Measuring Exercise-Induced Mood and Frontal Alpha Asymmetry Changes
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Chandrashekhar, Rohan
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The physical and mental health benefits of exercise are well established: physical activity is linked to a reduced risk of chronic disease (e.g., coronary disease and type 2 diabetes), as well as reduced risks for mental disorders including anxiety and depression. While mood-related studies in the field have predominantly used qualitative forms of measurement (i.e., ratings or surveys), there is much less utilization of quantitative methods to help assess exercise-induced mood changes. Electroencephalography (EEG), a tool useful in diagnosing a variety of brain disorders, can provide information by measuring alpha wave activity differences between the left and right frontal cortices. Such hemispheric differences can be quantified by the frontal alpha asymmetry (FAA) measure, which is pertinent to mood as higher FAA scores (i.e., greater relative left frontal activity) have been linked to positive mood improvements. In the present study, we use EEG and Positive and Negative Affect Schedule (PANAS) and Brunel Mood Scale (BRUMS) forms to assess our hypothesis that participants will exhibit greater mood improvements following an acute exercise bout compared to a quiet resting state. Results indicate poor test-retest reliability of PANAS, specifically negative affect (as it had greater day-to-day variability: ICC = 0.286), and that BRUMS may be a limitation due to its lower reproducibility compared to the better reliability shown by mood scores related to positive affect (ICC = 0.855; p = 0.044). The results partially supported our hypothesis as positive affect levels were significantly greater (p = 0.007) in the post-exercise condition compared to the post-control condition. However, regarding changes from the pre-exercise to post-exercise condition, we did not see a significant increase in FAA score or any decrease in levels of negative affect, tension, or depression. Larger sample sizes may be needed to find significant FAA changes that more closely align with our data predictions.
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Undergraduate Research Option Thesis