Wait a Minute? Uniting Physical Exercise and Data Science in Higher Learning

Purpose: This study explores the design and implementation of a novel Data Science course that integrates physical activity and physiological data collection. The course, scheduled twice weekly, combines a 30-minute physical exercise session with data collection activities focusing on metrics such as temperature, three separate heart rates, blood pressure, Functional Movement Screening (FMS), nutritional assessment using MyPlate.gov categories, urinalysis, and other metrics. The primary objective was to provide students with practical experience in data science by engaging them in real-time physiological data collection and analysis. Methods: This study involved a structured course format where students engaged in physical activities and recorded physiological data using standardized measurement tools and techniques. The course structure included instructional sessions on data science principles, data collection procedures, and statistical analysis. Students then applied their knowledge by undertaking a project that involved analyzing the collected data to address specific research questions or hypotheses related to physical health and performance. Each student presented their findings through a PowerPoint presentation, fostering peer review and collaborative learning. Results: Findings indicated that students successfully developed data science skills while gaining insights into the relationship between physical activity and physiological metrics. The projects revealed varied patterns and correlations, demonstrating the practical applicability of data science in health and fitness contexts. Discussion/Conclusions: The study highlights the effectiveness of integrating physical activity with data science education, enhancing both engagement and learning outcomes. The course equips students with technical skills while emphasizing the importance of holistic health data analysis.