92 results on '"Lisa Alcock"'
Search Results
2. Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device
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Cameron Kirk, Arne Küderle, M. Encarna Micó-Amigo, Tecla Bonci, Anisoara Paraschiv-Ionescu, Martin Ullrich, Abolfazl Soltani, Eran Gazit, Francesca Salis, Lisa Alcock, Kamiar Aminian, Clemens Becker, Stefano Bertuletti, Philip Brown, Ellen Buckley, Alma Cantu, Anne-Elie Carsin, Marco Caruso, Brian Caulfield, Andrea Cereatti, Lorenzo Chiari, Ilaria D’Ascanio, Judith Garcia-Aymerich, Clint Hansen, Jeffrey M. Hausdorff, Hugo Hiden, Emily Hume, Alison Keogh, Felix Kluge, Sarah Koch, Walter Maetzler, Dimitrios Megaritis, Arne Mueller, Martijn Niessen, Luca Palmerini, Lars Schwickert, Kirsty Scott, Basil Sharrack, Henrik Sillén, David Singleton, Beatrix Vereijken, Ioannis Vogiatzis, Alison J. Yarnall, Lynn Rochester, Claudia Mazzà, Bjoern M. Eskofier, Silvia Del Din, and Mobilise-D consortium
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Medicine ,Science - Abstract
Abstract This study aimed to validate a wearable device’s walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson’s Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and − 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application. Trial registration: ISRCTN – 12246987.
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- 2024
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3. Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study
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Felix Kluge, Yonatan E Brand, M Encarna Micó-Amigo, Stefano Bertuletti, Ilaria D'Ascanio, Eran Gazit, Tecla Bonci, Cameron Kirk, Arne Küderle, Luca Palmerini, Anisoara Paraschiv-Ionescu, Francesca Salis, Abolfazl Soltani, Martin Ullrich, Lisa Alcock, Kamiar Aminian, Clemens Becker, Philip Brown, Joren Buekers, Anne-Elie Carsin, Marco Caruso, Brian Caulfield, Andrea Cereatti, Lorenzo Chiari, Carlos Echevarria, Bjoern Eskofier, Jordi Evers, Judith Garcia-Aymerich, Tilo Hache, Clint Hansen, Jeffrey M Hausdorff, Hugo Hiden, Emily Hume, Alison Keogh, Sarah Koch, Walter Maetzler, Dimitrios Megaritis, Martijn Niessen, Or Perlman, Lars Schwickert, Kirsty Scott, Basil Sharrack, David Singleton, Beatrix Vereijken, Ioannis Vogiatzis, Alison Yarnall, Lynn Rochester, Claudia Mazzà, Silvia Del Din, and Arne Mueller
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Medicine - Abstract
BackgroundWrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. ObjectiveThe aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back–worn inertial sensors. MethodsParticipants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back–worn inertial sensors. ResultsThe best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. ConclusionsAlgorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. Trial RegistrationISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987 International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2021-050785
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- 2024
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4. Developing a novel dual-injection FDG-PET imaging methodology to study the functional neuroanatomy of gait
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Hilmar P. Sigurdsson, Lisa Alcock, Michael Firbank, Ross Wilson, Philip Brown, Ross Maxwell, Elizabeth Bennett, Nicola Pavese, David J. Brooks, and Lynn Rochester
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Aging ,Gait ,Posture ,Fluorodeoxyglucose ,Positron emission tomography ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Gait is an excellent indicator of physical, emotional, and mental health. Previous studies have shown that gait impairments in ageing are common, but the neural basis of these impairments are unclear. Existing methodologies are suboptimal and novel paradigms capable of capturing neural activation related to real walking are needed. In this study, we used a hybrid PET/MR system and measured glucose metabolism related to both walking and standing with a dual-injection paradigm in a single study session. For this study, 15 healthy older adults (10 females, age range: 60.5-70.7 years) with normal cognition were recruited from the community. Each participant received an intravenous injection of [18F]-2-fluoro-2-deoxyglucose (FDG) before engaging in two distinct tasks, a static postural control task (standing) and a walking task. After each task, participants were imaged. To discern independent neural functions related to walking compared to standing, we applied a bespoke dose correction to remove the residual 18F signal of the first scan (PETSTAND) from the second scan (PETWALK) and proportional scaling to the global mean, cerebellum, or white matter (WM). Whole-brain differences in walking-elicited neural activity measured with FDG-PET were assessed using a one-sample t-test. In this study, we show that a dual-injection paradigm in healthy older adults is feasible with biologically valid findings. Our results with a dose correction and scaling to the global mean showed that walking, compared to standing, increased glucose consumption in the cuneus (Z = 7.03), the temporal gyrus (Z = 6.91) and the orbital frontal cortex (Z = 6.71). Subcortically, we observed increased glucose metabolism in the supraspinal locomotor network including the thalamus (Z = 6.55), cerebellar vermis and the brainstem (pedunculopontine/mesencephalic locomotor region). Exploratory analyses using proportional scaling to the cerebellum and WM returned similar findings. Here, we have established the feasibility and tolerability of a novel method capable of capturing neural activations related to actual walking and extended previous knowledge including the recruitment of brain regions involved in sensory processing. Our paradigm could be used to explore pathological alterations in various gait disorders.
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- 2024
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5. Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium
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M. Encarna Micó-Amigo, Tecla Bonci, Anisoara Paraschiv-Ionescu, Martin Ullrich, Cameron Kirk, Abolfazl Soltani, Arne Küderle, Eran Gazit, Francesca Salis, Lisa Alcock, Kamiar Aminian, Clemens Becker, Stefano Bertuletti, Philip Brown, Ellen Buckley, Alma Cantu, Anne-Elie Carsin, Marco Caruso, Brian Caulfield, Andrea Cereatti, Lorenzo Chiari, Ilaria D’Ascanio, Bjoern Eskofier, Sara Fernstad, Marcel Froehlich, Judith Garcia-Aymerich, Clint Hansen, Jeffrey M. Hausdorff, Hugo Hiden, Emily Hume, Alison Keogh, Felix Kluge, Sarah Koch, Walter Maetzler, Dimitrios Megaritis, Arne Mueller, Martijn Niessen, Luca Palmerini, Lars Schwickert, Kirsty Scott, Basil Sharrack, Henrik Sillén, David Singleton, Beatrix Vereijken, Ioannis Vogiatzis, Alison J. Yarnall, Lynn Rochester, Claudia Mazzà, Silvia Del Din, and for the Mobilise-D consortium
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Real-world gait ,Algorithms ,DMOs ,Validation ,Wearable sensor ,Walking ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Abstract Background Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors
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- 2023
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6. Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases
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Robbin Romijnders, Francesca Salis, Clint Hansen, Arne Küderle, Anisoara Paraschiv-Ionescu, Andrea Cereatti, Lisa Alcock, Kamiar Aminian, Clemens Becker, Stefano Bertuletti, Tecla Bonci, Philip Brown, Ellen Buckley, Alma Cantu, Anne-Elie Carsin, Marco Caruso, Brian Caulfield, Lorenzo Chiari, Ilaria D'Ascanio, Silvia Del Din, Björn Eskofier, Sara Johansson Fernstad, Marceli Stanislaw Fröhlich, Judith Garcia Aymerich, Eran Gazit, Jeffrey M. Hausdorff, Hugo Hiden, Emily Hume, Alison Keogh, Cameron Kirk, Felix Kluge, Sarah Koch, Claudia Mazzà, Dimitrios Megaritis, Encarna Micó-Amigo, Arne Müller, Luca Palmerini, Lynn Rochester, Lars Schwickert, Kirsty Scott, Basil Sharrack, David Singleton, Abolfazl Soltani, Martin Ullrich, Beatrix Vereijken, Ioannis Vogiatzis, Alison Yarnall, Gerhard Schmidt, and Walter Maetzler
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deep learning (artificial intelligence) ,free-living ,gait analysis ,gait events detection ,inertial measurement unit (IMU) ,mobility ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
IntroductionThe 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.MethodsHere, 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 discussionThe 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
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- 2023
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7. Safety and tolerability of adjunct non-invasive vagus nerve stimulation in people with parkinson’s: a study protocol
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Hilmar P. Sigurdsson, Heather Hunter, Lisa Alcock, Ross Wilson, Ilse Pienaar, Elizabeth Want, Mark R. Baker, John-Paul Taylor, Lynn Rochester, and Alison J. Yarnall
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Attention ,Gait ,Cholinergic system ,Parkinsons disease ,Vagus nerve stimulation ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
Abstract Background Parkinson’s disease (PD) is the fastest growing neurological condition worldwide. Recent theories suggest that symptoms of PD may arise due to spread of Lewy-body pathology where the process begins in the gut and propagate transynaptically via the vagus nerve to the central nervous system. In PD, gait impairments are common motor manifestations that are progressive and can appear early in the disease course. As therapies to mitigate gait impairments are limited, novel interventions targeting these and their consequences, i.e., reducing the risk of falls, are urgently needed. Non-invasive vagus nerve stimulation (nVNS) is a neuromodulation technique targeting the vagus nerve. We recently showed in a small pilot trial that a single dose of nVNS improved (decreased) discrete gait variability characteristics in those receiving active stimulation relative to those receiving sham stimulation. Further multi-dose, multi-session studies are needed to assess the safety and tolerability of the stimulation and if improvement in gait is sustained over time. Design This will be an investigator-initiated, single-site, proof-of-concept, double-blind sham-controlled randomised pilot trial in 40 people with PD. Participants will be randomly assigned on a 1:1 ratio to receive either active or sham transcutaneous cervical VNS. All participants will undergo comprehensive cognitive, autonomic and gait assessments during three sessions over 24 weeks, in addition to remote monitoring of ambulatory activity and falls, and exploratory analyses of cholinergic peripheral plasma markers. The primary outcome measure is the safety and tolerability of multi-dose nVNS in PD. Secondary outcomes include improvements in gait, cognition and autonomic function that will be summarised using descriptive statistics. Discussion This study will report on the proportion of eligible and enrolled patients, rates of eligibility and reasons for ineligibility. Adverse events will be recorded informing on the safety and device tolerability in PD. This study will additionally provide us with information for sample size calculations for future studies and evidence whether improvement in gait control is enhanced when nVNS is delivered repeatedly and sustained over time. Trial registration This trial is prospectively registered at www.isrctn.com/ISRCTN19394828 . Registered August 23, 2021.
