Reduced medical spending associated with increased use of a remote diabetes management program and lower mean blood glucose values

Exploring the Feasibility of Digital Voice Assistants for Delivery of a Home-Based Exercise Intervention in Older Adults With Obesity and Type 2 Diabetes Mellitus: Randomized Controlled Trial

BACKGROUND

Current clinical guidelines for the management of type 2 diabetes mellitus (T2DM) in older adults recommend the use of antihyperglycemic medications, monitoring of blood glucose levels, regular exercise, and a healthy diet to improve glycemic control and reduce associated comorbidities. However, adherence to traditional exercise programs is poor (<35%). Common barriers to adherence include fear of hypoglycemia and the need for blood glucose level monitoring before exercise. Digital health strategies offer great promise for managing T2DM as they facilitate patient-practitioner communication, support self-management, and improve access to health care services for underserved populations. We have developed a novel web-based software program allowing practitioners to create tailored interventions and deliver them to patients via digital voice assistants (DVAs) in their own homes.

OBJECTIVE

We aim to evaluate the feasibility of a 12-week, home-based, personalized lifestyle intervention delivered and monitored by DVAs for older adults with obesity and T2DM.

METHODS

In total, 50 older adults with obesity aged 50-75 years with oral hypoglycemic agent-treated T2DM were randomized to the intervention (DVA, n=25) or a control group (n=25). Participants allocated to the DVA group were prescribed a home-based muscle strengthening exercise program (~20- to 30-min sessions) and healthy eating intervention, delivered via DVAs (Alexa Echo Show 8; Amazon) using newly developed software ("Buddy Link"; Great Australian Pty Ltd). Control group participants received generalized physical activity information via email. Outcomes were feasibility, DVA usability (System Usability Scale), and objectively assessed physical activity and sedentary time (wrist-worn accelerometers).

RESULTS

In total, 45 (90%) out of 50 participants completed this study. Mean adherence to prescribed exercise was 85% (SD 43%) with no intervention-related adverse events. System usability was rated above average (70.4, SD 16.9 out of 100). Compared with controls, the DVA group significantly decreased sedentary time (mean difference -67, SD 23; 95% CI -113 to -21 min/d), which was represented by a medium to large effect size (d=-0.6).

CONCLUSIONS

A home-based lifestyle intervention delivered and monitored by health professionals using DVAs was feasible for reducing sedentary behavior and increasing moderate-intensity activity in older adults with obesity and T2DM.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12621000307808; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=381364&isReview=true.

Measuring the effectiveness of hybrid diabetes care over 90 days through continuous data monitoring in type 2 diabetic patients

Background

Diabetes Mellitus, a global health challenge, affects 537 million individuals. Traditional management relies on periodic clinic visits, but technological advancements, including remote monitoring, offer transformative changes. Telemedicine enhances access, convenience, adherence, and glycemic control. Challenges include trust-building and limitations in face-to-face interactions. Integrating remote monitoring with in-person healthcare creates a hybrid approach. This study evaluates the impact on Type 2 Diabetes patients over 3 months.

Methods

A retrospective case-control observational study. Inclusion criteria involved previous Type 2 Diabetes diagnosis and a minimum 3-month GluCare model period with two physical visits. Patients in the case group had in-clinic visits, bi-weekly app engagement, and monthly body weight readings. Control group had in-clinic visits only. Outcomes measured included HbA1c, lipid profile, CV risk, eGFR, urine Albumin/Creatinine Ratio, Uric Acid, and CRP.

Results

Case group showed significant HbA1c improvements (-2.19%), especially in higher baseline levels. Weight, BMI, LDL, total cholesterol, and CVD risk also improved. Controls showed smaller improvements. Higher digital interactions correlated with better outcomes. Patients with ≥11 interactions showed significant reductions in HbA1c (-2.38%) and weight (-6.00 kg).

Conclusion

The GluCare.Health hybrid model demonstrates promising outcomes in Type 2 diabetes management. The integration of in-clinic consultations with continuous remote monitoring leads to substantial improvements in glycemic control and clinical parameters. The study highlights the importance of patient engagement in achieving positive outcomes, with higher digital interactions associated with greater reductions in HbA1c and weight. The hybrid approach proves more effective than digital-only interventions, emphasizing the need for comprehensive, end-to-end solutions in diabetes care.

