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Negative binomial mixed effects location-scale models for intensive longitudinal count-type physical activity data

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Wearable devices provide detailed physical activity (PA) data. We propose a statistical model to analyze PA mean and variability, improving health insights from activity counts.

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Area of Science:

  • Biomedical Engineering
  • Public Health
  • Statistics

Background:

  • Wearable devices like accelerometers are increasingly used for precise physical activity (PA) tracking.
  • PA data, such as steps and moderate-to-vigorous PA (MVPA) minutes, are crucial health indicators often recorded as counts.
  • Longitudinal PA data allows for modeling both the average PA level and individual variability in activity patterns.

Purpose of the Study:

  • To introduce a statistical model capable of analyzing intensive longitudinal PA count data.
  • To account for individual differences in both the average PA levels and the dispersion (variability) of PA.
  • To address the common issue of excess zeros in PA data by incorporating hurdle or zero-inflated components.

Main Methods:

  • Development of a negative binomial mixed-effects location-scale model for PA count data.
  • Application of the model to capture heterogeneity in both the mean and dispersion of PA across subjects.
  • Extension of the model to a hurdle/zero-inflated version to manage excessive zeros in PA measurements.

Main Results:

  • The proposed location-scale model effectively captures variations in PA mean and dispersion.
  • The hurdle/zero-inflated extension successfully models the probability of non-zero PA levels.
  • This approach provides a more comprehensive understanding of individual PA patterns.

Conclusions:

  • The negative binomial mixed-effects location-scale model offers a robust framework for analyzing wearable PA data.
  • Accounting for dispersion heterogeneity and excess zeros enhances the accuracy of PA pattern modeling.
  • This methodology can lead to more personalized health recommendations based on individual activity profiles.