Approved Research

Relationship between lifestyle behaviours, mental health and biomarkers on longevity and disease outcomes - a biological age approach to good health.

Lay summary

Positive lifestyle habits brings along numerous benefits, targeting improvements in a wide variety of health outcomes including improved longevity and prevent chronic diseases.  Healthy lifestyles can play an integral role in determining a person's biological age and with support of machine learning techniques, an algorithm can be developed to predict one's risk for mortality and development of chronic lifestyle diseases.

Our proposed research aims to:

1) To evaluate lifestyle behaviours metrics and quantify its relationship development of chronic diseases.

2) To develop a machine learning / statistical model to quantify biological age and predict risk for developing chronic diseases.

A deeper understanding of the relationship between lifestyle behaviours and health outcomes can provide strong insights to the development of a health engagement framework. The development of an algorithm, leveraging on deep machine learning, can provide a useful tool which can be integrated into a digital health engagement tool to:

1) Empower people to achieve a healthier way of living by being more physically active

2) Assess people's lifestyle, biological age, and health risks more precisely and provide targeted recommendations for disease prevention.

At ReMark, we have the relevant data science expertise and have proven in previous case study that machine learning applied on daily activity data could help detect minor signs of infections. This model was used to contain the spread of covid 19 in Fukuyama, Japan.

In this proposed research, we will increase the number of factors of good health and research their association with the most common cancers and cardiovascular diseases. We will then apply and test the usefulness of our research on a panel of participants external to UK Biobank.

The UK Biobank provides a unique opportunity to do this research using data from the wrist-worn activity tracker and participants' habits and hospital records.

Results from our research will be published to the scientific community and in a white paper for health care professionals and for the public.