The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Social sensing for epidemiological behavior change
Open accessAbstract
An important question in behavioral epidemiology and public health is to understand how individual behavior is affected by illness and stress. Although changes in individual behavior are intertwined with contagion, epidemiologists today do not have sensing or modeling tools to quantitatively measure its effects in real-world conditions. In this paper, we propose a novel application of ubiquitous computing. We use mobile phone based co-location and communication sensing to measure characteristic behavior changes in symptomatic individuals, reflected in their total communication, interactions with respect to time of day (e.g., late night, early morning), diversity and entropy of face-to-face interactions and movement. Using these extracted mobile features, it is possible to predict the health status of an individual, without having actual health measurements from the subject. Finally, we estimate the temporal information flux and implied causality between physical symptoms, behavior and mental health.
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“Finally the health status of an individual is predicted with a Bayesian network classifier, without having actual health measurements from the subjects.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [116]
In the citing paper
“Individual mobility behaviour changes in fever, influenza or mental stress situations [116].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [116]
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“Behavioural changes are reflected in total communication, interactions with respect to time of day (e.g. late night, early morning), diversity and entropy of face-to-face interactions and movement.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [116]
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Citations
Cites
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Sorted by citations
Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0041 · Cited by 1,235
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., & Campbell, A. T. (2014) · Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2632048.2632054 · Cited by 1,116
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-044949 · Cited by 802
Canzian, L., & Musolesi, M. (2015) · Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2750858.2805845 · Cited by 477
Social fMRI: Investigating and shaping social mechanisms in the real world
Aharony, N., Pan, W., Ip, C., Khayal, I. S., & Pentland, A. (2011) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2011.09.004 · Cited by 458
Self-citation
Metadata
- Authors
- Madan, A., Cebrian, M., Lazer, D., & Pentland, A.
- Affiliations
- Massachusetts Institute of Technology (US)
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Harvard University (US)
- Venue
- Proceedings of the 12th ACM international conference on Ubiquitous computing
- Publisher
- ACM
- Published
- 2010-09-26
- Pages
- 291-300
- OpenAlex
- W2171436624
- Cited by
- 219
- References
- 28
- Citations by year
- 2025: 3 · 2024: 4 · 2023: 6 · 2022: 7 · 2021: 14 · 2020: 14 · 2019: 14 · 2018: 17 · 2017: 15 · 2016: 24 · 2015: 22 · 2014: 19 · 2013: 20 · 2012: 25
- FWCI
- 42.39
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- social sensing, behavioral epidemiology, mobile phone data, face-to-face interaction, movement patterns, health prediction, mental health, ubiquitous computing
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