Using Digital Health Technology to Prevent and Treat Diabetes.

Abstract

Diabetes Technology & TherapeuticsVol. 25, No. S1 Original ArticlesFree AccessUsing Digital Health Technology to Prevent and Treat DiabetesMark Clements, Neal Kaufman, and Eran MelMark ClementsChildren's Mercy Hospital, Kansas City, MO, USA.University of Missouri-Kansas City, Kansas City, MO, USA.Search for more papers by this author, Neal KaufmanFielding School of Public Health, Geffen School of Medicine, University of California, Los Angeles, CA, USA.Canary Health Inc., Los Angeles, CA, USA.Search for more papers by this author, and Eran MelJesse Z. and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel.Search for more papers by this authorPublished Online:20 Feb 2023https://doi.org/10.1089/dia.2023.2506AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail IntroductionThis article is our annual attempt to highlight the key papers written in the prior 12 months (i.e., between July 1, 2021, and June 30, 2022) in which digital health technologies were used to provide digitally driven care for patients with diabetes mellitus or prediabetes. This article addresses both digital therapeutics and digital care solutions. Digital therapeutics deliver evidence-based interventions to an individual, either independently or in combination with other therapeutics (e.g., drugs, devices, and behavioral interventions). In contrast, digital care solutions may combine a suite of digital therapeutic strategies with a telemedical or remote care program, as well as with provider-facing population health management software to aid in identifying at-risk individuals or identifying the right patient and/or right time for a remote interaction.We selected papers that exemplified emerging themes related to digital care that deserve highlighting. First, there is an increasing focus on translating evidence-based diabetes prevention programs and similar strategies into validated digital delivery formats. With the increasing prevalence of connected glucose monitoring devices, fitness wearables, and smart scales, it has become possible for clinicians to remotely monitor individuals seeking to reverse dysglycemia, weight gain, insulin resistance, and other associated metabolic perturbations. With chatbots to provide guidance and artificial intelligence (AI) to predict short-term glycemic changes from self-monitored blood glucose data in an open-loop setting, it is becoming possible to automate at least some of the coaching that might typically be performed by a human. (AI to predict glycemia using continuous glucose monitor–derived data is already deployed across many closed-loop insulin delivery systems.) This trend is exciting, because coaches and healthcare professionals are expensive and time is a limited resource. And with the increasing use of text messaging, patient portals, digital social networks, and video telehealth, it has become easier for clinicians to interact remotely with patients. These trends also have the potential to positively impact access to care, although we will have to wait for new studies to be published before drawing a conclusion. Another theme relates to the power of digital therapeutics and digital care ecosystems to drive population health management processes and improve overall population health. Population health management operates on the underlying assumption (and fact) that many individuals with diabetes or prediabetes achieve suboptimal outcomes under a one-size-fits-all model. In other words, delivering the same “dose” and “type” of health care to all patients contributes to health inequity. In contrast, using population health approaches to tailor the dose and type of care to individuals—a strategy called risk-based management—has the potential to improve health equity and improve outcomes for individuals and for the population as a whole.Key Articles ReviewedEffects of a Digital Diabetes Prevention Program: An RCTKatula JA, Dressler EV, Kittel CA, Harvin LN, Almeida FA, Wilson KE, Michaud TL, Porter GC, Brito FA, Goessl CL, Jasik CB, Sweet CMC, Schwab R, Estabrooks PAAm J Prev Med 2022;62: 