Cost-Effectiveness Analysis of a Prescription Digital Therapeutic in Type 2 Diabetes.

INTRODUCTION: BT-001 (AspyreRx™) prescription digital therapy, a form of personalized cognitive behavioral therapy, has demonstrated clinically meaningful and durable hemoglobin A1c reductions in patients with type 2 diabetes (T2D). The current study examined the cost-effectiveness of BT-001 plus standard of care (SoC) versus SoC alone in T2D over a lifetime horizon from a healthcare payer perspective. METHODS: We modeled the T2D pathway using an individual patient-level simulation; clinical data were sourced from the intention-to-treat subset of the BT-001 randomized clinical trial (RCT). SoC across both arms included the composition of oral and injectable treatments for T2D. Events were simulated using the United Kingdom Prospective Diabetes Study Outcomes Model 2 risk equation. A 3-month model cycle length was used in the first year, then annual model cycles were used in line with the original risk engine specifications. Patient characteristics informed event equations and Monte Carlo random sampling was used to assess the occurrence of events within each model cycle. Incidence of hypoglycemic events, drug discontinuation, costs, and health utilities and disutility values were sourced from the literature. RESULTS: From a payer perspective, BT-001 plus SoC versus SoC alone was dominant with a gain in quality-adjusted life years (QALYs) of 0.101 and cost savings of $7343 per patient over the lifetime horizon (i.e., more effective and less costly). BT-001 plus SoC was cost-effective at a willingness-to-pay of $100,000 per QALY (incremental net monetary benefit was $17,443). Savings with BT-001 were primarily driven by a reduction in drug acquisition costs. The reduction in hemoglobin A1c with BT-001 was associated with fewer T2D complications. CONCLUSIONS: BT-001 plus SoC was estimated to dominate SoC alone over the lifetime horizon from a payer perspective, suggesting that using BT-001 can empower patients to better manage their diabetes with the potential for lifelong advantages.

Randomized, Controlled Trial of a Digital Behavioral Therapeutic Application to Improve Glycemic Control in Adults With Type 2 Diabetes.

OBJECTIVE: To evaluate the efficacy and safety of a digital therapeutic application (app) delivering cognitive behavioral therapy (CBT) designed to improve glycemic control in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: Adults with type 2 diabetes and an HbA1c of 7 to <11% were randomly assigned to receive access to a digital therapeutic app delivering CBT (BT-001) or a control app, both on top of standard of care management. CBT is an established form of psychological treatment that endeavors to identify and change unhelpful thinking patterns. The primary study end point was treatment group difference in mean HbA1c change from baseline to 90 days. RESULTS: Among 669 randomly assigned subjects who completed app onboarding, the mean age was 58 years, BMI 35 kg/m2, 54% were female, 28% Black, and 16% Latino. Baseline HbA1c was 8.2 and 8.1% in the BT-001 and control groups, respectively. After 90 days of app access, change in HbA1c was -0.28% (95% CI -0.41, -0.15) in the BT-001 group and +0.11% (95% CI -0.02, 0.23) in the control group (treatment group difference 0.39%; P < 0.0001). HbA1c reduction paralleled exposure to the therapeutic intervention, assessed as the number of modules completed on the app (P for trend <0.0001). No adverse events in either group were attributed to app use and no adverse device effects reported. CONCLUSIONS: Patients randomly assigned to the BT-001 arm relative to the control arm had significantly lower HbA1c at 90 days. The digital therapeutic may provide a scalable treatment option for patients with type 2 diabetes.

Cognitive behavioral therapy delivered via digital mobile application for the treatment of type 2 diabetes: Rationale, design, and baseline characteristics of a randomized, controlled trial.

BACKGROUND: The prevalence of type 2 diabetes (T2D) continues to rise in the United States and worldwide. Cognitive behavioral therapy (CBT) has been shown to improve glycemic control in patients with T2D, but broad implementation has been limited by inherent access and resource constraints. Digital therapeutics have the potential to overcome these obstacles. HYPOTHESIS: To describe the rationale and design of a trial evaluating the efficacy and safety of a digital therapeutic providing CBT to improve glycemic control in adults with T2D. METHODS: This randomized, controlled, multicenter, Phase 3 trial evaluates the hypothesis that BT-001, an investigational digital therapeutic intended to help patients with T2D improve their glycemic control, on top of standard of care therapy, will lower hemoglobin A1c (HbA1c) compared to a control app across a broad range of patients in a real-world setting. The study is designed to provide evidence to support FDA review of this device as a digital therapeutic. The intervention is provided within the digital application (app) and includes no person-to-person coaching. The primary endpoint is the difference in HbA1c change from baseline to 90 days for BT-001-allocated subjects compared with those assigned to the control app. Safety assessment includes adverse events and adverse device effects. The study incorporates pragmatic features including entirely remote conduct with at-home visits for physical measures and blood sample collection. CONCLUSIONS: This randomized, controlled trial evaluates a cognitive behavioral intervention delivered via smartphone app which has the potential to provide a scalable treatment option for patients with T2D.

