# RCT of a Measurement Feedback App to Improve Data Quality, Supervision & Outcomes in Behavioral Health

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $744,402

## Abstract

PROJECT SUMMARY/ABSTRACT
Decades of treatment studies demonstrate that youth with significant behavioral health needs make more
progress when their treatment planning is informed by ongoing quantitative data collection (e.g., by changing
treatment strategies, increasing therapy hours, and adding services), however, aides often do not collect high
quality data consistent with evidence-based practice. Measurement feedback systems (MFS), originally
developed to support data collection and inform treatment decisions in outpatient therapy as part of
measurement based care, may be an ideal starting point from which to improve aides' data collection; however,
MFS have not been applied and tested in this setting. Our application, Footsteps, comprises a low-cost MFS and
implementation strategy to support electronic data collection and target implementation mechanisms – aides'
intentions, attitudes, norms, and self-efficacy – associated with data collection to optimize clinical care. Footsteps
was developed in partnership with community behavioral health agencies, guided by behavioral economics
principles, user-centered design, and a conceptual model that integrates the science of behavior change with
organizational theory. Footsteps integrates digital data collection in a customizable, server-based, native app
with tools for supervisors to review data and provide feedback, and behavioral-economics informed features,
such as gamification, leaderboards, employee of the week emails, targeted reminders, celebratory/encouraging
messages, and in-app data collection tutorials, to increase motivation to collect data. As part of our Penn
ALACRITY Center (P50 MH127511), we conducted a pilot RCT in which we compared Footsteps with a data-
collection-only app in a pilot trial. We found that Footsteps was acceptable to aides and feasible to use, and that
it engaged our target mechanisms of attitudes, norms, self-efficacy, and motivation. We now are ready to test
the app in a fully powered trial. Our formative work also raised three key questions: a) which behavioral strategies
are most effective for increasing data collection; b) does Footsteps alter supervision processes; and c) does
Footsteps ultimately improve youth outcomes? We propose a randomized, hybrid type 2 effectiveness-
implementation pragmatic mixed-methods trial in which we enroll 150 aides and 30 supervisors. Specifically, we
propose to: (Aim 1) examine whether Footsteps improves data collection quality and youth outcomes in an RCT
on Footsteps vs. a “data collection only” application; (Aim 2) explore mediators of data collection quality,
specifically changes in aides' intentions, norms, attitudes, and self-efficacy regarding data collection via biweekly
surveys and interviews; (Aim 3) examine the impact of Footsteps on supervisory processes, team
communication, and changes to child treatment plans via interviews and biweekly surveys with 30 supervisors
and a subsample of 30 aides. The proposed study wo...

## Key facts

- **NIH application ID:** 10857896
- **Project number:** 1R01MH136153-01
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Heather J Nuske
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $744,402
- **Award type:** 1
- **Project period:** 2024-09-16 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10857896

## Citation

> US National Institutes of Health, RePORTER application 10857896, RCT of a Measurement Feedback App to Improve Data Quality, Supervision & Outcomes in Behavioral Health (1R01MH136153-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10857896. Licensed CC0.

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