![]() The present study aimed to analyze the electrical brain pattern of an elite chess player during different chess games: 15 + 10, blindfold 15 + 10, lightning game, and problem-solving chess tasks. In the sport of chess, there is a need to understand the mental demands of the sport of chess in order to manage training loads. The study of mental load is an emerging research topic in the field of sport sciences. Future research should use vagally-mediated HRV as a marker of self-regulation and adaptation in SEP, consult relevant HRV theories prior to hypothesis development, and follow methodological guidelines for HRV. ![]() Three key limitations within the field were discovered: limited application of theoretical frameworks, methodological issues with HRV measurement, and differing interpretations of HRV results. A narrative synthesis revealed that HRV was assessed within a range of topics such as stress, overtraining, anxiety, biofeedback, cognitive performance, and sporting performance. ![]() Risk of bias was assessed via the Mixed Methods Appraisal Tool. In February 2022 a systematic search of Web of Science, PubMed and Sport Discus identified 118 studies (4979 participants) using HRV in sport psychology (71) or exercise psychology (47). Exclusion criteria were non-peer reviewed work, animal studies, clinical populations, review or conference papers. Study inclusion criteria were examination of HRV in SEP, using athletes or healthy populations, peer-reviewed and published in English. The protocol was made available on the Open Science Framework. This paper aimed to provide a scoping review of the use of HRV within SEP. Sport and Exercise Psychology (SEP) often adopts physiological markers in theory and practice, and one measure receiving increasing attention is heart rate variability (HRV). The proposed method can contribute to quantifying MS and establishing viewer-friendly VR by determining its qualities. Among the MS classification models, the linear support vector machine achieves the highest average accuracy of 91.1% (10-fold cross validation) and has a significant permutation test outcome. The results of ANCOVA reveal a significant difference between 2D and VR viewing conditions, and the correlation coefficients between the subjective ratings and cardiac features have significant results in the range of −0.377 to −0.711 (for SDNN, pNN50, and ln HF) and 0.653 to 0.677 (for ln VLF and ln VLF/ln HF ratio). ![]() The proposed model for classifying MS was implemented in various classifiers using significant cardiac features. Cardiac features were statistically analyzed using analysis of covariance (ANCOVA). Twenty-eight undergraduate volunteers participated in the experiment by watching VR content on a 2D screen and HMD for 12 min each, and their electrocardiogram signals were measured. ![]() This paper proposes a method for assessing MS caused by watching VR content on an HMD using cardiac features. Head-mounted display (HMD) virtual reality devices can facilitate positive experiences such as co-presence and deep immersion however, motion sickness (MS) due to these experiences hinders the development of the VR industry. ![]()
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