Community-Based Care for Minority Adolescents With ADHD: Improving Fidelity With Machine Learning-Assisted Supervision and Fidelity Feedback.

NCT05135065 | Completed DMC

• 1 other identifier: R34MH125037
• Study Type: interventional
• Target: 51
• Locations: 1 country
• Sites: 1

Brief Summary

This project proposes to reduce disparities in care among disadvantaged racial/ethnic minority adolescents with ADHD by improving community therapist fidelity to evidence-based behavior therapy through a technology-assisted supervision intervention. In Y01, the research team will work with stakeholders to develop the proposed supervision intervention utilizing two novel technologies: Lyssn + Care4 (LC4S). In Y02, a preliminary clinical trial (N=72) will be conducted in three community mental health agencies in Miami, FL. Adolescent participants will be randomly assigned to receive supervision from a therapist who is trained in LCS4 or provides enhanced supervision as usual(ESAU)using a permuted block randomization strategy that randomizes within site. There will also be double randomization of agency therapists to supervisors. Supervisors will deliver both conditions and investigators will test for contamination to determine the integrity of this design prior to a future R01 that measures patient outcomes. Data from therapists, adolescents and their parents, and supervisors will be collected pre-training, post-training, weekly during service delivery, at EBT completion, and at the end of the trial. The proximal intervention target is therapist fidelity to EBT and the distal targets are service delivery outcomes that include quality, quantity, and speed of delivery. Investigators will also measure indices of consumer fit: cost, acceptability, feasibility, and fidelity to supervision procedures. Sources of data will be audio recorded therapy and supervision sessions, therapist and supervisor report, and project and electronic health records. In longitudinal analyses, time will be modeled as a person-specific variable representing months since baseline. Investigators will nest adolescents within therapists for all analyses.

Conditions

Attention Deficit Hyperactivity Disorder

Keywords

ADHD; Adolescents; Community Mental Health

Outcome Measures

Primary Outcomes (2)

• Therapist STAND Fidelity

  Behavior therapy content: STAND treatment fidelity checklists (Sibley et al., 2013, 2016, 2019). This data will be collected via therapist self-report, Lyssn (the machine learning tool), and coded by trained research assistants from audio recordings. If there is a discrepancy in sources, a trained RA will code the tape to resolve the discrepancy.

  Weekly for an average of 9 months

• Therapist MITI Fidelity

  Behavior therapy content:Motivational Interviewing Treatment Integrity (MITI) measure (Moyers et al., 2014) will measure MI integrity.This data will be collected via therapist self-report, Lyssn (the machine learning tool), and coded by trained research assistants from audio recordings. If there is a discrepancy in sources, a trained RA will code the tape to resolve the discrepancy.

  Weekly for an average of 9 months

Secondary Outcomes (2)

• Service Delivery Quality

  Weekly for duration that case is active in the agency, an average of 9 months

• Service Delivery Quantity

  Weekly for duration that case is active in the agency, an average of 9 months

Other Outcomes (7)

• Cost

  cost will be computed at the end of the year from study records and electronic heath records, an average of 9 months

• Technology Acceptability

  At end of the study, after an average of 9 months

• Satisfaction with supervision

  At end of the study, after an average of 9 months

Study Arms (2)

Artificial Intelligence-Assisted Supervision Protocol

EXPERIMENTAL

Measurement-based supervision protocol that incorporates fidelity measurement from a machine learning tool and feedback reports from this tool into a standardized supervision protocol for behavior therapy to task-shift burdensome supervision tasks to a machine, reducing costs and improving precision of fidelity measurement for agencies.

Other: Artificial Intelligence-Assisted Supervision Protocol

Enhanced Supervision as Usual (ESAU) Condition

NO INTERVENTION

ESAU therapists will be given standard, paper-based facilitation resources for STAND and will receive 4 hours of training on how to navigate these materials and self-assess fidelity. ESAU therapists will also be trained how to upload recordings into Care4 and complete self-assessments for each session. Supervisors will be given access to these data and recordings once uploaded (but not Lyssn scores or electronic facilitation resources).

Interventions

Artificial Intelligence-Assisted Supervision Protocol OTHER

Measurement-based supervision protocol that incorporates fidelity measurement from a machine learning tool and feedback reports from this tool into a standardized supervision protocol for behavior therapy to task-shift burdensome supervision tasks to a machine, reducing costs and improving precision of fidelity measurement for agencies.

Artificial Intelligence-Assisted Supervision Protocol

Eligibility Criteria

• Age: 11 Years - 17 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17)

You may qualify if:

• DSM-5 ADHD diagnosis, Enrollment in 6th-12th grade, IQ greater than 70, no history of autism spectrum disorder or thought disorder, client of one of two community mental health agencies

You may not qualify if:

• Autism Spectrum/Thought Disorders, IQ\<70

Sponsors & Collaborators

• Seattle Children's Hospital: lead
• Florida International University: collaborator

Study Sites (1)

Seattle Children's Research Institute

Seattle, Washington, 98145, United States

Location

Related Publications (1)

• Sibley MH, Bickman L, Atkins D, Coxe S, King J, Tanana M, Martin P, Page TF, Ortiz M, Tapia J, Sparber A, Zhao X. Improving Community-Based Care for Adolescents with ADHD: a Randomized Controlled Trial of Artificial Intelligence-Assisted Fidelity Supports. Prev Sci. 2025 Dec 24. doi: 10.1007/s11121-025-01868-x. Online ahead of print.

  PMID: 41436907 DERIVED

MeSH Terms

Conditions

Attention Deficit Disorder with Hyperactivity

Condition Hierarchy (Ancestors)

Attention Deficit and Disruptive Behavior Disorders; Neurodevelopmental Disorders; Mental Disorders

Study Officials

• Margaret H Sibley, Ph.D

  Seattle Children's Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: September 24, 2021
• First Posted: November 26, 2021
• Study Start: November 18, 2021
• Primary Completion: August 30, 2024
• Study Completion: December 1, 2024
• Last Updated: April 24, 2026

Record last verified: 2026-04

Locations

US (1)