Utility of sleep stage transitions in assessing sleep continuity.

IT IS BECOMING INCREASING EVIDENT THAT, IN ADDITION TO SLEEP DURATION, CONTINUITY OF SLEEP IS ESSENTIAL FOR ITS RESTORATIVE VALUE.1,2 Empirical evidence garnered over the last decade suggests that sleep disruption and poor sleep quality, even in the absence of sleep loss, can increase daytime sleep tendency, decrease psychomotor performance, cause deficits in attention and memory, and impair glucose homeostasis.3–7 In fact, fragmented sleep is a cardinal feature of several sleep related disorders such as obstructive sleep apnea and may independently contribute to the pathogenesis of associated adverse sequelae. In clinical practice and research, continuity of sleep is commonly assessed with polysomnographic measures such as the arousal index, sleep efficiency, sleep stage percentages, and wake time after sleep onset. While these metrics can provide useful insights on sleep architecture, each has its inherent limitations. For example, sleep stage percentages, while useful as global measures, provide little information on sleep micro-architecture and account for only a fraction of the total variance in clinical outcomes.8 Seminal work conducted over three decades ago by Baekeland et al.9 showed that the frequency of nocturnal awakenings and the duration of wakefulness are, in fact, better predictors of feeling rested upon awakening than the amount of slow wave or REM sleep. These findings have been subsequently confirmed in studies of experimental sleep disruption which have also shown that sleep stage summaries are not well correlated with impairments in performance or the degree of daytime sleepiness.10 Assessing the frequency of electroencephalographic (EEG) arousals, which does provide information on sleep micro-architecture has become common practice but is not without significant challenges. Scoring of EEG arousals is a tedious process and without rigorous training is fraught with considerable inter- and intra-rater variability.8 Moreover, a number of studies have found that the arousal index is modestly correlated with impairments in daytime function.11 Several complementary and potentially more sensitive approaches for characterizing sleep continuity have been proposed including quantifying autonomic events, assessing cyclic alternating pattern in the EEG, and using cardiopulmonary coupling as an index of sleep state instability.12,13 Although research on the utility of these measures in predicting health consequences is steadily growing, empirical evidence on their validity is currently limited. In addition to some of the polysomnographic measures highlighted above, the number14 and the rate15,16 of transitions between various sleep stages has also been proposed as a metric of sleep quality. Although still somewhat crude, sleep stage transition data can be readily derived from the conventional hypnogram. To date, a number of studies have examined the utility of the sleep stage transitions in the clinical and research arena using the number,14 rate,15,16 or the time between transitions17–23 as a metric of sleep quality. A consistent observation across these studies has been the significant correlation with other measures of sleep discontinuity such as the frequency of EEG arousals. Thus, it remains to be determined whether sleep stage transition rates, independent of the conventional measures of sleep architecture, can predict daytime outcomes. The primary aim of this study was to determine whether stage-specific and overall transition rates derived from the sleep hypnogram can offer additional information regarding daytime measures beyond that provided by conventional metrics. To achieve this goal, data from the community-based Sleep Heart Health Study were utilized to determine the utility of various stage-specific transition rates in predicting the subjective experience of sleep quality the morning after the sleep study. It was hypothesized that, independent of the measures such as sleep stage percentages, the arousal index, and wake time after sleep onset, sleep stage transition rates would predict the subjective experience of poor sleep quality.