Undergraduate and graduate student presentations from the School of Kinesiology & Recreation, 2021 Online University Research Symposium, Illinois State University
Background: Concussions are a growing public health concern, and emerging possible long-term health risks may negatively impact an individual’s quality of life. With concussions, there can be a diverse multitude of signs and symptoms, but frequent long-term sequelae of concussions including mood disturbances (e.g. anxiety and depression) and sleep disturbances (e.g. difficulties initiating and maintaining sleep) are given less attention. Few clinicians include mood and sleep disturbances in their concussion assessment and management plan, therefore potentially placing individuals at risk for prolonged disturbances beyond recovery. Purpose: To examine the significance of relationships between sleep quality and the prevalence of mood disturbances in healthy young adults with and without a history of concussions. Methods: Two hundred fifty healthy young adults from 4 universities across the country completed a one-time, 15-20-minute anonymous survey administered via Qualtrics. Survey components consisted of demographic information, concussion history, two mental health questionnaires (State-Trait Anxiety Inventory and Beck Depression Inventory) and two sleep quality questionnaires (Pittsburgh Sleep Quality Index and Insomnia Severity Index). Exclusion criteria consisted of having suffered a concussion or traumatic brain injury within the past 9 months, history of cancerous brain tumors, and diagnosed with post-traumatic stress disorder, schizophrenia, or bipolar disorder. Structural equation modeling (SEM) was used as the primary statistical analysis. Results: Data collection is still in progress. Using SEM, we hypothesize that history of concussion may directly impact anxiety and depression, but sleep quality mediates this relationship. Conclusion: This study will provide foundational insight into the relationship of sleep quality, anxiety, and depression in healthy young adults with concussion history. Understanding the relationship between these areas may help guide clinicians to better recognize and manage these prolonged sleep and mood disturbances that may continue well past recovery.
The purpose of this study was to identify the impact of social media on the mental health of student-athletes. Over 140 institutions across the 3 NCAA Divisions were invited to participate. In total, 9 schools sent the survey out to their student-athletes (5 Division I, 2 Division II and 2 Division III). The survey measured social media use including frequency, sites used, and a general social media use scale and various aspects of mental health including anxiety/depression, perceived stress, resilience, vitality, and self-esteem. It was completed by 94 student-athletes in its entirety. Additionally, the survey included a qualitative question that asked the respondents what impact they thought social media had on them. The results of this qualitative portion were split into 6 themes. The positive themes identified were increased communication, stress relief, and motivation. The negative themes identified included vulnerability, procrastination, and loss of sleep. Quantitative data from a multiple regression analysis indicated there were higher levels of depression/anxiety in the Division II and graduate student populations. Additionally, a negative relationship was identified between female studentathletes, self-esteem and Facebook use. Negative relationships were identified between male student-athletes Instagram use and depression/anxiety, as well as athletic identity and YouTube. The findings of this exploratory research identify that social media does have some impacts student-athlete mental health. Because of this finding, athletic departments should make an effort to educate themselves and student-athletes on the potential negative outcomes of using social media for stress relief. Future research should explore differences between social media use, and problematic social media use. At what point are student-athletes beginning to put themselves atrisk. Additional research should be completed on looking at student-athlete’s motivations for social media use and how these impact their well-being and success.
PURPOSE: The purpose was to describe barriers to being physically active based on reason for referral, gender identity, and incoming stage of change (SOC) for physical activity (PA) in university students referred to an Exercise is Medicine on Campus (EIMOC) program. METHODS: Students referred by university health and counseling services completed a 20-item questionnaire, which included the “Barriers to Being Active” quiz, an assessment of SOC (e.g., precontemplation), and students’ self-reported gender. Reason for referral, which was provided by a health practitioner, categorized as Obesity, Anxiety/Depression, General PA or Other. RESULTS: Barrier mean scores by group are included below. A score over 5 is considered an important barrier. Overall, a Lack of Willpower was the most highly rated barrier followed by Lack of Energy. A series of MANOVAs revealed non-significant differences in the set of barriers across the three grouping variables. However, follow-up ANOVAs showed that those classified as being at the pre-preparation stage (identified as contemplation or precontemplation) were higher on Lack of Willpower relative to those at action or maintenance, and higher than action on Lack of Skill. Results also showed the that women reported a higher Lack of Skill compared to men. CONCLUSION: Identifying client barriers is essential for promoting participation and adherence to physical activity for EIMOC. Lack of Energy and Lack of Willpower seem to be common, important barriers regardless of gender identity, reason for referral, or SOC.
