## 2 Methods
### Statistical Analyses  
Pre-processing: Cognitive data from the Membrain platform and survey data from SurveyMonkey were downloaded into Excel, scored, and exported into Python (version 3.9, Python Software Foundation, Wilmington, DE, USA) where data were merged. The author who performed the initial analyses ([PERSON]) was blinded to the conditions and all preliminary statistical analyses were performed prior to breaking the blind. Using a combination of scatterplots, descriptive statistics, and the Wilks-Shapiro test (\(p>0.05\)), normality was assessed. Normalization techniques were applied to transform variables that were not normally distributed. Using the pingouin library [56], an initial 3 (day) \(\times\) 3 (time) repeated measures Analysis of Variance (RM-ANOVA) to understand if there were any learning effects. No significant differences were noted for any measures (\(p>0.05\)) between days. The median for each trait variable (Trait Physical Energy = 6, Trait Physical Fatigue = 5, Trait Mental Energy = 6 and Trait Mental Fatigue = 6) was identified and then surrogate variables were created to represent these in dichotomous format with values of 1 assigned to the 50 th percentile and 2 otherwise. Primary Analysis: Using the rpy2 library [57] in Python, the afex package (version 1.2) was accessed in R [58] to examine significant interactions between 2 levels of traits (low v. high) \(\times\) 3 interventions (sitting, standing, intermittent walking) \(\times\) 3 time points (3-30 min, 34-61 min, 65-92 min) using a mixed ANOVA. No significant outliers were detected and variables that exhibited a poor fit were not removed or transformed. The justification for this comes from the nature of the repeated measures format where transformation of one measure must be evenly applied to all other measures of that variable, which would exacerbate analytical issues and make interpretation more difficult. Further, we are unaware of any non-parametric test that is equivalent to a 2 \(\times\) 3 \(\times\) 3 mixed ANOVA. Violations of sphericity were accounted for using the Greenhouse-Geisser correction to degrees of freedom [59]. If any of the results were significant, post hoc analyses were conducted using a combination of 2 \(\times\) 2 repeated measures ANOVAs, one-way ANOVAs, between- and within-subjects t-tests, Kruskal-Wallis, Mann-Whitney U and estimated means. All post hoc results were adjusted using a Benjamini-Hochberg False Discovery Rate (FDR) of 20% [60]. Due to the study design, generalized effect sizes were calculated for each result and are presented [61; 62]. Additionally, due to the large number of primary analyses, a Benjamini-Hochberg FDR was applied to all primary analyses to correct for Type I errors and a 20% FDR was considered acceptable [60]. If missing data were encountered for a test, the participant was removed from the analysis. All \(\alpha\) presented values are adjusted. An adjusted \(\alpha\) of 0.05 was used to justify significance. Post hoc machine learning analyses: To complete the exploratory machine learning analyses, the sklearn library was used. A Principal Component Analysis (PCA) was performed on the 4 trait measures. The components were set to explain 90% of variance. Using the Kaiser-Gutmann criterion, which recognizes PCs with an Eigenvalue \(\lambda>1.0\) as significant [63]. Data were then imputed for missing data using the Multiple Imputation by Chained Equation (MICE) Method [64]. Due to the small sample size, data were split to compare interventions (sitting vs. standing, standing vs. walking, and sitting vs. walking). All data were scaled and a combination of regressor and classifier models were used.