Sports-related concussions are complex injuries with biomechanical and biochemical etiology that present with central and autonomic nervous system dysfunction. Current methods for assessing concussions and basing return-to-play decisions rely on symptom resolution, rating scales, and neuropsychological testing, all of which are indirect measures of injury severity and detect functional capabilities but do not directly measure injury location or severity. In addition, these downstream measures are susceptible to false negatives because compensatory mechanism, such as unmasking and redundancies in brain circuitry can return functional capabilities before injury resolution. The multifactorial nature of concussion necessitates rapid, inexpensive, and easily applied multimodal analysis methods that can offer greater sensitivity and specificity. This article discusses how new approaches utilizing electrophysiology (e.g., QEEG, ERP, ECG, HRV), quantified balance measures, and biochemistry are necessary to advance the science of concussion assessment, treatment, recovery projections, and return-to-play decisions. These additional assessment tools offer a more direct window into the severity and location of the injury, real-time measures of brain function, and the ability to measure the multiple body systems negatively affected by concussion.
James W.G. Thompson and David Hagedorn
Harry G. Banyard, James J. Tufano, Jose Delgado, Steve W. Thompson and Kazunori Nosaka
Purpose: To compare kinetic and kinematic data from 3 different velocity-based training sessions and a 1-repetition-maximum (1RM)-percent-based training (PBT) session using full-depth, free-weight back squats with maximal concentric effort. Methods: Fifteen strength-trained men performed 4 randomized resistance-training sessions 96 h apart: PBT session involved 5 sets of 5 repetitions using 80% 1RM; load–velocity profile (LVP) session contained 5 sets of 5 repetitions with a load that could be adjusted to achieve a target velocity established from an individualized LVP equation at 80% 1RM; fixed sets 20% velocity loss threshold (FSVL20) session consisted of 5 sets at 80% 1RM, but sets were terminated once the mean velocity (MV) dropped below 20% of the threshold velocity or when 5 repetitions were completed per set; and variable sets 20% velocity loss threshold session comprised 25 repetitions in total, but participants performed as many repetitions in a set as possible until the 20% velocity loss threshold was exceeded. Results: When averaged across all repetitions, MV and peak velocity (PV) were significantly (P < .05) faster during the LVP (MV effect size [ES] = 1.05; PV ES = 1.12) and FSVL20 (MV ES = 0.81; PV ES = 0.98) sessions compared with PBT. Mean time under tension (TUT) and concentric TUT were significantly less during the LVP sessions compared with PBT. The FSVL20 sessions had significantly less repetitions, total TUT, and concentric TUT than PBT. No significant differences were found for all other measurements between any of the sessions. Conclusions: Velocity-based training permits faster velocities and avoids additional unnecessary mechanical stress but maintains similar measures of force and power output compared with strength-oriented PBT in a single training session.