AI-Based CBT Enhancement for Mental Health (AFFECT Trial)
Recruiting in Palo Alto (17 mi)
Overseen ByDavid Atkins, PhD
Age: 18+
Sex: Any
Travel: May be covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: University of Pennsylvania
No Placebo Group
Approved in 1 jurisdiction
Trial Summary
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.
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.
12345Is 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.
12367How 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.
12346Eligibility 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.
Participant Groups
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.
2Treatment groups
Active Control
Group I: LyssnCBTActive Control1 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 Control1 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
Find A Clinic Near You
Research locations nearbySelect from list below to view details:
The Penn Collaborative for CBT and Implementation SciencePhiladelphia, PA
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Who is running the clinical trial?
University of PennsylvaniaLead Sponsor
Lyssn.io, Inc.Industry Sponsor
National Institute of Mental Health (NIMH)Collaborator
References
Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol. [2023]Each year, millions of Americans receive evidence-based psychotherapies (EBPs) like cognitive behavioral therapy (CBT) for the treatment of mental and behavioral health problems. Yet, at present, there is no scalable method for evaluating the quality of psychotherapy services, leaving EBP quality and effectiveness largely unmeasured and unknown. Project AFFECT will develop and evaluate an AI-based software system to automatically estimate CBT fidelity from a recording of a CBT session. Project AFFECT is an NIMH-funded research partnership between the Penn Collaborative for CBT and Implementation Science and Lyssn.io, Inc. ("Lyssn") a start-up developing AI-based technologies that are objective, scalable, and cost efficient, to support training, supervision, and quality assurance of EBPs. Lyssn provides HIPAA-compliant, cloud-based software for secure recording, sharing, and reviewing of therapy sessions, which includes AI-generated metrics for CBT. The proposed tool will build from and be integrated into this core platform.
Design feasibility of an automated, machine-learning based feedback system for motivational interviewing. [2022]Direct observation of psychotherapy and providing performance-based feedback is the gold-standard approach for training psychotherapists. At present, this requires experts and training human coding teams, which is slow, expensive, and labor intensive. Machine learning and speech signal processing technologies provide a way to scale up feedback in psychotherapy. We evaluated an initial proof of concept automated feedback system that generates motivational interviewing quality metrics and provides easy access to other session data (e.g., transcripts). The system automatically provides a report of session-level metrics (e.g., therapist empathy) and therapist behavior codes at the talk-turn level (e.g., reflections). We assessed usability, therapist satisfaction, perceived accuracy, and intentions to adopt. A sample of 21 novice (n = 10) or experienced (n = 11) therapists each completed a 10-min session with a standardized patient. The system received the audio from the session as input and then automatically generated feedback that therapists accessed via a web portal. All participants found the system easy to use and were satisfied with their feedback, 83% found the feedback consistent with their own perceptions of their clinical performance, and 90% reported they were likely to use the feedback in their practice. We discuss the implications of applying new technologies to evaluation of psychotherapy. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Evaluating the Therapeutic Alliance With a Free-Text CBT Conversational Agent (Wysa): A Mixed-Methods Study. [2022]The present study aims to examine whether users perceive a therapeutic alliance with an AI conversational agent (Wysa) and observe changes in the t'herapeutic alliance over a brief time period. A sample of users who screened positively on the PHQ-4 for anxiety or depression symptoms (N = 1,205) of the digital mental health application (app) Wysa were administered the WAI-SR within 5 days of installing the app and gave a second assessment on the same measure after 3 days (N = 226). The anonymised transcripts of user's conversations with Wysa were also examined through content analysis for unprompted elements of bonding between the user and Wysa (N = 950). Within 5 days of initial app use, the mean WAI-SR score was 3.64 (SD 0.81) and the mean bond subscale score was 3.98 (SD 0.94). Three days later, the mean WAI-SR score increased to 3.75 (SD 0.80) and the mean bond subscale score increased to 4.05 (SD 0.91). There was no significant difference in the alliance scores between Assessment 1 and Assessment 2.These mean bond subscale scores were found to be comparable to the scores obtained in recent literature on traditional, outpatient-individual CBT, internet CBT and group CBT. Content analysis of the transcripts of user conversations with the CA (Wysa) also revealed elements of bonding such as gratitude, self-disclosed impact, and personification. The user's therapeutic alliance scores improved over time and were comparable to ratings from previous studies on alliance in human-delivered face-to-face psychotherapy with clinical populations. This study provides critical support for the utilization of digital mental health services, based on the evidence of the establishment of an alliance.
Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review. [2022]Emotional disorders are the most common mental disorders globally. Psychological treatments have been found to be useful for a significant number of cases, but up to 40% of patients do not respond to psychotherapy as expected. Artificial intelligence (AI) methods might enhance psychotherapy by providing therapists and patients with real- or close to real-time recommendations according to the patient's response to treatment. The goal of this investigation is to systematically review the evidence on the use of AI-based methods to enhance outcomes in psychological interventions in real-time or close to real-time. The search included studies indexed in the electronic databases Scopus, Pubmed, Web of Science, and Cochrane Library. The terms used for the electronic search included variations of the words "psychotherapy", "artificial intelligence", and "emotional disorders". From the 85 full texts assessed, only 10 studies met our eligibility criteria. In these, the most frequently used AI technique was conversational AI agents, which are chatbots based on software that can be accessed online with a computer or a smartphone. Overall, the reviewed investigations indicated significant positive consequences of using AI to enhance psychotherapy and reduce clinical symptomatology. Additionally, most studies reported high satisfaction, engagement, and retention rates when implementing AI to enhance psychotherapy in real- or close to real-time. Despite the potential of AI to make interventions more flexible and tailored to patients' needs, more methodologically robust studies are needed.
Improving the efficiency of psychological treatment using outcome feedback technology. [2018]This study evaluated the impact of applying computerized outcome feedback (OF) technology in a stepped care psychological service offering low and high intensity therapies for depression and anxiety.
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]The effect of an unguided internet-based cognitive behavioral therapy (iCBT) stress management program on depression may be enhanced by applying artificial intelligence (AI) technologies to guide participants adopting the program.
Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health. [2020]Introduction: Benefits from mental health early interventions may not be sustained over time, and longer-term intervention programs may be required to maintain early clinical gains. However, due to the high intensity of face-to-face early intervention treatments, this may not be feasible. Adjunctive internet-based interventions specifically designed for youth may provide a cost-effective and engaging alternative to prevent loss of intervention benefits. However, until now online interventions have relied on human moderators to deliver therapeutic content. More sophisticated models responsive to user data are critical to inform tailored online therapy. Thus, integration of user experience with a sophisticated and cutting-edge technology to deliver content is necessary to redefine online interventions in youth mental health. This paper discusses the development of the moderated online social therapy (MOST) web application, which provides an interactive social media-based platform for recovery in mental health. We provide an overview of the system's main features and discus our current work regarding the incorporation of advanced computational and artificial intelligence methods to enhance user engagement and improve the discovery and delivery of therapy content. Methods: Our case study is the ongoing Horyzons site (5-year randomized controlled trial for youth recovering from early psychosis), which is powered by MOST. We outline the motivation underlying the project and the web application's foundational features and interface. We discuss system innovations, including the incorporation of pertinent usage patterns as well as identifying certain limitations of the system. This leads to our current motivations and focus on using computational and artificial intelligence methods to enhance user engagement, and to further improve the system with novel mechanisms for the delivery of therapy content to users. In particular, we cover our usage of natural language analysis and chatbot technologies as strategies to tailor interventions and scale up the system. Conclusions: To date, the innovative MOST system has demonstrated viability in a series of clinical research trials. Given the data-driven opportunities afforded by the software system, observed usage patterns, and the aim to deploy it on a greater scale, an important next step in its evolution is the incorporation of advanced and automated content delivery mechanisms.