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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.

Self-tracking studies using smartphone sensing apps provide researchers with a great deal of detailed personal behavioral data. These methods promise to provide many insights into the links between everyday behaviors and well-being outcomes (e.g., physical and mental health). However, a better understanding of participants' compliance and experiences with self-tracking using smartphones is needed. Here we report on preliminary findings from a large scale self-tracking assignment that college students (N = 575) completed within the context of an online course. Specifically, we explored (1) compliance rates during the 14 days of the self-tracking assignment, and (2) students' experiences with the self-tracking assignment (e.g., satisfaction, perceived intrusiveness, self-insight). We also explored differences among iOS and Android users and discuss how the findings can be used in future study design.