Abstract
SleepSight is a novel approach to detecting early signs of relapse in psychosis, thereby allowing targeted intervention for relapse prevention. The system uses a wireless-enabled wearable device and smartphone to collect longitudinal accelerometry, heart-rate, ambient light and smartphone usage patterns from patients living in their homes. These data are encrypted and sent via the mobile data network to a secure server for real-Time analysis, using the Purple Robot mobile application. This study tested feasibility and acceptability of the SleepSight system in 15 participants with a diagnosis of schizophrenia. Patient recruitment and data collection was completed in January 2016, at the South London and Maudsley NHS Foundation Trust (SLaM), the largest mental health service provider in the EU. The project is a unique multidisciplinary collaboration between the NIHR Biomedical Research Centre for Mental Health, the Institute of Psychiatry, Psychology and Neuroscience, and the National Health Service.
| Original language | English |
|---|---|
| Title of host publication | UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 113-116 |
| Number of pages | 4 |
| ISBN (Print) | 9781450344623 |
| DOIs | |
| Publication status | Published - 12 Sept 2016 |
| Event | 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, Germany Duration: 12 Sept 2016 → 16 Sept 2016 |
Conference
| Conference | 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 |
|---|---|
| Country/Territory | Germany |
| City | Heidelberg |
| Period | 12/09/2016 → 16/09/2016 |
Keywords
- Mental Disorder
- Mobile Sensing
- Probabilistic Time Series Modelling
- Remote Monitoring
- Sensor Networks
- Wearable Computing
Access to Document
- SleepSight A Wearables_KERZ_Published12September2016_GREEN AAM
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