title: Missing value imputation for physical activity data measured by accelerometer.
abstract: An accelerometer, a wearable motion sensor on the hip or wrist, is becoming a popular tool in clinical and epidemiological studies for measuring the physical activity. Such data provide a series of activity counts at every minute or even more often and displays a person's activity pattern throughout a day. Unfortunately, the collected data can include irregular missing intervals because of noncompliance of participants and therefore make the statistical analysis more challenging. The purpose of this study is to develop a novel imputation method to handle the multivariate count data, motivated by the accelerometer data structure. We specify the predictive distribution of the missing data with a mixture of zero-inflated Poisson and Log-normal distribution, which is shown to be effective to deal with the minute-by-minute autocorrelation as well as under- and over-dispersion of count data. The imputation is performed at the minute level and follows the principles of multiple imputation using a fully conditional specification with the chained algorithm. To facilitate the practical use of this method, we provide an R package accelmissing. Our method is demonstrated using 2003-2004 National Health and Nutrition Examination Survey data.
authors.affiliation: 1 Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine in Saint Louis, Saint Louis, MO, USA.
authors.forename: Jung Ae
authors.initials: JA
authors.lastname: Lee
authors.affiliation: 2 Division of Public Health Sciences, Department of Surgery, School of Medicine and Department of Political Science, College of Arts and Sciences, Washington University in Saint Louis, Saint Louis, MO, USA.
authors.forename: Jeff
authors.initials: J
authors.lastname: Gill
mesh_terms: D061725:Accelerometry / Q000295:instrumentation* / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D056808:Biostatistics; D002648:Child; D003627:Data Interpretation, Statistical; D015444:Exercise; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000706:statistics & numerical data; D015999:Multivariate Analysis; D009749:Nutrition Surveys / Q000706:statistics & numerical data; D016012:Poisson Distribution; D000076251:Wearable Electronic Devices / Q000706:statistics & numerical data*; D055815:Young Adult
keywords: Accelerometer; Poisson log-normal; missing count data; multiple imputation; physical activity; zero-inflated model