Modeling the Economic Value of Cardiometabolic Virtual-First Care Programs

ABSTRACT

Objectives: This study simulated the potential multiyear health and economic benefits of participation in 4 cardiometabolic virtual-first care (V1C) programs: prevention, hypertension, diabetes, and diabetes plus hypertension.

Study Design: Using nationally available data and existing clinical and demographic information from members participating in cardiometabolic V1C programs, a microsimulation approach was used to estimate potential reduction in onset of disease sequelae and associated gross savings (ie, excluding the cost of V1C programs) in health care costs.

Methods: Members of each program were propensity matched to similar records in the combined 2012-2020 National Health and Nutrition Examination Survey files based on age, sex, race/ethnicity, body mass index, and diagnosis status of diabetes and/or hypertension. V1C program–attributed changes in clinical outcomes combined with baseline biometric levels and other risk factors were used as inputs to model disease onset and related gross health care costs.

Results: Across the V1C programs, sustained improvements in weight loss, hemoglobin A_1c, and blood pressure levels were estimated to reduce incidence of modeled disease sequelae by 2% to 10% over the 5 years following enrollment. As a result of sustained improvement in biometrics and reduced disease onset, the estimated gross savings in medical expenditures across the programs would be $892 to $1342 after 1 year, and cumulative estimated gross medical savings would be $2963 to $4346 after 3 years and $5221 to $7756 after 5 years. In addition, high program engagement was associated with greater health and economic benefits.

Conclusions: V1C programs for prevention and management of cardiometabolic chronic conditions have potential long-term health and financial implications.

Am J Manag Care. 2024;30(Spec Issue No. 6):SP430-SP436. https://doi.org/10.37765/ajmc.2024.89549

Takeaway Points

• This study simulated the potential reduction in disease sequelae (disease resulting from other existing complications or conditions) and associated gross health care savings for members participating in 4 virtual-first cardiometabolic programs (prevention, diabetes, hypertension, and diabetes plus hypertension).
• This study combined existing clinical and demographic data from 4 cardiometabolic programs with the latest published literature and nationally available data to build a Markov-based microsimulation model.
• Findings from this study estimate the potential long-term health and financial impact of participating in virtual cardiometabolic health programs.

Chronic diseases remain the leading drivers of rising health care costs, hospitalizations, disabilities, and death among adults in the US.¹ Diabetes, obesity, and hypertension are 3 of the most prevalent comorbid chronic diseases,² and adults with one condition are at higher risk for developing others.^3-5 The presence of these comorbid chronic diseases increases health complications such as retinopathy, kidney disease,^6-9 and cardiovascular disease.^1,10 Health care costs associated with hypertension account for $131 billion annually in the US,¹¹ and the total estimated cost of diagnosed diabetes in 2017 was $327 billion.¹²

Because chronic conditions are affected by similar lifestyle factors, holistic integrated care geared toward lifestyle changes can be effective in the management of multiple conditions.⁷ Use of digital solutions, such as telemedicine and virtual-first care (V1C), has increased significantly following the COVID-19 pandemic,¹³ and delivery of care through telehealth has been shown to be useful for chronic disease management, including diabetes,^14,15 hypertension,^16-18 and weight loss and chronic disease prevention.^19-22

V1C is defined by the Digital Medicine Society as “medical care for individuals or a community accessed through digital interactions where possible, guided by a clinician, and integrated into a person’s everyday life.”²³ V1C programs include a broad scope of remote services and emphasize care in which individuals are encouraged to be active participants in their care plan. Although many V1C programs utilize a point solution approach targeting one chronic condition, other platforms are embracing an integrated solution that focuses on the individual. By providing a continuum of care across conditions, the integrated approach supports individuals who are at risk for developing or have comorbid chronic conditions as well as employers or payers that otherwise have to manage multiple point solutions for different employees or beneficiaries.

