Sleep quality has a large impact on daily performance and general health. Among the different methods of objectivelymeasuring sleep quality, actigraphy continues to be very popular. It can take continuous activity measurementsover several days in order to determine sleep-wake cycles and calculate sleep variables, including the three standard sleepvariables used in determining sleep quality: total sleep time, sleep efficiency, and wake-after-sleep onset, in which studyanalyses use the mean of these variables. In the case of wake-after-sleep onset, which calculates the amount of time betweenfalling asleep and waking up, the average does not characterize wake-after-sleep times as it does not account for thetotal number of awakenings or the frequencies of wake-after-sleep times. Instead, we recommend using the entire distributionof wake-after-sleep onset times, which we will call waiting time distribution, which better characterizes wake-aftersleeponset than the average value. Sleep quality for each participant was determined by their total sleep time and sleep efficiency.Non-parametric statistics were utilized to determine differences in waiting time distributions between participantswith good and poor quality of sleep. Discriminant analysis was performed to show that a distribution of waitingtimes discriminates better between qualities of sleep than the average wake-after-sleep onset time does. Waiting time distributionswere also fit to standard probability distributions for utility and ease of understanding. Analyses show that thewaiting time distribution categorizes sleep qualities better than the average wake-after-sleep onset variable, as well as givingmore information and better characterizations.
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