Incidence, Mechanisms, and Characteristics of Injuries in Pole Dancers: A Prospective Cohort Study.

OBJECTIVE: Pole dancing is a challenging physical activity. Prospective injury studies in pole dancing are lacking. The aim of this study was to describe the incidence, mechanisms, and characteristics of injuries in pole dancers. METHODS: A total of 66 pole dancers from 41 studios across Australia were prospectively followed over 12 months. An intake questionnaire was administered including items on pole dancers' demographics and training characteristics. Exposure was assessed using a daily online training diary. Self-reported injury data were collected via an incident report form and subsequently coded using the Orchard Sports Injury Classification System. Injuries occurring during pole-specific and pole-related activities were included in the analyses. RESULTS: The sample included 63 females and 3 males, mean age 32.3 ± 8.9 years and mean pole training experience 3.5 ± 2.8 years. 25 of 66 participants completed the full study. The 1-year incidence of all new injuries was 8.95/1,000 exposure hours (95% CI 6.94 - 10.96), 7.65/1,000 hrs (95% CI 5.79 - 9.51) for pole-specific injuries and 1.29/1,000 hrs (95% CI 0.53 - 2.06) for pole-related injuries. A total of 103 injuries occurred, 62.1% of which were sudden onset and 37.9% gradual onset. Mechanism of onset included 54.4% acute and 45.6% repetitive in nature. Shoulder (20.4%) and thigh (11.7%, majority ham¬string) were the most reported anatomic injury sites. Non-contact mechanisms accounted for the majority of injuries (57.3%). The most reported primary contributor to injury onset at the shoulder were manoeuvres characterised by loaded internal humeral rotation (33.3%), and at the hamstring were manoeuvres and postures involving front splits (100.0%). CONCLUSION: The findings indicate that pole dancers are at high risk for injuries. Future research is needed to understand the biomechani¬cal demand of manoeuvres and training characteristics of pole dancing (e.g., workload and recovery) to guide the development of preventative interventions, particularly targeted toward the shoulder and hamstring.

Machine learning methods to support personalized neuromusculoskeletal modelling.

Many biomedical, orthopaedic, and industrial applications are emerging that will benefit from personalized neuromusculoskeletal models. Applications include refined diagnostics, prediction of treatment trajectories for neuromusculoskeletal diseases, in silico design, development, and testing of medical implants, and human-machine interfaces to support assistive technologies. This review proposes how physics-based simulation, combined with machine learning approaches from big data, can be used to develop high-fidelity personalized representations of the human neuromusculoskeletal system. The core neuromusculoskeletal model features requiring personalization are identified, and big data/machine learning approaches for implementation are presented together with recommendations for further research.

Different visual stimuli affect body reorientation strategies during sidestepping.

Sidestepping in response to unplanned stimuli is a high-risk maneuver for anterior cruciate ligament (ACL) injuries. Yet, differences in body reorientation strategies between high- and low-level soccer players prior to sidestepping in response to quasi-game-realistic vs non-game-realistic stimuli, remain unknown. Fifteen high-level (semi-professional) and 15 low-level (amateur) soccer players responded to a quasi-game-realistic one-defender scenario (1DS) and two-defender scenario (2DS), and non-game-realistic arrow-planned condition (AP) and arrow-unplanned condition (AUNP). The AP, 1DS, 2DS to AUNP represented increasing time constraints to sidestep. Selected biomechanics from the penultimate step to foot-off were assessed using a mixed-model (stimuli × skill) ANOVA (P < 0.05). Step length decreased in the defender scenarios compared with the arrow conditions. Support foot placement increased laterally, away from mid-pelvis, with increasing temporal constraints. Greater trunk lateral flexion in the 1DS, 2DS, and AUNP has been associated with ACL injury onsets. Higher level players pushed off closer to their pelvic midline at initial foot contact in the 2DS especially. Higher level perception of game-realistic visual information could have contributed to this safer neuromuscular strategy that, when understood better, could potentially be trained in lower level players to reduce ACL injury risk associated with dangerous sidestepping postures.