The V1C programs in this study combine human-led coaching, lifestyle modification curricula, and integrated behavioral health support to provide personalized virtual care for individuals with chronic conditions. These programs have previously demonstrated effectiveness at improving clinical outcomes.^22,24-31 However, less is known about their long-term impact on reducing chronic disease sequelae and related health care costs. This study used a Markov-based microsimulation approach to project the potential reduction in future onset of disease sequelae and associated direct health care savings. The model leveraged a combination of actual clinical and demographic information from members of 4 cardiometabolic V1C programs (prevention [PVN], diabetes [DM], hypertension [HTN], and diabetes plus hypertension [DM+HTN]) and information from published literature and nationally available data. This study also examined the role of program engagement on clinical outcomes and estimated gross health care cost savings.

METHODS

Study Sample and Program Description

The study sample consisted of insured adults who enrolled in 1 of 4 commercially available cardiometabolic V1C health programs between January 1, 2019, and October 31, 2022. Three programs are considered point solutions that address single conditions: PVN (addresses obesity),^{22,29,30,32,33} DM,^24,25 and HTN.²⁸ The fourth program, DM+HTN, focuses on an integrated approach that addresses 2 frequently comorbid conditions. These programs also provide behavioral health support for members with elevated symptoms. All programs are grounded by evidence-based guidelines; the PVN program is recognized by the CDC Diabetes Prevention Recognition Program, and the DM program has achieved accreditation from the National Committee for Quality Assurance’s Population Health Program Accreditation and the Association of Diabetes Care & Education Specialists.

Inclusion and exclusion criteria for each of the programs are summarized in Table 1. Program participation is limited by which program an employer chooses to sponsor. Although some members in the DM program have diagnosed hypertension and some in the HTN program have diagnosed diabetes, due to employer sponsorship decisions, these individuals are unable to participate in the DM+HTN program that simultaneously covers both diabetes and hypertension management.

Members receive customized support and resources to help them achieve their health goals. Resources include certified lifestyle coaches; curricula approved by relevant clinical quality organizations; digital tools for tracking body weight, blood pressure (BP), blood glucose, physical activity, and eating habits; and an online peer support forum. The interactive curriculum lessons can be accessed through a computer or mobile device, allowing members to engage when they choose and with the tools and resources that they find most useful.

The primary clinical outcomes are body weight (all programs), blood glucose level (DM and DM+HTN programs), and BP (HTN and DM+HTN programs). Each member with an elevated body weight (body mass index [BMI] of ≥ 25 kg/m²) receives a cellularly connected weight scale (BodyTrace Inc), and members in the HTN and DM programs also receive a cellularly connected BP monitor and blood glucose monitor (3G BioTel Care; Telcare LLC) and/or a continuous glucose monitor (FreeStyle Libre; Abbott) when prescribed. Members’ hemoglobin A_1c (HbA_1c) values are collected via self-report. Clinical outcomes modeled in this study are changes in body weight, BP, and HbA_1c levels calculated as the difference between recorded values at program start and at 12-month follow-up (recorded during the 12- to 15-month post–start date period). Other participant characteristics included age at program start, sex, and race/ethnicity.

The total number of participants differed by program type, with 172,406 in the PVN program, 2438 in the HTN program, 380 in the DM program, and 778 in the DM+HTN program (168 of whom reported change in blood glucose level and 703 of whom reported change in BP).

To fill in missing characteristics needed for simulation modeling (HbA_1c and BP for the PVN program, HbA_1c for the HTN program, BP for the DM program, either BP or HbA_1c for members of the DM+HTN program with partial data available, and total cholesterol and low-density lipoprotein cholesterol for all programs), we conducted propensity matching on each program member to find similar individuals in the combined 2012-2020 National Health and Nutrition Examination Survey (NHANES) files. Due to limitations on the available number of NHANES records and computational strength, we randomly selected half of members in the PVN program for the propensity match and simulation. A quality control check confirmed that the randomly selected members were representative of the member population on demographics and baseline clinical characteristics. For the DM, HTN, and DM+HTN programs, each member was matched to 3 similar control records for the following reasons: (1) Compared with the PVN program, fewer member records from the DM, HTN, and DM+HTN programs were available for this study, so larger sample sizes of these programs were needed to increase the power in the analysis; and (2) Some missing biometrics of members were filled in using information from matched controls. Increasing the match ratio can help improve precision of the parameters compared with a 1:1 match. The propensity score matched on age group, sex, race/ethnicity, BMI, and diagnosis status of diabetes and/or hypertension. We used the nearest neighbor method without replacement from the MatchIt package of R 4.2.3 for all propensity score–matching processes.³⁴ In addition, a caliper value of 0.1 times the SD of the propensity score was used to help limit the potential bias when using the fixed ratio.

