Data-driven digital health technologies in the remote clinical care of diabetic foot ulcers: a scoping review.

Background: The availability and effectiveness of Digital Health Technologies (DHTs) to support clinicians, empower patients, and generate economic savings for national healthcare systems are growing rapidly. Of particular promise is the capacity of DHTs to autonomously facilitate remote monitoring and treatment. Diabetic Foot Ulcers (DFUs) are characterised by high rates of infection, amputation, mortality, and healthcare costs. With clinical outcomes contingent on activities that can be readily monitored, DFUs present a promising focus for the application of remote DHTs. Objective: This scoping review has been conducted as a first step toward ascertaining fthe data-related challenges and opportunities for the development of more comprehensive, integrated, and individualised sense/act DHTs. We review the latest developments in the application of DHTs to the remote care of DFUs. We cover the types of DHTs in development and their features, technological readiness, and scope of clinical testing. Eligibility criteria: Only peer-reviewed original experimental and observational studies, case series and qualitative studies were included in literature searches. All reviews and manuscripts presenting pre-trial prototype technologies were excluded. Methods: An initial search of three databases (Web of Science, MEDLINE, and Scopus) generated 1,925 English-language papers for screening. 388 papers were assessed as eligible for full-text screening by the review team. 81 manuscripts were found to meet the eligibility criteria. Results: Only 19% of studies incorporated multiple DHTs. We categorised 56% of studies as 'Treatment-Manual', i.e. studies involving technologies aimed at treatment requiring manual data generation, and 26% as 'Prevention-Autonomous', i.e. studies of technologies generating data autonomously through wearable sensors aimed at ulcer prevention through patient behavioural change. Only 10% of studies involved more ambitious 'Treatment-Autonomous' interventions. We found that studies generally reported high levels of patient adherence and satisfaction. Conclusions: Our findings point to a major potential role for DHTs in remote personalised medical management of DFUs. However, larger studies are required to assess their impact. Here, we see opportunities for developing much larger, more comprehensive, and integrated monitoring and decision support systems with the potential to address the disease in a more complete context by capturing and integrating data from multiple sources from subjective and objective measurements.

Tackle direction and preferred side affect upper body loads and movements in rugby union tackling.

Tackling in Rugby Union is associated with most match injuries. New tackle regulations have been explored to reduce injuries, but limited quantitative evidence is available to inform any law changes. Using a novel tackle simulator, we investigated upper body loading under different tackling conditions: direction of approach (0° - frontal, 45° and 90° to the ball carrier direction) and side of body (dominant vs. non-dominant). Peak impact force between tackler and simulator , and head and upper trunk segment motions were measured from 10 male players. Impact load averages were 17% higher at (0°) compared with (90°), across the two different tackling sides (p = 0.093), with the highest impact force measured during dominant-side shoulder tackles at 0° (5.63 ± 1.14 kN). Trunk resultant accelerations were higher (+19%, p = 0.010) at 0° compared with 90°, with the highest resultant acceleration measured in frontal tackles with the dominant shoulder (17.52 ± 3.97 g). We observed higher head lateral bending around the impact when tackling with the non-dominant shoulder at 45° (p = 0.024) and 90° (p = 0.047). Tackling from an offset angle from frontal may be safer. Deficiencies in tackling techniques on the non-dominant side should be reduced.

Augmented screwdrivers can increase the performance of orthopaedic surgeons compared with use of normal screwdrivers.

Orthopaedic screws insertion can be trivialised as a simple procedure, however it is frequently performed poorly. Limited work exists defining how well surgeons insert screws or whether augmented screwdrivers can aid surgeons to reduce stripping rates and optimise tightness. We aimed to establish the performance of surgeons inserting screws and whether this be improved with screwdriver augmentation. 302 orthopaedic surgeons tightened 10 non-locking screws to what they determined to be optimum tightness into artificial bone sheets. The confidence in the screw purchase was given (1-10). A further 10 screws were tightened, using an augmented screwdriver that indicated when a predetermined optimum tightness was reached. The tightness for unstripped insertions under normal conditions and with the augmented screwdriver were 81% (95% CI 79-82%)(n = 1275) and 70% (95% CI 69-72%)(n = 2577) (p < 0.001). The stripping rates were 58% (95% CI 54-61%) and 15% (95% CI 12-17%) respectively (p < 0.001). The confidences when using the normal and augmented screwdrivers respectively were 7.2 and 7.1 in unstripped insertions and 6.2 and 6.5 in stripped insertions. Performance improved with an augmented screwdriver, both in reduced stripping rates and greater accuracy in detecting stripping. Augmenting screwdrivers to indicate optimum tightness offer potentially enormous clinical benefits by improving screw fixation.

