research-article

Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing

Authors:

Min S. H. Aung,

Andrew T. Campbell,

Tanzeem Choudhury,

Emily A. Scherer,

Megan WalshAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 3

Article No.: 110, Pages 1 - 24

https://doi.org/10.1145/3130976

Published: 11 September 2017 Publication History

Abstract

Continuously monitoring schizophrenia patients’ psychiatric symptoms is crucial for in-time intervention and treatment adjustment. The Brief Psychiatric Rating Scale (BPRS) is a survey administered by clinicians to evaluate symptom severity in schizophrenia. The CrossCheck symptom prediction system is capable of tracking schizophrenia symptoms based on BPRS using passive sensing from mobile phones. We present results from an ongoing randomized control trial, where passive sensing data, self-reports, and clinician administered 7-item BPRS surveys are collected from 36 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-12 months. We show that our system can predict a symptom scale score based on a 7-item BPRS within ±1.45 error on average using automatically tracked behavioral features from phones (e.g., mobility, conversation, activity, smartphone usage, the ambient acoustic environment) and user supplied self-reports. Importantly, we show our system is also capable of predicting an individual BPRS score within ±1.59 error purely based on passive sensing from phones without any self-reported information from outpatients. Finally, we discuss how well our predictive system reflects symptoms experienced by patients by reviewing a number of case studies.

References

[1]

Saeed Abdullah, Mark Matthews, Ellen Frank, Gavin Doherty, Geri Gay, and Tanzeem Choudhury. 2016. Automatic detection of social rhythms in bipolar disorder. Journal of the American Medical Informatics Association 23, 3 (2016), 538--543.

[2]

Min Aung, Faisal Alquaddoomi, Cheng-Kang Hsieh, Mashfiqui Rabbi, Longqi Yang, JP Pollak, Deborah Estrin, and Tanzeem Choudhury. 2016. Leveraging Multi-Modal Sensing for Mobile Health: a Case Review in Chronic Pain. IEEE Journal of Selected Topics in Signal Processing 10, 5 (2016), 1--13.

[3]

Gerald Bauer and Paul Lukowicz. 2012. Can smartphones detect stress-related changes in the behaviour of individuals?. In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2012 IEEE International Conference on. IEEE, 423--426.

[4]

D Ben-Zeev, R Brian, R Wang, W Wang, AT Campbell, MS Aung, M Merrill, VW Tseng, T Choudhury, M Hauser, and others. 2017. CrossCheck: Integrating Self-Report, Behavioral Sensing, and Smartphone Use to Identify Digital Indicators of Psychotic Relapse. Psychiatric rehabilitation journal (2017).

[5]

Dror Ben-Zeev, Gregory J McHugo, Haiyi Xie, Katy Dobbins, and Michael A Young. 2012. Comparing retrospective reports to real-time/real-place mobile assessments in individuals with schizophrenia and a nonclinical comparison group. Schizophrenia bulletin 38, 3 (2012), 396--404.

[6]

Dror Ben-Zeev, Rui Wang, Saeed Abdullah, Rachel Brian, Emily A Scherer, Lisa A Mistler, Marta Hauser, John M Kane, Andrew Campbell, and Tanzeem Choudhury. 2015. Mobile behavioral sensing for outpatients and inpatients with schizophrenia. Psychiatric services 67, 5 (2015), 558--561.

[7]

Max Birchwood, Jo Smith, Fiona Macmillan, Bridget Hogg, Rekha Prasad, Cathy Harvey, and Sandy Bering. 1989. Predicting relapse in schizophrenia: the development and implementation of an early signs monitoring system using patients and families as observers, a preliminary investigation. Psychological Medicine 19, 03 (1989), 649--656.

[8]

Andrey Bogomolov, Bruno Lepri, Michela Ferron, Fabio Pianesi, and Alex Sandy Pentland. 2014. Daily stress recognition from mobile phone data, weather conditions and individual traits. In Proceedings of the ACM international conference on multimedia. ACM, 477--486.

Digital Library

[9]

BPRS 2017. Brief Psychiatric Rating Scale (BPRS) Expanded Version (4.0). (2017). http://www.public-health.uiowa.edu/icmha/outreach/documents/bprs_expanded.pdf

[10]

P Burton, L Gurrin, and P Sly. 1998. Extending the simple linear regression model to account for correlated responses: an introduction to generalized estimating equations and multi-level mixed modelling. Statistics in medicine 17, 11 (jun 1998), 1261--91. http://www.ncbi. nlm.nih.gov/ /9670414

[11]

Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 1293--1304.

Digital Library

[12]

Thomas C Chalmers, Harry Smith, Bradley Blackburn, Bernard Silverman, Biruta Schroeder, Dinah Reitman, and Alexander Ambroz. 1981. A method for assessing the quality of a randomized control trial. Controlled clinical trials 2, 1 (1981), 31--49.

