UT1000: Exploring New Methods for Understanding College Life

In order to gain a greater understanding of how several factors that can affect health interact with each other, researchers must use a combination of measurement methods in tandem. In order to study the effectiveness of using a blend of sensing technology like smartphone apps and wearable technology, self-reported surveys, and readily available group-level information (grades, attendance), our team developed a study to understand more about the social lives, sleep habits, physical activity levels, and academic performance of 1000 UT undergraduate students in the 2019-2020 school year.  How does sleep quality affect physical activity? Or vice versa? Do people report greater feelings of loneliness when they are alone or in a crowded room? Do grades suffer when meals are skipped? While the broader Whole Communities–Whole Health study will focus on young families instead of young adults, the process of data collection with the students allows the team to learn how best to assist future participants with technology set-up and overcome new challenges related to handling large amounts of real-time data. In addition, the team can learn new methods for collecting relevant information, analyzing it, and returning it to participants in a useful way. 

Progress and Results: The process and data collection from this study laid the foundation for our future work in a number of important ways. First, collecting multimodal data from a large number of individuals requires advances in app development, secure data transfer, data wrangling and quality assessment and generation of summary statistics/results that will feed into a dashboard. The technology developed and tested during the initial UT1000 project laid the groundwork for the team to respond quickly to the coronavirus pandemic. Follow up from this early work will help us better understand how college students are responding to the crisis. 

Team Members


Edison Thomaz
Electrical and Computer Engineering
Cameron Craddock
Diagnostic Medicine
Sam Gosling
Psychology

Select Publications


Wu, C., Barczyk, A. N., Craddock, R. C., Harari, G. M., Thomaz, E., Shumake, J. D., Beevers, C. G., Gosling, S. D., & Schnyer, D. M. (2020). Improving Prediction of Real-Time Loneliness and Companionship Type Using Geosocial Features of Personal Smartphone Data. Smart Health, 11. 

Wu, C., Fritz, H., Nagy, Z., Maestre, J. P., Thomaz, E., Julien, C., Castelli, D. M., de, K., Harari, G. M., Craddock, R. C., Gosling, S. D., & Schnyer, D. M. (under review). Multi-Modal Data Collection for Measuring Health, Behavior, and Living Environment of Large-Scale Participant Cohorts: Conceptual Framework and Findings from Deployments. GigaScience.