March 5, 2024
2:00 – 4:30 PM Eastern
In person at CTBH and via Zoom
About the Tutorial: Passive sensing data from wearables provide exciting opportunities to quantify human behavior, e.g., sleep metrics, activity levels, and heart rate, in longitudinal research studies. While several options for wearables exist, there is a wide disparity in the quality of the data and the frequency at which researchers can access the data. Garmin smartwatches and fitness trackers stand out in this regard. They have high signal/data quality, and Garmin provides several options to access wearable data in different granularity, thus enabling researchers to integrate digital phenotyping in their existing and new studies. CTBH has partnered with Garmin to gain access to their Software Development Kits (SDKs) and Application Programming Interfaces (APIs) that enable passive data collection.
In this tutorial/workshop, we will discuss two different ways to use Garmin wearables in research studies and to passively collect digital phenotyping data: First, we will discuss the Garmin Health API, which allows researchers to obtain valuable all-day health metrics such as steps, heart rate, and sleep. Next, we will discuss the Garmin Health Companion SDK, which enables real-time continuous data collection, e.g., accelerometer, beat-to-beat intervals, and respiration rate. The key difference is the resolution of data: while Health API provides aggregated metrics throughout the day, the Companion SDK provides raw, time-series data, thus supporting a variety of study types and research questions. We will also look at some actual Garmin data from recent studies to understand the quality and resolution of the data. Finally, we will hear from colleagues at CTBH about their experiences using Garmin in their studies and potential opportunities, challenges, and limitations.
About the Presenters: Dr. Varun Mishra is an assistant professor at Northeastern University, holding a joint appointment with the Khoury College of Computer Sciences and the Bouvé College of Health Sciences. Dr. Mishra’s research focuses on leveraging ubiquitous technologies like smartphones and wearables to enable effective digital health interventions for mental and behavioral health outcomes. His research is in the broad field of Ubiquitous Computing and lies at the intersection of mobile/wearable sensing, human-centered computing, data science, and behavioral science. Dr. Mishra’s work is highly interdisciplinary, and he regularly collaborates with clinicians, psychologists, engineers, and other computer scientists to design, build, and deploy the tools and systems needed for their collective research goals.
Dr. Nick Jacobson is as an assistant professor in Biomedical Data Science and Psychiatry at Dartmouth’s Geisel School of Medicine, leading the AI and Mental Health: Innovation in Technology Guided Healthcare (AIM HIGH) Laboratory within the Center for Technology and Behavioral Health. His research focuses on leveraging technology to improve the assessment and treatment of anxiety and depression, emphasizing precision assessment through intensive data, multimethod evaluation using passive sensor data from smartphones and wearables, and scalable, personalized treatments. Dr. Jacobson’s assessment and intervention have been used by over 50,000 individuals worldwide. He is the principal investigator on a National Institute of Mental Health R01 project exploring personalized deep learning models for predicting changes in depression symptoms through passive sensor data.