Evaluating sleep patterns and intrinsic capacity with machine learning: Results from the Gan-Dau healthy longevity plan

Sep 24, 2025Archives of gerontology and geriatrics

Using machine learning to link sleep patterns with natural abilities in healthy older adults

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Longevity & Aging on OpenScience ↗PubMed ↗DOI ↗

Abstract

Low intrinsic capacity (IC) is associated with higher Pittsburgh Sleep Quality Index (PSQI) scores (OR 1.10, 95% CI 1.05-1.15, p<0.001).

Higher PSQI scores indicate poorer sleep quality and are linked to lower IC.
Individuals with a PSQI greater than 5 show lower scores in psychological wellbeing and vitality subdomains.
Three categories of sleep patterns identified through machine learning—worst sleepers, short and inefficient sleepers, and inefficient sleepers—are associated with increased risk for low IC.
The worst sleepers category has an odds ratio of 2.54 for low IC, while the short and inefficient and inefficient sleepers categories have odds ratios of 1.69 and 1.50, respectively.

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BACKGROUND: This study aims to examine the association between sleep and intrinsic capacity (IC), employing a machine-learning approach, to promote healthy aging and disability prevention in the community.

METHODS: A cohort of 810 community-dwelling individuals aged 50 years were enrolled. Sleep patterns were assessed using the Pittsburgh Sleep Quality Index (PSQI) and its subdomains. Unsupervised machine learning through K-means clustering was applied to classify sleep patterns into four distinct categories, enabling further analysis. IC was evaluated by assessing cognitive, locomotion, vitality, psychological, and sensory functions and was subsequently rescaled using the percent of the maximum possible method.

RESULTS: Low IC was linked to higher PSQI (OR 1.10, 95% CI 1.05-1.15, p<0.001), as well as subdomains indicating poor sleep quality, lower habitual sleep efficiency, and increased sleep disturbances. Poor sleep quality (PSQI >5) was associated with low IC and lower scores in the psychological wellbeing, and vitality subdomains. Results of K-means clustering analysis showed: Category 1 (worst sleepers) (OR 2.54, 95% CI 1.55-4.16, p<0.001), Category 2 (short and inefficient sleepers, OR 1.69, 95% CI 1.18-2.43, p=0.004), and Category 3 (inefficient sleepers, OR 1.50, 95% CI 1.02-2.20, p=0.037) exhibited a higher risk for low IC compared to robust sleepers.

CONCLUSIONS: The study highlights the crucial role of sleep quality in maintaining intrinsic capacity and promoting healthy aging. Impairments in psychological wellbeing and vitality were identified as the primary contributors. This emphasizes the importance of promoting healthy sleep habits for overall well-being.

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Evaluating sleep patterns and intrinsic capacity with machine learning: Results from the Gan-Dau healthy longevity plan

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