Automated Health Coaching for Type 2 Diabetes
(GODART-P&F Trial)
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: University of Alabama at Birmingham
Must not be taking: Insulin
Disqualifiers: Pregnancy, Cardiac event, Renal failure, others
No Placebo Group
Trial Summary
What is the purpose of this trial?
The purpose of this study is to pilot and assess the feasibility of implementing an artificial intelligence-assisted individualized lifestyle modification intervention for glycemic control in rural populations, which can be delivered even with regular landline phone service. This study will provide us with the knowledge to plan a well-powered optimization trial in the future to develop an optimal (low-cost) intervention package that can be delivered in a sustainable manner to the rural portions of America.
Do I have to stop taking my current medications for the trial?
The trial information does not specify whether you need to stop taking your current medications. However, it does exclude patients currently on insulin treatment.
What data supports the effectiveness of the treatment Automated Health Coaching for Type 2 Diabetes?
Research shows that digital health coaching, which is part of the treatment, can significantly improve diabetes management by enhancing behavior change and clinical outcomes. Virtual coaching combined with apps has been found to provide better glycemic control and higher satisfaction compared to apps alone, indicating the potential effectiveness of this approach.12345
Is automated health coaching for type 2 diabetes safe for humans?
How does automated health coaching differ from other treatments for type 2 diabetes?
Automated health coaching for type 2 diabetes is unique because it uses artificial intelligence to provide personalized, adaptive support, making it more accessible and cost-effective compared to traditional in-person counseling. This approach can offer real-time, individualized guidance and has shown potential to improve insulin resistance and other health outcomes, especially for under-resourced individuals.12389
Eligibility Criteria
This trial is for adults over 18 with Type 2 Diabetes, who speak and read English, have a doctor's approval to join, and HbA1c levels between 7% to 10.5%. It's not for those pregnant or planning pregnancy, in another diabetes study, on insulin treatment, with recent renal failure or major heart events.Inclusion Criteria
I am 18 years old or older.
Physician consent to participate in the study
Ability to converse in and read English
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Exclusion Criteria
I have not had renal failure in the last 6 months.
Present or soon-planned pregnancy
You are currently participating in a program to manage diabetes or weight.
See 2 more
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
2-4 weeks
Feasibility Phase
Participants test the feasibility, usability, and accessibility of the GODART platform
2 weeks
1 visit (virtual)
Intervention Phase
Participants receive the intervention with different experimental conditions for 6 months
6 months
Weekly virtual coaching sessions
Follow-up
Participants are monitored for safety and effectiveness after treatment
4 weeks
Treatment Details
Interventions
- Adapted Reward Level (Behavioural Intervention)
- Fixed Gamified Reward Level (Behavioural Intervention)
- Weekly Automated Health Coaching (Behavioural Intervention)
- Weekly Human Health Coaching (Behavioural Intervention)
Trial OverviewThe study tests an AI-assisted lifestyle change program aimed at controlling blood sugar in rural areas via regular phone service. Participants receive either automated or human health coaching weekly with varying reward systems to see which method works best.
Participant Groups
4Treatment groups
Experimental Treatment
Group I: Arm 4Experimental Treatment2 Interventions
Fixed Reward + Weekly human coaching
Group II: Arm 3Experimental Treatment2 Interventions
Fixed Reward + Weekly automated coaching
Group III: Arm 2Experimental Treatment2 Interventions
Adaptive Rewards + Weekly human coaching
Group IV: Arm 1Experimental Treatment2 Interventions
Adaptive Rewards + Weekly automated coaching
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Department of Family and Community Medicine, University of Alabama at BirminghamBirmingham, AL
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Who Is Running the Clinical Trial?
University of Alabama at BirminghamLead Sponsor
References
Virtual Coaching to Enhance Diabetes Care. [2020]Rates of diabetes are increasing worldwide and there is not a sufficient clinical workforce to care for these patients. Diabetes-related apps are a feasible way to provide diabetes education to large numbers of people with diabetes but attrition rates are high. Apps enhanced by virtual coaching may be a way to circumnavigate these problems. Virtual coaches are able to address common treatment barriers and other health disparities by providing data-driven individualized support in real time, at any time of day, and from anywhere. Enhanced glycemic benefits have been seen in those who use an app plus virtual coaching versus those who use an app alone, along with clinically meaningful behavior change, psychosocial effects, prolonged engagement, and high levels of satisfaction with the system. More research needs to be done to determine the longitudinal and widespread effects of virtual coaching in different populations.
