CrossCheck: Toward passive sensing and detection of mental health changes in people with schizophrenia

Rui Wang; Min S.H. Aung; Saeed Abdullah; Rachel Brian; Andrew T. Campbell; Tanzeem Choudhury; Marta Hauser; John Kane; Michael Merrill; Emily A. Scherer; Vincent W.S. Tseng; Dror Ben-Zeev

doi:10.1145/2971648.2971740

CrossCheck: Toward passive sensing and detection of mental health changes in people with schizophrenia

Rui Wang, Min S.H. Aung, Saeed Abdullah, Rachel Brian, Andrew T. Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Michael Merrill, Emily A. Scherer, Vincent W.S. Tseng, Dror Ben-Zeev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

167 Scopus citations

Abstract

Early detection of mental health changes in individuals with serious mental illness is critical for effective intervention. CrossCheck is the first step towards the passive monitoring of mental health indicators in patients with schizophrenia and paves the way towards relapse prediction and early intervention. In this paper, we present initial results from an ongoing randomized control trial, where passive smartphone sensor data is collected from 21 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-8.5 months. Our results indicate that there are statistically significant associations between automatically tracked behavioral features related to sleep, mobility, conversations, smartphone usage and self-reported indicators of mental health in schizophrenia. Using these features we build inference models capable of accurately predicting aggregated scores of mental health indicators in schizophrenia with a mean error of 7.6% of the score range. Finally, we discuss results on the level of personalization that is needed to account for the known variations within people. We show that by leveraging knowledge from a population with schizophrenia, it is possible to train accurate personalized models that require fewer individual-specific data to quickly adapt to new users.

Original language	English (US)
Title of host publication	UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Publisher	Association for Computing Machinery, Inc
Pages	886-897
Number of pages	12
ISBN (Electronic)	9781450344616
DOIs	https://doi.org/10.1145/2971648.2971740
State	Published - Sep 12 2016
Event	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, Germany Duration: Sep 12 2016 → Sep 16 2016

Publication series

Name	UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Other

Other	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
Country/Territory	Germany
City	Heidelberg
Period	9/12/16 → 9/16/16

All Science Journal Classification (ASJC) codes

Information Systems
Hardware and Architecture
Computer Networks and Communications
Software
Human-Computer Interaction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/2971648.2971740

Cite this

Wang, R., Aung, M. S. H., Abdullah, S., Brian, R., Campbell, A. T., Choudhury, T., Hauser, M., Kane, J., Merrill, M., Scherer, E. A., Tseng, V. W. S., & Ben-Zeev, D. (2016). CrossCheck: Toward passive sensing and detection of mental health changes in people with schizophrenia. In UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 886-897). (UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing). Association for Computing Machinery, Inc. https://doi.org/10.1145/2971648.2971740

Wang, Rui ; Aung, Min S.H. ; Abdullah, Saeed et al. / CrossCheck : Toward passive sensing and detection of mental health changes in people with schizophrenia. UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2016. pp. 886-897 (UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing).

@inproceedings{6a360b29160d4af5abec973304c1563b,

title = "CrossCheck: Toward passive sensing and detection of mental health changes in people with schizophrenia",

abstract = "Early detection of mental health changes in individuals with serious mental illness is critical for effective intervention. CrossCheck is the first step towards the passive monitoring of mental health indicators in patients with schizophrenia and paves the way towards relapse prediction and early intervention. In this paper, we present initial results from an ongoing randomized control trial, where passive smartphone sensor data is collected from 21 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-8.5 months. Our results indicate that there are statistically significant associations between automatically tracked behavioral features related to sleep, mobility, conversations, smartphone usage and self-reported indicators of mental health in schizophrenia. Using these features we build inference models capable of accurately predicting aggregated scores of mental health indicators in schizophrenia with a mean error of 7.6% of the score range. Finally, we discuss results on the level of personalization that is needed to account for the known variations within people. We show that by leveraging knowledge from a population with schizophrenia, it is possible to train accurate personalized models that require fewer individual-specific data to quickly adapt to new users.",

author = "Rui Wang and Aung, {Min S.H.} and Saeed Abdullah and Rachel Brian and Campbell, {Andrew T.} and Tanzeem Choudhury and Marta Hauser and John Kane and Michael Merrill and Scherer, {Emily A.} and Tseng, {Vincent W.S.} and Dror Ben-Zeev",