Long-Term Results of a Digital Diabetes Self-Management and Education Support Program Among Adults With Type 2 Diabetes: A Retrospective Cohort Study

PURPOSE

The purpose of this study is to examine the long-term impact of a digital diabetes self-management education and support (DSMES) program on A1C among adults with type 2 diabetes (T2DM).

METHODS

Data analyzed were from a retrospective cohort of commercially insured members with T2DM enrolled in the Omada for Diabetes program between January 1, 2019, and January 31, 2022 (n = 1,322). Linear mixed models measured changes in A1C and weight across 12 months (collected at baseline and every 3 months over 1 year) overall and stratified by A1C at baseline (≥8% vs <8%).

RESULTS

On average, members were 53.5 years old, 56.9% female, and 71.5% White, with a mean baseline body mass index (BMI) of 36.9 and A1C of 7.6%. Members with baseline A1C ≥8% demonstrated clinically and statistically significant adjusted mean reductions in A1C during follow-up, from 9.48% at baseline to 7.33%, 7.57%, 7.59%, and 7.47% at 3, 6, 9, and 12 months, respectively. Those with A1C <8% maintained glycemic stability (6.73%, 6.50%, 6.54%, 6.62%, and 6.51%, respectively). Collectively, members experienced a -1.17 kg/m2 mean reduction in BMI over 12 months.

CONCLUSIONS

This study provides real-world evidence that members with elevated baseline A1C (≥8%) enrolled in a digital DSMES program experienced clinically meaningful and statistically significant reductions in A1C. Those with baseline A1C within goal treatment range (<8%) maintained glycemic stability over 1 year. The findings support existing evidence that scalable digital DSMES solutions can help individuals with T2DM manage their condition.

Effectiveness of a hybrid technology enabled care model as measured by ICHOM standard set on established and managed type 2 diabetes already using medications: A RWE retrospective study

Aims

Diabetes is a pressing global health issue, demanding innovative strategies for improved treatment. However, traditional care often falls short of patient goals. To address this, digital health solutions, including smartphone apps and remote monitoring, have emerged as crucial in diabetes management. This study aims to assess a comprehensive intervention, combining remote continuous data monitoring (RCDM) with in-clinic care, for enhancing diabetes-related outcomes. Additionally, it seeks to evaluate the effectiveness of the digital RCDM component by comparing adherent and non-adherent patients.

Methods

Conducted in the United Arab Emirates, a retrospective study involved 89 patients primarily on anti-diabetic medications. They were split into two groups based on adherence to RCDM. Over time, significant improvements were observed across various parameters.

Results

Notably, patients exhibited weight loss (-4.0 ± 5.3, p < 0.001), reduced waist circumference (-4.74 ± 7.8, p < 0.001), lowered HbA1c levels (-1.00 ± 1.3, p < 0.001), decreased systolic BP (-3.1 ± 13.1, p = 0.035), and diminished diastolic BP (-3.4 ± 9.9, p = 0.002) annually. Furthermore, patients adhering to the GluCare model demonstrated substantial HbA1c reductions (-1.53 ± 1.5, p < 0.001), improved lipid profiles, notably decreased total Cholesterol (-16.6 ± 50.3, p = 0.034), and lowered LDL levels (-18.65 ± 42.6, p = 0.006).

Conclusions

The intervention model effectively managed T2D patients through a comprehensive approach, yielding notable improvements in HbA1c levels and other outcomes within a year. The study underscores the limitations of traditional care and reliance simply on pharmacotherapy, and emphasizes the need for a hyper-personalized, and continuous approach for T2D management.

Economic Evaluation Associated With Clinical-Grade Mobile App-Based Digital Therapeutic Interventions: Systematic Review

BACKGROUND

Digital therapeutics (DTx), a class of software-based clinical interventions, are promising new technologies that can potentially prevent, manage, or treat a spectrum of medical disorders and diseases as well as deliver unprecedented portability for patients and scalability for health care providers. Their adoption and implementation were accelerated by the need for remote care during the COVID-19 pandemic, and awareness about their utility has rapidly grown among providers, payers, and regulators. Despite this, relatively little is known about the capacity of DTx to provide economic value in care.

OBJECTIVE

This study aimed to systematically review and summarize the published evidence regarding the cost-effectiveness of clinical-grade mobile app-based DTx and explore the factors affecting such evaluations.