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- 2023
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8. Laboratory and free-living gait performance in adults with COPD and healthy controls
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Joren Buekers, Dimitrios Megaritis, Sarah Koch, Lisa Alcock, Nadir Ammour, Clemens Becker, Stefano Bertuletti, Tecla Bonci, Philip Brown, Ellen Buckley, Sara C. Buttery, Brian Caulfied, Andrea Cereatti, Nikolaos Chynkiamis, Heleen Demeyer, Carlos Echevarria, Anja Frei, Clint Hansen, Jeffrey M. Hausdorff, Nicholas S. Hopkinson, Emily Hume, Arne Kuederle, Walter Maetzler, Claudia Mazzà, Encarna M. Micó-Amigo, Arne Mueller, Luca Palmerini, Francesca Salis, Kirsty Scott, Thierry Troosters, Beatrix Vereijken, Henrik Watz, Lynn Rochester, Silvia Del Din, Ioannis Vogiatzis, and Judith Garcia-Aymerich
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Medicine - Abstract
Background Gait characteristics are important risk factors for falls, hospitalisations and mortality in older adults, but the impact of COPD on gait performance remains unclear. We aimed to identify differences in gait characteristics between adults with COPD and healthy age-matched controls during 1) laboratory tests that included complex movements and obstacles, 2) simulated daily-life activities (supervised) and 3) free-living daily-life activities (unsupervised). Methods This case–control study used a multi-sensor wearable system (INDIP) to obtain seven gait characteristics for each walking bout performed by adults with mild-to-severe COPD (n=17; forced expiratory volume in 1 s 57±19% predicted) and controls (n=20) during laboratory tests, and during simulated and free-living daily-life activities. Gait characteristics were compared between adults with COPD and healthy controls for all walking bouts combined, and for shorter (≤30 s) and longer (>30 s) walking bouts separately. Results Slower walking speed (−11 cm·s−1, 95% CI: −20 to −3) and lower cadence (−6.6 steps·min−1, 95% CI: −12.3 to −0.9) were recorded in adults with COPD compared to healthy controls during longer (>30 s) free-living walking bouts, but not during shorter (≤30 s) walking bouts in either laboratory or free-living settings. Double support duration and gait variability measures were generally comparable between the two groups. Conclusion Gait impairment of adults with mild-to-severe COPD mainly manifests during relatively long walking bouts (>30 s) in free-living conditions. Future research should determine the underlying mechanism(s) of this impairment to facilitate the development of interventions that can improve free-living gait performance in adults with COPD.
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- 2023
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9. Translating digital healthcare to enhance clinical management: a protocol for an observational study using a digital health technology system to monitor medication adherence and its effect on mobility in people with Parkinson’s
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Lynn Rochester, Silvia Del Din, Alison J Yarnall, Lisa Alcock, Fabio Ciravegna, Jordi Evers, Neil Ireson, Martijn Niessen, Emma Packer, Héloïse Debelle, Harry G B Bailey, and Jian Qing Shi
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Medicine - Abstract
Introduction In people with Parkinson’s (PwP) impaired mobility is associated with an increased falls risk. To improve mobility, dopaminergic medication is typically prescribed, but complex medication regimens result in suboptimal adherence. Exploring medication adherence and its impact on mobility in PwP will provide essential insights to optimise medication regimens and improve mobility. However, this is typically assessed in controlled environments, during one-off clinical assessments. Digital health technology (DHT) presents a means to overcome this, by continuously and remotely monitoring mobility and medication adherence. This study aims to use a novel DHT system (DHTS) (comprising of a smartphone, smartwatch and inertial measurement unit (IMU)) to assess self-reported medication adherence, and its impact on digital mobility outcomes (DMOs) in PwP.Methods and analysis This single-centre, UK-based study, will recruit 55 participants with Parkinson’s. Participants will complete a range of clinical, and physical assessments. Participants will interact with a DHTS over 7 days, to assess self-reported medication adherence, and monitor mobility and contextual factors in the real world. Participants will complete a motor complications diary (ON-OFF-Dyskinesia) throughout the monitoring period and, at the end, a questionnaire and series of open-text questions to evaluate DHTS usability. Feasibility of the DHTS and the motor complications diary will be assessed. Validated algorithms will quantify DMOs from IMU walking activity. Time series modelling and deep learning techniques will model and predict DMO response to medication and effects of contextual factors. This study will provide essential insights into medication adherence and its effect on real-world mobility in PwP, providing insights to optimise medication regimens.Ethics and dissemination Ethical approval was granted by London—142 Westminster Research Ethics Committee (REC: 21/PR/0469), protocol V.2.4. Results will be published in peer-reviewed journals. All participants will provide written, informed consent.Trial registration number ISRCTN13156149.
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- 2023
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10. Design and validation of a multi-task, multi-context protocol for real-world gait simulation
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Kirsty Scott, Tecla Bonci, Francesca Salis, Lisa Alcock, Ellen Buckley, Eran Gazit, Clint Hansen, Lars Schwickert, Kamiar Aminian, Stefano Bertuletti, Marco Caruso, Lorenzo Chiari, Basil Sharrack, Walter Maetzler, Clemens Becker, Jeffrey M. Hausdorff, Ioannis Vogiatzis, Philip Brown, Silvia Del Din, Björn Eskofier, Anisoara Paraschiv-Ionescu, Alison Keogh, Cameron Kirk, Felix Kluge, Encarna M. Micó-Amigo, Arne Mueller, Isabel Neatrour, Martijn Niessen, Luca Palmerini, Henrik Sillen, David Singleton, Martin Ullrich, Beatrix Vereijken, Marcel Froehlich, Gavin Brittain, Brian Caulfield, Sarah Koch, Anne-Elie Carsin, Judith Garcia-Aymerich, Arne Kuederle, Alison Yarnall, Lynn Rochester, Andrea Cereatti, Claudia Mazzà, and for the Mobilise-D consortium
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Digital mobility outcomes ,Technical validation ,Wearable sensors ,Neurological diseases ,Mobility monitoring ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Abstract 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.
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- 2022
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11. Acipimox in Mitochondrial Myopathy (AIMM): study protocol for a randomised, double-blinded, placebo-controlled, adaptive design trial of the efficacy of acipimox in adult patients with mitochondrial myopathy
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The AIMM Trial Group:, Alaa Abouhajar, Lisa Alcock, Theophile Bigirumurame, Penny Bradley, Laura Brown, Ian Campbell, Sylvia Del Din, Julie Faitg, Gavin Falkous, Gráinne S. Gorman, Rachel Lakey, Robert McFarland, Jane Newman, Lynn Rochester, Vicky Ryan, Hesther Smith, Alison Steel, Renae J. Stefanetti, Huizhong Su, Robert W. Taylor, Naomi J.P. Thomas, Helen Tuppen, Amy E. Vincent, Charlotte Warren, and Gillian Watson
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Acipimox ,Adenosine triphosphate ,Mitochondrial disease ,Mitochondria ,Myopathy ,Randomised controlled trial ,Medicine (General) ,R5-920 - Abstract
Abstract Background Mitochondrial disease is a heterogenous group of rare, complex neurometabolic disorders. Despite their individual rarity, collectively mitochondrial diseases represent the most common cause of inherited metabolic disorders in the UK; they affect 1 in every 4300 individuals, up to 15,000 adults (and a similar number of children) in the UK. Mitochondrial disease manifests multisystem and isolated organ involvement, commonly affecting those tissues with high energy demands, such as skeletal muscle. Myopathy manifesting as fatigue, muscle weakness and exercise intolerance is common and debilitating in patients with mitochondrial disease. Currently, there are no effective licensed treatments and consequently, there is an urgent clinical need to find an effective drug therapy. Aim To investigate the efficacy of 12-week treatment with acipimox on the adenosine triphosphate (ATP) content of skeletal muscle in patients with mitochondrial disease and myopathy. Methods AIMM is a single-centre, double blind, placebo-controlled, adaptive designed trial, evaluating the efficacy of 12 weeks’ administration of acipimox on skeletal muscle ATP content in patients with mitochondrial myopathy. Eligible patients will receive the trial investigational medicinal product (IMP), either acipimox or matched placebo. Participants will also be prescribed low dose aspirin as a non-investigational medical product (nIMP) in order to protect the blinding of the treatment assignment. Eighty to 120 participants will be recruited as required, with an interim analysis for sample size re-estimation and futility assessment being undertaken once the primary outcome for 50 participants has been obtained. Randomisation will be on a 1:1 basis, stratified by Fatigue Impact Scale (FIS) (dichotomised as < 40, ≥ 40). Participants will take part in the trial for up to 20 weeks, from screening visits through to follow-up at 16 weeks post randomisation. The primary outcome of change in ATP content in skeletal muscle and secondary outcomes relating to quality of life, perceived fatigue, disease burden, limb function, balance and walking, skeletal muscle analysis and symptom-limited cardiopulmonary fitness (optional) will be assessed between baseline and 12 weeks. Discussion The AIMM trial will investigate the effect of acipimox on modulating muscle ATP content and whether it can be repurposed as a new treatment for mitochondrial disease with myopathy. Trial registration EudraCT2018-002721-29 . Registered on 24 December 2018, ISRCTN 12895613. Registered on 03 January 2019, https://www.isrctn.com/search?q=aimm
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- 2022
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12. A multi-sensor wearable system for the assessment of diseased gait in real-world conditions
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Francesca Salis, Stefano Bertuletti, Tecla Bonci, Marco Caruso, Kirsty Scott, Lisa Alcock, Ellen Buckley, Eran Gazit, Clint Hansen, Lars Schwickert, Kamiar Aminian, Clemens Becker, Philip Brown, Anne-Elie Carsin, Brian Caulfield, Lorenzo Chiari, Ilaria D’Ascanio, Silvia Del Din, Bjoern M. Eskofier, Judith Garcia-Aymerich, Jeffrey M. Hausdorff, Emily C. Hume, Cameron Kirk, Felix Kluge, Sarah Koch, Arne Kuederle, Walter Maetzler, Encarna M. Micó-Amigo, Arne Mueller, Isabel Neatrour, Anisoara Paraschiv-Ionescu, Luca Palmerini, Alison J. Yarnall, Lynn Rochester, Basil Sharrack, David Singleton, Beatrix Vereijken, Ioannis Vogiatzis, Ugo Della Croce, Claudia Mazzà, Andrea Cereatti, and for the Mobilise-D consortium
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gait analysis ,IMU ,wearable sensors ,ecological conditions ,pressure insoles ,distance sensors ,Biotechnology ,TP248.13-248.65 - Abstract
Introduction: Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors).Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity.Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72–4.87 steps/min, stride length 0.04–0.06 m, walking speed 0.03–0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.
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- 2023
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13. Feasibility and usability of a digital health technology system to monitor mobility and assess medication adherence in mild-to-moderate Parkinson's disease
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Héloïse Debelle, Emma Packer, Esther Beales, Harry G. B. Bailey, Ríona Mc Ardle, Philip Brown, Heather Hunter, Fabio Ciravegna, Neil Ireson, Jordi Evers, Martijn Niessen, Jian Qing Shi, Alison J. Yarnall, Lynn Rochester, Lisa Alcock, and Silvia Del Din
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Parkinson's disease ,medication adherence ,smartwatch ,wearable technology ,remote monitoring ,mobility ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
IntroductionParkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management. This study investigates the feasibility and usability of a multi-component DHTS to remotely assess self-reported medication adherence and monitor mobility in people with Parkinson's (PwP).MethodsThirty participants with PD [Hoehn and Yahr stage I (n = 1) and II (n = 29)] were recruited for this cross-sectional study. Participants were required to wear, and where appropriate, interact with a DHTS (smartwatch, inertial measurement unit, and smartphone) for seven consecutive days to assess medication adherence and monitor digital mobility outcomes and contextual factors. Participants reported their daily motor complications [motor fluctuations and dyskinesias (i.e., involuntary movements)] in a diary. Following the monitoring period, participants completed a questionnaire to gauge the usability of the DHTS. Feasibility was assessed through the percentage of data collected, and usability through analysis of qualitative questionnaire feedback.ResultsAdherence to each device exceeded 70% and ranged from 73 to 97%. Overall, the DHTS was well tolerated with 17/30 participants giving a score > 75% [average score for these participants = 89%, from 0 (worst) to 100 (best)] for its usability. Usability of the DHTS was significantly associated with age (ρ = −0.560, BCa 95% CI [−0.791, −0.207]). This study identified means to improve usability of the DHTS by addressing technical and design issues of the smartwatch. Feasibility, usability and acceptability were identified as key themes from PwP qualitative feedback on the DHTS.ConclusionThis study highlighted the feasibility and usability of our integrated DHTS to remotely assess medication adherence and monitor mobility in people with mild-to-moderate Parkinson's disease. Further work is necessary to determine whether this DHTS can be implemented for clinical decision-making to optimise management of PwP.
