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Research Perspective|Volume 16, Issue 17|pp 12108—12122

Longitudinal activity monitoring and lifespan: quantifying the interface

Su I Iao1, Poorbita Kundu1, Han Chen1, James R. Carey2, Hans-Georg Müller1
  • 1Department of Statistics, University of California, Davis, CA 95616, USA
  • 2Department of Entomology, University of California, Davis, CA 95616, USA
* Equal contribution
Received: July 23, 2024Accepted: August 13, 2024Published: September 9, 2024

Copyright: © 2024 Iao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Understanding the relationship between activity over the entire lifespan and longevity is an important facet of aging research. We present a comprehensive framework for the statistical analysis of longitudinal activity and behavioral monitoring and their relationship with age-at-death at the individual level, highlighting the importance of advanced methodological approaches in aging research. The focus is on animal models, where continuous monitoring activity in terms of movement, reproduction and behaviors over the entire lifespan is feasible at the individual level. We specifically demonstrate the methodology with data on activity monitoring for Mediterranean fruit flies. Advanced statistical methodologies to explore the interface between activity and age-at-death include functional principal component analysis, concurrent regression, Fréchet regression and point processes. While the focus of this perspective is on relating age-at-death with data on movement, reproduction, behavior and nutrition of Mediterranean fruit flies, the methodology equally pertains to data from other species, including human data.