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Ilg W, Milne S, Schmitz-Hübsch T, Alcock L, Beichert L, Bertini E, Mohamed Ibrahim N, Dawes H, Gomez CM, Hanagasi H, Kinnunen KM, Minnerop M, Németh AH, Newman J, Ng YS, Rentz C, Samanci B, Shah VV, Summa S, Vasco G, McNames J, Horak FB. Quantitative Gait and Balance Outcomes for Ataxia Trials: Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers. CEREBELLUM (LONDON, ENGLAND) 2024; 23:1566-1592. [PMID: 37955812 PMCID: PMC11269489 DOI: 10.1007/s12311-023-01625-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/20/2023] [Indexed: 11/14/2023]
Abstract
With disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid, finely granulated, digital health measures are highly warranted to augment clinical and patient-reported outcome measures. Gait and balance disturbances most often present as the first signs of degenerative cerebellar ataxia and are the most reported disabling features in disease progression. Thus, digital gait and balance measures constitute promising and relevant performance outcomes for clinical trials.This narrative review with embedded consensus will describe evidence for the sensitivity of digital gait and balance measures for evaluating ataxia severity and progression, propose a consensus protocol for establishing gait and balance metrics in natural history studies and clinical trials, and discuss relevant issues for their use as performance outcomes.
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Affiliation(s)
- Winfried Ilg
- Section Computational Sensomotorics, Hertie Institute for Clinical Brain Research, Otfried-Müller-Straße 25, 72076, Tübingen, Germany.
- Centre for Integrative Neuroscience (CIN), Tübingen, Germany.
| | - Sarah Milne
- Bruce Lefroy Centre for Genetic Health Research, Murdoch Children's Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, Melbourne University, Melbourne, VIC, Australia
- Physiotherapy Department, Monash Health, Clayton, VIC, Australia
- School of Primary and Allied Health Care, Monash University, Frankston, VIC, Australia
| | - Tanja Schmitz-Hübsch
- Experimental and Clinical Research Center, a cooperation of Max-Delbrueck Center for Molecular Medicine and Charité, Universitätsmedizin Berlin, Berlin, Germany
- Neuroscience Clinical Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- NIHR Newcastle Biomedical Research Centre, Newcastle University, Newcastle upon Tyne, UK
| | - Lukas Beichert
- Department of Neurodegenerative Diseases and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Enrico Bertini
- Research Unit of Neuromuscular and Neurodegenerative Disorders, Bambino Gesu' Children's Research Hospital, IRCCS, Rome, Italy
| | | | - Helen Dawes
- NIHR Exeter BRC, College of Medicine and Health, University of Exeter, Exeter, UK
| | | | - Hasmet Hanagasi
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | | | - Martina Minnerop
- Institute of Neuroscience and Medicine (INM-1)), Research Centre Juelich, Juelich, Germany
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Andrea H Németh
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jane Newman
- NIHR Newcastle Biomedical Research Centre, Newcastle University, Newcastle upon Tyne, UK
- Wellcome Centre for Mitochondrial Research, Newcastle University, Newcastle upon Tyne, UK
| | - Yi Shiau Ng
- Wellcome Centre for Mitochondrial Research, Newcastle University, Newcastle upon Tyne, UK
| | - Clara Rentz
- Institute of Neuroscience and Medicine (INM-1)), Research Centre Juelich, Juelich, Germany
| | - Bedia Samanci
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Vrutangkumar V Shah
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
- APDM Precision Motion, Clario, Portland, OR, USA
| | - Susanna Summa
- Movement Analysis and Robotics Laboratory (MARLab), Neurorehabilitation Unit, Neurological Science and Neurorehabilitation Area, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Gessica Vasco
- Movement Analysis and Robotics Laboratory (MARLab), Neurorehabilitation Unit, Neurological Science and Neurorehabilitation Area, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - James McNames
- APDM Precision Motion, Clario, Portland, OR, USA
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR, USA
| | - Fay B Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
- APDM Precision Motion, Clario, Portland, OR, USA
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Ali F, Hogen CA, Miller EJ, Kaufman KR. Validation of Pelvis and Trunk Range of Motion as Assessed Using Inertial Measurement Units. Bioengineering (Basel) 2024; 11:659. [PMID: 39061741 PMCID: PMC11273649 DOI: 10.3390/bioengineering11070659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/13/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024] Open
Abstract
Trunk and pelvis range of motion (ROM) is essential to perform activities of daily living. The ROM may become limited with aging or with neuromusculoskeletal disorders. Inertial measurement units (IMU) with out-of-the box software solutions are increasingly being used to assess motion. We hypothesize that the accuracy (validity) and reliability (consistency) of the trunk and pelvis ROM during steady-state gait in normal individuals as measured using the Opal APDM 6 sensor IMU system and calculated using Mobility Lab version 4 software will be comparable to a gold-standard optoelectric motion capture system. Thirteen healthy young adults participated in the study. Trunk ROM, measured using the IMU was within 5-7 degrees of the motion capture system for all three planes and within 10 degrees for pelvis ROM. We also used a triad of markers mounted on the sternum and sacrum IMU for a head-to-head comparison of trunk and pelvis ROM. The IMU measurements were within 5-10 degrees of the triad. A greater variability of ROM measurements was seen for the pelvis in the transverse plane. IMUs and their custom software provide a valid and reliable measurement for trunk and pelvis ROM in normal individuals, and important considerations for future applications are discussed.
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Affiliation(s)
- Farwa Ali
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Cecilia A. Hogen
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55905, USA; (C.A.H.); (E.J.M.); (K.R.K.)
| | - Emily J. Miller
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55905, USA; (C.A.H.); (E.J.M.); (K.R.K.)
| | - Kenton R. Kaufman
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55905, USA; (C.A.H.); (E.J.M.); (K.R.K.)
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Voisard C, de l'Escalopier N, Ricard D, Oudre L. Automatic gait events detection with inertial measurement units: healthy subjects and moderate to severe impaired patients. J Neuroeng Rehabil 2024; 21:104. [PMID: 38890696 PMCID: PMC11184826 DOI: 10.1186/s12984-024-01405-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 06/11/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases. METHODS We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal. RESULTS We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3-13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8-39] ms) and 26 ms (IQR [12-50] ms). CONCLUSIONS Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.
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Affiliation(s)
- Cyril Voisard
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Gif-sur-Yvette, France.
