AI and Peer Coaching for Opioid Use Disorder

Recruiting at 1 trial location

Overseen By Babak Tofighi, MSc, MD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Friends Research Institute, Inc.

Must be taking: Buprenorphine

Must not be taking: OUD medications

Disqualifiers: Disability, Medications for OUD, others

Compensation: Varies

Number of Visits: Unknown

292 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a new support method for Black and Latinx individuals using opioids. It examines whether combining an AI-driven texting tool with peer coaching (a support system involving individuals with similar experiences) can facilitate easier access to buprenorphine treatment. Participants will be divided into three groups to compare different approaches: one with AI texts and peer support, one with AI texts only, and a control group receiving typical treatment information. The trial is ideal for Black and Latinx individuals who have used non-prescription opioids in the last month, are interested in starting buprenorphine treatment, and have a mobile phone with a data plan. As an unphased trial, this study provides a unique opportunity to explore innovative support methods that could enhance treatment accessibility.

Will I have to stop taking my current medications?

The trial does not specify if you need to stop taking your current medications, but you cannot participate if you've received medications for opioid use disorder in the past 30 days.

What prior data suggests that this AI and peer coaching method is safe for opioid use disorder treatment?

Research shows that artificial intelligence (AI) and peer coaching can help treat opioid use disorder. AI can predict and manage this condition, potentially improving patient outcomes. Studies have found that digital peer recovery coaches effectively help people access care after leaving the hospital.

These methods appear safe. AI-driven text messages and peer coaching are generally well-received, with no major negative effects reported. This trial focuses on the effectiveness of these treatments, not basic safety testing, suggesting their likely safety based on previous experiences.

In summary, using AI and peer coaching for opioid use disorder seems safe according to current data. The combination of human support and technology aims to manage treatment without significant safety concerns reported in past studies. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about these treatments because they integrate cutting-edge technology with personal support to tackle opioid use disorder (OUD) in a fresh way. Unlike traditional methods that rely mainly on medication and counseling, these treatments use AI-driven text messages tailored to social determinants of health. This personalized text-based approach aims to enhance access to buprenorphine specifically for Black and Latin individuals who use opioids. Additionally, one treatment arm includes peer recovery coaches, providing a unique blend of human support and AI assistance. This combination could offer a more accessible and culturally sensitive support system, potentially improving treatment outcomes for underserved communities.

What evidence suggests that this trial's treatments could be effective for opioid use disorder?

Research has shown that artificial intelligence (AI) can predict opioid addiction and help manage treatment by analyzing healthcare data, potentially leading to better patient outcomes. In this trial, one group of participants will receive a combination of AI-driven text messages and support from peer recovery coaches (PRCs). Studies suggest this combination may help people access treatments like buprenorphine. PRCs are trained to reduce stigma and guide patients through social services, which is especially helpful for Black and Latinx communities. Another group will receive AI-driven text messages without PRC support. Combining AI and PRCs might improve health and reduce opioid overdoses by addressing the unique social challenges these groups face. ^{1 3 6 7 8}

Who Is on the Research Team?

Babak Tofighi, MSc, MD

Principal Investigator

Friends Research Institute, Inc.

Are You a Good Fit for This Trial?

This trial is for Black and Latinx individuals over 18 years old who use opioids, speak English or Spanish, want to start buprenorphine treatment in primary care, have a positive opioid test result, and plan to stay in NYC for at least a year. They must be interested in using their mobile phone with data for the study.

Inclusion Criteria

I haven't used opioids without a prescription in the last 30 days.

I have given my informed consent.

I am interested in starting buprenorphine treatment in a primary care setting.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Participants receive AI-driven SDH-enhanced text messages, with or without Peer Recovery Coach support, to enhance the receipt of buprenorphine

26 weeks

Ongoing virtual interactions

Follow-up

Participants are monitored for the durability of treatment effect and social services received

26 weeks

What Are the Treatments Tested in This Trial?