To measure the level of program engagement, we calculated a total engagement score that combined several types of program activities (eg, tracking meals, engaging with group discussion boards, messaging their health coach/specialist, setting goals, self-monitoring health behaviors). Based on this score, we categorized members above and below the median engagement score as being in the high-engagement and low-engagement groups, respectively. Improvements in biometrics and associated potential economic savings are reported by engagement subgroups.

Markov-Based Microsimulation Model

Medical records for program members were unavailable, so we used a Markov-based microsimulation approach to estimate how improvements in these biometrics affect future onset of disease sequelae and associated gross medical savings (ie, excluding the cost of the V1C programs) for each member.^25,35-37 Members of each program with complete 12-month biometrics recorded constituted the intent-to-treat cohorts. The model has been used previously to simulate the potential direct medical savings from improvements in body weight, HbA_1c, systolic BP (SBP), and diastolic BP (DBP).^25,35-37 This model predicts the annual onset of disease sequelae and associated gross medical savings based on current demographics (age, sex, race/ethnicity), biometrics (body weight, HbA_1c, SBP, DBP, total cholesterol, and high-density lipoprotein cholesterol), smoking status, and the presence of the comorbidities diabetes, cardiovascular disease, and obesity. Modeled disease sequelae in this study included diabetes, hypertension, ischemic heart disease (IHD), congestive heart failure (CHF), stroke, myocardial infarction (MI), chronic kidney disease (CKD), and various cancers and other conditions linked to obesity. More detailed information on data, methods, assumptions, and limitations can be found in previous publications.^25,35-37

To project potential clinical and economic benefits, 2 scenarios were simulated for members of each program with supplemental biometrics information from matched NHANES records. First, a baseline scenario modeled each member’s annual changes in biometrics following the natural aging process derived based on analysis of public survey data sources and published references.³⁵ Second, the intervention scenario modeled each member’s actual and simulated changes in body weight, HbA_1c, SBP, and DBP over the first year as realized through the program and then maintained from year 2 through year 5. Prediction equations in the simulation model took these biometrics changes as inputs to project the onset of modeled disease sequelae and associated alteration in direct medical costs over the next 5 years. The differences in simulated health and economic outcomes between these 2 scenarios reflect the potential benefits of the programs.

A person entered the simulation with specific baseline characteristics including demographics, biometrics, and presence or history of various chronic diseases or adverse medical events. Demographics (age, sex, and race/ethnicity) were inputs to almost every prediction equation in the model. Alterations in biometrics combined with current biometric levels and other previously mentioned risk factors were used as inputs to the prediction equations for disease onset.

Intercorrelations between biometrics and prediction equations for disease states were based on results of published clinical trials, meta-analysis, and observational studies.^35,37 Based on program members’ demographic information and 12-month biometric improvements in BMI, HbA_1c, and BP, the simulation model extrapolated missing changes in biometrics for those programs that did not track these metrics. For example, results of the CONQUER clinical trial (NCT00553787) showed the correlation between mean change in body weight and mean change in HbA_1c level among individuals with a BMI of 27 to 45 in the US.³⁸ A meta-analysis of 25 clinical trial outcomes suggests that each 1-kg loss in body weight reduced SBP by 1.05 mm Hg.³⁹ Equations to predict incidence of IHD, MI, CHF, stroke, renal failure, retinopathy, and amputation among patients with diabetes came from the UK Prospective Diabetes Study Outcomes Model 2.⁴⁰

Equations to simulate medical expenditures for each participant were from an analysis of the combined 2018-2020 Medical Expenditure Panel Survey files, which estimated total annual medical expenditures of each member using a generalized linear model with γ distribution and a log link.³⁵ Explanatory variables included demographics; presence of diabetes, hypertension, CHF, IHD, retinopathy, and CKD; history of MI, stroke, and various cancers; smoking status; and body weight category. All medical costs are in 2022 US$, converted using the medical component of the Consumer Price Index.

