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Objective To evaluate the validity and reliability of inertial measurement unit (IMU) sensors in the assessment of spinal mobility in axial spondyloarthritis (axSpA). Methods A repeated measures study design involving 40 participants with axSpA was used. Pairs of IMU sensors were used to measure the maximum range of movement at the cervical (Cx) and lumbar (Lu) spine. A composite IMU score was defined by combining the IMU measures. Conventional metrology and physical function assessment were performed. Validation was assessed considering the agreement of IMU measures with conventional metrology and correlation with physical function. Reliability was assessed using intra-class correlation coefficients (ICCs). Results The composite IMU score correlated closely (r = 0.88) with the BASMI. Conventional Cx rotation and lateral flexion tests correlated closely with IMU equivalents (r = 0.85, 0.84). All IMU movement tests correlated strongly with BASFI, while this was true for only some of the BASMI tests. The reliability of both conventional and IMU tests (except for chest expansion) ranged from good to excellent. Test–retest ICCs for individual conventional tests varied between 0.57 and 0.91, in comparison to a range from 0.74 to 0.98 for each of the IMU tests. Each of the composite regional IMU scores had excellent test–retest reliability (ICCs=0.94–0.97), comparable to the reliability of the BASMI (ICC=0.96). Conclusion Cx and Lu spinal mobility measured using wearable IMU sensors is a valid and reliable assessment in multiple planes (including rotation), in patients with a wide range of axSpA severity.

Background Wearable devices can diagnose, monitor, and manage neurological disorders such as Parkinson disease. With a growing number of wearable devices, it is no longer a case of whether a wearable device can measure Parkinson disease motor symptoms, but rather which features suit the user. Concurrent with continued device development, it is important to generate insights on the nuanced needs of the user in the modern era of wearable device capabilities. Objective This study aims to understand the views and needs of people with Parkinson disease regarding wearable devices for disease monitoring and management. Methods This study used a mixed method parallel design, wherein survey and focus groups were concurrently conducted with people living with Parkinson disease in Munster, Ireland. Surveys and focus group schedules were developed with input from people with Parkinson disease. The survey included questions about technology use, wearable device knowledge, and Likert items about potential device features and capabilities. The focus group participants were purposively sampled for variation in age (all were aged >50 years) and sex. The discussions concerned user priorities, perceived benefits of wearable devices, and preferred features. Simple descriptive statistics represented the survey data. The focus groups analyzed common themes using a qualitative thematic approach. The survey and focus group analyses occurred separately, and results were evaluated using a narrative approach. Results Overall, 32 surveys were completed by individuals with Parkinson disease. Four semistructured focus groups were held with 24 people with Parkinson disease. Overall, the participants were positive about wearable devices and their perceived benefits in the management of symptoms, especially those of motor dexterity. Wearable devices should demonstrate clinical usefulness and be user-friendly and comfortable. Participants tended to see wearable devices mainly in providing data for health care professionals rather than providing feedback for themselves, although this was also important. Barriers to use included poor hand function, average technology confidence, and potential costs. It was felt that wearable device design that considered the user would ensure better compliance and adoption. Conclusions Wearable devices that allow remote monitoring and assessment could improve health care access for patients living remotely or are unable to travel. COVID-19 has increased the use of remotely delivered health care; therefore, future integration of technology with health care will be crucial. Wearable device designers should be aware of the variability in Parkinson disease symptoms and the unique needs of users. Special consideration should be given to Parkinson disease–related health barriers and the users’ confidence with technology. In this context, a user-centered design approach that includes people with Parkinson disease in the design of technology will likely be rewarded with improved user engagement and the adoption of and compliance with wearable devices, potentially leading to more accurate disease management, including self-management.

As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all-cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all-cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free-living settings, obtained for the “Healthy Ageing Initiative” study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random UnderSampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data-driven and disease-agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness.

