This document discusses using smartphone data to gain psychologically important insights. It summarizes past research that achieved 85% accuracy in predicting bipolar symptoms and 92% accuracy in detecting deception using location and usage data. The document then describes two of the author's own apps, ParkinsonEaston and Getting Log, which analyze location, movement patterns, and usage logs. It notes that recent changes in Android software now limit background access to location data, but discusses ways researchers can still gain insights while respecting users' privacy, such as focusing on places visited rather than movements.

1. Retrieving psychologically
important data from
smartphones
KRISTOFFER GEYER
DR DAVID ELLIS

2. Origin
Cattel (1958)Woodworths (1918)

3. Emergence of assisting technology

4. Smartphone research – utilizing location
data
85% accuracy of predicting bipolar patients symptoms (Palium, et al., 2017).
High accuracy in detecting social anxiety (Huang, et al., 2016).
Experience of sub-clinical depression (Saeb, et al., 2015, 2016).
92% accuracy in detecting deception (Sandham, et al., 2011).
◦ Accuracy of deception expert is 55%

5. Our applications – ParkinsonEaston Getting Log
Fuse location, rigorous,
Movement Patter
Heat map

6. Our applications – Usage log
Logs:
◦ Screen on
◦ Screen off
◦ Phone restarted
◦ User Present
Active use Passive use
Potentially problematic? Harmless?

7. Death of smartphone location
research…by Oreo

8. Oreo rational & Characteristics
All apps want to know the contents of your purse & where you are
Changes:
1. All operations which are running in the background must be declared
2. Location will only be provided per app approximately once an hour

9. Overcoming the Oreo
Working with participants
Working with developers
Focusing on place not movements
Ultimately exciting time for psychologists and developers

10. References
Huang, Y., Xiong, H., Leach, K., Zhang, Y., Chow, P., Fua, K., ... & Barnes, L. E. (2016, September). Assessing social anxiety
using GPS trajectories and point-of-interest data. In Proceedings of the 2016 ACM International Joint Conference on
Pervasive and Ubiquitous Computing (pp. 898-903). ACM.
Palmius, N., Tsanas, A., Saunders, K. E. A., Bilderbeck, A. C., Geddes, J. R., Goodwin, G. M., & De Vos, M. (2017). Detecting
bipolar depression from geographic location data. IEEE Transactions on Biomedical Engineering, 64(8), 1761-1771.
Woodworth, R. S. (1919). Examination of emotional fitness for war. Psychological Bulletin, 15, 59–60
Cattell, R. B. (1958). What is" objective" in" objective personality tests?". Journal of Counseling Psychology, 5(4), 285.
Saeb, S., Lattie, E. G., Schueller, S. M., Kording, K. P., & Mohr, D. C. (2016). The relationship between mobile phone
location sensor data and depressive symptom severity. PeerJ, 4, e2537.
Saeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P., & Mohr, D. C. (2015). Mobile phone sensor
correlates of depressive symptom severity in daily-life behavior: an exploratory study. Journal of medical Internet
research, 17(7).
Sandham, A., Ormerod, T., Dando, C., Bull, R., Jackson, M., & Goulding, J. (2011). Scent trails: Countering terrorism
through informed surveillance. Engineering Psychology and Cognitive Ergonomics, 452-460.