The proposed study will develop and evaluate a technology-assisted supervision protocol to promote treatment fidelity and more efficient delivery of evidence-based treatment (EBT) for ADHD in under-resourced community settings. The protocol (LC4S) will be grounded in the principles of measurement-based care. LC4S will task-shift basic supervision tasks to an existing machine learning tool that analyzes and codes therapy recordings for fidelity (Lyssn; lyssn.io; Tanana et al., 2019). These data will be automatically integrated into an established HIPAA-compliant online clinical dashboard that produces performance reports for therapists and supervisors (Care4; www.care4soft.net). Care4 also will be loaded with online facilitation resources for therapists, while supervisors will be trained to lead weekly in-person supervision sessions according to best practices in measurement-based care (Martino et al., 2016; Schoenwald et al., 2009; Webster-Stratton et al., 2014). This trial will be conducted in three community mental health agencies (Psych Solutions, Inc, Behavioral Aid Solutions, Jewish Community Services) in Miami-Dade County, FL that serve low income ethnic/racial minority youth and previously partnered with the research team in R01 MH106587. In Y01 of the project, we will conduct a stakeholder-focused development process for the LC4S intervention that begins with a thorough capacity and needs assessment using the Consolidated Framework for Implementation Research (CFIR; Damschroder et al., 2009) and is guided by the Knowledge to Action (K2A) Implementation Science Framework (Graham et al., 2006). In this phase, we will hold monthly videoconference meetings between development teams at each agency, Care4, and the investigative team to iterate the final Care4 interface with an emphasis user-centered design. In year 1, we also will conduct a small open trial to obtain user feedback (12 cases, 3 supervisors, and 6 therapists) on LC4S and we will make final adaptations to the technology and protocol. In year 2, we will conduct a pilot Hybrid Type 3 Implementation- Effectiveness randomized controlled trial (Curran et al., 2012; N=72 youth; 24 youth, 8 therapists, and 2 supervisors per agency) that will randomly assign both therapists and youth to the LC4S condition or enhanced supervision as usual (ESAU). Both supervision conditions will be delivered by endogenous, agency supervisors who are trained in delivery of the EBT and the core elements of its supervision and have access to monthly phone consultation from experts. The proximal target in this R34 pilot RCT is session by session fidelity scores. The distal service delivery outcomes in this trial are: (1) quality of EBT implementation, (2) quantity of EBT delivered in standard intervention time frame, and (3) speed of delivery (i.e., number of sessions and days to EBT completion) for each case. In support of a future R01 that measures the impact of LC4S on patient outcomes across multiple EBTs, this R34 will also focus on developing and validating appropriate study measures, testing for contamination across conditions, and establishing the costs of the LC4S intervention versus ESAU.
Evidence-based behavior therapy for ADHD demonstrates a decrement in fidelity and effectiveness when moving from university to community settings, largely due to limited resources in poor communities (Sibley, Graziano, Coxe, Bickman, & Martin, 2020). As a result, access to quality mental health care suffers among minority, socio-economically disadvantaged adolescents who commonly seek care in these settings and are at risk for the worst clinical outcomes (Sibley, Coxe, Stein, & Meinzer, 2020). Artificial Intelligence (AI) and web-based technology can be used to automate and task-shift key supervision and training tasks (i.e., fidelity monitoring and feedback, booster trainings) thus improving fidelity and effectiveness while reducing costs associated with burdensome measurement-based supervision protocols (Tanana et al., 2019).