567–577Evaluating the Implementation of the GREAT4Diabetes WhatsApp Chatbot To Educate People with Type 2 Diabetes During the Covid-19 Pandemic: Convergent Mixed Methods StudyMash R, Schouw D, Fischer AEJMIR Diabetes 2022;7: e37882Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market AnalysisKeller R, Hartmann S, Teepe GW, Lohse KM, Alattas A, Tudor Car L, Müller-Riemenschneider F, von Wangenheim F, Mair JL, Kowatsch TJ Med Internet Res 2022;24: e33348Implementation of an Intensive Telehealth Intervention for Rural Patients with Clinic-Refractory DiabetesKobe EA, Lewinski AA, Jeffreys AS, Smith VA, Coffman CJ, Danus SM, Sidoli E, Greck BD, Horne L, Saxon DR, Shook S, Aguirre LE, Esquibel MG, Evenson C, Elizagaray C, Nelson V, Zeek A, Weppner WG, Scodellaro S, Perdew CJ, Jackson GL, Steinhauser K, Bosworth HB, Edelman D, Crowley MJJ Gen Intern Med 2022 Jan 3; 1–9. doi: 10.1007/s11606-021-07281-8. Online ahead of print.Personalized Glycemic Response Led Digital Therapeutics Program Improves Time in Range in a Period of 14 DaysVerma R, Bhardwaj S, Lathia T, Kalra S, Ranadive R, Tanna S, Padsalge M, Juneja A, Samundra K, Thakkar PB, Jain V, Kini V, Kothari S, Guntur S, Joshi S, Singal AInt J Diabetes Dev Ctries 2022 Jul 22;1–8. doi: 10.1007/s13410-022-01111-1. Online ahead of print.A Randomized Controlled Trial of an Innovative, User-Friendly, Interactive Smartphone App-Based Lifestyle Intervention for Weight LossVaz CL, Carnes N, Pousti B, Zhao H, Williams KJObes Sci Pract 2021;7: 555–568New Digital Health Technologies for Insulin Initiation and Optimization for People with Type 2 DiabetesKerr D, Edelman S, Vespasiani G, Khunti KEndocr Prac 2022;28: 811–821Digital Health Coaching for Type 2 Diabetes: Randomized Controlled Trial of Healthy at HomeAzelton KR, Crowley AP, Vence N, Underwood K, Morris G, Kelly J, Landry MJFront Digit Health 2021;3: 764735Improvement in Glucose Regulation Using a Digital Tracker and Continuous Glucose Monitoring in Healthy Adults and Those with Type 2 DiabetesDehghani Zahedani A, Shariat Torbaghan S, Rahili S, Karlin K, Scilley D, Thakkar R, Saberi M, Hashemi N, Perelman D, Aghaeepour N, McLaughlin T, Snyder MP, January AI, Menlo Park, CADiabetes Ther 2021;12: 1871–1886Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults with Type 2 Diabetes Using One Drop: Retrospective Cohort StudyImrisek SD, Lee M, Goldner D, Nagra H, Lavaysse LM, Hoy-Rosas J, Dachis J, Sears LEJMIR Diabetes 2022;7: e34624Education at Scale: Improvements in Type 1 Diabetes Self-Management Following a Massive Open Online CourseMackenzie SC, Cumming KM, Mehar S, Wilson L, Cunningham SG, Bickerton A, Wake DJDiabet Med 2022;39: e14842Gamification for the Improvement of Diet, Nutritional Habits, and Body Composition in Children and Adolescents: A Systematic Review and Meta-AnalysisSuleiman-Martos N, García-Lara RA, Martos-Cabrera MB, Albendín-García L, Romero-Béjar JL, Cañadas-De la Fuente GA, Gómez-Urquiza JLNutrients 2021;13: 2478Efficacy of Personalized Diabetes Self-Care Using an Electronic Medical Record-Integrated Mobile App in Patients with Type 2 Diabetes: 6-Month Randomized Controlled TrialLee EY, Cha SA, Yun JS, Lim SY, Lee JH, Ahn YB, Yoon KH, Hyun MK, Ko SHJ Med Internet Res 2022;24: e37430On the Efficacy of Behavior Change Techniques in mHealth for Self-Management of Diabetes: A Meta-AnalysisEl-Gayar O, Ofori M, Nawar NJ Biomed Infor 2021;119: 103839Continuous Glucose Monitoring Data Sharing in Older Adults with Type 1 Diabetes: Pilot Intervention StudyAllen NA, Litchman ML, Chamberlain J, Grigorian EG, Iacob E, Berg CAJMIR Diabetes 2022;7: e35687Promoting Self-Management Behaviors in Adolescents with Type 1 Diabetes, Using Digital Storytelling: A Pilot Randomized Controlled TrialZarifsaniey N, Shirazi MO, Mehrabi M, Bagheri ZBMC Endocr Disord 2022;22: 74Effect of Digital Lifestyle Management on Metabolic Control and Quality of Life in Patients with Well-Controlled Type 2 DiabetesDwibedi C, Abrahamsson B, Rosengren AHDiabetes Ther 2022;13: 423–439Uptake and Impact of the English National Health Service Digital Diabetes Prevention Programme: Observational StudyRoss JAD, Barron