Brandon Hobson and Brooke Bossert
Purpose: The purpose of this study was to examine changes in body composition following a weight loss and fitness program in a group of university police officers. Methods: Subjects consisted of 9 Illinois State University police officers who planned to participate in a fitness improvement and weight loss program during the spring semester of 2021. The subjects (8 men, 1 woman) were 43.6 +12.2 years of age, with an average height of 70.6 +3.8 inches, and an average weight of 216.3 +30.7 lbs. Body composition was assessed prior to initiation of the program using an InBody bioelectrical impedance analyzer. Subjects were asked to follow a series of test preparation guidelines prior to participating in the InBody test. These guidelines prior to testing included: maintaining normal fluid intake the day before; removing any socks or pantyhose before the test; removing all heavy objects such a jewelry, watches, belts, wallets, and jackets; not eating or exercising for at least 3 hours; not consuming alcohol or excessive caffeine for at least 24 hours; and not using lotion on the hands or feet. Subjects are participating in a self-led fitness and weight loss regimen that differs from subject to subject. Results: Body composition will be assessed twice more over the course of the program, but initial body composition results indicated that the mean fat mass was 61.7 +23.3 lbs, the mean fat free mass was 87.8 + 16.7 lbs, and the mean body fat was 28.3 + 9.6 %. It is hypothesized that fat mass and percent fat will decrease, and fat-free mass will increase as a result of the training program.
Introduction: Numerous studies have been conducted to assess the biomechanical and neuromotor response to sudden ankle perturbation. The goal of such studies is often to explore the mechanisms that may contribute to ankle sprain prevention. However, when perturbations are invoked during walking, subsequent trials may be impacted by gait adaptations in response to the initial perturbation. Common gait strategy after experiencing a perturbation is a decrease in step length and an increase in step width. Purpose: Compare two drop conditions to determine if they elicit similar spatial-temporal adaptations, and if after repeated exposure adaptations return close to baseline. Methods: 12 healthy volunteers walked along a two-trapdoor walkway (6.10m in length & 0.25m tall) that elicited random, sudden inversion and inversion/plantarflexion drops. Participants performed trials of walking gait during normal walking (NW), inversion (ID), and inversion/plantarflexion (IPD) conditions. During all trials, subjects wore basketball vision blocking goggles to prevent them from seeing the walkway in front of them and were told to walk to a beat of 90bpm from a metronome. The means based on right and left heel strike and toe off were collected through motion capturing technology to determine spatial-temporal variables of step length, step width, and their respective standard deviations. Repeated ANOVAs were employed to assess differences across all three conditions during first and last wash trials in-regards-to step length, step width, and their respective standard deviations. Results: No significant differences were observed in spatial-temporal variables across conditions or time: Step Length (F (2,9) =.290, p=.751); Step Width (F (2,9) = .140, p=.870); Step Length Standard Deviation (F (2,9) = .708, p=.504); and Step Width Standard Deviation (F (2,9) = .926, p=.411). Conclusion: Due to the lack of significant differences found across all conditions, we can conclude both the inversion and inversion/plantarflexion drop elicited similar responses to gait strategy. Specifically, by examining the first and last wash trial per condition, it does not appear the perturbation influenced the gait characteristics during the subsequent trial or after several normalization trials. This confirms our experimental design of including six wash trials between each condition did not impact gait strategy. Alternatively, other spatial-temporal parameters (double-leg support time, swing phase, etc) that were not explored in the current study may have been impacted by the perturbations.
Hexagonal barbell (HB) loaded jumps are often used in training to increase lower extremity power. The effect of external load on lower extremity kinematics and kinetics during jumping has been described, but how individual muscles accommodate to these loads has not. Given the importance of coordinated muscular effort in achieving maximal power output, an understanding of how the lower extremity musculature individually performs during loaded jumps would be advantageous. The purpose of this study is to describe the effect of load on individual muscle forces during the concentric phase of loaded HB jumps. 10 male collegiate athletes (20.4 + 2.4 y; 108.8 + 14.0 kg) performed 5 maximal HB jumps at 0%, 20%, 40% and 60% of their HB deadlift 1-repetition maximum (216.6 + 10.9 kg). Filtered Ground reaction forces (300 Hz) and 3D lower extremity marker trajectories (13 Hz) were input into a 23 DOF musculoskeletal model and muscle forces were estimated with static optimization. Peak muscle force (xBW) was calculated for the gluteus maximum (GMAX), biceps femoris – long head (BFL), rectus femoris (RF), vastus intermedius (VAST), gastrocnemius (GAS), and soleus (SOL). Analysis of variance and LSD post hoc comparisons were used for analysis (p < 0.05). A significant increase in peak muscle force across loads existed for VAST (0%: 7.89 + 0.24 xBW; 20%: 8.22 + 0.28 xBW; 40%: 8.47 + 0.30 xBW; 60%: 8.64 + 0.33 xBW), with significant differences between 0% and 40%, 0% and 60%, and 20% and 60% (all p ≤ 0.015). Significant decreases were noted for RF (0%: 2.50 + 0.13 xBW; 20%: 2.32 + 0.17 xBW; 40%: 2.18 + 0.11 xBW; 60%: 1.98 + 0.20 xBW), with significant differences between 0% and all other conditions, and between 20% and 60% (all p ≤ 0.037). Significant increases were found in GAS across loads (0%: 2.14 + 0.10 xBW; 20%: 2.47 + 0.14 xBW; 40%: 2.72 + 0.12 xBW; 60%: 2.85 + 0.14 xBW), with significance between each load (all p ≤ 0.038). There was no significant difference in GMAX (p = 0.325), BFL (p = 0.369), or SOL (p = 0.122) across loads. Increases in demand were not met with equally distributed increases in peak force output across the lower extremity musculature. The varied effect of load on force output from individual muscles is important information to understand when using loaded jumps as part of training for athletic performance.