Effect of yoga and mindfulness on psychological correlates in young athletes: A meta-analysis

Home Anxiety Assessment and Influencing Factors among Adolescent Athletes in Yantai City

OBJECTIVE

To understand the prevalence of home-related anxiety among adolescent athletes during the novel coronavirus pandemic and to ascertain the factors influencing this anxiety.

METHODS

We employed cluster sampling to select 1150 adolescent athletes (aged 8-18 years) from six sports training schools in Yantai City, Shandong Province. Mental health status was assessed and recorded. Chi-square tests and multivariable logistic regression were used to analyze the factors contributing to athletes' anxiety.

RESULTS

The survey revealed a COVID-19 infection rate of 38.23% (437 individuals) with an anxiety score of 40.98 ± 8.20 and an anxiety detection rate of 11.29% (129 individuals) during the COVID-19 epidemic. Female athletes exhibited a higher anxiety rate of 14.40% compared to 8.40% in male athletes. Multivariate analysis identified female gender as a risk factor for anxiety (OR = 1.64), while participation in aquatics emerged as a protective factor (OR = 0.24, 95% CI: 1.08-2.48). Professional training duration exceeding three years increased anxiety risk (OR = 3.05, 95% CI: 1.67-5.58), as did not seeking help during difficulties (OR = 2.59, 95% CI: 1.33-5.01). Interestingly, parental care was linked to increased anxiety risk (OR = 2.44, 95% CI 1.34-4.44), while care from friends was protective (OR = 0.60, 95% CI: 0.36-1.01), which was possibly due to the pressure associated with parental expectations.

CONCLUSIONS

Adolescent athletes, particularly females and those with extended training durations, exhibit a heightened susceptibility to anxiety. This study also highlights that athletes who proactively seek assistance during challenging situations tend to experience lower anxiety levels. Additionally, a lack of COVID-19 infection and the involvement of concerned parents contribute to reduced anxiety among these young athletes.

Yoga Pose Estimation Using Angle-Based Feature Extraction

OBJECTIVE

This research addresses the challenges of maintaining proper yoga postures, an issue that has been exacerbated by the COVID-19 pandemic and the subsequent shift to virtual platforms for yoga instruction. This research aims to develop a mechanism for detecting correct yoga poses and providing real-time feedback through the application of computer vision and machine learning (ML) techniques.

METHODS AND PROCEDURES

This study utilized computer vision-based pose estimation methods to extract features and calculate yoga pose angles. A variety of models, including extremely randomized trees, logistic regression, random forest, gradient boosting, extreme gradient boosting, and deep neural networks, were trained and tested to classify yoga poses. Our study employed the Yoga-82 dataset, consisting of many yoga pose images downloaded from the web.

RESULTS

The results of this study show that the extremely randomized trees model outperformed the other models, achieving the highest prediction accuracy of 91% on the test dataset and 92% in a fivefold cross-validation experiment. Other models like random forest, gradient boosting, extreme gradient boosting, and deep neural networks achieved accuracies of 90%, 89%, 90%, and 85%, respectively, while logistic regression underperformed, having the lowest accuracy.

CONCLUSION

This research concludes that the extremely randomized trees model presents superior predictive power for yoga pose recognition. This suggests a valuable avenue for future exploration in this domain. Moreover, the approach has significant potential for implementation on low-powered smartphones with minimal latency, thereby enabling real-time feedback for users practicing yoga at home.