Thesis
The role of sensor data for the prediction of cardiovascular disease
- Abstract:
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The early identification of individuals at high risk of cardiovascular disease (CVD) is the cornerstone of effective primary prevention. Current risk stratification tools, such as QRISK3, rely heavily on non-modifiable factors and have limited predictive accuracy. This thesis explores the use of digital sensors to objectively measure physiological and lifestyle risk factors not captured by traditional methods to improve the prediction of incident CVD. In this thesis, I assess the added value ...
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- Files:
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(Preview, Dissemination version, pdf, 14.4MB, Terms of use)
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Authors
Contributors
+ Doherty, A
- Institution:
- University of Oxford
- Division:
- MSD
- Department:
- Nuffield Department of Population Health
- Sub department:
- Population Health
- Role:
- Supervisor
- ORCID:
- 0000-0003-1840-0451
+ van Duijvenboden, S
- Institution:
- University of Oxford
- Division:
- MSD
- Department:
- Nuffield Department of Population Health
- Sub department:
- Population Health
- Role:
- Supervisor
- ORCID:
- 0000-0001-8897-558X
+ Casadei, B
- Institution:
- Imperial College London (NHLI)
- Role:
- Supervisor
- ORCID:
- 0000-0002-6801-1617
+ Collins, G
- Institution:
- University of Oxford
- Division:
- MSD
- Department:
- NDORMS
- Role:
- Examiner
- ORCID:
- 0000-0002-2772-2316
+ Jefferis, B
- Institution:
- University College London
- Role:
- Examiner
- ORCID:
- 0000-0002-0850-3177
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- 2431966
- DOI:
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
- Language:
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English
- Keywords:
- Subjects:
- Deposit date:
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2026-04-09
- ARK identifier:
Terms of use
- Copyright holder:
- Adam Sturge
- Copyright date:
- 2025
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