title: A sensitivity analysis on the variability in accelerometer data processing for monitoring physical activity.
abstract: BACKGROUND
  Accelerometers are gaining popularity for measuring physical activity, but there are many different ways to process accelerometer data. A sensitivity analysis was conducted to study the effect of varying accelerometer data processing protocols on estimating the association between PA level and socio-demographic characteristics using the National Health and Nutrition Examination Survey (NHANES) accelerometer data.
  
  
  METHODS
  The NHANES waves 2003-2004 and 2005-2006 accelerometer data (n=14,072) were used to investigate the effect of changing the accelerometer non-wearing time and valid day definitions on the demographic composition of the filtered datasets and the association between physical activity (PA) and socio-demographic characteristics (sex, age, race, educational level, marital status).
  
  
  RESULTS
  Under different filtering rules (minimum number of valid day and definition of non-wear time), the demographic characteristics of the final sample varied. The proportion of participants aged 20-29 decreased from 18.9% to 15.8% when the minimum number of valid days required increased from 1 to 4 (p for trend<0.001), whereas that for aged ≥70 years increased from 18.9% to 20.6% (p for trend<0.001). Furthermore, with different filters, the effect of these demographic variables and PA varied, with some variables being significant under certain filtering rules but becoming insignificant under some other rules.
  
  
  CONCLUSIONS
  The sensitivity analysis showed that the significance of the association between socio-demographic variables and PA could be varied with the definition of non-wearing time and minimum number of valid days.
authors.affiliation: School of Nursing, Hong Kong Polytechnic University, Hong Kong. Electronic address: paul.h.lee@polyu.edu.hk.
authors.forename: Paul H
authors.initials: PH
authors.lastname: Lee
mesh_terms: D061725:Accelerometry / Q000379:methods* / Q000706:statistics & numerical data*; D000328:Adult; D000367:Age Factors; D000368:Aged; D004522:Educational Status; D001330:Electronic Data Processing / Q000379:methods / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008019:Life Style; D008297:Male; D017533:Marital Status; D008875:Middle Aged; D009043:Motor Activity / Q000502:physiology*; D009749:Nutrition Surveys; D012016:Reference Values; D055815:Young Adult
keywords: Accelerometry; Data analysis; Lifestyles; Motor activity; NHANES