Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality.

This study helps clinicians to interpret sleep measurements from wearable sensors in routine care. Researchers can use derived sleep parameters in large-scale accelerometer datasets to advance our understanding of the association between sleep and population subgroups with different clinical characteristics. Our findings further suggest that having a short overnight sleep is a risky behaviour regardless of the sleep quality, which requires immediate public attention to fight the social stigma that having a short sleep is acceptable as long as one sleeps well.