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Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From

Jake Turicchi1, Ruairi O'Driscoll1, Graham Finlayson1

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Body weight variability (BWV) estimation is accurate even with 80% missing data. Data imputation is not recommended for BWV analysis, as it can introduce significant errors.

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body weightdigital trackingenergy balanceimputationsmart scalesvalidationweight cyclingweight fluctuationweight instabilityweight variability

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Area of Science:

  • Biostatistics
  • Health Informatics
  • Obesity Research

Background:

  • Body weight variability (BWV) is prevalent and a potential risk factor for obesity and other diseases.
  • Advancements in smart scale technology enable frequent body weight data collection, offering new analytical opportunities.
  • Identifying BWV patterns holds prognostic and predictive value in clinical and research settings.

Purpose of the Study:

  • To compare various data imputation methods and body weight variability calculation approaches.
  • To evaluate linear and nonlinear methods for BWV estimation.
  • To assess the impact of missing data and imputation on BWV calculations.

Main Methods:

  • Utilized data from 50 participants in a weight loss maintenance study (NoHoW).
  • Simulated missing data (random and nonrandom patterns) to test 10 imputation strategies.
  • Calculated BWV using linear and nonlinear methods, analyzing the effects of missing data and imputation.

Main Results:

  • Kalman smoothing and exponentially weighted moving average showed best body weight imputation accuracy (0.62%-0.64% RMSE).
  • Imputation performance declined with increased missingness.
  • BWV estimation errors were low (2%-7%) with 80% missing data, but significantly higher with imputation methods.

Conclusions:

  • Data imputation is not advised for body weight variability analysis.
  • Linear and nonlinear BWV estimation methods yield accurate results even with substantial missing data (up to 80%).
  • The decision to impute body weight data should be based on the specific analytical objective.