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- 2023
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14. Acceptability of wearable devices for measuring mobility remotely: Observations from the Mobilise-D technical validation study
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Alison Keogh, Lisa Alcock, Philip Brown, Ellen Buckley, Marina Brozgol, Eran Gazit, Clint Hansen, Kirsty Scott, Lars Schwickert, Clemens Becker, Jeffrey M. Hausdorff, Walter Maetzler, Lynn Rochester, Basil Sharrack, Ioannis Vogiatzis, Alison Yarnall, Claudia Mazzà, and Brian Caulfield
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Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Background This study aimed to explore the acceptability of a wearable device for remotely measuring mobility in the Mobilise-D technical validation study (TVS), and to explore the acceptability of using digital tools to monitor health. Methods Participants ( N = 106) in the TVS wore a waist-worn device (McRoberts Dynaport MM + ) for one week. Following this, acceptability of the device was measured using two questionnaires: The Comfort Rating Scale (CRS) and a previously validated questionnaire. A subset of participants ( n = 36) also completed semi-structured interviews to further determine device acceptability and to explore their opinions of the use of digital tools to monitor their health. Questionnaire results were analysed descriptively and interviews using a content analysis. Results The device was considered both comfortable (median CRS (IQR; min-max) = 0.0 (0.0; 0–20) on a scale from 0–20 where lower scores signify better comfort) and acceptable (5.0 (0.5; 3.0–5.0) on a scale from 1–5 where higher scores signify better acceptability). Interviews showed it was easy to use, did not interfere with daily activities, and was comfortable. The following themes emerged from participants’ as being important to digital technology: altered expectations for themselves, the use of technology, trust, and communication with healthcare professionals. Conclusions Digital tools may bridge existing communication gaps between patients and clinicians and participants are open to this. This work indicates that waist-worn devices are supported, but further work with patient advisors should be undertaken to understand some of the key issues highlighted. This will form part of the ongoing work of the Mobilise-D consortium.
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- 2023
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15. Faster Walking Speeds Require Greater Activity from the Primary Motor Cortex in Older Adults Compared to Younger Adults
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Lisa Alcock, Rodrigo Vitório, Samuel Stuart, Lynn Rochester, and Annette Pantall
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functional near infrared spectroscopy ,oxygenated haemoglobin ,frontal lobe ,cortex ,preferred gait velocity ,fast gait velocity ,Chemical technology ,TP1-1185 - Abstract
Gait speed declines with age and slower walking speeds are associated with poor health outcomes. Understanding why we do not walk faster as we age, despite being able to, has implications for rehabilitation. Changes in regional oxygenated haemoglobin (HbO2) across the frontal lobe were monitored using functional near infrared spectroscopy in 17 young and 18 older adults while they walked on a treadmill for 5 min, alternating between 30 s of walking at a preferred and fast (120% preferred) speed. Gait was quantified using a triaxial accelerometer (lower back). Differences between task (preferred/fast) and group (young/old) and associations between regional HbO2 and gait were evaluated. Paired tests indicated increased HbO2 in the supplementary motor area (right) and primary motor cortex (left and right) in older adults when walking fast (p < 0.006). HbO2 did not significantly change in the young when walking fast, despite both groups modulating gait. When evaluating the effect of age (linear mixed effects model), greater increases in HbO2 were observed for older adults when walking fast (prefrontal cortex, premotor cortex, supplementary motor area and primary motor cortex) compared to young adults. In older adults, increased step length and reduced step length variability were associated with larger increases in HbO2 across multiple regions when walking fast. Walking fast required increased activation of motor regions in older adults, which may serve as a therapeutic target for rehabilitation. Widespread increases in HbO2 across the frontal cortex highlight that walking fast represents a resource-intensive task as we age.
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- 2023
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16. An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks
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Tecla Bonci, Francesca Salis, Kirsty Scott, Lisa Alcock, Clemens Becker, Stefano Bertuletti, Ellen Buckley, Marco Caruso, Andrea Cereatti, Silvia Del Din, Eran Gazit, Clint Hansen, Jeffrey M. Hausdorff, Walter Maetzler, Luca Palmerini, Lynn Rochester, Lars Schwickert, Basil Sharrack, Ioannis Vogiatzis, and Claudia Mazzà
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gait analysis ,spatio-temporal gait parameters ,gait cycle ,stride length ,stride duration ,stride speed ,Biotechnology ,TP248.13-248.65 - Abstract
There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, and accuracy were calculated. Absolute agreement [intraclass correlation coefficient (ICC2,1)] between marker-based and pressure insole stride parameters was also tested. In the YA cohort, the proposed method outperformed the existing ones, with sensitivity, PPV, and F1 scores ≥ 99% for both GEs and conditions, with a virtually null bias (
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- 2022
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17. Investigating the Impact of Environment and Data Aggregation by Walking Bout Duration on Parkinson’s Disease Classification Using Machine Learning
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Rana Zia Ur Rehman, Yu Guan, Jian Qing Shi, Lisa Alcock, Alison J. Yarnall, Lynn Rochester, and Silvia Del Din
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Parkinson’s disease ,gait ,real-world ,accelerometer ,machine learning ,laboratory ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Parkinson’s disease (PD) is a common neurodegenerative disease. PD misdiagnosis can occur in early stages. Gait impairment in PD is typical and is linked with an increased fall risk and poorer quality of life. Applying machine learning (ML) models to real-world gait has the potential to be more sensitive to classify PD compared to laboratory data. Real-world gait yields multiple walking bouts (WBs), and selecting the optimal method to aggregate the data (e.g., different WB durations) is essential as this may influence classification performance. The objective of this study was to investigate the impact of environment (laboratory vs. real world) and data aggregation on ML performance for optimizing sensitivity of PD classification. Gait assessment was performed on 47 people with PD (age: 68 ± 9 years) and 52 controls [Healthy controls (HCs), age: 70 ± 7 years]. In the laboratory, participants walked at their normal pace for 2 min, while in the real world, participants were assessed over 7 days. In both environments, 14 gait characteristics were evaluated from one tri-axial accelerometer attached to the lower back. The ability of individual gait characteristics to differentiate PD from HC was evaluated using the Area Under the Curve (AUC). ML models (i.e., support vector machine, random forest, and ensemble models) applied to real-world gait showed better classification performance compared to laboratory data. Real-world gait characteristics aggregated over longer WBs (WB 30–60 s, WB > 60 s, WB > 120 s) resulted in superior discriminative performance (PD vs. HC) compared to laboratory gait characteristics (0.51 ≤ AUC ≤ 0.77). Real-world gait speed showed the highest AUC of 0.77. Overall, random forest trained on 14 gait characteristics aggregated over WBs > 60 s gave better performance (F1 score = 77.20 ± 5.51%) as compared to laboratory results (F1 Score = 68.75 ± 12.80%). Findings from this study suggest that the choice of environment and data aggregation are important to achieve maximum discrimination performance and have direct impact on ML performance for PD classification. This study highlights the importance of a harmonized approach to data analysis in order to drive future implementation and clinical use.Clinical Trial Registration[09/H0906/82].
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- 2022
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18. Connecting real-world digital mobility assessment to clinical outcomes for regulatory and clinical endorsement–the Mobilise-D study protocol
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A. Stefanie Mikolaizak, Lynn Rochester, Walter Maetzler, Basil Sharrack, Heleen Demeyer, Claudia Mazzà, Brian Caulfield, Judith Garcia-Aymerich, Beatrix Vereijken, Valdo Arnera, Ram Miller, Paolo Piraino, Nadir Ammour, Mark Forrest Gordon, Thierry Troosters, Alison J. Yarnall, Lisa Alcock, Heiko Gaßner, Jürgen Winkler, Jochen Klucken, Christian Schlenstedt, Henrik Watz, Anne-Marie Kirsten, Ioannis Vogiatzis, Nikolaos Chynkiamis, Emily Hume, Dimitrios Megaritis, Alice Nieuwboer, Pieter Ginis, Ellen Buckley, Gavin Brittain, Giancarlo Comi, Letizia Leocani, Jorunn L. Helbostad, Lars Gunnar Johnsen, Kristin Taraldsen, Hubert Blain, Valérie Driss, Anja Frei, Milo A. Puhan, Ashley Polhemus, Magda Bosch de Basea, Elena Gimeno, Nicholas S. Hopkinson, Sara C. Buttery, Jeffrey M. Hausdorff, Anat Mirelman, Jordi Evers, Isabel Neatrour, David Singleton, Lars Schwickert, Clemens Becker, and Carl-Philipp Jansen
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Medicine ,Science - Abstract
Background The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. Methods/design The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson’s Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. Discussion The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility. Trial registration ISRCTN12051706.
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- 2022
19. Motor–Cognitive Treadmill Training With Virtual Reality in Parkinson’s Disease: The Effect of Training Duration
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Elisa Pelosin, Chiara Ponte, Martina Putzolu, Giovanna Lagravinese, Jeffrey M. Hausdorff, Alice Nieuwboer, Pieter Ginis, Lynn Rochester, Lisa Alcock, Bastiaan R. Bloem, Freek Nieuwhof, Andrea Cereatti, Ugo Della Croce, Anat Mirelman, and Laura Avanzino
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Parkinson’s disease ,treadmill training (TT) ,virtual reality ,gait ,cognitive functions ,falls ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Treadmill training with virtual reality (TT + VR) has been shown to improve gait performance and to reduce fall risk in Parkinson’s disease (PD). However, there is no consensus on the optimal training duration. This study is a sub-study of the V-TIME randomized clinical trial (NCT01732653). In this study, we explored the effect of the duration of training based on the motor–cognitive interaction on motor and cognitive performance and on fall risk in subjects with PD. Patients in Hoehn and Yahr stages II–III, aged between 40 and 70 years, were included. In total, 96 patients with PD were assigned to 6 or 12 weeks of TT + VR intervention, and 77 patients completed the full protocol. Outcome measures for gait and cognitive performance were assessed at baseline, immediately after training, and at 1- and 6-month follow-up. The incident rate of falls in the 6-month pre-intervention was compared with that in the 6-month post-intervention. Dual-task gait performance (gait speed, gait speed variability and stride length under cognitive dual task and obstacle negotiation, and the leading foot clearance in obstacle negotiation) improved similarly in both groups with gains sustained at 6-month follow-up. A higher decrease in fall rate and fear of falling were observed in participants assigned to the 12-week intervention than the 6-week intervention. Improvements in cognitive functions (i.e., executive functions, visuospatial ability, and attention) were seen only in participants enrolled in 12-week training up to 1-month follow-up but vanished at the 6-month evaluation. Our results suggest that a longer TT + VR training leads to greater improvements in cognitive functions especially those directly addressed by the virtual environment.
