Use of a Connected Glucose Meter and Certified Diabetes Educator Coaching to Decrease the Likelihood of Abnormal Blood Glucose Excursions: The Livongo for Diabetes Program

The dual challenge of diabesity: pathophysiology, management, and future directions

Diabesity, the concurrent occurrence of obesity and type-2 diabetes mellitus (T2DM), represents a pressing global health challenge characterized by intricate pathophysiological mechanisms and a wide range of associated comorbidities. Central to its development are insulin resistance, metabolic syndrome, and chronic low-grade inflammation mediated by dysregulated adipokine secretion and systemic metabolic dysfunction. These mechanisms underpin the progression of diabesity and its complications, including cardiovascular disease and hypertension. Management strategies encompass lifestyle interventions focusing on tailored dietary modifications and structured physical activity, pharmacological treatments targeting both glycemic control and weight loss, and surgical interventions such as bariatric surgery, which have demonstrated efficacy in achieving durable outcomes. Clinical trials and meta-analyses underscore the comparative advantages of different treatment modalities in terms of efficacy, safety, and sustainability. Moreover, long-term follow-up studies emphasize the critical need for sustained multidisciplinary interventions to prevent relapse and enhance patient outcomes. Future advancements in management include exploring precision medicine approaches that integrate individual metabolic profiles, lifestyle factors, and emerging therapeutic innovations. A multidisciplinary approach combining advanced therapeutic strategies and patient-centered care remains pivotal for optimizing management and improving prognoses for individuals with diabesity. This review highlights the complex interplay between obesity and T2DM, offering comprehensive insights into their pathophysiology, clinical presentation, and management paradigms.

A deep learning system for predicting time to progression of diabetic retinopathy

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

Development and validation of medical record-based logistic regression and machine learning models to diagnose diabetic retinopathy

PURPOSES

Many factors were reported to be associated with diabetic retinopathy (DR); however, their contributions remained unclear. We aimed to evaluate the prognostic and diagnostic accuracy of logistic regression and three machine learning models based on various medical records.

METHODS

This was a cross-sectional study. We investigated the prevalence and associations of DR among 757 participants aged 40 years or older in the 2005-2006 National Health and Nutrition Examination Survey (NHANES). We trained the models to predict if the participants had DR with 15 predictor variables. Area under the receiver operating characteristic (AUROC) and mean squared error (MSE) of each algorithm were compared in the external validation dataset using a replicate cohort from NHANES 2007-2008.

RESULTS

Among the 757 participants, 53 (7.00%) subjects had DR, the mean (standard deviation, SD) age was 57.7 (13.04), and 78.0% were male (n = 42). Logistic regression revealed that female gender (OR = 4.130, 95% CI: 1.820-9.380; P < 0.05), HbA1c (OR = 1.665, 95% CI: 1.197-2.317; P < 0.05), serum creatine level (OR = 2.952, 95% CI: 1.274-6.851; P < 0.05), and eGFR level (OR = 1.009, 95% CI: 1.000-1.014, P < 0.05) increased the risk of DR. The average performance obtained from internal validation was similar in all models (AUROC ≥ 0.945), and k-nearest neighbors (KNN) had the highest value with an AUROC of 0.984. In external validation, they remained robust or with modest reductions in discrimination with AUROC still ≥ 0.902, and KNN also performed the best with an AUROC of 0.982. Both logistic regression and machine learning models had good performance in the clinical diagnosis of DR.

CONCLUSIONS

This study highlights the utility of comparing traditional logistic regression to machine learning models. We found that logistic regression performed as well as optimized machine learning methods when classifying DR patients.

A data-driven approach to manage type 2 diabetes mellitus through digital health: The Klivo Intervention Program protocol (KIPDM)

BACKGROUND

Digital therapeutics, an emerging type of medical approach, is defined as evidence-based therapeutic interventions through qualified software programs that help prevent, manage, or treat chronic diseases such as type 2 diabetes mellitus (T2DM), which has high social and economic burden. Klivo, a startup certified by the Brazilian Society of Diabetes, developed the first digital therapeutic product for managing T2DM in Brazil, reaching 21 of 24 states. Klivo has continuously been improving its model of behavior change on the basis of an intensive lifestyle intervention method that addresses individuals' needs-the Klivo Intervention Program for T2DM (KIPDM). To test the most recent version of the KIPDM, we will evaluate the ongoing management of daily life habits in patients with T2DM by measuring clinically significant outcomes. To improve the transparency of further results, here we will present the study protocol and detail the plan for the research project, including the study design and the analysis strategies.

METHODS

The KIPDM will be sponsored by health plans and healthcare provider organizations and will be free for patients (adults aged ≥ 18 years and <65 years; and glycated hemoglobin ≥ 7%). The program will be based on a 6-month management process that will supervise patients remotely. The program will include educational classes via the Klivo app, text messages, or e-mails. Evaluation will include objectively assessing clinical, laboratory, and behavioral outcomes such as health-related quality of life, mental health, medication adherence, and healthcare utilization. For this, validated electronic questionnaires will be available through the Klivo app. The primary outcome will be glycated hemoglobin (HbA1c) values. The secondary outcome will be time in target blood glucose range (TIR) estimated by capillary glycemia. Other outcomes of interest will be evaluated at baseline and stipulated time points (3 and 6 months after the start of the program).

EXPECTED OUTCOMES

KIPDM patients should present improved HbA1c and TIR along the intervention as compared to baseline values. Findings from this study will provide insights into the health improvement of T2DM and other cardiometabolic conditions such as hypertension, dyslipidemia, and obesity by using a digital therapeutic strategy. By analyzing the patient's health over time, this study will also contribute to understanding comorbidities associated with this chronic condition in the Brazilian population.

Technology-Enabled Collaborative Care for Concurrent Diabetes and Distress Management During the COVID-19 Pandemic: Protocol for a Mixed Methods Feasibility Study

BACKGROUND

The COVID-19 pandemic disrupted the delivery of diabetes care and worsened mental health among many patients with type 2 diabetes (T2D). This disruption puts patients with T2D at risk for poor diabetes outcomes, especially those who experience social disadvantage due to socioeconomic class, rurality, or ethnicity. The appropriate use of communication technology could reduce these gaps in diabetes care created by the pandemic and also provide support for psychological distress.

