What question did this study set out to answer?

This study aims to assess how variability in sleep and heart rate can serve as indicators for early cognitive decline in individuals.

June 13, 2026Yonsei Medical JournalOpen Access

Longitudinal Variability of Wearable-Derived Sleep and Heart Rate as a Digital Biomarker for Early Detection of Mild Cognitive Impairment

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Randomized trial evaluates variability in sleep and heart rate for early mild cognitive impairment detection, suggesting new screening methods.

This study aims to assess how variability in sleep and heart rate can serve as indicators for early cognitive decline in individuals.
Collected nocturnal data from 162 participants using smart rings over an average of 70 days.
Derived variability-based features and compared them to mean-based models to evaluate predictive capabilities.
Employed logistic regression models with stepwise integration and model validation through cross-validation.
Variability-focused models showed better performance than mean-only models, achieving an area under the curve of 0.861 and a sensitivity of 0.820.
Top predictors identified include HR_drop variability and sleep-behavior rhythm, highlighting their significance in detecting cognitive decline.
Mean effects showed instability across different time windows, indicating the importance of variability over long-window averages.

Kim et al. (2026) studied this question.

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June 13, 2026Yonsei Medical JournalOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial evaluates variability in sleep and heart rate for early mild cognitive impairment detection, suggesting new screening methods.

This study aims to assess how variability in sleep and heart rate can serve as indicators for early cognitive decline in individuals.
Collected nocturnal data from 162 participants using smart rings over an average of 70 days.
Derived variability-based features and compared them to mean-based models to evaluate predictive capabilities.
Employed logistic regression models with stepwise integration and model validation through cross-validation.
Variability-focused models showed better performance than mean-only models, achieving an area under the curve of 0.861 and a sensitivity of 0.820.
Top predictors identified include HR_drop variability and sleep-behavior rhythm, highlighting their significance in detecting cognitive decline.
Mean effects showed instability across different time windows, indicating the importance of variability over long-window averages.

Kim et al. (2026) studied this question.

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