Doctoral Dissertation Research: Leveraging Intensive Time Series of Accelerometer Data to Assess Impulsivity and Inattention in Preschool Children

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This doctoral dissertation research project will develop and examine the validity of a sensor-based approach for assessing children's impulsive and inattentive behaviors. Understanding children's impulsive and inattentive behaviors is important because, even early in life, they predict later academic achievement and outcomes as diverse as employment, mental health, and substance use in adulthood.

However, current methods to assess these important behaviors in real-world contexts are limited because they are subjective, imprecise, and resource intensive. This project will develop an objective, precise method of assessment with low-participant burden and the ability to assess behavior continually throughout the day. The approach developed in this project will be of value to applied researchers from a variety of fields, including education, pediatrics, and developmental and clinical psychology.

Code and de-identified data from the project will be made available to the broader community. The investigators will recruit and mentor undergraduate research assistants from the diverse student body at the University of Texas at Austin.

This research project will develop and examine the validity of using wearable accelerometer devices to assess children's impulsive and inattentive behaviors in their preschool classrooms. Preschool-aged children will wear accelerometer devices while at school for one week. The devices will record data each second, capturing complex patterns of movement as children engage in classroom activities.

Accelerometers are an ideal choice of sensor because children's impulsivity and inattention involve physical movement, such as getting up from one's chair, that will register as movement on the accelerometers. The analyses will use confirmatory and exploratory machine learning techniques to identify patterns in the intensive accelerometer data that index impulsivity or inattention.

Analyses will go beyond the commonly used summaries of movement from accelerometers and instead take advantage of the rich patterns of movement that such summaries discard. The investigators will validate the accelerometer approach against existing measures of children's impulsivity and inattention. These results of this project will facilitate applied research and enhance our understanding of children's behavior in their real-world environments.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.