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- 2022
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20. Accelerometry-Based Digital Gait Characteristics for Classification of Parkinson's Disease: What Counts?
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Rana Zia Ur Rehman, Christopher Buckley, Maria Encarna Mico-Amigo, Cameron Kirk, Michael Dunne-Willows, Claudia Mazza, Jian Qing Shi, Lisa Alcock, Lynn Rochester, and Silvia Del Din
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Classification ,Machine Learning ,Digital Gait ,Parkinson's disease ,Partial least square-discriminant analysis (PLS-DA) ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Medical technology ,R855-855.5 - Abstract
Objective: Gait may be a useful biomarker that can be objectively measured with wearable technology to classify Parkinson's disease (PD). This study aims to: (i) comprehensively quantify a battery of commonly utilized gait digital characteristics (spatiotemporal and signal-based), and (ii) identify the best discriminative characteristics for the optimal classification of PD. Methods: Six partial least square discriminant analysis (PLS-DA) models were trained on subsets of 210 characteristics measured in 142 subjects (81 people with PD, 61 controls (CL)). Results: Models accuracy ranged between 70.42-88.73% (AUC: 78.4-94.5%) with a sensitivity of 72.84-90.12% and a specificity of 60.3-86.89%. Signal-based digital gait characteristics independently gave 87.32% accuracy. The most influential characteristics in the classification models were related to root mean square values, power spectral density, step velocity and length, gait regularity and age. Conclusions: This study highlights the importance of signal-based gait characteristics in the development of tools to help classify PD in the early stages of the disease.
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- 2020
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21. Technical validation of real-world monitoring of gait: a multicentric observational study
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Sarah Koch, Clint Hansen, Walter Maetzler, Anne-Elie Carsin, Kristin Taraldsen, Kamiar Aminian, Clemens Becker, Lorenzo Chiari, Anisoara Paraschiv-Ionescu, Jorunn L Helbostad, Beatrix Vereijken, Lynn Rochester, Philip Brown, Judith Garcia Aymerich, David Singleton, Basil Sharrack, Brian Caulfield, Ellen Buckley, Claudia Mazza, Nikolaos Chynkiamis, Felix Kluge, M Encarna Micó-Amigo, Francesca Salis, Lars Schwickert, Kirsty Scott, Ioannis Vogiatzis, Alison Yarnall, Alison Keogh, Silvia Del Din, Björn Eskofier, Lisa Alcock, Stefano Bertuletti, Tecla Bonci, Marina Brozgol, Marco Caruso, Andrea Cereatti, Fabio Ciravegna, Jordi Evers, Eran Gazit, Jeffrey M Hausdorff, Hugo Hiden, Emily Hume, Neil Ireson, Cameron Kirk, Arne Küderle, Vitaveska Lanfranchi, Arne Mueller, Isabel Neatrour, Martijn Niessen, Luca Palmerini, Lucas Pluimgraaff, Luca Reggi, Henrik Sillen, Abolfazi Soltani, Martin Ullrich, Linda Van Gelder, and Elke Warmerdam
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Medicine - Published
- 2021
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22. Correction: Acipimox in Mitochondrial Myopathy (AIMM): study protocol for a randomised, double-blinded, placebo-controlled, adaptive design trial of the efficacy of acipimox in adult patients with mitochondrial myopathy
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The AIMM Trial Group:, Alaa Abouhajar, Lisa Alcock, Theophile Bigirumurame, Penny Bradley, Laura Brown, Ian Campbell, Silvia Del Din, Julie Faitg, Gavin Falkous, Grainne S. Gorman, Rachel Lakey, Robert McFarland, Jane Newman, Lynn Rochester, Vicky Ryan, Hesther Smith, Alison Steel, Renae J. Stefanetti, Huizhong Su, Robert W. Taylor, Naomi J. P. Thomas, Helen Tuppen, Amy E. Vincent, Charlotte Warren, and Gillian Watson
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Medicine (General) ,R5-920 - Published
- 2022
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23. Balance Impairments as Differential Markers of Dementia Disease Subtype
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Ríona Mc Ardle, Stephanie Pratt, Christopher Buckley, Silvia Del Din, Brook Galna, Alan Thomas, Lynn Rochester, and Lisa Alcock
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dementia ,Alzheimer’s disease ,Lewy body disease ,Parkinson’s disease ,balance ,accelerometer ,Biotechnology ,TP248.13-248.65 - Abstract
BackgroundAccurately differentiating dementia subtypes, such as Alzheimer’s disease (AD) and Lewy body disease [including dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD)] is important to ensure appropriate management and treatment of the disease. Similarities in clinical presentation create difficulties for differential diagnosis. Simple supportive markers, such as balance assessments, may be useful to the diagnostic toolkit. This study aimed to identify differences in balance impairments between different dementia disease subtypes and normal aging using a single triaxial accelerometer.MethodsNinety-seven participants were recruited, forming four groups: cognitive impairment due to Alzheimer’s disease (AD group; n = 31), dementia with Lewy bodies (DLB group; n = 26), Parkinson’s disease dementia (PDD group; n = 13), and normal aging controls (n = 27). Participants were asked to stand still for 2 minutes in a standardized position with their eyes open while wearing a single triaxial accelerometer on their lower back. Seven balance characteristics were derived, including jerk (combined, mediolateral, and anterior–posterior), root mean square (RMS; combined, mediolateral, and anterior–posterior), and ellipsis. Mann–Whitney U tests identified the balance differences between groups. Receiver operating characteristics and area under the curve (AUC) determined the overall accuracy of the selected balance characteristics.ResultsThe PDD group demonstrated higher RMS [combined (p = 0.001), mediolateral (p = 0.005), and anterior–posterior (p = 0.001)] and ellipsis scores (p < 0.002) than the AD group (AUC = 0.71–0.82). The PDD group also demonstrated significantly impaired balance across all characteristics (p ≤ 0.001) compared to the controls (AUC = 0.79–0.83). Balance differences were not significant between PDD and DLB (AUC = 0.69–0.74), DLB and AD (AUC = 0.50–0.65), DLB and controls (AUC = 0.62–0.68), or AD and controls (AUC = 0.55–0.67) following Bonferroni correction.DiscussionAlthough feasible and quick to conduct, key findings suggest that an accelerometer-based balance during quiet standing does not differentiate dementia disease subtypes accurately. Assessments that challenge balance more, such as gait or standing with eyes closed, may prove more effective to support differential diagnosis.
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- 2021
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24. Gait Progression Over 6 Years in Parkinson’s Disease: Effects of Age, Medication, and Pathology
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Joanna Wilson, Lisa Alcock, Alison J. Yarnall, Sue Lord, Rachael A. Lawson, Rosie Morris, John-Paul Taylor, David J. Burn, Lynn Rochester, and Brook Galna
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gait ,walking ,Parkinson’s disease ,neurological disorders ,aging ,longitudinal ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Background: Gait disturbance is an early, cardinal feature of Parkinson’s disease (PD) associated with falls and reduced physical activity. Progression of gait impairment in Parkinson’s disease is not well characterized and a better understanding is imperative to mitigate impairment. Subtle gait impairments progress in early disease despite optimal dopaminergic medication. Evaluating gait disturbances over longer periods, accounting for typical aging and dopaminergic medication changes, will enable a better understanding of gait changes and inform targeted therapies for early disease. This study aimed to describe gait progression over the first 6 years of PD by delineating changes associated with aging, medication, and pathology.Methods: One-hundred and nine newly diagnosed PD participants and 130 controls completed at least two gait assessments. Gait was assessed at 18-month intervals for up to 6 years using an instrumented walkway to measure sixteen spatiotemporal gait characteristics. Linear mixed-effects models assessed progression.Results: Ten gait characteristics significantly progressed in PD, with changes in four of these characteristics attributable to disease progression. Age-related changes also contributed to gait progression; changes in another two characteristics reflected both aging and disease progression. Gait impairment progressed irrespective of dopaminergic medication change for all characteristics except step width variability.Conclusions: Discrete gait impairments continue to progress in PD over 6 years, reflecting a combination of, and potential interaction between, disease-specific progression and age-related change. Gait changes were mostly unrelated to dopaminergic medication adjustments, highlighting limitations of current dopaminergic therapy and the need to improve interventions targeting gait decline.
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- 2020
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25. Factors Influencing Habitual Physical Activity in Parkinson’s Disease: Considering the Psychosocial State and Wellbeing of People with Parkinson’s and Their Carers
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Ríona Mc Ardle, Silvia Del Din, Rosie Morris, Lisa Alcock, Alison J. Yarnall, David J. Burn, Lynn Rochester, Rachael A. Lawson, and on behalf of the ICICLE-PD Study Group
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Parkinson’s disease ,habitual physical activity ,wearable technology ,wellbeing ,carer ,accelerometer ,Chemical technology ,TP1-1185 - Abstract
Participating in habitual physical activity (HPA) may slow onset of dependency and disability for people with Parkinson’s disease (PwP). While cognitive and physical determinants of HPA are well understood, psychosocial influences are not. This pilot study aimed to identify psychosocial factors associated with HPA to guide future intervention development. Sixty-four PwP participated in this study; forty had carer informants. PwP participants wore a tri-axial accelerometer on the lower back continuously for seven days at two timepoints (18 months apart), measuring volume, pattern and variability of HPA. Linear mixed effects analysis identified relationships between demographic, clinical and psychosocial data and HPA from baseline to 18 months. Key results in PwP with carers indicated that carer anxiety and depression were associated with increased HPA volume (p < 0.01), while poorer carer self-care was associated with reduced volume of HPA over 18 months (p < 0.01). Greater carer strain was associated with taking longer walking bouts after 18 months (p < 0.01). Greater carer depression was associated with lower variability of HPA cross-sectionally (p = 0.009). This pilot study provides preliminary novel evidence that psychosocial outcomes from PwP’s carers may impact HPA in Parkinson’s disease. Interventions to improve HPA could target both PwP and carers and consider approaches that also support psychosocial wellbeing.
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- 2022
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26. A Quality Control Check to Ensure Comparability of Stereophotogrammetric Data between Sessions and Systems
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Kirsty Scott, Tecla Bonci, Lisa Alcock, Ellen Buckley, Clint Hansen, Eran Gazit, Lars Schwickert, Andrea Cereatti, Claudia Mazzà, and on behalf of the Mobilise-D Consortium
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optoelectronic stereophotogrammetry ,3D motion capture ,quality control ,spot check ,accuracy ,systematic errors ,Chemical technology ,TP1-1185 - Abstract
Optoelectronic stereophotogrammetric (SP) systems are widely used in human movement research for clinical diagnostics, interventional applications, and as a reference system for validating alternative technologies. Regardless of the application, SP systems exhibit different random and systematic errors depending on camera specifications, system setup and laboratory environment, which hinders comparing SP data between sessions and across different systems. While many methods have been proposed to quantify and report the errors of SP systems, they are rarely utilized due to their complexity and need for additional equipment. In response, an easy-to-use quality control (QC) check has been designed that can be completed immediately prior to a data collection. This QC check requires minimal training for the operator and no additional equipment. In addition, a custom graphical user interface ensures automatic processing of the errors in an easy-to-read format for immediate interpretation. On initial deployment in a multicentric study, the check (i) proved to be feasible to perform in a short timeframe with minimal burden to the operator, and (ii) quantified the level of random and systematic errors between sessions and systems, ensuring comparability of data in a variety of protocol setups, including repeated measures, longitudinal studies and multicentric studies.