Angular dynamics in vector coding: a new approach based on angular velocity.

The assessment of coordination variability in multi-joint human movements has traditionally started from angle-angle representations, and then used the angle change between subsequent time points as input for further analysis through vector coding. We propose an improvement to this approach, and suggest employing angular velocities as input data (Velocity Ellipse Method, VEM). We used experimental data and theoretical principles to contrast VEM with an existing standard (Difference Ellipse Method) and discuss its advantages and potential issues. Normalised cross-correlation was used to compare VEM and DEM in 36 angle couplings, from 20 participants running at 12 km/h on a treadmill. The hip flexion/extension-knee flexion/extension data were further investigated to discuss the robustness of the approach to measurement noise and outliers. Although DEM and VEM generally exhibited similar patterns (cross-correlation between 0.851 and 0.999), the variability curves from the two methods were noticeably different in some intervals. Also, using angular velocities as input appeared more robust to potential noise from raw data whilst retaining the following features: (a) more coherent with biomechanical conventions for calculating three-dimensional angular dynamics; (b) still suitable for coordination analysis; and, (c) more easily interpretable by practitioners when represented as relative motion plots.

The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review.

Wearable technologies are often indicated as tools that can enable the in-field collection of quantitative biomechanical data, unobtrusively, for extended periods of time, and with few spatial limitations. Despite many claims about their potential for impact in the area of injury prevention and management, there seems to be little attention to grounding this potential in biomechanical research linking quantities from wearables to musculoskeletal injuries, and to assessing the readiness of these biomechanical approaches for being implemented in real practice. We performed a systematic scoping review to characterise and critically analyse the state of the art of research using wearable technologies to study musculoskeletal injuries in sport from a biomechanical perspective. A total of 4952 articles were retrieved from the Web of Science, Scopus, and PubMed databases; 165 were included. Multiple study features-such as research design, scope, experimental settings, and applied context-were summarised and assessed. We also proposed an injury-research readiness classification tool to gauge the maturity of biomechanical approaches using wearables. Five main conclusions emerged from this review, which we used as a springboard to propose guidelines and good practices for future research and dissemination in the field.

Pulling the Trigger: The Effect of a 5-Minute Slow Diaphragmatic Breathing Intervention on Psychophysiological Stress Responses and Pressurized Pistol Shooting Performance.

This study examined the effect of slow diaphragmatic breathing on psychophysiological stress responses and pressurized performance. Sixty-seven participants (40 female; Mage = 20.17 ± 2.77 years) were randomly assigned to either a diaphragmatic-breathing, paced-breathing, or control group. Participants completed a nonpressurized shooting task and then received instructions about a pressurized version. Next, the diaphragmatic group was told to breathe at 6 breaths/min, the paced group at 12 breaths/min, and the control group received no instructions. Following a 5-min intervention period, participants completed the pressurized task while performance was assessed. Psychophysiological stress responses (e.g., cognitive anxiety, heart rate) were recorded throughout. Results revealed that diaphragmatic breathing had mixed effects on stress responses, with some unaffected (e.g., heart rate) and others reduced (e.g., cognitive anxiety), and little effect on performance. Findings suggested that slow diaphragmatic breathing might not aid pressurized performance but could benefit psychological stress responses.

A support vector machine algorithm can successfully classify running ability when trained with wearable sensor data from anatomical locations typical of consumer technology.