E, McGough B, Valabhji J, Daff K, Irwin J, Henley WE, Murray EBMJ Open Diab Res Care 2022;10: e002736The Use of Artificial Intelligence-Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical RecommendationsChew HSJJMIR Med Inform 2022;10: e32578A 12-Month Follow-Up of the Effects of a Digital Diabetes Prevention Program (VP Transform for Prediabetes) on Weight and Physical Activity Among Adults with Prediabetes: Secondary AnalysisBatten R, Alwashmi MF, Mugford G, Nuccio M, Besner A, Gao ZMIR Diabetes 2022;7: e23243Population-Level Management of Type 1 Diabetes via Continuous Glucose Monitoring and Algorithm-Enabled Patient Prioritization: Precision Health Meets Population HealthFerstad JO, Vallon JJ, Jun D, Gu A, Vitko A, Morales DP, Leverenz J, Lee MY, Leverenz B, Vasilakis C, Osmanlliu E, Prahalad P, Maahs DM, Johari R, Scheinker DPediatr Diabetes 2021;22: 982–991Teamwork, Targets, Technology, and Tight Control in Newly Diagnosed Type 1 Diabetes: The Pilot 4T StudyPrahalad P, Ding VY, Zaharieva DP, Addala A, Johari R, Scheinker D, Desai M, Hood K, Maahs DMJ Clin Endocrinol Metab 2022;107: 998–1008Effects of a Digital Diabetes Prevention Program: An RCTKatula JA1, Dressler EV2, Kittel CA2, Harvin LN2, Almeida FA3, Wilson KE4, Michaud TL3, Porter GC3, Brito FA3, Goessl CL3, Jasik CB5, Sweet CMC5, Schwab R6, Estabrooks PA31Department of Health & Exercise Science, Wake Forest University, Winston-Salem, NC; 2Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC; 3Department of Health Promotion, College of Public Health, University of Nebraska Medical Center, Omaha, NE; 4Department of Kinesiology and Health, College of Education & Human Development, Georgia State University, Atlanta, GA; 5Medical Affairs, Omada Health, Inc., San Francisco, CA; 6Department of Internal Medicine, College of Medicine, University of Nebraska Medical Center, Omaha, NEAm J Prev Med 2022;62: 567−577IntroductionAlthough many digital diabetes prevention programs (DDPPs) have been clinically proven, data from rigorous studies are still needed to demonstrate DDPP effectiveness. Furthermore, these programs must be made more accessible. Hence, the aim of this study was to compare a DDPP with enhanced standard care plus waitlist control for effectiveness in improving weight, HbA1c, and cardiovascular risk factors among people with prediabetes.Study DesignThis was a single-blind RCT among participants at risk of developing type 2 diabetes and included 12 months of follow-up.Setting/ParticipantsA total of 599 volunteer patients with prediabetes were recruited primarily through electronic medical records and primary care practices.InterventionParticipants were randomized to either a DDPP (n = 299) or a single-session small-group diabetes-prevention education class (n = 300) focused on action planning for weight loss. The DPP regimen consisted of 52 weekly sessions, lifestyle coaching, virtual peer support, and behavior-tracking tools.Main Outcome MeasuresThe primary outcome was a change in HbA1c from baseline to 12 months using intent-to-treat analyses. On the basis of multiple comparisons of endpoints, 95% CIs are presented, and two-sided P<.025 was required for statistical significance. Secondary outcomes included body weight and cardiovascular disease risk factors.ResultsAmong 599 randomized participants (mean age, 55.4 years; 61.4% women), 483 (80%) completed the study. The DDPP regimen produced significantly greater reductions in HbA1c (0.08%; 95% CI, −0.12 to −0.03) and percentage change in body weight (−5.5% vs −2.1%; P<.001) at 12 months. A greater proportion of the DDPP group achieved clinically significant weight loss ≥5% (43% vs 21%, P<.001), and more participants shifted from prediabetes to normal HbA1c range (58% vs 48%; P = .04). Engagement in a DDPP was significantly related to improved HbA1c and weight loss.ConclusionsThis DDPP demonstrated clinical effectiveness and has significant potential for widespread dissemination and impact, particularly considering the growing demand for telemedicine in preventive health-care services.CommentsLowering the rate of progression from prediabetes to type 2 diabetes is