[13]

Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. 2002. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research 16 (2002), 321--357.

[14]

Zhenyu Chen, Mu Lin, Fanglin Chen, Nicholas D Lane, Giuseppe Cardone, Rui Wang, Tianxing Li, Yiqiang Chen, Tonmoy Choudhury, and Andrew T Campbell. 2013. Unobtrusive sleep monitoring using smartphones. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2013 7th International Conference on. IEEE, 145--152.

Digital Library

[15]

Philip Chow, Wesley Bonelli, Yu Huang, Karl Fua, Bethany A Teachman, and Laura E Barnes. 2016. DEMONS: an integrated framework for examining associations between physiology and self-reported affect tied to depressive symptoms. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. ACM, 1139--1143.

Digital Library

[16]

Jeremy W Coid, Simone Ullrich, Paul Bebbington, Seena Fazel, and Robert Keers. 2016. Paranoid ideation and violence: meta-analysis of individual subject data of 7 population surveys. Schizophrenia bulletin 42, 4 (2016), 907--915.

[17]

Peter Diggle, Patrick Heagerty, Kung-Yee Liang, and Scott Zeger. 2013. Analysis of longitudinal data. OUP Oxford.

[18]

Lisa Dixon, Gretchen Haas, Peter J Weiden, John Sweeney, and Allen J Frances. 1991. Drug abuse in schizophrenic patients: clinical correlates and reasons for use. Am J Psychiatry 148, 2 (1991), 224--230.

[19]

Enrique Echeburúa, Montserrat Gómez, and Montserrat Freixa. 2017. Prediction of Relapse After Cognitive-Behavioral Treatment of Gambling Disorder in Individuals With Chronic Schizophrenia: A Survival Analysis. Behavior Therapy 48, 1 (2017), 69--75.

[20]

Jane Elith, John R Leathwick, and Trevor Hastie. 2008. A working guide to boosted regression trees. Journal of Animal Ecology 77, 4 (2008), 802--813.

[21]

Asma Ahmad Farhan, Chaoqun Yue, Reynaldo Morillo, Shweta Ware, Jin Lu, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, and Bing Wang. 2016. Behavior vs. Introspection: Refining prediction of clinical depression via smartphone sensing data. In 7th Conference on Wireless Health, WH.

[22]

Jerome H. Friedman. 2001. Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics 29, 5 (2001), 1189--1232. http://www.jstor.org/stable/2699986

[23]

Martin Gjoreski, Hristijan Gjoreski, Mitja Luštrek, and Matjaž Gams. 2016. Continuous Stress Detection Using a Wrist Device: In Laboratory and Real Life. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp ’16). ACM, New York, NY, USA, 1185--1193.

Digital Library

[24]

Google Activity Recognition Api. 2017. Google Activity Recognition Api. https://developers.google.com/android/reference/com/google/android/gms/location/ActivityRecognitionApi. (2017).

[25]

Isabelle Guyon and André Elisseeff. 2003. An introduction to variable and feature selection. Journal of machine learning research 3, Mar (2003), 1157--1182.

Digital Library

[26]

Katrin Hansel, Akram Alomainy, and Hamed Haddadi. 2016. Large Scale Mood and Stress Self-assessments on a Smartwatch. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp ’16). ACM, New York, NY, USA, 1180--1184.

Digital Library

[27]

C Haring, R Banzer, A Gruenerbl, S Oehler, G Bahle, P Lukowicz, and O Mayora. 2015. Utilizing Smartphones as an Effective Way to Support Patients with Bipolar Disorder: Results of the Monarca Study. European Psychiatry 30 (2015), 558.

[28]

Steven A Harvey, Elliot Nelson, John W Haller, and Terrence S Early. 1993. Lateralized attentional abnormality in schizophrenia is correlated with severity of symptoms. Biological Psychiatry 33, 2 (1993), 93--99.

[29]

William B Hawthorne, David P Folsom, David H Sommerfeld, Nicole M Lanouette, Marshall Lewis, Gregory A Aarons, Richard M Conklin, Ellen Solorzano, Laurie A Lindamer, and Dilip V Jeste. 2012. Incarceration among adults who are in the public mental health system: Rates, risk factors, and short-term outcomes. Psychiatric Services 63, 1 (2012), 26--32.

[30]

James Hedlund. 1980. The brief psychiatric rating scale (BPRS): A comprehensive review.

[31]

Kahyee Hor and Mark Taylor. 2010. Review: Suicide and schizophrenia: a systematic review of rates and risk factors. Journal of psychopharmacology 24, 4_suppl (2010), 81--90.

[32]

Karen Hovsepian, Mustafa al’Absi, Emre Ertin, Thomas Kamarck, Motohiro Nakajima, and Santosh Kumar. 2015. cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’15). ACM, New York, NY, USA, 493--504.