Prescribing joint co-ordinates during model preparation to improve inverse kinematic estimates of elbow joint angles.

To appropriately use inverse kinematic (IK) modelling for the assessment of human motion, a musculoskeletal model must be prepared 1) to match participant segment lengths (scaling) and 2) to align the model׳s virtual markers positions with known, experimentally derived kinematic marker positions (marker registration). The purpose of this study was to investigate whether prescribing joint co-ordinates during the marker registration process (within the modelling framework OpenSim) will improve IK derived elbow kinematics during an overhead sporting task. To test this, the upper limb kinematics of eight cricket bowlers were recorded during two testing sessions, with a different tester each session. The bowling trials were IK modelled twice: once with an upper limb musculoskeletal model prepared with prescribed participant specific co-ordinates during marker registration - MRPC - and once with the same model prepared without prescribed co-ordinates - MR; and by an established direct kinematic (DK) upper limb model. Whilst both skeletal model preparations had strong inter-tester repeatability (MR: Statistical Parametric Mapping (SPM1D)=0% different; MRPC: SPM1D=0% different), when compared with DK model elbow FE waveform estimates, IK estimates using the MRPC model (RMSD=5.2±2.0°, SPM1D=68% different) were in closer agreement than the estimates from the MR model (RMSD=44.5±18.5°, SPM1D=100% different). Results show that prescribing participant specific joint co-ordinates during the marker registration phase of model preparation increases the accuracy and repeatability of IK solutions when modelling overhead sporting tasks in OpenSim.

MRI development and validation of two new predictive methods of glenohumeral joint centre location identification and comparison with established techniques.

Identification of the centre of the glenohumeral joint (GHJ) is essential for three-dimensional (3D) upper limb motion analysis. A number of convenient, yet un-validated methods are routinely used to estimate the GHJ location in preference to the International Society of Biomechanics (ISB) recommended methods. The current study developed a new regression model, and simple 3D offset method for GHJ location estimation, employing easy to administer measures, and compared the estimates with the known GHJ location measured with magnetic resonance imaging (MRI). The accuracy and reliability of the new regression and simple 3D offset techniques were compared with six established predictive methods. Twenty subjects wore a 3D motion analysis marker set that was also visible in MRI. Immediately following imaging, they underwent 3D motion analysis acquisition. The GHJ and anatomical landmark positions of 15 participants were used to determine the new regression and simple 3D generic offset methods. These were compared for accuracy with six established methods using 10 subject's data. A cross validation on 5 participants not used for regression model development was also performed. Finally, 10 participants underwent a further two MRI's and subsequent 3D motion analysis analyses for inter-tester and intra-tester reliability quantification. When compared with any of the other established methods, our newly developed regression model found an average GHJ location closer to the actual MRI location, having an GHJ location error of 13+/-2 mm, and had significantly lower inter-tester reliability error, 6+/-4 mm (p<0.01).

Effects of different technical coordinate system definitions on the three dimensional representation of the glenohumeral joint centre.

This study aimed to find the most appropriate marker location, or combination thereof, for the centre of the humeral head (Wang et al. in J Biomech 31: 899-908, 1998) location representation during humeral motion. Ten male participants underwent three MRI scans in three different humeral postures. Seven technical coordinate systems (TCS) were defined from various combinations of an acromion, distal upper arm and proximal upper arm clusters of markers in a custom Matlab program. The CHH location was transformed between postures and then compared with the original MRI CHH location. The results demonstrated that following the performance of two near 180 degrees humeral elevations, a combined acromion TCS and proximal upper arm TCS produced an average error of 23 +/- 9 mm, and 18 +/- 4 mm, which was significantly smaller (p < 0.01) than any other TCS. A combination of acromion and proximal upper arm TCSs should therefore be used to reference the CHH location when analysing movements incorporating large ranges of shoulder motion.