A systematic review on incentive-driven mobile health technology: As used in diabetes management. [2022]Introduction Mobile health (mHealth) technologies have been shown to improve self-management of chronic diseases, such as diabetes. However, mHealth tools, e.g. apps, often have low rates of retention, eroding their potential benefits. Using incentives is a common mechanism for engaging, empowering and retaining patients that is applied by mHealth tools. We conducted a systematic review aiming to categorize the different types of incentive mechanisms employed in mHealth tools for diabetes management, which we defined as incentive-driven technologies (IDTs). As an auxiliary aim, we also analyzed barriers to adoption of IDTs. Methods Literature published in English between January 2008-August 2014 was identified through searching leading publishers and indexing databases: IEEE, Springer, Science Direct, NCBI, ACM, Wiley and Google Scholar. Results A total of 42 articles were selected. Of these, 34 presented mHealth tools with IDT mechanisms; Education was the most common mechanism ( n = 21), followed by Reminder ( n = 11), Feedback ( n = 10), Social ( n = 8), Alert ( n = 5), Gamification ( n = 3), and Financial ( n = 2). Many of these contained more than one IDT ( n = 19). The remaining eight articles, from which we defined barriers for adoption, were review papers and a qualitative study of focus groups and interviews. Discussion While mHealth technologies have advanced over the last five years, the core IDT mechanisms have remained consistent. Instead, IDT mechanisms have evolved with the advances in technology, such as moving from manual to automatic content delivery and personalization of content. Conclusion We defined the concept of IDT to be core features designed to act as motivating mechanisms for retaining and empowering users. We then identified seven core IDT mechanisms that are used by mHealth tools for diabetes management and classified 34 articles into these categories.
Digital Health Coaching for Type 2 Diabetes: Randomized Controlled Trial of Healthy at Home. [2021]Digital health coaching is an intervention for type 2 diabetes mellitus (T2DM) that has potential to improve the quality of care for patients. Previous research has established the efficacy of digital interventions for behavior change. This pilot study addresses a research gap in finding effective and accessible behavioral interventions for under-resourced individuals with T2DM. We examined the impact of Healthy at Home, a 12-week phone and SMS-based (short message service) digital health coaching program, on insulin resistance which is an upstream marker for T2DM progression. We compared this intervention to usual diabetic care in a family medicine residency clinic in a randomized controlled trial. Digital health coaching significantly improved participants' calculated Homeostatic Model Assessment for Insulin Resistance (HOMA2-IR) by -0.9 ± 0.4 compared with the control group (p = 0.029). This significance remained after controlling for years diagnosed with T2DM, enrollment in Medicaid, access to food, baseline stage of change, and race (p = 0.027). Increasing access to digital health coaching may lead to more effective control of diabetes for under-resourced patients. This study demonstrates the potential to implement a personalized, scalable, and effective digital health intervention to treat and manage T2DM through a lifestyle and behavioral approach to improve clinical outcomes (http://clinicaltrials.gov, NCT04872647).
An Individualized, Data-Driven Digital Approach for Precision Behavior Change. [2023]Chronic disease now affects approximately half of the US population, causes 7 in 10 deaths, and accounts for roughly 80% of US health care expenditure. Because the root causes of chronic diseases are largely behavioral, effective therapies require frequent, individualized interventions that extend beyond the hospital and clinic to reach patients in their day-to-day lives. However, a mismatch currently exists between what the health care system is equipped to provide and the interventions necessary to effectively address the chronic disease burden. To remedy this health crisis, we present an individualized, data-driven digital approach for chronic disease management and prevention through precision behavior change. The rapid growth of information, biological, and communication technologies makes this an opportune time to develop digital tools that deliver precision interventions for health behavior change to address the chronic disease crisis. Building on this rapid growth, we propose a framework that includes the precise targeting of risk-producing behaviors using real-time sensing technology, machine learning data analysis to identify the most effective intervention, and delivery of that intervention with health-reinforcing feedback to provide real-time, individualized support to empower sustainable health behavior change.
