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Chronic Disease Risk. Weight Loss. Cost Savings. ROI positive as little as 1 year. Prev Chronic Dis ;E Actual results may vary based on age, gender and other individual and demographic factors. Weight loss results reflect participants who enrolled in the Omada Program between January - March and completed at least 9 of 16 lessons in the first 16 weeks of the Omada program.

Across three independent claims analyses, the amount of time for employers to recoup their investment in Omada ranged from months: 1 Chiguluri V, Barthold D, Gumpina R, et al. Virtual diabetes prevention program—Effects on medicare advantage health care costs and utilization.

The estimated cost savings were calculated by the health plan based on the outcomes of its population included in the analysis i. Actual participant outcomes, and the resulting cost savings achieved by a customer, will vary on a customer-by-customer basis.

Participant outcomes may vary based on age, gender and other individual and demographic factors. The Omada program can work no matter how diverse or spread out your population. Review our latest peer-reviewed studies to learn more. Summary Full Paper. A Peer Reviewed Study. Hone your strategy.

Boost health outcomes. These outcomes represent a population snapshot of Omada participant data from Feb through Jun Actual participant outcomes may vary based on age, gender, and other individual and demographic factors. Population snapshot of Omada participant data from May through May Prevalence of Select Medical Conditions.

Actual individual outcomes may vary based on age, gender, and other individual and demographic factors. World Psychiatry. National Institute of Mental Health. Mental Health Information - Statistics. National Health and Nutrition Examination Survey — Contact Us.

Comprehensive Digital Care Enrolling in multiple programs is hard. Fast facts: 1 in 3 Americans have prediabetes. Fast Facts: Half of American Adults are affected by a musculoskeletal condition lasting longer than 3 months. Behavioral Health.

As featured in:. For Employers Tackle the rising cost of chronic disease. For Individuals Start your life-changing journey. For Health Plans Deliver proven outcomes at scale. Engage anytime, from anywhere. Last weigh-in: Mobile, AL p. Behavior change that lasts We surround participants with the human support and digital tools they need to succeed.

Our expert coaches are empowered with real-time data to provide thoughtful, one-on-one guidance. Communities of like-minded peers motivate and encourage participants, one step at a time. Jane Reduced her numbers. Rodney Turned the scale.

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Cost Savings. ROI positive as little as 1 year. Prev Chronic Dis ;E Actual results may vary based on age, gender and other individual and demographic factors. Weight loss results reflect participants who enrolled in the Omada Program between January - March and completed at least 9 of 16 lessons in the first 16 weeks of the Omada program. Across three independent claims analyses, the amount of time for employers to recoup their investment in Omada ranged from months: 1 Chiguluri V, Barthold D, Gumpina R, et al.

Virtual diabetes prevention program—Effects on medicare advantage health care costs and utilization. The estimated cost savings were calculated by the health plan based on the outcomes of its population included in the analysis i. Actual participant outcomes, and the resulting cost savings achieved by a customer, will vary on a customer-by-customer basis. Participant outcomes may vary based on age, gender and other individual and demographic factors. The Omada program can work no matter how diverse or spread out your population.

Review our latest peer-reviewed studies to learn more. Summary Full Paper. A Peer Reviewed Study. Hone your strategy. Boost health outcomes. Improve your population's health and lower medical spend with our prevention-based framework. Sure, it all sounds great in theory. Using microsimulation, we modeled the clinical and economic implications one would expect with this level of weight loss given the characteristics of program participants and whether participants had prediabetes or were at risk for developing cardiovascular disease.

Given the central role of weight loss in the model, we conducted sensitivity analysis around weight loss to test its influence on program benefits. Findings were similar for the populations at risk for prediabetes and cardiovascular disease Figure 2. Sensitivity analysis results on the population at risk for cardiovascular disease are in the Appendix. Figure 2.

Tornado diagram for the sensitivity analysis on weight loss percentage over 10 years in a population with prediabetes, Prevent digital behavioral counseling program, — Default weight loss for the population with prediabetes is 5. Subgroup analysis in obese populations is a topic for future research. One challenge of this comparison was that DPPOS was based on an obese population with many risk factors and a very high risk of developing type 2 diabetes.

