Digital markers and phenotypes of rest-activity rhythms in people with advanced dementia using real-time location data

No SJR dataJan 15, 2026The journals of gerontology. Series A, Biological sciences and medical sciences

Digital signs and daily rest-activity patterns in people with advanced dementia tracked by real-time location data

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Circadian Biology on OpenScience ↗PubMed ↗DOI ↗OA ↗

Abstract

Wrist-worn RTLS devices tracked location for up to 16 weeks in 47 participants with advanced dementia.

Higher activity intensity is correlated with increased scores of clinical motor agitation.
Disrupted sleep patterns and reduced time in bed are associated with difficulty falling asleep and increased nighttime motor agitation.
Unsupervised machine learning identified six distinct rest-activity phenotypes, which include high time in bed and severe rhythm disturbance.
These phenotypes vary based on factors such as age, cognition, mood disturbance, and functional status.
Increased activity intensity, reduced rhythmicity, and less nighttime time in bed are linked to higher levels of motor agitation and sleep disruption.

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BACKGROUND: Disrupted sleep and circadian rhythms in dementia affect rest-activity patterns and impact quality of life, safety, and caregiver burden. The objective of this study is to extend the application of real-time location system (RTLS) technology from nurse call and elopement prevention systems to measuring digital markers and phenotypes of rest-activity in people with advanced dementia in residential care.

METHODS: Wrist-worn RTLS devices continuously tracked location for up to 16 weeks in 47 participants (21 women, mean age 80.1 years) on a specialized dementia unit. Distance moved in 15-minute windows was used to derive digital markers including parametric and non-parametric features. Panel and mixed effect models were used to investigate the relationship between clinical assessments and RTLS-based digital markers and phenotypes of rest-activity rhythm.

RESULTS: Higher activity intensity was correlated with increased clinical motor agitation scores, and disrupted rhythmicity and reduced time in bed were associated with difficulty falling sleep and increased nighttime motor agitation. Unsupervised machine learning identified six distinct rest-activity phenotypes over one-week periods. These phenotypes include high time in bed, well-regulated, low stability, severe rhythm disturbance, nighttime active, and a highly active individual. Phenotypes differed by age, cognition, mood disturbance, and functional status. Increased activity intensity, decreased rhythmicity, and less nighttime time in bed were associated with higher motor agitation and sleep disruption.

CONCLUSIONS: RTLS-derived rest-activity markers and phenotypes were associated with changes in clinical assessments over time and may support data-driven, evidence-based dementia care.

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