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Longitudinal functional additive model with continuous proportional outcomes for physical activity data.

Haocheng Li1, Sarah Kozey-Keadle2, Victor Kipnis3

  • 1Departments of Oncology and Community Health Sciences, University of Calgary, Calgary, AB, T2N 1N4, Canada.

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|December 2, 2016
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Summary

Physical activity positively impacts sleep efficiency, with higher activity leading to better sleep. Increased physical activity also reduces the variability in sleep efficiency over time.

Keywords:
BodyMedia FIT devicecontinuous proportionsfunctional datamixed-effects modelphysical activitysleep efficiency

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Area of Science:

  • Biostatistics
  • Wearable Technology
  • Sleep Science

Background:

  • Longitudinal studies with continuous proportional outcomes present analytical challenges.
  • Wearable devices like BodyMedia FIT generate extensive physical activity data.
  • Understanding the relationship between physical activity and sleep quality is crucial for public health.

Purpose of the Study:

  • To develop a functional data analysis approach for longitudinal studies with continuous proportional outcomes.
  • To model the relationship between physical activity and sleep efficiency using a three-factor functional model.
  • To address the challenges of analyzing continuous proportion variables in this context.

Main Methods:

  • A three-factor functional model incorporating random effects with a correlation structure.
  • Summarization of continuous factor random curves using principal components analysis.
  • Application of a quasilikelihood approximation to handle continuous proportion variables.
  • Development of an efficient algorithm for model fitting, including principal component selection.

Main Results:

  • Sleep efficiency demonstrates a positive correlation with increased physical activity.
  • The variance of sleep efficiency decreases as physical activity levels rise.
  • The developed functional data approach effectively models the complex relationship.

Conclusions:

  • The functional data approach provides a robust method for analyzing physical activity and sleep efficiency from wearable device data.
  • Increased physical activity is associated with improved sleep efficiency and reduced sleep efficiency variability.
  • This methodology offers valuable insights for health and wellness interventions.