ArticleJMIR mHealth and uHealth2020
Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study.
Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Hair Cortisol Concentration, Weight Loss Maintenance and Body Weight Variability: A Prospective Study Based on Data From the European NoHoW Trial.Frontiers in endocrinology · 2021Trial
- Trial
- Utilization of locally estimated scatterplot smoothing (LOESS) regression to estimate missing weights in a longitudinal cohort of breast cancer patients.Annals of epidemiology · 2025Article
- A machine-learned model for predicting weight loss success using weight change features early in treatment.NPJ digital medicine · 2024Article
- MS Pattern Explorer: interactive visual exploration of temporal activity patterns for multiple sclerosis.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Handling Missing Data in COVID-19 Incidence Estimation: Secondary Data Analysis.JMIR public health and surveillance · 2024Article
- Evaluating a New Digital App-Based Program for Heart Health: Feasibility and Acceptability Pilot Study.JMIR formative research · 2024Article
- Weight Loss Trajectories and Related Factors in a 16-Week Mobile Obesity Intervention Program: Retrospective Observational Study.Journal of medical Internet research · 2022Observational
- Accuracy of Smart Scales on Weight and Body Composition: Observational Study.JMIR mHealth and uHealth · 2021Observational
- The impact of early body-weight variability on long-term weight maintenance: exploratory results from the NoHoW weight-loss maintenance intervention.International journal of obesity (2005) · 2021Article
- Body weight variability is not associated with changes in risk factors for cardiometabolic disease.International Journal of Cardiology. Hypertension · 2020Article
- Weight loss in a digital app-based diabetes prevention program powered by artificial intelligence.Digital healthArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
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 approaches.
methodsIn 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.
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