Chris Ardern

How do individual and community-level factors support or undermine people who are at high-risk for obesity and inactivity-related disease?

Chris Ardern studies the big picture in his research on obesity, physical inactivity, sedentary time, and the effect they have on health. He works at the population level, drawing on the expansive data collected by agencies like Statistics Canada and the National Center for Health Statistics in the U.S. to find patterns in chronic diseases. Focusing mostly on cardiometabolic health – diabetes, heart disease and stroke – his lab seeks to identify groups at high risk and evaluate the potential impact of lifestyle modifications to prevent these chronic diseases.


We need to recognize that the current healthcare model is not sustainable. We must start thinking about health more holistically. We need to shift the focus from taking care of people when they are sick, to preventing them from getting sick. I’m really interested in keeping communities and people healthier, longer.


We’re an aging population. If we want to limit the amount of time we spend in a diseased state, physical activity is amongst the most potent tools we have.

We use population-based data or clinical and administrative data from hospitals. We generalize to find patterns, and then also look at how those patterns persist across different subsets of the population. But the data represent real people in their care setting or in their community. It is so important to translate our findings from this research into practice. One example of this is work we did for the Ontario Brain Institute. Here, we examined the relationship between physical activity and Alzheimer’s disease risk by pooling 50 years of research on the topic. This work was subsequently used by the Ontario Brain Institute to convene an expert panel and develop a physical activity toolkit for healthcare providers and families of Alzheimer’s patients. We are now extending this work to identify how physical activity levels in early and middle adulthood may help to offset the development and progression of cognitive impairment and Alzheimer’s disease. Taken together, this work will help provide new insight into the optimal physical activity prescription for reducing the risk of Alzheimer’s disease, and help better understand the potential impact of population-wide prevention efforts.


One big data initiative involved examining the trajectories of metabolic dysfunction and obesity-related health risk. We looked at how people actually developed metabolic syndrome and gained weight over 20 years and found distinct group differences. Several different patterns were found, but to our surprise, there was a group that didn’t develop any metabolic dysfunction over 20 years, and a group that developed it very quickly in the first five years and then plateaued. We used a statistical technique borrowed from criminology and it allowed us to identify the best windows of time for early interventions. This research helped pinpoint when a lifestyle intervention of diet and exercise might be most beneficial, and the groups where more aggressive early intervention is most needed.

Another success was a study looking at physical activity in relation to the built environment. In this work, we found that regions with the highest amount of intersections and residential density were associated with higher levels of leisure-time physical activity and transport-related physical activity. This type of analysis can be used to identify inactivity hotspots, and better understand and target areas where physical activity or other health promoting resources can be introduced. This kind of strategic approach could be helpful in closing the gap in patterns of physical activity participation in traditionally marginalized groups.

What's Next?

I am interested in bringing research on the built environment together with research on the physical activity and nutrition environment. Some neighbourhoods could be described as healthy food “deserts.” And the design of some areas makes residents completely dependent on cars. We call this an obesogenic environment: the structure of the neighbourhood discourages exercise and encourages unhealthy eating. We want to better understand how people interact with that environment in terms of obesity-related health risks.