Interventions

• AI and Peer Coaching

Trial Overview The study tests an AI-driven texting tool combined with peer recovery coach support against AI-only texts and usual treatments. It aims to improve access to buprenorphine treatment among emergency department-enrolled Black/Latinx opioid users by addressing social health factors.

How Is the Trial Designed?

Group I: Intervention Arm-2: AI driven SDH-enhanced text only Experimental Treatment 1 Intervention

Participants in this arm will receive AI-driven SDH-enhanced text messages to enhance the receipt of buprenorphine in Black/ Latin people who use opioids. Unlike Arm-1, this intervention does not include the additional support and coordination provided by Peer Recovery Coaches.

Group II: Intervention Arm-1: PRC supported text+ AI driven SDH-enhanced text Experimental Treatment 1 Intervention

Participants in this arm will receive a combination of Peer Recovery Coaches (PRCs) supported text-based care/services coordination alongside AI-driven SDH-enhanced text messages to enhance the receipt of buprenorphine in Black/ Latin people who use opioids.

Group III: Control Arm 3- Treatment as Usual Placebo Group 1 Intervention

Control Arm-3 will receive treatment as usual (i.e., verbal instructions, NYC Dept of Health pamphlets detailing access to OUD and social services, health system smartphone application EMR patient portal).

Who Is Running the Clinical Trial?

Friends Research Institute, Inc.

Lead Sponsor

Trials

60

Recruited

22,500+

NYU Langone Health

Collaborator

Trials

1,431

Recruited

838,000+

Nathan Kline Institute for Psychiatric Research

Collaborator

Trials

40

Recruited

3,300+

Weill Medical College of Cornell University

Collaborator

Trials

1,103

Recruited

1,157,000+

Published Research Related to This Trial

Computer-assisted learning programs for addiction treatment can be highly adaptive, providing personalized feedback to users, which may enhance their effectiveness in promoting new learning behaviors.

Two specific programs targeting heroin relapse prevention in abstinent prisoners and promoting controlled drinking in alcohol abusers represent innovative approaches to substance abuse treatment, potentially reaching individuals who prefer self-help resources over formal therapy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer-assisted drug prevention. Barber, JG.[2019]

In a study of 170 adults with opioid use disorder, those receiving a digital therapeutic alongside standard treatment had significantly higher rates of opioid abstinence (77.3% vs. 62.1%) during the final weeks of treatment.

The digital therapeutic also improved treatment retention, with participants less likely to leave treatment compared to those receiving standard care alone, while showing no increase in adverse events, indicating it is a safe and effective addition to traditional therapies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Safety and efficacy of a prescription digital therapeutic as an adjunct to buprenorphine for treatment of opioid use disorder. Maricich, YA., Bickel, WK., Marsch, LA., et al.[2022]

An AI-based digital phenotype using social media language was found to be more effective than traditional psychometric assessments in predicting treatment outcomes for substance use disorder, based on a study of 269 patients.

The deep learning model (BERT) successfully identified high-risk individuals for treatment dropout, achieving a high accuracy (AUC = 0.81), suggesting that social media analysis could be a valuable tool for assessing risk in substance use treatment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

AI-based analysis of social media language predicts addiction treatment dropout at 90 days. Curtis, B., Giorgi, S., Ungar, L., et al.[2023]

A review of gray literature identified 29 unique AI interventions aimed at addressing opioid use disorder (OUD), including smartphone apps and healthcare data-related tools, highlighting the growing interest in using technology to improve OUD care.

While many of these interventions have not been rigorously tested in clinical trials, some have shown potential in identifying patterns of opioid use and detecting overdose risks, suggesting they could play a significant role in managing OUD in the future.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial intelligence interventions focused on opioid use disorders: A review of the gray literature. Beaulieu, T., Knight, R., Nolan, S., et al.[2021]

A study involving 189 outpatients with opioid-use disorder used a randomForest algorithm to predict cravings for heroin and cocaine, achieving high overall accuracy (up to 0.93) in predicting when cravings would not occur, but lower accuracy in predicting actual cravings.

