Contrast Set Mining for Actionable Insights into Associations Between Sleep and Glucose in a Normoglycemic Population

: Prior studies have suggested potential associations between poor sleep and glucose dysregulation among diabetic patients. However, little is known about the relationship between sleep and glucose regulation in healthy populations. In this study, we proposed a data mining pipeline based on contrast set mining to identify significant associations between sleep and glucose in a dataset collected from a normoglycemic population in free-living environments. Unlike traditional correlation analysis, our approach does not assume a linear relationship between sleep and glucose and can potentially discover associations when a pair of metrics fall within certain value ranges. The data mining result highlights the total sleep time as an important sleep metric associated with glucose regulation the next day, which is characterised by rules with high lift and confidence. Furthermore, the result suggests that having a higher time ratio in normal glucose range was associated with better sleep continuity at night. These results may provide insights that people can immediately act on for better sleep and better glucose control. Future research may leverage the proposed data mining protocol to develop healthy behaviour recommender systems.