RESULTS

As shown in Table 2, members of the DM+HTN program had slightly lower mean starting HbA_1c (7.3%) than DM members (7.5%) and slightly higher mean SBP (136.7 mm Hg) than HTN members (135.6 mm Hg). The propensity-matched samples that filled in unavailable biometric values had similar baseline demographics and clinical metrics compared with members of each program (Table 2).

Twelve months after enrollment, members in the PVN, HTN, DM, and DM+HTN programs had mean body weight reductions of 2.2%, 3.0%, 3.3%, and 2.9%, respectively (Table 3). DM and DM+HTN members experienced reductions in HbA_1c by 0.6% and 0.7%, respectively. HTN members lowered both SBP and DBP on average by 4.1 and 2.8 mm Hg, respectively, compared with reductions of 4.1 and 2.5 mm Hg among those in the DM+HTN program.

Simulation results suggest that sustaining improvements in weight loss, HbA_1c, and BP levels can reduce incidence of modeled disease sequelae by approximately 2% to 10% over the 5 years following enrollment (Table 3). As a result, PVN, HTN, DM, and DM+HTN members could each save $892, $908, $1046, and $1342, respectively, in total gross medical expenditures after 1 year (Figure). If the improvements in weight loss, HbA_1c, and BP were sustained, estimated cumulative gross medical savings were $2963 to $4346 over 3 years and $5221 to $7756 over 5 years (Table 3).

Improvement in biometrics was correlated with the engagement measurement across all program cohorts. In the PVN, HTN, and DM programs, members with low engagement reduced body weight by a mean of approximately 1% to 2% (Table 4). The less-engaged members in the DM and DM+HTN programs experienced a mean drop in HbA_1c of 0.5%, and those in the HTN and DM+HTN programs experienced a reduction in SBP by a mean of 1.3 mm Hg and 1.7 mm Hg, respectively, and in DBP by 1.4 mm Hg and 1.3 mm Hg. The estimated cumulative gross savings in health care costs for less-engaged members were $1535 to $6496 over 5 years across the 4 programs.

Program members with high engagement had a mean body weight reduction between 3.4% and 4.2% (Table 4). The more-engaged members in the DM and DM + HTN programs experienced mean drops in HbA_1c of 0.7% and 0.8%, respectively, and those in the HTN and DM+HTN programs experienced a reduction in SBP by a mean of 6.9 mm Hg and 6.2 mm Hg, respectively, and in DBP by 4.1 mm Hg and 3.6 mm Hg. The estimated cumulative gross savings in health care costs for more-engaged members were $8400 to $9016 over 5 years across the 4 programs.

DISCUSSION

This study simulated the long-term chronic disease onset and economic savings associated with 4 V1C programs for chronic conditions. Program data included 12-month reductions in weight, BP, and glucose levels, with modeling used to simulate changes in health outcomes and expected medical savings over the subsequent 5 years. Estimated gross savings in medical expenditures across the programs would be $892 to $1342 after 1 year, and cumulative estimated gross medical savings would be $2963 to $4346 after 3 years and $5221 to $7756 after 5 years. Further, members in the DM+HTN program demonstrated the highest estimated cumulative gross savings throughout 5 years compared with members in the other programs. Altogether, these findings reinforce the potential for V1C to improve long-term health outcomes and reduce the rising economic burden for those with chronic conditions⁴¹ and emphasize that an integrated approach that delivers comprehensive care across conditions may lead to even larger improvements in health benefits and reductions in health care costs.^42-44

Higher engagement was associated with larger decreases in weight loss, HbA_1c, and/or BP and, therefore, greater estimated gross medical savings after 1, 3, and 5 years. These findings support previous research on the critical role of individual engagement in the effectiveness of chronic disease prevention and management programs.⁴⁵ Because this study was not able to evaluate the potential dose-response relationship between engagement and improved clinical and economic outcomes or whether engagement is simply a marker of an underlying variable (eg, readiness to change health-related behavior), future studies should examine these relationships more closely.