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- 2021
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27. Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders
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Rana Zia Ur Rehman, Yuhan Zhou, Silvia Del Din, Lisa Alcock, Clint Hansen, Yu Guan, Tibor Hortobágyi, Walter Maetzler, Lynn Rochester, and Claudine J. C. Lamoth
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neurological disorders ,machine learning ,classification ,fall ,path signature ,gait ,Chemical technology ,TP1-1185 - Abstract
Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43–99% sensitivity and 48–98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.
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- 2020
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28. Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson’s Disease
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Rana Zia Ur Rehman, Philipp Klocke, Sofia Hryniv, Brook Galna, Lynn Rochester, Silvia Del Din, and Lisa Alcock
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inertial measurement unit (IMU) ,wearables ,upper body ,lower body ,spatial-temporal characteristics ,signal-based characteristics ,Chemical technology ,TP1-1185 - Abstract
Parkinson’s disease (PD) is a common neurodegenerative disorder resulting in a range of mobility deficits affecting gait, balance and turning. In this paper, we present: (i) the development and validation of an algorithm to detect turns during gait; (ii) a method to extract turn characteristics; and (iii) the classification of PD using turn characteristics. Thirty-seven people with PD and 56 controls performed 180-degree turns during an intermittent walking task. Inertial measurement units were attached to the head, neck, lower back and ankles. A turning detection algorithm was developed and validated by two raters using video data. Spatiotemporal and signal-based characteristics were extracted and used for PD classification. There was excellent absolute agreement between the rater and the algorithm for identifying turn start and end (ICC ≥ 0.99). Classification modeling (partial least square discriminant analysis (PLS-DA)) gave the best accuracy of 97.85% when trained on upper body and ankle data. Balanced sensitivity (97%) and specificity (96.43%) were achieved using turning characteristics from the neck, lower back and ankles. Turning characteristics, in particular angular velocity, duration, number of steps, jerk and root mean square distinguished mild-moderate PD from controls accurately and warrant future examination as a marker of mobility impairment and fall risk in PD.
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- 2020
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29. A Novel Energy-Based Composite Index for Assessing Motor State in Parkinson's Disease by Means of IMU-Based Digital Health Technology.
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Chiara Carissimo, Gianni Cerro, Gianfranco Miele, Héloïse Debelle, E. Packer, J. Sarvestan, Alison J. Yarnall, Lynn Rochester, Lisa Alcock, Luigi Ferrigno, Alessandro Marino, and Silvia Del Din
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- 2024
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30. The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control
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Christopher Buckley, Lisa Alcock, Ríona McArdle, Rana Zia Ur Rehman, Silvia Del Din, Claudia Mazzà, Alison J. Yarnall, and Lynn Rochester
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movement science ,Parkinson’s disease ,ataxia ,dementia ,machine learning ,deep learning ,risk prediction ,disease phenotyping ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions—including Parkinson’s disease, ataxia, and dementia—we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel ‘big data’ approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction.
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- 2019
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31. Enhancing remote monitoring and classification of motor state in Parkinson's disease using Wearable Technology and Machine Learning.
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Chiara Carissimo, Gianni Cerro, Héloïse Debelle, E. Packer, Alison J. Yarnall, Lynn Rochester, Lisa Alcock, Luigi Ferrigno, Alessandro Marino, Tommaso Di Libero, and Silvia Del Din
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- 2023
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32. A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings.
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Gaëlle Prigent, Kamiar Aminian, Andrea Cereatti, Francesca Salis, Tecla Bonci, Kirsty Scott, Claudia Mazzà, Lisa Alcock, Silvia Del Din, Eran Gazit, Clint Hansen, and Anisoara Paraschiv-Ionescu
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- 2023
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33. Automated Mobility Context Detection with Inertial Signals.
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Antonio Bevilacqua, Lisa Alcock, Brian Caulfield 0001, Eran Gazit, Clint Hansen, Neil Ireson, and Georgiana Ifrim
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- 2022
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34. A multi-sensor wearable system for the assessment of diseased gait in real-world conditions
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Salis, Francesca, Bertuletti, Stefano, Tecla, Bonci, Caruso, Marco, Kirsty, Scott, Lisa, Alcock, Ellen, Buckley, Eran, Gazit, Clint, Hansen, Lars, Schwickert, Kamiar, Aminian, Clemens, Becker, Philip, Brown, Anne-Elie, Carsin, Brian, Caulfield, Chiari, Lorenzo, Ilaria, D'Ascanio, Silvia Del Din, Eskofier, Bjoern M., Judith, Garcia-Aymerich, Hausdorff, Jeffrey M., Hume, Emily C., Cameron, Kirk, Felix, Kluge, Sarah, Koch, Arne, Kuederle, Walter, Maetzler, Mico'-Amigo, Encarna M., Arne, Mueller, Isabel, Neatrour, Anisoara, Paraschiv-Ionescu, Luca, Palmerini, Yarnall, Alison J., Lynn, Rochester, Basil, Sharrack, David, Singleton, Beatrix, Vereijken, Ioannis, Vogiatzis, Ugo Della Croce, Claudia, Mazza', Cereatti, Andrea, for the Mobilise-D consortium, Salis, Francesca, Carsin, Anne-Elie, García Aymerich, Judith, Koch, Sarah, and Mobilise-D consortium
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Histology ,wearable sensors ,gait analysis ,pressure insoles ,Biomedical Engineering ,Bioengineering ,ecological conditions ,spatial-temporal gait parameters ,ddc:600 ,IMU ,distance sensors ,imu ,Biotechnology - Abstract
Introduction:Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods:The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion:Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02m, walking speed ≤0.02m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72–4.87 steps/min, stride length 0.04–0.06m, walking speed 0.03–0.05m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.
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- 2023
35. A multi-sensor wearable system for gait assessment in real-world conditions: performance in individuals with impaired mobility
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Francesca Salis, Stefano Bertuletti, Tecla Bonci, Marco Caruso, Kirsty Scott, Lisa Alcock, Ellen Buckley, Eran Gazit, Clint Hansen, Lars Schwickert, Kamiar Aminian, Clemens Becker, Philip Brown, Anne-Elie Carsin, Brian Caulfield, Lorenzo Chiari, Ilaria D'Ascanio, Silvia Del Din, Bjoern M. Eskofier, Judith Garcia-Aymerich, Jeffrey M. Hausdorff, Emily C. Hume, Cameron Kirk, Felix Kluge, Sarah Koch, Arne Kuederle, Walter Maetzler, Encarna M. Micò-Amigo, Arne Mueller, Isabel Neatrour, Anisoara Paraschiv-Ionescu, Luca Palmerini, Alison J. Yarnall, Lynn Rochester, Basil Sharrack, David Singleton, Beatrix Vereijken, Ioannis Vogiatzis, Ugo Della Croce, Claudia Mazzà, and Andrea Cereatti
- Abstract
Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (SDA, including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-hours of real-world unsupervised activity. Excellent absolute agreement (ICC > 0.95) and very limited mean absolute errors were observed for all cohorts and DMOs (cadence ≤ 0.61 steps/min, stride length ≤ 0.02 m, walking speed ≤ 0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the SDA (cadence 2.72–4.87 steps/min, stride length 0.04–0.06 m, walking speed 0.03–0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-hours acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.
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- 2023
36. Design and validation of a multi-task, multi-phase protocol for real-world gait simulation
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Kirsty Scott, Tecla Bonci, Salis Francesca, Lisa Alcock, Ellen Buckley, Eran Gazit, Clint Hansen, Lars Schwickert, Kamiar Aminian, Stefano Bertuletti, Marco Caruso, Lorenzo Chiari, Basil Sharrack, Walter Maetzler, Clemens Becker, Jeffrey M. Haussdorff, Ioannis Vogiatzis, Philip Brown, Silvia Del Din, Björn Eskofier, Anisoara Paraschiv-Ionescu, Alison Keogh, Kirk Cameron, Felix Kluge, M. Encarna Micó-Amigo, Arne Mueller, Isabel Neatrur, Martijn Niessen, Luca Palmerini, Henrik Sillen, David Singleton, Martin Ullrich, Beatrix Vereijken, Marcel Froelich, Gavin Brittan, Brian Caulfield, Sarah Koch, Anne-Elie Carsin, Judith Garcia-Aymerich, Arne Kuederle, Alison Yarnall, Andrea Cereatti, and Claudia Mazzà
- Abstract
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 multi-task and multi-phase protocol for simulating real-world gait accounting for all these factors within a single set of observations carried out within a limited laboratory space, 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 cohort groups 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 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 relatively realistic representation of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life.Conclusions: The suitability of the protocol 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.
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- 2022
37. Acipimox in Mitochondrial Myopathy (AIMM): study protocol for a randomised, double-blinded, placebo-controlled, adaptive design trial of the efficacy of acipimox in adult patients with mitochondrial myopathy
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Alaa, Abouhajar, Lisa, Alcock, Theophile, Bigirumurame, Penny, Bradley, Laura, Brown, Ian, Campbell, Silvia, Del Din, Julie, Faitg, Gavin, Falkous, Gráinne S, Gorman, Rachel, Lakey, Robert, McFarland, Jane, Newman, Lynn, Rochester, Vicky, Ryan, Hesther, Smith, Alison, Steel, Renae J, Stefanetti, Huizhong, Su, Robert W, Taylor, Naomi J P, Thomas, Helen, Tuppen, Amy E, Vincent, Charlotte, Warren, and Gillian, Watson
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Adult ,Adenosine Triphosphate ,Aspirin ,Muscular Diseases ,Pyrazines ,Quality of Life ,Humans ,Mitochondrial Myopathies ,Child ,Fatigue ,Randomized Controlled Trials as Topic - Abstract
Mitochondrial disease is a heterogenous group of rare, complex neurometabolic disorders. Despite their individual rarity, collectively mitochondrial diseases represent the most common cause of inherited metabolic disorders in the UK; they affect 1 in every 4300 individuals, up to 15,000 adults (and a similar number of children) in the UK. Mitochondrial disease manifests multisystem and isolated organ involvement, commonly affecting those tissues with high energy demands, such as skeletal muscle. Myopathy manifesting as fatigue, muscle weakness and exercise intolerance is common and debilitating in patients with mitochondrial disease. Currently, there are no effective licensed treatments and consequently, there is an urgent clinical need to find an effective drug therapy.To investigate the efficacy of 12-week treatment with acipimox on the adenosine triphosphate (ATP) content of skeletal muscle in patients with mitochondrial disease and myopathy.AIMM is a single-centre, double blind, placebo-controlled, adaptive designed trial, evaluating the efficacy of 12 weeks' administration of acipimox on skeletal muscle ATP content in patients with mitochondrial myopathy. Eligible patients will receive the trial investigational medicinal product (IMP), either acipimox or matched placebo. Participants will also be prescribed low dose aspirin as a non-investigational medical product (nIMP) in order to protect the blinding of the treatment assignment. Eighty to 120 participants will be recruited as required, with an interim analysis for sample size re-estimation and futility assessment being undertaken once the primary outcome for 50 participants has been obtained. Randomisation will be on a 1:1 basis, stratified by Fatigue Impact Scale (FIS) (dichotomised as40, ≥ 40). Participants will take part in the trial for up to 20 weeks, from screening visits through to follow-up at 16 weeks post randomisation. The primary outcome of change in ATP content in skeletal muscle and secondary outcomes relating to quality of life, perceived fatigue, disease burden, limb function, balance and walking, skeletal muscle analysis and symptom-limited cardiopulmonary fitness (optional) will be assessed between baseline and 12 weeks.The AIMM trial will investigate the effect of acipimox on modulating muscle ATP content and whether it can be repurposed as a new treatment for mitochondrial disease with myopathy.EudraCT2018-002721-29 . Registered on 24 December 2018, ISRCTN 12895613. Registered on 03 January 2019, https://www.isrctn.com/search?q=aimm.
