Real-world deployment of remote sleep monitoring technologies reveals distinct patterns associated with cognitive decline

BACKGROUNDSleep disturbances and altered circadian rhythms are well-documented in both physiological and biological studies of dementia. The exact causal relationship remains unclear. Several other long-term health conditions may also influence sleep patterns. Examining sleep patterns in relation to chronological ageing and the variations and progression of dementia, considering factors such as age, sex, and disease stage, can offer new insights for developing early screening and risk identification measures. Remote sleep monitoring enables routine assessment of cognitive decline symptoms in high-risk groups, aiding early risk identification. Integrating predictive models with routine sleep data holds promise for shaping more proactive approaches to assessing and caring for older adults and people living with dementia (PLWD). METHODSWe developed a machine learning pipeline to estimate Sleep Age Index (SAI) from longitudinal remote sleep monitoring data collected using under-the-mattress sleep sensors and use it to identify dementia risk. The study utilised a dataset of nocturnal activity and physiology data (n=1,672; 18,369 person-samples) collected from individuals in the general population and a cohort of PLWD. Dementia risk scores were stratified into high, medium and low-risk categories to support clinical monitoring and decision-making. RESULTSOur study indicates that sleep patterns in dementia do not follow typical ageing processes. For individuals with dementia, our pre-trained machine learning model predicted age in a negative direction. Further investigation revealed distinct patterns associated with these predictions, including irregular times to bed and rise, lower variation in deep sleep duration, and elevated breathing and heart rates. While age predictions were largely similar for female and male participants, the patterns observed in the female group were more consistent. Chronological age was predicted from sleep data with a mean absolute error of 5.52 (95% CI: 5.37 - 5.67) on held-out test data. A sensitivity of 75.7% (95% CI: 71.4% - 79.9%) and specificity of 74.7% (95% CI: 69.2% - 80.0%) was achieved post-stratification on unseen data in dementia versus control. To evaluate the real-world clinical applicability of the model, we conducted a pilot study in a population with a higher risk of cognitive decline (n = 50). Pilot data analysis indicated a slight positive bias between model predictions and the clinical experts judgement (mean difference 0.98 units, limits of agreement from -0.83 to 2.78 units, reported to 3 s.f.). This analysis also revealed that the traffic light system, designed to indicate the risk of cognitive decline, can serve as a complementary source of information to enhance decision support. This is especially evaluated for its applicability in improving clinical decision-making for high-risk groups, where there is often insufficient data available regarding an individuals cognitive status. CONCLUSIONSThe study offers new insights into sleep and dementia, highlighting how age and sex differences manifest differently in typical ageing compared to dementia. We demonstrate the utility of leveraging sleep monitoring data and predictive analysis in identifying individuals who may benefit from further clinical evaluation and early disease-modifying interventions.