- Service de Neurologie, Service de Santé des Armées, HIA Percy, Clamart, France.
| | - Nicolas de l'Escalopier
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Paris, France
- Service de Chirurgie Orthopédique, Traumatologique et Réparatrice des Membres, Service de Santé des Armées, HIA Percy, Clamart, France
| | - Damien Ricard
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Paris, France
- Service de Neurologie, Service de Santé des Armées, HIA Percy, Clamart, France
- Ecole du Val-de-Grâce, Service de Santé des Armées, Paris, France
| | - Laurent Oudre
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Gif-sur-Yvette, France
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Warmerdam E, Wolff C, Orth M, Pohlemann T, Ganse B. Long-term continuous instrumented insole-based gait analyses in daily life have advantages over longitudinal gait analyses in the lab to monitor healing of tibial fractures. Front Bioeng Biotechnol 2024; 12:1355254. [PMID: 38497053 PMCID: PMC10940326 DOI: 10.3389/fbioe.2024.1355254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 02/15/2024] [Indexed: 03/19/2024] Open
Abstract
Introduction: Monitoring changes in gait during rehabilitation allows early detection of complications. Laboratory-based gait analyses proved valuable for longitudinal monitoring of lower leg fracture healing. However, continuous gait data recorded in the daily life may be superior due to a higher temporal resolution and differences in behavior. In this study, ground reaction force-based gait data of instrumented insoles from longitudinal intermittent laboratory assessments were compared to monitoring in daily life. Methods: Straight walking data of patients were collected during clinical visits and in between those visits the instrumented insoles recorded all stepping activities of the patients during daily life. Results: Out of 16 patients, due to technical and compliance issues, only six delivered sufficient datasets of about 12 weeks. Stance duration was longer (p = 0.004) and gait was more asymmetric during daily life (asymmetry of maximal force p < 0.001, loading slope p = 0.001, unloading slope p < 0.001, stance duration p < 0.001). Discussion: The differences between the laboratory assessments and the daily-life monitoring could be caused by a different and more diverse behavior during daily life. The daily life gait parameters significantly improved over time with union. One of the patients developed an infected non-union and showed worsening of force-related gait parameters, which was earlier detectable in the continuous daily life gait data compared to the lab data. Therefore, continuous gait monitoring in the daily life has potential to detect healing problems early on. Continuous monitoring with instrumented insoles has advantages once technical and compliance problems are solved.
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Affiliation(s)
- Elke Warmerdam
- Werner Siemens-Endowed Chair for Innovative Implant Development (Fracture Healing), Departments and Institutes of Surgery, Saarland University, Homburg, Germany
| | - Christian Wolff
- German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany
| | - Marcel Orth
- Department of Trauma, Hand and Reconstructive Surgery, Departments and Institutes of Surgery, Saarland University, Homburg, Germany
| | - Tim Pohlemann
- Department of Trauma, Hand and Reconstructive Surgery, Departments and Institutes of Surgery, Saarland University, Homburg, Germany
| | - Bergita Ganse
- Werner Siemens-Endowed Chair for Innovative Implant Development (Fracture Healing), Departments and Institutes of Surgery, Saarland University, Homburg, Germany
- Department of Trauma, Hand and Reconstructive Surgery, Departments and Institutes of Surgery, Saarland University, Homburg, Germany
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Regev K, Eren N, Yekutieli Z, Karlinski K, Massri A, Vigiser I, Kolb H, Piura Y, Karni A. Smartphone-based gait assessment for multiple sclerosis. Mult Scler Relat Disord 2024; 82:105394. [PMID: 38141562 DOI: 10.1016/j.msard.2023.105394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 12/19/2023] [Indexed: 12/25/2023]
Abstract
INTRODUCTION Multiple Sclerosis causes gait alteration, even in the early stages of the disease. Traditional methods to quantify gait impairment, such as performance-based measures, lab-based motion analyses, and self-report, have limited ecological relevance. The Mon4t® app is a digital tool that uses sensors embedded in standard smartphones to measure various gait parameters. OBJECTIVES To evaluate the use of Mon4t® technology in monitoring MS patients. METHODS 100 MS patients and age-matched healthy controls were evaluated using both a human rater and the Mon4t Clinic™ app. Three motor tasks were performed: 3m Timed up and go test (TUG), 10m TUG, and tandem walk. The digital markers were used to compare MS vs. HC, MS with EDSS=0 vs. HC, and MS with EDSS=0 vs. MS with EDSS>0. Within the MS EDSS>0 group, correlations between digital gait markers and the EDSS score were calculated. RESULTS Significant differences were found between MS patients and HC in multiple gait parameters. When comparing MS patients with minimal disability (EDSS=0) and HC: On the 3m TUG task, MS patients took longer to complete the task (mean difference 0.167seconds, p =0.034), took more steps (mean difference 1.32 steps, p =0.003), and had a weaker ML step-to-step correlation (mean difference 0.1, p = 0.001). The combination of features from the three motor tasks allowed distinguishing a nondisabled MS patient from a HC with high confidence (AUC of 85.65 on the ROC). When comparing MS patients with minimal disability (EDSS=0) to those with higher disability (EDSS>0): On the tandem walk task, patients with EDSS>0 took significantly longer to complete 10 steps than those with EDSS=0 (mean difference 4.63 seconds, p < 0.001), showed greater ML sway (mean difference 0.2, p < 0.001), and had larger angular velocity in the SI axis on average (mean difference 2.31 degrees/sec, p = 0.01). A classification model achieved 81.79 ROC AUC. In the subgroup of patients with EDSS>0, gait features significantly correlated with EDSS score in all three tasks. CONCLUSION The findings demonstrate the potential of digital gait assessment to augment traditional disease monitoring and support clinical decision making. The Mon4t® app provides a convenient and ecologically relevant tool for monitoring MS patients and detecting early changes in gait impairment.
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Affiliation(s)
- Keren Regev
- Neuroimmunology and Multiple Sclerosis Unit, Neurology Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | | | | | | | - Ashraf Massri
- Department of Rehabilitation, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Ifat Vigiser
- Neuroimmunology and Multiple Sclerosis Unit, Neurology Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Hadar Kolb
- Neuroimmunology and Multiple Sclerosis Unit, Neurology Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Yoav Piura
- Department of Neurology, Assuta Ashdod Medical Center, Ashdod, Israel
| | - Arnon Karni
- Neuroimmunology and Multiple Sclerosis Unit, Neurology Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
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Mason R, Barry G, Robinson H, O'Callaghan B, Lennon O, Godfrey A, Stuart S. Validity and reliability of the DANU sports system for walking and running gait assessment. Physiol Meas 2023; 44:115001. [PMID: 37852268 DOI: 10.1088/1361-6579/ad04b4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/18/2023] [Indexed: 10/20/2023]
Abstract
Objective. Gait assessments have traditionally been analysed in laboratory settings, but this may not reflect natural gait. Wearable technology may offer an alternative due to its versatility. The purpose of the study was to establish the validity and reliability of temporal gait outcomes calculated by the DANU sports system, against a 3D motion capture reference system.Approach. Forty-one healthy adults (26 M, 15 F, age 36.4 ± 11.8 years) completed a series of overground walking and jogging trials and 60 s treadmill walking and running trials at various speeds (8-14 km hr-1), participants returned for a second testing session to repeat the same testing.Main results. For validity, 1406 steps and 613 trials during overground and across all treadmill trials were analysed respectively. Temporal outcomes generated by the DANU sports system included ground contact time, swing time and stride time all demonstrated excellent agreement compared to the laboratory reference (intraclass correlation coefficient (ICC) > 0.900), aside from ground contact time during overground jogging which had good agreement (ICC = 0.778). For reliability, 666 overground and 511 treadmill trials across all speeds were examined. Test re-test agreement was excellent for all outcomes across treadmill trials (ICC > 0.900), except for swing time during treadmill walking which had good agreement (ICC = 0.886). Overground trials demonstrated moderate to good test re-test agreement (ICC = 0.672-0.750), which may be due to inherent variability of self-selected (rather than treadmill set) pacing between sessions.Significance. Overall, this study showed that temporal gait outcomes from the DANU Sports System had good to excellent validity and moderate to excellent reliability in healthy adults compared to an established laboratory reference.
