Simulation-Based Evaluation of Methods for Handling Nonwear Time in Accelerometer Studies of Physical Activity

Accelerometer data are widely used in research to provide objective measurements of physical activity. Frequently, participants may remove accelerometers during their observation period resulting in missing data referred to as nonwear periods. Common approaches for handling nonwear periods include discarding data (days with insufficient hours or individuals with insufficient valid days) from analyses and single imputation (SI) methods.

This study evaluates the performance of various discard-, SI-, and multiple imputation (MI)-based approaches on the ability to accurately and precisely characterize the relationship between a summarized measure of accelerometer counts (mean counts per minute) and an outcome (body mass index).

Realistic accelerometer data were simulated under various scenarios that induced nonwear. Data were analyzed using common and MI methods for handling nonwear. Bias, relative standard error, relative mean squared error, and coverage probabilities were compared across methods.

MI approaches were superior to commonly applied methods, with bias that ranged from -0.001 to -0.028 that was considerably lower than that of discard-based methods (ranging from -0.050 to -0.057) and SI methods (ranging from -0.061 to -0.081). We also reported substantial variation among MI strategies, with coverage probabilities ranging from .04 to .96.

Our findings demonstrate the benefit of applying MI methods over more commonly applied discard- and SI-based approaches. Additionally, we show that how you apply MI matters, where including data from previously observed acceleration measurements in the imputation model when using MI improves model performance.