Hip thrust and back squat training elicit similar gluteus muscle hypertrophy and transfer similarly to the deadlift

We examined how set-volume equated resistance training using either the back squat (SQ) or hip thrust (HT) affected hypertrophy and various strength outcomes. Untrained college-aged participants were randomized into HT (n = 18) or SQ (n = 16) groups. Surface electromyograms (sEMG) from the right gluteus maximus and medius muscles were obtained during the first training session. Participants completed 9 weeks of supervised training (15–17 sessions), before and after which gluteus and leg muscle cross-sectional area (mCSA) was assessed via magnetic resonance imaging. Strength was also assessed prior to and after the training intervention via three-repetition maximum (3RM) testing and an isometric wall push test. Gluteus mCSA increases were similar across both groups. Specifically, estimates [(−) favors HT (+) favors SQ] modestly favored the HT versus SQ for lower [effect ±SE, −1.6 ± 2.1 cm2; CI95% (−6.1, 2.0)], mid [−0.5 ± 1.7 cm2; CI95% (−4.0, 2.6)], and upper [−0.5 ± 2.6 cm2; CI95% (−5.8, 4.1)] gluteal mCSAs but with appreciable variance. Gluteus medius + minimus [−1.8 ± 1.5 cm2; CI95% (−4.6, 1.4)] and hamstrings [0.1 ± 0.6 cm2; CI95% (−0.9, 1.4)] mCSA demonstrated little to no growth with small differences between groups. mCSA changes were greater in SQ for the quadriceps [3.6 ± 1.5 cm2; CI95% (0.7, 6.4)] and adductors [2.5 ± 0.7 cm2; CI95% (1.2, 3.9)]. Squat 3RM increases favored SQ [14 ± 2 kg; CI95% (9, 18),] and hip thrust 3RM favored HT [−26 ± 5 kg; CI95% (−34, −16)]. 3RM deadlift [0 ± 2 kg; CI95% (−4, 3)] and wall push strength [−7 ± 12N; CI95% (−32, 17)] similarly improved. All measured gluteal sites showed greater mean sEMG amplitudes during the first bout hip thrust versus squat set, but this did not consistently predict gluteal hypertrophy outcomes. Squat and hip thrust training elicited similar gluteal hypertrophy, greater thigh hypertrophy in SQ, strength increases that favored exercise allocation, and similar deadlift and wall push strength increases

EXAMINATION OF SAGITTAL ANKLE KINEMATICS IN COMPRESSIVE NON-SLIP SOCKS ON DIFFERENT SURFACES

BACKGROUND: Non-slip hospital socks were implemented to reduce slips in clinical settings. However, these socks have not kept pace with the developments of sock enhancements. This study was conducted to investigate differences in maximal plantarflexion and mean sagittal plane kinematics while walking in compressive non-slip socks across common surfaces used in clinical settings. METHODS: Twelve female participants (age 22 ± 2 years; height 1.68 ± 0.04 meters; weight 73.68 ± 15.30 kilograms) completed three walking trials at a self-selected pace for each condition. In randomized order, they walked across either linoleum or rubber surfaces. Data was collected through Vicon and exported to Visual3D for processing. A paired sample t-test analyzed maximal plantarflexion and maximal sagittal angular position during the stance phase. RESULTS: No significant differences were found between maximal plantarflexion (t(11) = 0.43, p = 0.67) and mean sagittal plane kinematics (t(11) = 0.17, p = 0.87). CONCLUSION: The main finding of this study showed that there is no significant difference in sagittal plane ankle kinematics while wearing compressive non-slip socks on rubber and linoleum surfaces. The lack of differences between conditions indicates that common surfaces in clinical settings do not elicit alterations in sagittal ankle gait mechanics, regardless of friction variability. Future studies comparing different types of non-slip socks should be considered to determine which non-slip sock produces optimal walking biomechanics within clinical settings without changing gait kinetics

PLAYING SURFACE TRANSITION AND INJURY RATES IN NFL-A COMPARATIVE STUDY ON GRASS AND TURF

BACKGROUND Artificial turf is increasingly used in professional and collegiate sports due to cost and maintenance benefits. There is emerging evidence that differences in the sports field surface characteristics have considerable impact on the athletes’ biomechanics. Such biomechanical influences may result in variations in sport performance and injury rates in these athletes. This study investigates the impact of moving from one surface type to another (natural grass (G) vs. artificial turf (T)) on injury rates among American football athletes. METHODS Data on 700 first- and second-round drafted NFL athletes (2012-2022), their collegiate and professional stadium surfaces, and injuries were collected. For each athlete, a 4-factor category variable was included to capture their collegiate to professional teams’ surfaces (G to G; G to T; T to G; and T to T), and a 5-factor category variable that grouped the athlete based on his position (chase, evasive, linemen, QB, K). Chi-Square Test of Independence (χ2 test) and logistic regression were used to analyze the association between surface differences, position groups, and injury. Results are reported as probability of injury occurring in odds ratios (OR), and statistical significance alpha was set to 0.05. RESULTS 433 athletes suffering a LE injury that resulted in a minimum of 1 game absence (GG=126, GT=119, TG=107, TT=81) were included in the statistical analysis. No statistical significance was found in χ2 test results (p=0.61). Compared to GG, injury OR were 2.05 times higher in GT, and 1.87 in TG and TT. Among position groups on GG, evasive group’s injury OR were highest, followed by linemen, evasive, and then QB. When evaluating the interaction between surface and position group influencing injury, the injury OR were 6.49 higher for QBs in GT surface category than chase group in GG. There was no statistical significance for the effect of any combination of surface and position categories with injury. CONCLUSIONS Field surface type impacts athletes’ biomechanics, potentially leading to non-contact sports injuries. Athletes transitioning from collegiate to NFL on different surfaces had over 2 times higher injury odds. However, no statistical significance was found. Future research could further stratify the group to examine the impact of field surface type on injury and performance

Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021.

Objectives Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. Methods Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. Results Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation75% probability with +5 or more points). Conclusion Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints