The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles.

Purpose: To investigate the effects of ball drills and repeated-sprint-ability training during the regular season in basketball players. Methods: A total of 30 players were randomized into 3 groups: ball-drills training (BDT, n = 12, 4 × 4 min, 3 vs 3 with 3-min passive recovery), repeated-sprint-ability training (RSAT, n = 9, 3 × 6 × 20-m shuttle running with 20-s and 4-min recovery), and general basketball training (n = 9, basketball technical/tactical exercises), as control group. Players were tested before and after 8 wk of training using the following tests: , squat jump, countermovement jump, Yo-Yo Intermittent Recovery Test Level 1 (YIRT1), agility T test, line-drill test, 5-/10-/20-m sprints, and blood lactate concentration. A custom-developed survey was used to analyze players’ technical skills. Results: After training, significant improvements were seen in YIRT1 (BDT P = .014, effect size [ES] ± 90% CI = 0.8 ± 0.3; RSAT P = .022, ES ± 90% CI = 0.7 ± 0.3), the agility T test (BDT P = .018, ES ± 90% CI = 0.7 ± 0.5; RSAT P = .037, ES ± 90% CI = 0.7 ± 0.5), and the line-drill test (BDT P = .010, ES ± 90% CI = 0.3 ± 0.1; RSAT P < .0001, ES ± 90% CI = 0.4 ± 0.1). In the RSAT group, only 10-m sprint speeds (P = .039, ES ± 90% CI = 0.3 ± 0.2) and blood lactate concentration (P = .004, ES ± 90% CI = 0.8 ± 1.1) were improved. Finally, technical skills were increased in BDT regarding dribbling (P = .038, ES ± 90% CI = 0.8 ± 0.6), shooting (P = .036, ES ± 90% CI = 0.8 ± 0.8), passing (P = .034, ES ± 90% CI = 0.9 ± 0.3), rebounding (P = .023, ES ± 90% CI = 1.1 ± 0.3), defense (P = .042, ES ± 90% CI = 0.5 ± 0.5), and offense (P = .044, ES ± 90% CI = 0.4 ± 0.4) skills. Conclusions: BDT and RSAT are both effective in improving the physical performance of basketball players. BDT had also a positive impact on technical skills. Basketball strength and conditioning professionals should include BDT as a routine tool to improve technical skills and physical performance simultaneously throughout the regular training season.

Background Heart rate variability (HRV) reflects the autonomous nervous system modulation on heart rate and is associated with several pathologies, including cardiac mortality. While mechanistic studies show that smoking is associated with lower HRV, population-based studies present conflicting results. Methods We assessed the mutual effects of active smoking status, cumulative smoking history, and current smoking intensity, on HRV among 4751 adults from the Cooperative Health Research In South Tyrol (CHRIS) study. The HRV metrics standard deviation of normal-to-normal (NN) inter-beat intervals (SDNN), square root of the mean squared differences of consecutive NN intervals (RMSSD), total power (TP), low (LF) and high frequency (HF) power, and their ratio (LF/HF), were derived from 20-minute electrocardiograms. Smoking status, pack-years (PY), and tobacco grams/day from standardized questionnaires were the main exposures. We fitted linear mixed models to account for relatedness, non-linearity, and moderating effects, and including fractional polynomials. Results Past smokers had higher HRV levels than never smokers, independently of PY. The association of HRV with current smoking became apparent when accounting for the interaction between smoking status and PY. In current smokers, but not in past smokers, we observed HRV reductions between 2.0% (SDNN) and 4.9% (TP) every 5 PY increase. Furthermore, current smokers were characterized by dose-response reductions of 9.8% (SDNN), 8.9% (RMSSD), 20.1% (TP), 17.7% (LF), and 19.1% (HF), respectively, every 10 grams/day of smoked tobacco, independently of common cardiometabolic conditions and HRV-modifying drugs. The LF/HF ratio was not associated with smoking status, history, or intensity. Conclusions Smoking cessation was associated with higher HRV levels. In current smokers, heavier smoking intensity appears gradually detrimental on HRV, corroborating previous evidence. By affecting both the sympathetic and parasympathetic nervous system indexes, but not the LF/HF balance, smoking intensity seems to exert a systemic dysautonomic effect.