METHODS

A systematic review of economic evaluations of clinical-grade mobile app-based DTx was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Major electronic databases, including PubMed, Cochrane Library, and Web of Science, were searched for eligible studies published from inception to October 28, 2022. Two independent reviewers evaluated the eligibility of all the retrieved articles for inclusion in the review. Methodological quality and risk of bias were assessed for each included study.

RESULTS

A total of 18 studies were included in this review. Of the 18 studies, 7 (39%) were nonrandomized study-based economic evaluations, 6 (33%) were model-based evaluations, and 5 (28%) were randomized clinical trial-based evaluations. The DTx intervention subject to assessment was found to be cost-effective in 12 (67%) studies, cost saving in 5 (28%) studies, and cost-effective in 1 (6%) study in only 1 of the 3 countries where it was being deployed in the final study. Qualitative deficiencies in methodology and substantial potential for bias, including risks of performance bias and selection bias in participant recruitment, were identified in several included studies.

CONCLUSIONS

This systematic review supports the thesis that DTx interventions offer potential economic benefits. However, DTx economic analyses conducted to date exhibit important methodological shortcomings that must be addressed in future evaluations to reduce the uncertainty surrounding the widespread adoption of DTx interventions.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42022358616; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022358616.

Digital therapeutics in the clinic

Digital therapeutics are emerging as a new form of therapeutic interventions. Unlike conventional therapeutics, digital therapeutics deliver interventions directly to patients using an evidence-based, clinically evaluated software to treat, manage, or prevent diseases. Digital therapeutics manifest in diverse forms such as web-based applications, mobile applications on smart devices, virtual reality, and video games. As its own product category for FDA approval, digital therapeutics can function as stand-alone treatments or in combination with conventional therapeutics to improve adherence and/or efficacy. Here, we review the clinical landscape of digital therapeutics. We summarize FDA-approved products and their clinical use, overview >300 ongoing clinical trials, and discuss challenges for their clinical translation and strategies to overcome the same.

The Impact of Digital Health on Smoking Cessation

BACKGROUND

Smartphones have become useful tools for medicine, with the use of specific apps making it possible to bring health care closer to inaccessible areas, continuously monitor a patient's pathology at any time and place, promote healthy habits, and ultimately improve patients' quality of life and the efficiency of the health care system. Since 2020, the use of smartphones has reached unprecedented levels. There are more than 350,000 health apps, according to a 2021 IQVIA Institute report, that address, among other things, the management of patient appointments; communication among different services or professionals; the promotion of lifestyle changes related to adopting healthy habits; and the monitoring of different pathologies and chronic conditions, including smoking cessation. The number of mobile apps for quitting smoking is high. As early as 2017, a total of 177 unique smoking cessation-relevant apps were identified in the iPhone App Store, 139 were identified in Google Play, 70 were identified in the BlackBerry app store, and 55 were identified in the Windows Phone Store, but very few have adequate scientific support. It seems clear that efforts are needed to assess the quality of these apps, as well as their effectiveness in different population groups, to have tools that offer added value to standard practices.

OBJECTIVE

This viewpoint aims to highlight the benefits of mobile health (mHealth) and its potential as an adjuvant tool in health care.

METHODS

A review of literature and other data sources was performed in order to show the current status of mobile apps that can offer support for smoking cessation. For this purpose, the PubMed, Embase, and Cochrane databases were explored between May and November 2022.

RESULTS

In terms of smoking cessation, mHealth has become a powerful coadjuvant tool that allows health workers to perform exhaustive follow-ups for the process of quitting tobacco and provide support anytime and anywhere. mHealth tools are effective for different groups of smokers (eg, pregnant women, patients with chronic obstructive pulmonary disease, patients with mental illness, and the general population) and are cost-effective, generating savings for the health system. However, there are some patient characteristics that can predict the success of using mobile apps in the smoking cessation process, such as the lower age of patients, dependence on tobacco, the number of quit attempts, and the previous use of mobile apps, among others. Therefore, it is preferable to offer these tools to patients with a higher probability of quitting tobacco.

CONCLUSIONS

mHealth is a promising tool for helping smokers in the smoking cessation process. There is a need for well-designed clinical studies and economic evaluations to jointly assess the effectiveness of new interventions in different population groups, as well as their impact on health care resources.

The Impact of Reimbursement for Non-Face-to-Face Chronic Care Management on Comprehensive Metabolic Biomarkers Among Multimorbid Patients With Type 2 Diabetes

AIMS

We evaluated the impact of reimbursement for non-face-to-face chronic care management (NFFCCM) on comprehensive metabolic risk factors among multimorbid Medicare beneficiaries with type 2 diabetes in Louisiana.

MATERIALS AND METHODS

We implemented a propensity score method to obtain comparable treatment (n=1501 with NFFCCM) and control (n=17,524 without NFFCCM) groups. Patients with type 2 diabetes were extracted from the electronic health records stored in REACHnet. The study period was from 2013 to February 2020. The comprehensive metabolic risk factors included the primary outcome of glycated hemoglobin (HbA1c) (as the primary outcome) and the secondary outcomes of body mass index (BMI), systolic blood pressure (BP), and low-density lipoprotein cholesterol.

RESULTS

Receiving any NFFCCM was associated with improvement in all outcomes measures: a reduction in HbA1c of 0.063% (95% CI: 0.031%-0.094%; P <0.001), a reduction in BMI of 0.155 kg/m 2 (95% CI: 0.029-0.282 kg/m 2 ; P =0.016), a reduction in systolic BP of 0.816 mm Hg (95% CI: 0.469-1.163 mm Hg; P <0.001), and a reduction in low-density lipoprotein cholesterol of 1.779 mg/dL (95% CI: 0.988 2.570 mg/dL; P <0.001). Compared with the control group, the treatment group had 1.6% more patients with HbA1c <7% (95% CI: 0.3%-2.9%; P =0.013).

CONCLUSIONS

Patients with diabetes in Louisiana receiving NFFCCM experienced better control of HbA1c, BMI, BP, and low-density lipoprotein outcomes.

Digital health in older adults for the prevention and management of cardiovascular diseases and frailty. A clinical consensus statement from the ESC Council for Cardiology Practice/Taskforce on Geriatric Cardiology, the ESC Digital Health Committee and the ESC Working Group on e-Cardiology

Digital health technology is receiving increasing attention in cardiology. The rise of accessibility of digital health tools including wearable technologies and smart phone applications used in medical practice has created a new era in healthcare. The coronavirus pandemic has provided a new impetus for changes in delivering medical assistance across the world. This Consensus document discusses the potential implementation of digital health technology in older adults, suggesting a practical approach to general cardiologists working in an ambulatory outpatient clinic, highlighting the potential benefit and challenges of digital health in older patients with, or at risk of, cardiovascular disease. Advancing age may lead to a progressive loss of independence, to frailty, and to increasing degrees of disability. In geriatric cardiology, digital health technology may serve as an additional tool both in cardiovascular prevention and treatment that may help by (i) supporting self-caring patients with cardiovascular disease to maintain their independence and improve the management of their cardiovascular disease and (ii) improving the prevention, detection, and management of frailty and supporting collaboration with caregivers. Digital health technology has the potential to be useful for every field of cardiology, but notably in an office-based setting with frequent contact with ambulatory older adults who may be pre-frail or frail but who are still able to live at home. Cardiologists and other healthcare professionals should increase their digital health skills and learn how best to apply and integrate new technologies into daily practice and how to engage older people and their caregivers in a tailored programme of care.

Development, validation and transfer to clinical practice of a mobile application for the treatment of smoking

OBJECTIVE

The main objective is to transfer to clinical practice a new smoking cessation application ("Vive sin Tabaco" a) in all health centers of the public Basque Health Service.

DESIGN

An implementation study of a smoking cessation program previously validated. After implementation, a retrospective study has been carried out to evaluate its use under normal conditions. SITE: The process of transfer to clinical practice has been held in several phases; first a pilotage in four health centers of Alava and subsequently, when all reported incidents were resolved, it was extended to all health centers of the Basque Health Service.

INTERVENTION AND MAIN MEASUREMENT

Development of "Vive sin Tabaco"; a corporate tool for smoking cessation, and its transfer to clinical practice. All interested health care workers received training on how to use the application. User manuals for both patients and professionals were developed. Smoking cessation rates at 12 months during implementation were also collected.

RESULTS

The percentage of patients of post pilot phase who quit smoking at 12 months was 14.1%.