OBJECTIVE

The purpose of this study is to test the feasibility of an innovative co-designed Technology-Enabled Collaborative Care (TECC) model for diabetes management and mental health support among adults with T2D.

METHODS

We will recruit 30 adults with T2D residing in Ontario, Canada, to participate in our sequential explanatory mixed methods study. They will participate in 8 weekly web-based health coaching sessions with a registered nurse, who is a certified diabetes educator, who will be supported by a digital care team (ie, a peer mentor, an addictions specialist, a dietitian, a psychiatrist, and a psychotherapist). Assessments will be completed at baseline, 4 weeks, and 8 weeks, with a 12-week follow-up. Our primary outcome is the feasibility and acceptability of the intervention, as evident by the participant recruitment and retention rates. Key secondary outcomes include assessment completion and delivery of the intervention. Exploratory outcomes consist of changes in mental health, substance use, and physical health behaviors. Stakeholder experience and satisfaction will be explored through a qualitative descriptive study using one-on-one interviews.

RESULTS

This paper describes the protocol of the study. The recruitment commenced in June 2021. This study was registered on October 29, 2020, on ClinicalTrials.gov (Registry ID: NCT04607915). As of June 2022, all participants have been recruited. It is anticipated that data analysis will be complete by the end of 2022, with study findings available by the end of 2023.

CONCLUSIONS

The development of an innovative, technology-enabled model will provide necessary support for individuals living with T2D and mental health challenges. This TECC program will determine the feasibility of TECC for patients with T2D and mental health issues.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04607915; https://clinicaltrials.gov/ct2/show/NCT04607915.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/39724.

Remote glucose monitoring is feasible for patients and providers using a commercially available population health platform

OBJECTIVE

Remote patient monitoring (RPM) holds potential to enable more individualized and effective care for patients with type 1 diabetes (T1D), but requires population analytics to focus limited clinical resources on patients most in need. We explored the feasibility of RPM from patient and provider standpoints using a commercially available data analytic platform (glooko Population Health) among a cohort of youth with T1D.

STUDY DESIGN

Patients aged 1-20 years with established T1D (≥12 months) and CGM use (≥3 months) were recruited to participate. Participants' CGM devices were connected to the glooko app and linked to the research team's glooko account during a one-month baseline period. This was followed by a six-month intervention period during which participants with >15% of glucose values >250 mg/dl or >5% of values <70 mg/dl each month were contacted with personalized diabetes management recommendations. Participants were surveyed about their experiences, and effects on glycemic control were estimated via change in glucose management indicator (GMI) generated from CGM data at baseline and completion. Changes in time spent within various glucose ranges were also evaluated, and all glycemic metrics were compared to a non-randomized control group via difference-in-difference regression, adjusting for baseline characteristics.

RESULTS

Remote data-sharing was successful for 36 of 39 participants (92%). Between 33%-66% of participants merited outreach each month, and clinician outreach required a median of 10 minutes per event. RPM was reported to be helpful by 94% of participants. RPM was associated with a GMI change of -0.25% (P=0.047) for the entire cohort, and stratified analysis revealed greatest treatment effects among participants with baseline GMI of 8.0-9.4% (GMI change of -0.68%, P=0.047; 19.84% reduction in time spent >250 mg/dl, P=0.005).

CONCLUSIONS

This study demonstrates the feasibility of RPM for patients with T1D using a commercially available population health platform, and suggests that RPM with clinician-initiated outreach may be particularly beneficial for patients with suboptimal glycemic control at entry. However, larger randomized studies are needed to fully explore the glycemic impact of RPM.

CLINICAL TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT04696640, identifier NCT04696640.

Real-World Evidence of Improved Glycemic Control in People with Diabetes Using a Bluetooth-Connected Blood Glucose Meter with a Mobile Diabetes Management App

Background: The OneTouch Verio Reflect (OTVR) meter provides ColorSure Dynamic Range Indicator (DCRI) and Blood Sugar Mentor (BSM) features that are complemented by the OneTouch Reveal (OTR) mobile app. We sought to provide real-world evidence that these products support improved glycemic control. Methods: Anonymised glucose and app analytics were extracted from the LifeScan server for 4154 people with type 1 diabetes (PwT1D) and 13,623 people with type 2 diabetes (PwT2D). Data from their first 14 days were compared with the 14 days before the 90-day time point using paired within-subject differences. Results: Percentage glucose readings in range (RIR) 70-180 mg/dL improved by +8.1% (from 58% to 66.1%) in PwT1D and by +11.2% (from 72.4% to 83.6%) in PwT2D. Hyperglycemic readings (>180 mg/dL) reduced by -8.5% (from 37.1% to 28.6%) in PwT1D and by -11.3% (from 26.4% to 15.1%) in PwT2D. Mean glucose reduced on average by -14.5 mg/dL (from 174.8 to 160.2 mg/dL) in PwT1D and -18.2 mg/dL (from 157.8 to 139.6 mg/dL) in PwT2D. Glycemic improvement was strongly associated with OTR app engagement. Two to three sessions or 11 to 20 min/week in the app improved readings in range in PwT1D by +7.0% or +8.4%, respectively. Similar engagement trends for glycemic improvement were observed in PwT2D. Proportions of subjects achieving a 5% or 10% improvement in RIR were 46.9%/36.6% for PwT1D and 48.7%/37.7% for PwT2D. Conclusions: Real-world data from over 17,000 people with diabetes (PWDs) demonstrated significantly improved readings in range and reduced the burden of hyperglycemia in PWDs using the OTVR meter and OTR app.

Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults With Type 2 Diabetes Using One Drop: Retrospective Cohort Study

Background

Personalized feedback is an effective behavior change technique frequently incorporated into mobile health (mHealth) apps. Innovations in data science create opportunities for leveraging the wealth of user data accumulated by mHealth apps to generate personalized health forecasts. One Drop’s digital program is one of the first to implement blood glucose forecasts for people with type 2 diabetes. The impact of these forecasts on behavior and glycemic management has not been evaluated to date.

