title: Statistical approaches to account for missing values in accelerometer data: Applications to modeling physical activity.
abstract: Physical inactivity is a recognized risk factor for many chronic diseases. Accelerometers are increasingly used as an objective means to measure daily physical activity. One challenge in using these devices is missing data due to device nonwear. We used a well-characterized cohort of 333 overweight postmenopausal breast cancer survivors to examine missing data patterns of accelerometer outputs over the day. Based on these observed missingness patterns, we created psuedo-simulated datasets with realistic missing data patterns. We developed statistical methods to design imputation and variance weighting algorithms to account for missing data effects when fitting regression models. Bias and precision of each method were evaluated and compared. Our results indicated that not accounting for missing data in the analysis yielded unstable estimates in the regression analysis. Incorporating variance weights and/or subject-level imputation improved precision by >50%, compared to ignoring missing data. We recommend that these simple easy-to-implement statistical tools be used to improve analysis of accelerometer data.
authors.affiliation: 1 Department of Mathematics, UC San Diego, La Jolla, USA.
authors.forename: Selene
authors.initials: S
authors.lastname: Yue Xu
authors.affiliation: 2 Graduate School of Public Health, San Diego State University, San Diego, USA.|3 Department of Family Medicine and Public Health, UC San Diego, La Jolla, USA.
authors.forename: Sandahl
authors.initials: S
authors.lastname: Nelson
authors.affiliation: 3 Department of Family Medicine and Public Health, UC San Diego, La Jolla, USA.|4 Moores UC San Diego Cancer Center, UC San Diego, La Jolla, USA.|5 Center for Wireless and Population Health Sciences, UC San Diego, La Jolla, USA.
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Kerr
authors.affiliation: 5 Center for Wireless and Population Health Sciences, UC San Diego, La Jolla, USA.
authors.forename: Suneeta
authors.initials: S
authors.lastname: Godbole
authors.affiliation: 3 Department of Family Medicine and Public Health, UC San Diego, La Jolla, USA.|4 Moores UC San Diego Cancer Center, UC San Diego, La Jolla, USA.
authors.forename: Ruth
authors.initials: R
authors.lastname: Patterson
authors.affiliation: 5 Center for Wireless and Population Health Sciences, UC San Diego, La Jolla, USA.
authors.forename: Gina
authors.initials: G
authors.lastname: Merchant
authors.affiliation: 1 Department of Mathematics, UC San Diego, La Jolla, USA.
authors.forename: Ian
authors.initials: I
authors.lastname: Abramson
authors.affiliation: 6 Department of Mathematics and Statistics, University of Massachusetts, Amherst, USA.
authors.forename: John
authors.initials: J
authors.lastname: Staudenmayer
authors.affiliation: 3 Department of Family Medicine and Public Health, UC San Diego, La Jolla, USA.|4 Moores UC San Diego Cancer Center, UC San Diego, La Jolla, USA.
authors.forename: Loki
authors.initials: L
authors.lastname: Natarajan
mesh_terms: D061725:Accelerometry; D015982:Bias; D001943:Breast Neoplasms; D000073116:Cancer Survivors; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D015444:Exercise; D005260:Female; D006801:Humans; D050177:Overweight; D012044:Regression Analysis
keywords: Accelerometer data; linear mixed effects model; missing data; physical activity; weighted regression