note = "Funding Information: The research reported in this article is supported the National Institute of Mental Health, grant number R01MH103148. Publisher Copyright: {\textcopyright} 2016 ACM.; 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 ; Conference date: 12-09-2016 Through 16-09-2016",

year = "2016",

month = sep,

day = "12",

doi = "10.1145/2971648.2971740",

language = "English (US)",

series = "UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing",

publisher = "Association for Computing Machinery, Inc",

pages = "886--897",

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Wang, R, Aung, MSH, Abdullah, S, Brian, R, Campbell, AT, Choudhury, T, Hauser, M, Kane, J, Merrill, M, Scherer, EA, Tseng, VWS & Ben-Zeev, D 2016, CrossCheck: Toward passive sensing and detection of mental health changes in people with schizophrenia. in UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Association for Computing Machinery, Inc, pp. 886-897, 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016, Heidelberg, Germany, 9/12/16. https://doi.org/10.1145/2971648.2971740

CrossCheck: Toward passive sensing and detection of mental health changes in people with schizophrenia. / Wang, Rui; Aung, Min S.H.; Abdullah, Saeed et al.
UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2016. p. 886-897 (UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - CrossCheck

T2 - 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016

AU - Wang, Rui

AU - Aung, Min S.H.

AU - Abdullah, Saeed

AU - Brian, Rachel

AU - Campbell, Andrew T.

AU - Choudhury, Tanzeem

AU - Hauser, Marta

AU - Kane, John

AU - Merrill, Michael

AU - Scherer, Emily A.

AU - Tseng, Vincent W.S.

AU - Ben-Zeev, Dror

PY - 2016/9/12

Y1 - 2016/9/12

N2 - Early detection of mental health changes in individuals with serious mental illness is critical for effective intervention. CrossCheck is the first step towards the passive monitoring of mental health indicators in patients with schizophrenia and paves the way towards relapse prediction and early intervention. In this paper, we present initial results from an ongoing randomized control trial, where passive smartphone sensor data is collected from 21 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-8.5 months. Our results indicate that there are statistically significant associations between automatically tracked behavioral features related to sleep, mobility, conversations, smartphone usage and self-reported indicators of mental health in schizophrenia. Using these features we build inference models capable of accurately predicting aggregated scores of mental health indicators in schizophrenia with a mean error of 7.6% of the score range. Finally, we discuss results on the level of personalization that is needed to account for the known variations within people. We show that by leveraging knowledge from a population with schizophrenia, it is possible to train accurate personalized models that require fewer individual-specific data to quickly adapt to new users.

AB - Early detection of mental health changes in individuals with serious mental illness is critical for effective intervention. CrossCheck is the first step towards the passive monitoring of mental health indicators in patients with schizophrenia and paves the way towards relapse prediction and early intervention. In this paper, we present initial results from an ongoing randomized control trial, where passive smartphone sensor data is collected from 21 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-8.5 months. Our results indicate that there are statistically significant associations between automatically tracked behavioral features related to sleep, mobility, conversations, smartphone usage and self-reported indicators of mental health in schizophrenia. Using these features we build inference models capable of accurately predicting aggregated scores of mental health indicators in schizophrenia with a mean error of 7.6% of the score range. Finally, we discuss results on the level of personalization that is needed to account for the known variations within people. We show that by leveraging knowledge from a population with schizophrenia, it is possible to train accurate personalized models that require fewer individual-specific data to quickly adapt to new users.

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DO - 10.1145/2971648.2971740

M3 - Conference contribution

AN - SCOPUS:84991447114

T3 - UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

SP - 886

EP - 897

BT - UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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ER -

Wang R, Aung MSH, Abdullah S, Brian R, Campbell AT, Choudhury T et al. CrossCheck: Toward passive sensing and detection of mental health changes in people with schizophrenia. In UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc. 2016. p. 886-897. (UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing). doi: 10.1145/2971648.2971740

CrossCheck: Toward passive sensing and detection of mental health changes in people with schizophrenia

Abstract

Publication series

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All Science Journal Classification (ASJC) codes

UN SDGs

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