Objective

This study sought to evaluate the impact of exposure to blood glucose forecasts on blood glucose logging behavior, average blood glucose, and percentage of glucose points in range.

Methods

This retrospective cohort study examined people with type 2 diabetes who first began using One Drop to record their blood glucose between 2019 and 2021. Cohorts included those who received blood glucose forecasts and those who did not receive forecasts. The cohorts were compared to evaluate the effect of exposure to blood glucose forecasts on logging activity, average glucose, and percentage of glucose readings in range, after controlling for potential confounding factors. Data were analyzed using analysis of covariance (ANCOVA) and regression analyses.

Results

Data from a total of 1411 One Drop users with type 2 diabetes and elevated baseline glucose were analyzed. Participants (60.6% male, 795/1311; mean age 50.2 years, SD 11.8) had diabetes for 7.1 years on average (SD 7.9). After controlling for potential confounding factors, blood glucose forecasts were associated with more frequent blood glucose logging (P=.004), lower average blood glucose (P<.001), and a higher percentage of readings in range (P=.03) after 12 weeks. Blood glucose logging partially mediated the relationship between exposure to forecasts and average glucose.

Conclusions

Individuals who received blood glucose forecasts had significantly lower average glucose, with a greater amount of glucose measurements in a healthy range after 12 weeks compared to those who did not receive forecasts. Glucose logging was identified as a partial mediator of the relationship between forecast exposure and week-12 average glucose, highlighting a potential mechanism through which glucose forecasts exert their effect. When administered as a part of a comprehensive mHealth program, blood glucose forecasts may significantly improve glycemic management among people living with type 2 diabetes.

Telehealth Technologies and Their Benefits to People With Diabetes

This article reviews the current diabetes technology landscape and how recent advancements are being used to help overcome barriers in the management of diabetes. The authors offer case examples of how digital tools and platforms can facilitate diabetes care via telehealth and remote patient monitoring for individuals in special populations. They also provide tips to ensure success in implementing diabetes technology to provide the best possible care for people with diabetes in outpatient settings.

Efficacy of Digitally Supported and Real-Time Self-Monitoring of Blood Glucose-Driven Counseling in Patients with Type 2 Diabetes Mellitus: A Real-World, Retrospective Study in North India

PURPOSE

Poor glycemic control is prevalent in patients with type 2 diabetes mellitus (T2DM) in India. This study aims to understand the effectiveness of a smartphone-connected glucometer, real-time feedback, and contextualized counseling on glycemic control and hypoglycemic episodes in T2DM patients.

METHODS

This retrospective, multicenter study reviewed the medical records of T2DM patients belonging to several cities of north India, who were digitally engaged with a smartphone-connected glucometer and who had received at least one counseling session between September 2019 and July 2020. Intervention included self-monitoring of blood glucose (SMBG) using a smartphone-connected glucometer enabled with real-time transmission of information to certified diabetes educators (CDE) and their corresponding counseling based on SMBG findings.

RESULTS

Of 7111 adult T2DM patients included in this study, majority (75%) of the patients received a single session of counseling, and the remaining patients received 2 (16.7%), 3 (5%), 4 (2%), or ≥5 (1.3%) sessions. The mean age of the patients was 51.6 years, and the majority (77.9%) were males. Digital monitoring of BG and counseling with CDE significantly reduced the mean fasting (by 9.6%), pre-prandial (by 9.9%), and post-prandial (by 9.2%) BG values in 53%, 52%, and 54% of patients, respectively. The majority (81.4%) of patients showed no hypoglycemic episode (≤70 mg/dL) post-counseling. The hypoglycemia episodes observed with FBG, pre-prandial, and post-prandial BG values were reduced significantly by 58.5%, 48.1%, and 61.8%, respectively, post-counseling.

CONCLUSION

Digitally supported and real-time SMBG-driven counselling was effective in glycemic control and reduction of hypoglycemic episodes in T2DM patients in India. Moreover, reduction in hypoglycemia may be due to back end real-time support of CDE intervention.

Association of physical activity on blood glucose in individuals with type 2 diabetes

Regular physical activity (PA) has been shown to improve glycemic control in persons with type 2 diabetes. This study aimed to investigate the impact of PA on blood glucose after controlling for medication use, demographics, and week of activation using a real-world population of individuals with type 2 diabetes. A longitudinal, retrospective study was performed evaluating weekly PA of Livongo members (N = 9,509), which analyzed fasting blood glucose (FBG), step counts, and daily active minutes. Linear mixed-effect modeling technique was used to investigate within member and between member effects of input variables on average weekly FBG. Of members enrolled, 6,336 (32%) had self-reported body mass index, qualified week with diabetes medications, and FBG measures. Members' baseline average age was 49.4 (SD 10.1) years old, 43% female, and 45,496 member weeks with an average of 7.2 qualified weeks (PA observable in ≥4 days) per member. Average weekly FBG was 140.5 mg/dL (SD 39.8), and average daily step counts were 4,833 (SD 3,266). Moving from sedentary (<5,000 steps per day) to active (≥5,000 steps per day) resulted in mean weekly FBG reduction of 13 mg/dL (95% CI: -22.6 to -3.14). One additional day of ≥8,000 steps reduced mean weekly FBG by 0.47 mg/dL (95% CI: -0.77 to -0.16). Members who completed 30 min of moderate to vigorous PA above the population average reduced mean weekly FBG by 7.7 mg/dL (95% CI: -13.4 to -2.0). PA is associated with a mean weekly FBG reduction of 13 mg/dL when changing from a sedentary to active lifestyle while participating in a remote diabetes monitoring program.

