Automated Health Coaching for Type 2 Diabetes

(GODART-P&F Trial)

Overseen By Tejossy John, MPH

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Alabama at Birmingham

Must not be taking: Insulin

Disqualifiers: Pregnancy, Cardiac event, Renal failure, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests different methods to help people with type 2 diabetes manage their blood sugar levels through automated and human health coaching. The researchers aim to determine if a combination of coaching and rewards can be effective in rural areas, even with just a landline phone. Participants will join one of four groups to receive various combinations of coaching and rewards, including the "Adapted Reward Level" and "Fixed Gamified Reward Level" interventions. The trial seeks individuals diagnosed with type 2 diabetes who have regular blood sugar levels between 7% and 10.5% (measured by HbA1C). As an unphased trial, this study provides a unique opportunity to contribute to innovative diabetes management strategies that could benefit rural communities.

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.

Is there any evidence suggesting that this trial's treatments are likely to be safe?

Research has shown that AI-powered health coaching, like the one being tested, is generally safe and easy to use. In some studies, over 70% of people with type 2 diabetes who used AI coaching improved their blood sugar levels, with many reducing or even stopping their medications. This suggests that AI coaching is effective and doesn't cause major issues for most users.

Regarding human coaching, studies indicate it is a promising way to help manage type 2 diabetes. Participants often experience better health outcomes, and no significant safety concerns have been reported. The use of rewards, whether fixed or changing, has not been shown to cause safety problems either.

Overall, these methods appear safe and offer health benefits. However, those considering joining a trial should consult with their doctor first. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the automated health coaching trial for type 2 diabetes because it explores innovative ways to manage the condition beyond traditional medication and lifestyle changes. This trial features both adaptive and fixed gamified reward systems, combined with either automated or human-led weekly coaching. Unlike standard treatments that might focus solely on medication or diet, these approaches use behavioral strategies to encourage patients to make healthier lifestyle choices. The potential for personalized rewards and the integration of technology in coaching could significantly enhance patient engagement and improve long-term management of diabetes. By examining different combinations of rewards and coaching styles, this trial aims to discover more effective ways to support people with type 2 diabetes.

What evidence suggests that this trial's treatments could be effective for type 2 diabetes?

Research shows that artificial intelligence (AI) and game-like reward systems can help people manage type 2 diabetes by encouraging healthier lifestyle choices. This trial assigns participants to different arms to evaluate these methods. One arm receives AI-driven coaching, which helped over 70% of participants in previous studies lower their HbA1c levels, an important measure of blood sugar control. Another arm experiences game-like rewards, which effectively promote weight loss and better blood sugar management. Additionally, some participants receive digital health coaching, either by a computer or a person, which significantly improved diabetes management in past studies by encouraging positive behavior changes. These methods offer effective and accessible support for managing diabetes, especially in areas with limited healthcare resources. ^{6 7 8 9 10}

Who Is on the Research Team?

Tapan Mehta, MD

Principal Investigator

University of Alabama at Birmingham

Mohanraj Thirumalai, PHD

Principal Investigator

University of Alabama at Birmingham

Are You a Good Fit for This Trial?

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

Physician consent to participate in the study

Ability to converse in and read English

Your HbA1c levels need to be between 7% and 10.5% for the first phase of the study, and between 8% and 10.5% for the second phase.

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.

Timeline for a Trial Participant

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

What Are the Treatments Tested in This Trial?

Interventions

• Adapted Reward Level
• Fixed Gamified Reward Level
• Weekly Automated Health Coaching
• Weekly Human Health Coaching

Trial Overview

The 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.

How Is the Trial Designed?

Group I: Arm 4 Experimental Treatment 2 Interventions

Fixed Reward + Weekly human coaching

Group II: Arm 3 Experimental Treatment 2 Interventions

Fixed Reward + Weekly automated coaching

Group III: Arm 2 Experimental Treatment 2 Interventions

Adaptive Rewards + Weekly human coaching

Group IV: Arm 1 Experimental Treatment 2 Interventions

Adaptive Rewards + Weekly automated coaching

Who Is Running the Clinical Trial?

University of Alabama at Birmingham

Lead Sponsor

Trials

1,677

Recruited

2,458,000+

Published Research Related to This Trial

Diabetes-related apps combined with virtual coaching can significantly improve glycemic control and promote meaningful behavior changes compared to using apps alone, addressing common treatment barriers effectively.

