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