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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Data imputation and body weight variability calculation using linear and nonlinear methods in data collected from digital smart scales : Simulation and validation study. / Turicchi, Jake; O'Driscoll, Ruairi; Finlayson, Graham; Duarte, Cristiana; Palmeira, A. L.; Larsen, Sofus C.; Heitmann, Berit L.; James Stubbs, R.
I: JMIR mHealth and uHealth, Bind 8, Nr. 9, e17977, 2020.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Data imputation and body weight variability calculation using linear and nonlinear methods in data collected from digital smart scales
T2 - Simulation and validation study
AU - Turicchi, Jake
AU - O'Driscoll, Ruairi
AU - Finlayson, Graham
AU - Duarte, Cristiana
AU - Palmeira, A. L.
AU - Larsen, Sofus C.
AU - Heitmann, Berit L.
AU - James Stubbs, R.
PY - 2020
Y1 - 2020
N2 - Background: 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. 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. 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. Results: 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. Conclusions: 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.
AB - Background: 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. 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. 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. Results: 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. Conclusions: 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.
KW - Body weight
KW - Digital tracking
KW - Energy balance
KW - Imputation
KW - Smart scales
KW - Validation
KW - Weight cycling
KW - Weight fluctuation
KW - Weight instability
KW - Weight variability
U2 - 10.2196/17977
DO - 10.2196/17977
M3 - Journal article
C2 - 32915155
AN - SCOPUS:85085263273
VL - 8
JO - J M I R mHealth and uHealth
JF - J M I R mHealth and uHealth
SN - 2291-5222
IS - 9
M1 - e17977
ER -
ID: 252764783