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Quality Control Methods in Accelerometer Data Processing: Identifying Extreme Counts

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  • Carly Rich
  • Marco Geraci
  • Lucy Griffiths
  • Francesco Sera
  • Carol Dezateux
  • Mario Cortina-Borja

Abstract

Background: Accelerometers are designed to measure plausible human activity, however extremely high count values (EHCV) have been recorded in large-scale studies. Using population data, we develop methodological principles for establishing an EHCV threshold, propose a threshold to define EHCV in the ActiGraph GT1M, determine occurrences of EHCV in a large-scale study, identify device-specific error values, and investigate the influence of varying EHCV thresholds on daily vigorous PA (VPA). Methods: We estimated quantiles to analyse the distribution of all accelerometer positive count values obtained from 9005 seven-year old children participating in the UK Millennium Cohort Study. A threshold to identify EHCV was derived by differentiating the quantile function. Data were screened for device-specific error count values and EHCV, and a sensitivity analysis conducted to compare daily VPA estimates using three approaches to accounting for EHCV. Results: Using our proposed threshold of ≥ 11,715 counts/minute to identify EHCV, we found that only 0.7% of all non-zero counts measured in MCS children were EHCV; in 99.7% of these children, EHCV comprised

Suggested Citation

  • Carly Rich & Marco Geraci & Lucy Griffiths & Francesco Sera & Carol Dezateux & Mario Cortina-Borja, 2014. "Quality Control Methods in Accelerometer Data Processing: Identifying Extreme Counts," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-6, January.
    • Handle: RePEc:plo:pone00:0085134
    DOI: 10.1371/journal.pone.0085134
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