Making Progress Monitoring Easier and More Motivating: Developing a Client Data Collection App Incorporating User-Centered Design and Behavioral Economics Insights

Heather J. Nuske, Jacqueline E. Buck, Brinda Ramesh, Emily M. Becker-Haimes, Kelly Zentgraf and David S. Mandell
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Heather J. Nuske: Penn Center for Mental Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Jacqueline E. Buck: Sandra Rosenbaum School of Social Work, University of Wisconsin, Madison, WI 53706, USA
Brinda Ramesh: Penn Center for Mental Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Emily M. Becker-Haimes: Penn Center for Mental Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Kelly Zentgraf: Department of Psychiatry, Penn Medicine Nudge Unit, University of Pennsylvania Health System, Philadelphia, PA 19104, USA
David S. Mandell: Penn Center for Mental Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA

Social Sciences, 2022, vol. 11, issue 3, 1-15

Abstract: Data collection is an important component of evidence-based behavioral interventions for children with autism, but many one-to-one aides (i.e., behavioral support staff) do not systemically collect quantitative data that are necessary for best-practice client progress monitoring. Data collection of clients’ behaviors often involves labor-intensive pen-and-paper practices. In addition, the solitary nature of one-to-one work limits opportunities for timely supervisor feedback, potentially reducing motivation to collect data. We incorporated principles from behavioral economics and user-centered design to develop a phone-based application, Footsteps, to address these challenges. We interviewed nine one-to-one aides working with children with autism and seven supervisors to ask for their app development ideas. We then developed the Footsteps app prototype and tested the prototype with 10 one-to-one aides and supervisors through three testing cycles. At each cycle, one-to-one aides rated app usability. Participants provided 76 discrete suggestions for improvement, including 29 new app features (e.g., behavior timer), 20 feature modifications (e.g., numeric type-in option for behavior frequency), four flow modifications (e.g., deleting a redundant form), and 23 out-of-scope suggestions. Of the participants that tested the app, 90% rated usability as good or excellent. Results support continuing to develop Footsteps and testing its impact in a clinical trial.

Keywords: digital mental health; m-heath applications; behavioral data collection; autism spectrum disorder; behavioral economics; user-centered design; participatory design (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2022
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