Wearables Group

We develop reproducible methods to analyse time-series wearable sensor data in very large population health studies to better understand the causes and consequences of disease.

Please do get in contact if you are interested in joining, or collaborating with, our research group. We produce open papers, code, and data where our work can be broadly categorised into three major areas:

Machine learning: We develop methods to measure physical activity and sleep status from wearables data in large-scale biobanks that are actively used by researchers worldwide to demonstrate new associations with cardiovascular disease, cancer, depression, mood disorders, and others. These methods generally involve reproducible time-series machine learning methods to robustly identify both behavioural (e.g. walking) and disease-specific (e.g. heart failure) phenotypes. We are actively working on new accelerometer phenotypes and new sensing modalities including cameras, electrocardiograms, and photoplethysmograms.
Epidemiology: We develop methods to account for the unique nature of wearable phenotypes in relation to health association and risk prediction analyses. This has helped us show a clear inverse association between the amount of total physical activity and cardiovascular disease incidence, with no threshold of effect at low or high levels. Our methods generally involve reproducible compositional data analysis to account for the relative time spent in different behaviours over the 24-hour day. We are actively working to expand our work to include large-scale wearable epidemiology studies across a diverse range of cohorts.
Genomics: The combination of wearables with genomics is important to identify and prioritise which wearable measurements are causally associated with disease and to discover new target mechanisms for disease. This has helped us discover the first genetic variants associated with machine-learned sensor phenotypes, showing the first genetic evidence that physical activity might causally lower blood pressure. We are actively working to expand this work to include more datasets and more detailed genomic information.

Our team

Aiden Doherty

Professor of Biomedical Informatics
Stefan van Duijvenboden

Researcher in Health Data Science
Shing Chan

Postdoctoral Research Scientist in Statistical Machine Learning for Cardiovascular Medicine
Laura Portas

Researcher in Genomic Discovery
Charlie Harper

Medical Statistician
Shang-Kuan Lin

Postdoctoral Researcher
Ben Maylor

Post doctoral researcher
Hang Yuan

NDPH Early Career Research Fellow
Karl Smith-Byrne

Senior Molecular Epidemiologist
Peter KaHung Chan

Oxford British Heart Foundation Centre of Research Excellence Intermediate Transition Research Fellow in Cardiovascular Research
Evelynne Fulda

Trainee Fellow
Alaina Shreves
Charilaos Zisou

DPhil student
Adam Sturge

CDT Student
Aidan Acquah

CDT Student
Abram Schonfeldt
Lynne Morley-Johnson

Personal Assistant