Effects of physiotherapy and home-based training in parkinsonian syndromes: protocol for a randomised controlled trial (MobilityAPP).

INTRODUCTION: Gait and mobility impairment are pivotal signs of parkinsonism, and they are particularly severe in atypical parkinsonian disorders including multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). A pilot study demonstrated a significant improvement of gait in patients with MSA of parkinsonian type (MSA-P) after physiotherapy and matching home-based exercise, as reflected by sensor-based gait parameters. In this study, we aim to investigate whether a gait-focused physiotherapy (GPT) and matching home-based exercise lead to a greater improvement of gait performance compared with a standard physiotherapy/home-based exercise programme (standard physiotherapy, SPT). METHODS AND ANALYSIS: This protocol was deployed to evaluate the effects of a GPT versus an active control undergoing SPT and matching home-based exercise with regard to laboratory gait parameters, physical activity measures and clinical scales in patients with Parkinson's disease (PD), MSA-P and PSP. The primary outcomes of the trial are sensor-based laboratory gait parameters, while the secondary outcome measures comprise real-world derived parameters, clinical rating scales and patient questionnaires. We aim to enrol 48 patients per disease group into this double-blind, randomised-controlled trial. The study starts with a 1 week wearable sensor-based monitoring of physical activity. After randomisation, patients undergo a 2 week daily inpatient physiotherapy, followed by 5 week matching unsupervised home-based training. A 1 week physical activity monitoring is repeated during the last week of intervention. ETHICS AND DISSEMINATION: This study, registered as 'Mobility in Atypical Parkinsonism: a Trial of Physiotherapy (Mobility_APP)' at clinicaltrials.gov (NCT04608604), received ethics approval by local committees of the involved centres. The patient's recruitment takes place at the Movement Disorders Units of Innsbruck (Austria), Erlangen (Germany), Lausanne (Switzerland), Luxembourg (Luxembourg) and Bolzano (Italy). The data resulting from this project will be submitted to peer-reviewed journals, presented at international congresses and made publicly available at the end of the trial. TRIAL REGISTRATION NUMBER: NCT04608604.

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases.

Introduction: The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings. Methods: Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data. Results and discussion: The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of -0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, -0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.

EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors.

Wearable sensors are able to monitor physical health in a home environment and detect changes in gait patterns over time. To ensure long-term user engagement, wearable sensors need to be seamlessly integrated into the user's daily life, such as hearing aids or earbuds. Therefore, we present EarGait, an open-source Python toolbox for gait analysis using inertial sensors integrated into hearing aids. This work contributes a validation for gait event detection algorithms and the estimation of temporal parameters using ear-worn sensors. We perform a comparative analysis of two algorithms based on acceleration data and propose a modified version of one of the algorithms. We conducted a study with healthy young and elderly participants to record walking data using the hearing aid's integrated sensors and an optical motion capture system as a reference. All algorithms were able to detect gait events (initial and terminal contacts), and the improved algorithm performed best, detecting 99.8% of initial contacts and obtaining a mean stride time error of 12 ± 32 ms. The existing algorithms faced challenges in determining the laterality of gait events. To address this limitation, we propose modifications that enhance the determination of the step laterality (ipsi- or contralateral), resulting in a 50% reduction in stride time error. Moreover, the improved version is shown to be robust to different study populations and sampling frequencies but is sensitive to walking speed. This work establishes a solid foundation for a comprehensive gait analysis system integrated into hearing aids that will facilitate continuous and long-term home monitoring.

Aligning Federated Learning with Existing Trust Structures in Health Care Systems.