a noble cause and one that has eluded nearly all who have attempted to accomplish it at scale. One promising trend is the ability for digital interventions to be successful. This randomized controlled trial adds to the literature by demonstrating the positive impact of Omada's digital intervention. The major limitation of the study is the relatively short timeline for measuring outcomes (1 year). Also, beyond the scope of this study are the methods used to recruit participants to the study and the generalizability of that approach to overcome the main challenge to these programs: the ability to cost-effectively recruit large proportions of the target population to yield population-based impact.Evaluating the Implementation of the GREAT4Diabetes WhatsApp Chatbot to Educate People with Type 2 Diabetes During the Covid-19 Pandemic: Convergent Mixed Methods StudyMash R1, Schouw D1, Fischer AE21Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; 2Aviro Health, Cape Town, South AfricaJMIR Diabetes 2022;7: e37882BackgroundIn South Africa, one of the leading causes of morbidity and mortality is diabetes, a problem that became even worse during the COVID-19 pandemic. As a result of the lockdown, most diabetes-related education and counseling were discontinued. To help compensate for the loss of these services, the GREAT4Diabetes WhatsApp Chatbot was created.ObjectiveThis study aimed to evaluate the implementation of the chatbot in Cape Town, South Africa, between May 2021 and October 2021.MethodsConvergent mixed methods were used to evaluate the implementation outcomes: acceptability, adoption, appropriateness, feasibility, fidelity, cost, coverage, effects, and sustainability. Quantitative data were derived from the chatbot and analyzed using the SPSS. Qualitative data were collected from key informants and analyzed using the framework method assisted by ATLAS.ti. The chatbot provided users with 16 voice messages and graphics in English, Afrikaans, or Xhosa. Messages focused on COVID-19 infection and self-management of type 2 diabetes.ResultsThe chatbot was adopted by the Metro Health Services to assist people with diabetes who had restricted health care during the lockdown and were at a higher risk of hospitalization and death from COVID-19 infection. The chatbot was disseminated via health-care workers in primary care facilities and local nonprofit organizations and via local media and television. Two technical glitches interrupted the dissemination but did not substantially affect user behavior. Minor changes were made to the chatbot to improve its utility. Many patients had access to smartphones and were able to use the chatbot via WhatsApp. Overall, 8158 people connected with the chatbot and 4577 (56.1%) proceeded to listen to the messages, with 12.56% (575/4577) of them listening to all 16 messages, mostly within 32 days. The incremental setup costs were ZAR 255,000 (US $16,876) and operational costs over 6 months were ZAR 462,473 (US $30,607). More than 90% of the users who listened to each message found the messages useful. Of the 533 who completed the whole program, 351 (71.1%) said they changed their self-management a lot, and 87.6% (369/421) were more confident. Most users changed their lifestyles in terms of diet (315/414, 76.1%) and physical activity (222/414, 53.6%). Health-care workers also saw benefits to patients and recommended that the service continue. Sustainability of the chatbot will depend on the future policy of the provincial Department of Health toward mobile health and the willingness to contract with Aviro Health. There is the potential to go to scale and include other languages and chronic conditions.ConclusionsBenefits were seen with the chatbot. This promising tool may complement traditional health care for people with diabetes and may help more extensively educate patients. Additional research is needed to further understand patients' experience with the chatbot and determine its effectiveness.CommentsThis convergent mixed-methods study evaluated implementation outcomes for the GREAT4Diabetes WhatsApp Chatbot, which was initially implemented in Cape Town, South