Wearable sensor technology has steadily grown in availability within a wide variety of well-established consumer and medical devices. Wearable sensors have been used in many healthcare applications to monitor patients at home and throughout their rehabilitation. Data collected from wearable sensors allow monitoring of patient recovery during rehabilitation and assist clinicians in diagnosing. Activities of Daily Living (ADL) is considered as an assessment criterion for various disease conditions. Wearable devices enable the collection of information associated with different range of movement (ROM) tests that measure ADL. In an ambulatory monitoring setting, the volume of data collected by wearable sensors can become complex and challenging to process. Extraction of ROM tests can be laboursome, and often fraught with misclassification of movement. Hence it is difficult to analyse and make conclusions/predictions from movement datasets using manual assessment techniques. This paper examines whether ROM tests can be automatically detected and extracted from wearable sensor data using Artificial Intelligence (AI) techniques.This research examines and discusses clinical trial data collected from patients suffering from Axial SpondyloArthritis (AxSpA). AxSpA is a disease that affects spinal cord mobility. In this trial, Inertial Measurement Unit (IMU) sensors are attached to the lower back and neck of the patient, and data corresponding to clinical trial movements are recorded. An AI system is trained and tested using these datasets, and the prediction accuracy of the system is examined. The system will be capable of detecting ROM tests within long-term datasets once the AI system used in this analysis is sufficiently trained by an adequate amount of data for efficient pattern recognition.

The increased use of sensor technology has been crucial in releasing the potential for remote rehabilitation. However, it is vital that human factors, that have potential to affect real-world use, are fully considered before sensors are adopted into remote rehabilitation practice. The smart sensor devices for rehabilitation and connected health (SENDoc) project assesses the human factors associated with sensors for remote rehabilitation of elders in the Northern Periphery of Europe. This article conducts a literature review of human factors and puts forward an objective scoring system to evaluate the feasibility of balance assessment technology for adaption into remote rehabilitation settings. The main factors that must be considered are: Deployment constraints, usability, comfort and accuracy. This article shows that improving accuracy, reliability and validity is the main goal of research focusing on developing novel balance assessment technology. However, other aspects of usability related to human factors such as practicality, comfort and ease of use need further consideration by researchers to help advance the technology to a state where it can be applied in remote rehabilitation settings.

Background Older adults with diabetes take fewer steps per day than those without diabetes. The purpose of the present study was to investigate the association of daily step count with incident diabetes in community-dwelling 70-year-olds. Methods This prospective cohort study included N = 3055 community-dwelling 70-year-olds (52% women) who participated in a health examination in Umeå, Sweden during 2012–2017, and who were free from diabetes at baseline. Daily step count was measured for 1 week using Actigraph GT3X+ accelerometers. Cases of diabetes were collected from the Swedish National Patient Register. The dose-response association was evaluated graphically using a flexible parametric model, and hazard ratios (HR) with 95% confidence intervals (CI) were calculated using Cox regressions. Results During a mean follow-up of 2.6 years, diabetes was diagnosed in 81 participants. There was an inverse nonlinear dose-response association between daily step count and incident diabetes, with a steep decline in risk of diabetes from a higher daily step count until around 6000 steps/day. From there, the risk decreased at a slower rate until it leveled off at around 8000 steps/day. A threshold of 4500 steps/day was found to best distinguish participants with the lowest risk of diabetes, where those taking ≥ 4500 steps/day, had 59% lower risk of diabetes, compared to those taking fewer steps (HR, 0.41, 95% CI, 0.25–0.66). Adjusting for visceral adipose tissue (VAT) attenuated the association (HR, 0.64, 95% CI, 0.38–1.06), which was marginally altered after further adjusting for sedentary time, education and other cardiometabolic risk factors and diseases (HR, 0.58, 95% CI, 0.32–1.05). Conclusions A higher daily step count is associated with lower risk of incident diabetes in community-dwelling 70-year-olds. The greatest benefits occur at the lower end of the activity range, and much earlier than 10,000 steps/day. With the limitation of being an observational study, these findings suggest that promoting even a modest increase in daily step count may help to reduce the risk of diabetes in older adults. Because VAT appears to partly mediate the association, lifestyle interventions targeting diabetes should apart from promoting physical activity also aim to prevent and reduce central obesity.

The symptoms of (PD) often begins on one side of the body before separating bilaterally. Early symptoms of PD are rest tremors, bradykinesia and rigidity. Common signs are a decrease in arm swing, shuffling foot movement and slowness arising from a chair. Rigidity of muscles commonly affects a patient limbs, neck and shoulder PD is currently diagnosed after the neuro degenerative process has started. To optimize and improve PD quality of care, it should be diagnosed early in its onset once symptoms are not yet evident. The current gold standard for patient assessment of PD is the completion of symptom diaries. These diaries are subjective and usually do not accurately reflect what is taking place throughout the day. It is exceedingly difficult for an untrained observer such as family members to provide accurate description of movement characteristic in PD. An alternative way to exercise the abnormal movement in PD is to use a wearable technology system which could aid early detection of the disease and help clinicians manage medication when symptoms fluctuate throughout the day. This paper will evaluate the effectiveness of a wearable multi-sensor kinematic system capable of detecting the tell-tale signs of PD when performing tailored exercise routines. Each exercise routine will examine movement for potential PD symptoms and report finding for deeper clinician analysis.