A second challenge was that after completion of the DPP study at 3 years all participants were offered the lifestyle intervention, thus diluting the potential long-term benefits in the DPPOS. However, our simulation results align with those of the study in the following ways. In the first 3 years, the benefits of lifestyle intervention were smaller in our simulation, which can be explained by a lower-risk population.

Over 10 years, the benefits became larger — consistent with no cross-contamination of the intervention and control groups as occurred when the DPP control group later began the lifestyle intervention. We collected weight loss data for 26 weeks, and the return-on-investment analysis assumes that participants retain this weight loss with natural weight changes one would expect associated with aging with average annual weight gain through approximately age Findings from early Prevent participants found that after 2 years, a large proportion of the population has maintained their weight loss Specifically, program completers lost an average 4.

The DPPOS found that in the 5 years following DPP lifestyle intervention there was gradual weight gain, with participants sustaining approximately one-third of their original weight loss between years 5 and 10 4. Prevent has an integrated 3-year Sustain component aiming at maintaining initial weight loss for an extended period of time. Nevertheless, we tested a scenario in which participants regain weight after year 1 at the same speed observed in DPPOS. Clinical trials and community-based programs have shown that lifestyle intervention can be effective in reducing body weight and improving health outcomes.

An estimated 86 million adults have prediabetes, and many of these adults are candidates for lifestyle intervention 1. Treating a population this size requires alternative strategies to those tested in the in-person DPP. This study suggests that online programs may offer a scalable, cost-effective solution. Using Internet-based technologies can both help overcome geographic and scheduling barriers and allow participants to review material at their own pace.

Using a previously published and validated microsimulation model, we simulated how the clinical outcomes achieved by Prevent participants translate into reduced future prevalence of disease and reduced medical expenditures. Model strengths and limitations are discussed elsewhere in detail 8,9. Strengths include the ability to simulate outcomes over an extended period, using disease prediction equations based on published epidemiologic studies and accounting for the characteristics and outcomes of program participants.

Limitations include the use of data from multiple sources both US and non-US , older data such as those from the Framingham study when newer data were unavailable, and some disease onset predication equations based on a general population rather than a population with prediabetes or risk factors for cardiovascular disease. Additional limitations specific to this study include. Prevent participants chose to participate in the intervention.

This means that results can be generalized to other populations of voluntary participants but not necessarily to the general population. We could not directly observe if Prevent participants had prediabetes or risk factors for cardiovascular disease. Unreported findings suggest that extremely obese people have higher return on investment from weight loss relative to less obese people, but younger people have lower short-term return on investment relative to older people.

DPP-based programs offered online can increase access to a cost-effective lifestyle intervention to millions of adults with prediabetes or who are at high risk for cardiovascular disease. In addition to improving health outcomes, such an intervention can provide a positive return on investment for payers. Prevent is a registered trademark of Omada Health Inc.

Funding for this study was provided by Omada Health Inc, who provided raw data for analysis and reviewed the draft of this manuscript. The study sponsor approved the study design developed by T. Data provided by study sponsor was analyzed solely by F.

Telephone: Email: wayne. Abbreviation: QALYs, quality-adjusted life years. This file is available for download as a Microsoft Word document word icon. The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U. Skip directly to site content Skip directly to page options Skip directly to A-Z link. Preventing Chronic Disease. Section Navigation. Facebook Twitter LinkedIn Syndicate. Minus Related Pages. View Page In: pdf icon.

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Starter and completer subgroups were similar to the overall intent-to-treat cohort. On average, participants with prediabetes lost 5. Average weight loss recorded on or before week 26 among program completers 6. Both groups had simulated positive return on investment within 3 years. An estimated Weight loss among this analytic cohort averaged 5.

The average weight loss among starters and completers was 5. Simulated medical savings over 3, 5, and 10 years were higher for program completers versus program starters. In the intent-to-treat cohort, the projected break-even point was 3 years for both the population with prediabetes and the population with cardiovascular disease risk factors Figure 1.

