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Sleep and cognition in ageing: how to measure what?

The demographic changes observed in many countries, agerelated deterioration in cognition and the prevalence of neurodegenerative disorders, all represent a considerable economic and quality of life burden. The desire to reduce this burden drives considerable research funding opportunities in this area. The sleep research community is well placed to contribute to research efforts to ameliorate the impact of ageing on cognition, not least because sleep duration, and its timing and structure change profoundly across the lifespan. Other reasons include the notion that sleep supports brain function, and indeed associations between age-related changes in sleep structure and waking function have been reported e.g. (Lafortune et al., 2014). Sleep also contributes to physical health and disease which, in turn, interact with ageing and brain function. Finally, sleep complaints are highly prevalent in ageing and for that reason alone sleep is an attractive target. In fact, when thinking about the possible approaches to sleep, ageing and cognition, research the opportunities appear near infinite, but where to start? Research on the function of sleep in humans has focused on young adults and perhaps the developing human, but has almost completely excluded older or very old individuals from the study populations. Most of the research was laboratorybased and the ecological validity of the sleep manipulations used in most studies can be considered limited. At a very basic level, one can argue that there is no a priori reason to assume that dose–response studies of sleep duration and cognition are invariable across age groups. In fact, the effects of total sleep deprivation on some measures of waking function are reported to be smaller in healthy older than in young participants (Landolt et al., 2012). Similarly, there is no reason to assume that good-quality sleep means the same to a 20and 80-year-old man or woman. The effects of sleep disorders, such as sleep-disordered breathing, on daytime sleepiness, cognition and mortality have been shown to vary with age (Addison-Brown et al., 2014; Lavie and Lavie, 2009). How to measure sleep anyway, and what are the most relevant response–outcome measures? What is the value of cross-sectional studies or longitudinal cohort studies without intervention? There is little doubt that the n = 12 laboratorybased studies on the effects of sleep restriction on cognition in young and older people are valuable, but translation of any findings to a population-based intervention is not obvious. How to proceed? In this issue of the journal the age–sleep problem is addressed in four papers which use four different approaches. Wilckens and colleagues investigated the association between sleep parameters and aspects of executive function (Wilckens et al., 2014). Sleep is thought to benefit executive function and executive function declines with ageing, although in some individuals it is well preserved. The study was conducted among 54 young and 45 older men and women with no evidence of cognitive impairment as assessed by the Mini Mental State Examination. An interesting aspect of the present study is that wake after sleep onset (WASO) and total sleep time (TST) were assessed during at least four nights while the participants were at home. The method used was an armband-like sleep detection device, and this may indeed indicate that we are entering an era of new methods of assessing sleep. Executive function was assessed using a task-switching paradigm, and both ‘global’ and ‘local’ switch costs were quantified. The data show age effects on executive function and, when analysed across age groups, significant contributions of both WASO and TST, in particular to the global switch-costs parameters of reactiontime and accuracy were identified. However, these associations appeared to be stronger in the younger participants. Aoki and colleagues investigated the association between sleep-disordered breathing and cognitive function in 111 patients with dementia (Aoki et al., 2014). In this study, sleep-breathing was monitored using an unconstrained sleep apneaexamination deviceconsisting of a sheetwith anarrayof 162 highly sensitive pressure sensors, placed underneath the patient. This approach has the obvious advantage that no sensors need to be connected to the patient, which is particularly relevant in dementia. Moderate to severe sleepdisordered breathing (respiratory distress index >20 h ) was an independent predictor of the severity of dementia. Of interest, when the analyseswere conducted in two agegroups, this effect was observed only in the younger group (average age 72.3 years), and not in the older group (86.4 years). Ramos and colleagues assessed the association between sleep duration and white matter hyperintensity (WMH) volume, which is a marker of cerebral small vessel disease and predictor of stroke and dementia (Ramos et al., 2014). The study was conducted in 1244 participants with a mean age of 70 years (standard deviation = 9). Sleep was assessed by questionnaires which included questions about sleep duration, snoring and daytime sleepiness. A positive correlation between sleep duration and WMH, assessed by quantitative magnetic resonance imaging, was observed. This positive correlation remained significant when adjusted for age, sex, years of education, habitual snoring and daytime sleepiness. Interestingly, the negative effects of long sleep were present primarily in those with diabetes mellitus. In the fourth paper, Fang and colleagues report on the history of stroke and sleep duration in an extremely large sample of 154 599 participants with very good coverage over a very broad age range (Fang et al., 2014). In this representative sample, all participants were interviewed and asked