Digital Therapeutics: Leading the Way to Improved Outcomes for People With Diabetes. [2020]IN BRIEF The shift from acute illness to the epidemic of chronic conditions is the hallmark of the past 50 years. In addition to the provision of high-quality, accessible, and comprehensive medical care, one key to improving outcomes for individuals with diabetes and other chronic conditions is to increase their ability to self-manage. Having a high degree of self-efficacy is key to a person's ability to self-manage. One major challenge is finding scalable and affordable approaches that successfully increase self-efficacy. The field of digital health encompasses a variety of technology-enabled tools to make clinical care and patient self-management easier and more impactful by providing effective treatments that lead to improved clinical and economic outcomes. Better Choices, Better Health, a self-efficacy-based, self-management support intervention, is an example of a successful digital therapeutic for adults with one or more chronic conditions, including diabetes.
Clinical outcomes of a digitally supported approach for self-management of type 2 diabetes mellitus. [2023]Self-management of Type 2 diabetes mellitus (T2D) is challenging. Regular self-monitoring of blood glucose and healthy lifestyles are required to improve glycometabolic control, thus delaying diabetes complications, and reducing hospitalizations. Digital technologies can empower patients in their disease management promoting self-management and motivation to change behaviors. We report the results of an exploratory trial aimed at evaluating the metabolic outcomes of using digital solutions for T2D self-management developed in the ProEmpower project, a European Commission funded Pre-Commercial Procurement.
Digital games for type 1 and type 2 diabetes: underpinning theory with three illustrative examples. [2022]Digital games are an important class of eHealth interventions in diabetes, made possible by the Internet and a good range of affordable mobile devices (eg, mobile phones and tablets) available to consumers these days. Gamifying disease management can help children, adolescents, and adults with diabetes to better cope with their lifelong condition. Gamification and social in-game components are used to motivate players/patients and positively change their behavior and lifestyle. In this paper, we start by presenting the main challenges facing people with diabetes-children/adolescents and adults-from a clinical perspective, followed by three short illustrative examples of mobile and desktop game apps and platforms designed by Ayogo Health, Inc. (Vancouver, BC, Canada) for type 1 diabetes (one example) and type 2 diabetes (two examples). The games target different age groups with different needs-children with type 1 diabetes versus adults with type 2 diabetes. The paper is not meant to be an exhaustive review of all digital game offerings available for people with type 1 and type 2 diabetes, but rather to serve as a taster of a few of the game genres on offer today for both types of diabetes, with a brief discussion of (1) some of the underpinning psychological mechanisms of gamified digital interventions and platforms as self-management adherence tools, and more, in diabetes, and (2) some of the hypothesized potential benefits that might be gained from their routine use by people with diabetes. More research evidence from full-scale evaluation studies is needed and expected in the near future that will quantify, qualify, and establish the evidence base concerning this gamification potential, such as what works in each age group/patient type, what does not, and under which settings and criteria.
Optimizing Health Coaching for Patients With Type 2 Diabetes Using Machine Learning: Model Development and Validation Study. [2022]Health coaching is an emerging intervention that has been shown to improve clinical and patient-relevant outcomes for type 2 diabetes. Advances in artificial intelligence may provide an avenue for developing a more personalized, adaptive, and cost-effective approach to diabetes health coaching.
A Fully Automated Conversational Artificial Intelligence for Weight Loss: Longitudinal Observational Study Among Overweight and Obese Adults. [2020]Type 2 diabetes is the most expensive chronic disease in the United States. Two-thirds of US adults have prediabetes or are overweight and at risk for type 2 diabetes. Intensive in-person behavioral counseling can help patients lose weight and make healthy behavior changes to improve their health outcomes. However, with the shortage of health care providers and associated costs, such programs do not adequately service all patients who could benefit. The health care system needs effective and cost-effective interventions that can lead to positive health outcomes as scale. This study investigated the ability of conversational artificial intelligence (AI), in the form of a standalone, fully automated text-based mobile coaching service, to promote weight loss and other health behaviors related to diabetes prevention. This study also measured user acceptability of AI coaches as alternatives to live health care professionals.