Figure 1. Projected average return on investment on weight loss program participation, Prevent digital behavioral counseling program, — Previous work found that Prevent is effective in reducing body weight and improving HbA1c levels among a population with prediabetes 11, Using microsimulation, we modeled the clinical and economic implications one would expect with this level of weight loss given the characteristics of program participants and whether participants had prediabetes or were at risk for developing cardiovascular disease.

Given the central role of weight loss in the model, we conducted sensitivity analysis around weight loss to test its influence on program benefits. Findings were similar for the populations at risk for prediabetes and cardiovascular disease Figure 2. Sensitivity analysis results on the population at risk for cardiovascular disease are in the Appendix. Figure 2. Tornado diagram for the sensitivity analysis on weight loss percentage over 10 years in a population with prediabetes, Prevent digital behavioral counseling program, — Default weight loss for the population with prediabetes is 5.

Subgroup analysis in obese populations is a topic for future research. One challenge of this comparison was that DPPOS was based on an obese population with many risk factors and a very high risk of developing type 2 diabetes. A second challenge was that after completion of the DPP study at 3 years all participants were offered the lifestyle intervention, thus diluting the potential long-term benefits in the DPPOS.

However, our simulation results align with those of the study in the following ways. In the first 3 years, the benefits of lifestyle intervention were smaller in our simulation, which can be explained by a lower-risk population. Over 10 years, the benefits became larger — consistent with no cross-contamination of the intervention and control groups as occurred when the DPP control group later began the lifestyle intervention.

We collected weight loss data for 26 weeks, and the return-on-investment analysis assumes that participants retain this weight loss with natural weight changes one would expect associated with aging with average annual weight gain through approximately age Findings from early Prevent participants found that after 2 years, a large proportion of the population has maintained their weight loss Specifically, program completers lost an average 4.

The DPPOS found that in the 5 years following DPP lifestyle intervention there was gradual weight gain, with participants sustaining approximately one-third of their original weight loss between years 5 and 10 4. Prevent has an integrated 3-year Sustain component aiming at maintaining initial weight loss for an extended period of time.

Nevertheless, we tested a scenario in which participants regain weight after year 1 at the same speed observed in DPPOS. Clinical trials and community-based programs have shown that lifestyle intervention can be effective in reducing body weight and improving health outcomes. An estimated 86 million adults have prediabetes, and many of these adults are candidates for lifestyle intervention 1.

Treating a population this size requires alternative strategies to those tested in the in-person DPP. This study suggests that online programs may offer a scalable, cost-effective solution. Using Internet-based technologies can both help overcome geographic and scheduling barriers and allow participants to review material at their own pace. Using a previously published and validated microsimulation model, we simulated how the clinical outcomes achieved by Prevent participants translate into reduced future prevalence of disease and reduced medical expenditures.

Model strengths and limitations are discussed elsewhere in detail 8,9. Strengths include the ability to simulate outcomes over an extended period, using disease prediction equations based on published epidemiologic studies and accounting for the characteristics and outcomes of program participants.

Limitations include the use of data from multiple sources both US and non-US , older data such as those from the Framingham study when newer data were unavailable, and some disease onset predication equations based on a general population rather than a population with prediabetes or risk factors for cardiovascular disease. Additional limitations specific to this study include. Prevent participants chose to participate in the intervention. This means that results can be generalized to other populations of voluntary participants but not necessarily to the general population.

We could not directly observe if Prevent participants had prediabetes or risk factors for cardiovascular disease. Unreported findings suggest that extremely obese people have higher return on investment from weight loss relative to less obese people, but younger people have lower short-term return on investment relative to older people.

DPP-based programs offered online can increase access to a cost-effective lifestyle intervention to millions of adults with prediabetes or who are at high risk for cardiovascular disease. In addition to improving health outcomes, such an intervention can provide a positive return on investment for payers. Prevent is a registered trademark of Omada Health Inc.

