Harnessing data from wearable devices for the prognosis of long-term conditions and mortality: application to clinical risk scores

How we lead our lives effects our health, yet this information is typically not considered when predicting future risk of chronic disease or mortality.

For example, an individual with a high genetic risk of a disease or a high risk based on routine clinical data is placed in the same high risk pool regardless of how physically active they are, despite the fact that physical activity can modify much of this risk for some long-term conditions and mortality outcomes.

The studentship will include training in cutting edge analytical approaches for using data gathered from wrist worn accelerometers (similar to those found in smart watches and physical activity trackers) in generating patterns of physical behaviours, ranging from sleep to physical activity.

The relevance of these novel metrics to predicting the onset of common chronic disease or early mortality will then be explored using flexible parametric models, which will include developing new risk prediction tools.