Greater understanding of differences in technique between runners may allow more beneficial feedback related to improving performance and decreasing injury risk. The purpose of this study was to develop and test a support vector machine classifier, which could automatically differentiate running technique between experienced and novice participants using only wearable sensor data. Three-dimensional linear accelerations and angular velocities were collected from six wearable sensors secured to current common smart device locations. Cross-validation was used to test the classification accuracy of models trained with a variety of combinations of sensor locations, with participants running at different speeds. Average classification accuracies ranged from 71.3% to 98.4% across the sensor combinations and running speeds tested. Models trained with only a single sensor location still showed effective classification. With the models trained with only upper arm data achieving an average accuracy of 96.4% across all tested running speeds. A post-hoc comparison of biomechanical variables between the two subgroups showed significant differences in upper body biomechanics throughout the stride. Both the methodology used to perform the classifications and the biomechanical differences identified could prove useful when aiming to shift a novice runner's technique towards movement patterns more akin to those with greater experience.

Electromyography-Assisted Neuromusculoskeletal Models Can Estimate Physiological Muscle Activations and Joint Moments Across the Neck Before Impacts.

Knowledge of neck muscle activation strategies before sporting impacts is crucial for investigating mechanisms of severe spinal injuries. However, measurement of muscle activations during impacts is experimentally challenging and computational estimations are not often guided by experimental measurements. We investigated neck muscle activations before impacts with the use of electromyography (EMG)-assisted neuromusculoskeletal models. Kinematics and EMG recordings from four major neck muscles of a rugby player were experimentally measured during rugby activities. A subject-specific musculoskeletal model was created with muscle parameters informed from MRI measurements. The model was used in the calibrated EMG-informed neuromusculoskeletal modeling toolbox and three neural solutions were compared: (i) static optimization (SO), (ii) EMG-assisted (EMGa), and (iii) MRI-informed EMG-assisted (EMGaMRI). EMGaMRI and EMGa significantly (p < 0.01) outperformed SO when tracking cervical spine net joint moments from inverse dynamics in flexion/extension (RMSE = 0.95, 1.14, and 2.32 N·m) but not in lateral bending (RMSE = 1.07, 2.07, and 0.84 N·m). EMG-assisted solutions generated physiological muscle activation patterns and maintained experimental cocontractions significantly (p < 0.01) outperforming SO, which was characterized by saturation and nonphysiological "on-off" patterns. This study showed for the first time that physiological neck muscle activations and cervical spine net joint moments can be estimated without assumed a priori objective criteria before impacts. Future studies could use this technique to provide detailed initial loading conditions for theoretical simulations of neck injury during impacts.

Screw tightness and stripping rates vary between biomechanical researchers and practicing orthopaedic surgeons.

BACKGROUND: Screws are the most frequently inserted orthopaedic implants. Biomechanical, laboratory-based studies are used to provide a controlled environment to investigate revolutionary and evolutionary improvements in orthopaedic techniques. Predominantly, biomechanical trained, non-surgically practicing researchers perform these studies, whilst it will be orthopaedic surgeons who will put these procedures into practice on patients. Limited data exist on the comparative performance of surgically and non-surgically trained biomechanical researchers when inserting screws. Furthermore, any variation in performance by surgeons and/or biomechanical researchers may create an underappreciated confounder to biomechanical research findings. This study aimed to identify the differences between surgically and non-surgically trained biomechanical researchers' achieved screw tightness and stripping rates with different fixation methods. METHODS: Ten orthopaedic surgeons and 10 researchers inserted 60 cortical screws each into artificial bone, for three different screw diameters (2.7, 3.5 and 4.5 mm), with 50% of screws inserted through plates and 50% through washers. Screw tightness, screw hole stripping rates and confidence in screw purchase were recorded. Three members of each group also inserted 30 screws using an augmented screwdriver, which indicated when optimum tightness was achieved. RESULTS: Unstripped screw tightness for orthopaedic surgeons and researchers was 82% (n = 928, 95% CI 81-83) and 76% (n = 1470, 95% CI 75-76) respectively (p < 0.001); surgeons stripped 48% (872/1800) of inserted screws and researchers 18% (330/1800). Using washers was associated with increased tightness [80% (95% CI 80-81), n = 1196] compared to screws inserted through plates [76% (95% CI 75-77), n = 1204] (p < 0.001). Researchers were more accurate in their overall assessment of good screw insertion (86% vs. 62%). No learning effect occurred when comparing screw tightness for the first 10 insertions against the last 10 insertions for any condition (p = 0.058-0.821). Augmented screwdrivers, indicating optimum tightness, reduced stripping rates from 34 to 21% (p < 0.001). Experience was not associated with improved performance in screw tightness or stripping rates for either group (p = 0.385-0.965). CONCLUSIONS: Surgeons and researchers showed different screw tightness under the same in vitro conditions, with greater rates of screw hole stripping by surgeons. This may have important implications for the reproducibility and transferability of research findings from different settings depending on who undertakes the experiments.

Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running.

The accurate detection of foot-strike and toe-off is often critical in the assessment of running biomechanics. The gold standard method for step event detection requires force data which are not always available. Although kinematics-based algorithms can also be used, their accuracy and generalisability are limited, often requiring corrections for speed or foot-strike pattern. The purpose of this study was to develop FootNet, a novel kinematics and deep learning-based algorithm for the detection of step events in treadmill running. Five treadmill running datasets were gathered and processed to obtain segment and joint kinematics, and to identify the contact phase within each gait cycle using force data. The proposed algorithm is based on a long short-term memory recurrent neural network and takes the distal tibia anteroposterior velocity, ankle dorsiflexion/plantar flexion angle and the anteroposterior and vertical velocities of the foot centre of mass as input features to predict the contact phase within a given gait cycle. The chosen model architecture underwent 5-fold cross-validation and the final model was tested in a subset of participants from each dataset (30%). Non-parametric Bland-Altman analyses (bias and [95% limits of agreement]) and root mean squared error (RMSE) were used to compare FootNet against the force data step event detection method. The association between detection errors and running speed, foot-strike angle and incline were also investigated. FootNet outperformed previously published algorithms (foot-strike bias = 0 [-10, 7] ms, RMSE = 5 ms; toe-off bias = 0 [-10, 10] ms, RMSE = 6 ms; and contact time bias = 0 [-15, 15] ms, RMSE = 8 ms) and proved robust to different running speeds, foot-strike angles and inclines. We have made FootNet's source code publicly available for step event detection in treadmill running when force data are not available.

Variations in non-locking screw insertion conditions generate unpredictable changes to achieved fixation tightness and stripping rates.

BACKGROUND: Screws are the most commonly inserted orthopaedic implants. However, several variables related to screw insertion and tightening have not been evaluated. This study aimed firstly to assess the effect of insertion variables on screw tightness, secondly to improve methodologies used by researchers when testing screw insertion techniques and thirdly to assess for any learning or fatigue effects when inserting screws. METHODS: Two surgeons tightened a total of 2280 non-locking, 3.5 mm cortical screws, with 120 screws inserted to what they felt to be optimum tightness whilst varying each of the following factors: different screwdrivers for measuring torque, screwdriver orientation, gloves usage, dominant/non-dominant hand usage, awareness to the applied torque (blinded, unblinded and re-blinded), four bone densities and seven cortical thicknesses. Screws were tightened to failure to determine stripping torque, which was used to calculate screw tightness - ratio between stopping and stripping torque. FINDINGS: Screw tightness increased with glove usage, being blinded to the applied torque and with denser artificial bone and with thinner cortices. Considering all the insertions performed, the two surgeons stopped tightening screws at difference values of tightness ((77% versus 66% (p < 0.001)). A learning effect was observed with some parameters including sterile gloves usage and non-dominant hand application. INTERPRETATION: Different insertion conditions frequently changed screw tightness for both surgeons. Given the influence of screw tightness on fixation stability, the variables investigated within this study should be carefully reported and controlled when performing biomechanical testing alongside practicing screw insertion under different conditions during surgical training.

Stripping torques in human bone can be reliably predicted prior to screw insertion with optimum tightness being found between 70% and 80% of the maximum.