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- 2022
38. Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers
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Silvia Del Din, Lisa Alcock, Yu Guan, Clint Hansen, Rana Zia Ur Rehman, Tibor Hortobágyi, Yuhan Zhou, Walter Maetzler, Lynn Rochester, Claudine J. C. Lamoth, SMART Movements (SMART), Movement Disorder (MD), and Personalized Healthcare Technology (PHT)
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Male ,medicine.medical_specialty ,fall ,neurological disorders ,STRIDE ,Wearable computer ,Walking ,lcsh:Chemical technology ,gait ,Biochemistry ,Article ,Poor quality ,Analytical Chemistry ,Wearable Electronic Devices ,03 medical and health sciences ,0302 clinical medicine ,Physical medicine and rehabilitation ,path signature ,Humans ,Medicine ,lcsh:TP1-1185 ,030212 general & internal medicine ,Electrical and Electronic Engineering ,Instrumentation ,fall risk assessment ,Aged ,Fall risk assessment ,business.industry ,inertial measurement unit ,Fall risk ,Atomic and Molecular Physics, and Optics ,Random forest ,machine learning ,wearables ,classification ,Gait analysis ,Quality of Life ,Accidental Falls ,Female ,Nervous System Diseases ,Gait Analysis ,business ,human activities ,030217 neurology & neurosurgery ,data pre-processing - Abstract
Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43&ndash, 99% sensitivity and 48&ndash, 98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.
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- 2020
39. Gait-Related Metabolic Covariance Networks at Rest in Parkinson's Disease
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Hilmar P. Sigurdsson, Alison J. Yarnall, Brook Galna, Sue Lord, Lisa Alcock, Rachael A. Lawson, Sean J. Colloby, Michael J. Firbank, John‐Paul Taylor, Nicola Pavese, David J. Brooks, John T. O'Brien, David J. Burn, Lynn Rochester, Sigurdsson, Hilmar P [0000-0002-0624-065X], Yarnall, Alison J [0000-0002-3126-9163], Galna, Brook [0000-0002-5890-1894], Alcock, Lisa [0000-0002-8364-9803], Lawson, Rachael A [0000-0003-2608-8285], Firbank, Michael J [0000-0002-9536-0185], Taylor, John-Paul [0000-0001-7958-6558], Pavese, Nicola [0000-0002-6801-6194], Brooks, David J [0000-0003-2602-2518], O'Brien, John T [0000-0002-0837-5080], Rochester, Lynn [0000-0001-5774-9272], and Apollo - University of Cambridge Repository
- Subjects
Parkinson's disease ,Parkinson Disease ,gait ,multivariate covariance networks ,Magnetic Resonance Imaging ,nervous system diseases ,Levodopa ,PET ,Glucose ,Neurology ,[18F]-2-fluoro-2-deoxyglucose ,Quality of Life ,Humans ,Neurology (clinical) ,[ F]-2-fluoro-2-deoxyglucose ,health care economics and organizations - Abstract
Funder: Lockhart Parkinson's Disease Research Fund, Funder: Newcastle Biomedical Research Centre; Id: http://dx.doi.org/10.13039/501100012295, BACKGROUND: Gait impairments are characteristic motor manifestations and significant predictors of poor quality of life in Parkinson's disease (PD). Neuroimaging biomarkers for gait impairments in PD could facilitate effective interventions to improve these symptoms and are highly warranted. OBJECTIVE: The aim of this study was to identify neural networks of discrete gait impairments in PD. METHODS: Fifty-five participants with early-stage PD and 20 age-matched healthy volunteers underwent quantitative gait assessment deriving 12 discrete spatiotemporal gait characteristics and [18 F]-2-fluoro-2-deoxyglucose-positron emission tomography measuring resting cerebral glucose metabolism. A multivariate spatial covariance approach was used to identify metabolic brain networks that were related to discrete gait characteristics in PD. RESULTS: In PD, we identified two metabolic gait-related covariance networks. The first correlated with mean step velocity and mean step length (pace gait network), which involved relatively increased and decreased metabolism in frontal cortices, including the dorsolateral prefrontal and orbital frontal, insula, supplementary motor area, ventrolateral thalamus, cerebellum, and cuneus. The second correlated with swing time variability and step time variability (temporal variability gait network), which included relatively increased and decreased metabolism in sensorimotor, superior parietal cortex, basal ganglia, insula, hippocampus, red nucleus, and mediodorsal thalamus. Expression of both networks was significantly elevated in participants with PD relative to healthy volunteers and were not related to levodopa dosage or motor severity. CONCLUSIONS: We have identified two novel gait-related brain networks of altered glucose metabolism at rest. These gait networks could serve as a potential neuroimaging biomarker of gait impairments in PD and facilitate development of therapeutic strategies for these disabling symptoms. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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- 2022
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40. Temporal dynamics of cortical activity and postural control in response to the first levodopa dose of the day in people with Parkinson's disease
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Fabio Augusto Barbieri, Aline Prieto de Barros Silveira, Felipe Balistieri Santinelli, Lisa Alcock, Luis Felipe Itikawa Imaizumi, Fabiana Araújo-Silva, Luiz Henrique Palucci Vieira, Universidade Estadual Paulista (UNESP), Hasselt University, Newcastle University Newcastle upon Tyne, Araújo-Silva, Fabiana, BALISTIERI SANTINELLI, Felipe, Felipe I. Imaizumi, Luis, Silveira, Aline P.B., Vieira, Luiz H.P., Alcock, Lisa, and Barbieri, Fabio A.
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Male ,medicine.medical_specialty ,Levodopa ,Parkinson's disease ,Brain activity and meditation ,Posture ,Alpha (ethology) ,Electroencephalography ,Medication ,Antiparkinson Agents ,Physical medicine and rehabilitation ,medicine ,Humans ,Force platform ,Molecular Biology ,Postural Balance ,Balance (ability) ,Aged ,Aged, 80 and over ,Cerebral Cortex ,medicine.diagnostic_test ,business.industry ,General Neuroscience ,Parkinson Disease ,Middle Aged ,medicine.disease ,medicine.anatomical_structure ,Parkinson’s disease ,Female ,Neurology (clinical) ,Ankle ,business ,Brain activity ,Developmental Biology ,medicine.drug ,Muscle activity - Abstract
Made available in DSpace on 2022-05-01T11:07:20Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-01-15 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Background: Our understanding of how balance control responds to levodopa over the course of a single day in people with Parkinson's disease (PD) is limited with the majority of studies focused on isolated comparisons of ON vs. OFF levodopa medication. Objective: To evaluate the temporal dynamics of postural control following the first levodopa dose of the day during a challenging standing task in a group of people with PD. Methods: Changes in postural control were evaluated by monitoring cortical activity (covering frontal, motor, parietal and occipital areas), body sway parameters (force platform), and lower limb muscle activity (tibialis anterior and gastrocnemius medialis) in 15 individuals with PD during a semi-tandem standing task. Participants were assessed during two 60 second trials every 30 minutes (ON-30 ON-60 etc.) for 3 hours after the first matinal dose (ON-180). Results: Compared to when tested OFF-medication, cortical activity was increased across all four regions from ON-60 to ON-120 with early increases in alpha and beta band activity observed at ON-30. Levodopa was associated with increased gastrocnemius medialis activity (ON-30 to ON-120) and ankle co-contraction (ON-60 to ON-120). Changes in body sway outcomes (particularly in the anterior-posterior direction) were evident from ON-60 to ON-120. Conclusions: Our results reveal a 60-minute window within which postural control outcomes may be obtained that are different compared to OFF-state and remain stable (from 60-minutes to 120-minutes after levodopa intake). Identifying a window of opportunity for measurement when individuals are optimally medicated is important for observations in a clinical and research setting. São Paulo State University (UNESP) School of Sciences Graduate Program in Movement Sciences Department of Physical Education Human Movement Research Laboratory (MOVI-LAB) REVAL Rehabilitation Research Center Faculty of Rehabilitation Sciences Hasselt University Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle upon Tyne São Paulo State University (UNESP) School of Sciences Graduate Program in Movement Sciences Department of Physical Education Human Movement Research Laboratory (MOVI-LAB)
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- 2022
41. Design and validation of a multi-task, multi-context protocol for real-world gait simulation
- Author
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Kirsty, Scott, Tecla, Bonci, Francesca, Salis, Lisa, Alcock, Ellen, Buckley, Eran, Gazit, Clint, Hansen, Lars, Schwickert, Kamiar, Aminian, Stefano, Bertuletti, Caruso, Marco, Lorenzo, Chiari, Basil, Sharrack, Walter, Maetzler, Clemens, Becker, Hausdorff, Jeffrey M., Ioannis, Vogiatzis, Philip, Brown, Silvia Del Din, Björn, Eskofier, Anisoara, Paraschiv-Ionescu, Alison, Keogh, Cameron, Kirk, Felix, Kluge, Mic('(o))-Amigo, Encarna M., Arne, Mueller, Isabel, Neatrour, Martijn, Niessen, Luca, Palmerini, Henrik, Sillen, David, Singleton, Martin, Ullrich, Beatrix, Vereijken, Marcel, Froehlich, Gavin, Brittain, Brian, Caulfield, Sarah, Koch, Anne-Elie, Carsin, Judith, Garcia-Aymerich, Arne, Kuederle, Alison, Yarnall, Lynn, Rochester, Cereatti, Andrea, and Claudia, Mazza'
- Subjects
parameters ,Mobility monitoring ,Digital mobility outcomes ,Neurological diseases ,Technical validation ,Wearable sensors ,Rehabilitation ,ddc:000 ,Health Informatics ,mobility - Abstract
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. This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 820820. This JU receives support from the European Union’s Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). This study was also supported by the National Institute for Health Research (NIHR) through the Sheffield Biomedical Research Centre (BRC, Grant Number IS-BRC-1215–20017). AY, LA, LR and SDD are also supported by the National Institute for Health Research (NIHR) Newcastle Biomedical Research Center (BRC) based at Newcastle Upon Tyne Hospital NHS Foundation Trust and Newcastle University. AY, LA, LR and SDD are also supported by the NIHR/Wellcome Trust Clinical Research Facility (CRF) infrastructure at Newcastle upon Tyne Hospitals NHS Foundation Trust. ISGlobal acknowledges support from the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019–2023” Program (CEX2018-000806-S), and from the Generalitat de Catalunya through the CERCA Program. All opinions are those of the authors and not the funders. Neither IMI nor the European Union, EFPIA, NHS, NIHR, DHSC or any Associated Partners are responsible for any use that may be made of the information contained herein.
