View clinical trials related to Sleep Inertia.
Filter by:Sleep inertia (sometimes also referred to as sleep drunkenness) is a disabling state of increased sleepiness, impaired mood and reduced vigilance immediately upon awakening. Sleep inertia is highly prevalent in various neurological diseases, including neurodegenerative, affective and circadian sleep-wake rhythms disorders, as well as in frequent societal conditions such as chronic sleep restriction, jetlag and shiftwork. Reactive countermeasures against sleep inertia, i.e., strategies implemented upon wake-up, are not sufficiently effective, yet current recommendations are limited to proactive strategies, including long enough sleep at optimal times of day. These recommendations are not always easy and sometimes impossible to apply. To address this unmet medical need, the investigators developed an innovative, time-controlled, pulsatile-release formulation of 160 mg caffeine targeting an efficacious dose briefly before planned awakening.
Pilot data suggests that working professionals and college students routinely use alarms and snooze. Alarm usage and snoozing is associated with several negative health biomarkers including lighter sleep, higher resting heart rate, and reduced sleep duration. It is unclear when this behavior is established, but it is likely in the teenage years when chronic sleep restriction begins to effect a large percentage of Americans. We will ask teens about psychological traits (e.g. personality) and snoozing behavior in a repeated measures design. In addition, we will implement a smartphone based intervention which notifies teens when they are awake past their minimum bedtime for adequate sleep. throughout the study, we will monitor sleep and heart-rate via wearable. From this data, we will establish the prevalence of alarm and snoozing behaviors in teens. We will determine what demographic, psychological, and behavioral traits predict snoozing, and if there are any differences in health biomarkers (e.g. sleep duration, resting heart rate)between snooze and/or alarm users. We will use data from the wearables and smartphones to generate features that can detect snoozing, and will validate them against self-report. Finally, we seek to determine if alarm and snoozing behavior can be reduced via a smartphone intervention aimed at increasing sleep duration.