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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

Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study

All statistical analyses were conducted using R version 3.5.1. All statistical codes used can be found in Git Hub [29]. A flow diagram of the study is shown in Figure 1. First, we removed outliers based on the limits of physiological plausibility (detailed in Multimedia Appendix 1).

Jake Turicchi, Ruairi O'Driscoll, Graham Finlayson, Cristiana Duarte, A L Palmeira, Sofus C Larsen, Berit L Heitmann, R James Stubbs

JMIR Mhealth Uhealth 2020;8(9):e17977

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

All minutes in which energy expenditure data were available (ie, face mask was not removed) were included in this analysis, and the aggregation of the data sets by time was conducted in Python 3.7.6 and R version 3.6.3 (R Core Team). For activity-specific analyses, we grouped activities into broader categories. Activities of daily living, which involved folding, sweeping, typing, ironing, and wiping surfaces. Distinct categories were assigned for cycling, elliptical, rowing, running, and walking.

Ruairi O'Driscoll, Jake Turicchi, Mark Hopkins, Cristiana Duarte, Graham W Horgan, Graham Finlayson, R James Stubbs

JMIR Mhealth Uhealth 2021;9(8):e23938

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