Patient-centered health care information systems (PHSs) on peer-to-peer (P2P) networks (e.g., decentralized personal health records) enable storing data locally at the edge to enhance data sovereignty and resilience to single points of failure. Nonetheless, these systems raise concerns on trust and adoption in medical workflow due to non-alignment to current health care processes and stakeholders' needs. The distributed nature of the data makes it more challenging to train and deploy machine learning models (using traditional methods) at the edge, for instance, for disease prediction. Federated learning (FL) has been proposed as a possible solution to these limitations. However, the P2P PHS architecture challenges current FL solutions because they use centralized engines (or random entities that could pose privacy concerns) for model update aggregation. Consequently, we propose a novel conceptual FL framework, CareNetFL, that is suitable for P2P PHS multi-tier and hybrid architecture and leverages existing trust structures in health care systems to ensure scalability, trust, and security. Entrusted parties (practitioners' nodes) are used in CareNetFL to aggregate local model updates in the network hierarchy for their patients instead of random entities that could actively become malicious. Involving practitioners in their patients' FL model training increases trust and eases access to medical data. The proposed concepts mitigate communication latency and improve FL performance through patient-practitioner clustering, reducing skewed and imbalanced data distributions and system heterogeneity challenges of FL at the edge. The framework also ensures end-to-end security and accountability through leveraging identity-based systems and privacy-preserving techniques that only guarantee security during training.

Mobile digital gait analysis objectively measures progression in hereditary spastic paraplegia.

Progressive spasticity and gait impairment is the functional hallmark of hereditary spastic paraplegia (HSP), but due to inter-individual variability, longitudinal studies on its progression are scarce. We investigated the progression of gait deficits via mobile digital measurements in conjunction with clinical and patient-reported outcome parameters. Our cohort included adult HSP patients (n = 55) with up to 77 months of follow-up. Gait speed showed a significant association with SPRS progression. Changes in stride time and gait variability correlated to fear of falling and quality of life, providing evidence that gait parameters are meaningful measures of HSP progression.

Design and validation of a multi-task, multi-context protocol for real-world gait simulation.

BACKGROUND: Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. METHODS: The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants' strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. RESULTS: The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. CONCLUSIONS: The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. TRIAL REGISTRATION: ISRCTN-12246987.

Inertial Gait Sensors to Measure Mobility and Functioning in Hereditary Spastic Paraplegia: A Cross-Sectional Multicenter Clinical Study.

BACKGROUND AND OBJECTIVES: Hereditary spastic paraplegia (HSP) causes progressive spasticity and weakness of the lower limbs. As neurological examination and the clinical Spastic Paraplegia Rating Scale (SPRS) are subject to potential patient- and clinician-dependent bias, instrumented gait analysis bears the potential to objectively quantify impaired gait. The aim of the present study was to investigate gait cyclicity parameters by application of a mobile gait analysis system in a cross sectional cohort of HSP patients and a longitudinal fast progressing subcohort. METHODS: Using wearable sensors attached to the shoes, HSP patients and controls performed a 4x10 meters walking test during regular visits in three outpatient centers. Patients were also rated according to the SPRS and in a subset, questionnaires on quality of life and fear of falling were obtained. An unsupervised segmentation algorithm was employed to extract stride parameters and respective coefficients of variation. RESULTS: Mobile gait analysis was performed in a total of 112 ambulatory HSP patients and 112 age and gender matched controls. While swing time was unchanged compared to controls, there were significant increases in the duration of the total stride phase and the duration of the stance phase, both regarding absolute values and coefficients of variation values. While stride parameters did not correlate to age, weight or height of the patients, there were significant associations of absolute stride parameters to single SPRS items reflecting impaired mobility (|r| > 0.50), to patients' quality of life (|r| > 0.44), and notably to disease duration (|r| > 0.27). Sensor-derived coefficients of variation, on the other hand, were associated with patient-reported fear of falling (|r| > 0.41) and cognitive impairment (|r| > 0.40). In a small 1-year follow-up analysis of patients with complicated HSP and fast progression, absolute values of mobile gait parameters had significantly worsened compared to baseline. DISCUSSION: The presented wearable sensor system provides parameters of stride characteristics which appear clinically valid to reflect gait impairment in HSP. Due to the feasibility with regard to time, space and costs, the present study forms the basis for larger scale longitudinal and interventional studies in HSP.

