Methods and Results

We identified 69 apps for LBP self-management from a systematic search in the UK iTunes and Google Play stores. The most recommended interventions are muscle stretching (n=51, 73.9%), muscle strengthening (n=42, 60.9%), and core stability exercises (n=32, 46.4%). The average MARS (SD) overall score for the included apps was 2.4 (0.44) out of a possible 5 points, with the engagement and information dimension scoring the lowest at 2.1. In terms of theoretical and risk management approaches, no apps offered a theoretical care model and all failed to specify the age group targeted; only one (1.4%) provided a tailored care approach; 18 (26.1%) included intervention progression; and 11 (15.9%) reported management safety checks.

Methods

AMRI was assessed using the Feagin-Thomas test in 7 isolated ACL (iACL) injured participants, 3 combined ACL and superficial fibre MCL (sMCL) injuries, 5 combined ACL and deep fibre MCL injuries, and 21 healthy controls. Displacement values were recorded using an optical motion capture (OMC) system and bespoke processing pipeline which map and model the knee's anterior displacement values relative to the medial compartment. Since absolute values (mm) of rotational laxity vary dependant on the person, values were recorded as a proportion of the rotational laxity obtained from the subject's contralateral leg. Values were compared between each patient group using an ANOVA test and Tukey's honesty significant difference post hoc test. 3.

Objectives

Biomechanics is an essential form of measurement in the understanding of the development and progression of osteoarthritis (OA). However, the number of participants in biomechanical studies are often small and there is limited ways to share or combine data from across institutions or studies. This is essential for applying modern machine learning methods, where large, complex datasets can be used to identify patterns in the data. Using these data-driven approaches, it could be possible to better predict the optimal interventions for patients at an early stage, potentially avoiding pain and inappropriate surgery or rehabilitation. In this project we developed a prototype database platform for combining and sharing biomechanics datasets. The database includes methods for importing and standardising data and associated variables, to create a seamless, searchable combined dataset of both healthy and knee OA biomechanics.

Methods

Data was curated through calls to members of the OATech Network+ (https://www.oatechnetwork.org/). The requirements were 3D motion capture data from previous studies that related to analysing the biomechanics of knee OA, including participants with OA at any stage of progression plus healthy controls. As a minimum we required kinematic data of the lower limbs, plus associated kinetic data (i.e. ground reaction forces). Any additional, complementary data such as EMG could also be provided. Relevant ethical approvals had to be in place that allowed re-use of the data for other research purposes. The datasets were uploaded to a University hosted cloud platform. The database platform was developed using Javascript and hosted on a Windows server, located and managed within the department.

OBJECTIVES

Application of deep learning approaches to marker trajectories and ground reaction forces (mocap data), is often hampered by small datasets. Enlarging dataset size is possible using some simple numerical approaches, although these may not be suited to preserving the physiological relevance of mocap data. We propose augmenting mocap data using a deep learning architecture called “generative adversarial networks” (GANs). We demonstrate appropriate use of GANs can capture variations of walking patterns due to subject- and task-specific conditions (mass, leg length, age, gender and walking speed), which significantly affect walking kinematics and kinetics, resulting in augmented datasets amenable to deep learning analysis approaches.

METHODS

A publicly available (https://www.nature.com/articles/s41597-019-0124-4) gait dataset (733 trials, 21 women and 25 men, 37.2 ± 13.0 years, 1.74 ± 0.09 m, 72.0 ± 11.4 kg, walking speeds ranging from 0.18 m/s to 2.04 m/s) was used as the experimental dataset. The GAN comprised three neural networks: an encoder, a decoder, and a discriminator. The encoder compressed experimental data into a fixed-length vector, while the decoder transformed the encoder's output vector and a condition vector (containing information about the subject and trial) into mocap data. The discriminator distinguished between the encoded experimental data from randomly sampled vectors of the same size. By training these networks jointly using the experimental dataset, the generator (decoder) could generate synthetic data respecting specified conditions from randomly sampled vectors. Synthetic mocap data and lower limb joint angles were generated and compared to the experimental data, by identifying the statistically significant differences across the gait cycle for a randomly selected subset of the experimental data from 5 female subjects (73 trials, aged 26–40, weighing 57–74 kg, with leg lengths between 868–931 mm, and walking speeds ranging from 0.81–1.68 m/s). By conducting these comparisons for this subset, we aimed to assess the synthetic data generated using multiple conditions.