This study is among the first to explore the relationship between integrated V1C and long-term clinical outcomes and estimated medical cost savings. Members in the DM+HTN program demonstrated similar improvements in HbA_1c to those in the DM-only program; higher reductions in SBP and DBP compared with those in the HTN-only program; and greater reductions in the simulated 5-year onset of stroke, CKD, and IHD compared with the other programs. These findings suggest that integrated V1C with a multicondition approach may have a greater impact on improving health outcomes and reducing chronic disease risk and associated economic burden.

Limitations

Despite its significant strengths, this study also had limitations. Because of the real-world digital delivery of these programs, the sample size for each was limited to members with complete primary clinical outcomes at 12 months, and missing data due to attrition and/or lack of clinical outcomes reporting were a challenge across the programs. Further exploration into the drivers of long-term member engagement is warranted. Because this study population consisted of insured adults who chose to enroll in the programs, findings may not be generalizable to other populations with similar conditions. In addition, due to employer sponsorship decisions, certain programs may have not been available to members, hindering access to programs. Notably, there were fewer members in the DM program, which can be attributed to a combination of factors: the overall lower prevalence of diabetes compared with obesity and hypertension, making fewer individuals eligible to enroll in the program at baseline; lower penetration in the market of the DM program relative to the PVN and HTN programs; and the reliance on self-reported data collection of HbA_1c, which requires members to acquire their own HbA_1c level from a laboratory and self-report it in the program app^46,47 vs the cellularly connected scale and BP monitors used for the PVN and HTN programs, which automatically sync with the program interface.

As previously noted, missing values of clinical outcomes that were not collected across the programs were simulated by matching program members to similar individuals in the combined 2012-2020 NHANES files. Lastly, findings in this study were estimated using simulation modeling because medical records were unavailable for members. Future studies with more accurate estimates of the effectiveness of these integrated V1C programs on long-term outcomes using real-world data (eg, health care claims) should be conducted to strengthen the methodology and validate the findings.

Finally, V1C programs are primarily individually focused and do not address broader socioeconomic factors that contribute to health outcomes.^48,49 Furthermore, these programs are offered through insurance and employee benefits packages and require access to the internet. To the extent that access to a V1C program is limited for communities that already experience disparities in chronic disease outcomes, V1C programs may exacerbate existing disparities.⁵⁰ Population-level improvements in chronic disease outcomes and health care costs in the US will require policy implementations to increase equitable access to V1C as well as innovations that address social and environmental factors affecting chronic disease outcomes.

CONCLUSIONS

Study findings indicate the potential long-term health and financial impact of cardiometabolic V1C programs. As V1C continues to increase the availability, accessibility, and affordability of care, future research on integrated approaches is needed to better understand its effectiveness and value for chronic disease prevention and management at a population level. 

Author Affiliations: Omada Health Inc (MN, SL, JN), San Francisco, CA; GlobalData PLC (FC, TMD), New York, NY.

Source of Funding: Funding for this study was provided by Omada Health Inc.

Author Disclosures: Ms Noble, Dr Linke, and Dr Napoleone are employed by and own stock in Omada Health Inc, which provides cardiometabolic virtual-first care services. Dr Chen and Mr Dall are consultants for GlobalData PLC, which received funding for modeling and analysis.

Authorship Information: Concept and design (MN, FC, SL, TMD, JN); acquisition of data (JN); analysis and interpretation of data (FC, SL, TMD, JN); drafting of the manuscript (MN, FC, TMD, JN); critical revision of the manuscript for important intellectual content (MN, FC, SL, TMD, JN); statistical analysis (FC, JN); administrative, technical, or logistic support (MN); and supervision (SL, JN).

Address Correspondence to: Madison Noble, MPH, Omada Health Inc, 500 Sansome St #200, San Francisco, CA 94111. Email: madison.noble@omadahealth.com.