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Affiliation(s)
- Rachel Mason
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Gillian Barry
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | | | | | | | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcasle upon Tyne, United Kingdom
| | - Samuel Stuart
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States of America
- Northumbria Healthcare NHS Foundation Trust, North Shields, United Kingdom
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Buekers J, Megaritis D, Koch S, Alcock L, Ammour N, Becker C, Bertuletti S, Bonci T, Brown P, Buckley E, Buttery SC, Caulfied B, Cereatti A, Chynkiamis N, Demeyer H, Echevarria C, Frei A, Hansen C, Hausdorff JM, Hopkinson NS, Hume E, Kuederle A, Maetzler W, Mazzà C, Micó-Amigo EM, Mueller A, Palmerini L, Salis F, Scott K, Troosters T, Vereijken B, Watz H, Rochester L, Del Din S, Vogiatzis I, Garcia-Aymerich J. Laboratory and free-living gait performance in adults with COPD and healthy controls. ERJ Open Res 2023; 9:00159-2023. [PMID: 37753279 PMCID: PMC10518872 DOI: 10.1183/23120541.00159-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 06/29/2023] [Indexed: 09/28/2023] Open
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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Affiliation(s)
- Joren Buekers
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Dimitrios Megaritis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Sarah Koch
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Health and Care Research Newcastle Biomedical Research Centre, Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Nadir Ammour
- Clinical Science and Operations, GlobalDevelopment, Sanofi R&D, Chilly-Mazarin, France
| | - Clemens Becker
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Tecla Bonci
- Department of Mechanical Engineering and INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Philip Brown
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Ellen Buckley
- Department of Mechanical Engineering and INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Sara C. Buttery
- National Lung and Heart Institute, Imperial College, London, UK
| | - Brian Caulfied
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Andrea Cereatti
- Polytechnic University of Torino, Department of Electronics and Telecommunications, Turin, Italy
| | - Nikolaos Chynkiamis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
- Thorax Research Foundation and First Department of Respiratory Medicine, National and Kapodistrian University of Athens, Sotiria General Chest Hospital, Athens, Greece
| | - Heleen Demeyer
- KU Leuven, Department of Rehabilitation Sciences and Pulmonary Rehabilitation, Respiratory Division, University Hospital Gasthuisberg, Leuven, Belgium
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium
| | - Carlos Echevarria
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Anja Frei
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Clint Hansen
- Department of Neurology, University Hospital Schleswig-Holstein and Kiel University, Kiel, Germany
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience and Department of Physical Therapy, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer's Disease Center and Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | | | - Emily Hume
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Arne Kuederle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Walter Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein and Kiel University, Kiel, Germany
| | - Claudia Mazzà
- Department of Mechanical Engineering and INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Encarna M. Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Arne Mueller
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kirsty Scott
- Department of Mechanical Engineering and INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Thierry Troosters
- KU Leuven, Department of Rehabilitation Sciences and Pulmonary Rehabilitation, Respiratory Division, University Hospital Gasthuisberg, Leuven, Belgium
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Henrik Watz
- Pulmonary Research Institute at LungenClinic Grosshansdorf, Airway Research Center North, German Center for Lung Research (DZL), Grosshansdorf, Germany
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Health and Care Research Newcastle Biomedical Research Centre, Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Health and Care Research Newcastle Biomedical Research Centre, Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
- Thorax Research Foundation and First Department of Respiratory Medicine, National and Kapodistrian University of Athens, Sotiria General Chest Hospital, Athens, Greece
| | - Judith Garcia-Aymerich
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
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Hafer JF, Vitali R, Gurchiek R, Curtze C, Shull P, Cain SM. Challenges and advances in the use of wearable sensors for lower extremity biomechanics. J Biomech 2023; 157:111714. [PMID: 37423120 PMCID: PMC10529245 DOI: 10.1016/j.jbiomech.2023.111714] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/23/2023] [Accepted: 07/03/2023] [Indexed: 07/11/2023]
Abstract
The use of wearable sensors for the collection of lower extremity biomechanical data is increasing in popularity, in part due to the ease of collecting data and the ability to capture movement outside of traditional biomechanics laboratories. Consequently, an increasing number of researchers are facing the challenges that come with utilizing the data captured by wearable sensors. These challenges include identifying/calculating meaningful measures from unfamiliar data types (measures of acceleration and angular velocity instead of positions and joint angles), defining sensor-to-segment alignments for calculating traditional biomechanics metrics, using reduced sensor sets and machine learning to predict unmeasured signals, making decisions about when and how to make algorithms freely available, and developing or replicating methods to perform basic processing tasks such as recognizing activities of interest or identifying gait events. In this perspective article, we present our own approaches to common challenges in lower extremity biomechanics research using wearable sensors and share our perspectives on approaching several of these challenges. We present these perspectives with examples that come mostly from gait research, but many of the concepts also apply to other contexts where researchers may use wearable sensors. Our goal is to introduce common challenges to new users of wearable sensors, and to promote dialogue amongst experienced users towards best practices.
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Affiliation(s)
- Jocelyn F Hafer
- Department of Kinesiology and Applied Physiology, University of Delaware, Newark, DE, United States.
| | - Rachel Vitali
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, United States
| | - Reed Gurchiek
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Carolin Curtze
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, United States
| | - Peter Shull
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, China
| | - Stephen M Cain
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, United States
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9
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Woelfle T, Bourguignon L, Lorscheider J, Kappos L, Naegelin Y, Jutzeler CR. Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective. J Med Internet Res 2023; 25:e44428. [PMID: 37498655 PMCID: PMC10415952 DOI: 10.2196/44428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/19/2022] [Accepted: 05/04/2023] [Indexed: 07/28/2023] Open
Abstract
BACKGROUND Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains. OBJECTIVE This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades. METHODS We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms "multiple sclerosis" and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. RESULTS Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good. CONCLUSIONS Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS. TRIAL REGISTRATION PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249.
