Despite these limitations, the unique features of our study design and data sources set it apart from previous studies, yielding results that are both novel and clinically relevant. To our knowledge, this is the largest study to objectively analyze sleep patterns longitudinally across many years using direct measures of sleep. Using data from commercial wearable devices, we were able to assess detailed sleep patterns across nearly 6.5 million person-nights, which was previously not possible with surveys or was prohibitively expensive with polysomnograms. Previous studies have often focused on a narrow set of phenotypes, whereas we are able to analyze associations across 1,636 diverse phenotypes because of the AoU’s linkage of sleep data with EHR data. This study also includes time-varying analyses, which account for changes in sleep behavior over time, unlike previous cross-sectional studies. Finally, our study also accounts for the impact of sleep on disease risk accounting for concomitant, longitudinal activity behavior, which is novel compared with other studies and shows the independent impact of sleep.

There are several limitations to this study. First, the study cohort is relatively young, majority female, white and college educated. The generalizability of the findings to underrepresented communities or those in areas of deprivation is unclear and, thus, a high priority for future studies. Notably, there is an active effort within the AoU Research Program to expand the diversity of participants with Fitbit data through the WEAR study by providing free Fitbit devices to invited participants from underrepresented communities. Nonetheless, many of our findings are supported by previous studies using diverse populations with survey or polysomnogram data. In addition, our findings will be highly relevant to the increasing proportion of the general US population that owns a commercial wearable device, which reached nearly 30% in 202042. Moreover, because the owners of commercial wearable devices are broadly healthier than the general population, the reported effect sizes and impact of poor sleep health in this study may actually be stronger in the general population22. Second, the sleep data included in our analyses are reported from and calculated by Fitbit. The Fitbit algorithms have been evaluated against gold-standard polysomnograms in many studies14,15,16,17,18,19,20,43,44. The largest of these validation studies showed that Fitbit did not significantly differ in estimation of total sleep time or deep sleep compared with polysomnograms, but underestimated REM sleep by 11.4 min (ref. 18).

decreased daily sleep duration and increased sleep irregularity are associated with obesity and sleep apnea

Increased REM sleep percentage was associated with decreased risk of incident heart failure (HR 0.51; 95% 0.26–0.99) and generalized anxiety disorder (0.80; 0.69–0.92). Increased light sleep percentage was associated with increased risk of incident heart failure (2.30; 1.05–5.04), generalized anxiety disorder (1.31; 1.13–1.52) and atrial fibrillation (1.76; 1.02–3.05). Increased deep sleep percentage was associated with decreased risk of atrial fibrillation (0.59; 0.35–0.99) and generalized anxiety disorder (0.84; 0.72–0.98).