Digital Library

[33]

Peter J Huber and others. 1964. Robust estimation of a location parameter. The Annals of Mathematical Statistics 35, 1 (1964), 73--101.

[34]

Maximilian Kerz, Amos Folarin, Nicholas Meyer, Mark Begale, James MacCabe, and Richard J Dobson. 2016. SleepSight: a wearables-based relapse prevention system for schizophrenia. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. ACM, 113--116.

Digital Library

[35]

Ron Kohavi and others. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai, Vol. 14. Stanford, CA, 1137--1145.

Digital Library

[36]

Helen Koo, Ivan Hebrio, Megan Johnston, Nicholas Hosein, and Kris Fallon. 2016. Stresssense: Skin Conductivity Monitoring Garment with a Mobile App. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp ’16). ACM, New York, NY, USA, 487--490.

Digital Library

[37]

Alex Kopelowicz, Joseph Ventura, Robert Paul Liberman, and Jim Mintz. 2007. Consistency of Brief Psychiatric Rating Scale factor structure across a broad spectrum of schizophrenia patients. Psychopathology 41, 2 (2007), 77--84.

[38]

Ai Koyanagi, Andrew Stickley, and Josep Maria Haro. 2015. Psychotic-like experiences and nonsuidical self-injury in England: results from a national survey. PloS one 10, 12 (2015), e0145533.

[39]

Kurt Kroenke and Robert L Spitzer. 2002. The PHQ-9: a new depression diagnostic and severity measure. Psychiatric Annals 32, 9 (2002), 509--515.

[40]

Kurt Kroenke, Robert L Spitzer, and Janet BW Williams. 2001. The Phq-9. Journal of general internal medicine 16, 9 (2001), 606--613.

[41]

Nicholas D Lane, Emiliano Miluzzo, Hong Lu, Daniel Peebles, Tanzeem Choudhury, and Andrew T Campbell. 2010. A survey of mobile phone sensing. Communications Magazine, IEEE 48, 9 (2010), 140--150.

Digital Library

[42]

Nicholas D Lane, Mashfiqui Mohammod, Mu Lin, Xiaochao Yang, Hong Lu, Shahid Ali, Afsaneh Doryab, Ethan Berke, Tanzeem Choudhury, and Andrew Campbell. 2011. Bewell: A smartphone application to monitor, model and promote wellbeing. In 5th international ICST conference on pervasive computing technologies for healthcare. 23--26.

[43]

Kung-Yee Liang and Scott L Zeger. 1986. Longitudinal data analysis using generalized linear models. Biometrika 73, 1 (1986), 13--22.

[44]

Hong Lu, Denise Frauendorfer, Mashfiqui Rabbi, Marianne Schmid Mast, Gokul T Chittaranjan, Andrew T Campbell, Daniel Gatica-Perez, and Tanzeem Choudhury. 2012. Stresssense: Detecting stress in unconstrained acoustic environments using smartphones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 351--360.

Digital Library

[45]

Naoki Maeda, Yuko Hirabe, Yutaka Arakawa, and Keiichi Yasumoto. 2016. COSMS: Unconscious Stress Monitoring System for Office Worker. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp ’16). ACM, New York, NY, USA, 329--332.

Digital Library

[46]

Jörg Sander Martin Ester, Hans-Peter Kriegel and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD ’96. AAAI Press, 226--231.

Digital Library

[47]

Alban Maxhuni, Angelica Muñoz-Meléndez, Venet Osmani, Humberto Perez, Oscar Mayora, and Eduardo F Morales. 2016. Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients. Pervasive and Mobile Computing (2016).

Digital Library

[48]

Abhinav Mehrotra, Robert Hendley, and Mirco Musolesi. 2016. Towards multi-modal anticipatory monitoring of depressive states through the analysis of human-smartphone interaction. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. ACM, 1132--1138.

Digital Library

[49]

Venet Osmani. 2015. Smartphones in mental health: detecting depressive and manic episodes. IEEE Pervasive Computing 14, 3 (2015), 10--13.

Digital Library

[50]

Venet Osmani, Alban Maxhuni, Agnes Grünerbl, Paul Lukowicz, Christian Haring, and Oscar Mayora. 2013. Monitoring activity of patients with bipolar disorder using smart phones. In Proceedings of International Conference on Advances in Mobile Computing 8 Multimedia. ACM, 85.

Digital Library

[51]

John E Overall and Donald R Gorham. 1962. The brief psychiatric rating scale. Psychological reports 10, 3 (1962), 799--812.

[52]

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830.

Digital Library

[53]

Mashfiqui Rabbi, Shahid Ali, Tanzeem Choudhury, and Ethan Berke. 2011. Passive and in-situ assessment of mental and physical well-being using mobile sensors. In Proceedings of the 13th international conference on Ubiquitous computing. ACM, 385--394.

