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As a consequence of related lifestyle changes, job strain and social stress, shift-work and insufficient sleep is reportedly a risk factor for the development of cardiovascular disease. Although the causal mechanisms remain unclear, disruption of the sleep/wake cycle is known to affect the time-of-day variation in biological functions. This variation also exists in most aspects of the cardiovascular physiology, including cardiac electrophysiology, where daily rhythms in heart rate (HR) and electrocardiogram (ECG) parameters can be readily observed. Recent experimental work has demonstrated that disruptions of the circadian rhythm can significantly affect the autonomic control of the heart potentially rendering it more vulnerable to arrhythmias. However, the impact of circadian misalignment on the functioning of the heart and its control remains poorly understood.

Our team has driven key innovations in large-scale use of wrist-worn accelerometers to objectively measure physical activity and sleep duration using state-of-the-art machine learning algorithms. The proposed Dphill offers a unique opportunity to investigate the impact of circadian misalignment on the functioning and control of the heart using by key biomarkers derived from ECG and imaging data in a large cohort of healthy individuals.  


The project will encompass research experience in (wearable) signal processing and epidemiological analyses in a large data population dataset. The project focuses on four main research questions:

  1. Is circadian misalignment associated with 12-lead ECG measurements of heart disease? A short-term follow-up study to identify accelerometer derived circadian misalignment (exposure) and its association with ECG markers (outcome).
  2.  Is circadian misalignment associated with future cardiac and brain structure measurements? Accelerometer-derived measurements of sleep, to construct new measurements of circadian misalignment, will be associated with key Imaging parameters from cardiac and brain imaging data derived by our collaborators.
  3. Is circadian misalignment associated with an increased risk of arrythmias? In a unique dataset comprising long term continuous and simultaneous recording of both ECG and accelerometer data, you will investigate whether circadian misalignment is associated with increased risk of arrhythmias. 
  4. Are wearable ECG measurements of circadian misalignment associated with abnormalities in structural and functional parameters from heart and brain imaging during follow-up? This study will use brain and heart imaging data recorded after ECG measurements were taken.


There will be several opportunities for training in signal processing and statistical/quantitative analyses of health data.


Candidates ideally will have postgraduate training in biomedical engineering, epidemiology, or related discipline. Interest in cardiovascular disease, analysis of large scale sensor data, and good communication skills are necessary.