BackgroundThere are limited sensitive evaluation methods to distinguish people’s symptoms of peripheral fatigue and central fatigue simultaneously. The purpose of this study is to identify and evaluate them after acute exercise with a simple and practical scale.MethodsThe initial scale was built through a literature review, experts and athlete population survey, and a small sample pre-survey. Randomly selected 1,506 students were evaluated with the initial scale after exercise. Subjective fatigue self-assessments were completed at the same time.ResultsThe Acute Exercise-Induced Fatigue Scale was (...) determined after performing a factor analysis. In the exploratory factor analysis, the cumulative variance contribution rate was 65.464%. The factor loadings of the total 8 questions were 0.661–0.816. In the confirmatory factor analysis, χ2/df = 2.529, GFI = 0.985, AGFI = 0.967, NFI = 0.982, IFI = 0.989, CFI = 0.989, and RMSEA = 0.048. The Cronbach’s alpha coefficient for the scale was 0.872, and it was 0.833 for peripheral fatigue and 0.818 for central fatigue. The intra-class correlation coefficient for the scale was 0.536, and the intra-class correlation coefficients for peripheral fatigue and central fatigue were 0.421 and 0.548, respectively. The correlation coefficient between the total score of the AEIFS and the SFSA score was 0.592.ConclusionOur results demonstrate that the AEIFS can distinguish peripheral fatigue and central fatigue and can also reflect their correlation. This scale can be a useful evaluation tool not only for measuring fatigue after acute exercise but also for guiding reasonable exercise, choosing objective testing indicators, and preventing sports injuries resulting from acute exercise-induced fatigue. (shrink)

Industry 4.0 has changed the paradigm in the business practice and business model, and digital technology has brought radical transformations to enterprises. To support this transformation, digital leaders are required to help enterprises transform and lead them to a more promising future. Based on job demands-resources model and person-organization fit theory, this study examines the relationship between digital leadership and employee creativity. Based on a sample of 357 employees from various Chinese companies, this study used SPSS 22.0 and MPLUS 7.0 (...) to examine the hypotheses. The findings indicate the following digital leadership has a positive effect on employee creativity. employee job crafting mediate the relationship between digital leadership and employee creativity. person-organization fit positively moderates the relationship between digital leadership and employee job crafting. person-organization fit positively moderates the indirect effect of digital leadership on employee creativity via employee job crafting. The findings reveal the effect mechanism of digital leaders on employee creativity and enrich the literature on antecedents of employee creativity. Practical implications and future research are also discussed. (shrink)