Digital Responsibility Goals - A Framework for a Human-Centered Sustainable Digital Economy with a Focus on Trusted Digital Solutions.

This paper describes the Digital Responsibility Goals, their purpose, and the associated guiding criteria and their relevance particularly for health. In addition, the document makes a first proposal for measuring digital responsibility.

Technical validation of real-world monitoring of gait: a multicentric observational study.

INTRODUCTION: Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users' perspective on the device. METHODS AND ANALYSIS: This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs.After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson's disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users' perspective on the deployed technology and relevance of the mobility assessment. ETHICS AND DISSEMINATION: The study has been granted ethics approval by the centre's committees (London-Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available. TRIAL REGISTRATION NUMBER: ISRCTN (12246987).

Characterization of gait variability in multiple system atrophy and Parkinson's disease.

BACKGROUND: Gait impairment is a pivotal feature of parkinsonian syndromes and increased gait variability is associated with postural instability and a higher risk of falls. OBJECTIVES: We compared gait variability at different walking velocities between and within groups of patients with Parkinson-variant multiple system atrophy, idiopathic Parkinson's disease, and a control group of older adults. METHODS: Gait metrics were recorded in 11 multiple system atrophy, 12 Parkinson's disease patients, and 18 controls using sensor-based gait analysis. Gait variability was analyzed for stride, swing and stance time, stride length and gait velocity. Values were compared between and within the groups at self-paced comfortable, fast and slow walking speed. RESULTS: Multiple system atrophy patients displayed higher gait variability except for stride time at all velocities compared with controls, while Parkinson's patients did not. Compared with Parkinson's disease, multiple system atrophy patients displayed higher variability of swing time, stride length and gait velocity at comfortable speed and at slow speed for swing and stance time, stride length and gait velocity (all P < 0.05). Stride time variability was significantly higher in slow compared to comfortable walking in patients with multiple system atrophy (P = 0.014). Variability parameters significantly correlated with the postural instability/gait difficulty subscore in both disease groups. Conversely, significant correlations between variability parameters and MDS-UPDRS III score was observed only for multiple system atrophy patients. CONCLUSION: This analysis suggests that gait variability parameters reflect the major axial impairment and postural instability displayed by multiple system atrophy patients compared with Parkinson's disease patients and controls.

Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement.

Aim: This paper introduces Apkinson, a mobile application for motor evaluation and monitoring of Parkinson's disease patients. Materials & methods: The App is based on previously reported methods, for instance, the evaluation of articulation and pronunciation in speech, regularity and freezing of gait in walking, and tapping accuracy in hand movement. Results: Preliminary experiments indicate that most of the measurements are suitable to discriminate patients and controls. Significance is evaluated through statistical tests. Conclusion: Although the reported results correspond to preliminary experiments, we think that Apkinson is a very useful App that can help patients, caregivers and clinicians, in performing a more accurate monitoring of the disease progression. Additionally, the mobile App can be a personal health assistant.

Assessment of gait parameters and physical function in patients with advanced cancer participating in a 12-week exercise and nutrition programme: A controlled clinical trial.

OBJECTIVE: Gait is a sensitive marker for functional declines commonly seen in patients treated for advanced cancer. We tested the effect of a combined exercise and nutrition programme on gait parameters of advanced-stage cancer patients using a novel wearable gait analysis system. METHODS: Eighty patients were allocated to a control group with nutritional support or to an intervention group additionally receiving whole-body electromyostimulation (WB-EMS) training (2×/week). At baseline and after 12 weeks, physical function was assessed by a biosensor-based gait analysis during a six-minute walk test, a 30-s sit-to-stand test, a hand grip strength test, the Karnofsky Index and EORTC QLQ-C30 questionnaire. Body composition was measured by bioelectrical impedance analysis and inflammation by blood analysis. RESULTS: Final analysis included 41 patients (56.1% male; 60.0 ± 13.0 years). After 12 weeks, the WB-EMS group showed higher stride length, gait velocity (p < .05), six-minute walking distance (p < .01), bodyweight and skeletal muscle mass, and emotional functioning (p < .05) compared with controls. Correlations between changes in gait and in body composition, physical function and inflammation were detected. CONCLUSION: Whole-body electromyostimulation combined with nutrition may help to improve gait and functional status of cancer patients. Sensor-based mobile gait analysis objectively reflects patients' physical status and could support treatment decisions.