AIMS: To devise a method to quantify and optimize tightness when inserting cortical screws, based on bone characterization and screw geometry. METHODS: Cortical human cadaveric diaphyseal tibiae screw holes (n = 20) underwent destructive testing to firstly establish the relationship between cortical thickness and experimental stripping torque (Tstr), and secondly to calibrate an equation to predict Tstr. Using the equation's predictions, 3.5 mm screws were inserted (n = 66) to targeted torques representing 40% to 100% of Tstr, with recording of compression generated during tightening. Once the target torque had been achieved, immediate pullout testing was performed. RESULTS: Cortical thickness predicted Tstr (R2 = 0.862; p < 0.001) as did an equation based on tensile yield stress, bone-screw friction coefficient, and screw geometries (R2 = 0.894; p < 0.001). Compression increased with screw tightness up to 80% of the maximum (R2 = 0.495; p < 0.001). Beyond 80%, further tightening generated no increase in compression. Pullout force did not change with variations in submaximal tightness beyond 40% of Tstr (R2 = 0.014; p = 0.175). CONCLUSION: Screw tightening between 70% and 80% of the predicted maximum generated optimum compression and pullout forces. Further tightening did not considerably increase compression, made no difference to pullout, and increased the risk of the screw holes being stripped. While further work is needed for development of intraoperative methods for accurate and reliable prediction of the maximum tightness for a screw, this work justifies insertion torque being considerably below the maximum.Cite this article: Bone Joint Res 2020;9(8):493-500.

Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout.

Observing, classifying and assessing human movements is important in many applied fields, including human-computer interface, clinical assessment, activity monitoring and sports performance. The redundancy of options in planning and implementing motor programmes, the inter- and intra-individual variability in movement execution, and the time-continuous, high-dimensional nature of motion data make segmenting sequential movements into a smaller set of discrete classes of actions non-trivial. We aimed to develop and validate a method for the automatic classification of four popular functional fitness drills, which are commonly performed in current circuit training routines. Five inertial measurement units were located on the upper and lower limb, and on the trunk of fourteen participants. Positions were chosen by keeping into account the dynamics of the movement and the positions where commercially-available smart technologies are typically secured. Accelerations and angular velocities were acquired continuously from the units and used to train and test different supervised learning models, including k-Nearest Neighbors (kNN) and support-vector machine (SVM) algorithms. The use of different kernel functions, as well as different strategies to segment continuous inertial data were explored. Classification performance was assessed from both the training dataset (k-fold cross-validation), and a test dataset (leave-one-subject-out validation). Classification from different subsets of the measurement units was also evaluated (1-sensor and 2-sensor data). SVM with a cubic kernel and fed with data from 600 ms windows with a 10% overlap gave the best classification performances, yielding to an overall accuracy of 97.8%. This approach did not misclassify any functional fitness movement for another, but confused relatively frequently (2.8-18.9%) a fitness movement phase with the transition between subsequent repetitions of the same task or different drills. Among 1-sensor configurations, the upper arm achieved the best classification performance (96.4% accuracy), whereas combining the upper arm and the thigh sensors obtained the highest level of accuracy (97.6%) from 2-sensors movement tracking. We found that supervised learning can successfully classify complex sequential movements such as those of functional fitness workouts. Our approach, which could exploit technologies currently available in the consumer market, demonstrated exciting potential for future on-field applications including unstructured training.

Surgical performance when inserting non-locking screws: a systematic review.

Billions of screws are inserted by surgeons each year, making them the most commonly inserted implant. When using non-locking screws, insertion technique is decided by the surgeon, including how much to tighten each screw. The aims of this study were to assess, through a systematic review, the screw tightness and rate of material stripping produced by surgeons and the effect of different variables related to screw insertion.Twelve studies were included, with 260 surgeons inserting a total of 2793 screws; an average of 11 screws each, although only 1510 screws have been inserted by 145 surgeons where tightness was measured - average tightness was 78±10% for cortical (n = 1079) and 80±6% for cancellous screw insertions (n = 431).An average of 26% of all inserted screws irreparably damaged and stripped screw holes, reducing the construct pullout strength. Furthermore, awareness of bone stripping is very poor, meaning that screws must be considerably overtightened before a surgeon will typically detect it.Variation between individual surgeons' ability to optimally insert screws was seen, with some surgeons stripping more than 90% of samples and others hardly any. Contradictory findings were seen for the relationship between the tightness achieved and bone density.The optimum tightness for screws remains unknown, thus subjectively chosen screw tightness, which varies greatly, remains without an established target to generate the best possible construct for any given situation. Work is needed to establish these targets, and to develop methods to accurately and repeatably achieve them. Cite this article: EFORT Open Rev 2020;5:26-36. DOI: 10.1302/2058-5241.5.180066.