Gait-based person identification suffers from the problem of different covariate factors such as clothing and carrying objects, which drastically reduce the recognition rate. Most existing methods capture dynamic and static information and remove the covariate factors without any systematic study. However, it has been reported in the literature that the head is one of the important features and the removal of the head from static information decreases the recognition rate. In our preliminary study, we developed a novel random walk (RW)-based gait extraction method that retains the head portion and removes certain static body parts to reduce the effect of covariate factors. The RW-based method is a novel gait feature extraction method and should be exploited more for its discriminative power to separate different body parts efficiently. However, the dynamic part is also significant in gait information, which is not very effectively represented in the RW-based gait extraction method. Therefore, a discrete Fourier transform (DFT)-based frequency component of the gait is considered to represent the dynamic part of gait information. Furthermore, we propose a novel gait recognition algorithm that fuses dynamic and static information from DFT- and RW-based representations. The proposed method systematically retains the discriminative static gait information along with the frequency attribute embedded as the dynamic gait information. Extensive experiments on the Chinese Academy of Sciences, Institute of Automation and the HumanID datasets have been carried out to demonstrate that the proposed fused gait features-based approach outperforms the existing methods, particularly when there are substantial appearance changes.

In the last decade the total global number of displaced refugees has increased significantly and consistently. This has been further inflated by 5 million Syrians who were externally displaced in 2017. Often these individuals flee their home countries without identification documentation and thus find it extremely difficult to verify their true identities when they attempt to reconstruct their lives in their new homelands. The requirement for identification documentation is a constant element of daily life within modern societies. This functions to both protect the rights of individuals and also the entities and organisations they interact with. The need to provide identification documentation can present significant challenges for displaced people trying to reconstruct their lives. In the digital age where individuals are creating large digital footprints, online accounts and activities can prove to be a valuable source of information that may contribute to ‘proof’ that an asserted identity is genuine. Online social contextual data - or ‘Digital identities' - pertaining to real people are built over time and bolstered by associated accounts, relationships and attributes. This data is difficult to fake and therefore may have the capacity to provide proof of a ‘real’ identity. This paper outlines the design and initial development of a solution that could potentially allow refugees who are resettling to build a digital identity document over time as they settle in a new land, without the need for multiple sources of traditional identity documentation. This solution utilizes data sourced from an individual's digital footprint to assess the likelihood that it pertains to a ‘real’ identity. This is achieved through application of machine learning and Bayesian probabilistic modelling techniques. Where identity sources are considered reliable, a secure and intelligent digital identification artefact will be created. This artefact will emulate a blockchain-inspired ledger and may subsequently be used to prove identity in place of traditional paper documentation.