Emerging technologies for the management of type 2 diabetes mellitus

Diabetes mellitus is a global health problem affecting 422 million people worldwide, of which 34.2 million live in the United States alone. Complications due to diabetes can lead to considerable morbidity and mortality related to both microvascular and macrovascular disease. While glycosylated hemoglobin testing is the standard test utilized to evaluate glycemic control, emerging targets like "time in range" and "glycemic variability" often provide more accurate assessments of glycemic fluctuations and have implications for diabetes complications and quality of life. Patients with diabetes face considerable burdens of self-care including frequent glucose monitoring, multiple insulin injections, dietary management, and the need to track daily activities, all of which lead to reduced adherence and psychological burnout. From the provider perspective, limited patient data and access to self-management tools lead to treatment inertia and a reduced ability to help patients achieve and maintain their glycemic goals. In the past few decades, there have been considerable advances in treatment-based technology and technological applications designed to help reduce patient burden and provide tools for better self-management. These advances make real-time clinical data available for clinicians to make necessary changes in treatment regimens. In this review, we discuss the latest emerging technologies available for the management of people with type 2 diabetes mellitus.

A Machine Learning Approach to Passively Informed Prediction of Mental Health Risk in People with Diabetes: Retrospective Case-Control Analysis

Background

Proactive detection of mental health needs among people with diabetes mellitus could facilitate early intervention, improve overall health and quality of life, and reduce individual and societal health and economic burdens. Passive sensing and ecological momentary assessment are relatively newer methods that may be leveraged for such proactive detection.

Objective

The primary aim of this study was to conceptualize, develop, and evaluate a novel machine learning approach for predicting mental health risk in people with diabetes mellitus.

Methods

A retrospective study was designed to develop and evaluate a machine learning model, utilizing data collected from 142,432 individuals with diabetes enrolled in the Livongo for Diabetes program. First, participants’ mental health statuses were verified using prescription and medical and pharmacy claims data. Next, four categories of passive sensing signals were extracted from the participants’ behavior in the program, including demographics and glucometer, coaching, and event data. Data sets were then assembled to create participant-period instances, and descriptive analyses were conducted to understand the correlation between mental health status and passive sensing signals. Passive sensing signals were then entered into the model to train and test its performance. The model was evaluated based on seven measures: sensitivity, specificity, precision, area under the curve, F₁ score, accuracy, and confusion matrix. SHapley Additive exPlanations (SHAP) values were computed to determine the importance of individual signals.

Results

In the training (and validation) and three subsequent test sets, the model achieved a confidence score greater than 0.5 for sensitivity, specificity, area under the curve, and accuracy. Signals identified as important by SHAP values included demographics such as race and gender, participant’s emotional state during blood glucose checks, time of day of blood glucose checks, blood glucose values, and interaction with the Livongo mobile app and web platform.

Conclusions

Results of this study demonstrate the utility of a passively informed mental health risk algorithm and invite further exploration to identify additional signals and determine when and where such algorithms should be deployed.

Medication Optimization Among People With Type 2 Diabetes Participating in a CGM-Driven Virtual Care Program: Prospective Trial (Preprint)

Background

The Onduo virtual care program for people with type 2 diabetes (T2D) includes a mobile app, remote lifestyle coaching, connected devices, and telemedicine consultations with endocrinologists for medication management and prescription of real-time continuous glucose monitoring (RT-CGM) devices. In a previously described 4-month prospective study of this program, adults with T2D and baseline glycated hemoglobin (HbA_1c) ≥8.0% to ≤12.0% experienced a mean HbA_1c decrease of 1.6% with no significant increase in hypoglycemia.

Objective

The objective of this analysis was to evaluate medication optimization and management in the 4-month prospective T2D study.

Methods

Study participants received at least 1 telemedicine consultation with an Onduo endocrinologist for diabetes medication management and used RT-CGM intermittently to guide therapy and dosing. Medication changes were analyzed.

Results

Of 55 participants, 48 (87%) had a medication change consisting of a dose change, addition, or discontinuation. Of these, 15 (31%) participants had a net increase in number of diabetes medication classes from baseline. Mean time to first medication change for these participants was 36 days. The percentage of participants taking a glucagon-like peptide-1 receptor agonist increased from 25% (12/48) to 56% (n=27), while the percentages of participants taking a sulfonylurea or dipeptidyl peptidase 4 inhibitor decreased from 56% (n=27) to 33% (n=16) and 17% (n=8) to 6% (n=3), respectively. Prescriptions of other antidiabetic medication classes including insulin did not change significantly.

Conclusions

The Onduo virtual care program can play an important role in providing timely access to guideline-based diabetes management medications and technologies for people with T2D.

Trial Registration

ClinicalTrials.gov NCT03865381; https://clinicaltrials.gov/ct2/show/NCT03865381

Optimizing Healthcare Through Digital Health and Wellness Solutions to Meet the Needs of Patients With Chronic Disease During the COVID-19 Era

The COVID-19 pandemic exposed and exacerbated longstanding inefficiencies and deficiencies in chronic disease management and treatment in the United States, such as a fragmented healthcare experience and system, narrowly focused services, limited resources beyond office visits, expensive yet low quality care, and poor access to comprehensive prevention and non-pharmacological resources. It is feared that the addition of COVID-19 survivors to the pool of chronic disease patients will burden an already precarious healthcare system struggling to meet the needs of chronic disease patients. Digital health and telemedicine solutions, which exploded during the pandemic, may address many inefficiencies and deficiencies in chronic disease management, such as increasing access to care. However, these solutions are not panaceas as they are replete with several limitations, such as low uptake, poor engagement, and low long-term use. To fully optimize digital health and telemedicine solutions, we argue for the gamification of digital health and telemedicine solutions through a pantheoretical framework-one that uses personalized, contextualized, and behavioral science algorithms, data, evidence, and theories to ground treatments.

Connected health for growth hormone treatment research and clinical practice: learnings from different sources of real-world evidence (RWE)-large electronically collected datasets, surveillance studies and individual patients' cases

BACKGROUND

A range of factors can reduce the effectiveness of treatment prescribed for the long-term management of chronic health conditions, such as growth disorders. In particular, prescription medications may not achieve the positive outcomes expected because approximately half of patients adhere poorly to the prescribed treatment regimen.