Athletic groin pain patients and healthy athletes demonstrate consistency in their movement strategy selection when performing multiple repetitions of a change of direction test.

OBJECTIVES: To report the consistency in movement strategy selection in athletic groin pain patients and to assess whether there are differences in consistency between athletic groin pain patients and healthy athletes. DESIGN: Cross sectional exploratory study. METHODS: Twenty athletic groin pain patients and 21 healthy athletes performed 15 repetitions of 110° change of direction task. Lower limb and trunk kinematics alongside ground reaction forces were collected. A correlation-to-mean algorithm was used to allocate each trial to a movement strategy using kinematic and kinetic features. Mann-Whitney U tests were used to compare the frequency of the most selected strategy (i.e. consistency) and fuzziness between athletic groin pain patients and healthy athletes. Chi-squared tests were used to compare the strategy selection between athletic groin pain patients and healthy athletes. RESULTS: There were no differences between groups in consistency in movement strategy selection (>80%). Athletic groin pain patients tended to select a knee dominant movement strategy whereas healthy athletes preferred an ankle dominant movement strategy. CONCLUSIONS: The consistency observed in athletic groin pain patients supports the implementation of movement strategy assessments to inform AGP rehabilitation programmes tailored to athletes' deficiencies. Such assessments could help enhance the success of athletic groin pain rehabilitation. Differences in movement strategy selection might not be associated with injury state since there were no differences between athletic groin pain patients and healthy athletes.

Non-locking screw insertion: No benefit seen if tightness exceeds 80% of the maximum torque.

BACKGROUND: Millions of non-locking screws are manually tightened during surgery each year, but their insertion frequently results in overtightening and damage to the surrounding bone. We postulated that by calculating the torque limit of a screw hole, using bone and screw properties, the risk of overtightening during screw insertion could be reduced. Additionally, predicted maximum torque could be used to identify optimum screw torque, as a percentage of the maximum, based on applied compression and residual pullout strength. METHODS: Longitudinal cross-sections were taken from juvenile bovine tibial diaphyses, a validated surrogate of human bone, and 3.5 mm cortical non-locking screws were inserted. Fifty-four samples were used to define the association between stripping torque and cortical thickness. The relationship derived enabled prediction of insertion torques representing 40 to 100% of the theoretical stripping torque (Tstr) for a further 170 samples. Screw-bone compression generated during insertion was measured, followed immediately by axial pullout testing. FINDINGS: Screw-bone compression increased linearly with applied torque up to 80% of Tstr (R2 = 0.752, p < 0.001), but beyond this, no significant further compression was generated. After screw insertion, with all screw threads engaged, more tightening did not create any significant (R2 = 0.000, p = 0.498) increase in pullout strength. INTERPRETATION: Increasing screw tightness beyond 80% of the maximum did not increase screw-bone compression. Variations in torques below Tstr, did not affect pullout forces of inserted screws. Further validation of these findings in human bone and creation of clinical guidelines based on this research approach should improve surgical outcomes and reduce operative costs.

Musculoskeletal modelling of the human cervical spine for the investigation of injury mechanisms during axial impacts.