Achieving Rapid Blood Pressure Control With Digital Therapeutics: Retrospective Cohort and Machine Learning Study.

BACKGROUND: Behavioral therapies, such as electronic counseling and self-monitoring dispensed through mobile apps, have been shown to improve blood pressure, but the results vary and long-term engagement is a challenge. Machine learning is a rapidly advancing discipline that can be used to generate predictive and responsive models for the management and treatment of chronic conditions and shows potential for meaningfully improving outcomes. OBJECTIVE: The objectives of this retrospective analysis were to examine the effect of a novel digital therapeutic on blood pressure in adults with hypertension and to explore the ability of machine learning to predict participant completion of the intervention. METHODS: Participants with hypertension, who engaged with the digital intervention for at least 2 weeks and had paired blood pressure values, were identified from the intervention database. Participants were required to be ≥18 years old, reside in the United States, and own a smartphone. The digital intervention offers personalized behavior therapy, including goal setting, skill building, and self-monitoring. Participants reported blood pressure values at will, and changes were calculated using averages of baseline and final values for each participant. Machine learning was used to generate a model of participants who would complete the intervention. Random forest models were trained at days 1, 3, and 7 of the intervention, and the generalizability of the models was assessed using leave-one-out cross-validation. RESULTS: The primary cohort comprised 172 participants with hypertension, having paired blood pressure values, who were engaged with the intervention. Of the total, 86.1% participants were women, the mean age was 55.0 years (95% CI 53.7-56.2), baseline systolic blood pressure was 138.9 mmHg (95% CI 136.6-141.3), and diastolic was 86.2 mmHg (95% CI 84.8-87.7). Mean change was -11.5 mmHg for systolic blood pressure and -5.9 mmHg for diastolic blood pressure over a mean of 62.6 days (P<.001). Among participants with stage 2 hypertension, mean change was -17.6 mmHg for systolic blood pressure and -8.8 mmHg for diastolic blood pressure. Changes in blood pressure remained significant in a mixed-effects model accounting for the baseline systolic blood pressure, age, gender, and body mass index (P<.001). A total of 43% of the participants tracking their blood pressure at 12 weeks achieved the 2017 American College of Cardiology/American Heart Association definition of blood pressure control. The 7-day predictive model for intervention completion was trained on 427 participants, and the area under the receiver operating characteristic curve was .78. CONCLUSIONS: Reductions in blood pressure were observed in adults with hypertension who used the digital therapeutic. The degree of blood pressure reduction was clinically meaningful and achieved rapidly by a majority of the studied participants. Greater improvement was observed in participants with more severe hypertension at baseline. A successful proof of concept for using machine learning to predict intervention completion was presented.

Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study.

OBJECTIVES: Development of digital biomarkers to predict treatment response to a digital behavioural intervention. DESIGN: Machine learning using random forest classifiers on data generated through the use of a digital therapeutic which delivers behavioural therapy to treat cardiometabolic disease. Data from 13 explanatory variables (biometric and engagement in nature) generated in the first 28 days of a 12-week intervention were used to train models. Two levels of response to treatment were predicted: (1) systolic change ≥10 mm Hg (SC model), and (2) shift down to a blood pressure category of elevated or better (ER model). Models were validated using leave-one-out cross validation and evaluated using area under the curve receiver operating characteristics (AUROC) and specificity- sensitivity. Ability to predict treatment response with a subset of nine variables, including app use and baseline blood pressure, was also tested (models SC-APP and ER-APP). SETTING: Data generated through ad libitum use of a digital therapeutic in the USA. PARTICIPANTS: Deidentified data from 135 adults with a starting blood pressure ≥130/80, who tracked blood pressure for at least 7 weeks using the digital therapeutic. RESULTS: The SC model had an AUROC of 0.82 and a sensitivity of 58% at a specificity of 90%. The ER model had an AUROC of 0.69 and a sensitivity of 32% at a specificity at 91%. Dropping explanatory variables related to blood pressure resulted in an AUROC of 0.72 with a sensitivity of 42% at a specificity of 90% for the SC-APP model and an AUROC of 0.53 for the ER-APP model. CONCLUSIONS: Machine learning was used to transform data from a digital therapeutic into digital biomarkers that predicted treatment response in individual participants. Digital biomarkers have potential to improve treatment outcomes in a digital behavioural intervention.

