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Measurement-Based Peer Supervision App for Non-Specialist Providers

D

Dimagi

Status

Enrolling

Conditions

Training of Frontline Health Workers
Training of Mental Health Professionals

Treatments

Device: MBPS App

Study type

Interventional

Funder types

Other
Industry
NIH

Identifiers

NCT07082010
R44MH130305 (U.S. NIH Grant/Contract)
2R44MH130305-02 (U.S. NIH Grant/Contract)

Details and patient eligibility

About

The goal of this project is to enhance and evaluate the efficacy of a scalable digital solution designed to help train and support the work of non-specialist providers (NSPs), such as community health workers, to deliver therapy to individuals with anxiety and/or depression. The main questions this project aims to answer are:

  • Can a digital tool help NSPs feel more confident in counseling patients?
  • How can a digital tool be designed to optimally support the work of NSPs?

Full description

There is a critical shortage of mental health specialists (i.e., psychiatrists, psychologists, licensed mental health counselors) and a growing mental health crisis in the US. Task sharing, whereby highly qualified health workers share specific tasks non-specialist providers (NSPs) with less training and fewer qualifications in order to make more efficient use of the available human resources, is an approach that can scale and address unmet mental health needs, particularly in hard-to-reach communities with limited to no access to specialist care. The investigators propose a comprehensive solution to support a task sharing approach for delivering high quality brief psychosocial interventions. Our proposed digital platform is designed to support measurement-based peer supervision (MBPS), which includes the ability for NSPs to record therapy sessions, rate them using a validated quality scale, and review ratings safely and securely with peers in moderated group discussions. The investigators developed and successfully demonstrated acceptability, usability, and potential feasibility of digitally-enabled MBPS to support NSP performance and quality of care in Phase I. The goal of this Phase II project is to enhance and evaluate the efficacy of this digitally-enabled MBPS tool with NSPs working directly in communities. The investigators will address this goal with three specific aims.

In Aim 1, the investigators will build an enhanced version of the digital platform based on Phase I feedback and direct input from NSPs.

In Aim 2, the investigators will conduct a mixed methods evaluation of digitally-enabled MBPS with 50 target end users, i.e., NSPs recruited from a large health system in Texas and recently trained in behavioral activation. During this period, NSPs will engage in mock sessions with standardized patients. Primary outcomes of interest will be NSP session quality in delivering behavioral activation therapy and counselor self-efficacy.

Results of this Phase II work can provide important insights to guide the implementation and scaling of effective delivery of psychosocial interventions and program quality improvement, with the long-term goal of improving mental health outcomes in hard-to-reach communities that can benefit the most from NSP-led support.

Enrollment

55 estimated patients

Sex

All

Ages

18+ years old

Volunteers

Accepts Healthy Volunteers

Inclusion criteria

  • 18 years of age or older;
  • English-speaking; and
  • Completed EMPOWER Behavioral Activation training

Exclusion criteria

  • Unable to participate in virtual meetings

Trial design

Primary purpose

Health Services Research

Allocation

N/A

Interventional model

Single Group Assignment

Masking

None (Open label)

55 participants in 1 patient group

MBPS App
Experimental group
Description:
Study participants (i.e., non-specialist providers) will use a web application to support measurement-based peer supervision (MBPS) activities (e.g., rating and reviewing therapy sessions) performed with standardized patients in mock scenarios
Treatment:
Device: MBPS App

Trial contacts and locations

1

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Central trial contact

Delta-Marie Lewis, BA; Yun Xian Ho, PhD

Data sourced from clinicaltrials.gov

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