Background Axial Spondyloarthritis (axSpA) is characterised by progressive loss of spinal mobility, due to inflammation and structural damage. Conventional mobility tests have been used alongside radiographic structural damage and disease activity scores in order to explore the overall relationship between spinal mobility, structural damage and disease activity. This has rarely been done at the segmental level e.g. at the level of the lumbar spine only. The ViMove inertial motion sensor-based system allows segmental spinal mobility to be directly measured, namely at the lumbar spine level. Objectives To analyse the relationship between mobility, structural damage and disease activity in the lumbar spine of patients with axSpA. Methods Lumbar spinal mobility was measured using the the ViMove system with a pair of sensors located at L1 and the sacrum. Lumbar ROM in the three planes (anterior flexion - LFF, extension - LFE, lateral flexion – LL, and rotation - LR) was measured. Radiographs were obtained to calculate the lumbar part of the mSASSS (LmSASSS). Other outcome measures were: BASMI, Schober and lateral flexion (measured with a tape), BASDAI and ASDAS-CRP. Subgroups of patients with high and low levels of lumbar structural damage were defined, by dichotomising the population based on the median LmSASSS (5.5). Pearson correlations between measures, Student T tests for significant differences between groups and linear regression analysis, to analyse relationship between structural damage and mobility, were obtained. Results 44 axSpA patients were recruited from the COSPAR cohort of the Reina Sofia University Hospital (39% females, age 43±12 years, disease duration 17±14 years, BMI 26±3). All mobility measurements (except for LFE) in the severe damage group were significantly reduced compared to the group with less damage (p Conclusion Other studies have shown a relationship between mobility, structural damage and disease activity using a more global assessment. Our study focuses on the analysis of structural damage and mobility in the lumbar vertebrae, and our results suggest that structural damage is the dominant factor determining spinal mobility in this region (especially lumbar frontal flexion). Further studies are needed combining these results with MRI. Acknowledgement This study was funded by FOREUM (www.foreum.org), Junta de Andalucia (PIN-0079-2016) and XXI Research Promotion Program of the University of Cordoba (Spain). Disclosure of Interests Juan L. Garrido-Castro: None declared, Inmaculada Concepcion Aranda-Valera: None declared, Cristina Gonzalez-Navas: None declared, Pedro Machado Consultant for: Abbvie, BMS, Celgene, Janssen, MSD, Novartis, Pfizer, Roche and UCB, Speakers bureau: Abbvie, BMS, Celgene, Janssen, MSD, Novartis, Pfizer, Roche and UCB, Philip Gardiner: None declared, Joan Condell: None declared, Eduardo Collantes Estevez: None declared

Background Loss of spinal mobility is one of the most characteristic problems for people with axial spondyloarthritis (axSpA) and is predictive of loss of function. Traditional measures such as the BASMI fail to capture many elements of spinal mobility and lack responsiveness to change. Inertial Motion Unit (IMU) sensors can be used to accurately measure spinal movement without requiring significant operator expertise. Objectives The primary objective of this study was to test the reliability of these new tools in patients with axSpA and to develop a new composite spinal mobility index. Our secondary objective was to apply the OMERACT ‘Truth’ filter to evaluate the new index for bias, for clinical relevance, and for convergent validity with existing measures1. Methods Patients with axSpA fulfilling ASAS classification criteria were recruited. ViMove system was used to obtain ROM by attaching two IMU sensors at the cervical (Occiput-T3) and lumbar spine (L1-Sacrum). Intra-rater, inter-rater and test-retest reliability of IMU tests were assessed by intraclass correlation coefficients (ICC). The maximum range of movement for anterior flexion/extension (AFE), lateral flexion (Left+Right) and rotation (Left+Right) were obtained for the lumbar and cervical region. These six values were used in a composite score (IMU-ASMI) which referenced equivalent ROM values from normal subjects in an earlier criterion validity study. Pearson correlation coefficients with BASFI were calculated for each component as well as the overall score. Results The study included 40 patients (12 females, 28 males) with a mean age of 48 (27-41). Subjects had a wide range of severity of axSpA. The mean BASMI was 4.8 (range 0.7 to 8.2, SD 1.9). The mean IMU-ASMI was 4.0 (range 0.1-9.2, SD 2.1). The sensor based measurements had good to excellent reliability (Table 1) and correlated closely with BASMI (r=0.79). The mean BASFI was 4.6 and the IMU-ASMI correlated closely with BASFI (r=0.71). Face Validity: Each IMU test presents spinal movement in angles and can also be represented as a normalized severity index analogous to BASMI. The mean cervical and lumbar IMU-ASMIs were 3.5 and 4.4 units, respectively. Construct Validity: Do IMU movements correlate with their corresponding traditional measurements? As expected, the closest correlations were between IMU and goniometer cervical rotation (r=0.85) and between IMU and tape measure lumbar side flexion (r=0.84). Correlations between Schober’s test and IMU lumbar AFE and between tragus to wall and IMU cervical FE were moderate (r=0.62, 0.65). Do IMU movements correlate with BASFI? Correlation coefficients were as follows: lumbar AFE -0.57; rotation -0.59; side flexion -0.45; cervical F/E -0.55; rotation -0.61; side flexion -0.39. BASFI correlations with BASMI were comparable. Content Validity/Comprehensiveness: No major ceiling or floor bias issues were found in the composite indices. Intermalleolar distance (BASMI) represents hip rather than spinal mobility, but it correlates with BASFI and is not in the IMU-ASMI. IMU-ASMI includes lumbar rotation which accounts for 27% (0-53%) of the lumbar mobility score. Conclusion IMU sensors can be used by non-experts to accurately and reliably measure spinal mobility in patients with axSpA. Lumbar rotation is an important new outcome measure. References [1] Tugwell P, et al. J Rheum 2014 41(5):1000-1004 Acknowledgement This study was funded by FOREUM (www.foreum.org) Disclosure of Interests Philip Gardiner Grant/research support from: Educational support to attend rheumatology conferences from AbbVie, Abbott, MSD, Novartis, Amgen, Pfizer, Roche, UCB., Speakers bureau: Menarini, Dawn Small Employee of: Medical representative - Farmitalia Carlo Erba, Pharmacia, Pharmacia & Upjohn, Yamanouchi/Astellas., Paid instructor for: Medical representative - Farmitalia Carlo Erba, Pharmacia, Pharmacia & Upjohn, Yamanouchi/Astellas., Pedro Machado Consultant for: Abbvie, BMS, Celgene, Janssen, MSD, Novartis, Pfizer, Roche and UCB, Speakers bureau: Abbvie, BMS, Celgene, Janssen, MSD, Novartis, Pfizer, Roche and UCB, Karla Munoz Esquivel: None declared, Joan Condell: None declared, Antonio Cuesta-Vargas: None declared, Juan L. Garrido-Castro: None declared

