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摘要
Objectives: 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. Design: 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.
- 出版日期2015-11