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Affiliation(s)
- Tim Woelfle
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Lucie Bourguignon
- Department of Health Sciences and Technology, ETH Zurich, Zürich, Switzerland
| | - Johannes Lorscheider
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Ludwig Kappos
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Yvonne Naegelin
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
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10
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Micó-Amigo ME, Bonci T, Paraschiv-Ionescu A, Ullrich M, Kirk C, Soltani A, Küderle A, Gazit E, Salis F, Alcock L, Aminian K, Becker C, Bertuletti S, Brown P, Buckley E, Cantu A, Carsin AE, Caruso M, Caulfield B, Cereatti A, Chiari L, D'Ascanio I, Eskofier B, Fernstad S, Froehlich M, Garcia-Aymerich J, Hansen C, Hausdorff JM, Hiden H, Hume E, Keogh A, Kluge F, Koch S, Maetzler W, Megaritis D, Mueller A, Niessen M, Palmerini L, Schwickert L, Scott K, Sharrack B, Sillén H, Singleton D, Vereijken B, Vogiatzis I, Yarnall AJ, Rochester L, Mazzà C, Del Din S. Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium. J Neuroeng Rehabil 2023; 20:78. [PMID: 37316858 PMCID: PMC10265910 DOI: 10.1186/s12984-023-01198-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 05/26/2023] [Indexed: 06/16/2023] Open
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 < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. CONCLUSIONS Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987.
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Affiliation(s)
- M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Tecla Bonci
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Abolfazl Soltani
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Clemens Becker
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Stefano Bertuletti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Philip Brown
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Ellen Buckley
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Alma Cantu
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Anne-Elie Carsin
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Marco Caruso
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Ilaria D'Ascanio
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy
| | - Bjoern Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sara Fernstad
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | | | - Judith Garcia-Aymerich
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience and Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Hugo Hiden
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Emily Hume
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Dimitrios Megaritis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Arne Mueller
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | | | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Lars Schwickert
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Kirsty Scott
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | | | - David Singleton
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Alison J Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
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11
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Meyer BM, Cohen JG, Donahue N, Fox SR, O'Leary A, Brown AJ, Leahy C, VanDyk T, DePetrillo P, Ceruolo M, Cheney N, Solomon AJ, McGinnis RS. Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons With Multiple Sclerosis. IEEE Trans Neural Syst Rehabil Eng 2023; 31:2132-2139. [PMID: 37067975 PMCID: PMC10408383 DOI: 10.1109/tnsre.2023.3267807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Typical assessments of balance impairment are subjective or require data from cumbersome and expensive force platforms. Researchers have utilized lower back (sacrum) accelerometers to enable more accessible, objective measurement of postural sway for use in balance assessment. However, new sensor patches are broadly being deployed on the chest for cardiac monitoring, opening a need to determine if measurements from these devices can similarly inform balance assessment. Our aim in this work is to validate postural sway measurements from a chest accelerometer. To establish concurrent validity, we considered data from 16 persons with multiple sclerosis (PwMS) asked to stand on a force platform while also wearing sensor patches on the sacrum and chest. We found five of 15 postural sway features derived from the chest and sacrum were significantly correlated with force platform-derived features, which is in line with prior sacrum-derived findings. Clinical significance was established using a sample of 39 PwMS who performed eyes-open, eyes-closed, and tandem standing tasks. This cohort was stratified by fall status and completed several patient-reported measures (PRM) of balance and mobility impairment. We also compared sway features derived from a single 30-second period to those derived from a one-minute period with a sliding window to create individualized distributions of each postural sway feature (ID method). We find traditional computation of sway features from the chest is sensitive to changes in PRMs and task differences. Distribution characteristics from the ID method establish additional relationships with PRMs, detect differences in more tasks, and distinguish between fall status groups. Overall, the chest was found to be a valid location to monitor postural sway and we recommend utilizing the ID method over single-observation analyses.
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12
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Jung S, de l’Escalopier N, Oudre L, Truong C, Dorveaux E, Gorintin L, Ricard D. A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment. SENSORS (BASEL, SWITZERLAND) 2023; 23:4000. [PMID: 37112339 PMCID: PMC10145775 DOI: 10.3390/s23084000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/03/2023] [Accepted: 04/13/2023] [Indexed: 06/19/2023]
Abstract
This paper presents a novel approach to creating a graphical summary of a subject's activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time series collected while monitoring patients in Semi Free-Living Environments are often long and complex, our contribution relies on an innovative pipeline of signal processing methods and machine learning algorithms. Once learned, the graphical representation is able to sum up all activities present in the data and can quickly be applied to newly acquired time series. In a nutshell, raw data from inertial measurement units are first segmented into homogeneous regimes with an adaptive change-point detection procedure, then each segment is automatically labeled. Then, features are extracted from each regime, and lastly, a score is computed using these features. The final visual summary is constructed from the scores of the activities and their comparisons to healthy models. This graphical output is a detailed, adaptive, and structured visualization that helps better understand the salient events in a complex gait protocol.
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Affiliation(s)
- Sylvain Jung
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France
- Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430 Villetaneuse, France
- AbilyCare, 130 Rue de Lourmel, F-75015 Paris, France
- ENGIE Lab CRIGEN, F-93249 Stains, France
| | - Nicolas de l’Escalopier
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, France
- Service de Neurologie, Service de Santé des Armées, HIA Percy, F-92190 Clamart, France
| | - Laurent Oudre
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France
| | - Charles Truong
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France
| | - Eric Dorveaux
- AbilyCare, 130 Rue de Lourmel, F-75015 Paris, France
| | - Louis Gorintin
- Novakamp, 10-12 Avenue du Bosquet, F-95560 Baillet en France, France
| | - Damien Ricard
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, France
- Service de Neurologie, Service de Santé des Armées, HIA Percy, F-92190 Clamart, France
- Ecole du Val-de-Grâce, Service de Santé des Armées, F-75005 Paris, France
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13
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da Rosa Tavares JE, Ullrich M, Roth N, Kluge F, Eskofier BM, Gaßner H, Klucken J, Gladow T, Marxreiter F, da Costa CA, da Rosa Righi R, Victória Barbosa JL. uTUG: An unsupervised Timed Up and Go test for Parkinson’s disease. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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14
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Cuerda-Ballester M, Martínez-Rubio D, García-Pardo MP, Proaño B, Cubero L, Calvo-Capilla A, Sancho-Cantus D, de la Rubia Ortí JE. Relationship of Motor Impairment with Cognitive and Emotional Alterations in Patients with Multiple Sclerosis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1387. [PMID: 36674140 PMCID: PMC9864158 DOI: 10.3390/ijerph20021387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 06/17/2023]
Abstract
Introduction. Multiple sclerosis (MS) is a neurodegenerative disease that, despite mainly affecting women, is more severe in men and causes motor, cognitive and emotional alterations. The objective of this study was to determine the possible relationship between motor, cognitive and emotional alterations. Materials and Methods. This is a descriptive, observational and cross-sectional study, with 67 patients with MS (20 men and 47 women), who were given the following questionnaires: Expanded Disability Status Scale (EDSS), Two-Minute Walk Test (2MWT), Berg Balance Scale, Beck’s Depression Inventory (BDI-II), State-Trait Anxiety Inventory (STAI) and Prefrontal Symptoms Inventory (PSI) to analyze their cognitive level, body mass index (BMI) and percentage of muscle mass. In addition, regression analysis was conducted to study the relationship among variables. Results. No significant differences were found between men and women in any of the variables. Regarding the relationship between parameters, the regression analysis was statistically significant, showing an effect of age on the walking and balance performance (β ≅ −0.4, p < 0.05); in addition, there was a relationship between 2MWT and STAI A/S, indicating that both older age and a high anxiety state could impact walking performance. On the other hand, prefrontal symptoms showed moderate relationships with both anxiety and depression (β ≅ 0.6, p < 0.05); thus, high levels of anxiety and depression could increase prefrontal alterations. Conclusions. There is a relationship between motor and emotional variables. Specifically, state anxiety is related to walking resistance. No relationship was found between depression and cognitive alteration and balance or walking ability. Only age has an effect in these relationships.