Interindividual Balance Adaptations in Response to Perturbation Treadmill Training in Persons With Parkinson Disease.

BACKGROUND AND PURPOSE: Perturbation training is a promising approach to reduce fall incidence in persons with Parkinson disease (PwPD). This study aimed to evaluate interindividual differences in balance adaptations in response to perturbation treadmill training (PTT) and identify potential outcome predictors. METHODS: PwPD (n = 43, Hoehn & Yahr stage 1-3.5) were randomly assigned to either 8 weeks of PTT or conventional treadmill training (CTT) without perturbations. At baseline and following intervention, data from 4 domains of balance function (reactive, anticipatory, dynamic postural control, and quiet stance) were collected. Using responder analysis we investigated interindividual differences (responder rates and magnitude of change) and potential predictive factors. RESULTS: PTT showed a significantly higher responder rate in the Mini Balance Evaluation Systems Test (Mini-BESTest) subscore reactive postural control, compared with CTT (PTT = 44%; CTT = 10%; risk ratio = 4.22, confidence interval = 1.03-17.28). Additionally, while between-groups differences were not significant, the proportion of responders in the measures of dynamic postural control was higher for PTT compared with CTT (PTT: 22%-39%; CTT: 5%-10%). The magnitude of change in responders and nonresponders was similar in both groups. PTT responders showed significantly lower initial balance performance (4/8 measures) and cognitive function (3/8 measures), and were older and at a more advanced disease stage, based on descriptive evaluation. DISCUSSION AND CONCLUSIONS: Our findings suggest that PTT is beneficial to improve reactive balance in PwPD. Further, PTT appeared to be effective only for a part of PwPD, especially for those with lower balance and cognitive function, which needs further attention.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A1).

Turning Analysis during Standardized Test Using On-Shoe Wearable Sensors in Parkinson's Disease.

Mobile gait analysis systems using wearable sensors have the potential to analyze and monitor pathological gait in a finer scale than ever before. A closer look at gait in Parkinson's disease (PD) reveals that turning has its own characteristics and requires its own analysis. The goal of this paper is to present a system with on-shoe wearable sensors in order to analyze the abnormalities of turning in a standardized gait test for PD. We investigated turning abnormalities in a large cohort of 108 PD patients and 42 age-matched controls. We quantified turning through several spatio-temporal parameters. Analysis of turn-derived parameters revealed differences of turn-related gait impairment in relation to different disease stages and motor impairment. Our findings confirm and extend the results from previous studies and show the applicability of our system in turning analysis. Our system can provide insight into the turning in PD and be used as a complement for physicians' gait assessment and to monitor patients in their daily environment.

[Wearables in the treatment of neurological diseases-where do we stand today?] / "Wearables" in der Behandlung neurologischer Erkrankungen ­ wo stehen wir heute?

Fitness and lifestyle trackers raise the awareness for wearable sensors in medical applications for clinical trials and healthcare. Various functional impairments of patients with neurological diseases are an ideal target to generate wearable-derived and patient-centered parameters that have the potential to support prevention, prediction, diagnostic procedures and therapy monitoring during the clinical work-up; however, substantial differences between clinical grade wearables and fitness trackers have to be acknowledged. For the application in clinical trials or individualized patient care distinct technical and clinical validation trials have to be conducted. The different test environments under laboratory conditions during standardized tests or under unsupervised home monitoring conditions have to be included in the algorithmic processing of sensor raw data in order to enable a clinical decision support under real-life conditions. This article presents the general understanding of the technical application for the most relevant functional impairments in neurology. While wearables used for sleep assessment have already reached a high level of technological readiness due to the defined test environment (bed, sleep), other wearable applications, e.g. for gait and mobility during home monitoring require further research in order to transfer the technical capabilities into real-life patient care.