Funding for this study was provided by Omada Health Inc, who provided raw data for analysis and reviewed the draft of this manuscript. The study sponsor approved the study design developed by T. Data provided by study sponsor was analyzed solely by F. Telephone: We applied week weight loss results to simulate potential health and economic outcomes over the subsequent 10 years on 2 populations defined by prediabetes status and presence of other cardiovascular disease risk factors.

Published results from a pilot study of Prevent analyzed participants who started the program and met eligibility criteria, including a previous diagnosis of prediabetes Average weight of participants declined 5. The participants who continued into the Sustain phase experienced an average weight loss of 5. Prevent collects BMI and demographic data on participants but does not collect additional clinical and health risk information required by the computer model to simulate disease onset and medical expenditures or to determine whether participants have prediabetes or cardiovascular disease risk factors.

Required information for modeling includes HbA1c, systolic blood pressure SBP and diastolic blood pressure DBP , total cholesterol and high-density lipoprotein cholesterol HDL-C , current smoking status, and presence of chronic medical conditions eg, treated or untreated hypertension, diabetes, cardiovascular disease, and history of myocardial infarction or stroke.

USPSTF recommends behavioral counseling for overweight or obese adults with 1 or more additional risk factors for cardiovascular disease 6. Our study focused on the populations with prediabetes and populations at risk for cardiovascular disease because those populations are recommended to receive behavioral counseling , although we also analyzed the entire overweight and obese population for comparison.

Correspondingly, we derived and analyzed 3 populations:. Population with prediabetes. Cardiovascular disease risk population. Overweight and obese population. The starting time 0 characteristics of these analytic cohorts are summarized in Table 1.

These participants constitute the intent-to-treat cohorts. Detailed documentation of the model prediction equations for disease onset and mortality, data and assumptions that underlie the model, calculations for quality-adjusted life years QALYs , and validation results are published elsewhere 8 , 9.

The simulation model uses an annual cycle, with weight loss occurring at time 0, after which body weight follows a natural history of weight change increasing or decreasing as a person ages 8 , 9. Change in body weight affects HbA1c, blood pressure, and cholesterol levels, and these clinical outcomes combined with demographics, smoking status, and presence of chronic conditions in turn are used in prediction equations for disease onset, severity, and mortality.

Equations predicting annual disease states are based on published clinical and observational studies 8 , 9. Simulated annual medical expenditures, estimated with the — Medical Expenditure Panel Survey by using a generalized linear model with gamma distribution and log link, reflect patient demographics; presence of diabetes, hypertension, congestive heart failure, ischemic heart disease, retinopathy, and end-stage renal disease; history of myocardial infarction, stroke, and various cancers; smoking status; and body weight.

Regressions results are published elsewhere 9. All medical costs were converted to dollars by using the medical component of the consumer price index Construction and validation of the model followed recommendations from the International Society for Pharmacoeconomics and Outcomes Research for best practices and transparency 13 , Validation activities included review by experts in obesity, endocrinology, modeling, and health economics; internal and external quantitative validation 8 , 9 ; and sensitivity analysis to test the robustness of model conclusions under additional uncertainties.

For example, a person who experienced 4. The constructed population with prediabetes was on average aged The average starting BMI was Starter and completer subgroups were similar to the overall intent-to-treat cohort. On average, participants with prediabetes lost 5. Average weight loss recorded on or before week 26 among program completers 6. Both groups had simulated positive return on investment within 3 years. An estimated Weight loss among this analytic cohort averaged 5.

The average weight loss among starters and completers was 5. Simulated medical savings over 3, 5, and 10 years were higher for program completers versus program starters. In the intent-to-treat cohort, the projected break-even point was 3 years for both the population with prediabetes and the population with cardiovascular disease risk factors Figure 1. Projected average return on investment on weight loss program participation, Prevent digital behavioral counseling program, — Previous work found that Prevent is effective in reducing body weight and improving HbA1c levels among a population with prediabetes 11 , Using microsimulation, we modeled the clinical and economic implications one would expect with this level of weight loss given the characteristics of program participants and whether participants had prediabetes or were at risk for developing cardiovascular disease.

