Considerable variability in tibial acceleration slope (AS) values, and different interpretations of injury risk based on these values, have been reported. Acceleration slope variability may be due in part to variations in the quantification methods used. Therefore, the purpose of this study was to quantify differences in tibial AS values determined using end points at various percentage ranges between impact and peak tibial acceleration, as a function of either amplitude or time. Tibial accelerations were recorded from 20 participants (21.8 ± 2.9 years, 1.7 m ± 0.1 m, 75.1 kg ± 17.0 kg) during 24 unshod heel impacts using a human pendulum apparatus. Nine ranges were tested from 5–95% (widest range) to 45–55% (narrowest range) at 5% increments. ASAmplitude values increased consistently from the widest to narrowest ranges, whereas the ASTime values remained essentially the same. The magnitudes of ASAmplitude values were significantly higher and more sensitive to changes in percentage range than ASTime values derived from the same impact data. This study shows that tibial AS magnitudes are highly dependent on the method used to calculate them. Researchers are encouraged to carefully consider the method they use to calculate AS so that equivalent comparisons and assessments of injury risk across studies can be made.
Adriana M. Duquette and David M. Andrews
Joshua Twaites, Richard Everson, Joss Langford and Melvyn Hillsdon
resolution and difficulty in assigning a single behavior type to the entire window ( Staudenmayer, Pober, Crouter, Bassett, & Freedson, 2009 ). Automatically segmenting the acceleration data allows for the creation of variable duration windows without the same compromises as fixed duration windows ( Noor
Ewald M. Hennig and Mario A. Lafortune
Using data from six male subjects, this study compared ground reaction force and tibial acceleration parameters for running. A bone-mounted triaxial accelerometer and a force platform were employed for data collection. Low peak values were found for the axial acceleration, and a time shift toward the occurrence of the first peak in the vertical force data was present. The time to peak axial acceleration differed significantly from the time to the first force peak, and the peak values of force and acceleration demonstrated only a moderate correlation. However, a high negative correlation was found for the comparison of the peak axial acceleration with the time to peak vertical force. Employing a multiple regression analysis, the peak tibial acceleration could be well estimated using vertical force loading rate and peak horizontal ground reaction force as predictors.
Erin M.R. Bigelow, Niell G. Elvin, Alex A. Elvin and Steven P. Arnoczky
To determine whether peak vertical and horizontal impact accelerations were different while running on a track or on a treadmill, 12 healthy subjects (average age 32.8 ± 9.8 y), were fitted with a novel, wireless accelerometer capable of recording triaxial acceleration over time. The accelerometer was attached to a custom-made acrylic plate and secured at the level of the L5 vertebra via a tight fitting triathlon belt. Each subject ran 4 miles on a synthetic, indoor track at a self-selected pace and accelerations were recorded on three perpendicular axes. Seven days later, the subjects ran 4 miles on a treadmill set at the individual runner’s average pace on the track and the peak vertical and horizontal impact magnitudes between the track and treadmill were compared. There was no difference (P = .52) in the average peak vertical impact accelerations between the track and treadmill over the 4 mile run. However, peak horizontal impact accelerations were greater (P = .0012) on the track when compared with the treadmill. This study demonstrated the feasibility for long-term impact accelerations monitoring using a novel wireless accelerometer.
Alison Schinkel-Ivy, Timothy A. Burkhart and David M. Andrews
To date, there has not been a direct examination of the effect that tissue composition (lean mass/muscle, fat mass, bone mineral content) differences between males and females has on how the tibia responds to impacts similar to those seen during running. To evaluate this, controlled heel impacts were imparted to 36 participants (6 M and 6 F in each of low, medium and high percent body fat [BF] groups) using a human pendulum. A skin-mounted accelerometer medial to the tibial tuberosity was used to determine the tibial response parameters (peak acceleration, acceleration slope and time to peak acceleration). There were no consistent effects of BF or specific tissue masses on the un-normalized tibial response parameters. However, females experienced 25% greater peak acceleration than males. When normalized to lean mass, wobbling mass, and bone mineral content, females experienced 50%, 62% and 70% greater peak acceleration, respectively, per gram of tissue than males. Higher magnitudes of lean mass and bone mass significantly contributed to decreased acceleration responses in general.
Joseph J. Crisco, Laura Costa, Ryan Rich, Joel B. Schwartz and Bethany Wilcox
Girls’ lacrosse is fundamentally a different sport than boys’ lacrosse, and girls are not required to wear protective headgear. Recent epidemiological studies have found that stick checks are the leading cause of concussion injury in girls’ lacrosse. The purpose of this study was to determine stick check speeds and estimate the head acceleration associated with direct checks to the head. In addition, we briefly examine if commercially available headgear can mitigate the accelerations. Seven (n = 7) experienced female lacrosse players checked, with varying severity, a NOSCAE and an ASTM headform. Stick speed at impact and the associated peak linear accelerations of the headform were recorded. The NOCSAE headform was fitted with four commercially available headgear and similar stick impact testing was performed. The median stick impact speed was 8.1 m/s and 777 deg/s. At these speeds, peak linear acceleration was approximately 60g. Three out of the four headgear significantly reduced the peak linear acceleration when compared with the bare headform. These data serve as baseline for understanding the potential mechanism and reduction of concussions from stick impacts in girls’ lacrosse.
