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Updated: Jan 12, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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Aging and activity patterns: actigraphy evidence from NHANES studies.

Wen Luo1, Matthew T Scharf2, Ioannis P Androulakis1

  • 1Biomedical Engineering Department, Rutgers University, New Brunswick, NJ, United States.

Frontiers in Systems Biology
|October 30, 2025
PubMed
Summary
This summary is machine-generated.

As people age, their sleep-wake cycles and activity patterns shift, with older adults showing more structured schedules and younger individuals having later sleep times. These changes highlight the need for personalized health strategies across the lifespan.

Keywords:
NHANESactigraphyagingchronotypecircadian rhythmsmachine learningphysical activitysleep-wake cycle

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Area of Science:

  • Chronobiology and Sleep Science
  • Gerontology and Aging Research
  • Systems Biology and Network Dynamics

Background:

  • Activity patterns and sleep-wake cycles undergo significant age-related changes.
  • Understanding these shifts is crucial for developing targeted interventions for healthy aging.
  • Actigraphy provides a valuable tool for phenotyping rest-activity behavior across the lifespan.

Purpose of the Study:

  • To examine age-related variations in activity patterns using National Health and Nutrition Examination Survey (NHANES) actigraphy data.
  • To identify distinct, age-dependent rest-activity phenotypes using machine learning.
  • To uncover changes in chronotype and physical engagement associated with aging.

Main Methods:

  • Analysis of actigraphy data from NHANES (2011-2013) across four age groups.
  • Application of unsupervised machine learning to cluster average-day rest-activity profiles.
  • Derivation of key metrics such as winding down activity and time to alertness.

Main Results:

  • Younger individuals displayed delayed chronotypes with later sleep/wake times; older adults showed advanced, structured schedules.
  • Winding down periods extended with age, and overall activity levels declined progressively.
  • Time to alertness correlation with wake time weakened with age, indicating reduced circadian influence.

Conclusions:

  • Aging is linked to significant shifts in sleep-wake cycles and activity patterns, reflecting biological and behavioral adaptations.
  • Actigraphy-derived "dynamical phenotypes" offer insights for systems-level models of sleep-wake regulation.
  • Findings support personalized interventions for sleep and activity alignment throughout life, informing public health and clinical approaches to aging.