AI-Assisted Telehealth for Chronic Conditions

Overseen By Mohanraj Thirumalai, PhD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Alabama at Birmingham

Disqualifiers: Cognitive impairment, Depression, Cardiac event, others

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

Compensation: Varies

Number of Visits: Unknown

Duration: 24 weeks

212 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial explores a new AI-assisted program called AI4CHRON, designed to improve the quality of life for people with chronic health conditions and mobility challenges. Participants receive lifestyle support through coaching calls and rewards, with some groups experiencing a gamified, interactive approach. The study seeks individuals with heart disease, chronic lung disease, or Type 2 diabetes who also have a permanent physical disability and can use a smartphone or computer. As an unphased trial, it offers a unique opportunity to contribute to innovative research that could enhance daily living for those with chronic conditions.

Do I need to stop my current medications for the trial?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What prior data suggests that this AI-assisted telehealth platform is safe for chronic condition management?

Research has shown that AI technologies, such as those used in AI4CHRON, are generally safe and helpful for managing long-term health issues. Studies have found that these AI systems assist people in handling their medications, symptoms, and daily routines. Similar AI programs have reported no major safety problems. The AI4CHRON program aims to enhance the quality of life for people with mobility challenges and other ongoing health conditions. Although this program is new and undergoing testing, similar AI tools in healthcare have demonstrated safety. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about AI4CHRON because it integrates AI-driven telehealth with innovative approaches like scheduled coaching calls and gamified rewards. Unlike traditional treatments for chronic conditions, which often involve regular in-person visits and medication adjustments, AI4CHRON offers a more personalized and accessible experience. The gamified rewards and coaching calls aim to boost patient engagement and adherence, making it easier for individuals to manage their health from home. This trial explores how these unique features can enhance patient outcomes and provide a more flexible solution for chronic condition management.

What evidence suggests that AI4CHRON could be effective for improving health-related quality of life in adults with chronic conditions?

Research shows that artificial intelligence (AI) can help manage long-term health issues. Studies have found that AI assists with weight loss and blood sugar control. In this trial, the AI4CHRON platform will assign participants to different treatment arms, suggesting personalized lifestyle changes. This method has successfully improved people's quality of life. Some participants will receive scheduled coaching calls, while others will not, and some will experience gamified rewards. Additionally, combining AI with health coaching has encouraged positive changes in health habits. This evidence suggests that AI4CHRON could be effective for people with ongoing health conditions and mobility challenges. ^{1 2 3 6 7}

Are You a Good Fit for This Trial?

This trial is for adults over 18 with heart disease, chronic lung disease, or Type 2 diabetes who also live with a permanent physical disability like spina bifida. Participants must be able to communicate and read in English and have access to a smartphone or computer.

Inclusion Criteria

I have a smartphone or computer.

I can speak and read English.

I live with a permanent physical disability like spina bifida or MS.

Exclusion Criteria

Current enrollment in any structured intervention

I have a visual or hearing impairment.

I have significant memory or thinking problems.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

Participants engage in a lifestyle modification intervention with AI assistance for health-related quality of life

24 weeks

Telehealth sessions as per group assignment

Follow-up

Participants are monitored for safety and effectiveness after treatment

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

• AI4CHRON

Trial Overview

The study tests an AI-assisted telehealth platform designed for people with chronic diseases and impaired mobility. It compares four approaches: two without scheduled coaching calls (with gamified or independent rewards) and two with calls (also with gamified or independent rewards).

How Is the Trial Designed?

Group I: Scheduled Coaching Calls & Independent rewards (No Gamification) Experimental Treatment 1 Intervention

Group II: Scheduled Coaching Calls & Gamified Rewards Experimental Treatment 1 Intervention

Group III: No Scheduled Coaching Calls & Independent Rewards (No Gamification) Experimental Treatment 1 Intervention

Group IV: No Scheduled Coaching Call & Gamified Rewards Experimental Treatment 1 Intervention

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

The use of the AI-powered telehealth platform, Laura Digital Emergency Room, in Brazil facilitated around 130,000 interactions and helped triage 24,162 patients during the COVID-19 pandemic, effectively classifying most patients with mild to moderate symptoms.

This approach demonstrated that AI can enhance healthcare accessibility and safety, potentially reducing the burden on Brazil's public health system, which serves 75% of the population, especially during a crisis like the pandemic.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

COVID-19 in Brazil-Preliminary Analysis of Response Supported by Artificial Intelligence in Municipalities. Morales, HMP., Guedes, M., Silva, JS., et al.[2021]

The study involved 1,667 patients over a 24-month period and developed an AI-assisted telecare platform that effectively predicts acute exacerbation events for chronic diseases, achieving an average accuracy of 88.46%.

By integrating real-time monitoring of lifestyle and environmental factors, the platform demonstrated that these factors are strongly linked to health outcomes, outperforming traditional questionnaire-based data in predicting future health issues.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Precision Health Service for Chronic Diseases: Development and Cohort Study Using Wearable Device, Machine Learning, and Deep Learning. Wu, CT., Wang, SM., Su, YE., et al.[2022]

A study involving 10 adult patients with ulcerative colitis showed high acceptance of the home automated telemanagement (UC HAT) system, with 90% of participants willing to use it in the future, indicating its potential for improving patient engagement and adherence to treatment.