REFERENCES

1. National Center for Chronic Disease Prevention and Health Promotion. About chronic diseases. CDC. Updated July 21, 2022. Accessed April 27, 2023. https://www.cdc.gov/chronicdisease/about/index.htm 

2. National Diabetes Statistics Report: Estimates of Diabetes and Its Burden in the United States. CDC. Updated November 29, 2023. Accessed August 11, 2022. https://www.cdc.gov/diabetes/data/statistics-report/index.html

3. Having one chronic condition can boost the risk for others. Harvard Health Publishing. August 4, 2023. Accessed August 30, 2023. https://www.health.harvard.edu/staying-healthy/having-one-chronic-condition-can-boost-the-risk-for-others

4. Barnes AS. The epidemic of obesity and diabetes: trends and treatments. Tex Heart Inst J. 2011;38(2):142-144.

5. Landsberg L, Aronne LJ, Beilin LJ, et al. Obesity-related hypertension: pathogenesis, cardiovascular risk, and treatment: a position paper of The Obesity Society and the American Society of Hypertension. J Clin Hypertens (Greenwich). 2013;15(1):14-33. doi:10.1111/jch.12049

6. The link between diabetes and hypertension. Medical News Today. Updated April 28, 2023. Accessed April 30, 2023. https://www.medicalnewstoday.com/articles/317220

7. Long AN, Dagogo-Jack S. Comorbidities of diabetes and hypertension: mechanisms and approach to target organ protection. J Clin Hypertens (Greenwich). 2011;13(4):244-251. doi:10.1111/j.1751-7176.2011.00434.x

8. Petrie JR, Guzik TJ, Touyz RM. Diabetes, hypertension, and cardiovascular disease: clinical insights and vascular mechanisms. Can J Cardiol. 2018;34(5):575-584. doi:10.1016/j.cjca.2017.12.005

9. de Boer IH, Bangalore S, Benetos A, et al. Diabetes and hypertension: a position statement by the American Diabetes Association. Diabetes Care. 2017;40(9):1273-1284. doi:10.2337/dci17-0026

10. Sowers JR, Epstein M, Frohlich ED. Diabetes, hypertension, and cardiovascular disease: an update. Hypertension. 2001;37(4):1053-1059. doi:10.1161/01.hyp.37.4.1053

11. Kirkland EB, Heincelman M, Bishu KG, et al. Trends in healthcare expenditures among US adults with hypertension: national estimates, 2003-2014. J Am Heart Assoc. 2018;7(11):e008731. doi:10.1161/JAHA.118.008731

12. American Diabetes Association. Economic costs of diabetes in the U.S. in 2017. Diabetes Care. 2018;41(5):917-928. doi:10.2337/dci18-0007

13. Bestsennyy O, Gilbert G, Harris A, Rost J. Telehealth: a quarter-trillion-dollar post-COVID-19 reality? McKinsey & Company. July 9, 2021. Accessed May 1, 2023. https://www.mckinsey.com/industries/healthcare/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality

14. Mullur RS, Hsiao JS, Mueller K. Telemedicine in diabetes care. Am Fam Physician. 2022;105(3):281-288.

15. Lee SWH, Chan CKY, Chua SS, Chaiyakunapruk N. Comparative effectiveness of telemedicine strategies on type 2 diabetes management: a systematic review and network meta-analysis. Sci Rep. 2017;7(1):12680. doi:10.1038/s41598-017-12987-z

16. Margolis KL, Bergdall AR, Crain AL, et al. Comparing pharmacist-led telehealth care and clinic-based care for uncontrolled high blood pressure: the Hyperlink 3 pragmatic cluster-randomized trial. Hypertension. 2022;79(12):2708-2720. doi:10.1161/HYPERTENSIONAHA.122.19816

17. Wang JG, Li Y, Chia YC, et al; Hypertension Cardiovascular Outcome Prevention, Evidence (HOPE) Asia Network. Telemedicine in the management of hypertension: evolving technological platforms for blood pressure telemonitoring. J Clin Hypertens (Greenwich). 2021;23(3):435-439. doi:10.1111/jch.14194

