Data analysis
fatigue
Data analysis was performed with SPSS (IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp). Normality was assessed by visually inspecting histograms and skewness and kurtosis and homogeneity of variance for linear mixed models by plotting the residuals against the predicted values. Variables collected via PSG, actigraphy, and subjective sleep diaries as well as physical activity indices were measured with paired t-tests; any variables not normally distributed were analysed with non-parametric Wilcoxon signed rank tests. Actigraphy and subjective sleep data is an average of the 7 supplementation nights. Core temperature, KSS, fatigue, and all cognitive function and blood outcomes were analysed with linear mixed models. Mixed models were preferred to repeated measures ANOVAs because they better account for missing data. Time, condition (CP vs. CON), and time*condition interaction effects were included as fixed factors, and participant as a random factor. Models were run using the keep it maximal approach recommended by Barr et al. [