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

Examining sleep patterns in relation to chronological ageing and dementia can provide insights for risk screening. Integrating predictive models with remote sleep monitoring enables routine assessment of cognitive decline symptoms in high-risk groups, aiding early risk identification. We developed a machine learning pipeline to estimate Sleep Age Index from longitudinal under-the-mattress sleep sensor data in the general population and a dementia cohort (n = 1672; person-samples = 18,369), using it to identify dementia risk. Risk scores were stratified into high, medium and low-risk categories to support clinical decision-making. Our study indicates that sleep patterns in dementia do not follow typical ageing processes, with the pre-trained model showing greater deviation from "normative" age-related patterns. These deviations were associated with irregular bed- and rise-times and reduced night-to-night variability in deep sleep. Chronological age was predicted from sleep data with a mean absolute error of 5.52 (95% CI: 5.37-5.67) on held-out data. In dementia versus control, the model achieved 75.7% (95% CI: 71.4%-79.9%) sensitivity and 74.7% (95% CI: 69.2%-80.0%) specificity post-stratification on unseen data. In a pilot high-risk cohort (n = 50), model predictions showed slight positive bias relative to clinical judgement (mean difference 0.98, limits of agreement -0.83-2.78). These findings demonstrate the potential of remote sleep monitoring and predictive modelling in identifying individuals who may benefit from further clinical evaluation and early intervention.