Change in Glycemic Control With Use of a Digital Therapeutic in Adults With Type 2 Diabetes: Cohort Study.

BACKGROUND: Intensive lifestyle change can treat and even reverse type 2 diabetes. Digital therapeutics have the potential to deliver lifestyle as medicine for diabetes at scale. OBJECTIVE: This 12-week study investigates the effects of a novel digital therapeutic, FareWell, on hemoglobin A1c (HbA1c) and diabetes medication use. METHODS: Adults with type 2 diabetes and a mobile phone were recruited throughout the United States using Facebook advertisements. The intervention aim was to effect a sustainable shift to a plant-based dietary pattern and regular exercise by advancing culinary literacy and lifestyle skill acquisition. The intervention was delivered by an app paired with specialized human support, also delivered digitally. Health coaching was provided every 2 weeks by telephone, and a clinical team was available for participants requiring additional support. Participants self-reported current medications and HbA1c at the beginning and end of the 12-week program. Self-efficacy related to managing diabetes and maintaining dietary changes was assessed via survey. Engagement was recorded automatically through the app. RESULTS: We enrolled 118 participants with a baseline HbA1c >6.5%. Participants were 81.4% female (96/118) and resided in 38 US states with a mean age of 50.7 (SD 9.4) years, baseline body mass index of 38.1 (SD 8.8) kg/m2, and baseline HbA1c of 8.1% (SD 1.6). At 12 weeks, 86.2% (94/109) of participants were still using the app. Mean change in HbA1c was -0.8% (97/101, SD 1.3, P<.001) for those reporting end-study data. For participants with a baseline HbA1c >7.0% who did not change medications midstudy, HbA1c change was -1.1% (67/69, SD 1.4, P<.001). The proportion of participants with an end-study HbA1c <6.5% was 28% (22/97). After completion of the intervention, 17% (16/97) of participants reported a decrease in diabetic medication while 8% (8/97) reported an increase. A total of 57% (55/97) of participants achieved a composite outcome of reducing HbA1c, reducing diabetic medication use, or both; 92% (90/98) reported greater confidence in their ability to manage their diabetes compared to before the program, and 91% (89/98) reported greater confidence in their ability to maintain a healthy dietary pattern. Participants engaged with the app an average of 4.3 times per day. We observed a significantly greater decrease in HbA1c among participants in the highest tertile of app engagement compared to those in the lowest tertile of app engagement (P=.03). CONCLUSIONS: Clinically meaningful reductions in HbA1c were observed with use of the FareWell digital therapeutic. Greater glycemic control was observed with increasing app engagement. Engagement and retention were both high in this widely distributed sample.

Soy protein with isoflavones has favorable effects on endothelial function that are independent of lipid and antioxidant effects in healthy postmenopausal women.

BACKGROUND: Controversy exists about the ability of soy protein and isoflavones to modulate vascular reactivity and biochemical cardiovascular disease risk markers in healthy, normolipidemic postmenopausal women. OBJECTIVE: The objective was to investigate whether the consumption of soy protein with isoflavones would result in improved vascular reactivity and decreased biochemical markers of endothelial dysfunction and inflammation, independent of enhanced lipid and antioxidant effects. DESIGN: Healthy postmenopausal women (n = 28) were enrolled in a randomized, double-blind, crossover study, and they consumed 25 g of 3 protein products/d for 6 wk each, with intervening washout periods. The products were isolated soy protein with isoflavones, ethanol-washed isolated soy protein with trace isoflavones, and total milk protein, which supplied 107, 2, and 0 mg total isoflavone (aglycone) units/d, respectively. We studied vascular function by using brachial artery reactivity values, plasma concentrations of vasoactive factors, endothelial inflammatory markers, and plasma isoflavone concentrations. The resistance of whole plasma and isolated LDL to copper-mediated oxidation was measured by conjugated diene formation. RESULTS: Postocclusion peak flow velocity of the brachial artery was significantly (P = 0.03) lower after treatment with isolated soy protein with isoflavones, which is consistent with a vasodilatory response, than after treatment with total milk protein. Plasma isoflavones and metabolites were significantly (P < 0.01) higher after treatment with isolated soy protein with isoflavones. There were no significant changes in biochemical cardiovascular disease risk markers or conjugated diene formation between the 3 dietary groups. CONCLUSION: Daily consumption of soy protein with isoflavones can result in positive vascular effects that are independent of lipid and antioxidant effects in healthy postmenopausal women.