Martin Buchheit, Hani Al Haddad, Ben M. Simpson, Dino Palazzi, Pitre C. Bourdon, Valter Di Salvo and Alberto Mendez-Villanueva
The aims of the current study were to examine the magnitude of between-GPS-models differences in commonly reported running-based measures in football, examine between-units variability, and assess the effect of software updates on these measures. Fifty identical-brand GPS units (15 SPI-proX and 35 SPIproX2, 15 Hz, GPSports, Canberra, Australia) were attached to a custom-made plastic sled towed by a player performing simulated match running activities. GPS data collected during training sessions over 4 wk from 4 professional football players (N = 53 files) were also analyzed before and after 2 manufacturersupplied software updates. There were substantial differences between the different models (eg, standardized difference for the number of acceleration >4 m/s2 = 2.1; 90% confidence limits [1.4, 2.7], with 100% chance of a true difference). Between-units variations ranged from 1% (maximal speed) to 56% (number of deceleration >4 m/s2). Some GPS units measured 2–6 times more acceleration/deceleration occurrences than others. Software updates did not substantially affect the distance covered at different speeds or peak speed reached, but 1 of the updates led to large and small decreases in the occurrence of accelerations (–1.24; –1.32, –1.15) and decelerations (–0.45; –0.48, –0.41), respectively. Practitioners are advised to apply care when comparing data collected with different models or units or when updating their software. The metrics of accelerations and decelerations show the most variability in GPS monitoring and must be interpreted cautiously.
Raviraj Nataraj, Musa L. Audu, Robert F. Kirsch and Ronald J. Triolo
This pilot study investigated the potential of using trunk acceleration feedback control of center of pressure (COP) against postural disturbances with a standing neuroprosthesis following paralysis. Artificial neural networks (ANNs) were trained to use three-dimensional trunk acceleration as input to predict changes in COP for able-bodied subjects undergoing perturbations during bipedal stance. Correlation coefficients between ANN predictions and actual COP ranged from 0.67 to 0.77. An ANN trained across all subject-normalized data was used to drive feedback control of ankle muscle excitation levels for a computer model representing a standing neuroprosthesis user. Feedback control reduced average upper-body loading during perturbation onset and recovery by 42% and peak loading fby 29% compared with optimal, constant excitation.
Jace A. Delaney, Grant M. Duthie, Heidi R. Thornton, Tannath J. Scott, David Gay and Ben J. Dascombe
Rugby league involves frequent periods of high-intensity running including acceleration and deceleration efforts, often occurring at low speeds.
To quantify the energetic cost of running and acceleration efforts during rugby league competition to aid in prescription and monitoring of training.
Global positioning system (GPS) data were collected from 37 professional rugby league players across 2 seasons. Peak values for relative distance, average acceleration/deceleration, and metabolic power (Pmet) were calculated for 10 different moving-average durations (1–10 min) for each position. A mixed-effects model was used to assess the effect of position for each duration, and individual comparisons were made using a magnitude-based-inference network.
There were almost certainly large differences in relative distance and Pmet between the 10-min window and all moving averages <5 min in duration (ES = 1.21–1.88). Fullbacks, halves, and hookers covered greater relative distances than outside backs, edge forwards, and middle forwards for moving averages lasting 2–10 min. Acceleration/deceleration demands were greatest in hookers and halves compared with fullbacks, middle forwards, and outside backs. Pmet was greatest in hookers, halves, and fullbacks compared with middle forwards and outside backs.
Competition running intensities varied by both position and moving-average duration. Hookers exhibited the greatest Pmet of all positions, due to high involvement in both attack and defense. Fullbacks also reached high Pmet, possibly due to a greater absolute volume of running. This study provides coaches with match data that can be used for the prescription and monitoring of specific training drills.
David M. Andrews and James J. Dowling
A fourth order mass/spring/damper (MSD) mechanical model with linear coefficients was used to estimate axial tibial accelerations following impulsive heel impacts. A generic heel pad with constant stiffness was modeled to improve the temporal characteristics of the model. Subjects (n = 14) dropped (~5 cm) onto a force platform (3 trials), landing on the right heel pad with leg fully extended at the knee. A uni-axial accelerometer was mounted over the skin on the anterior aspect of the medial tibial condyle inferior to the tibial plateau using a Velcro™ strap (normal preload ~45 N). Model coefficients for stiffness (k1, k2) and damping (c1, c2) were varied systematically until the minimum difference in peak tibial acceleration (%PTAmin) plus maximum rate of tibial acceleration (%RTAmax) between the estimated and measured curves was achieved for each trial. Model responses to mean subject and mean group model coefficients were also determined. Subject PTA and RTA magnitudes were reproduced well by the model (%PTAmin = 1.4 ± 1.0 %, %RTAmin = 2.2 ± 2.7%). Model estimates of PTA were fairly repeatable for a given subject despite generally high variability in the model coefficients, for subjects and for the group (coefficients of variation: CVk1 = 57; CVk2 = 59; CVc1 = 48; CVc2 = 85). Differences in estimated parameters increased progressively when subject and group mean coefficients (%PTAsub = 8.4 ± 6.3%, %RTAsub = 18.9 ± 18.6%, and %PTAgrp = 19.9 ± 15.2 %, %RTAgrp = 30.2 ± 30.2%, respectively) were utilized, suggesting that trial specific calibration of coefficients for each subject is required. Additional model refinement seems warranted in order to account for the large intra-subject variability in coefficients.