Tracking Mental Well-Being: Balancing Rich Sensing and Patient Needs
Abstract
Serious mental illnesses are among the most pressing public healthcare concerns. Continuous and unobtrusive sensing of social and physical functioning has tremendous potential to support lifelong health management by acting as an early warning system to detect changes in mental well-being, delivering context-aware micro-interventions to patients when and where they need them and significantly accelerating patients' understanding of their illness. We have presented a range of techniques from our previous and ongoing work to suggest how technologists can make significant advances in this area. While we are still shaping and evaluating these solutions, there is an opportunity for other researchers in our field to help ease the burden of SMIs by striking a balance between cutting-edge sensing and patient needs. This could empower patients by giving them a hand in their own treatment and ultimately lead to more effective, lower-cost treatment.
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- Authors
- Matthews, M., Abdullah, S., Gay, G., & Choudhury, T.
- Affiliations
- Cornell University (US)
- Venue
- Computer
- Published
- 2014-04
- Volume
- 47
- Issue
- 4
- Pages
- 36-43
- ISSN
- 0018-9162 (ISSN-L)
- 1558-0814
- OpenAlex
- W1942459798
- Cited by
- 64
- References
- 12
- Citations by year
- 2025: 3 · 2024: 4 · 2023: 2 · 2022: 3 · 2021: 5 · 2020: 9 · 2019: 6 · 2018: 5 · 2017: 10 · 2016: 8 · 2015: 6 · 2014: 3
- FWCI
- 14.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computer (publishedVersion)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- severe mental illness, unobtrusive sensing, continuous sensing, early warning systems, context-aware interventions, patient empowerment, patient-centered design, digital mental health
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