Utilising electrodermal activity sensor signals to quantify nociceptive response during movement activities.

OBJECTIVE: With an increasingly ageing population and osteoarthritis prevalence, the quantification of nociceptive signals responsible for painful movements and individual responses could lead to better treatment and monitoring solutions. Changes in electrodermal activity (EDA) can be detected via changes in skin conductance (SC) and measured using finger electrodes on a wearable sensor, providing objective information for increased physiological stress response. RESULTS: To provide EDA response preliminary data, this was recorded with healthy volunteers on an array of activities while receiving a noxious stimulus. This provides a defined scenario that can be utilised as protocol feasibility testing. Raw signal extraction, processing and statistical analysis was performed using mean SC values on all participant data. The application of the stimuli resulted in a significant average increase (p < 0.05) in mean SC in four out of five activities with significant gender differences (p < 0.05) in SC and self-reported pain scores and large effect sizes. Though EDA parameters are a promising tool for nociceptive response indicators, limitations including motion artifact sensitivities and lack of previous movement-based EDA published data result in restricted analysis understanding. Refined processing pipelines with signal decomposition tools could be utilised in a protocol that quantifies nociceptive response clinically meaningfully.

Biomechanics beyond the lab: Remote technology for osteoarthritis patient data-A scoping review.

The objective of this project is to produce a review of available and validated technologies suitable for gathering biomechanical and functional research data in patients with osteoarthritis (OA), outside of a traditionally fixed laboratory setting. A scoping review was conducted using defined search terms across three databases (Scopus, Ovid MEDLINE, and PEDro), and additional sources of information from grey literature were added. One author carried out an initial title and abstract review, and two authors independently completed full-text screenings. Out of the total 5,164 articles screened, 75 were included based on inclusion criteria covering a range of technologies in articles published from 2015. These were subsequently categorised by technology type, parameters measured, level of remoteness, and a separate table of commercially available systems. The results concluded that from the growing number of available and emerging technologies, there is a well-established range in use and further in development. Of particular note are the wide-ranging available inertial measurement unit systems and the breadth of technology available to record basic gait spatiotemporal measures with highly beneficial and informative functional outputs. With the majority of technologies categorised as suitable for part-remote use, the number of technologies that are usable and fully remote is rare and they usually employ smartphone software to enable this. With many systems being developed for camera-based technology, such technology is likely to increase in usability and availability as computational models are being developed with increased sensitivities to recognise patterns of movement, enabling data collection in the wider environment and reducing costs and creating a better understanding of OA patient biomechanical and functional movement data.

Immediate effect of a spinal mobilisation intervention on muscle stiffness, tone and elasticity in subjects with lower back pain - A randomized cross-over trial.

BACKGROUND: Despite the lack of objective evidence, spinal manual therapies have been common practice for many years, particularly for treatment of lower back pain (LBP). This exploratory study measured and analysed the effect of a spinal mobilisation intervention on muscle tissue quality in LBP sufferers. METHODS: 40 people with LBP participated in a within-subject repeated measures cross-over study with intervention and control conditions. A myometer was used to assess the change in para-spinal muscle tissue quality before and after the intervention. Analysis considered the magnitude of muscle response together with individual covariates as potential contributors. RESULTS: A significant post-intervention reduction was observed in muscle stiffness (p = 0.012, η 2 partial = 0.15), tone (p = 0.001, η 2 partial = 0.25) and elasticity (p = 0.001, η 2 partial = 0.24). Significant increases were seen in 2 variables post-control: stiffness (p = 0.004, η 2 partial = 0.19), tone (p = 0.006, η 2 partial = 0.18) and a significant decrease in elasticity (p ˂ 0.000, η 2 partial = 0.3). Significant contributing covariates include baseline stiffness, BMI, waist circumference and sex. Baseline stiffness and tone were significantly correlated to their response levels. CONCLUSIONS: The significant reduction in all muscle tissue qualities following the intervention provide preliminary data for an evidence-based LBP therapeutic. Baseline stiffness, BMI, waist circumference and sex could act as significant contributors to magnitude of response. The results warrant further investigation into spinal mobilisation therapies to further build the objective evidence base.

Data sharing across osteoarthritis research groups and disciplines: Opportunities and challenges.

Background: Osteoarthritis is a heterogeneous condition characterised by a wide variety of factors and represents a worldwide healthcare challenge. There are multiple clinical and research specialisms involved in the diagnosis, prognosis and treatment of osteoarthritis, and there may be opportunities to share or pool data which are currently not being utilised. However, there are challenges to doing so which require carefully structured solutions and partnership working. Methods: Interviews were conducted with nine experts from various fields within osteoarthritis research. A semi-structured approach was used, and thematic analysis applied to the results. Results: Generally, osteoarthritis researchers were supportive of data sharing, provided it is done responsibly and without impacting data integrity. Benefits identified included increasing typically low-powered data, the potential for machine learning opportunities, and the potential for improved patient outcomes. However, a number of challenges were identified, relating to: data security, data harmonisation, storage costs, ethical considerations and governance. Conclusions: There is clear support for increased data sharing and partnership working in osteoarthritis research. Further investigation will be required to navigate the complex issues identified; however, it is clear that collaborative opportunities should be better facilitated and there may be innovative ways to do this. It is also clear that nomenclature within different disciplines could be better streamlined, to improve existing opportunities to harmonise data.