CONCLUSIONS

The conception of "Vive sin tabaco" as a corporate tool for smoking cessation, available in all health centers of Basque Health Service, has been long and arduous, and has required the participation of health professionals and patients as end-users in order to obtain a tool that adapts to their expectations and guarantees greater usability and satisfaction. This application is being effective as an adjuvant tool to health advice.

Impact of a Digitally Enhanced Diabetes Self-Management Program on Glycemia and Medical Costs

OBJECTIVE

To analyze economic savings and health impacts associated with a virtual digitally enhanced diabetes self-management education and support (DSMES) program.

RESEARCH DESIGN AND METHODS

Participants (n = 1,494) were nonpregnant adults with diagnosed type 2 diabetes and baseline body mass index (BMI) of 25 kg/m² (23 kg/m² if of Asian descent) or higher who enrolled in virtual DSMES between February 2019 and April 2020 for at least 4 months. Participants' changes in glycated hemoglobin (A1C) and body weight were calculated as the difference between program start and last recorded values between months 4 and 6. Outcomes for all participants were analyzed; subanalyses were done on 628 participants with starting A1C >7% (53 mmol/mol), who could benefit most from DSMES. Markov-based microsimulation approach was used to model the potential reductions in diabetes sequalae and medical expenditures if observed improvements in A1C and BMI were maintained.

RESULTS

DSMES participants with starting A1C >7% experienced average reductions of 0.9% A1C and 2.1 kg of body weight (-1.7% of BMI) within 6 months. If these improvements were maintained, simulated outcomes include reduced 5-year onset of ischemic heart disease by 9.2%, myocardial infarction by 10.6%, stroke by 12.1%, chronic kidney disease by 16.5%, and reduced onset of other sequelae. Simulated cumulative reduction in medical expenditures is $1160 after 1 year, $4150 after 3 years, $7790 after 5 years, and $18 020 after 10 years.

CONCLUSIONS

Participation in virtual DSMES improves A1C and body weight, with the potential to slow onset of diabetes sequelae and reduce medical expenditures.

Changing face of healthcare: digital therapeutics in the management of diabetes

Diagnosis, prevention, management and treatment of acute and chronic medical conditions have improved with technological advancements in terms of scalability, efficacy, access, and personalized approach. Digital therapeutic applications (DTx) (Blue Star, Diabeo System, Livongo Diabetes Program, Tidepool etc.,) use web-based applications/cloud platforms to provide evidence-based, personalized, rapid point of care management of chronic, behavior-modifiable conditions, including diabetes mellitus (DM). DTx has improved patient compliance, therapeutic success and economic outcomes in DM management by enabling active patient engagement, lifestyle change, comprehensive medical care, and periodic monitoring of glycemic status. Addressing concerns along DTx data vulnerability, comparative efficacy with conventional treatments, ability to accommodate diverse population needs, and resolving ambiguous regulatory policies and reimbursement guidelines are critical for increasing access to DTx, and overcoming availability, accessibility, and affordability issues in the existing resource limited healthcare environment. In this commentary the authors explore the potential, prospects, and challenges of DTx in the management of 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.

The Effect of 24/7, Digital-First, NHS Primary Care on Acute Hospital Spending: Retrospective Observational Analysis

BACKGROUND

Digital health has the potential to revolutionize health care by improving accessibility, patient experience, outcomes, productivity, safety, and cost efficiency. In England, the NHS (National Health Service) Long Term Plan promised the right to access digital-first primary care by March 31, 2024. However, there are few global, fully digital-first providers and limited research into their effects on cost from a health system perspective.

OBJECTIVE

The aim of this study was to evaluate the impact of highly accessible, digital-first primary care on acute hospital spending.

METHODS

A retrospective, observational analysis compared acute hospital spending on patients registered to a 24/7, digital-first model of NHS primary care with that on patients registered to all other practices in North West London Collaboration of Clinical Commissioning Groups. Acute hospital spending data per practice were obtained under a freedom of information request. Three versions of NHS techniques designed to fairly allocate funding according to need were used to standardize or "weight" the practice populations; hence, there are 3 results for each year. The weighting adjusted the populations for characteristics that impact health care spending, such as age, sex, and deprivation. The total spending was divided by the number of standardized or weighted patients to give the spending per weighted patient, which was used to compare the 2 groups in the NHS financial years (FY) 2018-2019 (FY18/19) and 2019-2020 (FY19/20). FY18/19 costs were adjusted for inflation, so they were comparable with the values of FY19/20.

