Evidence map›Paper›PMID 41436907›Full record

Trial reportPrevention science : the official journal of the Society for Prevention Research2026

Improving Community-Based Care for Adolescents with ADHD: a Randomized Controlled Trial of Artificial Intelligence-Assisted Fidelity Supports.

Margaret H Sibley, Leonard Bickman, David Atkins, Stefany Coxe, Julian King, Michael Tanana, Pablo Martin, Timothy F Page, Mercedes Ortiz, Joshua Tapia and 2 more

Registry-linked trialAbstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in Prevention science : the official journal of the Society for Prevention Research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05135065 (Community-Based Care for Minority Adolescents With ADHD), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05135065 nacompletednot on this map

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

TypeinterventionalSponsorSeattle Children's HospitalRan2021 to 2024Enrolled51ConditionsAttention Deficit Hyperactivity DisorderArmsArtificial Intelligence-Assisted Supervision Protocol
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Margaret H SibleyDepartment of Psychiatry & Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA. margaret.sibley@seattlechildrens.org.
Leonard BickmanDepartment of Psychology and Human Development, Vanderbilt University, Nashville, TN, USA.
David Atkins, Lyssn, Seattle, WA, USA.
Stefany CoxeBiostatistics Shared Resource, Cedars-Sinai Cancer, Department of Computational Biomedicine, Cedars-Sinai, Los Angeles, CA, USA.
Julian KingCare4Software, Inc, Edmonds, WA, USA.
Michael Tanana, Lyssn, Seattle, WA, USA.
Pablo MartinDepartment of Psychology, Florida International University, Miami, FL, USA.
Timothy F PageDepartment of Management and Entrepreneurship, Nova Southeastern University, Fort Lauderdale, FL, USA.
Mercedes OrtizSeattle Children's Research Institute, Seattle, WA, USA.
Joshua TapiaDepartment of Psychiatry & Behavioral Health, Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA.
Ashley SparberDepartment of Psychiatry & Behavioral Health, Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA.
Xin ZhaoDepartment of Psychiatry & Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA.

Funding

Community-Based Care for Minority Adolescents with ADHD: Improving Fidelity with Machine Learning-Assisted Supervision and Fidelity FeedbackR34MH125037 · NIMH · SEATTLE CHILDREN'S HOSPITAL · PI SIBLEY, MARGARET HARPER · 2020 to 2021
$692k
NIMH NIH HHS R34 MH125037
6 · The paper itself

Abstract

Cognitive-behavioral treatments (CBTs) for adolescents with ADHD demonstrate promise of long-term effects on outcome. However, their implementation in routine care community clinics faces barriers that impact quantity, efficiency, and quality of delivery, as well as client outcomes. This study is a randomized controlled trial designed to evaluate the impact of an AI-assisted service delivery model on therapist implementation of Supporting Teens' Autonomy Daily (STAND), a CBT blended with Motivational Interviewing (MI) for adolescents with ADHD. Adolescents with ADHD (N = 51), who were clients at three community mental health agencies, received treatment from 23 therapists. There was randomization of adolescents and therapists to AI-assisted or standard implementation supports. In addition to standard supports (i.e., training, standard facilitation resources, technical assistance, case supervision), AI-assisted support package included digitized facilitation resources housed in a clinical dashboard (Care4), feedback on content fidelity, and AI-generated feedback on MI implementation quality. The AI-assisted group was associated with more efficient treatment delivery and lower number of appointments attended by the adolescent. There was also a significant decrement in MI quality over time in the AI-assisted group compared to the standard support group. Feedback in focus groups indicated that therapists perceived a task-oriented mindset to be associated with receipt of the AI-assisted support package, leading therapists to prioritize efficiency over relational aspects of therapy. Following the results of this trial, a future, larger RCT should examine the impact of the AI-assisted implementation model on mental health outcomes and cost savings to organizations, third party payers, and clients. Trial registration number: NCT05135065; https://www.clinicaltrials.gov ; Registered September 2021.

Indexed as

Artificial IntelligenceAttention Deficit Disorder with HyperactivityCognitive Behavioral TherapyAdolescentFemaleHumansMaleMotivational InterviewingADHDArtificial intelligenceCommunity-based researchMotivational interviewing

Identifiers

PMID41436907

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.