Over the last decade, the field of sleep research and clinical sleep medicine has dramatically expanded in its capability for measuring sleep naturalistically, longitudinally, and on a large scale. At the forefront of this sleep revolution is the exponential growth of miniaturized sleep-tracking technology like wearables and nearables (e.g. multisensor wristbands, noncontact radar/sonar bedside devices, dry EEG-based sleep headbands), mainly within the consumer space. For example, in 2021, Statista reported 111 million users of Fitbit devices [1], which is still only the tip of the iceberg when considering the widespread use of sleep-tracking technology. These devices offer the enticing opportunity to monitor sleep (e.g. sleep/wake patterns, sleep timings, sleep stages, naps) and related events (e.g. nighttime heart rate and its variability, atrial fibrillation, oxygen desaturation, breathing patterns) through analytical approaches (e.g. machine-learning algorithms) that rely on sensors to collect physiological, environmental, and behavioral data…