An increasingly large number and variety of sensors are collecting and transmitting data about many of the smallest actions in our lives. Often, these data are used by companies to try to sell us things. But they are also increasingly being used to help us with tasks in our lives. Sensor data from our phones is collected to help us choose driving routes that avoid traffic. Step-counting sensors try to help us stay active. The aim of these studies is to learn how to use these kinds of sensor data to improve the lives of people with mental health problems. The personal sensing research at the Center for Behavioral Intervention Technologies harnesses sensor data collected from mobile phones and other devices to identify behaviors and states related to mental health. These algorithms can then be used to improve the quality and effectiveness of digital mental health interventions such as apps and bots.
Studies targeted the general population and people with symptoms of depression. As a result, we published a number of papers showing that mobile phone sensor data can be used identify people who may be at greater risk for depression, as well as more specific behaviors such as when people go to bed and get up.
The PIs are David C. Mohr, PhD, and Konrad Kording, PhD.
- National Institute of Mental Health grant R01-MH111610
- Mohr DC, Zhang M, Schueller SM. Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. Annu Rev Clin Psychol. 2017 May 08;13:23-47. PMID: 28375728. doi: 10.1146/annurev-clinpsy-032816-044949.
- Saeb S, Cybulski TR, Schueller SM, Kording KP, Mohr DC. Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles. J Med Internet Res. 2017 Apr 18;19(4):e118. PMID: 28420605. doi: 10.2196/jmir.6821.
- Saeb S, Lattie E, Schueller SM, Kording K, Mohr DC. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ. 2016;4(e2537). doi: 10.7717/peerj.2537.
- Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP, et al. Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study. J Med Internet Res. 2015;17(7):e175. PMID: 26180009. doi: 10.2196/jmir.4273.
- Liu, T., Nicholas, J., Theilig, M.M., Guntuku, S., Kording, K., Mohr, D.C., Unger, L. (2019) Machine learning for phone-based relationship estimation: The need to consider population heterogeneity. Wearable Ubiquitous Technologies, 3, Article 145.
- Nicholas J, Shilton K, Schueller SM, Gray EL, Kwasny MJ, Mohr DC. The Role of Data Type and Recipient in Individuals' Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study. JMIR Mhealth Uhealth. 2019;7(4):e12578.
- Meyerhoff, J. Liu, T. , Kording, K.P., Ungar, L.H., Kaiser, S.M., Karr, C.J., Mohr, D.C. (under review) Evaluation of changes in depression, anxiety, and social anxiety using smartphone sensor features: Longitudinal cohort study. JMIR Mental Health.