title: A multilevel approach to examining time-specific effects in accelerometer-assessed physical activity.
abstract: OBJECTIVE
  Popular methods for analyzing accelerometer data often use a single physical activity outcome variable such as average-weekly or total physical activity. These approaches limit the types of research questions that can be answered and fail to utilize the detailed, time-specific information available from accelerometers. This study proposes the use of multilevel modeling, which tested intervention effects at specific time periods.
  
  
  METHODS
  The motivating example was the Active by Choice Today trial. Simulations were used to test whether the application of time-specific hypotheses about when physical activity intervention treatment effects were expected to occur (e.g., after-school hours) increased power to detect effects compared to traditional methods.
  
  
  METHODS
  Six simulation conditions were tested: (1) no treatment effects (to test the type 1 error rate), (2) time-specific effects, but no traditionally-tested effects, (3) traditionally-tested effects, but no time-specific effects, and (4) combinations of traditional and time-specific effects in 3 proportions.
  
  
  RESULTS
  Results showed the proposed multilevel approach demonstrated appropriate type 1 error rates and increased power to detect treatment effects during hypothesized times by 31-38 percentage points compared to traditional approaches. This was consistent across varying proportions of traditional versus time-specific effects, and there was no loss of power using the multilevel approach when only traditional effects were present.
  
  
  CONCLUSIONS
  The current study showed potential advantages of testing time-specific hypotheses about intervention effects using a multilevel time-specific approach. This approach may show intervention effects when traditional approaches do not. Future research should explore the application of this additional analytic tool for accelerometer physical activity estimates.
authors.affiliation: Center for Obesity Research and Education, Temple University, USA. Electronic address: hlawman86@gmail.com.
authors.forename: Hannah G
authors.initials: HG
authors.lastname: Lawman
authors.affiliation: Department of Psychology, University of South Carolina, USA.
authors.forename: M Lee Van
authors.initials: ML
authors.lastname: Horn
authors.affiliation: Department of Psychology, University of South Carolina, USA.
authors.forename: Dawn K
authors.initials: DK
authors.lastname: Wilson
authors.affiliation: Department of Exercise Science, Arnold School of Public Health, University of South Carolina, USA.
authors.forename: Russell R
authors.initials: RR
authors.lastname: Pate
mesh_terms: D061725:Accelerometry; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D023381:Endpoint Determination; D005260:Female; D006293:Health Promotion; D006801:Humans; D016014:Linear Models; D009043:Motor Activity; D017063:Outcome Assessment, Health Care / Q000379:methods*; D012574:Schools; D013997:Time Factors
keywords: Actical; Actigraph; CSA/MTI; Hierarchical linear models; Random coefficient models