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Missing value imputation for physical activity data measured by accelerometer.

Jung Ae Lee1, Jeff Gill2

  • 11 Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine in Saint Louis, Saint Louis, MO, USA.

Statistical Methods in Medical Research
|March 20, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to fill in missing physical activity data from wearable accelerometers. The technique improves statistical analysis of activity patterns, even with irregular data gaps.

Keywords:
AccelerometerPoisson log-normalmissing count datamultiple imputationphysical activityzero-inflated model

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Area of Science:

  • Biomedical Engineering
  • Epidemiology
  • Biostatistics

Background:

  • Accelerometers are widely used wearable motion sensors for measuring physical activity in clinical and epidemiological research.
  • Accelerometer data, recorded at minute-level intervals, reveal daily activity patterns but are prone to missing data due to participant noncompliance.
  • Missing data in accelerometer recordings pose significant challenges for accurate statistical analysis of physical activity.

Purpose of the Study:

  • To develop a novel imputation method specifically designed for multivariate count data from accelerometers.
  • To address the complexities of irregular missing intervals in wearable sensor data.
  • To enhance the statistical analysis of physical activity patterns derived from accelerometer measurements.

Main Methods:

  • A novel imputation method was developed using a mixture of zero-inflated Poisson and Log-normal distributions for the predictive distribution of missing data.
  • The imputation process was conducted at the minute level, employing multiple imputation principles via a fully conditional specification (FCS) chained algorithm.
  • An R package, accelmissing, was created to facilitate the practical application of the proposed imputation method.

Main Results:

  • The developed imputation method effectively handles minute-by-minute autocorrelation in count data.
  • The approach successfully addresses under- and over-dispersion issues common in accelerometer count data.
  • Demonstration using National Health and Nutrition Examination Survey (NHANES) data validated the method's utility.

Conclusions:

  • The novel imputation method provides a robust solution for handling missing multivariate count data from accelerometers.
  • The accelmissing R package offers a practical tool for researchers to improve the analysis of physical activity data.
  • Accurate imputation of missing accelerometer data is crucial for reliable insights in physical activity research.