StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones

By Rui Wang, Zhenyu Chen, Tianxing Li, Gabriella M. Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, Andrew T. Campbell. Ubiquitous Computing, 2014

1,192 citations recorded by the source corpus.

Subfields: Applied Psychology, Clinical Psychology, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/4909210fc73cc4a432fbe1fd68b8445beb7f02f2

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In Applied Psychology

In Clinical Psychology

In Experimental and Cognitive Psychology

Citations in the corpus

Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study (2015, background): “Other groups have found that phone sensors were effective at detecting social and sleep behaviors among patients with depression [20,21], and such features correlated significantly with severity of depressive symptoms [22].”
Lab And/Or Field? Measuring Personality Processes and Their Social Consequences (2015, background): “Therefore, we are convinced that classical experience sampling is not only here to stay but should also ideally be a standard component of any mobile sensing study (Wang et al., 2014).”
Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing (2018, methodology): “The StudentLife study [75] investigates the relationship between passive sensing behaviors from mobile phones including conversation, sleep, activity and co-location with mental health outcomes, such as, depression, stress, loneliness, and flourishing for 48 college students over a 10-week term.”
Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis (2015, background): “Several recent projects have investigated the potential use of mobile technologies for monitoring stress, depression and other mental disorders (see, for example, [25, 6, 31, 24, 36, 1, 5, 39], providing new ways for supporting both patients and healthcare officers [8, 20].”
Internet of Things: Architectures, Protocols, and Applications (2017, background): “[30] describe a mobile application that is based completely on a smartphone.”
Academic performance prediction in a gender-imbalanced environment (2017, background): “[21] showed that the academic performance can also be predicted from behavioral data collected using smartphones.”
Monitoring stress with a wrist device using context (2017, methodology): “In our previous work, we used the data from the Wang’s et al. [25] study to build machine-learning models for stress detection based only on the smartphone data.”
Using Smartphones to Collect Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges (2016, neutral): “…infer stress levels from features of a participant’s voice; Lu et al., 2012) to those that collect data from many sensors (e.g., StudentLife, in which accelerometer, Bluetooth, GPS, microphone, and WiFi were used to chart behaviors associated with well-being and performance; Wang et al., 2014).”
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, background): “Such sleep period markers have also been correlated with the severity of depressive symptoms (Wang et al. 2014).”
Systematic review of smartphone-based passive sensing for health and wellbeing (2017, methodology): “In mental health studies, ﬁ ndings included signi ﬁ cant correlations with validation measures and successful prediction models for some or all the studied variables [29,34,44,45,53,56,57,61 – 63].”
Smartphone Sensors for Health Monitoring and Diagnosis (2019, background): “For example, in Reference [114], the authors analyzed the data from the smartphone’s GPS, accelerometer, light sensor and microphone as well as call history, application usage, and SMS patterns of 48 university students.”
LibRA (2019, neutral): “To passively sense participants’ behavior and wellbeingmeasures of participants, the study deployed three modalities of sensing technologies [12, 98] — 1) bluetooth beacons were provided to the participants (two static and two portable Gimbal beacons [4]) to essentially sense their presence at work ”