Effect of Night Float Call on Sleep and Activity Patterns Among Anesthesia Residents

With increasing awareness about physician fatigue and its effect on patient safety, residency programs are increasingly transitioning to a night float call system. In other industries, multiple night shifts in a row can cause a disruption in the circadian rhythm, sleep debt, shift work disorder, that is related to chronic medical conditions such as obesity and cardiovascular disease. We will evaluate the effect of different call structures on resident activity, sleep and self reported measures of wellness using a commercially available Fitbit device.

Participants will be recruited via an email once they are scheduled for a week of night float in the Stanford Main Operating room or on their Obstetric Anesthesia rotation. Potential participants who are taking prescription medications that might affect their alertness during the study period will not be enrolled. The investigators will quantify the changes in sleep pattern and activity during different call rotations. Self reported aspects of well being including fatigue, physical function, and positive affect will be assessed with NIH PROMIS surveys periodically during the study period. The investigators will use the Fitbit Alta HR data to quantify the change in total amount of sleep, sleep interruption and sleep phase and steps per day. Data will be analyzed only when it is coincident with heart rate data to correct for periods when the device is not used. Activity will not alter from the participants normal except that they will wear the Fitbit and respond to the NIH PROMIS surveys over the study period. Data from NIH PROMIS surveys, Fitbit and provided demographic information including age, sex and BMI, and number of previous night float periods previously completed. After association, data will be completely de-identified. All data will be normalized to the participant's baseline value during the run-in week. The call week and post call week will be analyzed with a time series mixed effects model using R and/or NONMEM (a program for Nonlinear Mixed Effects Modeling). The effects of the above demographic variables will be assessed as potential covariates. The investigators anticipate that this observational study will be the first of many studies looking at the effects of changing sleep patterns on activity and well-being