Digital Library

[54]

Matthia Sabatelli, Venet Osmani, Oscar Mayora, Agnes Gruenerbl, and Paul Lukowicz. 2014. Correlation of significant places with self-reported state of bipolar disorder patients. In Wireless Mobile Communication and Healthcare (Mobihealth), 2014 EAI 4th International Conference on. IEEE, 116--119.

[55]

Sohrab Saeb, Emily G Lattie, Stephen M Schueller, Konrad P Kording, and David C Mohr. 2016. The relationship between mobile phone location sensor data and depressive symptom severity. Peer J 4 (2016), e2537.

[56]

Sohrab Saeb, Mi Zhang, Christopher J Karr, Stephen M Schueller, Marya E Corden, Konrad P Kording, and David C Mohr. 2015. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. Journal of medical Internet research 17, 7 (2015).

[57]

Akihide Sano and Rosalind W Picard. 2013. Stress recognition using wearable sensors and mobile phones. In Affective Computing and Intelligent Interaction (ACII), 2013 Humaine Association Conference on. IEEE, 671--676.

Digital Library

[58]

Moushumi Sharmin, Andrew Raij, David Epstien, Inbal Nahum-Shani, J. Gayle Beck, Sudip Vhaduri, Kenzie Preston, and Santosh Kumar. 2015. Visualization of Time-series Sensor Data to Inform the Design of Just-in-time Adaptive Stress Interventions. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’15). ACM, New York, NY, USA, 505--516.

Digital Library

[59]

Robert L Spitzer, Kurt Kroenke, Janet BW Williams, Patient Health Questionnaire Primary Care Study Group, and others. 1999. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Jama 282, 18 (1999), 1737--1744.

[60]

Marie Stentebjerg-Olesen, Stephen J Ganocy, Robert L Findling, Kiki Chang, Melissa P DelBello, John M Kane, Mauricio Tohen, Pia Jeppesen, and Christoph U Correll. 2015. Early response or nonresponse at week 2 and week 3 predict ultimate response or nonresponse in adolescents with schizophrenia treated with olanzapine: results from a 6-week randomized, placebo-controlled trial. European child 8 adolescent psychiatry 24, 12 (2015), 1485--1496.

[61]

Gregory P Strauss, Martin Harrow, Linda S Grossman, and Cherise Rosen. 2010. Periods of recovery in deficit syndrome Schizophrenia: a 20-year multi--follow-up longitudinal study. Schizophrenia bulletin 36, 4 (2010), 788--799.

[62]

Cheryl D Swofford, John W Kasckow, Geri Scheller-Gilkey, and Lawrence B Inderbitzin. 1996. Substance use: a powerful predictor of relapse in schizophrenia. Schizophrenia research 20, 1 (1996), 145--151.

[63]

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, and Dror Ben-Zeev. 2016. CrossCheck: Toward Passive Sensing and Detection of Mental Health Changes in People with Schizophrenia. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’16). ACM, New York, NY, USA, 886--897.

Digital Library

[64]

Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T. Campbell. 2014. StudentLife: Assessing Mental Health, Academic Performance and Behavioral Trends of College Students Using Smartphones. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’14). ACM, New York, NY, USA, 3--14.

Digital Library

[65]

Gary S Wilkinson and GJ Robertson. 2006. Wide range achievement test (WRAT4). Psychological Assessment Resources, Lutz (2006).

[66]

Danny Wyatt, Tanzeem Choudhury, and Jeff A Bilmes. 2007. Conversation detection and speaker segmentation in privacy-sensitive situated speech data. In INTERSPEECH. 586--589.

[67]

Danny Wyatt, Tanzeem Choudhury, Jeff A Bilmes, and Henry A Kautz. 2007. A Privacy-Sensitive Approach to Modeling Multi-Person Conversations. In IJCAI, Vol. 7. 1769--1775.

Digital Library

[68]

Danny Wyatt, Tanzeem Choudhury, and Henry Kautz. 2007. Capturing spontaneous conversation and social dynamics: A privacy-sensitive data collection effort. In Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, Vol. 4. IEEE, IV--213.

[69]

S L Zeger and K Y Liang. 1992. An overview of methods for the analysis of longitudinal data. Statistics in medicine 11, 14-15 (Jan 1992), 1825--39. http://www.ncbi.nlm.nih.gov/ /1480876

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Index Terms

Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing
1. Applied computing
  1. Life and medical sciences
2. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 3

September 2017

2023 pages

EISSN:2474-9567

DOI:10.1145/3139486

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Publication History

Published: 11 September 2017

Accepted: 01 July 2017

Revised: 01 May 2017

Received: 01 February 2017

Published in IMWUT Volume 1, Issue 3

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