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Affiliation(s)
- María Cuerda-Ballester
- Doctoral School, Catholic University of Valencia San Vicente Mártir, 46001 Valencia, Spain
| | - David Martínez-Rubio
- Psicoforma Integral Psychology Center, 46001 Valencia, Spain
- Department of Psychology, European University of Valencia, 46010 Valencia, Spain
| | - María Pilar García-Pardo
- Department of Psychology and Sociology, University of Zaragoza, Campus Teruel, 44003 Teruel, Spain
| | - Belén Proaño
- Department of Basic Medical Sciences, Catholic University of Valencia, 46001 Valencia, Spain
| | - Laura Cubero
- Department of Basic Medical Sciences, Catholic University of Valencia, 46001 Valencia, Spain
| | - Antonio Calvo-Capilla
- Department of Medicine and Animal Surgery, Catholic University of Valencia, 46001 Valencia, Spain
| | - David Sancho-Cantus
- Department of Basic Medical Sciences, Catholic University of Valencia, 46001 Valencia, Spain
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Connecting real-world digital mobility assessment to clinical outcomes for regulatory and clinical endorsement-the Mobilise-D study protocol. PLoS One 2022; 17:e0269615. [PMID: 36201476 PMCID: PMC9536536 DOI: 10.1371/journal.pone.0269615] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 06/17/2022] [Indexed: 11/19/2022] Open
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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16
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Baroudi L, Yan X, Newman MW, Barton K, Cain SM, Shorter KA. Investigating walking speed variability of young adults in the real world. Gait Posture 2022; 98:69-77. [PMID: 36057208 DOI: 10.1016/j.gaitpost.2022.08.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/10/2022] [Accepted: 08/15/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Walking speed strongly correlates with health outcomes, making accurate assessment essential for clinical evaluations. However, assessments tend to be conducted over short distances, often in a laboratory or clinical setting, and may not capture natural walking behavior. To address this gap, the following questions are investigated in this work: Is walking speed significantly influenced by the continuity and duration of a walking bout? Can preferred walking speed be inferred by grouping walking bouts using duration and continuity? METHODS We collected two weeks of continuous data from fifteen healthy young adults using a thigh-worn accelerometer and a heart rate monitor. Walking strides were identified and grouped into walking periods. We quantified the duration and the continuity of each walking period. Continuity is used to parameterize changes in stepping rate related to pauses during a bout of walking. Finally, we analyzed the influence of duration and continuity on estimates of stride speed, and examined how the distribution of walking speed varies depending on different walking modes (defined by duration and continuity). RESULTS We found that continuity and duration can be used to explain some of the variability in real-world walking speed (p<0.001). Speeds estimated from long continuous walks with many strides (42% of all recorded strides) had the lowest standard deviation. Walking speed during these bouts was 1.41ms-1 (SD = 0.26ms-1). SIGNIFICANCE Walking behavior in the real world is largely variable. Features of real-world walks, like duration and continuity, can be used to explain some of the variability observed in walking speed. As such, we recommend using long continuous walks to confidently isolate the preferred walking behavior of an individual.
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Affiliation(s)
- Loubna Baroudi
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
| | - Xinghui Yan
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Mark W Newman
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Kira Barton
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Stephen M Cain
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, USA
| | - K Alex Shorter
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
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17
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Sasaki JE, Bertochi GFA, Meneguci J, Motl RW. Pedometers and Accelerometers in Multiple Sclerosis: Current and New Applications. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11839. [PMID: 36142112 PMCID: PMC9517119 DOI: 10.3390/ijerph191811839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/13/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
Pedometers and accelerometers have become commonplace for the assessment of physical behaviors (e.g., physical activity and sedentary behavior) in multiple sclerosis (MS) research. Current common applications include the measurement of steps taken and the classification of physical activity intensity, as well as sedentary behavior, using cut-points methods. The existing knowledge and applications, coupled with technological advances, have spawned new opportunities for using those motion sensors in persons with MS, and these include the utilization of the data as biomarkers of disease severity and progression, perhaps in clinical practice. Herein, we discuss the current state of knowledge on the validity and applications of pedometers and accelerometers in MS, as well as new opportunities and strategies for the improved assessment of physical behaviors and disease progression, and consequently, personalized care.
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Affiliation(s)
- Jeffer Eidi Sasaki
- Graduate Program in Physical Education, Federal University of Triangulo Mineiro, Uberaba 38025-180, MG, Brazil
| | | | - Joilson Meneguci
- Graduate Program in Physical Education, Federal University of Triangulo Mineiro, Uberaba 38025-180, MG, Brazil
| | - Robert W. Motl
- Department of Kinesiology and Nutrition, College of Applied Health Sciences, University of Illinois Chicago, Chicago, IL 60612, USA
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18
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Meyer BM, Depetrillo P, Franco J, Donahue N, Fox SR, O’Leary A, Loftness BC, Gurchiek RD, Buckley M, Solomon AJ, Ng SK, Cheney N, Ceruolo M, McGinnis RS. How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22186982. [PMID: 36146348 PMCID: PMC9503816 DOI: 10.3390/s22186982] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/10/2022] [Accepted: 09/13/2022] [Indexed: 06/12/2023]
Abstract
Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to capture representative variability in impairment measures, are needed to balance patient burden, data quality, and study cost. Prior investigations have examined the duration required for resolving a variety of movement variables (e.g., gait speed, sit-to-stand tests), but these studies use differing methodologies and have only examined a small subset of potential measures of gait and balance impairment. Notably, postural sway measures have not yet been considered in these analyses. Here, we propose a three-level framework for examining this problem. Difference testing and intra-class correlations (ICC) are used to examine the agreement in features computed from potential wear durations (levels one and two). The association between features and established patient reported outcomes at each wear duration is also considered (level three) for determining the necessary wear duration. Utilizing wearable accelerometer data continuously collected from 22 persons with multiple sclerosis (PwMS) for 6 weeks, this framework suggests that 2 to 3 days of monitoring may be sufficient to capture most of the variability in gait and sway; however, longer periods (e.g., 3 to 6 days) may be needed to establish strong correlations to patient-reported clinical measures. Regression analysis indicates that the required wear duration depends on both the observation frequency and variability of the measure being considered. This approach provides a framework for evaluating wear duration as one aspect of the comprehensive assessment, which is necessary to ensure that wearable sensor-based methods for capturing gait and balance impairment in the free-living environment are fit for purpose.