Background:Spinal mobility is an important outcome in axial spondyloarthritis (axSpA). Standardized metrology tests have been defined and validated. Nevertheless, conventional metrology measures lack responsiveness, particularly in the context of assessing short term response to treatments. Advanced metrology devices have also been used in axSpA, namely optical motion capture systems (MoCap) and Inertial Measurement Systems (IMU). These systems have better psychometric properties in terms of accuracy and reliability for analysing spinal mobility.Objectives:To compare the responsiveness of spinal mobility using conventional and advanced metrology.Methods:Spinal mobility was measured using an IMU based system (ViMove©) with sensors located at: occiput and T3 for cervical mobility, L1 and sacrum for lumbar mobility, and T3 and sacrum for thoracic+lumbar mobility. A MoCap system (UCOTrack©) using 13 markers was also used to record the movements in 3 planes (flexion+extension, rotation, and lateral flexion). The UCOASMI [1] index was calculated using measures obtained by MoCap and by the IMU system. Other metrology indexes based on IMU measures developed by the authors [2} were also calculated. Patients were assessed before (PRE) and 3 months after (POST) TNF blocker treatment. The Wilcoxon signed-rank test was used to test differences between pairs of pre- and post-treatment. Cohen’s d was use for calculating the effect size of improvement.Results:12 axSpA patients were recruited from the CASTRO cohort (42% female, age 46±11 years, disease duration 16±13 years). Results PRE and POST are shown in Table 1: means (sd), statistical significance, and effect size. Disease activity and functional indexes were lower in POST and these differences were significant, especially in ASDAS. Conventional metrology lacked responsiveness, except for cervical rotation. MoCap showed moderate responsiveness in cervical rotation, in thoracolumbar mobility and with regards to the UCOASMI. Using IMU measures, responsiveness was good in terms of cervical flexion and rotation. For Lumbar (L_) and Lumbar Thoracic (LT_), lateral flexion shown significant improvement and lumbar rotation shown the best responsiveness.PREPOSTSignESPREPOSTSignESActivityIMUBASDAI6.5 (1.6)4.9 (2.7)*0.64C_FLEX77 (22)83 (19)*-0.25BASFI6.5 (2.3)4.8 (3.2)*0.58C_ROT78 (25)84 (22)*-0.22ASDAS3.6 (1.0)2.5 (1.2)**0.87C_FL54 (19)54 (17)NSMetrologyL_FLEX46 (16)44 (19)NSLatFlex9.3 (4.1)10.6 (3.3)NSL_ROT21 (9)27 (9)***-0.66TWD13.2 (4.2)12.8 (3.6)*0.06L_FL32 (1)38 (10)*-0.52Schober4.7 (1.5)4.9 (1.5)NSLT_FLEX62 (28)63 (24)NSIMD89 (15)90 (15)NSLT_ROT63 (19)70 (19)NSCervRot104 (28)118 (24)**-0.51LT_FL52 (15)60 (15)**-0.51BASMI3.8 (1.3)3.4 (1.2)NSTrk_FLEX109 (28)112 (24)NSMoCapTrk_ROT98 (31)116 (21)**-0.59C_FLEX81 (22)87 (20)*-0.28Trk_FL39 (9)46 (10)**-0.7C_ROT101 (28)112 (25)***-0.38TTrk_FLEX119 (36)128 (30)NSC_FL58 (20)58 (18)NSTTrk_ROT137 (34)161 (35)**-0.67L_FLEX107 (24)114 (21)***-0.26TTrk_FL59 (15)69 (16)**-0.6L_ROT67 (21)73 (20)NSiUCOASMIL5.7 (1.5)5.0 (1.4)***0.48L_FL57 (18)65 (20)**-0.41iUCOASMILT6.0 (1.5)5.4 (1.3)**0.39UCOASMI6.1 (1.6)5.3 (1.6)**0.50iCxLPASMI3.9 (1.8)3.2 (1.5)**0.40iCxLuASMI3.2 (1.4)2.7 (1.3)***0.40NS, not significant; *, pConclusion:Conventional metrology lacks short-term responsiveness. Advanced metrology using MoCap and IMU showed, in this pilot study, better responsiveness compared to conventional measures. Furthermore, IMU provide a feasibility similar to conventional metrology, and could be easily implemented in the context of clinical trials and observational studies.References:[1]Validation of a new objective index to measure spinal mobility: the University of Cordoba Ankylosing Spondylitis Metrology Index (UCOASMI). Rheumatol Int. 2014 Mar;34(3):401-6.[2]Validity and Reliability of a Sensor-Based Electronic Spinal Mobility Index for Axial Spondyloarthritis. Rheumatology (Oxford). 2020 Apr 28;keaa122.Disclosure of Interests:None declared

