The UWEXP Study has been publishing research on a range of topics. Here are some publications to date
The population health project describes UWEXP’s focus on translation in a recent blog post, as one of six faculty-led, interdisciplinary research teams committed to addressing critical population health challenges.Project seeks to better tailor responses to student mental health at the UW
Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. Doryab, Afsaneh, et al. “Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning … Continue reading Detecting Loneliness
The rate of depression in college students is rising, which is known to increase suicide risk, lower academic performance and double the likelihood of dropping out. Researchers have used passive mobile sensing technology to assess mental health. Existing work on finding relationships between mobile sensing and depression, as well as identifying depression via sensing features, … Continue reading Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students
See the UW News article featuring this study! A deeper understanding of how discrimination impacts psychological health and well-being of students would allow us to better protect individuals at risk and support those who encounter discrimination. While the link between discrimination and diminished psychological and physical well-being is well established, existing research largely focuses on … Continue reading Passively-sensed Behavioral Correlates of Discrimination Events in College Students
Chikersal, P., Doryab, A., Tumminia, M., Villalba, D.K., Dutcher, J.M., Liu, X., Cohen, S., Creswell, K.G., Mankoff, J., Creswell, J.D., Goel, M., & Dey, A.K. (2021) Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection. ACM Transactions on Computer-Human Interaction (TOCHI), 28, 1, Article 3 (January 2021).
Kuehn, K.S., Sefidgar, Y.S., Nurius, P., Browning, A., Riskin, E., Dey, A., & Mankoff, J. (October, 2019). Using Passive Data Monitoring and Machine Learning Algorithms to Examine Negative Affect and Coping Behaviors Among College Students Experiencing Suicidal Ideation. Paper presented at the 2019 IASR/AFSP International Summit on Suicide Research, Miami, FL.
A Tech-Forward Approach
UW EXP uses data from surveys, phones, Fitbits, and more to capture a comprehensive understanding of the UW student experience.
The UWEXP study is an expensive study to run. Our funders have been crucial to our success. Currently, the study is funded by internally by the College of Engineering (including the Department of Electrical and Computer Engineering and the Allen School of Computer Science and Engineering), Population Health Initiative, and Alcohol and Drug Abuse Institute. In addition, we have funding from the National Science Foundation; Samsung Advanced Institute of Technology; and Google.
Overview of Findings
UW EXP is analyzing data from 2018 to understand who reports discrimination and how micro-climates in the College of Engineering may correlate with lower stress and depression.