Patients found the UC HAT system easy to use and reported that it could enhance monitoring of their symptoms and side effects, suggesting that it may lead to better clinical outcomes and increased patient satisfaction in managing ulcerative colitis.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Home telemanagement for patients with ulcerative colitis (UC HAT). Cross, RK., Cheevers, N., Finkelstein, J.[2021]

The study analyzed 16,453 lines of dialog from 424 patients with inflammatory bowel diseases (IBD) to explore the feasibility of using natural language processing (NLP) for categorizing patient communications, demonstrating that NLP can effectively categorize patient data into relevant categories such as symptoms, medications, and appointments.

The categorization algorithm showed a high agreement with independent physician evaluations, with 95% of cases having minor or no differences, indicating that NLP can reliably assist in developing chatbots for chronic disease management, potentially improving patient monitoring and education.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

An Exploration Into the Use of a Chatbot for Patients With Inflammatory Bowel Diseases: Retrospective Cohort Study. Zand, A., Sharma, A., Stokes, Z., et al.[2020]

In a study involving 200 patients with acute decompensated heart failure, an avatar-based app aimed at improving patient education showed that only 36 high-risk patients were randomized to use it, highlighting challenges in engaging elderly and critically ill patients with technology.

Despite the potential of AI-based education to enhance self-care and quality of life, only 20% of enrolled patients completed the program, indicating significant barriers to effective use and engagement in this demographic.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

An m-Health intervention to improve education, self-management, and outcomes in patients admitted for acute decompensated heart failure: barriers to effective implementation. Zisis, G., Carrington, MJ., Oldenburg, B., et al.[2023]

The 'Nurse Chatbot' aims to enhance patient access to healthcare information and support chronic disease management, potentially addressing the global shortage of healthcare providers by utilizing artificial intelligence.

The development of the 'Nurse Chatbot' is designed to be scalable and customizable, promoting effective communication and patient satisfaction while also considering ethical and safety issues in its implementation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Network Diffusion and Technology Acceptance of A Nurse Chatbot for Chronic Disease Self-Management Support : A Theoretical Perspective. Hernandez, JPT.[2019]

The study successfully co-designed an autonomous virtual agent to support self-management for patients with chronic obstructive pulmonary disease (COPD), focusing on key scenarios like crisis support and emotional well-being, based on input from both patients and health professionals.

Participants found the virtual agent acceptable and engaging, particularly appreciating its potential to assist individuals living alone, highlighting the importance of integrating mental health support into chronic disease management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Virtual Agent to Support Individuals Living With Physical and Mental Comorbidities: Co-Design and Acceptability Testing. Easton, K., Potter, S., Bec, R., et al.[2022]

A review of 26 studies on conversational agents (CAs) in healthcare for chronic conditions shows that users generally find these tools helpful, satisfying, and easy to use, indicating promising acceptance for self-management.

Despite positive user feedback, there is a significant gap in reliable evidence regarding the efficacy of AI-enabled CAs due to insufficient reporting on their technical implementation, which limits the ability to compare their effectiveness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions. Bin Sawad, A., Narayan, B., Alnefaie, A., et al.[2022]

The SENSING-AI project aims to develop an artificial intelligence-driven digital health solution to support the self-management of patients with long COVID-19 and assist healthcare professionals in managing their care, utilizing data from 10 patients for training predictive algorithms.

By collecting psychometric and biometric data through wearable devices and validated questionnaires, the project seeks to improve patient outcomes and provide timely clinical decision support, highlighting the importance of innovative digital health solutions in managing the aftermath of the COVID-19 pandemic.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

An Artificial Intelligence-Driven Digital Health Solution to Support Clinical Management of Patients With Long COVID-19: Protocol for a Prospective Multicenter Observational Study. Fuster-Casanovas, A., Fernandez-Luque, L., Nuñez-Benjumea, FJ., et al.[2022]

The CC-Guardian AI system, developed for managing congenital cataract, shows high sensitivity and specificity in predicting complications and scheduling follow-ups, indicating its effectiveness in real-world applications.

By integrating this AI with a smartphone app and a cloud platform, the system not only detects complications earlier but also reduces socioeconomic burdens compared to traditional monitoring methods.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing. Long, E., Chen, J., Wu, X., et al.[2023]

A study involving 54 participants found that 29% of the actions suggested by conversational assistants for medical problems could potentially lead to patient harm, with 16% of those actions posing a risk of death.

Only 43% of the medical queries were successfully completed, highlighting the unreliability of conversational assistants for providing safe and actionable medical information, emphasizing the need for users to consult healthcare providers for medical advice.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: An Observational Study of Siri, Alexa, and Google Assistant. Bickmore, TW., Trinh, H., Olafsson, S., et al.[2019]

A new AI-driven clinical decision support system (CDSS) was developed using data from 27,904 diabetes patients, which predicts the effectiveness of different treatment strategies to help achieve care goals.

This CDSS was successfully integrated with the Epic electronic health record (EHR) system, demonstrating improved prediction accuracy over previous methods and the potential for application in other chronic conditions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus. Tarumi, S., Takeuchi, W., Chalkidis, G., et al.[2021]

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