della Monica, C. | bioRxiv Science

bioRxiv2025

Within and between sleep and cognition: associations in older community dwelling individuals

Cross sectional and interventional studies have demonstrated that sleep has a significant impact on waking brain function including alertness and cognitive performance. Few studies have assessed whether spontaneous night-to-night variation in sleep associates with variation in brain function within an individual. How this compares to the between-individual variation in sleep and cognition and their associations also remains largely unknown. These questions are of particular interest in the context of ageing because both sleep and cognitive abilities are altered in ageing. Furthermore, older people have been reported to be less sensitive to sleep loss. Here we investigated the relationship between sleep and cognition by quantifying associations between intraindividual variation in sleep and cognition as well as associations between interindividual variation in sleep and cognition in 35 cognitively intact older adults (70.8 {+/-} 4.9 years; mean {+/-} SD; 14 females) living in the community. Subjective and actigraphic sleep measures and daily digital assessments of cognition (nine cognitive tests; 19 variables) were obtained over a two-week period. The cognitive test battery probed a wide range of cognitive functions including reaction time, working memory, attention, and problem solving. Principal Component Analysis (PCA) identified four principal sleep components which were labelled Sleep Duration, Sleep Efficiency, Subjective Sleep Quality, and Nap-Effect. Mixed model analyses were conducted with mean and deviation-from-the-mean cognitive variables to quantify how inter- and intra-individual variation in sleep associated with inter and intra individual variation in cognition. Longer sleep duration was associated with faster reaction times in both the inter- and intra-individual analyses and with reduced errors in the inter-individual analyses. Higher sleep efficiency was associated with faster reaction times in both the intra- and inter-individual analyses. By contrast, aspects of cognition relating to learning, visual memory, verbal reasoning, and verbal fluency did not associate with sleep. The data show that in older people some aspects of waking function are sensitive to normal variation in sleep duration and efficiency which implies that interventions that target these aspects of sleep may be beneficial for waking function in ageing.

physiology↗

bioRxiv2023

Method to determine whether sleep phenotypes are driven by endogenous circadian rhythmicity or environmental light by combining longitudinal data and personalised mathematical models

Sleep timing varies between individuals and can be altered in mental and physical health conditions. Sleep and circadian sleep phenotypes, including circadian rhythm sleep-wake disorders, may be driven by endogenous physiological processes, exogeneous environmental light exposure along with social constraints and behavioural factors. Identifying the relative contributions of these driving factors to different phenotypes is essential for the design of personalised interventions. The timing of the human sleep-wake cycle has been modelled as an interaction of a relaxation oscillator (the sleep homeostat), a stable limit cycle oscillator with a near 24-hour period (the circadian process), man-made light exposure and the natural light-dark cycle generated by the Earths rotation. However, these models have rarely been used to quantitatively describe sleep at the individual level. Here, we present a new Homeostatic-Circadian-Light model (HCL) which is simpler, more transparent and more computationally efficient than other available models and is designed to run using longitudinal sleep and light exposure data from wearable sensors. We carry out a systematic sensitivity analysis for all model parameters and discuss parameter identifiability. We demonstrate that individual sleep phenotypes in each of 34 older participants (65-83y) can be described by feeding individual participant light exposure patterns into the model and fitting two parameters that capture individual average sleep duration and timing. The fitted parameters describe endogenous drivers of sleep phenotypes. We then quantify exogenous drivers using a novel metric which encodes the circadian phase dependence of the response to light. Combining endogenous and exogeneous drivers better explains individual mean mid-sleep (adjusted R-squared 0.64) than either driver on its own (adjusted R-squared 0.08 and 0.17 respectively). Critically, our model and analysis highlights that different people exhibiting the same sleep phenotype may have different driving factors and opens the door to personalised interventions to regularize sleep-wake timing that are readily implementable with current digital health technology. Author summaryDisrupted sleep has long term health consequences and affects our day-to-day ability to function physically, mentally and emotionally. But what determines when and how long we sleep? It is well-known that daily light exposure patterns determine the timing of the body clock. However, creating mathematical models that can take realistic light exposure patterns and predict plausible sleep timing has been challenging. Furthermore, nearly all previous studies have focused on developing models for average behaviour, yet sleep timing and duration are highly individual. In this paper, we present a simple model that combines sleep regulatory and circadian processes. The model can take individual light exposure patterns and, by fitting physiologically plausible parameters, describe individual mean sleep timing and duration. We test our model on data collected from 34 older participants. Our modelling approach suggests that some of the participants slept late because of physiological factors, while for other individuals, late sleep was a consequence of their light environment. This approach of combining a model with longitudinal data could be implemented in digital health technology such that your smart watch could tell you not only how you slept last night, but also how to change your light environment to sleep better tomorrow.

neuroscience↗