To determine the ability of different punch trackers (PT) (Corner (CPT), Everlast (EPT), and Hykso (HPT)) to recognize specific punch types (lead and rear straight punches, lead and rear hooks, and lead and rear uppercuts) thrown by trained (TR, n = 10) and untrained punchers (UNTR, n = 11), subjects performed different punch combinations, and PT data were compared to data from video recordings to determine how well each PT recognized the punches that were actually thrown. Descriptive statistics and multilevel modelling were used to analyze the data. The CPT, EPT and HPT detected punches more accurately in TR than UNTR, evidenced by a lower percentage error in TR (p = 0.007). The CPT, EPT, and HPT detected straight punches better than uppercuts and hooks, with a lower percentage error for straight punches (p < 0.001). The recognition of punches with CPT and HPT depended on punch order, with earlier punches in a sequence recognized better. The same may or may not have occurred with EPT, but EPT does not allow for data to be exported, meaning the order of individual punches could not be analyzed. The CPT and HPT both seem to be viable options for tracking punch count and punch type in TR and UNTR.

Omcirk, D, Vetrovsky, T, Padecky, J, Malecek, J, and Tufano, JJ. Validity of commercially available punch trackers. J Strength Cond Res 37(11): 2273–2281, 2023—This study determined how well data from commercially available punch trackers (Corner, Hykso, and StrikeTec) related to gold-standard velocity and force measures during full-contact punches. In a quasi-randomized order, 20 male subjects performed 6 individual rear straight punches, rear hooks, and rear uppercuts against a wall-mounted force plate. Punch tracker variables were compared with the peak force of the force plate and to the peak (QPV) and mean velocity (QMV) assessed through Qualisys 3-dimensional tracking. For each punch tracker variable, Pearson's correlation coefficient, mean absolute percentage error (MAPE), and mean percentage error (MPE) were calculated. There were no strong correlations between punch tracker data and gold-standard force and velocity data. However, Hykso “velocity” was moderately correlated with QMV (r = 0.68, MAPE 0.64, MPE 0.63) and QPV (r = 0.61, MAPE 0.21, MPE −0.06). Corner Power G was moderately correlated with QMV (r = 0.59, MAPE 0.65, MPE 0.58) and QPV (r = 0.58, MAPE 0.27, MPE −0.09), but Corner “velocity” was not. StrikeTec “velocity” was moderately correlated with QMV (r = 0.56, MAPE 1.49, MPE 1.49) and QPV (r = 0.55, MAPE 0.46, MPE 0.43). Therefore, none of the devices fared particularly well for all of their data output, and if not willing to accept any room for error, none of these devices should be used. Nevertheless, these devices and their proprietary algorithms may be updated in the future, which would warrant further investigation.

Purpose: Assisted jumping can supplement resistance training and traditional plyometric training to increase vertical jump performance. However, as coaches may choose to make field-based decisions based on lab-based research, this study determined whether a field-based assisted jumping set-up results in different ground contact times (CT), take off forces (TOF), flight times (FT), and impact forces (IF) compared to a lab-based set-up. Methods: Eighteen active males (24.8 ± 3.0 yr; 178.8 ± 7.8 cm; 77.8 ± 7.8 kg) performed two sessions of assisted jumping: one with each hand holding a commercially available resistance band (1m) that was attached to a pull-up bar (_FIELD), and the other with assistance from a custom-built system of ropes, pulleys, and long (3 m) elastic bands (_LAB). With each set-up, subjects performed five sets of five countermovement jumps on a force plate. Each set was performed with either bodyweight (BW), 90, 80, 70, or 60% of BW, which was achieved by either grabbing higher or lower on the bands during _FIELD, or by being pulled upward via a full-body harness during _LAB. The order of each visit was counter-balanced, and the order of jumps within each visit was quasi-randomized. Data from the 90, 80, 70, and 60% trials for each set-up were then expressed relative to the data of BW jumps, and these relative values were then used for analysis. Results: CT_FIELD was less than CT_LAB at 80, 70, and 60%. FT_FIELD was greater than FT_LAB at 90 and 80%, but FT_LAB became greater at 60%. TOF and IF remained unchanged during _LAB, but TOF_FIELD was consistently less than TOF during BW, with IF_FIELD generally being greater than IF_LAB. Conclusion: If the purpose of assisted jumping is to spend less time on the ground without decreasing force, systems with finite adjustments and longer bands like _LAB should be used. However, shorter bands similar to _FIELD may also be used; but due to the larger variability of assistance throughout the range of motion, such systems may alter the neuromuscular characteristics of the jump in other ways that should be investigated in future research.

