Exercise provides individuals with various physical benefits, improving cardiovascular health, strengthening bones and muscles, and increasing flexibility and mobility. Additionally, exercise promotes numerous mental benefits, reducing stress, improving mood, and enhancing cognitive function. Unfortunately, with various types of exercise, ranging from strength training to interval training, it's difficult for individuals to choose the one that will provide the best results. In this study, we decided to examine the cognitive effects of interval training compared to a prescribed fitness plan amongst UWEC's Community Fitness Program (CFP) members. To do this, participants of the interval training and the control group were asked to commit to 2-3 workouts a week and took a baseline test, examining their cognitive abilities. After 4 weeks, the two groups were retested, took a week-long break, and resumed training. Through this experiment, our group hoped to discover if interval training, compared to a prescribed fitness plan, had a greater impact on cognitive function, providing more insight into the vast world of exercise, potentially directing new exercising individuals looking to seek higher cognitive function or redirecting veteran exercisers to help them realign their goals.
PURPOSE: Evidence demonstrates that undergraduate students experience a chronic lack of sleep with 60% being qualified as poor sleepers. Additionally, poor quality and quantity of sleep and sedentary behavior has been shown to increase all-cause mortality in the general population. Given the high prevalence of poor sleep quality in undergraduate students raises the question of how various exercise modalities would affect the quantity and quality of sleep of undergraduate students. METHODS: A total of 23 college students were recruited. Participants were split into three groups: journaling (control), aerobic training (AT), and resistance training (RT), and completed three, 30-minute sessions of the intervention for three weeks. Two accelerometer and inclinometer devices were worn at baseline and the third week of intervention to track quantity and quality of sleep. Group x Time two-way repeated measures analysis of variance was employed to compare the differences in outcome variables across the three groups. RESULTS: There was no interaction effect between group and time on total sleep time, number of awakenings, average length of awakening, and sleep efficiency (p>.05). CONCLUSION: Three weeks of AT and RT compared to journaling did not have significant effects on undergraduate students’ sleep quality and quantity.
The research question is: is the 8-minute self-paced (8SPV) VO2max test a valid method for measuring VO2 max? VO2 max is the highest rate at which oxygen can be consumed during intense exercise and reflects the efficiency of the cardiovascular and respiratory systems. There are validated VO2 max protocols (ie. Bruce and 10-minute self-paced), however, existing protocols have evidence pointing toward peripheral fatigue being a limiting factor, therefore a shorter 8-minute test could lead to better VO2 max result. UWEC students will be recruited and three VO2max tests will be performed for each participant. The three protocols will be conducted using a treadmill and the VO2max will be measured using the metabolic cart. The tests include the 8-minute self-paced, 10-minute self-paced, and Bruce protocol. One-way repeated measures ANOVA will be implemented for comparing the VO2max across three protocols. The anticipated result is that the 8SPV will provide consistent results with the validated protocols. If validated, the 8SPV protocol can be used in future research.
The purpose of this study if to investigate the effectiveness of exergaming in combination with resistance training to reduce fall risk among individuals with Parkinson’s Disease (PD). PD causes many balance impairments and fall risks due to the underlying physiology of the disease. Six participants were recruited from the Parkinson’s Exercise Program at the University of Wisconsin – Eau Claire and were split into two groups. For the first four weeks, Group 1 will engage in two 15-minute exergaming sessions along with 75 minutes of resistance training per week while Group 2 will only be doing 75 minutes of resistance training. After the initial four weeks, the groups will switch, and Group 1 will only do resistance training while Group 2 will do the two exergaming sessions along with resistance training for four weeks. Fall risk will be assessed at the beginning, middle, and end of the study using a fall risk assessment on the BioDex Balance System and a Timed Up and Go - Cognitive assessment. This research is still in progress. We hope that this study will provide valuable insight into effective strategies for decreasing fall risk among individuals with PD.
Muscle growth is a complex topic, with many influencing and detracting factors, many of which vary in effectiveness from person to person. In order to better understand these factors in the context of strength training, research was performed on contributors to an individual’s perceived muscle stimulus, which likely correlates with the amount of actual muscle growth detected. A survey was distributed to multiple individuals of varying levels of experience in strength training, focusing on variables such as contraction type, range of motion, volume, repetition amount, and amount of effort in each exercise. Other contributors like diet, growth supplements, age, sex, experience level, and build were also monitored.
This study examined the reliability of bioelectrical impedance analysis (BIA) compared to dual-energy x-ray absorptiometry (DEXA) to track body composition changes in Division III football athletes across an entire off-season training program. A sample of Division III male college football athletes (n=32) participated in this study. Total body composition analysis was measured using DEXA and BIA at three time points throughout the off-season training program. These time points represented the beginning (January), middle (May), and end (August) of the off-season training program. Change scores (post-pre) were calculated between time points to quantify changes across time points. Paired-samples t-tests were employed to examine any significant differences (p<0.05) between DEXA and BIA. Intraclass correlation coefficients (ICC) were used to examine agreement between methods across time. There were no significant differences (p>0.05) in FM, FFM, and FFMI changes between DEXA and BIA within the January-May time frame. A significant difference (p<0.05) between DEXA and BIA was detected in FM, FFM, and FFMI changes in the May-August time points. BIA showed an acceptable level of reliability in tracking early off-season changes in body composition compared to DEXA, however, the BIA significantly underestimated changes in FM and overestimated changes in FFM during late off-season programming.