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Affiliation(s)
- Brett M. Meyer
- M-Sense Research Group, University of Vermont, Burlington, VT 05405, USA
| | - Paolo Depetrillo
- Medidata Solutions, A Dassault Systèmes Company, New York, NY 10014, USA
| | - Jaime Franco
- Medidata Solutions, A Dassault Systèmes Company, New York, NY 10014, USA
| | - Nicole Donahue
- M-Sense Research Group, University of Vermont, Burlington, VT 05405, USA
| | - Samantha R. Fox
- M-Sense Research Group, University of Vermont, Burlington, VT 05405, USA
| | - Aisling O’Leary
- M-Sense Research Group, University of Vermont, Burlington, VT 05405, USA
| | - Bryn C. Loftness
- M-Sense Research Group, University of Vermont, Burlington, VT 05405, USA
| | - Reed D. Gurchiek
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Maura Buckley
- Medidata Solutions, A Dassault Systèmes Company, New York, NY 10014, USA
| | - Andrew J. Solomon
- Department of Neurological Sciences, University of Vermont, Burlington, VT 05405, USA
| | - Sau Kuen Ng
- Medidata Solutions, A Dassault Systèmes Company, New York, NY 10014, USA
| | - Nick Cheney
- M-Sense Research Group, University of Vermont, Burlington, VT 05405, USA
| | - Melissa Ceruolo
- Medidata Solutions, A Dassault Systèmes Company, New York, NY 10014, USA
| | - Ryan S. McGinnis
- M-Sense Research Group, University of Vermont, Burlington, VT 05405, USA
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19
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Shieh V, Zampieri C, Sansare A, Collins J, Bulea TC, Jain M. Validation of Body-Worn Sensors for Gait Analysis During a 2-min Walk Test in Children. JOURNAL FOR THE MEASUREMENT OF PHYSICAL BEHAVIOUR 2022; 5:111-119. [PMID: 37538346 PMCID: PMC10398795 DOI: 10.1123/jmpb.2021-0035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Introduction Instrumented gait mat systems have been regarded as one of the gold standard methods for measuring spatiotemporal gait parameters. However, their portable walkways confine walking to a restricted area and limit the number of gait cycles collected. Wearable inertial sensors are a potential alternative that allow more natural walking behavior and have fewer space restrictions. The objective of this pilot study was to establish the concurrent validity of body-worn sensors against the portable walkway system in older children. Methods Twenty-one participants (10 males) 7-17 years old performed 2-min walk tests at a self-selected and fast pace in a 25-m-long hallway, while wearing three inertial sensors. Data collection were synchronized between devices and the portions of the walk when subjects passed on the walkway were used to compare gait speed, stride length, gait cycle duration, cadence, and double support time. Regression models and Bland-Altman analysis were completed to determine agreement between systems for the selected gait parameters. Results Gait speed, cadence, gait cycle duration, and stride length as measured by inertial sensors demonstrated strong agreement overall. Double support time was found to have lower validity due to a combined bias of age, height, weight, and walking pace. Conclusion These results support the validity of wearable inertial sensors in measuring gait speed, cadence, gait cycle duration, and stride length in children 7 years old and above during a 2-min walking test. Future studies are warranted with a broader age range to thoroughly represent the pediatric population.
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Affiliation(s)
- Vincent Shieh
- Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Cris Zampieri
- Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Ashwini Sansare
- Department of Physical Therapy, University of Delaware, Newark, DE, USA
| | - John Collins
- Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Thomas C Bulea
- Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Minal Jain
- Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA
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20
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Mobbs RJ, Perring J, Raj SM, Maharaj M, Yoong NKM, Sy LW, Fonseka RD, Natarajan P, Choy WJ. Gait metrics analysis utilizing single-point inertial measurement units: a systematic review. Mhealth 2022; 8:9. [PMID: 35178440 PMCID: PMC8800203 DOI: 10.21037/mhealth-21-17] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 08/27/2021] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Wearable sensors, particularly accelerometers alone or combined with gyroscopes and magnetometers in an inertial measurement unit (IMU), are a logical alternative for gait analysis. While issues with intrusive and complex sensor placement limit practicality of multi-point IMU systems, single-point IMUs could potentially maximize patient compliance and allow inconspicuous monitoring in daily-living. Therefore, this review aimed to examine the validity of single-point IMUs for gait metrics analysis and identify studies employing them for clinical applications. METHODS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) were followed utilizing the following databases: PubMed; MEDLINE; EMBASE and Cochrane. Four databases were systematically searched to obtain relevant journal articles focusing on the measurement of gait metrics using single-point IMU sensors. RESULTS A total of 90 articles were selected for inclusion. Critical analysis of studies was conducted, and data collected included: sensor type(s); sensor placement; study aim(s); study conclusion(s); gait metrics and methods; and clinical application. Validation research primarily focuses on lower trunk sensors in healthy cohorts. Clinical applications focus on diagnosis and severity assessment, rehabilitation and intervention efficacy and delineating pathological subjects from healthy controls. DISCUSSION This review has demonstrated the validity of single-point IMUs for gait metrics analysis and their ability to assist in clinical scenarios. Further validation for continuous monitoring in daily living scenarios and performance in pathological cohorts is required before commercial and clinical uptake can be expected.
