TY - GEN
T1 - Social Sensing
T2 - 2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020
AU - Wang, Weichen
AU - Mirjafari, Shayan
AU - Harari, Gabriella
AU - Ben-Zeev, Dror
AU - Brian, Rachel
AU - Choudhury, Tanzeem
AU - Hauser, Marta
AU - Kane, John
AU - Masaba, Kizito
AU - Nepal, Subigya
AU - Sano, Akane
AU - Scherer, Emily
AU - Tseng, Vincent
AU - Wang, Rui
AU - Wen, Hongyi
AU - Wu, Jialing
AU - Campbell, Andrew
N1 - Funding Information:
The research reported in this article is supported by the National Institute of Mental Health, grant number R01MH103148. We are deeply grateful to the guidance offered by the editors that helped shepherd our paper.
Publisher Copyright:
© 2020 ACM.
PY - 2020/4/21
Y1 - 2020/4/21
N2 - 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.
AB - 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.
KW - health
KW - mobile sensing
KW - social functioning
KW - social sensing
UR - http://www.scopus.com/inward/record.url?scp=85088691416&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85088691416&partnerID=8YFLogxK
U2 - 10.1145/3313831.3376855
DO - 10.1145/3313831.3376855
M3 - Conference contribution
AN - SCOPUS:85088691416
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 25 April 2020 through 30 April 2020
ER -