RESULTS

The NHS spending on acute hospital care for 2.43 million and 2.54 million people (FY18/19 and FY19/20) across 358 practices and 49 primary care networks was £1.6 billion and £1.65 billion (a currency exchange rate of £1=US $1.38 is applicable), respectively. The spending on acute care per weighted patient for Babylon GP at Hand members was 12%, 31%, and 54% (£93, P=.047; £223, P<.001; and £389, P<.001) lower than the regional average in FY18/19 for the 3 weighting methodologies used. In FY19/20, it was 15%, 35%, and 51% (£114, P=.006; £246, P<.001; and £362, P<.001) lower. This amounted to lower costs for the Babylon GP at Hand population of £1.37, £4.40 million, and £11.6 million, respectively, in FY18/19; and £3.26 million, £9.54 million, and £18.8 million, respectively, in FY19/20.

CONCLUSIONS

Patients with access to 24/7, digital-first primary care incurred significantly lower acute hospital costs.

2021 ISHNE/HRS/EHRA/APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/ Heart Rhythm Society/ European Heart Rhythm Association/ Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE / HRS / EHRA / APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE/HRS/EHRA/APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE/HRS/EHRA/APHRS Expert Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society describes the current status of mobile health technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mobile health. The promises of predictive analytics but also operational challenges in embedding mobile health into routine clinical care are explored.

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.

The improved survival rate and cost-effectiveness of a 7-day continuous subcutaneous insulin infusion set

AIMS

The purpose of this article is to compare the insulin cost-savings of the Medtronic Extended Infusion Set (or EIS, a.k.a. Extended Wear Infusion Set) designed and labeled for up to 7-day use with rapid-acting insulins to the current standard of care, 2- to 3-day infusion sets.

METHODS

There are three major improvements (reducing insulin waste, plastic waste, and adverse events) with the extended duration of infusion set wear. This analysis focuses on cost savings from reduced insulin wastage during set changes. Studies published on insulin infusion set survival and EIS clinical trial data (NCT04113694) were used to estimate device lifetime performance using a Markov chain Monte Carlo model, including the assessment of adverse effects and device failure. Total costs associated with infusion set change or failure were systematically found in published literature or estimated based on physical usage, and the direct impact on insulin costs was calculated.

RESULTS

Based on the model and clinical data, EIS users can expect to change their infusion sets about 75 fewer times than standard set users each year. The costs related to unrecoverable insulin during an infusion set and reservoir change in the US were estimated to range from $19.79 to $22.48, resulting in approximately $1324 to $1677 in annual cost-savings for the typical user from minimizing insulin wastage.

LIMITATIONS

The study only assessed devices used within a monitored setting, that is, clinical trials. In addition, the variability associated with healthcare standards and costs and individual treatment variability including insulin dosages, contribute to the uncertainties with the calculations.

CONCLUSIONS

Our analysis demonstrates that by extending the duration of infusion set wear, there may be substantial cost savings by reducing insulin wastage.

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.

Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists

Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. Machine learning software programs that disclose the reasoning behind a prediction allow for “what-if” models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that “affect” the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.

Digital Diabetes Care System Observations from a Pilot Evaluation Study in Vietnam

Digital technologies are gaining an important role in the management of patients with diabetes. We assessed clinical outcomes and user satisfaction of incorporating a digital diabetes care system in diabetes clinics of a developing country. The system integrated a wireless blood glucose monitor that communicates data to any smartphone utilizing a patented acoustic data transfer method, a mobile-app, and cloud-based software that stores, analyzes, and presents data. Five hospital endocrinology clinics in Vietnam sequentially recruited all patients willing to join the study, providing they had a smartphone and access to internet connectivity. Face-to-face visits were conducted at baseline and at 12 weeks, with monthly digital visits scheduled in the interim and additional digital visits performed as needed. HbA1c levels were measured at baseline and at 12 weeks (±20 days). The study included 300 patients of whom 279 completed the evaluation. Average glucose levels declined from 170.4 ± 64.6 mg/dL in the first 2 weeks to 150.8 ± 53.2 mg/dL in the last 2 weeks (n = 221; p < 0.001). HbA1c levels at baseline and 12 weeks declined from 8.3% ± 1.9% to 7.6% ± 1.3% (n = 126; p < 0.001). The digital solution was broadly accepted by both patients and healthcare professionals and improved glycemic outcomes. The durability, scalability, and cost-effectiveness of this approach merits further study.