METHODS

Adherence to treatment has previously been assessed using relatively unreliable subjective methods, such as patient self-reporting during clinical follow-up, or counting prescriptions filled or vials returned by patients. Here, we report on a new approach, the use of electronically recorded objective evidence of date, time, and dose taken which was obtained through a comprehensive eHealth ecosystem, based around the easypod™ electromechanical auto-injection device and web-based connect software. The benefits of this eHealth approach are also illustrated here by two case studies, selected from the Finnish cohort of the easypod™ Connect Observational Study (ECOS), a 5-year, open-label, observational study that enrolled children from 24 countries who were being treated with growth hormone (GH) via the auto-injection device.

RESULTS

Analyses of data from 9314 records from the easypod™ connect database showed that, at each time point studied, a significantly greater proportion of female patients had high adherence (≥ 85%) than male patients (2849/3867 [74%] vs 3879/5447 [71%]; P < 0.001). Furthermore, more of the younger patients (< 10 years for girls, < 12 years for boys) were in the high adherence range (P < 0.001). However, recursive partitioning of data from ECOS identified subgroups with lower adherence to GH treatment ‒ children who performed the majority of injections themselves at an early age (~ 8 years) and teenagers starting treatment aged ≥ 14 years.

CONCLUSIONS

The data and case studies presented herein illustrate the importance of adherence to GH therapy and how good growth outcomes can be achieved by following treatment as described. They also show how the device, software, and database ecosystem can complement normal clinical follow-up by providing HCPs with reliable information about patient adherence between visits and also providing researchers with real-world evidence of adherence and growth outcomes across a large population of patients with growth disorders treated with GH via the easypod™ device.

Evaluation of a Diabetes Remote Monitoring Program Facilitated by Connected Glucose Meters for Patients With Poorly Controlled Type 2 Diabetes: Randomized Crossover Trial

BACKGROUND

Patients with poorly controlled type 2 diabetes (T2D) experience increased morbidity, increased mortality, and higher cost of care. Self-monitoring of blood glucose (SMBG) is a critical component of diabetes self-management with established diabetes outcome benefits. Technological advancements in blood glucose meters, including cellular-connected devices that automatically upload SMBG data to secure cloud-based databases, allow for improved sharing and monitoring of SMBG data. Real-time monitoring of SMBG data presents opportunities to provide timely support to patients that is responsive to abnormal SMBG recordings. Such diabetes remote monitoring programs can provide patients with poorly controlled T2D additional support needed to improve critical outcomes.

OBJECTIVE

To evaluate 6 months of a diabetes remote monitoring program facilitated by cellular-connected glucose meter, access to a diabetes coach, and support responsive to abnormal blood glucose recordings greater than 400 mg/dL or below 50 mg/dL in adults with poorly controlled T2D.

METHODS

Patients (N=119) receiving care at a diabetes center of excellence participated in a two-arm, 12-month randomized crossover study. The intervention included a cellular-connected glucose meter and phone-based diabetes coaching provided by Livongo Health. The coach answered questions, assisted in goal setting, and provided support in response to abnormal glucose levels. One group received the intervention for 6 months before returning to usual care (IV/UC). The other group received usual care before enrolling in the intervention (UC/IV) for 6 months. Change in hemoglobin A_1c (HbA_1c) was the primary outcome, and change in treatment satisfaction was the secondary outcome.

RESULTS

Improvements in mean HbA_1c were seen in both groups during the first 6 months (IV/UC -1.1%, SD 1.5 vs UC/IV -0.8%, SD 1.5; P<.001). After crossover, there was no significant change in HbA_1c in IV/UC (mean HbA_1c change +0.2, SD 1.7, P=.41); however, those in UC/IV showed further improvement (mean HbA_1c change -0.4%, SD 1.0, P=.008). A mixed-effects model showed no significant treatment effect (IV vs UC) over 12 months (P=.06). However, participants with higher baseline HbA_1c and those in the first time period experienced greater improvements in HbA_1c. Both groups reported similar improvements in treatment satisfaction throughout the study.

CONCLUSIONS

Patients enrolled in the diabetes remote monitoring program intervention experienced improvements in HbA_1c and treatment satisfaction similar to usual care at a specialty diabetes center. Future studies on diabetes remote monitoring programs should incorporate scheduled coaching components and involve family members and caregivers.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03124043; https://clinicaltrials.gov/ct2/show/NCT03124043.

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.

A pragmatic methodology for the evaluation of digital care management in the context of multimorbidity

Multimorbidity is a defining challenge for health systems and requires coordination of care delivery and care management. Care management is a clinical service designed to remotely engage patients between visits and after discharge in order to support self-management of chronic and emergent conditions, encourage increased use of scheduled care and address the use of unscheduled care. Care management can be provided using digital technology - digital care management. A robust methodology to assess digital care management, or any traditional or digital primary care intervention aimed at longitudinal management of multimorbidity, does not exist outside of randomized controlled trials (RCTs). RCTs are not always generalizable and are also not feasible for most healthcare organizations. We describe here a novel and pragmatic methodology for the evaluation of digital care management that is generalizable to any longitudinal intervention for multimorbidity irrespective of its mode of delivery. This methodology implements propensity matching with bootstrapping to address some of the major challenges in evaluation including identification of robust outcome measures, selection of an appropriate control population, small sample sizes with class imbalances, and limitations of RCTs. We apply this methodology to the evaluation of digital care management at a U.S. payor and demonstrate a 9% reduction in ER utilization, a 17% reduction in inpatient admissions, and a 29% increase in the utilization of preventive medicine services. From these utilization outcomes, we drive forward an estimated cost saving that is specific to a single payor's payment structure for the study time period of $641 per-member-per-month at 3 months. We compare these results to those derived from existing observational approaches, 1:1 and 1:n propensity matching, and discuss the circumstances in which our methodology has advantages over existing techniques. Whilst our methodology focuses on cost and utilization and is applied in the U.S. context, it is applicable to other outcomes such as Patient Reported Outcome Measures (PROMS) or clinical biometrics and can be used in other health system contexts where the challenge of multimorbidity is prevalent.

