CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia
Open accessAbstract
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, smart-phone 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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Audio sensing has been applied to mental health conditions including depression, bipolar, schizophrenia, autism, stress, and mood.
“Depression, as one of the leading mental disorders, has been studied using audio sounds [76]. Other mental disorders have also been investigated using audio sensing, including bipolar [66], schizophrenia [77], autism [78]. Stress and mood which are highly correlated to mental health have also been studied using audio sounds [67,76].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [77]
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References 55 · Cited by 257
Research works printed but not linked: 2
- HOVSEPIAN, K., AL'ABSI, M., ERTIN, E., KAMARCK, T., NAKAJIMA, M., AND KUMAR, S. 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 (New York, NY, USA, 2015), UbiComp '15, ACM, pp. 493-504.
- LU, H., FRAUENDORFER, D., RABBI, M., MAST, M. S., CHITTARANJAN, G. T., CAMPBELL, A. T., GATICA-PEREZ, D., AND CHOUDHURY, T. Stresssense: Detecting stress in unconstrained acoustic environments using smartphones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (2012), ACM, pp. 351-360.
- Authors
- 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.
- Affiliations
- Dartmouth College (US)
- Cornell University
- Cornell University (US)
- Hofstra University (US)
- Dartmouth College
- ORCID
- Venue
- Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
- Publisher
- ACM
- Published
- 2016-09-12
- Pages
- 886-897
- OpenAlex
- W2516086211
- Cited by
- 257
- References
- 55
- Citations by year
- 2026: 18 · 2025: 25 · 2024: 25 · 2023: 22 · 2022: 30 · 2021: 30 · 2020: 35 · 2019: 30 · 2018: 22 · 2017: 19 · 2013: 1
- FWCI
- 55.44
- Type
- conference paper
- Language
- en
- Open access
- green
- ueaeprints.uea.ac.uk
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH103148)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- schizophrenia, mental health monitoring, passive sensing, smartphone sensors, recurrence prediction, early intervention, sleep behavior, mobility (physical function), conversation analysis, smartphone use, personalized models, outpatients
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