Variability in Self-Reported Normal Sleep Across the Adult Age Span

Poor sleep is a significant problem for older adults, with almost half of adults aged 65 years and older reporting difficulties sleeping on several nights of the week (Foley et al., 1995). There are also changes in sleep architecture occurring across adulthood. Sleep becomes lighter with age and shifts between sleep stages more frequent (Ohayon, Carskadon, Guilleminault, & Vitiello, 2004), with older adults experiencing more awakenings and more time awake (Lichstein, Durrence, Riedel, Taylor, & Bush, 2004; Ohayon et al., 2004). The sleep phase appears to advance in some older adults, which may contribute to sleep-onset and wake-up times several hours earlier than desired (Myers & Badia, 1995). Despite these age-related changes in sleep structure, poor sleep is not an inevitable result of aging. Instead, this increased prevalence of sleep disturbance in older adults is related to the higher levels of medical comorbidities (e.g., chronic pain) and other life events (e.g., retirement, loss of spouse) that accompany aging. Given the myriad of potential influences on older adults’ sleep and the negative consequences that sleep problems have on their health and functioning, there is a need for more research and interventions focused on this population. The purpose of this study is to examine intraindividual variability (IIV) in sleep, or the extent to which sleep varies from night to night, across the adult age span. Most research on sleep averages values across several nights or uses a single-point estimate of habitual sleep. However, this may not be an accurate representation of a person’s sleep and could overlook valuable information about age-related change processes. Developing normative values of IIV in sleep will provide baseline levels of short-term variability that can increase our understanding of how pathological sleep patterns develop and/or resolve at different stages of life. There is already an important role for psychologists in the delivery of behavioral treatments to improve sleep (e.g., multicomponent cognitive behavioral interventions for insomnia; Morin et al., 2006). Given the unique nature of sleep in older adults, there is a role for clinical geropsychologists who are already skilled in recognizing the biopsychosocial influences affecting such a dynamic process in late life. However, in order to develop and deliver efficacious interventions to this population, we need a complete understanding of sleep across the life span, which includes short-term variability. In fact, a recommended geropsychology competency is an understanding of normative and successful aging (Molinari, 2012). Furthermore, there may be functional implications of sleep variability that influence psychological and physical health of older adults. For instance, greater IIV in sleep has been associated with increased stressful life events (Mezick et al., 2009), negative affect (Fuligni & Hardway, 2006), depression and suicidal ideation (Bernert, 2009), and poor sleep quality (Carney, Edinger, Meyer, Lindman, & Istre, 2006), and nightly fluctuations in sleep have been linked to subsequent daily fluctuations in mood and health complaints in community samples (Fuligni & Hardway, 2006; McCrae et al., 2008). Older adults with insomnia show more night-to-night variability (Buysse et al., 2010), and daily changes in sleep duration are associated with worse cognitive functioning in older adults (Gamaldo, Allaire, & Whitfield, 2010). Finally, the amount of nightly variability seems to be independent of aggregate measures of sleep. A study of napping behavior in community-dwelling older adults found that day-to-day variability in naptime was predictive of medical morbidity while average nap duration was not (Dautovich et al., 2012). Therefore, sleep variability may be a potential target for geropsychology interventions. Although studies have documented IIV in objective measures of sleep in the general adult population (Knutson, Rathouz, Yan, Liu, & Lauderdale, 2007; Mezick et al., 2009; Tworoger, Davis, Vitiello, Lentz, & McTiernan, 2005; van Hilten et al., 1993), little is known about age differences in IIV. Two studies failed to find age effects on objective measures of sleep variability (Mezick et al., 2009; van Hilten et al., 1993). However, both study samples were limited to the higher end of the age span and age differences may exist outside of these ranges. A study looking at sleep–wake patterns in healthy adult males found that younger males (19–26 years) showed more day-to-day variability in the timing of their sleep schedules (times entering and leaving bed) than older males (59–74 years; Kramer, Kerkhof, & Hofman, 1999). If younger adults have more variability in the timing of their sleep schedules, they will likely exhibit greater variability in other sleep parameters (i.e., sleep-onset latency [SOL], sleep duration) as well. Some researchers theorize that greater regularity in older adults’ sleep schedules is due to lifestyle changes that tend to occur with increasing age. Indeed, lifestyle regularity does increase with age (Monk et al., 1997) and greater lifestyle regularity is associated with better sleep quality (Monk, Reynolds, Buysse, DeGrazia, & Kupfer, 2003). Perhaps there is more heterogeneity in the daily schedules of younger and middle-aged adults as they balance work schedules with other family and life demands, and this continues into the night, leading to greater variability in sleep patterns. Furthermore, subsets of the older adult population may exhibit greater variability. Rowe, McCrae, Campbell, Benito, and Cheng (2008) found that older adult caregivers to persons with dementia had greater night-to-night variation in both objective and subjective sleep than noncaregiving older adults. Finally, sex and race differences may exist in sleep variability, and may interact with age. Studies addressing sex differences suggest females have greater IIV than males in most measures of adult (Knutson et al., 2007; Mezick et al., 2009; van Hilten et al., 1993) and adolescent (Fuligni & Hardway, 2006) sleep. Studies have also addressed racial differences, reporting greater IIV in some measures of sleep for blacks compared with whites (Knutson et al., 2007), although results are not consistent across all sleep outcomes (Knutson et al., 2007; Mezick et al., 2009; Moore et al., 2011). We used an existing epidemiological sample to examine variability in normal sleep across 14 days of sleep diaries. Any variable measured over time has at least two sources of variation: within-person and between-person. Therefore, our first aim was to examine sources of variability in four aspects of self-reported sleep; total sleep time (TST), number of nighttime awakenings (NWAK), SOL, and wake-time after sleep onset (WASO). Based on prior research documenting high IIV in sleep, we hypothesized that the majority of variability in all four sleep parameters would be due to within-person as opposed to between-person differences. Furthermore, the distribution/sources of variability were examined for three age groups (young, middle age, and older adults) and by sex and race. The second aim was to investigate individual differences and potential correlates of IIV. Specifically, we examined whether the amount of IIV in each sleep parameter differed by age, sex, or race. Consistent with prior research showing that regularity in sleep–wake behaviors increases (becomes less variable) with age, we expected IIV in TST to decrease with increasing age. However, because the average number of awakenings tends to increase with older age, we did not expect age-related changes in IIV in NWAK. Based on a few previous studies, we hypothesized that women and blacks would exhibit greater IIV in self-reported TST and NWAK than men and whites. Due to a lack of prior research on IIV in SOL and WASO, hypotheses were not made for these sleep parameters.