Early detection of Rheumatoid Arthritis (RA) and other neurological conditions is vital for effective treatment. Existing methods of detecting RA rely on observation, questionnaires, and physical measurement, each with their own weaknesses. Pharmaceutical medications and procedures aim to reduce the debilitating effect, preventing the progression of the illness and bringing the condition into remission. There is still a great deal of ambiguity around patient diagnosis, as the difficulty of measurement has reduced the importance that joint stiffness plays as an RA identifier. The research areas of medical rehabilitation and clinical assessment indicate high impact applications for wearable sensing devices. As a result, the overall aim of this research is to review current sensor technologies that could be used to measure an individual’s RA severity. Other research teams within RA have previously developed objective measuring devices to assess the physical symptoms of hand steadiness through to joint stiffness. Unfamiliar physical effects of these sensory devices restricted their introduction into clinical practice. This paper provides an updated review among the sensor and glove types proposed in the literature to assist with the diagnosis and rehabilitation activities of RA. Consequently, the main goal of this paper is to review contact systems and to outline their potentialities and limitations. Considerable attention has been paid to gloved based devices as they have been extensively researched for medical practice in recent years. Such technologies are reviewed to determine whether they are suitable measuring tools.

BackgroundIncreasing numbers of women living with chronic conditions are becoming pregnant (Jølving et al., 2016). The presence of chronic conditions can contribute to adverse outcomes in pregnancy and in later life for mother and infant (Gilmore et al., 2015; Schetter and Tanner, 2012). Over recent years, the role of increasing BMI in pregnancy has gained prominence, with risk factors for maternal obesity well documented in literature. AimTo determine the relationship between BMI and other chronic conditions (multimorbidity) and pregnancy and birth outcomes in Northern Ireland. MethodA cross-sectional cohort study was undertaken using data from the NIMATS database (2014-2015), accessed via the Honest Broker Service, with approval by the Office for Research Ethics Committees Northern Ireland (ORECNI) and the Honest Broker Governance Board. ResultsRegression analyses demonstrated statistically significant relationships between increasing BMI and maternal conditions such as asthma, diabetes, epilepsy, hypertension, mental health and thyroid disease as well as a number of labour and birth outcomes. Mostly, these relationships were shown to increase with increasing BMI. DiscussionThe findings from this study have important implications for the provision of preconception care, maternity services (including health professional training), child health services and the public health agenda.

