as CGM users if they indicated using real-time CGM during the prior 30 days.

Results

Nine percent of participants used CGM (6% of children <13 years old, 4% of adolescents 13 to <18 years, 6% of young adults 18 to <26 years, and 21% of adults ≥26 years). CGM use was more likely with higher education, higher household income, private health insurance, longer duration of diabetes, and use of insulin pump (P < 0.01 all factors). CGM use was associated with lower HbA1c in children (8.3% vs. 8.6%, P < 0.001) and adults (7.7% vs. 7.9%, P < 0.001). In adults, more frequent use of CGM (≥6 days/week) was associated with lower mean HbA1c. Only 27% of users downloaded data from their device at least once per month, and ≤15% of users reported downloading their device at least weekly. Among participants who used CGM at baseline, 41% had discontinued within 1 year.

Conclusions

CGM use is uncommon but associated with lower HbA1c in some age-groups, especially when used more frequently. Factors associated with discontinuation and infrequent use of retrospective analysis of CGM data should be considered in developing next-generation devices and education on CGM use.

Sodium-glucose cotransporter (SGLT) inhibitors are new oral antidiabetes medications shown to effectively reduce glycated hemoglobin (A1C) and glycemic variability, blood pressure, and body weight without intrinsic properties to cause hypoglycemia in people with type 1 diabetes. However, recent studies, particularly in individuals with type 1 diabetes, have demonstrated increases in the absolute risk of diabetic ketoacidosis (DKA). Some cases presented with near-normal blood glucose levels or mild hyperglycemia, complicating the recognition/diagnosis of DKA and potentially delaying treatment. Several SGLT inhibitors are currently under review by the U.S. Food and Drug Administration and European regulatory agencies as adjuncts to insulin therapy in people with type 1 diabetes. Strategies must be developed and disseminated to the medical community to mitigate the associated DKA risk. This Consensus Report reviews current data regarding SGLT inhibitor use and provides recommendations to enhance the safety of SGLT inhibitors in people with type 1 diabetes.

Background

A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.

Methods

We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.

Results

The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.

Conclusion

The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.