- Published
- 2022
42. Enhanced Obstacle Contrast to Promote Visual Scanning in Fallers with Parkinson’s Disease: Role of Executive Function
- Author
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Lynn Rochester, Jeffrey M. Hausdorff, Sue Lord, Brook Galna, and Lisa Alcock
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0301 basic medicine ,medicine.medical_specialty ,Eye Movements ,genetic structures ,media_common.quotation_subject ,Poison control ,Walking ,Executive Function ,03 medical and health sciences ,0302 clinical medicine ,Physical medicine and rehabilitation ,medicine ,Humans ,Contrast (vision) ,Gait ,media_common ,Visual search ,General Neuroscience ,Eye movement ,Parkinson Disease ,Cognition ,Gaze ,030104 developmental biology ,Obstacle ,Eye tracking ,Psychology ,030217 neurology & neurosurgery - Abstract
The ability to perceive differences in environmental contrast is critical for navigating complex environments safely. People with Parkinson's disease (PD) report a multitude of visual and cognitive deficits which may impede safe obstacle negotiation and increase fall risk. Enhancing obstacle contrast may influence the content of visual information acquired within complex environments and thus target environmental fall risk factors. 17 PD with a history of falls and 18 controls walked over an obstacle covered in a high and low contrast material in separate trials whilst eye movements were recorded. Measures of visual function and cognition were obtained. Gaze location was extracted during the approach phase. PD spent longer looking at the obstacle compared to controls regardless of contrast (p .05), however group differences were largest for the low contrast obstacle. When accounting for group differences in approach time, PD spent longer looking at the low contrast obstacle and less time looking at the ground beyond the low contrast obstacle compared to controls (p .05). The response to obstacle contrast in PD (high-low) was significantly associated with executive function. Better executive function was associated with spending longer looking at the low contrast obstacle and at the ground beyond the high contrast obstacle. Enhancing the contrast of ground-based trip hazards may improve visual processing of environmental cues in PD, particularly for individuals with better executive function. Manipulating contrast to attract visual attention is already in use in the public domain, however its utility for reducing fall risk in PD is yet to be formally tested in habitual settings.
- Published
- 2020
43. Trajectories of pain over 6 years in early Parkinson’s disease: ICICLE-PD
- Author
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Lynn Rochester, Brook Galna, David J. Burn, Lisa Alcock, Rachael A Lawson, Jenni Naisby, and Alison J. Yarnall
- Subjects
medicine.medical_specialty ,Longitudinal study ,Neurology ,Parkinson's disease ,Psychological intervention ,Pain ,B100 ,Disease ,03 medical and health sciences ,0302 clinical medicine ,Surveys and Questionnaires ,medicine ,Humans ,Cognitive Dysfunction ,Longitudinal Studies ,030212 general & internal medicine ,Neuroradiology ,Original Communication ,business.industry ,Incidence (epidemiology) ,Parkinson Disease ,Pain management ,medicine.disease ,Parkinson’s disease ,Longitudinal ,Physical therapy ,Neurology (clinical) ,business ,030217 neurology & neurosurgery - Abstract
Introduction Pain is a common non-motor symptom in Parkinson’s disease (PD), affecting up to 85% of patients. The frequency and stability of pain over time has not been extensively studied. There is a paucity of high-quality studies investigating pain management in PD. To develop interventions, an understanding of how pain changes over the disease course is required. Methods One hundred and fifty-four participants with early PD and 99 age-and-sex-matched controls were recruited as part of a longitudinal study (Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation in PD, ICICLE-PD). Pain data were collected at 18-month intervals over 72 months in both groups using the Nonmotor Symptom Questionnaire (NMSQ), consisting of a binary yes/no response. Two questions from the Parkinson’s Disease Questionnaire (PDQ-39) were analysed for the PD group only. Results Unexplained pain was common in the PD group and occurred more frequently than in age-matched controls. ‘Aches and pains’ occurred more frequently than ‘cramps and muscle spasms’ at each time point (p Conclusions This study shows that pain is prevalent even in the early stages of PD, yet the frequency and type of pain fluctuates as symptoms progress. People with PD should be asked about their pain at clinical consultations and given support with describing pain given the different ways this can present.
- Published
- 2021
44. Free-water imaging of the cholinergic basal forebrain and pedunculopontine nucleus in Parkinson's disease
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Nicola J Ray, Rachael A Lawson, Sarah L Martin, Hilmar P Sigurdsson, Joanna Wilson, Brook Galna, Sue Lord, Lisa Alcock, Gordon W Duncan, Tien K Khoo, John T O’Brien, David J Burn, John-Paul Taylor, River C Rea, Maurizio Bergamino, Lynn Rochester, and Alison J Yarnall
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Neurology (clinical) - Abstract
Free-water imaging can predict and monitor dopamine system degeneration in people with Parkinson’s disease. It can also enhance the sensitivity of traditional diffusion tensor imaging (DTI) metrics for indexing neurodegeneration. However, these tools are yet to be applied to investigate cholinergic system degeneration in Parkinson’s disease, which involves both the pedunculopontine nucleus and cholinergic basal forebrain. Free-water imaging, free-water-corrected DTI and volumetry were used to extract structural metrics from the cholinergic basal forebrain and pedunculopontine nucleus in 99 people with Parkinson’s disease and 46 age-matched controls. Cognitive ability was tracked over 4.5 years. Pearson’s partial correlations revealed that free-water-corrected DTI metrics in the pedunculopontine nucleus were associated with performance on cognitive tasks that required participants to make rapid choices (behavioural flexibility). Volumetric, free-water content and DTI metrics in the cholinergic basal forebrain were elevated in a sub-group of people with Parkinson’s disease with evidence of cognitive impairment, and linear mixed modelling revealed that these metrics were differently associated with current and future changes to cognition. Free water and free-water-corrected DTI can index cholinergic degeneration that could enable stratification of patients in clinical trials of cholinergic interventions for cognitive decline. In addition, degeneration of the pedunculopontine nucleus impairs behavioural flexibility in Parkinson’s disease, which may explain this region’s role in increased risk of falls.
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- 2021
45. Technical validation of real-world monitoring of gait: A multicentric observational study
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Martin Ullrich, Silvia Del Din, Lars Schwickert, Vitaveska Lanfranchi, Eran Gazit, Alison Keogh, Tecla Bonci, S. Bertuletti, Claudia Mazzà, Felix Kluge, David Singleton, Lynn Rochester, Lucas Pluimgraaff, Francesca Salis, Luca Palmerini, Basil Sharrack, Nikolaos Chynkiamis, Jordi Evers, Sarah Koch, Elke Warmerdam, Clemens Becker, Philip M. Brown, M. Encarna Micó-Amigo, Neil Ireson, Judith Garcia Aymerich, Arne Küderle, Jeffrey M Hausdorff, Emily Hume, Lorenzo Chiari, Fabio Ciravegna, Luca Reggi, Anne-Elie Carsin, Isabel Neatrour, Linda Van Gelder, Cameron Kirk, Walter Maetzler, Andrea Cereatti, Abolfazi Soltani, Beatrix Vereijken, Björn M. Eskofier, Martijn Niessen, Arne Mueller, Jorunn L. Helbostad, Alison J. Yarnall, Ioannis Vogiatzis, Marina Brozgol, Hugo Hiden, Kristin Taraldsen, Kirsty Scott, Henrik Sillen, Lisa Alcock, M. Caruso, Anisoara Paraschiv-Ionescu, Kamiar Aminian, Clint Hansen, Brian Caulfield, Ellen Buckley, Mazza C., Alcock L., Aminian K., Becker C., Bertuletti S., Bonci T., Brown P., Brozgol M., Buckley E., Carsin A.-E., Caruso M., Caulfield B., Cereatti A., Chiari L., Chynkiamis N., Ciravegna F., Del Din S., Eskofier B., Evers J., Garcia Aymerich J., Gazit E., Hansen C., Hausdorff J.M., Helbostad J.L., Hiden H., Hume E., Paraschiv-Ionescu A., Ireson N., Keogh A., Kirk C., Kluge F., Koch S., Kuderle A., Lanfranchi V., Maetzler W., Mico-Amigo M.E., Mueller A., Neatrour I., Niessen M., Palmerini L., Pluimgraaff L., Reggi L., Salis F., Schwickert L., Scott K., Sharrack B., Sillen H., Singleton D., Soltani A., Taraldsen K., Ullrich M., Van Gelder L., Vereijken B., Vogiatzis I., Warmerdam E., Yarnall A., and Rochester L.
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medicine.medical_specialty ,hip ,Population ,heart failure ,multiple sclerosis ,01 natural sciences ,Wearable Electronic Devices ,03 medical and health sciences ,Units of measurement ,0302 clinical medicine ,disability instrument ,medicine ,Humans ,media_common.cataloged_instance ,Relevance (information retrieval) ,Medical physics ,European union ,education ,Gait ,Diagnostics ,Wearable technology ,Aged ,media_common ,Protocol (science) ,education.field_of_study ,Research ethics ,business.industry ,010401 analytical chemistry ,Parkinson Disease ,speed ,General Medicine ,calibration ,C600 ,0104 chemical sciences ,3. Good health ,Research Design ,chronic airways disease ,multiple sclerosi ,Medicine ,Observational study ,parkinson-s disease ,late-life function ,business ,Parkinson-s disease ,030217 neurology & neurosurgery - Abstract
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). We acknowledge support from the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. SDD, AY and LRo are also supported by the Newcastle Biomedical Research Centre (BRC) based at Newcastle upon Tyne and Newcastle University. CM, BS, LVG and EB are also supported by the Sheffield Biomedical Research Centre (BRC) based at the Sheffield Teaching Hospital and the University of Sheffield. The work was also supported by the NIHR/Wellcome Trust Clinical Research Facility (CRF) infrastructure at Newcastle upon Tyne Hospitals NHS Foundation Trust and the CRF at the Sheffield Teaching Hospital. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care or the funders.This study was co-funded by the European Union’s Horizon 2020 research and innovation programme and EFPIA via the Innovative Medicine Initiative 2 (Mobilise-D project, grant number IMI22017-13-7-820820). The views expressed are those of the authors and not necessarily those of the IMI, the European Union, the EFPIA, or any Associated Partners. We acknowledge the support of Grünenthal GmbH via the funding of a PhD scholarship directly dedicated to the technical validation protocol.