Africa from May 2021 through Oct 2021 with 8158 people who have type 2 diabetes and restricted health access due to the pandemic. The chatbot delivered short audio education, sometimes along with a still picture. Users answered a question about the usefulness of the education in order to access the next education module. Results showed that 56.1% of those who registered for the program listened to the messages and 71.1% of those who completed the program made significant changes to self-management. This study is an excellent example of using qualitative methods for implementation science. Implementation outcomes include acceptability, adoption, appropriateness, feasibility, fidelity, cost, coverage, effects, and sustainability. We should hope to see more implementation science studies on digital applications for diabetes in the future.Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market AnalysisKeller R1,2, Hartmann S3, Teepe GW4, Lohse KM4, Alattas A1, Tudor Car L5,6, Müller-Riemenschneider F2,7, von Wangenheim F1,4, Mair JL1,2, Kowatsch T1,3,41Future Health Technologies Programme, Campus for Research Excellence and Technological Enterprise, Singapore-ETH Centre, Singapore; 2Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore; 3Centre for Digital Health Interventions, Institute of Technology Management, University of St Gallen, St Gallen, Switzerland; 4Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland; 5Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; 6Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK; 7Yong Loo Lin School of Medicine, National University of Singapore, SingaporeJ Med Internet Res 2022;24: e33348BackgroundAs technology evolves, new methods arise for preventing and managing type 2 diabetes. Exemplifying such advancements are digital behavioral change interventions (DCBIs), which are being developed by digital diabetes companies and funded by venture capitalists. However, the scientific evidence behind DCBIs has not been closely examined, nor has the use of conversational agents (CAs) or just-in-time adaptive intervention (JITAI) approaches.ObjectiveOur objectives were to identify the top-funded companies offering DBCIs for type 2 diabetes management and prevention, review the level of scientific evidence underpinning the DBCIs, identify which DBCIs are recognized as evidence-based programs by quality assurance authorities, and examine the degree to which these DBCIs include novel automated approaches such as CAs and JITAI mechanisms.MethodsA systematic search was conducted using two venture capital databases (Crunchbase Pro and Pitchbook) to identify the top-funded companies offering interventions for type 2 diabetes prevention and management. Scientific publications relating to the identified DBCIs were identified via PubMed, Google Scholar, and the DBCIs' websites; then data regarding intervention effectiveness were extracted. The Diabetes Prevention Recognition Program (DPRP) of the Center for Disease Control and Prevention in the United States was used to identify the recognition status. The DBCIs' publications, websites, and mobile apps were reviewed with regard to the intervention characteristics.ResultsThe 16 top-funded companies offering DBCIs for type 2 diabetes received a total funding of US $2.4 billion as of June 15, 2021. Only four out of the 50 identified publications associated with these DBCIs were about fully powered randomized controlled trials (RCTs). Further, one of those four RCTs showed a significant difference in glycated hemoglobin A1c (HbA1c) outcomes between the intervention and control groups. However, all the studies reported HbA1c improvements ranging from 0.2% to 1.9% over the course of 12 months. In addition, six interventions were fully recognized by the DPRP to deliver evidence-based programs, and two interventions had a pending recognition status. Health professionals were included in the majority of DBCIs (13/16, 81%), whereas only 10% (1/10) of accessible apps involved a CA as part of the intervention delivery. Self-reports represented most of the data sources (74/119, 62%) that could be used to tailor