Digital Diabetes Congress 2019

The Impact of Digital Self-Monitoring of Weight on Improving Diabetes Clinical Outcomes: Quasi-Randomized Study

BACKGROUND

The management of type 2 diabetes (T2D) and obesity, particularly in the context of self-monitoring, remains a critical challenge in health care. As nearly 80% to 90% of patients with T2D have overweight or obesity, there is a compelling need for interventions that can effectively manage both conditions simultaneously. One of the goals in managing chronic conditions is to increase awareness and generate behavioral change to improve outcomes in diabetes and related comorbidities, such as overweight or obesity. There is a lack of real-life evidence to test the impact of self-monitoring of weight on glycemic outcomes and its underlying mechanisms.

OBJECTIVE

This study aims to assess the efficacy of digital self-monitoring of weight on blood glucose (BG) levels during diabetes management, investigating whether the weight changes may drive glucose fluctuations.

METHODS

In this retrospective, real-world quasi-randomized study, 50% of the individuals who regularly used the weight monitoring (WM) feature were propensity score matched with 50% of the users who did not use the weight monitoring feature (NWM) based on demographic and clinical characteristics. All the patients were diagnosed with T2D and tracked their BG levels. We analyzed monthly aggregated data 6 months before and after starting their weight monitoring. A piecewise mixed model was used for analyzing the time trajectories of BG and weight as well as exploring the disaggregation effect of between- and within-patient lagged effects of weight on BG.

RESULTS

The WM group exhibited a significant reduction in BG levels post intervention (P<.001), whereas the nonmonitoring group showed no significant changes (P=.59), and both groups showed no differences in BG pattern before the intervention (P=.59). Furthermore, the WM group achieved a meaningful decrease in BMI (P<.001). Finally, both within-patient (P<.001) and between-patient (P=.008) weight variability was positively associated with BG levels. However, 1-month lagged back BMI was not associated with BG levels (P=.36).

CONCLUSIONS

This study highlights the substantial benefits of self-monitoring of weight in managing BG levels in patients with diabetes, facilitated by a digital health platform, and advocates for the integration of digital self-monitoring tools in chronic disease management. We also provide initial evidence of testing the underlying mechanisms associated with BG management, underscoring the potential role of patient empowerment.

Improving Glycemic Control in Type 2 Diabetes Using Mobile Applications and e-Coaching: A Mixed Treatment Comparison Network Meta-Analysis

OBJECTIVES

This study compared the effectiveness of glycemic control among usual care, care management using a mobile application (app), and management using an app with additional e-coaching for patients with type 2 diabetes mellitus (T2DM) using a mixed treatment comparison (MTC) network meta-analysis (NMA).

METHODS

A systematic search for published randomized controlled trials (RCTs) was conducted, which included Pubmed, Web of Science, Cochrane Central Register of Controlled Trials, CINAL, Koreamed, KMbase, and ScienceOn, until October 2020. Among the 10,391 studies identified after removing duplicates, 14 RCTs were finally included in the MTC NMA. Data extraction and methodological quality assessment using version 2 of the Cochrane tool for assessing the risk-of-bias in randomized trials (RoB 2) was performed. The comparative efficacy was analyzed using the random-effects NMA based on a frequentist model by the intervention group and main outcome variables.

RESULTS

At the 3-month follow-up after each intervention, a comparison of the P-scores revealed the app plus e-coaching intervention to be the most effective method for reducing the HbA1c level in a homogeneous gender ratio group (P-score 0.92). At the 6-month follow-up period, app intervention was the best in reducing the HbA1c level in the homogeneous gender ratio and under 60 years of age group (P-score 1.00).

CONCLUSIONS

Based on MTC analysis using the data from published RCTs, mobile apps or apps with e-coaching interventions for T2DM patients were more effective in improving the HbA1c values, FBS, and hypoglycemia frequency than usual care. Nevertheless, further research will be needed to clarify the effects of adding e-coaching to the app.

STUDY REGISTRATION

Research Registry UIN (reviewregistry780).

Blood Pressure Monitoring as a Digital Health Tool for Improving Diabetes Clinical Outcomes: Retrospective Real-world Study

BACKGROUND

Remote data capture for blood glucose (BG) or blood pressure (BP) monitoring and the use of a supportive digital app are becoming the model in diabetes and hypertension chronic care. One of the goals in chronic condition management is to increase awareness and generate behavioral change in order to improve outcomes in diabetes and related comorbidities, such as hypertension. In addition, there is a lack of understanding of the association between BG and BP levels when using digital health tools.

OBJECTIVE

By applying a rigorous study framework to digital health data, this study investigated the relationship between BP monitoring and BG and BP levels, as well as a lagged association between BP and BG. We hypothesized that during the first 6 months of BP monitoring, BG and BP levels would decrease. Finally, we suggested a positive association between BP levels and the following month's BG levels.

METHODS

In this retrospective, real-world case-control study, we extracted the data of 269 people with type 2 diabetes (T2D) who tracked their BG levels using the Dario digital platform for a chronic condition. We analyzed the digital data of the users who, in addition to BG, monitored their BP using the same app (BP-monitoring [BPM] group, n=137) 6 months before and after starting their BP monitoring. Propensity score matching established a control group, no blood pressure monitoring (NBPM, n=132), matched on demographic and baseline clinical measures to the BPM group. A piecewise mixed model was used for analyzing the time trajectories of BG, BP, and their lagged association.