Given the central role of weight loss in the model, we conducted sensitivity analysis around weight loss to test its influence on program benefits. Findings were similar for the populations at risk for prediabetes and cardiovascular disease Figure 2. Sensitivity analysis results on the population at risk for cardiovascular disease are in the Appendix.

Tornado diagram for the sensitivity analysis on weight loss percentage over 10 years in a population with prediabetes, Prevent digital behavioral counseling program, — Default weight loss for the population with prediabetes is 5. Subgroup analysis in obese populations is a topic for future research.

One challenge of this comparison was that DPPOS was based on an obese population with many risk factors and a very high risk of developing type 2 diabetes. A second challenge was that after completion of the DPP study at 3 years all participants were offered the lifestyle intervention, thus diluting the potential long-term benefits in the DPPOS. However, our simulation results align with those of the study in the following ways.

In the first 3 years, the benefits of lifestyle intervention were smaller in our simulation, which can be explained by a lower-risk population. Over 10 years, the benefits became larger — consistent with no cross-contamination of the intervention and control groups as occurred when the DPP control group later began the lifestyle intervention.

We collected weight loss data for 26 weeks, and the return-on-investment analysis assumes that participants retain this weight loss with natural weight changes one would expect associated with aging with average annual weight gain through approximately age Findings from early Prevent participants found that after 2 years, a large proportion of the population has maintained their weight loss Specifically, program completers lost an average 4.

The DPPOS found that in the 5 years following DPP lifestyle intervention there was gradual weight gain, with participants sustaining approximately one-third of their original weight loss between years 5 and 10 4. Prevent has an integrated 3-year Sustain component aiming at maintaining initial weight loss for an extended period of time. Nevertheless, we tested a scenario in which participants regain weight after year 1 at the same speed observed in DPPOS.

Clinical trials and community-based programs have shown that lifestyle intervention can be effective in reducing body weight and improving health outcomes. An estimated 86 million adults have prediabetes, and many of these adults are candidates for lifestyle intervention 1. Treating a population this size requires alternative strategies to those tested in the in-person DPP. This study suggests that online programs may offer a scalable, cost-effective solution. Using Internet-based technologies can both help overcome geographic and scheduling barriers and allow participants to review material at their own pace.

Using a previously published and validated microsimulation model, we simulated how the clinical outcomes achieved by Prevent participants translate into reduced future prevalence of disease and reduced medical expenditures. Model strengths and limitations are discussed elsewhere in detail 8 , 9.

Strengths include the ability to simulate outcomes over an extended period, using disease prediction equations based on published epidemiologic studies and accounting for the characteristics and outcomes of program participants. Limitations include the use of data from multiple sources both US and non-US , older data such as those from the Framingham study when newer data were unavailable, and some disease onset predication equations based on a general population rather than a population with prediabetes or risk factors for cardiovascular disease.

Additional limitations specific to this study include. Prevent participants chose to participate in the intervention. This means that results can be generalized to other populations of voluntary participants but not necessarily to the general population. We could not directly observe if Prevent participants had prediabetes or risk factors for cardiovascular disease. Unreported findings suggest that extremely obese people have higher return on investment from weight loss relative to less obese people, but younger people have lower short-term return on investment relative to older people.

DPP-based programs offered online can increase access to a cost-effective lifestyle intervention to millions of adults with prediabetes or who are at high risk for cardiovascular disease. In addition to improving health outcomes, such an intervention can provide a positive return on investment for payers. Prevent is a registered trademark of Omada Health Inc. Funding for this study was provided by Omada Health Inc, who provided raw data for analysis and reviewed the draft of this manuscript.

The study sponsor approved the study design developed by T. Data provided by study sponsor was analyzed solely by F. This file is available for download as a Microsoft Word document. Click here to view. The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.

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Cost Savings. ROI positive as little as 1 year. Prev Chronic Dis ;E Actual results may vary based on age, gender and other individual and demographic factors. Weight loss results reflect participants who enrolled in the Omada Program between January - March and completed at least 9 of 16 lessons in the first 16 weeks of the Omada program. Across three independent claims analyses, the amount of time for employers to recoup their investment in Omada ranged from months: 1 Chiguluri V, Barthold D, Gumpina R, et al.

