Abstract: HRV data is a promising way to assess effectiveness in digital health interventions; however, there are challenges in data collection and analysis. We developed a pipeline for collecting and transforming heart rate variability data collected in a trial involving personalized Social Emotional Learning (SEL) tools – specifically, using the emWave software – which coaches patients to regulate their breathing. We present HRV data, measured during a 3-minute breathing sample, and discuss implementation and programming challenges.

Describe the new knowledge and additional skills the participant will gain after attending your presentation.: 1. Participants will see screen shots of a technology-based intervention that we used to teach biofeedback as part of a social and emotional learning intervention in the inpatient psychiatry setting.
2. Participants will gain knowledge about our implementation procedures, including challenges to implementation of a clinical intervention in a hospital setting.
3. Participants will learn the process for taking the data provided by the emWave program and manipulating it to create the variables needed to measure Heart Rate Variability (HRV).
4. We will show the results of our initial sample, demonstrating heart rate variability at baseline of the research subjects who used the biofeedback program, by age and diagnosis.

Authors:

Annie Chen, University of Washington
Marco Ornelas-Mendoza (Presenter)
Seattle Children's Hospital

Carol Rockhill, University of Washington