RESULTS

Analysis revealed a significant difference in BG time trajectories associated with BP monitoring in BPM and NBPM groups (t=-2.12, P=.03). The BPM group demonstrated BG reduction improvement in the monthly average BG levels during the first 6 months (t=-3.57, P<.001), while BG did not change for the NBPM group (t=0.39, P=.70). Both groups showed similarly stable BG time trajectories (B=0.98, t=1.16, P=.25) before starting the use of the BP-monitoring system. In addition, the BPM group showed a significant reduction in systolic (t=-6.42, P<.001) and diastolic (t=-4.80, P<.001) BP during the first 6 months of BP monitoring. Finally, BG levels were positively associated with systolic (B=0.24, t=2.77, P=.001) and diastolic (B=0.30, t=2.41, P=.02) BP.

CONCLUSIONS

The results of this study shed light on the association between BG and BP levels and on the role of BP self-monitoring in diabetes management. Our findings also underscore the need and provide a basis for a comprehensive approach to understanding the mechanism of BP regulation associated with BG.

The Challenge of Sustainable Access to Telemonitoring Tools for People with Diabetes in Europe: Lessons from COVID-19 and Beyond

Telemedicine in diabetes care has been evolving over several years, particularly since the advent of cloud-connected technologies for diabetes management, such as glucose monitoring devices, including continuous glucose monitoring (CGM) systems, that facilitate sharing of glucose data between people with diabetes and their healthcare professionals in near-real time. Extreme social distancing and shielding in place for vulnerable patients during the COVID-19 pandemic has created both the challenge and the opportunity to provide care at a distance on a large scale. Available evidence suggests that glucose control has in fact improved during this period for people with diabetes who are able to use CGM devices for remote glucose monitoring. The development of telemedicine as part of the standard of care in diabetes faces significant challenges in the European context, particularly in terms of providing consistent and effective care at a distance to large populations of patients while using robust systems that can be supported by large regional and national healthcare services. These challenges include a fragmented approach to healthcare technology assessment and reimbursement, lack of eHealth education and literacy, particularly amongst healthcare professionals, lack of data integration, as well as concerns about electronic health records, patient consent and privacy. Here we review the benefits of and challenges to wider application of telemedicine and telemonitoring in the post-pandemic future, with the aim to ensure that the value of these eHealth services is provided to patients, healthcare providers and health systems.

Role of Digital Engagement in Diabetes Care Beyond Measurement: Retrospective Cohort Study

BACKGROUND

The use of remote data capture for monitoring blood glucose and supporting digital apps is becoming the norm in diabetes care. One common goal of such apps is to increase user awareness and engagement with their day-to-day health-related behaviors (digital engagement) in order to improve diabetes outcomes. However, we lack a deep understanding of the complicated association between digital engagement and diabetes outcomes.

OBJECTIVE

This study investigated the association between digital engagement (operationalized as tagging of behaviors alongside glucose measurements) and the monthly average blood glucose level in persons with type 2 diabetes during the first year of managing their diabetes with a digital chronic disease management platform. We hypothesize that during the first 6 months, blood glucose levels will drop faster and further in patients with increased digital engagement and that difference in outcomes will persist for the remainder of the year. Finally, we hypothesize that disaggregated between- and within-person variabilities in digital engagement will predict individual-level changes in blood glucose levels.

METHODS

This retrospective real-world analysis followed 998 people with type 2 diabetes who regularly tracked their blood glucose levels with the Dario digital therapeutics platform for chronic diseases. Subjects included "nontaggers" (users who rarely or never used app features to notice and track mealtime, food, exercise, mood, and location, n=585) and "taggers" (users who used these features, n=413) representing increased digital engagement. Within- and between-person variabilities in tagging behavior were disaggregated to reveal the association between tagging behavior and blood glucose levels. The associations between an individual's tagging behavior in a given month and the monthly average blood glucose level in the following month were analyzed for quasicausal effects. A generalized mixed piecewise statistical framework was applied throughout.

RESULTS

Analysis revealed significant improvement in the monthly average blood glucose level during the first 6 months (t=-10.01, P<.001), which was maintained during the following 6 months (t=-1.54, P=.12). Moreover, taggers demonstrated a significantly steeper improvement in the initial period relative to nontaggers (t=2.15, P=.03). Additional findings included a within-user quasicausal nonlinear link between tagging behavior and glucose control improvement with a 1-month lag. More specifically, increased tagging behavior in any given month resulted in a 43% improvement in glucose levels in the next month up to a person-specific average in tagging intensity (t=-11.02, P<.001). Above that within-person mean level of digital engagement, glucose levels remained stable but did not show additional improvement with increased tagging (t=0.82, P=.41). When assessed alongside within-person effects, between-person changes in tagging behavior were not associated with changes in monthly average glucose levels (t=1.30, P=.20).

CONCLUSIONS

This study sheds light on the source of the association between user engagement with a diabetes tracking app and the clinical condition, highlighting the importance of within-person changes versus between-person differences. Our findings underscore the need for and provide a basis for a personalized approach to digital health.