International Journal of Physical Education, Fitness and Sports

Predictive Modeling of Heart Rate Dynamics based on Physical Characteristics and Exercise Parameters: A Machine Learning Approach

Mahmoud Ali, Ahmed Abdelsallam, Ahmed Rasslan, Abdallah Rabee — Mon, 06 May 2024 00:00:00 +0000

To accurately forecast heart rate changes during exercise, which is essential for customized health monitoring and improving training regimens, it is necessary to comprehend both the physiological foundations and the technical capacities for data processing. This research utilizes Machine Learning (ML) methodologies to predict heart rate reactions based on physical characteristics and activity variables. Our research focuses on the health and sports aspects of our results, using a comprehensive dataset that includes a wide range of activity types and ambient circumstances across 12,000 sets. We establish a connection between the ability of models such as Linear Regression (LR) and Extreme Gradient Boosting (XGB) to predict outcomes and their practical use in exercise management and optimizing athlete performance. These models accurately forecast variations in heart rate and also provide insights into the cardiovascular demands of various physical activities. Standard metrics measure the effectiveness of these models. The Linear Regression (LR) model achieved a Mean Absolute Error (MAE) of 0.419, a Mean Squared Error (MSE) of 0.294, a Root Mean Squared Error (RMSE) of 0.543, and an R-Squared value of 0.997. On the other hand, the Extreme Gradient Boosting (XGB) Regressor model achieved a Mean Absolute Error (MAE) of 0.421, a Mean Squared Error (MSE) of 0.335, a Root Mean Squared Error (RMSE) of 0.578, and an R-Squared value of 0.996. These metrics demonstrate the usefulness of these models in real-world scenarios. Our study's findings demonstrate that the combination of physiological data and powerful machine learning models may improve an individual's comprehension of fitness levels and the requirements for adaptive training. This study not only adds to the field of computational physiology, but it also aids in the creation of adaptive, real-time therapies for improving health and performance.

Challenges and Interventions of Physical Education Teachers in Assessing Students' Learning in the Online Modality

Claire Irish C. Balay-as, Maureen Jane O. Bandoc — Sat, 15 Jun 2024 00:00:00 +0000

Despite the emergence of several studies on online learning in Physical Education (PE) research, little has been mentioned about the assessment of learning outcomes in PE. This qualitative research aimed to understand the challenges and intervention strategies of PE teachers in assessing learning outcomes in the online modality. Nine elementary PE teachers from the laboratory elementary schools in Baguio City, Philippines participated in a semi-structured interview. The findings revealed that elementary PE teachers were faced with challenges in assessing PE learning online, but made interventions to these challenges. Three themes surfaced to describe the challenges: challenges in viewing students’ output, challenges in trusting, and challenges in monitoring students. The two themes described the interventions as differentiating students’ requirements and collaborating with parents. In assessing the PE learning outcomes online, the participants found it difficult; however, they realized that finding ways to overcome these difficulties is part of what teachers should do. The findings offer valuable insights that can inform and guide PE teachers in effectively assessing students' learning outcomes in the online learning modality.

Exploring the Influence of Sports on Student-Athletes’ Competitive Mindsets during the COVID-19 Pandemic

Sun, 16 Jun 2024 00:00:00 +0000

Sports aim to enhance an individual’s formation through organized physical and mental involvement. As sports participation promotes teamwork and dedication, athletes develop sportsmanship, mindfulness, and mental toughness, which brings out their confidence. While the COVID-19 pandemic has caused distress and tested the resilience of people, the effects of the situation on student-athlete mindset and competitiveness remain unresolved. With Bandura’s Self-Efficacy Theory as its foreshadowing theory, this qualitative study focused on the experiences of 10 high school to collegiate student-athletes from a private university in Manila who were asked about their mindsets during the COVID-19 pandemic. After analyzing the data from the transcribed interviews through thematic analysis and the use of the Tracy Coding Matrix, findings revealed the effects of Bandura’s sources of self-efficacy expectations that influenced the student-athletes’ will to win, particularly performance accomplishments, vicarious experience, verbal persuasion, and emotional arousal. The researchers found that sports affect student-athletes’ competitive mindsets by developing their traits, such as self-confidence, motivation, self-assurance, self-reformation, and wellness. Although sports participation also caused feelings of insecurity and pressure among the participants, especially during the COVID-19 pandemic, student-athletes were able to adapt, particularly through being supported by their environment. With this, some of the findings suggest that a social circle's presence may ignite student-athletes' drive for victory but may also demotivate them. Finally, the findings of the study may be used to understand student-athlete behavior and strategies such as positive reinforcement and goal-setting may be used to improve their mentalities.