Relationship Between Diabetes, Stress, and Self-Management to Inform Chronic Disease Product Development: Retrospective Cross-Sectional Study

Background

Technology is rapidly advancing our understanding of how people with diabetes mellitus experience stress.

Objective

The aim of this study was to explore the relationship between stress and sequelae of diabetes mellitus within a unique data set composed of adults enrolled in a digital diabetes management program, Livongo, in order to inform intervention and product development.

Methods

Participants included 3263 adults under age 65 who were diagnosed with diabetes mellitus and had access to Livongo through their employer between June 2015 and August 2018. Data were collected at time of enrollment and 12 months thereafter, which included demographic information, glycemic control, presence of stress, diabetes distress, diabetes empowerment, behavioral health diagnosis, and utilization of behavioral health-related medication and services. Analysis of variance and chi-square tests compared variables across groups that were based on presence of stress and behavioral health diagnosis or utilization.

Results

Fifty-five percent of participants (1808/3263) reported stress at the time of at least 1 blood glucose reading. Fifty-two percent of participants (940/1808) also received at least 1 behavioral health diagnosis or intervention. Compared to their peers, participants with stress reported greater diabetes distress, lower diabetes empowerment, greater insulin use, and poorer glycemic control. Participants with stress and a behavioral health diagnosis/utilization additionally had higher body mass index and duration of illness.

Conclusions

Stress among people with diabetes mellitus is associated with reduced emotional and physical health. Digital products that focus on the whole person by offering both diabetes mellitus self-management tools and behavioral health skills and support can help improve disease-specific and psychosocial outcomes.

Predicting the risk of developing diabetic retinopathy using deep learning

BACKGROUND

Diabetic retinopathy screening is instrumental to preventing blindness, but scaling up screening is challenging because of the increasing number of patients with all forms of diabetes. We aimed to create a deep-learning system to predict the risk of patients with diabetes developing diabetic retinopathy within 2 years.

METHODS

We created and validated two versions of a deep-learning system to predict the development of diabetic retinopathy in patients with diabetes who had had teleretinal diabetic retinopathy screening in a primary care setting. The input for the two versions was either a set of three-field or one-field colour fundus photographs. Of the 575 431 eyes in the development set 28 899 had known outcomes, with the remaining 546 532 eyes used to augment the training process via multitask learning. Validation was done on one eye (selected at random) per patient from two datasets: an internal validation (from EyePACS, a teleretinal screening service in the USA) set of 3678 eyes with known outcomes and an external validation (from Thailand) set of 2345 eyes with known outcomes.

FINDINGS

The three-field deep-learning system had an area under the receiver operating characteristic curve (AUC) of 0·79 (95% CI 0·77-0·81) in the internal validation set. Assessment of the external validation set-which contained only one-field colour fundus photographs-with the one-field deep-learning system gave an AUC of 0·70 (0·67-0·74). In the internal validation set, the AUC of available risk factors was 0·72 (0·68-0·76), which improved to 0·81 (0·77-0·84) after combining the deep-learning system with these risk factors (p<0·0001). In the external validation set, the corresponding AUC improved from 0·62 (0·58-0·66) to 0·71 (0·68-0·75; p<0·0001) following the addition of the deep-learning system to available risk factors.

INTERPRETATION

The deep-learning systems predicted diabetic retinopathy development using colour fundus photographs, and the systems were independent of and more informative than available risk factors. Such a risk stratification tool might help to optimise screening intervals to reduce costs while improving vision-related outcomes.

FUNDING

Google.

A Virtual Type 2 Diabetes Clinic Using Continuous Glucose Monitoring and Endocrinology Visits

The Onduo Virtual Diabetes Clinic (VDC) telehealth technology/care model for adults with type 2 diabetes (T2D) combines connected devices, remote lifestyle coaching, and clinical support with a mobile App. Key differentiating program features are the availability of live video consultations with board-certified endocrinologists for medication management and real-time continuous glucose monitor use for higher-risk participants. Preliminary data (n = 740) suggest that participation was associated with a significant improvement in HbA1c with up to 6 months follow-up in those not meeting treatment targets. HbA1c decreased by 2.3% ± 1.9%, 0.7% ± 1.0%, and 0.2% ± 0.8% across baseline categories of >9.0%, 8.0%-9.0% and 7.0% to <8.0%, respectively (all P < .001). These findings suggest that the VDC has potential to support individuals with T2D and their clinicians in diabetes management between office visits.

The Effect of a Cellular-Enabled Glucose Meter on Glucose Control for Patients With Diabetes: Prospective Pre-Post Study

Background

Diabetes is a global epidemic affecting approximately 30 million people in the United States. The World Health Organization recommends using technology and telecommunications to improve health care delivery and disease management. The Livongo for Diabetes Program offers a remote monitoring technology with Certified Diabetes Educator outreach.

Objective

The purpose of this study was to examine health outcomes measured by changes in HbA_1c, in time in target blood glucose range, and in depression symptoms for patients enrolled in a remote digital diabetes management program in a Diabetes Center of Excellence setting.

Methods

The impact of the Livongo for Diabetes program on hemoglobin A_1c (HbA_1c), blood glucose ranges, and depression screening survey results (Patient Health Questionnaire-2 [PHQ-2]) were assessed over 12 months in a prospective cohort recruited from the University of South Florida Health Diabetes Home for Healthy Living. Any patient ≥18 years old with a diagnosis of diabetes was approached for voluntary inclusion into the program. The analysis was a pre-post design for those members enrolled in the study. Data was collected at outpatient clinic visits and remotely through the Livongo glucose meter.

Results

A total of 86 adults were enrolled into the Livongo for Diabetes program, with 49% (42/86) female, an average age of 50 (SD 15) years, 56% (48/86) with type 2 diabetes mellitus, and 69% (59/86) with insulin use. The mean HbA_1c drop amongst the group was 0.66% (P=.17), with all participants showing a decline in HbA_1c at 12 months. A 17% decrease of blood glucose checks <70 mg/dL occurred concurrently. Participants with type 2 diabetes not using insulin had blood glucose values within target range (70-180 mg/dL) 89% of the time. Participants with type 2 diabetes using insulin were in target range 68% of the time, and type 1 diabetes 58% of the time. Average PHQ-2 scores decreased by 0.56 points during the study period.

