Mark Berman - Academia.edu (original) (raw)

Papers by Mark Berman

Objective To evaluate the efficacy and safety of a digital therapeutic application (app) deliv... more 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 HbA1c 7 to <11% were randomized 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 endpoint was treatment group difference in mean HbA1c change from baseline to 90 days. Results Among 669 randomized subjects who completed app on-boarding, mean age was 58 years, body mass index 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...

Journal of the Endocrine Society, 2020

Despite advances in pharmacological treatment, half of U.S. patients with type 2 diabetes are not... more Despite advances in pharmacological treatment, half of U.S. patients with type 2 diabetes are not achieving glycemic control (1). Even when glycemic control is achieved via pharmacotherapy, a substantial residual risk to all-cause mortality still exists (2). An overlooked contributor to both poor glycemic control and mortality risk is the need to address the behavioral determinants of type 2 diabetes. We tested a novel software application (app) designed to change behaviors that are root causes of type 2 diabetes and improve glycemic control. If behavioral therapy is effective and can be delivered using software installed on a patient’s smartphone, it may provide a cost-effective means of improving outcomes in this patient population. To study this hypothesis, we examined data from app users with suboptimal glycemic control, as defined by having a 3-day average of self-reported fasting blood glucose (FBG) values ≥ 152 mg/dL, who used the app for 12 weeks. 74 participants residing in...

BMJ Open, 2019

ObjectivesDevelopment of digital biomarkers to predict treatment response to a digital behavioura... more ObjectivesDevelopment of digital biomarkers to predict treatment response to a digital behavioural intervention.DesignMachine 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).SettingData generated through ad lib...

BACKGROUND Behavioral therapies, such as e-counseling and self-monitoring dispensed through mobil... more BACKGROUND Behavioral therapies, such as e-counseling and self-monitoring dispensed through mobile apps, have been shown to improve blood pressure but 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 This retrospective analysis of data from the digital therapeutic, Better, examines its effect on blood pressure in hypertensive adults and explores the use of machine learning methods to predict intervention completion. METHODS The Better database was queried for participants with hypertension, who engaged with the intervention for at least 2 weeks and had paired blood pressure (BP) values. Participants were required to be ≥ 18 years old, reside in the United States and own a smartphone. The digital intervention offers personalized behavior therapy, i...

American Journal of Lifestyle Medicine, 2017

The what of Lifestyle Medicine, including a whole foods dietary pattern, has been well establishe... more The what of Lifestyle Medicine, including a whole foods dietary pattern, has been well established, but the how has remained elusive. How do we apply what we know in a cost-effective and widely accessible manner to prevent, treat, and even reverse chronic disease? Over the decade ahead, we believe the field of Lifestyle Medicine and the people who need it most will benefit from addressing the how. This article summarizes the founding and operational principles of FareWell Inc. - a digital therapeutics company targeting lifestyle-related cardiometabolic diseases. We outline our current use of mobile health technology and artificial intelligence and describe our early clinical experience, business model, and key anticipated challenges.

JMIR diabetes, Jan 14, 2018

Intensive lifestyle change can treat and even reverse type 2 diabetes. Digital therapeutics have ... more 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. This 12-week study investigates the effects of a novel digital therapeutic, FareWell, on hemoglobin A (HbA) and diabetes medication use. 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 HbA at the beginning and end of the 12-week program. Self-efficacy related to managing diabetes and mai...

American Journal of Preventive Medicine, 2011

Objective To evaluate the efficacy and safety of a digital therapeutic application (app) deliv... more 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 HbA1c 7 to <11% were randomized 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 endpoint was treatment group difference in mean HbA1c change from baseline to 90 days. Results Among 669 randomized subjects who completed app on-boarding, mean age was 58 years, body mass index 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...

Journal of the Endocrine Society, 2020

Despite advances in pharmacological treatment, half of U.S. patients with type 2 diabetes are not... more Despite advances in pharmacological treatment, half of U.S. patients with type 2 diabetes are not achieving glycemic control (1). Even when glycemic control is achieved via pharmacotherapy, a substantial residual risk to all-cause mortality still exists (2). An overlooked contributor to both poor glycemic control and mortality risk is the need to address the behavioral determinants of type 2 diabetes. We tested a novel software application (app) designed to change behaviors that are root causes of type 2 diabetes and improve glycemic control. If behavioral therapy is effective and can be delivered using software installed on a patient’s smartphone, it may provide a cost-effective means of improving outcomes in this patient population. To study this hypothesis, we examined data from app users with suboptimal glycemic control, as defined by having a 3-day average of self-reported fasting blood glucose (FBG) values ≥ 152 mg/dL, who used the app for 12 weeks. 74 participants residing in...

BMJ Open, 2019

ObjectivesDevelopment of digital biomarkers to predict treatment response to a digital behavioura... more ObjectivesDevelopment of digital biomarkers to predict treatment response to a digital behavioural intervention.DesignMachine 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).SettingData generated through ad lib...

BACKGROUND Behavioral therapies, such as e-counseling and self-monitoring dispensed through mobil... more BACKGROUND Behavioral therapies, such as e-counseling and self-monitoring dispensed through mobile apps, have been shown to improve blood pressure but 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 This retrospective analysis of data from the digital therapeutic, Better, examines its effect on blood pressure in hypertensive adults and explores the use of machine learning methods to predict intervention completion. METHODS The Better database was queried for participants with hypertension, who engaged with the intervention for at least 2 weeks and had paired blood pressure (BP) values. Participants were required to be ≥ 18 years old, reside in the United States and own a smartphone. The digital intervention offers personalized behavior therapy, i...

American Journal of Lifestyle Medicine, 2017

The what of Lifestyle Medicine, including a whole foods dietary pattern, has been well establishe... more The what of Lifestyle Medicine, including a whole foods dietary pattern, has been well established, but the how has remained elusive. How do we apply what we know in a cost-effective and widely accessible manner to prevent, treat, and even reverse chronic disease? Over the decade ahead, we believe the field of Lifestyle Medicine and the people who need it most will benefit from addressing the how. This article summarizes the founding and operational principles of FareWell Inc. - a digital therapeutics company targeting lifestyle-related cardiometabolic diseases. We outline our current use of mobile health technology and artificial intelligence and describe our early clinical experience, business model, and key anticipated challenges.

JMIR diabetes, Jan 14, 2018

Intensive lifestyle change can treat and even reverse type 2 diabetes. Digital therapeutics have ... more 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. This 12-week study investigates the effects of a novel digital therapeutic, FareWell, on hemoglobin A (HbA) and diabetes medication use. 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 HbA at the beginning and end of the 12-week program. Self-efficacy related to managing diabetes and mai...

American Journal of Preventive Medicine, 2011