Abstract
BackgroundBody 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.ObjectiveThis study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approachesMethodsIn 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.ResultsBody 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.ConclusionsThe 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.
Highlights
BackgroundRecently, the idea of remote health care monitored through a network of internet-connected devices, termed The (Medical) Internet of Things [1,2,3], has become popular, and in 2020, it is thought that 40% of internet of things–related technology is health related, accounting for US $117 billion [4]
This study evaluated the performance of various imputation methods applied to body weight data and presented a protocol for estimating Body weight variability (BWV) under varying amounts of missing data
We showed that structural modeling with a Kalman smoother and exponentially weighted moving average (EWMA) performed an imputation most effectively
Summary
BackgroundRecently, the idea of remote health care monitored through a network of internet-connected devices, termed The (Medical) Internet of Things [1,2,3], has become popular, and in 2020, it is thought that 40% of internet of things–related technology is health related, accounting for US $117 billion [4]. Regular self-weighing in research environments using tracking technologies will allow for more accurate recognition of body weight patterns, which are currently not well understood. Objective: This study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches Methods: In total, 50 participants from an ongoing weight loss maintenance study (the NoHoW study) were selected to develop the procedure. 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. Linear and nonlinear methods of estimating BWV provide reasonably accurate estimates under high proportions (80%) of missing data
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