Wang, W., Mirjafari, S., Harari, G. M., Ben-Zeev, D., Brain, R., Choudhury, T., Hauser, M., Kane, J., Masaba, K., Nepal, S., Sano, A., Scherer, E., Tseng, V., Wang, R., Wen, H., Wu, J., & Campbell, A. (2020) Social Sensing: Assessing Social Functioning of Patients Living with Schizophrenia using Mobile Phone Sensing. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI’20), Honolulu.
Impaired social functioning is a symptom of mental illness (e.g., depression, schizophrenia) and a wide range of other conditions (e.g., cognitive decline in the elderly, dementia). Today, assessing social functioning relies on subjective evaluations and self assessments. We propose a different approach and collect detailed social functioning measures and objective mobile sensing data from N=55 outpatients living with schizophrenia to study new methods of passively accessing social functioning. We identify a number of behavioral patterns from sensing data, and discuss important correlations between social function sub-scales and mobile sensing features. We show we can accurately predict the social functioning of outpatients in our study including the following sub-scales: prosocial activities (MAE = 7.79, r = 0.53), which indicates engagement in common social activities; interpersonal behavior (MAE = 3.39, r = 0.57), which represents the number of friends and quality of communications; and employment/occupation (MAE = 2.17, r = 0.62), which relates to engagement in productive employment or a structured program of daily activity. Our work on automatically inferring social functioning opens the way to new forms of assessment and intervention across a number of areas including mental health and aging in place.