JITAIs.ConclusionsIn this study, the amount of funding for a DCBI did not correlate with the strength of the scientific evidence supporting a DCBI's effectiveness in preventing and managing type 2 diabetes. Overall, more rigorous trials are needed to determine effectiveness, and quality assurance authorities need to make reporting more transparent. As of now, automated approaches such as CAs and JITAIs are not used by many DCBIs, so the scalability and reach of these solutions are limited.CommentsFor more than 20 years, numerous emerging digital companies have attempted to improve outcomes for people with or at risk for diabetes. Many have been extraordinarily well funded, and the authors of this paper analyzed the published outcomes from the top 16 funded digital diabetes companies (ranging from US $15.5 million to $657.3 million, for a total of $2.4 billion). The majority of the interventions were for the prevention of type 2 diabetes. While the 16 companies published 50 papers, only four studies were randomized controlled trials, the gold standard of intervention assessment. On the promising side are the number of well-funded companies, the number of participants served, and the maturing of the marketplace and of the vendors. On the negative side are the less than stellar study designs and outcomes. The methodologies to structure these types of studies are also evolving, so we can expect many more in the near future.Implementation of an Intensive Telehealth Intervention for Rural Patients with Clinic-Refractory DiabetesKobe EA1, Lewinski AA2,3, Jeffreys AS2, Smith VA2,4,5, Coffman CJ2,6, Danus SM2, Sidoli E7, Greck BD7, Horne L8, Saxon DR9,10, Shook S11, Aguirre LE11, Esquibel MG11, Evenson C12, Elizagaray C12, Nelson V13, Zeek A13, Weppner WG14,15, Scodellaro S15, Perdew CJ,15 Jackson GL2,4,5,16, Steinhauser K2,4, Bosworth HB17, Edelman D2,15, Crowley MJ2,181Duke University School of Medicine, Durham, NC; 2Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC; 3School of Nursing, Duke University School of Medicine, Durham, NC; 4Department of Population Health Sciences, Duke University School of Medicine, Durham, NC; 5Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC; 6Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC; 7Western North Carolina Veteran Affairs Health Care System, Asheville, NC; 8VISN 19 Rocky Mountain Regional, Denver, CO; 9Division of Endocrinology, Metabolism and Diabetes, University of Colorado School of Medicine, Aurora, CO; 10Division of Endocrinology, Rocky Mountain Veterans Affairs Medical Center, Aurora, CO; 11New Mexico Veteran Affairs Health Care System, University of New Mexico School of Medicine, Albuquerque, NM; 12Montana Veteran Affairs Health Care System, Kalispell, MT; 13Veterans Affairs Central Ohio Healthcare System, Columbus, OH; 14Division of General Internal Medicine, University of Washington School of Medicine, Seattle, WA; 15Boise Veteran Affairs Medical Center, Boise, ID; 16Department of Family Medicine and Community Health, Duke University School of Medicine, Durham, NC; 17Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC; 18Division of Endocrinology, Department of Medicine, Duke University School of Medicine, Durham, NCJ Gen Intern Med 2022 Jan 3; 1–9. doi: 10.1007/s11606-021-07281-8. Online ahead of print.BackgroundPatients with type 2 diabetes (T2D) who live in rural areas may not be able to control their condition well because they may not be able to fully access specialty care and self-management support. Although telehealth can be used to deliver comprehensive care to T2D patients who live too far for in-person access, implementing this option can be difficult in clinical practice.ObjectiveTo examine the implementation of Advanced Comprehensive Diabetes Care (ACDC), an evidence-based, comprehensive telehealth intervention for clinic-refractory, uncontrolled T2D. ACDC leverages existing Veterans Health Administration (VHA) Home Telehealth (HT) infrastructure, making delivery practical in rural areas.DesignMixed-methods implementation study.ParticipantsA total of 230 patients with clinic-refractory, uncontrolled T2D.InterventionACDC