Conclusions

Participants provided with a cellular-enabled blood glucose meter with real-time feedback and access to coaching from a certified diabetes educator in an outpatient clinical setting experienced improved mean glucose values and fewer episodes of hypoglycemia relative to the start of the program.

Aggregation-induced emission of copper nanoclusters triggered by synergistic effect of dual metal ions and the application in the detection of H2O2 and related biomolecules

Recently, the aggregation-induced emission (AIE) of nanoclusters triggered by metal ions has been received great attentions. However, the good AIE efficiency usually requires excessive metal ions, which may result in an undesired competition between metal ions and targets. In this work, by the synergistic effect of Pb²⁺ and Zr⁴⁺, a fewer amounts of metal ions can induce more aggregates of glutathione-capped Cu nanoclusters (CSH-CuNCs), resulting in a higher AIE efficiency. Next, by virtue of the oxidative property of H₂O₂, the AIE of GSH-CuNCs-Pb²⁺-Zr⁴⁺ system quenches linearly with the concentration of H₂O₂ from 1 to 60 μmol/L. Moreover, many biological substrates, such as glucose and cholesterol, can generate H₂O₂ in the presence of their specific oxidases and O₂. Therefore, the detection of glucose or cholesterol can also be achieved by the proposed method, and the limits of detection of glucose and cholesterol are 0.37 and 2.7 μmol/L, respectively. Finally, this method has been validated to be sensitive and selective for glucose or cholesterol detection in human serum samples.

Improving patient self-care using diabetes technologies

Diabetes technologies are an unstoppable phenomenon. They offer opportunities to improve patient self-care through empowerment. However, they can be a challenge for both patients and clinicians. Thus, the use of technology may empower or burden. To understand and benefit from the use of diabetes technologies, one must understand the currently unmet needs in diabetes management. These unmet needs call for perspectives beyond glycated hemoglobin and an evaluation of technology solutions. Optimal use of these technologies is necessary to obtain benefits and achieve cost-effectiveness; this process depends on diabetes education and training. This review evaluates clinician and patient perspectives regarding diabetes technologies, followed by an evaluation of technology solutions. Diabetes technology solutions are evaluated according to available results about their effectiveness and their potential to empower people living with diabetes.

The Internet of Things in Health Care in Oxford: Protocol for Proof-of-Concept Projects

Background

Demands on health services across are increasing because of the combined challenges of an expanding and aging population, alongside complex comorbidities that transcend the classical boundaries of modern health care. Continuing to provide and coordinate care in the current manner is not a viable route to sustain the improvements in health outcomes observed in recent history. To ensure that there continues to be improvement in patient care, prevention of disease, and reduced burden on health systems, it is essential that we adapt our models of delivery. Providers of health and social care are evolving to face these pressures by changing the way they think about the care system and, importantly, how to involve patients in the planning and delivery of services.

Objective

The objective of this paper is to provide (1) an overview of the current state of Internet of Things (IoT) and key implementation considerations, (2) key use cases demonstrating technology capabilities, (3) an overview of the landscape for health care IoT use in Oxford, and (4) recommendations for promoting the IoT via collaborations between higher education institutions and industry proof-of-concept (PoC) projects.

Methods

This study describes the PoC projects that will be created to explore cost-effectiveness, clinical efficacy, and user adoption of Internet of Medical Things systems. The projects will focus on 3 areas: (1) bring your own device integration, (2) chronic disease management, and (3) personal health records.

Results

This study is funded by Research England’s Connecting Capability Fund. The study started in March 2018, and results are expected by the end of 2019.

Conclusions

Embracing digital solutions to support the evolution and transformation of health services is essential. Importantly, this should not simply be undertaken by providers in isolation. It must embrace and exploit the advances being seen in the consumer devices, national rollout of high-speed broadband services, and the rapidly expanding medical device industry centered on mobile and wearable technologies. Oxford University Hospitals and its partner providers, patients, and stakeholders are building on their leading position as an exemplar site for digital maturity in the National Health Service to implement and evaluate technologies and solutions that will capitalize on the IoT. Although early in the application to health, the IoT and the potential it provides to make the patient a partner at the center of decisions about care represent an exciting opportunity. If achieved, a fully connected and interoperable health care environment will enable continuous acquisition and real-time analysis of patient data, offering unprecedented ability to monitor patients, manage disease, and potentially deliver early diagnosis. The clinical benefit of this is clear, but additional patient benefit and value will be gained from being able to provide expert care at home or close to home.

International Registered Report Identifier (IRRID)

DERR1-10.2196/12077

Diabetes Educators' Insights Regarding Connecting Mobile Phone- and Wearable Tracker-Collected Self-Monitoring Information to a Nationally-Used Electronic Health Record System for Diabetes Education: Descriptive Qualitative Study

BACKGROUND

Diabetes educators are integral to a clinical team in providing diabetes self-management education and support; however, current mobile and Web-based self-management tools are not integrated into clinical diabetes care to support diabetes educators' education efforts.

OBJECTIVE

The objective of our study was to seek diabetes educators' insights regarding the development of an interface within the Chronicle Diabetes system, a nationally used electronic health record (EHR) system for diabetes education documentation with behavioral goal-setting functions, to transfer mobile phone- and wearable tracker-collected self-monitoring information from patients to diabetes educators to facilitate behavioral goal monitoring.

METHODS

A descriptive qualitative study was conducted to seek educators' perspectives on usability and interface development preferences in developing a connected system. Educators can use the Chronicle Diabetes system to set behavioral goals with their patients. Individual and group interviews were used to seek educators' preferences for viewing mobile phone- and wearable tracker-collected information on diet, physical activity, and sleep in the Chronicle Diabetes system using open-ended questions. Interview data were transcribed verbatim and analyzed for common themes.

