Quality control methods in accelerometer data processing: identifying extreme counts.
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 < 1% of total non-zero counts. Only 11 MCS children (0.12% of sample) returned accelerometers that contained negative counts; out of 237 such values, 211 counts were equal to -32,768 in one child. The medians of daily minutes spent in VPA obtained without excluding EHCV, and when using a higher threshold (≥19,442 counts/minute) were, respectively, 6.2% and 4.6% higher than when using our threshold (6.5 minutes; p<0.0001).


CONCLUSIONS
Quality control processes should be undertaken during accelerometer fieldwork and prior to analysing data to identify monitors recording error values and EHCV. The proposed threshold will improve the validity of VPA estimates in children's studies using the ActiGraph GT1M by ensuring only plausible data are analysed. These methods can be applied to define appropriate EHCV thresholds for different accelerometer models.
Medical Research Centre of Epidemiology for Child Health, University College London, London, United Kingdom.
Carly
C
Rich
Medical Research Centre of Epidemiology for Child Health, University College London, London, United Kingdom.
Marco
M
Geraci
Medical Research Centre of Epidemiology for Child Health, University College London, London, United Kingdom.
Lucy
L
Griffiths
Medical Research Centre of Epidemiology for Child Health, University College London, London, United Kingdom.
Francesco
F
Sera
Medical Research Centre of Epidemiology for Child Health, University College London, London, United Kingdom.
Carol
C
Dezateux
Medical Research Centre of Epidemiology for Child Health, University College London, London, United Kingdom.
Mario
M
Cortina-Borja
D061725:Accelerometry / Q000379:methods* / Q000592:standards*; D002648:Child; D001330:Electronic Data Processing; D006801:Humans; D009043:Motor Activity / Q000502:physiology; D011786:Quality Control; D013997:Time Factors