On Providing Multi-Level Quality of Service for Operating Rooms of the Future.

The Operating Room (OR) plays an important role in delivering vital medical services to patients in hospitals. Such environments contain several medical devices, equipment, and systems producing valuable information which might be combined for biomedical and surgical workflow analysis. Considering the sensibility of data from sensors in the OR, independently of processing and network loads, the middleware that provides data from these sensors have to respect applications quality of service (QoS) demands. In an OR middleware, there are two main bottlenecks that might suffer QoS problems and, consequently, impact directly in user experience: (i) simultaneous user applications connecting the middleware; and (ii) a high number of sensors generating information from the environment. Currently, many middlewares that support QoS have been proposed by many fields; however, to the best of our knowledge, there is no research on this topic or the OR environment. OR environments are characterized by being crowded by persons and equipment, some of them of specific use in such environments, as mobile x-ray machines. Therefore, this article proposes QualiCare, an adaptable middleware model to provide multi-level QoS, improve user experience, and increase hardware utilization to middlewares in OR environments. Our main contributions are a middleware model and an orchestration engine in charge of changing the middleware behavior to guarantee performance. Results demonstrate that adapting middleware parameters on demand reduces network usage and improves resource consumption maintaining data provisioning.

Exploring gait adaptations to perturbed and conventional treadmill training in Parkinson's disease: Time-course, sustainability, and transfer.

BACKGROUND: Gait impairment is a major motor symptom in Parkinson's disease (PD), and treadmill training is an effective non-pharmacological treatment option. RESEARCH QUESTION: In this study, the time course, sustainability and transferability of gait adaptations to treadmill training with and without additional postural perturbations were investigated. METHODS: 38 PD patients (Hoehn & Yahr 1-3.5) were randomly allocated to eight weeks of treadmill training, performed twice-weekly for 40 min either with (perturbation treadmill training [PTT], n = 18) or without (conventional treadmill training [CTT], n = 20) additional perturbations to the treadmill surface. Spatiotemporal gait parameters were assessed during treadmill walking on a weekly basis (T0-T8), and after three months follow-up (T9). Additional overground gait analyses were performed at T0 and T8 to investigate transfer effects. RESULTS: Treadmill gait variability reduced linearly over the course of 8 weeks in both groups (p < .001; Cohen's d (range): -0.53 to -0.84). Only the PTT group significantly improved in other gait parameters (stride length/time, stance-/swing time), with stride time showing a significant between-group interaction effect (Cohen's d = 0.33; p = .05). Additional between-group interactions indicated more sustained improvements in stance (Cohen's d = 0.85; p = .02) and swing time variability in the PTT group (Cohen's d = 0.82; p = .03) at T9. Overground gait improvements at T8 existed only in stance (d = -0.73; p = .04) and swing time (d = 0.73; p = .04). DISCUSSION: Treadmill stride-to-stride variability reduced substantially and linearly, but transfer to overground walking was limited. Adding postural perturbations tended to increase efficacy and sustainability of several gait parameters. However, since between-group effects were small, more work is necessary to support these findings.

Multimodal Assessment of Parkinson's Disease: A Deep Learning Approach.

Parkinson's disease is a neurodegenerative disorder characterized by a variety of motor symptoms. Particularly, difficulties to start/stop movements have been observed in patients. From a technical/diagnostic point of view, these movement changes can be assessed by modeling the transitions between voiced and unvoiced segments in speech, the movement when the patient starts or stops a new stroke in handwriting, or the movement when the patient starts or stops the walking process. This study proposes a methodology to model such difficulties to start or to stop movements considering information from speech, handwriting, and gait. We used those transitions to train convolutional neural networks to classify patients and healthy subjects. The neurological state of the patients was also evaluated according to different stages of the disease (initial, intermediate, and advanced). In addition, we evaluated the robustness of the proposed approach when considering speech signals in three different languages: Spanish, German, and Czech. According to the results, the fusion of information from the three modalities is highly accurate to classify patients and healthy subjects, and it shows to be suitable to assess the neurological state of the patients in several stages of the disease. We also aimed to interpret the feature maps obtained from the deep learning architectures with respect to the presence or absence of the disease and the neurological state of the patients. As far as we know, this is one of the first works that considers multimodal information to assess Parkinson's disease following a deep learning approach.