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Affiliation(s)
- Ralph Jasper Mobbs
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
- Department of Neurosurgery, Prince of Wales Hospital, Sydney, Australia
| | - Jordan Perring
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | | | - Monish Maharaj
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | - Nicole Kah Mun Yoong
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | - Luke Wicent Sy
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
| | - Rannulu Dineth Fonseka
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | - Pragadesh Natarajan
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
| | - Wen Jie Choy
- Faculty of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia
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21
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Atrsaei A, Hansen C, Elshehabi M, Solbrig S, Berg D, Liepelt-Scarfone I, Maetzler W, Aminian K. Effect of Fear of Falling on Mobility Measured During Lab and Daily Activity Assessments in Parkinson's Disease. Front Aging Neurosci 2021; 13:722830. [PMID: 34916920 PMCID: PMC8669821 DOI: 10.3389/fnagi.2021.722830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 10/21/2021] [Indexed: 12/24/2022] Open
Abstract
In chronic disorders such as Parkinson’s disease (PD), fear of falling (FOF) is associated with falls and reduced quality of life. With inertial measurement units (IMUs) and dedicated algorithms, different aspects of mobility can be obtained during supervised tests in the lab and also during daily activities. To our best knowledge, the effect of FOF on mobility has not been investigated in both of these settings simultaneously. Our goal was to evaluate the effect of FOF on the mobility of 26 patients with PD during clinical assessments and 14 days of daily activity monitoring. Parameters related to gait, sit-to-stand transitions, and turns were extracted from IMU signals on the lower back. Fear of falling was assessed using the Falls Efficacy Scale-International (FES-I) and the patients were grouped as with (PD-FOF+) and without FOF (PD-FOF−). Mobility parameters between groups were compared using logistic regression as well as the effect size values obtained using the Wilcoxon rank-sum test. The peak angular velocity of the turn-to-sit transition of the timed-up-and-go (TUG) test had the highest discriminative power between PD-FOF+ and PD-FOF− (r-value of effect size = 0.61). Moreover, PD-FOF+ had a tendency toward lower gait speed at home and a lower amount of walking bouts, especially for shorter walking bouts. The combination of lab and daily activity parameters reached a higher discriminative power [area under the curve (AUC) = 0.75] than each setting alone (AUC = 0.68 in the lab, AUC = 0.54 at home). Comparing the gait speed between the two assessments, the PD-FOF+ showed higher gait speeds in the capacity area compared with their TUG test in the lab. The mobility parameters extracted from both lab and home-based assessments contribute to the detection of FOF in PD. This study adds further evidence to the usefulness of mobility assessments that include different environments and assessment strategies. Although this study was limited in the sample size, it still provides a helpful method to consider the daily activity measurement of the patients with PD into clinical evaluation. The obtained results can help the clinicians with a more accurate prevention and treatment strategy.
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Affiliation(s)
- Arash Atrsaei
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Clint Hansen
- Department of Neurology, UKSH, Christian-Albrechts-University, Kiel, Germany
| | - Morad Elshehabi
- Department of Neurology, UKSH, Christian-Albrechts-University, Kiel, Germany
| | - Susanne Solbrig
- Department of Neurodegeneration, Center for Neurology and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Daniela Berg
- Department of Neurology, UKSH, Christian-Albrechts-University, Kiel, Germany.,Department of Neurodegeneration, Center for Neurology and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Inga Liepelt-Scarfone
- Department of Neurodegeneration, Center for Neurology and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany.,IB-Hochschule, Stuttgart, Germany
| | - Walter Maetzler
- Department of Neurology, UKSH, Christian-Albrechts-University, Kiel, Germany
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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22
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Jung S, Oudre L, Truong C, Dorveaux E, Gorintin L, Vayatis N, Ricard D. Adaptive Change-Point Detection for Studying Human Locomotion. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2020-2024. [PMID: 34891684 DOI: 10.1109/embc46164.2021.9629775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This paper presents an innovative method to analyze inertial signals recorded in a semi-controlled environment. It uses an adaptive and supervised change point detection procedure to decompose the signals into homogeneous segments, allowing a refined analysis of the successive phases within a gait protocol. Thanks to a training procedure, the algorithm can be applied to a wide range of protocols and handles different levels of granularity. The method is tested on a cohort of 15 healthy subjects performing a complex protocol composed of different activities and shows promising results for the automated and adaptive study of human gait and activity.Clinical relevance- A new approach to study human activity and locomotion in Free-Living Environments FLEs through an adaptive change-point detection which isolates homogeneous phases.
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23
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Landers M, Dorsey R, Saria S. Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption. Digit Biomark 2021; 5:216-223. [PMID: 34703976 DOI: 10.1159/000517885] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 06/08/2021] [Indexed: 11/19/2022] Open
Abstract
The assessment of health and disease requires a set of criteria to define health status and progression. These health measures are referred to as "endpoints." A "digital endpoint" is defined by its use of sensor-generated data often collected outside of a clinical setting such as in a patient's free-living environment. Applicable sensors exist in an array of devices and can be applied in a diverse set of contexts. For example, a smartphone's microphone might be used to diagnose or predict mild cognitive impairment due to Alzheimer's disease or a wrist-worn activity monitor (such as those found in smartwatches) may be used to measure a drug's effect on the nocturnal activity of patients with sickle cell disease. Digital endpoints are generating considerable excitement because they permit a more authentic assessment of the patient's experience, reveal formerly untold realities of disease burden, and can cut drug discovery costs in half. However, before these benefits can be realized, effort must be applied not only to the technical creation of digital endpoints but also to the environment that allows for their development and application. The future of digital endpoints rests on meaningful interdisciplinary collaboration, sufficient evidence that digital endpoints can realize their promise, and the development of an ecosystem in which the vast quantities of data that digital endpoints generate can be analyzed. The fundamental nature of health care is changing. With coronavirus disease 2019 serving as a catalyst, there has been a rapid expansion of home care models, telehealth, and remote patient monitoring. The increasing adoption of these health-care innovations will expedite the requirement for a digital characterization of clinical status as current assessment tools often rely upon direct interaction with patients and thus are not fit for purpose to be administered remotely. With the ubiquity of relatively inexpensive sensors, digital endpoints are positioned to drive this consequential change. It is therefore not surprising that regulators, physicians, researchers, and consultants have each offered their assessment of these novel tools. However, as we further describe later, the broad adoption of digital endpoints will require a cooperative effort. In this article, we present an analysis of the current state of digital endpoints. We also attempt to unify the perspectives of the parties involved in the development and deployment of these tools. We conclude with an interdependent list of challenges that must be collaboratively addressed before these endpoints are widely adopted.
