The current study utilized a random forest regression analysis to predict post-experiment fatigue in a sample of 212 healthy participants (mean age = 20.5, SD = 2.21; 52% women) between the ages of 18 and 30 following a mildly stressful experiment. We used a total of 30 features of demographic variables, lifestyle variables, alcohol and other drug use behaviors and problems, state anxiety and depressive symptoms, and physiological indicators that were lab assessed or self-reported. A random forest regression analysis with 10-fold cross-validation resulted in accurate prediction of post-experiment fatigue (R2 equivalent = 0.93) with the average "out-of-bag" (OOB) R2 = 0.52. Not surprisingly, self-reported pre-experiment fatigue was the most important variable (54%) in the prediction of post-experiment fatigue. Feeling anxious (state anxiety) pre- and post-experiment (3%, 7%), feeling less vigorous post experiment (3%), systolic and diastolic blood pressure (3%, 2%) and LF HRV (2%) assessed at baseline, and self-reported alcohol-related problems (3%) and sleep (2%) additionally contributed to the prediction of post-experiment fatigue. Other remaining input variables had relatively minimal importance. Substantively, this study suggests that complex interactions across multiple systems domains that support regulation may be linked to fatigue. A random forest regression analysis can relatively easily be implemented with a built-in cross-validation function and reveal a web of connections undergirding health behavior and risks.
Keywords: fatigue; machine learning; random forests; regulation; stress.
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