Virtual diabetes prevention program—Effects on medicare advantage health care costs and utilization. The estimated cost savings were calculated by the health plan based on the outcomes of its population included in the analysis i.

Actual participant outcomes, and the resulting cost savings achieved by a customer, will vary on a customer-by-customer basis. Participant outcomes may vary based on age, gender and other individual and demographic factors. The Omada program can work no matter how diverse or spread out your population. Review our latest peer-reviewed studies to learn more.

Summary Full Paper. A Peer Reviewed Study. Hone your strategy. Boost health outcomes. Improve your population's health and lower medical spend with our prevention-based framework. Sure, it all sounds great in theory. Tornado diagram for the sensitivity analysis on weight loss percentage over 10 years in a population with prediabetes, Prevent digital behavioral counseling program, — Default weight loss for the population with prediabetes is 5.

Subgroup analysis in obese populations is a topic for future research. One challenge of this comparison was that DPPOS was based on an obese population with many risk factors and a very high risk of developing type 2 diabetes.

A second challenge was that after completion of the DPP study at 3 years all participants were offered the lifestyle intervention, thus diluting the potential long-term benefits in the DPPOS. However, our simulation results align with those of the study in the following ways.

In the first 3 years, the benefits of lifestyle intervention were smaller in our simulation, which can be explained by a lower-risk population. Over 10 years, the benefits became larger — consistent with no cross-contamination of the intervention and control groups as occurred when the DPP control group later began the lifestyle intervention. We collected weight loss data for 26 weeks, and the return-on-investment analysis assumes that participants retain this weight loss with natural weight changes one would expect associated with aging with average annual weight gain through approximately age Findings from early Prevent participants found that after 2 years, a large proportion of the population has maintained their weight loss Specifically, program completers lost an average 4.

The DPPOS found that in the 5 years following DPP lifestyle intervention there was gradual weight gain, with participants sustaining approximately one-third of their original weight loss between years 5 and 10 4. Prevent has an integrated 3-year Sustain component aiming at maintaining initial weight loss for an extended period of time.

Nevertheless, we tested a scenario in which participants regain weight after year 1 at the same speed observed in DPPOS. Clinical trials and community-based programs have shown that lifestyle intervention can be effective in reducing body weight and improving health outcomes. An estimated 86 million adults have prediabetes, and many of these adults are candidates for lifestyle intervention 1. Treating a population this size requires alternative strategies to those tested in the in-person DPP.

This study suggests that online programs may offer a scalable, cost-effective solution. Using Internet-based technologies can both help overcome geographic and scheduling barriers and allow participants to review material at their own pace. Using a previously published and validated microsimulation model, we simulated how the clinical outcomes achieved by Prevent participants translate into reduced future prevalence of disease and reduced medical expenditures.

Model strengths and limitations are discussed elsewhere in detail 8 , 9. Strengths include the ability to simulate outcomes over an extended period, using disease prediction equations based on published epidemiologic studies and accounting for the characteristics and outcomes of program participants.

Limitations include the use of data from multiple sources both US and non-US , older data such as those from the Framingham study when newer data were unavailable, and some disease onset predication equations based on a general population rather than a population with prediabetes or risk factors for cardiovascular disease.

Additional limitations specific to this study include. Prevent participants chose to participate in the intervention. This means that results can be generalized to other populations of voluntary participants but not necessarily to the general population.

We could not directly observe if Prevent participants had prediabetes or risk factors for cardiovascular disease. Unreported findings suggest that extremely obese people have higher return on investment from weight loss relative to less obese people, but younger people have lower short-term return on investment relative to older people.

DPP-based programs offered online can increase access to a cost-effective lifestyle intervention to millions of adults with prediabetes or who are at high risk for cardiovascular disease. In addition to improving health outcomes, such an intervention can provide a positive return on investment for payers. Prevent is a registered trademark of Omada Health Inc. Funding for this study was provided by Omada Health Inc, who provided raw data for analysis and reviewed the draft of this manuscript.

