title: Data imputation for accelerometer-measured physical activity: the combined approach.
abstract: BACKGROUND
  Accelerometers are gaining popularity for the assessment of the physical activity level; however, compliance is a problem that results in missing data. Data from study days in which the accelerometer is not worn for a number of hours that are sufficient to reach a predetermined cutoff value are considered invalid and discarded. The problem of missing data is commonly handled by imputation; however, all traditional imputation methods ignore the available information from invalid days.
  
  
  OBJECTIVE
  In this study, I propose a new approach to the imputation of missing accelerometer data that takes into account the data available from invalid days.
  
  
  METHODS
  A total of 4069 participants in NHANES waves 2003-2004 and 2005-2006 who provided 7 d of valid accelerometer data were used to illustrate this new approach. The method of imputation was a combined approach that combined the available data from valid days and invalid days to impute missing values. Simulation studies were carried out to compare this new combined approach with the traditional imputation method for 1) accuracy and 2) effect-size estimation of the sex-physical activity relation by using the root mean squared error (RMSE).
  
  
  RESULTS
  The combined approach performed significantly better than traditional imputation method (all t tests P < 0.001), with the percentage reduction of the RMSE for accuracy and effect-size estimation that ranged from 12.4% to 17.3% and 19.8% to 32.9%, respectively.
  
  
  CONCLUSIONS
  The combined approach significantly outperforms the traditional imputation algorithm.
authors.affiliation: School of Public Health, University of Hong Kong, Hong Kong. honglee@graduate.hku.hk
authors.forename: Paul H
authors.initials: PH
authors.lastname: Lee
mesh_terms: D061725:Accelerometry / Q000295:instrumentation* / Q000379:methods*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D002648:Child; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009043:Motor Activity; D009749:Nutrition Surveys / Q000379:methods*; D011336:Probability; D055815:Young Adult