Background Improving joint mobility is an important outcome for patients with arthritis, but finger joint range of motion is rarely measured in clinic. Electronic gloves with movement sensors have been developed to measure joint movement accurately and it is now possible to assess dynamic mobility of the finger joints. However these gloves are expensive and it is likely that when carrying out measurements in the patient population they would be used with inner disposable gloves to avoid nonsocomical infection. Establishing accuracy and usability of electronic gloves whilst wearing disposable inner gloves is therefore an important pre-requisite for studies in patients with arthritis. Objectives To establish the accuracy and repeatability of measurements of finger movement obtained using two different electrogoniometric gloves worn with and without an inner disposable glove. Methods We used two different types of electrogoniometric glove for the purpose of this study. One is the commercially available 5DT dataglove 14 Ultra (5DT, 2011) and the other was produced to our specifications by Tyndall National Institute, University College Cork. We called this the “IMU glove”. We developed a graphical interface for both devices to facilitate detailed evaluation of joint movement in each finger. Both gloves were tested using a protocol adapted from Dipietro, Sabatini, & Dario, (2003). Results Table 1 displays comparison of Coefficient of Variation (CV) readings for both data gloves. Figure shows this information graphically. Results show no significant change for 5DT angular readings with and without a surgical glove worn underneath the data glove. Results for PIP sensors show an improvement in repeatability with a surgical glove. CV variance was smaller for MCP sensors with a surgical glove worn underneath the data glove compared with no surgical glove. CV for the IMU data glove show negligible changes in MCP readings when a surgical glove is worn underneath. PIP readings show small changes when using a surgical glove. Conclusions Inner disposable gloves can be worn when using electrogoniometric gloves for testing finger movement without loss of accuracy or any significant discomfort in patients with arthritis. References 5DT, 2011. 5DT Data Glove 14 Ultra [WWW Document]. URL http://www.5dt.com/products/pdataglove14.html(accessed 1.10.12). Dipietro, L., Sabatini, A.M., Dario, P., 2003. Evaluation of an instrumented glove for hand-movement acquisition. J. Rehabil. Res. Dev. 40, 179–89. Disclosure of Interest None declared

Background Patients with Rheumatoid arthritis suffer from pain, stiffness and reduced mobility of their finger joints. Instrumented electronic goniometric gloves have been developed which can enable dynamic measurements to be made with accuracy and efficiency [1]. This technology could be employed in assessing patients with rheumatoid arthritis and could also be used as an aid to rehabilitation. However, pain and swelling in the joints might limit the applicability of these measurement devices as to date they have not been tested in this patient group. Objectives The purpose of this pilot study was to establish the usability of two different electronic gloves in patients with mild to moderate Rheumatoid arthritis. We wished to establish if patients experienced difficulty donning or doffing either glove or if carrying out a series of measurements with the gloves on caused a change in pain and stiffness. We also investigated differences in the usability and preference between the two Datagloves. Methods We compared a commercially available electronic glove (the 5DT dataglove 14 Ultra) with a bespoke IMU based electronic glove produced to our specifications by Tyndall National Institute, University College Cork (Figure). We developed a programming interface for both devices to facilitate calibration and detailed evaluation of joint movement. Nine patients with mild to moderate rheumatoid arthritis who were experiencing significant but not severe pain and early morning stiffness in their hands were recruited. After calibration, the patients worked through a protocol of finger flexion and extension movements, which were repeated for each glove and again to test repeatability. The patients completed questionnaires before and after using the gloves on their pain and stiffness levels and at the end of the session on glove donning and doffing usability and preference between the two gloves. Results All nine patients were able to don and doff the IMU glove without any difficulty compared to 4/9 for the 5DT glove. Seven of 9 patients expressed a preference for the IMU glove. In 2/9 patients, joint stiffness increased after using the gloves and 4/9 patients reported an increase in pain. Seven out of nine patients were not able to complete the fourth set of movement tests because of discomfort. Conclusions Electrogoniometric gloves are usable in patients with rheumatoid arthritis, but patients cannot tolerate longer protocols without an increase in discomfort. References Dipietro L, et al. (2003). Evaluation of an instrumented glove for hand-movement acquisition. Journal of Rehabilitation Research and Development, 40(2), 179–89. Disclosure of Interest None declared

