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Analysing longitudinal wearable physical activity data using non-stationary time series models.

Melina Del Angel1, Matthew Nunes2, Oliver Peacock1

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Summary

This study introduces a novel Trend Locally Stationary Wavelet (TLSW) model to analyze longitudinal physical activity data from wearable devices. The new method, including Time in Reference Region of Variability (TIRRV), offers robust insights into temporal changes in physical activity for individuals and groups.

Keywords:
Non-stationary time seriesPhysical activity data analysisTrend estimationWearable devices

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Area of Science:

  • Biomedical Engineering
  • Data Science
  • Statistics

Background:

  • Wearable devices offer new ways to monitor physical activity.
  • Traditional methods often ignore temporal changes, focusing on averages or snapshots.
  • A novel statistical method is needed to analyze longitudinal physical activity data, accounting for temporal structure.

Purpose of the Study:

  • To develop and validate a novel statistical method for analyzing longitudinal physical activity data from wearable devices.
  • To account for the temporal structure and dynamics within the data.
  • To provide enhanced understanding of physical activity patterns over time.

Main Methods:

  • Utilized secondary data from the Multidimensional Individualised Physical Activity (MIPACT) randomized controlled trial.
  • Applied a Trend Locally Stationary Wavelet (TLSW) model to analyze 12-week physical activity data from 80 participants.
  • Introduced the Time in Reference Region of Variability (TIRRV) metric to assess individual changes relative to baseline.

Main Results:

  • Demonstrated the effectiveness of the TLSW approach for analyzing hourly resolution physical activity data.
  • The TLSW model accounts for time dependency and data structure, enabling detailed trend and confidence interval analysis.
  • The TIRRV metric provides a baseline-informed assessment of individual and group progress over time, yielding robust insights.

Conclusions:

  • The TLSW-based approach is a novel and effective method for analyzing high-resolution physical activity data from wearable technology.
  • TLSW trends robustly characterize individual and group behavior over extended periods.
  • This approach enhances the understanding of temporal changes in device-measured physical activity for researchers, clinicians, and patients.