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

      other
      1 , 2 , , 3 , 4 , 5 , 6 , 1 , 2 , 7 , 8 , 1 , 2 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 13 , 17 , 18 , 19 , 1 , 20 , 3 , 21 , 22 , 9 , 10 , 11 , 8 , 23 , 8 , 24 , 25 , 26 , 19 , 4 , 1 , 20 , 15 , 16 , 3 , 21 , 9 , 10 , 11 , 21 , 1 , 20 , 23 , 3 , 27 , 3 , 22 , 17 , 18 , 22 , 18 , 6 , 5 , 1 , 2 , 28 , 29 , 15 , 16 , 4 , 25 , 21 , 1 , 2 , 25 , 19 , 23 , 3 , 7 , 3 , 7
      BMJ Open
      BMJ Publishing Group
      multiple sclerosis, parkinson-s disease, hip, chronic airways disease, heart failure

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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).

          Related collections

          Most cited references54

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          The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.

          To develop a 10-minute cognitive screening tool (Montreal Cognitive Assessment, MoCA) to assist first-line physicians in detection of mild cognitive impairment (MCI), a clinical state that often progresses to dementia. Validation study. A community clinic and an academic center. Ninety-four patients meeting MCI clinical criteria supported by psychometric measures, 93 patients with mild Alzheimer's disease (AD) (Mini-Mental State Examination (MMSE) score > or =17), and 90 healthy elderly controls (NC). The MoCA and MMSE were administered to all participants, and sensitivity and specificity of both measures were assessed for detection of MCI and mild AD. Using a cutoff score 26, the MMSE had a sensitivity of 18% to detect MCI, whereas the MoCA detected 90% of MCI subjects. In the mild AD group, the MMSE had a sensitivity of 78%, whereas the MoCA detected 100%. Specificity was excellent for both MMSE and MoCA (100% and 87%, respectively). MCI as an entity is evolving and somewhat controversial. The MoCA is a brief cognitive screening tool with high sensitivity and specificity for detecting MCI as currently conceptualized in patients performing in the normal range on the MMSE.
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            MDS clinical diagnostic criteria for Parkinson's disease.

            This document presents the Movement Disorder Society Clinical Diagnostic Criteria for Parkinson's disease (PD). The Movement Disorder Society PD Criteria are intended for use in clinical research but also may be used to guide clinical diagnosis. The benchmark for these criteria is expert clinical diagnosis; the criteria aim to systematize the diagnostic process, to make it reproducible across centers and applicable by clinicians with less expertise in PD diagnosis. Although motor abnormalities remain central, increasing recognition has been given to nonmotor manifestations; these are incorporated into both the current criteria and particularly into separate criteria for prodromal PD. Similar to previous criteria, the Movement Disorder Society PD Criteria retain motor parkinsonism as the core feature of the disease, defined as bradykinesia plus rest tremor or rigidity. Explicit instructions for defining these cardinal features are included. After documentation of parkinsonism, determination of PD as the cause of parkinsonism relies on three categories of diagnostic features: absolute exclusion criteria (which rule out PD), red flags (which must be counterbalanced by additional supportive criteria to allow diagnosis of PD), and supportive criteria (positive features that increase confidence of the PD diagnosis). Two levels of certainty are delineated: clinically established PD (maximizing specificity at the expense of reduced sensitivity) and probable PD (which balances sensitivity and specificity). The Movement Disorder Society criteria retain elements proven valuable in previous criteria and omit aspects that are no longer justified, thereby encapsulating diagnosis according to current knowledge. As understanding of PD expands, the Movement Disorder Society criteria will need continuous revision to accommodate these advances.
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              Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS).