Reviewing U.S. Connected Diabetes Care: The Newest Member of the Team

Recent years have brought about an explosion in the number of companies offering connected diabetes care products, defined as digital diabetes management systems based around (1) smartphone apps, (2) devices with built-in connectivity, and (3) remote human and automated coaching and support. These nascent models aim to provide more continuous and on-demand care, aligning with the 24/7 demands of chronic disease. It has been enabled by multiple factors, including the rising use of connected devices and apps to help people manage their chronic conditions, growing appreciation for the importance of outcomes beyond A1c, and the lofty and growing cost of health care. Despite the potential of these programs to improve the outcomes and well-being of people with diabetes and reduce the burdens on health care providers and systems, awareness and use of these programs and approaches remain low in the medical community. In this article, we present a snapshot of this dynamic field, including a taxonomy of various connected diabetes care products available to employers, health plans, health systems, and people with diabetes in the United States, and we identify meaningful distinctions among them: (1) health conditions managed, (2) peer support interactions, (3) prescribing providers on the care team, (4) provision of connected medical devices and/or continuous glucose monitors, (5) degree of treatment personalization, and (6) clinical and real-world evidence. We also discuss broad trends in connected diabetes care. Given the urgency and scale of the diabetes epidemic, it is vital that a range of medical and clinical communities find meaningful ways to scale individualized, timely care under reimbursement models that better align incentives for various stakeholders, particularly health care providers themselves. This would not only address deficiencies in care but could also make diabetes care more attractive to future clinicians.

Is Digital Health for Diabetes in an Investment Bubble?

An investment bubble occurs when there is a surge in asset prices that is not warranted by asset fundamentals because of irrationally exuberant market behavior. When prices rise to a level where no additional investors are willing to buy at the elevated price, then a massive sell-off typically occurs. Digital health investments represent approximately 10% of venture capital-backed startup investments, and diabetes digital health startups represent 4% of digital health investments. Attributes of a bubble indicate evidence for and against the current time period being in an investment bubble for digital health startups. After analyzing these attributes as well as the overall economy and the demand for healthcare products, we conclude that digital health startups and particularly digital health startups for diabetes are not in a bubble.

Estimating the Impact of Novel Digital Therapeutics in Type 2 Diabetes and Hypertension: Health Economic Analysis

BACKGROUND

Behavioral interventions can meaningfully improve cardiometabolic conditions. Digital therapeutics (DTxs) delivering these interventions may provide benefits comparable to pharmacologic therapies, displacing medications for some patients.

OBJECTIVE

Our objective was to estimate the economic impact of a digital behavioral intervention in type 2 diabetes mellitus (T2DM) and hypertension (HTN) and estimate the impact of clinical inertia on deprescribing medications.

METHODS

Decision analytic models estimated health resource savings and cost effectiveness from a US commercial payer perspective. A 3-year time horizon was most relevant to the intervention and payer. Effectiveness of the DTx in improving clinical outcomes was based on cohort studies and published literature. Health resource utilization (HRU), health state utilities, and costs were drawn from the literature with costs adjusted to 2018 dollars. Future costs and quality-adjusted life years (QALYs) were discounted at 3%. Sensitivity analyses assessed uncertainty.

RESULTS

Average HRU savings ranged from $97 to $145 per patient per month, with higher potential benefits in T2DM. Cost-effectiveness acceptability analyses using a willingness-to-pay of $50,000/QALY indicated that the intervention would be cost effective at total 3-year program costs of $6468 and $6620 for T2DM and HTN, respectively. Sensitivity analyses showed that reduced medication costs are a primary driver of potential HRU savings, and the results were robust within values tested. A resistance to deprescribe medications when a patient's clinical outcomes improve can substantially reduce the estimated economic benefits. Our models rely on estimates of clinical effectiveness drawn from limited cohort studies with DTxs and cannot account for other disease management programs that may be implemented. Performance of DTxs in real-world settings is required to further validate their economic benefits.

CONCLUSIONS

The DTxs studied may provide substantial cost savings, in part by reducing the use of conventional medications. Clinical inertia may limit the full cost savings of DTxs.

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.