Arthritis remains a disabling and painful disease, and involvement of finger joints is a major cause of disability and loss of employment. Traditional arthritis measurements require labour intensive examination by clinical staff. These manual measurements are inaccurate and open to observer variation. This paper presents the development and testing of a next generation wireless smart glove to facilitate the accurate measurement of finger movement through the integration of multiple IMU sensors, with bespoke controlling algorithms. Our main objective was to measure finger and thumb joint movement. These dynamic measurements will provide clinicians with a new and accurate way to measure loss of movement in patients with rheumatoid arthritis. Commercially available gaming gloves are not fitted with sufficient sensors for this particular application, and require calibration for each glove wearer. Unlike these state-of-the-art data gloves, the inertial measurement unit glove uses a combination of novel stretchable substrate material and nine degree of freedom inertial sensors in conjunction with complex data analytics to detect joint movement. Our novel iSEG-Glove requires minimal calibration and is therefore particularly suited to the healthcare environment. Inaccuracies may arise for wearers who have varying degrees of movement in their finger joints, variance in hand size or deformities. The developed glove is fitted with sensors to overcome these issues. This glove will help quantify joint stiffness and monitor patient progression during the arthritis rehabilitation process.

This paper outlines the initial ideas surrounding the development of an accurate hand measuring tool to assist Medical clinicians and Rheumatologists in the accurate measurement of finger and thumb movement of the human hand. Clinicians rely heavily on invasive x-rays or manual evaluation methods which are dependent on training and can vary between observers. Angle measuring instruments, tape measure and grip strength dynamometer are used to assess the joint range and function of a patient to determine their hand function. Self questionnaires used determine a persons ability to perform daily tasks are open to interpretation problems.

This paper outlines the initial ideas and results surrounding the development of an accurate hand movement measurement tool. This tool will assist medical clinicians, specifically rheumatologists and orthopeadic hand surgeons, with measuring the loss of movement in the human hand. This has many direct applications within medical practice including diagnosis, prognosis and recovery assessment of patients with conditions specific to the hand e.g. to measure how far a patient can close their fingers (with a flare up in arthritis patients may not be able to make a fist).

This paper outlines the initial ideas surrounding the development of an accurate hand movement measurement tool. This tool will assist medical clinicians, specifically rheumatologists and orthopeadic hand surgeons, with the accurate measurement of the loss of movement in the human hand. This has many direct applications within medical practice including diagnosis, prognosis and recovery assessment of patients with conditions specific to the hand e.g. to measure how far a patient can close their fingers (with a flare up in arthritis patients may not be able to make a fist). Current measurement techniques available to clinicians are either invasive (x-rays) or rely heavily on manual evaluation such as vision and touch which are dependent on training and experience and results often vary between observers. Measuring tape is commonly used to measure distances e.g. between palm and fingertip which also leads to issues with accuracy. Also patient self questionnaires are often used which introduce interpretation problems.

This paper outlines the initial ideas and results surrounding the development of an accurate hand movement measurement tool. The system consists of a wearable glove measurement tool and a 3D interface. Real time data captured from each glove sensor will be displayed numerically and graphically. It will accurately quantify patients’ flexion, extension, adduction and abduction of finger and thumb joint movements in degrees, maximum and minimum joint range and compare joint range with normal ROM values to determine the degree of deformity of the hand and stiffness of moving finger joints. The system can simultaneously record angles from multiple fingers to detect previously unidentifiable movement patterns. It measures a shift in the position of fingers in relation to the direction of the thumb by measuring web space and recording the minimum, maximum and average values during a number of tests to analyse joint movement and identify areas for joint protection benefit. Data is recorded and used for future comparison analysis. It will be the first ambulatory system to detect joint stiffness at home and will help quantify and understand the symptom of ‘early morning stiffness’.

Rheumatoid arthritis affects 0.5 – 1% of the general population. The prediction and prognosis of the disease varies for each individual and its course can detrimentally affect the psychosocial condition of the patient. Clinicians and Therapists aim to quickly diagnose and treat those with this debilitating disease. Detection relies heavily on manual evaluation methods that are dependent on training and can vary between observers. Angle measuring instrument, tape measure and grip strength dynamometer are used to assess the joint range and strength of a patient to determine their hand function. Joint stiffness can be a determining factor when diagnosing the advancement and improvement of Rheumatoid Arthritis (RA). This paper outlines the development of a hand movement measurement tool to accurately quantify patients' flexion, extension, abduction and adduction movement of each finger joint and quantifies the symptom of “early morning stiffness”. It also describes the problems that arise when using a data glove to accurately measure Range Of Movement and discusses alternative methods to overcome these issues.