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Activity Number: 408 - SPAAC Poster Competition
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Scientific and Public Affairs Advisory Committee
Abstract #330935
Title: Predicting Mood Using Multivariate Mobile Sensor Data Streams for Medical Interns
Author(s): Timothy NeCamp* and Zhenke Wu and Srijan Sen and Edward Ionides
Companies: University of Michigan and University of Michigan and University of Michigan and University of Michigan
Keywords: Mobile Health; Intensive Longitudinal Data; Time Series
Abstract:

There is a critical need to understand the temporal dynamics of depression using real-time objective measures. The Intern Health Study (IHS) seeks to identify predictors of depressive symptoms by following a large cohort of medical interns. For the entire medical internship year, the IHS uses a phone app to collect the interns' daily self-reported mood scores (1-10), and uses a smartphone and wristband to collect objective measurements such as minute level activity data, nightly sleep time and duration, heart rate data, and geolocations via a smartphone or wristband.

We introduce a flexible multivariate time series model to analyze multiple sensor data streams collected at different time scales with occasional missingness (due to failure to wear wristbands or carry smartphones). Our model predicts interns' mood and estimates the lagged effects of each data stream by sharing information both across time, to account for smooth time-varying associations, and across similar subjects. We illustrate our methods using data from the 2015-16 IHS cohort recruited at University of Michigan. Lastly, we discuss computational issues and the practical implications of our results.


Authors who are presenting talks have a * after their name.

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