Current research uses the 6-factor model, which includes motivation (enthusiasm for sport), confidence (belief in performance), concentration (focused attention), vigour (perceived vitality), tirelessness (lack of vitality), and calmness (absence of anxiety), 18-item Athletic Mental Energy Scale (AMES, Lu et al., 2018) to examine its influence on athletes’ cognition, behaviour, and emotion. However, two factors (i.e., vigour and tirelessness) in AMES are conceptually redundant and overlapping. We aimed to revise the AMES to improve its brevity. In Study 1, a 5-factor AMES (i.e., vigour, motivation, confidence, concentration, and calmness) was tested and analysed. In Study 2, we tested the gender invariance of the 5-factor AMES and examined nomological validity. In Study 3, we examined the test-retest reliability of the 5-factor AMES. Across these phases, the results showed that the 5-factor, 10-item AMES had an adequate factor structure, construct validity, reliability, and gender invariance. Future studies may utilise the 5-factor, 10-item AMES to examine its influence on athletes’ psychological and physical responses in field or laboratory settings. We further recommend that sports psychologists adopt the 5-factor, 10-item AMES.

Introduction: Quality sleep is crucial for optimal sports performance, yet the psychological mechanisms underpinning the sleep-sports performance relationship require further examination. Methods: This cross-sectional study explored the effects of athletic mental energy (AME) and sleep quality on basketball three-point shooting outcomes with a particular emphasis on the mediating role of AME. One hundred and forty-five collegiate basketball athletes (71 males and 74 females; M _age = 19.62 ± 1.35) with highly trained levels were recruited to evaluate sleep quality, AME, and basketball three-point shooting performance and percentage. Data were collected through validated questionnaires and a standardized three-point shooting test, and analyzed using structural equation modeling (SEM) with bootstrapping. Results: Sleep quality was positively associated with basketball three-point shooting performance ( r = 0.22, p = 0.007) and shooting percentage ( r = 0.22, p = 0.009). AME partially and fully mediated these associations (indirect effect = 0.18, p = 0.031, 95% bias-corrected CI [0.02–0.42] for shooting performance; indirect effect = 0.27, p = 0.019, 95% bias-corrected CI [0.04–0.60] for shooting percentage). Conclusions: The findings contribute to the literature on the relationship between sleep and competition-relevant sports performance and suggest AME as one of the potential psychological mechanisms underlying these associations. These results highlight the importance of considering athletes’ sleep quality and AME in performance contexts, while further research is needed to strengthen and generalize these conclusions.

Introduction: The purpose of this study was to conduct a systematic review and meta-analysis to examine the specialization–injury relationship, and explore whether the specialization–injury relationship is moderated by study design, sport type, age, sex, and injury measurement type, injury mechanism, and anatomical location. Methods: We searched eight databases by related keywords and assessed the quality of the included studies using the JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies and the Newcastle–Ottawa Scale. The Comprehensive Meta-Analysis (CMA) statistical software 3.7 examined heterogeneity, sensitivity, publication bias, overall effect size of specialization–injury relationship, and moderation effects. This review was prospectively registered in PROSPERO (CRD420251233318). Searches were conducted in eight electronic databases from inception to March, 2026. Results: The 15 included studies showed moderate heterogeneity, stable sensitivity analyses, and no publication bias. The overall odds ratio of the sports specialization–injury relationship was 2.00 (95% confidence interval [1.58–2.55], p < .001). Moderation analyses indicated that sport type significantly influenced the specialization–injury relationship, whereas no significant moderation effects were observed for study design, sex, age, injury measurement type, injury mechanism, and injury anatomical location. Conclusions: The findings indicate a positive association between sport specialization and injury risk among youth athletes, with variation across sport participation contexts. Specifically, athletes in both contact and non-contact sports demonstrated higher pooled odds of injury than those involved in multiple sports. Although the evidence is heterogeneous and should be interpreted with caution, these findings highlight the potential role of diversified sport participation in relation to injury.

Athletic mental energy is a multidimensional psychological resource associated with athletes’ performance and well-being. The present study examined the cross-validation and cross-cultural measurement invariance of the 10-item Athletic Mental Energy Scale (AMES-10) across three Asian countries. Study 1 recruited 263 Taiwanese collegiate athletes and employed confirmatory factor analysis (CFA) and item analysis to evaluate the factorial validity, reliability, and item discrimination of the AMES-10. Study 2 recruited collegiate athletes from Taiwan (n = 338), Thailand (n = 349), and India (n = 325) and conducted multiple-group CFA to examine measurement invariance across cultural groups. Results supported the proposed five-factor structure of the AMES-10 and demonstrated satisfactory reliability, convergent validity, and item discrimination. Acceptable model fit was observed across all three cultural samples. Multiple-group analyses supported configural and metric invariance and provided partial support for more restrictive invariance models. Overall, the findings provide further evidence for the psychometric robustness and cross-cultural applicability of the AMES-10 among collegiate athletes from Taiwan, Thailand, and India.