18. Omboni S, McManus RJ, Bosworth HB, et al. Evidence and recommendations on the use of telemedicine for the management of arterial hypertension: an international expert position paper. Hypertension. 2020;76(5):1368-1383. doi:10.1161/HYPERTENSIONAHA.120.15873

19. Ufholz K, Bhargava D. A review of telemedicine interventions for weight loss. Curr Cardiovasc Risk Rep. 2021;15(9):17. doi:10.1007/s12170-021-00680-w

20. Beleigoli AM, Andrade AQ, Cançado AG, Paulo MN, Diniz MFH, Ribeiro AL. Web-based digital health interventions for weight loss and lifestyle habit changes in overweight and obese adults: systematic review and meta-analysis. J Med Internet Res. 2019;21(1):e298. doi:10.2196/jmir.9609

21. Van Rhoon L, Byrne M, Morrissey E, Murphy J, McSharry J. A systematic review of the behaviour change techniques and digital features in technology-driven type 2 diabetes prevention interventions. Digit Health. 2020;6:2055207620914427. doi:10.1177/2055207620914427

22. Katula JA, Dressler EV, Kittel CA, et al. Effects of a digital diabetes prevention program: an RCT. Am J Prev Med. 2022;62(4):567-577. doi:10.1016/j.amepre.2021.10.023

23. Defining virtual first care (V1C). Digital Medicine Society. Accessed May 1, 2023. https://impact.dimesociety.org/V1C/

24. Wilson-Anumudu F, Quan R, Castro Sweet C, et al. Early insights from a digitally enhanced diabetes self-management education and support program: single-arm nonrandomized trial. JMIR Diabetes. 2021;6(1):e25295. doi:10.2196/25295

25. Chen F, Jasik CB, Dall TM, Siego CV. Impact of a digitally enhanced diabetes self-management program on glycemia and medical costs. Sci Diabetes Self Manag Care. 2022;48(4):258-269. doi:10.1177/26350106221100779

26. Hallberg SJ, McKenzie AL, Williams PT, et al. Effectiveness and safety of a novel care model for the management of type 2 diabetes at 1 year: an open-label, non-randomized, controlled study. Diabetes Ther. 2018;9(2):583-612. doi:10.1007/s13300-018-0373-9

27. Athinarayanan SJ, Adams RN, Hallberg SJ, et al. Long-term effects of a novel continuous remote care intervention including nutritional ketosis for the management of type 2 diabetes: a 2-year non-randomized clinical trial. Front Endocrinol (Lausanne). 2019;10:348. doi:10.3389/fendo.2019.00348

28. Wilson-Anumudu F, Quan R, Cerrada C, et al. Pilot results of a digital hypertension self-management program among adults with excess body weight: single-arm nonrandomized trial. JMIR Form Res. 2022;6(3):e33057. doi:10.2196/33057

29. Sepah SC, Jiang L, Ellis RJ, McDermott K, Peters AL. Engagement and outcomes in a digital Diabetes Prevention Program: 3-year update. BMJ Open Diabetes Res Care. 2017;5(1):e000422. doi:10.1136/bmjdrc-2017-000422

30. Sepah SC, Jiang L, Peters AL. Long-term outcomes of a web-based diabetes prevention program: 2-year results of a single-arm longitudinal study. J Med Internet Res. 2015;17(4):e92. doi:10.2196/jmir.4052

31. Castro Sweet CM, Chiguluri V, Gumpina R, et al. Outcomes of a digital health program with human coaching for diabetes risk reduction in a Medicare population. J Aging Health. 2018;30(5):692-710. doi:10.1177/0898264316688791

32. Sepah SC, Jiang L, Peters AL. Translating the Diabetes Prevention Program into an online social network: validation against CDC standards. Diabetes Educ. 2014;40(4):435-443. doi:10.1177/0145721714531339

33. Almeida FA, Michaud TL, Wilson KE, et al. Preventing diabetes with digital health and coaching for translation and scalability (PREDICTS): a type 1 hybrid effectiveness-implementation trial protocol. Contemp Clin Trials. 2020;88:105877. doi:10.1016/j.cct.2019.105877

34. Ho DE, Imai K, King G, Stuart EA. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal. 2007;15(3):199-236. doi:10.1093/pan/mpl013

