| Title: | Leveraging objective physical activity measures and MRI images to dissect the relationships of physical activity with cardiovascular and cerebrovascular health |
| Lead Institution: | City University of Hong Kong |
| Principal investigator: | Dr Xinyue Li |
Application 67990
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About
Insufficient physical activity is a known risk factor for cardiovascular and cerebrovascular health, but the underlying mechanisms are still unclear. Wearable devices and MRI have been increasingly used recently to provide comprehensive information on daily physical activity and heart and brain health, which offers us unprecedented opportunities to study their associations. It is noted that recent studies primarily focus on summary metrics of wearable device data and MRI, while rich information contained in the time-series physical activity data and two-dimensional images remains to be fully exploited. Therefore, we propose to apply advanced statistical models to derive novel features from time-series wearable device data and characterize daily physical activity patterns. By leveraging both extracted physical activity features and collected MRI measures, we will study the phenotypic associations as well as examine the genetic associations through genome-wide association studies and genetic correlation estimation. Our proposed method will pinpoint activity features that may serve as cardiovascular and cerebrovascular health risk indicators, uncover new heart and brain MRI features associated with physical activity, and further yield novel insights into the underlying biological mechanisms. The methods proposed will also be developed into code packages for public use. We anticipate that this project will take three years. The novelty of our research includes integrative analysis of wearable device data and imaging data, a detailed examination of the phenotypic and genetic correlation of physical activity with cardiovascular and cerebrovascular health, and the evaluation of the causal and likely beneficial effects of physical activity. Our findings can inform public policies and strategies integrating physical activity as an essential component to improve population health as well as open up the possibility of using wearable devices to evaluate and monitor cardiovascular and cerebrovascular risks and call for timely intervention.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 15734 | Towards proactively improving sleep: machine learning and wearable device data forecast sleep efficiency 4-8 hours before sleep onset | Collin Sakal (+5) | 2025 | Sleep |
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