bundles telemonitoring, self-management support, and specialist-guided medication management and is delivered over 6 months using existing VHA HT clinical staffing/equipment. Patients may continue in a maintenance protocol after the initial 6-month intervention period.Main MeasuresImplementation was evaluated using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. The primary effectiveness outcome was hemoglobin A1c (HbA1c).Key ResultsFrom 2017 to 2020, ACDC was delivered to 230 patients across seven geographically diverse VHA sites; on average, patients were 59 years of age, 95% male, 80% white, and 14% Hispanic/Latinx. Patients completed an average of 10.1 of 12 scheduled encounters during the 6-month intervention period. Model-estimated mean baseline HbA1c was 9.56% and improved to 8.14% at 6 months (−1.43%; 95% CI, −1.64 to −1.21; P<.001). Benefits persisted at 12 months (−1.26%, 95% CI, −1.48 to −1.05; P<.001) and 18 months (−1.08%; 95% CI, −1.35 to −0.81; P<.001). Patients reported increased engagement in self-management and awareness of glycemic control, while clinicians and HT nurses reported a moderate workload increase. As of this submission, some sites have maintained delivery of ACDC for up to 4 years.ConclusionsComprehensive care can be delivered to rural areas through telehealth when telehealth systems are designed to take advantage of existing infrastructure. Through ACDC, participants with clinic-refractory diabetes achieved sustained improvements in glycemic control.CommentsThis study represents another example of implementation science, this time using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework. The authors studied Advanced Comprehensive Diabetes Care (ACDC), which is essentially an evidence-based remote patient monitoring program for clinic-refractory, uncontrolled type 2 diabetes. ACDC bundles telemonitoring, self-management support, and medication management and is delivered over 6 months. Prior randomized controlled trial data have demonstrated that ACDC improves A1c by 1.0% at 6 months, while in the present implementation study, A1c improved by 1.43%, with the effect being significantly maintained at 12 and 18 months. This study provides yet another example of the positive benefits of remote patient monitoring. How long will it take the field to release new consensus statements that recognize a new reality, that “connected care” adds significant value to the few in-person clinic visits a year to which we are all accustomed?Personalized Glycemic Response Led Digital Therapeutics Program Improves Time in Range in a Period of 14 DaysVerma R1, Bhardwaj S1, Lathia T2, Kalra S3, Ranadive R1, Tanna S4, Padsalge M5, Juneja A6, Samundra K7, Thakkar PB8, Jain V9, Kini V10, Kothari S11, Guntur S1, Joshi S1, Singal A11Fitterfly Healthtech Pvt Ltd, Akshar Blue Chip Corporate Park Turbhe MIDC, Navi Mumbai, India; 2Apollo Hospital, Navi Mumbai, India; 3Bharti Research Institute of Diabetes & Endocrinology, Haryana, India; 4Jupiter Hospital, Thane, India; 5Diabecare Diabetes & Thyroid Clinic, Navi Mumbai, India; 6Kokilaben Hospital, Mumbai, India; 7Diabetes Care Clinic, Navi Mumbai, India; 8Bombay Hospital, Marine Lines, Mumbai, India; 9Advanced Eye Hospital and Institute, Navi Mumbai, India; 10Care n Cure Speciality Clinic, Navi Mumbai, India; 11Global Hospitals, Mumbai, IndiaInt J Diabetes Dev Ctries 2022 Jul 22;1–8. doi: 10.1007/s13410-022-01111-1. Online ahead of print.BackgroundFor individuals with type 2 diabetes (T2D), making lifestyle changes is critical for managing the disease. However, it is often difficult to make sure that personalized lifestyle advice is accurate. Diabefly-Pro is a digital therapeutic system providing personalized advice based on an individual's glycemic responses, In this study, Diabefly-Pro is analyzed for its real-world effectiveness in improving glycemic control.MethodsData from continuous glucose monitoring (CGM) of 64 participants with T2D were analyzed. All participants were provided with modified lifestyle plan based on their personalized glycemic response. The CGM data were analyzed for a period of 7 days before