35. Dall TM, Storm MV, Semilla AP, Wintfeld N, O’Grady M, Narayan KM. Value of lifestyle intervention to prevent diabetes and sequelae. Am J Prev Med. 2015;48(3):271-280. doi:10.1016/j.amepre.2014.10.003

36. Su W, Huang J, Chen F, et al. Modeling the clinical and economic implications of obesity using microsimulation. J Med Econ. 2015;18(11):886-897. doi:10.3111/13696998.2015.1058805

37. Chen F, Su W, Becker SH, et al. Clinical and economic impact of a digital, remotely-delivered intensive behavioral counseling program on Medicare beneficiaries at risk for diabetes and cardiovascular disease. PLoS One. 2016;11(10):e0163627. doi:10.1371/journal.pone.0163627

38. Gadde KM, Allison DB, Ryan DH, et al. Effects of low-dose, controlled-release, phentermine plus topiramate combination on weight and associated comorbidities in overweight and obese adults (CONQUER): a randomised, placebo-controlled, phase 3 trial. Lancet. 2011;377(9774):1341-1352. doi:10.1016/S0140-6736(11)60205-5

39. Neter JE, Stam BE, Kok FJ, Grobbee DE, Geleijnse JM. Influence of weight reduction on blood pressure: a meta-analysis of randomized controlled trials. Hypertension. 2003;42(5):878-884. doi:10.1161/01.HYP.0000094221.86888.AE

40. Hayes AJ, Leal J, Gray AM, Holman RR, Clarke PM. UKPDS outcomes model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia. 2013;56(9):1925-1933. doi:10.1007/s00125-013-2940-y

41. Boyle EL. The cost of being chronic in 2023: a special report. HealthCentral LLC. April 28, 2023. Accessed April 30, 2023. https://www.healthcentral.com/chronic-health/the-cost-of-being-chronic-in-2023-a-special-report

42. Rocks S, Berntson D, Gil-Salmerón A, et al. Cost and effects of integrated care: a systematic literature review and meta-analysis. Eur J Health Econ. 2020;21(8):1211-1221. doi:10.1007/s10198-020-01217-5

43. Kadu M, Ehrenberg N, Stein V, Tsiachristas A. Methodological quality of economic evaluations in integrated care: evidence from a systematic review. Int J Integr Care. 2019;19(3):17. doi:10.5334/ijic.4675

44. Desmedt M, Vertriest S, Hellings J, et al. Economic impact of integrated care models for patients with chronic diseases: a systematic review. Value Health. 2016;19(6):892-902. doi:10.1016/j.jval.2016.05.001

45. Beck J, Greenwood DA, Blanton L, et al. 2017 National Standards for Diabetes Self-Management Education and Support. Sci Diabetes Self Manag Care. 2021;47(1):14-29. doi:10.1177/0145721720987926

46. Kamath A, Imes CC. Discordance between self-reported and lab-measured A1c among adults with diabetes. J Nurse Pract. 2023;19(10):104769. doi:10.1016/j.nurpra.2023.104769

47. Løvaas KF, Cooper JG, Sandberg S, Røraas T, Thue G. Feasibility of using self-reported patient data in a national diabetes register. BMC Health Serv Res. 2015;15:553. doi:10.1186/s12913-015-1226-0

48. Clark ML, Utz SW. Social determinants of type 2 diabetes and health in the United States. World J Diabetes. 2014;5(3):296-304. doi:10.4239/wjd.v5.i3.296

49. Nguyen KH, Cemballi AG, Fields JD, Brown W, Pantell MS, Lyles CR. Applying a socioecological framework to chronic disease management: implications for social informatics interventions in safety-net healthcare settings. JAMIA Open. 2022;5(1):ooac014. doi:10.1093/jamiaopen/ooac014

50. 2022 National Healthcare Quality and Disparities Report. Agency for Healthcare Research and Quality; October 2022. AHRQ publication No. 22(23)-0030. Accessed September 15, 2023. https://www.ahrq.gov/sites/default/files/wysiwyg/research/findings/nhqrdr/2022qdr.pdf