Ankle angle variability during running in athletes with chronic ankle instability and copers.

BACKGROUND: Individuals with chronic ankle instability (CAI) demonstrate altered ankle kinematics during running compared to uninjured individuals; however, little is known about differences between individuals with CAI and those who recover successfully from an index sprain (copers). METHODS: Thirty-two young male athletes with prior ankle sprain were investigated, eighteen with CAI and fourteen copers. Instrumented running analysis was performed on a treadmill at two velocities: moderate (2.63 ± 0.20 m/s, rate of perceived of exertion = 14/20); and high velocity (3.83 ± 0.20 m/s). Mean ankle kinematics and stride-to-stride variability were analyzed applying the statistical parametric mapping method. RESULTS: At both running velocities, no statistically significant differences in mean ankle kinematics were observed. At high running velocity, athletes with CAI demonstrated significantly increased frontal plane variability at 17-19% of the running gait cycle (p = 0.009). Additionally, large between-group effect sizes (Hedges' g ≥ 0.8) may potentially indicate increased frontal plane variability during initial contact and terminal swing, as well as decreased variability in sagittal plane at 34-35% in CAI. A similar tendency existed at moderate velocity, with large effect sizes indicating decreased dorsiflexion at 75-89% in CAI, as well as an increased frontal plane variability at 16-25%, and 97-99%. DISCUSSION: Compared to copers, individuals with CAI demonstrate increased variability of ankle kinematics - mainly in the frontal plane and particularly during stance phase - while mean ankle kinematics seems minimally affected. Increased ankle variability at high running velocity may best reflect persisting sensorimotor control deficits in athletes with chronically instable ankles.

Thigh-Derived Inertial Sensor Metrics to Assess the Sit-to-Stand and Stand-to-Sit Transitions in the Timed Up and Go (TUG) Task for Quantifying Mobility Impairment in Multiple Sclerosis.

Introduction: Inertial sensors generate objective and sensitive metrics of movement disability that may indicate fall risk in many clinical conditions including multiple sclerosis (MS). The Timed-Up-And-Go (TUG) task is used to assess patient mobility because it incorporates clinically-relevant submovements during standing. Most sensor-based TUG research has focused on the placement of sensors at the spine, hip or ankles; an examination of thigh activity in TUG in multiple sclerosis is wanting. Methods: We used validated sensors (x-IMU by x-io) to derive transparent metrics for the sit-to-stand (SI-ST) transition and the stand-to-sit (ST-SI) transition of TUG, and compared effect sizes for metrics from inertial sensors on the thighs to effect sizes for metrics from a sensor placed at the L3 level of the lumbar spine. Twenty-three healthy volunteers were compared to 17 ambulatory persons with MS (PwMS, HAI ≤ 2). Results: During the SI-ST transition, the metric with the largest effect size comparing healthy volunteers to PwMS was the Area Under the Curve of the thigh angular velocity in the pitch direction-representing both thigh and knee extension; the peak of the spine pitch angular velocity during SI-ST also had a large effect size, as did some temporal measures of duration of SI-ST, although less so. During the ST-SI transition the metric with the largest effect size in PwMS was the peak of the spine angular velocity curve in the roll direction. A regression was performed. Discussion: We propose for PwMS that the diminished peak angular velocity during SI-ST directly represents extensor weakness, while the increased roll during ST-SI represents diminished postural control. Conclusions: During the SI-ST transition of TUG, angular velocities can discriminate between healthy volunteers and ambulatory PwMS better than temporal features. Sensor placement on the thighs provides additional discrimination compared to sensor placement at the lumbar spine.