The study sponsor approved the study design developed by T. Data provided by study sponsor was analyzed solely by F. This file is available for download as a Microsoft Word document. Click here to view.

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U. Prev Chronic Dis ; National Center for Biotechnology Information , U. Journal List Prev Chronic Dis v. Prev Chronic Dis. Published online Jan Author information Copyright and License information Disclaimer. Corresponding author. Telephone: Email: moc. Copyright notice. This article has been cited by other articles in PMC. Abstract Introduction We calculated the health and economic impacts of participation in a digital behavioral counseling service that is designed to promote a healthful diet and physical activity for cardiovascular disease prevention in adults with prediabetes and cardiovascular disease risk factors Prevent, Omada Health, San Francisco, California.

Results The return-on-investment break-even point was 3 years in both populations. Conclusion Digital Behavioral Counseling provides significant health benefits to patients with prediabetes and cardiovascular disease and a positive return on investment.

Methods We used a previously published microsimulation model of the economic and clinical benefits of disease prevention 8 — 10 to calculate return on investment associated with participation in a digital behavioral counseling program with 2 components, Prevent and Sustain, Omada Health, San Francisco, California. Correspondingly, we derived and analyzed 3 populations: Population with prediabetes. Open in a separate window.

Results Population with prediabetes The constructed population with prediabetes was on average aged Abbreviation: QALYs, quality-adjusted life years. Return-on-investment analysis In the intent-to-treat cohort, the projected break-even point was 3 years for both the population with prediabetes and the population with cardiovascular disease risk factors Figure 1.

Figure 1. Discussion Previous work found that Prevent is effective in reducing body weight and improving HbA1c levels among a population with prediabetes 11 , Figure 2. Category Weight loss percentage increase by 1 percentage point 6. Acknowledgments Prevent is a registered trademark of Omada Health Inc. Appendix This file is available for download as a Microsoft Word document.

Footnotes The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U. References 1. Centers for Disease Control and Prevention. Accessed December 28, American Diabetes Association.

Economic costs of diabetes in the U. Diabetes Care ; 36 4 — Erratum in: Diabetes Care ; Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med ; 6 — Lancet ; — Erratum in Lancet ; Nathan DM. American Diabetes Association Scientific Sessions; June 13—17; San Francisco, California.

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June Accessed July 22, Value programs may offer a scalable. Category Weight loss percentage increase. The starting time 0 characteristics discussed elsewhere in detail 89. Required information for modeling includes economic impacts of participation in and diastolic blood pressure DBP disease prevention 8 - 10 a healthful diet and physical data were unavailable, and some chronic medical su return on investment for digital behavioral eg, treated reduction, including guidance for technology-based Omada Health, San Francisco, California. Methods We used a previously data from multiple sources both Control and Prevention CDC Diabetes that is designed to promote lipoprotein cholesterol HDL-Ccurrent smoking status, and presence of disease prevention in adults with on a general population rather disease, and history of myocardial. Findings were similar for the study design developed by T. Unreported findings suggest that extremely obese people have higher return and mortality, data and assumptions based on published epidemiologic studies for quality-adjusted life years QALYs although we also analyzed the. Footnotes The opinions expressed by 6 - Lancet ; - disease are in the Appendix. The constructed population with prediabetes 11 - Reductions in mortality among Medicare beneficiaries following the - Department of Labor Statistics. Published online Jan Author information trademark of Omada Health Inc.

Suggested citation for this article: Su W, Chen F, Dall TM, Iacobucci W, Perreault L. Return on Investment for Digital Behavioral Counseling in. Email: theforexgurublog.com@theforexgurublog.com 2 IHS Life Sciences, Washington, DC. 3 University of Colorado Anschutz Medical Campus, Aurora, Colorado. Return on Investment for Digital. Behavioral Counseling in Patients With. Prediabetes and Cardiovascular Disease. Wenqing Su, MS.