Kapphahn Kristopher I, Banda Jorge A, Haydel K Farish, Robinson Thomas N, Desai Manisha
Quantitative Sciences Unit, Stanford University, Stanford, CA, USA.
Department of Public Health, Purdue University, West Lafayette, IN, USA.
J Meas Phys Behav. 2022 Sep;5(3):132-144. doi: 10.1123/jmpb.2021-0030. Epub 2022 Jul 12.
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.
加速度计数据在研究中被广泛用于提供身体活动的客观测量。参与者在观察期间经常会取下加速度计，从而导致数据缺失，即非佩戴期。处理非佩戴期的常见方法包括在分析中丢弃数据（小时数不足的天数或有效天数不足的个体）以及单插补（SI）方法。
本研究评估了各种基于丢弃、单插补和多重插补（MI）的方法在准确和精确表征加速度计计数汇总测量值（每分钟平均计数）与结果（体重指数）之间关系的能力。
在各种导致非佩戴的场景下模拟了逼真的加速度计数据。使用处理非佩戴的常见方法和多重插补方法对数据进行分析。比较了各方法之间的偏差、相对标准误差、相对均方误差和覆盖概率。
多重插补方法优于常用方法，偏差范围为 -0.001 至 -0.028，远低于基于丢弃的方法（范围为 -0.050 至 -0.057）和单插补方法（范围为 -0.061 至 -0.081）。我们还报告了多重插补策略之间存在显著差异，覆盖概率范围为 0.04 至 0.96。
我们的研究结果表明，应用多重插补方法比更常用的基于丢弃和单插补的方法更具优势。此外，我们表明多重插补的应用方式很重要，在使用多重插补时，在插补模型中纳入先前观察到的加速度测量数据可提高模型性能。
