1. Information about the paper


Wang, Rui, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T. Campbell. “Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones.” In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 3-14. ACM, 2014.


Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-today and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 week term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.


2. My review of the paper

  1. There are studies that conclude that people can stay healthy with less sleep in quantity, but with good quality sleep.
  2. http://lifehacker.com/how-to-stay-productive-on-a-4-to-6-hour-sleep-cycle-1477407799
  3. http://www.webmd.com/sleep-disorders/features/7-myths-about-sleep
  4. To maximize the data quality of Secondary users, the authors can instead provide iOS version of the app, because they use iPhone as their primary phones.
  5. Figures should be placed on the same page where they are discussed, for better readability. This would not take much effort from the authors.
  6. Because there are many possible privacy leaks, I think T-Shirt is not enough as fair incentives for the participants. Better incentives were drawn randomly to subsets of more active participants. Therefore, some active participants will still get only T-Shirt. This unfairness can cause lower data quality and might lower their willingness to participate in the future. I think a better than T-Shirt incentives which are given fairly (not drawn randomly) might encourage them to fully participate.
  7. This approach will demand high energy cost, because several hardware sensors are activated and used (accelerometer, microphone, light sensor, GPS/Bluetooth). It would be better to use virtual sensor as discussed in previous class, unless hardware sensor is really necessary.