title: Is missing geographic positioning system data in accelerometry studies a problem, and is imputation the solution?
abstract: The main purpose of the present study was to assess the impact of global positioning system (GPS) signal lapse on physical activity analyses, discover any existing associations between missing GPS data and environmental and demographics attributes, and to determine whether imputation is an accurate and viable method for correcting GPS data loss. Accelerometer and GPS data of 782 participants from 8 studies were pooled to represent a range of lifestyles and interactions with the built environment. Periods of GPS signal lapse were identified and extracted. Generalised linear mixed models were run with the number of lapses and the length of lapses as outcomes. The signal lapses were imputed using a simple ruleset, and imputation was validated against person-worn camera imagery. A final generalised linear mixed model was used to identify the difference between the amount of GPS minutes pre- and post-imputation for the activity categories of sedentary, light, and moderate-to-vigorous physical activity. Over 17% of the dataset was comprised of GPS data lapses. No strong associations were found between increasing lapse length and number of lapses and the demographic and built environment variables. A significant difference was found between the pre- and postimputation minutes for each activity category. No demographic or environmental bias was found for length or number of lapses, but imputation of GPS data may make a significant difference for inclusion of physical activity data that occurred during a lapse. Imputing GPS data lapses is a viable technique for returning spatial context to accelerometer data and improving the completeness of the dataset.
authors.affiliation: Department of Family Medicine and Public Health, University of California, La Jolla, CA. kmeseck@ucsd.edu.
authors.forename: Kristin
authors.initials: K
authors.lastname: Meseck
authors.forename: Marta M
authors.initials: MM
authors.lastname: Jankowska
authors.forename: Jasper
authors.initials: J
authors.lastname: Schipperijn
authors.forename: Loki
authors.initials: L
authors.lastname: Natarajan
authors.forename: Suneeta
authors.initials: S
authors.lastname: Godbole
authors.forename: Jordan
authors.initials: J
authors.lastname: Carlson
authors.forename: Michelle
authors.initials: M
authors.lastname: Takemoto
authors.forename: Katie
authors.initials: K
authors.lastname: Crist
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Kerr
mesh_terms: D056044:Actigraphy / Q000592:standards*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003627:Data Interpretation, Statistical; D015444:Exercise; D005260:Female; D040362:Geographic Information Systems / Q000592:standards*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012111:Residence Characteristics; D062206:Spatial Analysis; D055815:Young Adult