Users of these enhanced apps report high satisfaction and prolonged engagement, suggesting that virtual coaching may be a valuable tool in diabetes education, although further research is needed to explore its long-term effects across diverse populations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Virtual Coaching to Enhance Diabetes Care. Ramchandani, N.[2020]

The study involving 70 overweight and obese participants demonstrated that using a conversational AI health coach led to an average weight loss of 2.38% over approximately 15 weeks, indicating its effectiveness in promoting weight loss similar to traditional in-person interventions.

Participants reported a high level of satisfaction with the AI coach, scoring 87 out of 100 on usability, and showed a significant increase in healthy meal choices by 31%, suggesting that AI can effectively support behavior change in diabetes prevention.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Fully Automated Conversational Artificial Intelligence for Weight Loss: Longitudinal Observational Study Among Overweight and Obese Adults. Stein, N., Brooks, K.[2020]

A pilot study involving 177 patients demonstrated that a reinforcement learning model could effectively recommend personalized diabetes health coaching interventions, potentially improving clinical outcomes like hemoglobin A1c reduction and quality of life.

The model's recommendations led to better cumulative health outcomes compared to traditional coaching methods, suggesting that AI-driven approaches could enhance the effectiveness of diabetes management interventions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Optimizing Health Coaching for Patients With Type 2 Diabetes Using Machine Learning: Model Development and Validation Study. Di, S., Petch, J., Gerstein, HC., et al.[2022]

The use of digital self-management solutions for Type 2 diabetes, such as DM4All and DiaWatch, led to significant improvements in metabolic outcomes, including reductions in HbA1c, body weight, blood pressure, and cholesterol levels over an average follow-up of 8 months in 100 patients.

Compared to a control group, the ProEmpower participants showed significantly better improvements in cardiovascular risk factors, highlighting the efficacy of digital technologies in enhancing diabetes management and promoting healthier lifestyles.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical outcomes of a digitally supported approach for self-management of type 2 diabetes mellitus. De Luca, V., Bozzetto, L., Giglio, C., et al.[2023]

The 12-week Healthy at Home digital health coaching program significantly improved insulin resistance in individuals with type 2 diabetes, showing a reduction in HOMA2-IR scores compared to usual care (p = 0.029).

This pilot study suggests that digital health coaching can be an effective and accessible intervention for under-resourced patients with type 2 diabetes, potentially leading to better diabetes management and clinical outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Digital Health Coaching for Type 2 Diabetes: Randomized Controlled Trial of Healthy at Home. Azelton, KR., Crowley, AP., Vence, N., et al.[2021]

A systematic review of 42 articles identified seven core incentive mechanisms used in mobile health (mHealth) tools for diabetes management, with education being the most common, followed by reminders and feedback.

Despite advancements in technology, the fundamental incentive-driven mechanisms have remained consistent, evolving mainly in terms of content delivery and personalization to enhance user engagement and retention.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A systematic review on incentive-driven mobile health technology: As used in diabetes management. de Ridder, M., Kim, J., Jing, Y., et al.[2022]

Chronic diseases affect about half of the US population and are responsible for 70% of deaths and 80% of healthcare costs, highlighting the need for effective, individualized interventions beyond traditional healthcare settings.

The proposed digital approach utilizes real-time sensing technology and machine learning to deliver personalized behavior change interventions, aiming to empower individuals to manage and prevent chronic diseases more effectively.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

An Individualized, Data-Driven Digital Approach for Precision Behavior Change. Wongvibulsin, S., Martin, SS., Saria, S., et al.[2023]

Improving self-management skills is crucial for individuals with chronic conditions like diabetes, and having high self-efficacy plays a significant role in this process.

The digital health intervention 'Better Choices, Better Health' effectively enhances self-efficacy and supports self-management in adults with chronic conditions, demonstrating a scalable and affordable approach to improving health outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Digital Therapeutics: Leading the Way to Improved Outcomes for People With Diabetes. Kaufman, N.[2020]

Digital games can effectively support diabetes management for both children and adults by using gamification and social components to motivate behavior change and improve lifestyle habits.

While the paper highlights examples of mobile and desktop games designed for type 1 and type 2 diabetes, it emphasizes the need for more comprehensive research to evaluate their effectiveness across different age groups and patient needs.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Digital games for type 1 and type 2 diabetes: underpinning theory with three illustrative examples. Kamel Boulos, MN., Gammon, S., Dixon, MC., et al.[2022]

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