RESULTS

Five common themes emerged from the discussion. First, educators expressed enthusiasm for and concerns about viewing diet and physical activity data in Chronicle Diabetes system. Second, educators valued viewing detailed dietary macronutrients and activity data; however, they preferred different kinds of details depending on patients' needs, conditions, and behavioral goals and educators' training background. Third, all educators liked the integration of mobile phone-collected data into Chronicle Diabetes system and preferably with current EHR systems. Fourth, a need for a health care team and a central EHR system to be formed was realized for educators to share summaries of self-monitoring data with other providers. Fifth, educators desired advanced features for the mobile app and the connected interface that can show self-monitoring data.

CONCLUSIONS

Flexibility is needed for educators to track the details of mobile phone- and wearable tracker-collected diet and activity information, and the integration of such data into Chronicle Diabetes and EHR systems is valuable for educators to track patients' behavioral goals, provide diabetes self-management education and support, and share data with other health care team members to faciliate team-based care in clinical practice.

Hypoglycemia Among Patients with Type 2 Diabetes: Epidemiology, Risk Factors, and Prevention Strategies

PURPOSE OF REVIEW

Hypoglycemia is the most common and often treatment-limiting serious adverse effect of diabetes therapy. Despite being potentially preventable, hypoglycemia in type 2 diabetes incurs substantial personal and societal burden. We review the epidemiology of hypoglycemia in type 2 diabetes, discuss key risk factors, and introduce potential prevention strategies.

RECENT FINDINGS

Reported rates of hypoglycemia in type 2 diabetes vary widely as there is marked heterogeneity in how hypoglycemia is defined, measured, and reported. In randomized controlled trials, rates of severe hypoglycemia ranged from 0.7 to 12 per 100 person-years. In observational studies, hospitalizations or emergency department visits for hypoglycemia were experienced by 0.2 (patients treated without insulin or sulfonylurea) to 2.0 (insulin or sulfonylurea users) per 100 person-years. Patient-reported hypoglycemia is much more common. Over the course of 6 months, 1-4% non-insulin users reported need for medical attention for hypoglycemia; 1-17%, need for any assistance; and 46-58%, any hypoglycemia symptoms. Similarly, over a 12-month period, 4-17% of insulin-treated patients reported needing assistance and 37-64% experienced any hypoglycemic symptoms. Hypoglycemia is most common among older patients with multiple or advanced comorbidities, patients with long diabetes duration, or patients with a prior history of hypoglycemia. Insulin and sulfonylurea use, food insecurity, and fasting also increase hypoglycemia risk. Clinical decision support tools may help identify at-risk patients. Prospective trials of efforts to reduce hypoglycemia risk are needed, and there is emerging evidence supporting multidisciplinary interventions including treatment de-intensification, use of diabetes technologies, diabetes self-management, and social support. Hypoglycemia among patients with type 2 diabetes is common. Patient-centered multidisciplinary care may help proactively identify at-risk patients and address the multiplicity of factors contributing to hypoglycemia occurrence.

A Behavioral Lifestyle Intervention Enhanced With Multiple-Behavior Self-Monitoring Using Mobile and Connected Tools for Underserved Individuals With Type 2 Diabetes and Comorbid Overweight or Obesity: Pilot Comparative Effectiveness Trial

Background

Self-monitoring is a cornerstone of behavioral lifestyle interventions for obesity and type 2 diabetes mellitus. Mobile technology has the potential to improve adherence to self-monitoring and patient outcomes. However, no study has tested the use of a smartphone to facilitate self-monitoring in overweight or obese adults with type 2 diabetes mellitus living in the underserved community.

Objective

The aim of this study was to examine the feasibility of and compare preliminary efficacy of a behavioral lifestyle intervention using smartphone- or paper-based self-monitoring of multiple behaviors on weight loss and glycemic control in a sample of overweight or obese adults with type 2 diabetes mellitus living in underserved communities.

Methods

We conducted a randomized controlled trial to examine the feasibility and preliminary efficacy of a behavioral lifestyle intervention. Overweight or obese patients with type 2 diabetes mellitus were recruited from an underserved minority community health center in Houston, Texas. They were randomly assigned to one of the three groups: (1) behavior intervention with smartphone-based self-monitoring, (2) behavior intervention with paper diary-based self-monitoring, and (3) usual care group. Both the mobile and paper groups received a total of 11 face-to-face group sessions in a 6-month intervention. The mobile group received an Android-based smartphone with 2 apps loaded to help them record their diet, physical activity, weight, and blood glucose, along with a connected glucometer, whereas the paper group used paper diaries for these recordings. Primary outcomes of the study included percentage weight loss and glycated hemoglobin (HbA_1c) changes over 6 months.

Results

A total of 26 patients were enrolled: 11 in the mobile group, 9 in the paper group, and 6 in the control group. We had 92% (24/26) retention rate at 6 months. The sample is predominantly African Americans with an average age of 56.4 years and body mass index of 38.1. Participants lost an average of 2.73% (mobile group) and 0.13% (paper group) weight at 6 months, whereas the control group had an average 0.49% weight gain. Their HbA_1c changed from 8% to 7 % in mobile group, 10% to 9% in paper group, and maintained at 9% for the control group. We found a significant difference on HbA_1c at 6 months among the 3 groups (P=.01). We did not find statistical group significance on percentage weight loss (P=.20) and HbA_1c changes (P=.44) overtime; however, we found a large effect size of 0.40 for weight loss and a medium effect size of 0.28 for glycemic control.

Conclusions

Delivering a simplified behavioral lifestyle intervention using mobile health–based self-monitoring in an underserved community is feasible and acceptable and shows higher preliminary efficacy, as compared with paper-based self-monitoring. A full-scale randomized controlled trial is needed to confirm the findings in this pilot study.

Trial Registration

ClinicalTrials.gov NCT02858648; https://clinicaltrials.gov/ct2/show/NCT02858648 (Archived by WebCite at http://www.webcitation.org/6ySidjmT7)