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

Integrating predictive models with remote sleep monitoring enables routine assessment of cognitive decline symptoms in high-risk groups, aiding early risk identification.

Risk scores were stratified into high, medium and low-risk categories to support clinical decision-making.

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 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).