Head collisions in sport can result in catastrophic injuries to the cervical spine. Musculoskeletal modelling can help analyse the relationship between motion, external forces and internal loads that lead to injury. However, impact specific musculoskeletal models are lacking as current viscoelastic values used to describe cervical spine joint dynamics have been obtained from unrepresentative quasi-static or static experiments. The aim of this study was to develop and validate a cervical spine musculoskeletal model for use in axial impacts. Cervical spine specimens (C2-C6) were tested under measured sub-catastrophic loads and the resulting 3D motion of the vertebrae was measured. Specimen specific musculoskeletal models were then created and used to estimate the axial and shear viscoelastic (stiffness and damping) properties of the joints through an optimisation algorithm that minimised tracking errors between measured and simulated kinematics. A five-fold cross validation and a Monte Carlo sensitivity analysis were conducted to assess the performance of the newly estimated parameters. The impact-specific parameters were integrated in a population specific musculoskeletal model and used to assess cervical spine loads measured from Rugby union impacts compared to available models. Results of the optimisation showed a larger increase of axial joint stiffness compared to axial damping and shear viscoelastic parameters for all models. The sensitivity analysis revealed that lower values of axial stiffness and shear damping reduced the models performance considerably compared to other degrees of freedom. The impact-specific parameters integrated in the population specific model estimated more appropriate joint displacements for axial head impacts compared to available models and are therefore more suited for injury mechanism analysis.

Applying circular statistics can cause artefacts in the calculation of vector coding variability: A bivariate solution.

BACKGROUND: Coordination variability is thought to provide meaningful insights into motor learning, skill level and injury prevention. Current analytical techniques, based on vector coding (VC) methods, use calculations from circular statistics. However a statistical artefact associated with the application of circular statistics may artificially increase the estimated coordination variability, especially when VC vectors are short. RESEARCH QUESTION: Are two popular methods for calculating vector coding coordination variability susceptible to contamination by statistical artefacts and if so, how can coordination variability be calculated without statistical artefact? METHODS: A combination of simulated and experimental data was used to prove the existence of the statistical artefact and to understand the extent to which it may affect experimental running gait data, respectively. An alternative approach that uses ellipse area as a bivariate measure of variability was proposed, applied to the same dataset, and compared to two popular methods of coordination variability analysis. RESULTS: The simulated data showed the existence of a statistical artefact, which was greater for shorter VC vector lengths in coordination variability measures that used circular statistics. The statistical artefact typically manifests itself as inflated peaks in the coordination variability trace. The experimental data also indicated that short vector lengths are prevalent in running gait. The Ellipse Area Method of coordination variability was not affected by the VC vector length. SIGNIFICANCE: Researchers using current VC variability measures should be particularly aware of the possible effect of the statistical artefact on their data, which is most likely to occur when vector lengths are short. The novel approach we have suggested for calculating VC coordination variability may provide the foundation for future research into vector coding coordination variability.

Cervical Spine Injuries: A Whole-Body Musculoskeletal Model for the Analysis of Spinal Loading.

Cervical spine trauma from sport or traffic collisions can have devastating consequences for individuals and a high societal cost. The precise mechanisms of such injuries are still unknown as investigation is hampered by the difficulty in experimentally replicating the conditions under which these injuries occur. We harness the benefits of computer simulation to report on the creation and validation of i) a generic musculoskeletal model (MASI) for the analyses of cervical spine loading in healthy subjects, and ii) a population-specific version of the model (Rugby Model), for investigating cervical spine injury mechanisms during rugby activities. The musculoskeletal models were created in OpenSim, and validated against in vivo data of a healthy subject and a rugby player performing neck and upper limb movements. The novel aspects of the Rugby Model comprise i) population-specific inertial properties and muscle parameters representing rugby forward players, and ii) a custom scapula-clavicular joint that allows the application of multiple external loads. We confirm the utility of the developed generic and population-specific models via verification steps and validation of kinematics, joint moments and neuromuscular activations during rugby scrummaging and neck functional movements, which achieve results comparable with in vivo and in vitro data. The Rugby Model was validated and used for the first time to provide insight into anatomical loading and cervical spine injury mechanisms related to rugby, whilst the MASI introduces a new computational tool to allow investigation of spinal injuries arising from other sporting activities, transport, and ergonomic applications. The models used in this study are freely available at simtk.org and allow to integrate in silico analyses with experimental approaches in injury prevention.