              J. Kurtzke (1983)
              One method of evaluating the degree of neurologic impairment in MS has been the combination of grades (0 = normal to 5 or 6 = maximal impairment) within 8 Functional Systems (FS) and an overall Disability Status Scale (DSS) that had steps from 0 (normal) to 10 (death due to MS). A new Expanded Disability Status Scale (EDSS) is presented, with each of the former steps (1,2,3 . . . 9) now divided into two (1.0, 1.5, 2.0 . . . 9.5). The lower portion is obligatorily defined by Functional System grades. The FS are Pyramidal, Cerebellar, Brain Stem, Sensory, Bowel & Bladder, Visual, Cerebral, and Other; the Sensory and Bowel & Bladder Systems have been revised. Patterns of FS and relations of FS by type and grade to the DSS are demonstrated.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2021
                2 December 2021
                : 11
                : 12
                : e050785
                Affiliations
                [1 ]departmentINSIGNEO Institute for in silico Medicine , The University of Sheffield , Sheffield, UK
                [2 ]departmentDepartment of Mechanical Engineering , The University of Sheffield , Sheffield, UK
                [3 ]departmentTranslational and Clinical Research Institute, Faculty of Medical Sciences , Newcastle University , Newcastle upon Tyne, UK
                [4 ]departmentLaboratory of Movement Analysis and Measurement , Ecole Polytechnique Federale de Lausanne , Lausanne, Switzerland
                [5 ]Robert Bosch Gesellschaft für Medizinische Forschung , Stuttgart, Germany
                [6 ]departmentDepartment of Biomedical Sciences , University of Sassari , Sassari, Sardegna, Italy
                [7 ]The Newcastle Upon Tyne Hospitals NHS Foundation Trust , Newcastle Upon Tyne, UK
                [8 ]departmentCenter for the Study of Movement, Cognition and Mobility, Neurological Institute , Tel Aviv Sourasky Medical Center , Tel Aviv, Israel
                [9 ]ISGlobal , Barcelona, Spain
                [10 ]Universitat Pompeu Fabra (UPF) , Barcelona, Spain
                [11 ]CIBER Epidemiología y Salud Pública (CIBERESP) , Madrid, Spain
                [12 ]IMIM (Hospital del Mar Medical Research Institute) , Barcelona, Spain
                [13 ]departmentDipartimento di Elettronica e Telecomunicazioni , Politecnico di Torino , Torino, Italy
                [14 ]departmentPolitoBIOMed Lab – Biomedical Engineering Lab , Politecnico di Torino , Torino, Italy
                [15 ]departmentInsight Centre for Data Analytics, O’Brien Science Centre , University College Dublin , Dublin, Ireland
                [16 ]departmentUCD School of Public Health, Physiotherapy and Sports Science , University College Dublin , Dublin, Ireland
                [17 ]departmentDepartment of Electrical, Electronic and Information Engineering «Guglielmo Marconi» , University of Bologna , Bologna, Italy
                [18 ]departmentHealth Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV) , University of Bologna , Bologna, Italy
                [19 ]departmentDepartment of Sport, Exercise and Rehabilitation , Northumbria University Newcastle , Newcastle upon Tyne, UK
                [20 ]departmentDepartment of Computer Science , The University of Sheffield , Sheffield, UK
                [21 ]departmentMachine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering , Friedrich-Alexander-Universität Erlangen-Nürnberg , Erlangen, Germany
                [22 ]McRoberts BV , Den Haag, Zuid-Holland, Netherlands
                [23 ]departmentDepartment of Neurology , University Medical Center Schleswig-Holstein Campus Kiel , Kiel, Germany
                [24 ]departmentDepartment of Physical Therapy, Sackler Faculty of Medicine & Sagol School of Neuroscience , Tel Aviv University , Tel Aviv, Israel
                [25 ]departmentDepartment of Neuromedicine and Movement Science , Norwegian University of Science and Technology , Trondheim, Norway
                [26 ]departmentSchool of Computing , Newcastle University , Newcastle upon Tyne, UK
                [27 ]departmentNovartis Institutes of Biomedical Research , Novartis Pharma AG , Basel, Switzerland
                [28 ]departmentDepartment of Neuroscience and Sheffield NIHR Translational Neuroscience BRC , Sheffield Teaching Hospitals NHS Foundation Trust , Sheffield, UK
                [29 ]departmentDigital Health R&D , AstraZeneca Sweden , Sodertalje, Sweden
                Author notes
                [Correspondence to ] Professor Claudia Mazzà; c.mazza@ 123456sheffield.ac.uk
                Author information
                http://orcid.org/0000-0002-5215-1746
                http://orcid.org/0000-0002-0968-6286
                http://orcid.org/0000-0003-3981-977X
                http://orcid.org/0000-0003-4813-3868
                Article
                bmjopen-2021-050785
                10.1136/bmjopen-2021-050785
                8640671
                34857567
                c0c2a164-0e29-4866-b29f-04dec0de12b9
                © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 02 March 2021
                : 28 October 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100010767, Innovative Medicines Initiative;
                Award ID: IMI22017-13-7-820820
                Categories
                Diagnostics
                1506
                1689
                Protocol
                Custom metadata
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                Medicine
                multiple sclerosis,parkinson-s disease,hip,chronic airways disease,heart failure
                Medicine
                multiple sclerosis, parkinson-s disease, hip, chronic airways disease, heart failure

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