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of individuals’ performance on tasks that require alertness, vigilance, motor


responsiveness, and concentration.


Measuring Sleep Quantity and Quality. A number of objective indices of


sleep quantity are employed in the clinical assessment of sleep disorders, and in


academic investigations of sleep variation. These indices use sophisticated instru-


mentation for capturing brain-wave activity or movement, observation, or


self-report, to calculate variables such as sleep onset latency (SOL, duration of time


between bedtime and sleep onset), total sleep time (TST, combined duration of all


sleep bouts in a single night), wakings after sleep onset (WASO, duration of


nighttime awakenings, a measure of sleep fragmentation), and sleep efficiency (SE,


proportion of time in bed spent asleep). These measures provide a quantitative


baseline of an individual’s sleep functioning and can be used to assess whether


sleep function is responding to a treatment intervention (Marin et al. 2005 ). In


comparative studies, the above indices may be compared across samples in an


attempt to characterize the sleep parameters of particular groups of individuals; for


instance by age (Ohayon et al. 2004 ), occupation (Mitler et al. 1997 ), or environ-


ment (Freedman et al. 2001 ). The above sleep parameters are sometimes combined


by researchers in order to attempt an objective assessment of sleep quality (Young


et al. 2003 ); however,‘sleep quality’is also used to imply an aspect of sleep that is


not simply reducible to‘sleep quantity’and may be used to refer to either the


experience of sleep or the individual sense of‘well-restedness’or satisfaction with


sleep upon waking.
Polysomnography (PSG) is considered the‘gold standard’for studying sleep


physiology; it allows one to capture information about sleep architecture, such as


relative or absolute duration of stage 1 sleep, stage 2 sleep, slow-wave sleep, and


rapid eye movement (REM) sleep. Standard sleep indices (e.g., SOL, TST, WASO,


amount and percentage of sleep stages, REM latency) can be computed to provide


information on (a) the quantity and timing of sleep, (b) the number of pathological


events, and (c) the duration and relative distribution of sleep stages. These are


useful for assessing the outcomes of sleep disorder treatments (i.e., how sleep


parameters have been altered by a clinical intervention). PSG has limitations,


however, in that sleep stage scoring is a crude mechanism that characterizes the


nature of sleep according to four patterns, creating an artificial segmentation of a


continuous process. It also requires electrodes to be attached to the participant’s


head, with leads running to a computer that interprets the electrical signals received


from the brain (electroencephalography or EEG). This does not make this method


suitable for research in naturalistic settings. As a result of these limitations, other


more sensitive methods are now emerging, and portable PSG devices with limited


capacities are becoming available for investigations based on the home environment


(Cantero et al. 2002 ; Kelly et al. 2012 ).


Non-REM (NREM) spectral analysisexamines the frequency content of EEG


signals obtained during NREM sleep, with the frequencies being transformed to


generate measures of the amount of activity in a series of bands of different fre-


quencies (delta, theta, alpha, sigma, beta, and gamma), producing a more contin-


uous measure of the nature of sleep compared with PSG sleep staging. Although it


3 Baby-Lag: Methods for Assessing Parental Tiredness and Fatigue 31

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