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- 2021
46. Review for 'Parkinson’s‐disease‐related changes in the behavioural synergy between eye movements and postural movements'
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Lisa Alcock
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medicine.medical_specialty ,Physical medicine and rehabilitation ,Parkinson's disease ,business.industry ,medicine ,Eye movement ,business ,medicine.disease - Published
- 2020
47. Cholinergic Basal Forebrain Volumes Predict Gait Decline in Parkinson's Disease
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Joanna Wilson, Rachael A Lawson, John T. O'Brien, John-Paul Taylor, Brook Galna, Gordon W Duncan, Lynn Rochester, Rosie Morris, Nicola J. Ray, Chesney E. Craig, Alison J. Yarnall, Sue Lord, Tien K. Khoo, David J. Burn, Lisa Alcock, Lawson, Rachael A [0000-0003-2608-8285], and Apollo - University of Cambridge Repository
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0301 basic medicine ,medicine.medical_specialty ,Movement disorders ,Parkinson's disease ,Basal Forebrain ,Cholinergic Agents ,B100 ,Regular Issue Articles ,Nucleus basalis ,03 medical and health sciences ,0302 clinical medicine ,Physical medicine and rehabilitation ,Gait (human) ,medicine ,Humans ,Gait ,Research Articles ,Basal forebrain ,Gait Disturbance ,business.industry ,Dopaminergic ,Parkinson Disease ,A300 ,medicine.disease ,structural MRI ,gait ,NBM ,acetylcholine ,030104 developmental biology ,Cross-Sectional Studies ,Neurology ,Cholinergic ,Neurology (clinical) ,medicine.symptom ,business ,human activities ,030217 neurology & neurosurgery ,Research Article - Abstract
Background Gait disturbance is an early, disabling feature of Parkinson's disease (PD) that is typically refractory to dopaminergic medication. The cortical cholinergic system, originating in the nucleus basalis of Meynert of the basal forebrain, has been implicated. However, it is not known if degeneration in this region relates to a worsening of disease‐specific gait impairment. Objective To evaluate associations between sub‐regional cholinergic basal forebrain volumes and longitudinal progression of gait impairment in PD. Methods 99 PD participants and 47 control participants completed gait assessments via an instrumented walkway during 2 minutes of continuous walking, at baseline and for up to 3 years, from which 16 spatiotemporal characteristics were derived. Sub‐regional cholinergic basal forebrain volumes were measured at baseline via MRI and a regional map derived from post‐mortem histology. Univariate analyses evaluated cross‐sectional associations between sub‐regional volumes and gait. Linear mixed‐effects models assessed whether volumes predicted longitudinal gait changes. Results There were no cross‐sectional, age‐independent relationships between sub‐regional volumes and gait. However, nucleus basalis of Meynert volumes predicted longitudinal gait changes unique to PD. Specifically, smaller nucleus basalis of Meynert volume predicted increasing step time variability (P = 0.019) and shortening swing time (P = 0.015); smaller posterior nucleus portions predicted shortening step length (P = 0.007) and increasing step time variability (P = 0.041). Conclusions This is the first study to demonstrate that degeneration of the cortical cholinergic system predicts longitudinal progression of gait impairments in PD. Measures of this degeneration may therefore provide a novel biomarker for identifying future mobility loss and falls. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., March Infographic: Cholinergic Basal Forebrain Volumes Predict Gait Decline in Parkinson's Disease
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- 2020
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48. A consensus guide to using functional near-infrared spectroscopy in posture and gait research
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Dennis Hamacher, Sue Peters, Roberta Vasta, Anat Mirelman, Meltem Izzetoglu, Vera Gramigna, Jasmine C. Menant, Sarah Fraser, Fabian Herold, Shannon B. Lim, Annette Pantall, Roee Holtzer, Eling D. de Bruin, Paulo H.S. Pelicioni, Samuel Stuart, Antonio Cerasa, Rodrigo Vitório, Rebecca J. St George, Inbal Maidan, Andrea L. Rosso, Lisa Alcock, David J. Clark, and Emad Al-Yahya
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Balance ,medicine.medical_specialty ,Consensus ,Standardization ,Posture ,Biophysics ,Validity ,physiology [Gait] ,Functional-Near Infrared Spectroscopy ,gait balance ,cerebral hemodynamics [Guidelines] ,methods [Spectroscopy, Near-Infrared] ,03 medical and health sciences ,0302 clinical medicine ,Physical medicine and rehabilitation ,Gait (human) ,medicine ,Humans ,Orthopedics and Sports Medicine ,guidelines ,ddc:796 ,Set (psychology) ,Research question ,Gait ,Balance (ability) ,Spectroscopy, Near-Infrared ,functional near infrared spectroscopy ,Rehabilitation ,Reproducibility of Results ,Cognition ,030229 sport sciences ,C600 ,B900 ,Functional near-infrared spectroscopy ,Psychology ,physiology [Posture] ,030217 neurology & neurosurgery - Abstract
BACKGROUND: Functional near-infrared spectroscopy (fNIRS) is increasingly used in the field of posture and gait to investigate patterns of cortical brain activation while people move freely. fNIRS methods, analysis and reporting of data vary greatly across studies which in turn can limit the replication of research, interpretation of findings and comparison across works.\ud \ud RESEARCH QUESTION AND METHODS: Considering these issues, we propose a set of practical recommendations for the conduct and reporting of fNIRS studies in posture and gait, acknowledging specific challenges related to clinical groups with posture and gait disorders.\ud \ud RESULTS: Our paper is organized around three main sections: 1) hardware set up and study protocols, 2) artefact removal and data processing and, 3) outcome measures, validity and reliability; it is supplemented with a detailed checklist.\ud \ud SIGNIFICANCE: This paper was written by a core group of members of the International Society for Posture and Gait Research and posture and gait researchers, all experienced in fNIRS research, with the intent of assisting the research community to lead innovative and impactful fNIRS studies in the field of posture and gait, whilst ensuring standardization of research.
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- 2020
49. Effect of Parkinson’s disease and two therapeutic interventions on muscle activity during walking: a systematic review
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A Islam, Lisa Alcock, Kianoush Nazarpour, Lynn Rochester, and Annette Pantall
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medicine.medical_specialty ,Deep brain stimulation ,Parkinson's disease ,medicine.medical_treatment ,0206 medical engineering ,Psychological intervention ,Neurophysiology ,Review Article ,02 engineering and technology ,lcsh:RC346-429 ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,0302 clinical medicine ,Physical medicine and rehabilitation ,Medicine ,lcsh:Neurology. Diseases of the nervous system ,Rehabilitation ,business.industry ,Dopaminergic ,Diagnostic markers ,medicine.disease ,020601 biomedical engineering ,Gait ,Subthalamic nucleus ,Neurology ,Neurology (clinical) ,business ,Biophysical models ,030217 neurology & neurosurgery - Abstract
Gait deficits are a common feature of Parkinson’s disease (PD) and predictors of future motor and cognitive impairment. Understanding how muscle activity contributes to gait impairment and effects of therapeutic interventions on motor behaviour is crucial for identifying potential biomarkers and developing rehabilitation strategies. This article reviews sixteen studies that investigate the electromyographic (EMG) activity of lower limb muscles in people with PD during walking and reports on their quality. The weight of evidence establishing differences in motor activity between people with PD and healthy older adults (HOAs) is considered. Additionally, the effect of dopaminergic medication and deep brain stimulation (DBS) on modifying motor activity is assessed. Results indicated greater proximal and decreased distal activity of lower limb muscles during walking in individuals with PD compared to HOA. Dopaminergic medication was associated with increased distal lower limb muscle activity whereas subthalamic nucleus DBS increased activity of both proximal and distal lower limb muscles. Tibialis anterior was impacted most by the interventions. Quality of the studies was not strong, with a median score of 61%. Most studies investigated only distal muscles, involved small sample sizes, extracted limited EMG features and lacked rigorous signal processing. Few studies related changes in motor activity with functional gait measures. Understanding mechanisms underpinning gait impairment in PD is essential for development of personalised rehabilitative interventions. Recommendations for future studies include greater participant numbers, recording more functionally diverse muscles, applying multi-muscle analyses, and relating EMG to functional gait measures.
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- 2020
50. Do Patients With Parkinson's Disease With Freezing of Gait Respond Differently Than Those Without to Treadmill Training Augmented by Virtual Reality?
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Ugo Della Croce, Elisa Pelosin, Bastiaan R. Bloem, Lisa Alcock, Esther M.J. Bekkers, Lynn Rochester, Alice Nieuwboer, Laura Avanzino, Andrea Cereatti, Freek Nieuwhof, Anat Mirelman, and Jeffrey M. Hausdorff
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Male ,medicine.medical_specialty ,Parkinson's disease ,genetic structures ,medicine.medical_treatment ,Population ,Trail Making Test ,Virtual reality ,postural control ,freezing of gait ,rehabilitation ,240 Systems Neurology ,Physical medicine and rehabilitation ,falls ,Outcome Assessment, Health Care ,medicine ,Humans ,Treadmill ,education ,Postural Balance ,Gait Disorders, Neurologic ,Balance (ability) ,Aged ,Parkinson’s disease ,virtual reality ,Aged, 80 and over ,education.field_of_study ,Rehabilitation ,business.industry ,Neurological Rehabilitation ,Virtual Reality ,Parkinson Disease ,General Medicine ,Middle Aged ,medicine.disease ,Disorders of movement Donders Center for Medical Neuroscience [Radboudumc 3] ,Gait ,Exercise Therapy ,Accidental Falls ,Female ,business - Abstract
Contains fulltext : 220639pub.pdf (Publisher’s version ) (Open Access) Contains fulltext : 220639pos.pdf (Author’s version postprint ) (Open Access) Background. People with Parkinson's disease and freezing of gait (FOG+) have more falls, postural instability and cognitive impairment compared with FOG-. Objective. To conduct a secondary analysis of the V-TIME study, a randomized, controlled investigation showing a greater reduction of falls after virtual reality treadmill training (TT + VR) compared with usual treadmill walking (TT) in a mixed population of fallers. We addressed whether these treadmill interventions led to similar gains in FOG+ as in FOG-. Methods. A total of 77 FOG+ and 44 FOG- were assigned randomly to TT + VR or TT. Participants were assessed pre- and posttraining and at 6 months' follow-up. Main outcome was postural stability assessed by the Mini Balance Evaluation System Test (Mini-BEST) test. Falls were documented using diaries. Other outcomes included the New Freezing of Gait Questionnaire (NFOG-Q) and the Trail Making Test (TMT-B). Results. Mini-BEST scores and the TMT-B improved in both groups after training (P = .001), irrespective of study arm and FOG subgroup. However, gains were not retained at 6 months. Both FOG+ and FOG- had a greater reduction of falls after TT + VR compared with TT (P = .008). NFOG-Q scores did not change after both training modes in the FOG+ group. Conclusions. Treadmill walking (with or without VR) improved postural instability in both FOG+ and FOG-, while controlling for disease severity differences. As found previously, TT + VR reduced falls more than TT alone, even among those with FOG. Interestingly, FOG itself was not helped by training, suggesting that although postural instability, falls and FOG are related, they may be controlled by different mechanisms.
- Published
- 2020
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