AI-Based CBT Enhancement for Mental Health

(AFFECT Trial)

Overseen By Leah Salama, MS

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Pennsylvania

Disqualifiers: Unwilling to share recordings

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

Approved in 1 Jurisdiction This treatment is already approved in other countries

Compensation: Varies

Number of Visits: 4

1957 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It seems focused on therapy sessions rather than medication use.

What data supports the effectiveness of the treatment LyssnCBT, LyssnCBT, Lyssn AI Platform, AI-Based Fidelity Feedback Tool?

Research shows that AI-based tools can enhance psychotherapy by providing real-time feedback, improving therapist performance, and increasing patient satisfaction and engagement. Studies have found that AI can help therapists deliver more effective cognitive behavioral therapy (CBT) by offering objective feedback and quality assurance, which can lead to better mental health outcomes. ^{1 2 3 4 5}

Is AI-based CBT enhancement safe for humans?

The available research does not specifically address safety concerns for AI-based CBT enhancement tools like LyssnCBT, but these tools are generally used to support therapy by providing feedback and improving therapy quality, which suggests they are safe to use as they do not directly interact with patients in a harmful way. ^{1 2 3 6 7}

How is the treatment LyssnCBT different from other treatments for mental health?

LyssnCBT is unique because it uses artificial intelligence to automatically evaluate the quality of cognitive behavioral therapy (CBT) sessions, providing feedback to therapists to improve their practice. This AI-based approach is scalable, cost-efficient, and helps ensure high-quality therapy by offering real-time recommendations and metrics, which is not typically available in traditional therapy settings. ^{1 2 3 4 6}

What is the purpose of this trial?

This trial tests a digital tool called LyssnCBT, which helps therapists improve their therapy sessions by providing feedback. The study targets therapists and their clients at community mental health agencies. LyssnCBT uses advanced technology to analyze recorded therapy sessions and offer suggestions for improvement. LyssnCBT is an AI-based software system developed to automatically evaluate therapy sessions, supporting high-quality training and supervision.

Research Team

Torrey A Creed, PhD

Principal Investigator

Director, The Penn Collaborative for CBT and Implementation Science

David Atkins, PhD

Principal Investigator

CEO, Lyssn

Eligibility Criteria

This trial is for therapists, their supervisors, and clients at Philadelphia community mental health centers. Participants must be able to conduct therapy in English, agree to use session recordings for research, and have internet access.

Inclusion Criteria

I can participate in therapy sessions in English.

Therapists: Employed at a Philadelphia CMH treatment center that allows the recruitment and participation of therapists in research-related activities

I can participate in therapy sessions in English.

Exclusion Criteria

I am not willing to let researchers access my therapy session recordings.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Pilot Field Trial

A single-arm pilot field trial to identify and address barriers to implementing LyssnCBT in a community mental health context

2 weeks

4 sessions per therapist

Main Trial

A randomized stepped-wedge study comparing LyssnCBT to services as usual across multiple community mental health clinics

18 months

Weekly therapy sessions

Follow-up

Participants are monitored for safety and effectiveness after treatment

4 weeks

Treatment Details

Interventions

• LyssnCBT

Trial Overview The study tests LyssnCBT, a digital tool designed to improve the quality of Cognitive Behavioral Therapy (CBT). It will compare the effectiveness of CBT with LyssnCBT support against usual care without this technology.

Participant Groups

Group I: LyssnCBT Active Control 1 Intervention

Therapists will use the LyssnCBT tool with clients for recording and session-sharing functionalities. Therapists and supervisors will also have access to LyssnCBT features like speech-to-text transcription, annotation tools, and AI-generated metrics.

Group II: SAU (services-as-usual) Active Control 1 Intervention

Therapists will use the LyssnCBT tool with clients for recording and session-sharing functionalities. No other LyssnCBT features will be available for therapist or supervisor review.

LyssnCBT is already approved in United States for the following indications:

Approved in United States as LyssnCBT for:

• Cognitive Behavioral Therapy (CBT) for mental health disorders and addiction

Who Is Running the Clinical Trial?

University of Pennsylvania

Lead Sponsor

Trials

2,118

Recruited

45,270,000+

Lyssn.io, Inc.

Industry Sponsor

Trials

4

Recruited

2,500+

National Institute of Mental Health (NIMH)

Collaborator

Trials

3,007

Recruited

2,852,000+

Findings from Research

Project AFFECT aims to create an AI-based software, LyssnCBT, to automatically assess the quality of cognitive behavioral therapy (CBT) sessions, which could enhance training and supervision for therapists and improve treatment outcomes for clients.

In a planned Phase II trial involving 1,875 clients, the effectiveness of LyssnCBT will be evaluated to see if it can improve therapist skills, client outcomes, and reduce client drop-out rates, marking a significant step towards scalable quality assurance in psychotherapy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol. Creed, TA., Salama, L., Slevin, R., et al.[2023]

An automated feedback system using machine learning and speech processing technologies was developed to provide therapists with real-time quality metrics on their motivational interviewing skills, making the feedback process more efficient and scalable.

In a study with 21 therapists, the majority found the system easy to use and reported that the feedback aligned with their self-assessment, indicating strong potential for integrating this technology into regular psychotherapy training and practice.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Design feasibility of an automated, machine-learning based feedback system for motivational interviewing. Imel, ZE., Pace, BT., Soma, CS., et al.[2022]

In a study involving 1,205 users of the Wysa app for mental health, users reported a strong therapeutic alliance with the AI conversational agent, with scores comparable to traditional therapy methods like CBT.

Over a brief period of 3 days, users' perceptions of their bond with Wysa improved, indicating that digital mental health services can effectively foster a therapeutic relationship similar to that found in face-to-face therapy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluating the Therapeutic Alliance With a Free-Text CBT Conversational Agent (Wysa): A Mixed-Methods Study. Beatty, C., Malik, T., Meheli, S., et al.[2022]

A systematic review of 10 studies found that using artificial intelligence (AI), particularly conversational AI agents like chatbots, can significantly enhance psychotherapy outcomes and reduce symptoms in patients with emotional disorders.

Patients reported high satisfaction and engagement when AI was integrated into therapy, suggesting that AI can make psychological interventions more personalized and effective, although further robust research is needed.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review. Gual-Montolio, P., Jaén, I., Martínez-Borba, V., et al.[2022]

The study involving 594 patients showed that implementing computerized outcome feedback (OF) technology in therapy for depression and anxiety did not significantly change clinical outcomes, as measured by PHQ-9 and GAD-7 scores, compared to traditional methods.

However, the use of OF led to a significant reduction in the average duration and cost of treatment, suggesting that OF can enhance the efficiency of stepped care psychological services.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Improving the efficiency of psychological treatment using outcome feedback technology. Delgadillo, J., Overend, K., Lucock, M., et al.[2018]

This study will evaluate the effectiveness of an innovative internet-based cognitive behavioral therapy (iCBT) program enhanced by artificial intelligence (AI) technologies to improve depression among 1400 healthy workers during the COVID-19 pandemic.

The program, called SMART-CBT, consists of a 6-week course with machine-guided exercises and aims to provide a cost-effective mental health solution that can be widely disseminated, marking the first randomized controlled trial of its kind.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Effectiveness of an Internet-Based Machine-Guided Stress Management Program Based on Cognitive Behavioral Therapy for Improving Depression Among Workers: Protocol for a Randomized Controlled Trial. Kawakami, N., Imamura, K., Watanabe, K., et al.[2021]

The moderated online social therapy (MOST) web application is being developed as a cost-effective and engaging alternative for youth mental health interventions, particularly for those recovering from early psychosis, as part of a 5-year randomized controlled trial.

MOST aims to enhance user engagement and therapy content delivery through advanced computational and artificial intelligence methods, including natural language analysis and chatbot technologies, demonstrating its potential viability in clinical research settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health. D'Alfonso, S., Santesteban-Echarri, O., Rice, S., et al.[2020]

References

1. England pubmed.ncbi.nlm.nih.gov

Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol. [2023]

2. United States pubmed.ncbi.nlm.nih.gov

Design feasibility of an automated, machine-learning based feedback system for motivational interviewing. [2022]

3. Switzerland pubmed.ncbi.nlm.nih.gov

Evaluating the Therapeutic Alliance With a Free-Text CBT Conversational Agent (Wysa): A Mixed-Methods Study. [2022]

4. Switzerland pubmed.ncbi.nlm.nih.gov

Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review. [2022]

5. England pubmed.ncbi.nlm.nih.gov

Improving the efficiency of psychological treatment using outcome feedback technology. [2018]

6. Canada pubmed.ncbi.nlm.nih.gov

Effectiveness of an Internet-Based Machine-Guided Stress Management Program Based on Cognitive Behavioral Therapy for Improving Depression Among Workers: Protocol for a Randomized Controlled Trial. [2021]

7. Switzerland pubmed.ncbi.nlm.nih.gov

Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health. [2020]