OBJECTIVES

Bone health deterioration is a major public health issue. General guidelines for the limitation of bone loss prescribe a healthy lifestyle and a minimum level of physical activity. However, there is no specific recommendation regarding targeted activities that can effectively maintain lumbar spine bone health. To provide a better understanding of such influencing activities, a new predictive modelling framework was developed to study bone remodelling under various loading conditions.

METHODS

The approach is based on a full-body subject-specific musculoskeletal model [1] combined with structural finite element models of the lumbar vertebrae. Using activities recorded with the subject, musculoskeletal simulations provide physiological loading conditions to the finite element models which simulate bone remodelling using a strain-driven optimisation algorithm [2]. With a combination of daily living activities representative of a healthy lifestyle including locomotion activities (walking, stair ascent and descent, sitting down and standing up) and spine-focused activities involving twisting and reaching, this modelling framework generates a healthy bone architecture in the lumbar vertebrae. The influence of spine-focused tasks was studied by adapting healthy vertebrae to an altered loading scenario where only locomotion activities were performed.

Objectives

Osteoporosis of the pelvis and femur is diagnosed in a high proportion of lower-limb amputees which carries an increased fracture risk and subsequently serious implications on mobility, physical dependency and morbidity. Through the development of biofidelic musculoskeletal and finite element (FE) models, we aim to determine the effect of lower-limb amputation on long-term bone remodelling in the hip and to understand the potential underpinning mechanisms for bone degradation in the younger amputee population.

Methods

Our models are patient specific and anatomically accurate. Geometries are derived from MRI-scans of one bilateral, above-knee, amputee and one body-matched control subject. Musculoskeletal modelling enables comparison of muscle and joint reaction-forces throughout gait. This provides the loading scenario implemented in FE. FE modelling demonstrates the effect of loading on the amputated limb via a prosthetic socket by comparing bone mechanical stimulation in amputee and control cases.

Methods

Ninety-seven subjects participated in the study (mean age 50 years (SD 12)). 3T MRI was used to acquire T2 weighted images (L1-S1). LDD was determined using Pfirrmann grading and LBP using the numerical rating scale (NRS). A bespoke perturbation platform was designed to deliver postural perturbations. Electrical activity was analysed from 16 trunk and lower limb muscles during four typical APA and CPA epochs. A Kruskal-Wallis H test with Bonferroni correction for multiple comparisons was conducted.

Background

A feasibility-RCT suggested REFS achieved a meaningful impact in disability and pain self-efficacy compared to ‘usual care’ (p=0.014, p=0.007).

In keeping with MRC guidance a qualitative evaluation was undertaken to understand possible mechanisms of action.

Method

The spine was modelled according to easily identifiable anatomical landmarks, including upper thoracic (T1-T6), lower thoracic (T7-T12) and lumbar (L1-L5) segments. Pelvis, thigh, shank and foot segments were included. A 10-camera 3D motion capture system was used to track retro-reflective markers, which were used to define each segment of 10 healthy participants as they walked 3 times at a comfortable speed over a 6km walkway. The relative peak angles between each segment were calculated using the Joint Coordinate System convention and Intraclass Correlation Coefficients (ICCs) were used to determine intra-rater and inter-rater reliability (between an experienced clinician and biomechanical scientist).

Methods

A sample of 21 healthy, asymptomatic subjects participated (mean age 56.9 years). T2-weighted axial lumbar images were obtained (L3/L4 to L5/S1), with slices oriented through the centre of each disc. Scans were examined by a Consultant MRI specialist and divided into 2 groups dependent on Modic presence (M) or absence (NM). Bilateral measurements of the CSA and FCSA of the erector spinae, multifidus, psoas major and quadratus lumborum were made using Image-J software. Muscle composition was determined using the equation [(FCSA/CSA)*100] and asymmetry using the equation [(Largest FCSA-smallest FCSA)/largest FCSA*100]. Data were analysed using Mann-Whitney U tests (p value set at). Intrarater reliability was examined using Intraclass Correlations (ICCs).

Aims

To investigate the experiences of patients seeking a neurosurgical opinion for back and leg pain.

