Objective: To assess the responses to taper in endurance athletes using meta-analysis. Methods: Systematic searches were conducted in China National Knowledge Infrastructure, PubMed, Web of Science, SPORTDiscus, and EMBASE databases. Standardized mean difference (SMD) and 95% confidence interval (CI) of outcome measures were calculated as effect sizes. Results: 14 studies were included in this meta-analysis. Significant improvements were found between pre- and post-tapering in time–trial (TT) performance (SMD = −0.45; P < 0.05) and time to exhaustion (TTE) performance (SMD = 1.28; P < 0.05). However, There were no improvements in maximal oxygen consumption ( V˙O2max ) and economy of movement (EM) (P > 0.05) between pre- and post-tapering. Further subgroup analysis showed that tapering combined with pre-taper overload training had a more significant effect on TT performance than conventional tapering (P < 0.05). A tapering strategy that reduced training volume by 41–60%, maintained training intensity and frequency, lasted ≤7 days, 8–14 days, or 15–21 days, used a progressive or step taper could significantly improve TT performance (P < 0.05). Conclusions: The tapering applied in conjunction with pre-taper overload training seems to be more conducive to maximize performance gains. Current evidence suggests that a ≤21-day taper, in which training volume is progressively reduced by 41–60% without changing training intensity or frequency, is an effective tapering strategy.

A successful snowboarding performance is a multifactorial accomplishment resulting from a complex interaction of the major fitness components. This review aimed to examine the anthropometric, physiological, and physical profiles of elite snowboarding athletes. A database search was performed in the China National Knowledge Infrastructure, PubMed, SPORT Discus, and Web of Science. The findings indicate that (a) elite speed-based and skill-based athletes generally had low body fat percentages. The height and body mass of men speed-based athletes are slightly larger than those of skill-based athletes. (b) The energy system predominantly engaged in is anaerobic metabolism. Muscle oxidative metabolism could help to promote recovery in the repeated high-intensity runs. (c) Top levels of lower-body strength, especially the eccentric force production and explosive concentric power production of athletes, have a determinant role in rapid riding and turning, jump amplitude, trick difficulty, and successful landing. The take-off velocity of skill-based snowboarding and the starting and turning speed of speed-based snowboarding also have a major effect on success in competitions. Further research is warranted to investigate the anthropometry, the relative energy system contributions from aerobic and anaerobic metabolism, and the effect of physical training on performance while considering the possible differentiated effects of different snowboarding disciplines.

With advancements in AI, smart home gyms are becoming increasingly popular for providing fitness assistance in indoor environments. In this research, we propose a layer-by-layer framework, called Wi-Fitness, which bridges video perception with Wi-Fi sensing for smart fitness. At the data preprocessing layer, the singular value decomposition-based channel state information denoising mechanism is leveraged to do the Wi-Fi data calibration. Diverse and high-quality training samples are generated by a random quantization-based data augmentation method. At the bimodal fusion layer, the heterogeneity between the Wi-Fi and video is mitigated by the local attention mechanism and the bimodal feature integration mechanism. For the video modality, the attention-based spatio-temporal graph convolutional network (AST-GCN Net) is proposed to refine spatial information. The spatio-temporal semantic alignment module is proposed to transfer spatial information from video to Wi-Fi and maintain temporal consistency across modalities. The fitness assessment layer provides exercise visualization. The generalization of Wi-Fitness is enhanced by layer-by-layer collaboration. Wi-Fitness demonstrates its effectiveness by achieving an average F1-Score of 92.68% in three typical indoor environments.

In this study, the impact of Plyometric Training (PT) on handball players' performance was explored. Databases such as PubMed, Web of Science, and EBSCOhost were systematically searched, using mean difference (MD) and 95% confidence interval (CI) of the calculation result indicators as effect sizes. 11 studies involving 302 subjects were included. Results showed that PT was correlated with improved jumping ability: countermovement jump (MD = 4.14, P < 0.00001) and squat jump (MD = 3.83, P < 0.00001). In change-of-direction ability, T-half performance improved (MD=-0.55, P < 0.0001). As for the sprint ability, 5 m (MD=-0.07, P < 0.00001), 10 m (MD=-0.12, P < 0.00001), and 20 m (MD=-0.15, P<0.00001) sprint times were shortened by PT. PT enhanced throwing abilities: 3 Step Running Throw (MD = 2.78, P = 0.02), Jumping Throw(MD = 1.13, P = 0.04), and Penalty Throw (MD = 1.43, P = 0.0002). In repeated sprint ability (RSA), RSA-total time (MD=-1.03, P < 0.00001), mean time(MD=-0.29, P = 0.03), and best time (MD=-0.15, P<0.00001) decreased, but RSA-fatigue index showed no significant improvement. PT improved limb strength indices such as upper limb relative peak power (MD = 0.52, P < 0.00001) but not maximum braking force. It enhanced left and right handgrip (e.g., right: MD = 59.07, P < 0.00001) yet not balance (e.g., right leg: MD=-0.22, P = 0.23). PT thus improved the performance of handball players. Scientific training designs could maximize their benefits. Therefore, future studies should investigate long-term effects and adaptation mechanisms to provide more precise theoretical support.

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments.