Assessing Sexual Behavior Patterns among Student Athletes of Senior High Schools in the Upper East Region, Ghana

The purpose of this study was to investigate the sexual behavior patterns of student athletes of senior high schools in the Upper East Region of Ghana and to assess the differences in sexual behavior patterns between male and females. A sample of 400 student athletes using a convenience sampling technique from public senior high schools was drawn to complete a self-designed research study. Descriptive statistics and the Chi-square test tool were used to analyze the collected data. The results showed that student athletes practiced various forms of sexual behaviors such as celibacy, foreplay, vaginal-penile sex, sexual fantasy, masturbation, oral sex, and anal sex. The Chi-square analysis showed significant gender differences in prevalence of masturbation (χ2 (1, n = 400) = 4.6962, probability = 0.030) and sexual fantasy (χ2 (1, n = 400) = 6.8477, probability = 0.009), but not vaginal-penile intercourse (χ2 (1, n = 400) = 1.3197, probability = 0.251) and celibacy (χ2, (1, n = 400) = 0.0721, probability = 0.788). The study concludes that student athletes of senior high schools might be vulnerable to unplanned parenthood and are at risk of STIs, including HIV. Regular health promotion campaigns on sexual risk-taking behaviors are required to help reduce the prevalence of student athletes’ indulgence in risky sexual behavior patterns that can harm their health. It is essential to implement gender-specific interventions (e.g., decision-making skills) when addressing the problems of sexual behaviors among the student athletes in the region.

Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning

Purpose: To determine and characterize the radiomics features from structural MRI (MPRAGE) and Diffusion Tensor Imaging (DTI) associated with the presence of mild traumatic brain injuries on student athletes with post-concussive syndrome (PCS).Material and Methods: 122 student athletes (65 M, 57 F), median (IQR) age 18.8 (15–20) years, with a mixed level of play and sports activities, with a known history of concussion and clinical PCS, and 27 (15 M, 12 F), median (IQR) age 20 (19, 21) years, concussion free athlete subjects were MRI imaged in a clinical MR machine. MPRAGE and DTI-FA and DTI-ADC images were used to extract radiomic features from white and gray matter regions within the entire brain (2 ROI) and the eight main lobes of the brain (16 ROI) for a total of 18 analyzed regions. Radiomic features were divided into five different data sets used to train and cross-validate five different filter-based Support Vector Machines. The top selected features of the top model were described. Furthermore, the test predictions of the top four models were ensembled into a single average prediction. The average prediction was evaluated for the association to the number of concussions and time from injury.Results: Ninety-one PCS subjects passed inclusion criteria (91 Cases, 27 controls). The average prediction of the top four models had a sensitivity of 0.80, 95% CI: [0.71, 0.88] and specificity of 0.74 95%CI [0.54, 0.89] for distinguishing subjects from controls. The white matter features were strongly associated with mTBI, while the whole-brain analysis of gray matter showed the worst association. The predictive index was significantly associated with the number of concussions (p < 0.0001) and associated with the time from injury (p < 0.01).Conclusion: MRI Radiomic features are associated with a history of mTBI and they were successfully used to build a predictive machine learning model for mTBI for subjects with PCS associated with a history of one or more concussions.

Big Data Analysis of the Key Attributes Related to Stress and Mental Health in Korean Taekwondo Student Athletes

In the present study, we used big data analysis to examine the key attributes related to stress and mental health among Korean Taekwondo student-athletes. Keywords included “Taekwondo + Student athlete + Stress + Mental health”. Naver and Google databases were searched to identify research published between 1 January 2010 and 31 December 2019. Text-mining analysis was performed on unstructured texts using TEXTOM 4.5, with social network analysis performed using UCINET 6. In total, 3149 large databases (1.346 MB) were analyzed. Two types of text-mining analyses were performed, namely, frequency analysis and term frequency-inverse document frequency analysis. For the social network analysis, the degree centrality and convergence of iterated correlation analysis were used to deduce the node-linking degree in the network and to identify clusters. The top 10 most frequently used terms were “stress”, “Taekwondo”, “health”, “player”, “student”, “mental”, “exercise”, “mental health”, “relieve”, and “child.” The top 10 most frequently occurring results of the TF-IDF analysis were “Taekwondo”, “health”, “player”, “exercise”, “student”, “mental”, “stress”, “mental health”, “child” and “relieve”. The degree centrality analysis yielded similar results regarding the top 10 terms. The convergence of iterated correlation analysis identified six clusters: student, start of dream, diet, physical and mental, sports activity, and adult Taekwondo center. Our results emphasize the importance of designing interventions that attenuate stress and improve mental health among Korean Taekwondo student-athletes.

Student Athlete Activism in a Millennial World

College student athletes are among the most recognized students in their communities, across the country, and in some cases around the world. Their voices hold a significant esteem, and they can impact many societal and political issues. Some have postulated that college student-athletes are hesitant to be a part of these politics, but during the past few years, many have taken stands through social media and through protests on their campuses that have been in opposition to the stances of their coaches, their university's administration, and their teammates. Many, however, challenge the role that student athletes have in these protests. This chapter will explore the history of student athlete activism and its developmental aspects, highlight the more recent instances of such activism, and finally discuss how university administration and others can support and be responsive to the concerns that are expressed by this unique set of students.