Methods:

A reliable and valid electronic survey was designed to include theoretical constructs relating to training and education, general knowledge, assessment and management practices. Clinicians from the Society of Back Pain Research U.K. were invited to take part. Quantitative data was collated and coded using Bristol on-line survey software, and content analysis was used to systematically code and categorize qualitative data.

Methods

We applied force sensors to the 1st and 5th metatarsal heads and the plantar surface of the calcaneum of 14 healthy volunteers. Force measurements were taken without a cast applied and then with a Sarmiento Cast, a below knee cast, and a below knee cast with Böhler Walker™ fitted.

Methods

Healthy volunteers (n=16) and subjects with hLBP (n=10) were subjected to 31 identical postural disturbances at varying time intervals while standing atop a moving platform. Skeletal kinematics and muscle activation were recorded using a 10-camera Vicon system (Oxford, UK) and Myon electromyography (EMG) at the trunk (lumbar, lower thoracic, and upper thoracic segments), pelvis, thigh, calf, and foot. Joint angles were calculated using Body Builder (Vicon) and a unilateral seven-segment custom model.

Methods

80 participants were recruited into the following groups: 25 surgery(S), 20 chronic LBP(CH), 14 spinal injection(SI), and 21 controls(C). Parameters of corticospinal control were examined before, at 6, 26 and 52 weeks following lumbar decompression surgery and equivalent intervals. Electromyographic(EMG) activity was recorded from tibialis anterior(TA), soleus(SOL), rectus abdominis(RA), external oblique(EO) and erector spinae(ES) muscles at the T12&L4 levels in response to transcranial magnetic stimulation of the motor cortex. Motor evoked potentials (MEP) and cortical silent periods(cSP) recruitment curves(RC) were analysed.

Methods

In this study, an alternative technical pelvic coordinate system to the standard right and left anterior superior iliac spine (ASIS) and posterior superior iliac spine (PSIS) is developed and evaluated in two healthy male subjects (slim and overweight). The alternative system consists of a cluster of 3 retro-reflective markers attached to the Sacrum, thus allowing position and motion of the pelvis to be measured. In order to use these technical markers a static trial must be performed. The ASISs were calibrated relative to the technical frame; and the anatomical frame of the pelvis was defined relative to the technical coordinate frame. Each participant completed 5 walking trials and the angular rotations of the two methods were investigated using Euler angles.

Methods

This was a multi-centre, factorial, randomised controlled trial on the post operative management of spinal surgery patients, with randomisation stratified by surgeon and operative procedure. The study compared the effectiveness of a rehabilitation programme and an education booklet for the postoperative management of patients undergoing discectomy or lateral nerve root decompression surgery, each compared with “usual care” using a 2 × 2 factorial design, randomising patient to four groups; rehabilitation-only, booklet-only, rehabilitation-plus-booklet, and usual care only. The primary outcome measure was the Oswestry Disability Index (ODI) at 12 months, with secondary outcomes including visual analogue scale measures of back and leg pain. An economic analysis was also performed.

Methods

Initially a cadaveric dissection of the fascia was performed to gain an appreciation of the 3-D orientation and representation of the TLF in the lumbar region. A conventional ultrasound system (Diasus, Dynamic Imaging Ltd) was then used to image the 3 layers of the fascia on 40 normal subjects (18 males and 22 females, mean age 27.3±5.8 years) and the reliability of these measures was investigated on a subset of this population.

Sensor Development, Testing and Results

The system devised was based on inertial sensor technology combined with wireless Body Sensor Network (BSN) platform. This was tested on 16 healthy volunteers for ten common movements (including sit to stand, lifting, walking, and stairs) with results validated by optical tracking.

Preliminary findings suggest good agreement between the optical tracker and device with mean average orientation error (°) ranging from 0.1 ± 2.3 to 4.2 ± 2.6. The sensor repeatability errors range from 0 to 4° while subject movement variability ranged from 4% to 14%. Parameters of angular motion suggest greater movement of the lumbar spine compared to the pelvis with mean velocities (°/s) for lumbar spine ranging from 15.3 to 74.13 and pelvis ranging from 5.6 to 40.74. Further analysis revealed the extent to which the pelvis was engaged, as a proportion of the total movement. This demonstrated that the pelvis underwent smooth transitions from low (0.02), moderate (0.4) to high (0.99) use during different movement phases.