Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study.
Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study.
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DOI：
10.2196/17977
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发表时间：
2020-09-11
影响因子：
5
通讯作者：
Stubbs RJ
中科院分区：
文献类型：
--
作者：
Turicchi J;O'Driscoll R;Finlayson G;Duarte C;Palmeira AL;Larsen SC;Heitmann BL;Stubbs RJ
体重变异性（BWV）在一般人群中很常见，可能是肥胖或疾病的危险因素。这些模式的正确识别可能在临床和研究环境中具有预后或预测价值。随着技术的进步，允许从电子智能秤频繁收集体重数据，分析和识别体重数据模式的新机会是可用的。本研究旨在比较使用线性和非线性方法进行数据插补和BWV计算的多种方法。总共选择了来自正在进行的减肥维持研究（NoHoW研究）的50名参与者来开发该程序。我们讨论了数据分析的以下方面：清理、插补、去趋势以及总BWV和局部BWV的计算。为了检验插补，随机模拟缺失数据并使用缺失的真实的模式。总共测试了10种插补策略。接下来，使用线性和非线性方法计算BWV，并研究缺失数据和数据插补对这些估计值的影响。使用Kalman平滑或指数加权移动平均的结构建模进行体重插补，与观察值的一致性最好（均方根误差范围为0.62%-0.64%）。插补性能随缺失而下降，随机和非随机模拟之间相似。与插补策略相比，缺失模拟数据集的BWV估计误差较低（2%-7%，80%缺失数据或平均值为67，SD 40.1可用体重），插补策略的误差显著更大，不同插补方法不同。插补体重数据的决定取决于分析目的。最好的执行插补方法的方向提供。为了估计BWV，不应进行数据插补。估计BWV的线性和非线性方法在高比例（80%）缺失数据下提供了合理准确的估计值。
Body weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With advancements in technology allowing for the frequent collection of body weight data from electronic smart scales, new opportunities to analyze and identify patterns in body weight data are available. This study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches In total, 50 participants from an ongoing weight loss maintenance study (the NoHoW study) were selected to develop the procedure. We addressed the following aspects of data analysis: cleaning, imputation, detrending, and calculation of total and local BWV. To test imputation, missing data were simulated at random and using real patterns of missingness. A total of 10 imputation strategies were tested. Next, BWV was calculated using linear and nonlinear approaches, and the effects of missing data and data imputation on these estimates were investigated. Body weight imputation using structural modeling with Kalman smoothing or an exponentially weighted moving average provided the best agreement with observed values (root mean square error range 0.62%-0.64%). Imputation performance decreased with missingness and was similar between random and nonrandom simulations. Errors in BWV estimations from missing simulated data sets were low (2%-7% with 80% missing data or a mean of 67, SD 40.1 available body weights) compared with that of imputation strategies where errors were significantly greater, varying by imputation method. The decision to impute body weight data depends on the purpose of the analysis. Directions for the best performing imputation methods are provided. For the purpose of estimating BWV, data imputation should not be conducted. Linear and nonlinear methods of estimating BWV provide reasonably accurate estimates under high proportions (80%) of missing data.
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DOI：
10.1002/oby.21925
发表时间：
2017-09
期刊：
Obesity (Silver Spring, Md.)
影响因子：
--
作者：
Feig EH;Lowe MR
通讯作者：
Lowe MR
影响因子：
3.7
作者：
Chen C;Twycross J;Garibaldi JM
通讯作者：
Garibaldi JM
影响因子：
2.9
作者：
Aucott LS;Philip S;Avenell A;Afolabi E;Sattar N;Wild S;Scottish Diabetes Research Network Epidemiology Group
通讯作者：
Scottish Diabetes Research Network Epidemiology Group
影响因子：
158.5
作者：
Bangalore, Sripal;Fayyad, Rana;Waters, David D.
通讯作者：
Waters, David D.
影响因子：
15.2
作者：
Hicks, Jennifer L.;Althoff, Tim;Delp, Scott L.
通讯作者：
Delp, Scott L.