Introduction: A lack of sleep can pose a risk during military operations due to the associated decreases in physical and cognitive performance. However, fast-acting ergogenic aids, such as ammonia inhalants (AI), may temporarily mitigate those adverse effects of total sleep deprivation (TSD). Therefore, the present study aimed to investigate the acute effect of AI on cognitive and physical performance throughout 36 hours of TSD in military personnel. Methods: Eighteen male military cadets (24.1 ± 3.0 y; 79.3 ± 8.3 kg) performed 5 identical testing sessions during 36 hours of TSD (after 0 [0], 12 [–12], 24 [–24], and 36 [–36] hours of TSD), and after 8 [+8] hours of recovery sleep. During each testing session, the following assessments were conducted: Epworth sleepiness scale (ESS), simple reaction time (SRT), shooting accuracy (SA), rifle disassembling and reassembling (DAS), and countermovement jump height (JH). Heart rate (HR) was continuously monitored during the SA task, and a rating of perceived exertion (RPE) was obtained during the JH task. At each time point, tests were performed twice, either with AI or without AI as control (CON), in a counterbalanced order. Results: There was faster SRT (1.6%; p < 0.01) without increasing the number of errors, higher JH (1.5%; p < 0.01), lower RPE (9.4%; p < 0.001), and higher HR (5.0%; p < 0.001) after using AI compared to CON regardless of TSD. However, neither SA nor DAS were affected by AI or TSD (p > 0.05). Independent of AI, the SRT was slower (3.2–9.3%; p < 0.001) in the mornings (-24, +8) than in the evening (-12), JH was higher (3.0–4.7%, p < 0.001) in the evenings (-12, -36) than in the mornings (0, -24, +8), and RPE was higher (20.0–40.1%; p < 0.001) in the sleep-deprived morning (-24) than all other timepoints (0, -12, -36, +8). Furthermore, higher ESS (59.5–193.4%; p < 0.001) was reported at -24 and -36 than the rest of the time points (0, -12, and + 8). Conclusion: Although there were detrimental effects of TSD, the usage of AI did not reduce those adverse effects. However, regardless of TSD, AI did result in a short-term increase in HR, improved SRT without affecting the number of errors, and improved JH while concurrently decreasing the RPE. No changes, yet, were observed in SA and DAS. These results suggest that AI could potentially be useful in some military scenarios, regardless of sleep deprivation.

Ammonium inhalants (AIs) are used to improve athletic performance, but their use has preceded the research process. Oftentimes, strength-based athletes use AIs to postpone acute fatigue or increase arousal. Despite the widespread use of AIs, the amount of research examining its physiological effects, efficacy, and safety is low compared with other ergogenic aids that have been extensively researched. Therefore, the purpose of this review is to provide sports science researchers, strength and conditioning professionals, medical professionals, and other practitioners with the most up to date information about the benefits, risks, and physiological effects of AIs. To date, there is a lack of evidence to support anecdotal claims of increased cognitive arousal and greater strength performance. However, there may be a short-term effect of AIs on the cardiorespiratory system (possibly increasing breathing rate and heart rate approximately 15–30 seconds), but further research is needed to support these findings and to determine how the short-term cardiorespiratory effects may affect other physiological and performance measures. Finally, although evidence does not indicate that AIs are dangerous in healthy populations, sport and health professionals should be aware of the potential risks of AIs to prevent any unlikely, but possible, difficulties.

Despite the recognized influence of body mass on combat techniques, the relationship between body segment mass (BSM) and combat moves remains unexplored. This study aimed to examine the relationship between the striking arm mass (SAM), kicking leg mass (KLM), and body mass (BM) and the dynamic forces of direct punch (DP), palm strike (PS), elbow strike (ES), front kick (FK), and countermovement jump (CMJ) performance. Sixteen male military cadets (22.3 ± 1.8 years, 181.4 ± 7.0 cm, 82.1 ± 8.5 kg) performed combat techniques, with their performance measured by using a force plate and their body segment mass assessed by dual-energy X-ray absorptiometry. Spearman’s correlation analysis, the Wilcoxon test, and Cohen’s d were applied. The results indicated the relationship between the KLM or BM and the FK impulse (r = 0.64, p = 0.01; r = 0.52, p = 0.04, respectively) and CMJ impact force (r = 0.80, p ≤ 0.01; r = 0.70, p ≤ 0.01, respectively). The FK peak and impact forces were moderately correlated with the CMJ jump height (r = 0.74, p ≤ 0.01; r = 0.77, p ≤ 0.01). Moreover, the FK peak force was significantly higher than that for DP, PS, and ES (p ≤ 0.01, d = 3.32; p ≤ 0.01, d = 1.6; and p = 0.013, d = 1.3, respectively). The highest relationship was found between the KLM and the FK impulse; however, the difference in variability explained by the KLM versus the body mass was only 12%. This suggests that knowledge of the BSM did not provide a significantly better estimate of the dynamic forces of the punches and FKs than the knowledge of the BM.