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

NCT05135065 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 51

Last updated 2026-04-24

No results posted yet for this study

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

Interventions

OTHER

Artificial Intelligence-Assisted Supervision Protocol

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.

Sponsors & Collaborators

  • Seattle Children's Hospital

    lead OTHER

  • Florida International University

    collaborator OTHER

Principal Investigators

  • Margaret H Sibley, Ph.D · Seattle Children's Hospital

Study Design

Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
11 Years
Max Age
17 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-11-18
Primary Completion
2024-08-30
Completion
2024-12-01

Countries

  • United States

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05135065 on ClinicalTrials.gov