The findings suggest that while passive monitoring through GPS data can provide some predictive insights, accurately predicting subtle events like cravings or stress may require active user input, as high overall accuracy can sometimes hide the prevalence of false alarms.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data. Epstein, DH., Tyburski, M., Kowalczyk, WJ., et al.[2023]

An automated risk-modeling framework was developed using data from 40,005 appointments of 2,742 patients in a medication for opioid use disorder (MOUD) program to predict opioid abstinence and medication adherence at the next appointment.

The XGBoost machine-learning model demonstrated similar accuracy (88%) to traditional logistic regression models but provided better calibration of risk estimates, highlighting its potential as a valuable tool for improving treatment adherence in MOUD patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using Machine Learning to Predict Treatment Adherence in Patients on Medication for Opioid Use Disorder. Burgess-Hull, AJ., Brooks, C., Epstein, DH., et al.[2023]

A study involving data from 474,208 patients over 10 years demonstrated that an AI-based model can predict opioid use disorder (OUD) more effectively than traditional clinical tools, achieving an area under the curve (AUC) of 0.742.

The AI model outperformed various other predictive models, including logistic regression and random forest, suggesting that integrating AI into clinical practice could enhance risk assessment and management for patients on opioid therapy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models. Fouladvand, S., Talbert, J., Dwoskin, LP., et al.[2023]

Citations

1. clinicaltrials.gov clinicaltrials.gov/study/NCT06569667

Using AI and Peer Coaching to Address Racial Disparities ... This interventional study evaluates an AI-driven texting tool combined with peer recovery coach support to deliver social services, reduce stigma, and provide ...

2. withpower.com withpower.com/trial/phase-opioid-use-disorder-oud-7-2024-5fdf8

AI and Peer Coaching for Opioid Use Disorder Research shows that AI can effectively predict opioid use disorder and help manage treatment by analyzing healthcare data, which may improve patient outcomes.

3. reporter.nih.gov reporter.nih.gov/search/XOaaP7l5p0G9-meVXLqprQ/project-details/10800295

Project Details This proposal aims to adapt the evidence-supported CSH training to interactive video modules for peer recovery coaches (PRCs), and refine an artificial ...

4. clinicaltrials.gov clinicaltrials.gov/study/NCT06573476

Addressing Racial Disparities in Opioid Overdose Using ... The study aims to adapt an existing Cultural Structural Humility (CSH) training into a video format for peer recovery coaches (PRCs) and refine an AI-driven ...

5. cdn.clinicaltrials.gov cdn.clinicaltrials.gov/large-docs/67/NCT06569667/Prot_SAP_000.pdf

Leveraging social determinants via artificial intelligence ... SDH-focused cultural and structural humility (CSH) training for peer recovery coaches. (PRCs) is uniquely positioned to address racial disparities among ...

6. centerwatch.com centerwatch.com/clinical-trials/listings/NCT06569667/using-ai-and-peer-coaching-to-address-racial-disparities-among-people-who-use-opioids

Using AI and Peer Coaching to Address Racial Disparities ... The proposed study assesses the efficacy of an open source, multimodal artificial intelligence-driven texting tool combined with peer recovery ...

7. reporter.nih.gov reporter.nih.gov/project-details/11126614

Project Details - NIH RePORTER Among people who use opioids (PWUO) some communities bear a disproportionate burden of opioid-related overdose deaths. Health outcomes among PWUO may be ...

8. formative.jmir.org formative.jmir.org/2023/1/e43304

Digitally Assisted Peer Recovery Coach to Facilitate Linkage ... The results demonstrated that a digitally assisted peer recovery coach may be feasible in facilitating linkage to care following discharge from inpatient ...