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New research · AI in Medicine
NPJ digital medicine · 1d
Cohort studyNPJ digital medicine · 2026

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

Nan Fletcher-Lloyd, Nathalia Céspedes Gómez, Alexander Capstick … Payam Barnaghi
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AI in MedicineCohort study

Remote sleep monitoring model identifies dementia with 75.7% sensitivity.

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

Nan Fletcher-Lloyd et al. · NPJ digital medicine · 2026
Background

Examining sleep patterns in relation to chronological ageing and dementia can provide insights for risk screening.

Methods

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.

n = 1672
Results

This is the percentage of people with dementia that the sleep model correctly identified

mean difference 0.98 (95% CI -0.83 to 2.78)
null = 0-0.832.78
CI crosses the null - not significant
More results

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.

More results

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).

“
Conclusion

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.

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