3 Clinical Trials for Various Conditions
The purpose of this online research study is to determine whether or not a gradual caffeine reduction program developed at Johns Hopkins can help people reduce their caffeine use. The investigators will provide materials to help guide caffeine reduction and ask questions to track caffeine use over several weeks. The investigators will also assess how reducing caffeine may benefit common caffeine-related problems such as anxiety, sleep disturbances, and gastrointestinal distress. The study will also determine whether or not people like participating in this caffeine reduction program in an online format.
Sleep deprivation (SD) has a powerful degrading effect on cognitive performance, particularly psychomotor vigilance (PV) and reaction time. Caffeine is well known to be an effective countermeasure to the effects of SD. However, individuals differ in both their response to SD and to the administration of caffeine. This has made it difficult to provide individualized recommendations regarding the use of caffeine to sustain alertness when needed. For the past two decades, the Army's Biotechnology HPC Institute (BHSAI), in collaboration with the Walter Reed Army Institute of Research, have been developing statistical models to predict individual performance during prolonged SD. Recently, this resulted in the publication of the 2B-Alert app, a computer algorithm based on large datasets that can learn an individual's response to SD by combining actigraphic sleep data with simultaneously acquired PV performance data. The 2B-Alert algorithm can predict an individual's sleep need and performance after \~2 weeks of training the model. Recently, the model has been extended to incorporate individualized responses to caffeine. This was recently validated in a retrospective study published by BHSAI in 2019. The present study is designed to test the predictive capacity of the 2B-Alert app in real time. During Phase 1 a total of 21 healthy participants will wear an actigraph \& complete multiple daily PV tests on a personal cell phone. After 2 weeks, these individuals will attend Phase 2 involving an in-laboratory stay \& SD. Participants will have an 8-hour period of sleep in the laboratory, followed by 62 hours of continuous wakefulness. During these 62 hours, participants will complete PV and mood testing every 3 hours. The 2B-Alert app will be used to predict individual caffeine need to sustain performance at near-baseline levels based on the statistical model. At 44 hours SD, participants will undergo a 6-hour "alertness window" where they may receive individualized doses of caffeine based on the recommendations of the model. After 62 hours of SD, Phase 3 begins, involving a night of monitored recovery sleep and additional sessions of PV and mood testing until release from the study at 6 pm on the final day. It is hypothesized that the 2B-Alert app will be effective at providing caffeine dosing recommendations that return PV and mood performance to normal levels during the alertness window.
The aim of this study is to develop information about the acute and residual effects of a new product being targeted to young adults. Using a double placebo-controlled 2 X 2 factorial model study design, we will compare the acute and residual effects on driving impairment of caffeinated alcohol, non-caffeinated alcohol, caffeinated placebo, and non-caffeinated placebo. Under the alcohol conditions, participants will receive sufficient alcoholic beverage to attain a blood alcohol concentration (BAC) of .12 g%. Participants will be 144 undergraduate and graduate students, and recent college graduates.