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Affiliation(s)
- Matthew Landers
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Ray Dorsey
- Center for Health + Technology, University of Rochester, Rochester, New York, USA
| | - Suchi Saria
- Departments of Computer Science and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.,Bayesian Health, New York, New York, USA
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24
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Polhemus A, Delgado-Ortiz L, Brittain G, Chynkiamis N, Salis F, Gaßner H, Gross M, Kirk C, Rossanigo R, Taraldsen K, Balta D, Breuls S, Buttery S, Cardenas G, Endress C, Gugenhan J, Keogh A, Kluge F, Koch S, Micó-Amigo ME, Nerz C, Sieber C, Williams P, Bergquist R, Bosch de Basea M, Buckley E, Hansen C, Mikolaizak AS, Schwickert L, Scott K, Stallforth S, van Uem J, Vereijken B, Cereatti A, Demeyer H, Hopkinson N, Maetzler W, Troosters T, Vogiatzis I, Yarnall A, Becker C, Garcia-Aymerich J, Leocani L, Mazzà C, Rochester L, Sharrack B, Frei A, Puhan M. Walking on common ground: a cross-disciplinary scoping review on the clinical utility of digital mobility outcomes. NPJ Digit Med 2021; 4:149. [PMID: 34650191 PMCID: PMC8516969 DOI: 10.1038/s41746-021-00513-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/09/2021] [Indexed: 02/08/2023] Open
Abstract
Physical mobility is essential to health, and patients often rate it as a high-priority clinical outcome. Digital mobility outcomes (DMOs), such as real-world gait speed or step count, show promise as clinical measures in many medical conditions. However, current research is nascent and fragmented by discipline. This scoping review maps existing evidence on the clinical utility of DMOs, identifying commonalities across traditional disciplinary divides. In November 2019, 11 databases were searched for records investigating the validity and responsiveness of 34 DMOs in four diverse medical conditions (Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, hip fracture). Searches yielded 19,672 unique records. After screening, 855 records representing 775 studies were included and charted in systematic maps. Studies frequently investigated gait speed (70.4% of studies), step length (30.7%), cadence (21.4%), and daily step count (20.7%). They studied differences between healthy and pathological gait (36.4%), associations between DMOs and clinical measures (48.8%) or outcomes (4.3%), and responsiveness to interventions (26.8%). Gait speed, step length, cadence, step time and step count exhibited consistent evidence of validity and responsiveness in multiple conditions, although the evidence was inconsistent or lacking for other DMOs. If DMOs are to be adopted as mainstream tools, further work is needed to establish their predictive validity, responsiveness, and ecological validity. Cross-disciplinary efforts to align methodology and validate DMOs may facilitate their adoption into clinical practice.
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Affiliation(s)
- Ashley Polhemus
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
| | - Laura Delgado-Ortiz
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Gavin Brittain
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, England
| | - Nikolaos Chynkiamis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle, UK
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Michaela Gross
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rachele Rossanigo
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kristin Taraldsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Diletta Balta
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Sofie Breuls
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
| | - Sara Buttery
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Gabriela Cardenas
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Christoph Endress
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Julia Gugenhan
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sarah Koch
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Corinna Nerz
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Chloé Sieber
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Parris Williams
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Ronny Bergquist
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Magda Bosch de Basea
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Ellen Buckley
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | | | - Lars Schwickert
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Kirsty Scott
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Sabine Stallforth
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Janet van Uem
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Andrea Cereatti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Heleen Demeyer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium
| | | | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Thierry Troosters
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle, UK
| | - Alison Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Clemens Becker
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Judith Garcia-Aymerich
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Letizia Leocani
- Department of Neurology, San Raffaele University, Milan, Italy
| | - Claudia Mazzà
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, England
| | - Anja Frei
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Milo Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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25
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Alexander S, Peryer G, Gray E, Barkhof F, Chataway J. Wearable technologies to measure clinical outcomes in multiple sclerosis: A scoping review. Mult Scler 2021; 27:1643-1656. [PMID: 32749928 PMCID: PMC8474332 DOI: 10.1177/1352458520946005] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/01/2020] [Accepted: 07/06/2020] [Indexed: 11/15/2022]
Abstract
Wearable technology refers to any sensor worn on the person, making continuous and remote monitoring available to many people with chronic disease, including multiple sclerosis (MS). Daily monitoring seems an ideal solution either as an outcome measure or as an adjunct to support rater-based monitoring in both clinical and research settings. There has been an increase in solutions that are available, yet there is little consensus on the most appropriate solution to use in either MS research or clinical practice. We completed a scoping review (using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines) to summarise the wearable solutions available in MS, to identify those approaches that could potentially be utilised in clinical trials, by evaluating the following: scalability, cost, patient adaptability and accuracy. We identified 35 unique products that measure gait, cognition, upper limb function, activity, mood and fatigue, with most of these solutions being phone applications.
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Affiliation(s)
- Sarah Alexander
- Queen Square MS Centre and Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK
| | - Guy Peryer
- School of Health Sciences, University of East
Anglia, Norwich, UK
| | - Emma Gray
- The Multiple Sclerosis Society, London, UK
| | - Frederik Barkhof
- Queen Square MS Centre and Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK/Centre for Medical Image
Computing (CMIC), Department of Medical Physics and Biomedical Engineering,
University College London, London, UK/National Institute for Health Research
(NIHR), Biomedical Research Centre, University College London Hospitals
(UCLH), London, UK/Department of Radiology and Nuclear Medicine, VU
University Medical Centre, Amsterdam, The Netherlands
| | - Jeremy Chataway
- Queen Square MS Centre and Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK/National Institute for
Health Research (NIHR), Biomedical Research Centre, University College
London Hospitals (UCLH), London, UK/MRC CTU at UCL, Institute of Clinical
Trials and Methodology, University College London, London, UK
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26
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Weed L, Little C, Kasser SL, McGinnis RS. A Preliminary Investigation of the Effects of Obstacle Negotiation and Turning on Gait Variability in Adults with Multiple Sclerosis. SENSORS (BASEL, SWITZERLAND) 2021; 21:5806. [PMID: 34502697 PMCID: PMC8434341 DOI: 10.3390/s21175806] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/23/2021] [Accepted: 08/27/2021] [Indexed: 11/16/2022]
Abstract
Many falls in persons with multiple sclerosis (PwMS) occur during daily activities such as negotiating obstacles or changing direction. While increased gait variability is a robust biomarker of fall risk in PwMS, gait variability in more ecologically related tasks is unclear. Here, the effects of turning and negotiating an obstacle on gait variability in PwMS were investigated. PwMS and matched healthy controls were instrumented with inertial measurement units on the feet, lumbar, and torso. Subjects completed a walk and turn (WT) with and without an obstacle crossing (OW). Each task was partitioned into pre-turn, post-turn, pre-obstacle, and post-obstacle phases for analysis. Spatial and temporal gait measures and measures of trunk rotation were captured for each phase of each task. In the WT condition, PwMS demonstrated significantly more variability in lumbar and trunk yaw range of motion and rate, lateral foot deviation, cadence, and step time after turning than before. In the OW condition, PwMS demonstrated significantly more variability in both spatial and temporal gait parameters in obstacle approach after turning compared to before turning. No significant differences in gait variability were observed after negotiating an obstacle, regardless of turning or not. Results suggest that the context of gait variability measurement is important. The increased number of variables impacted from turning and the influence of turning on obstacle negotiation suggest that varying tasks must be considered together rather than in isolation to obtain an informed understanding of gait variability that more closely resembles everyday walking.
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Affiliation(s)
- Lara Weed
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA;
| | - Casey Little
- Department of Rehabilitation and Movement Science, University of Vermont, Burlington, VT 05405, USA; (C.L.); (S.L.K.)
| | - Susan L. Kasser
- Department of Rehabilitation and Movement Science, University of Vermont, Burlington, VT 05405, USA; (C.L.); (S.L.K.)
| | - Ryan S. McGinnis
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA;
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27
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Kluge F, Del Din S, Cereatti A, Gaßner H, Hansen C, Helbostad JL, Klucken J, Küderle A, Müller A, Rochester L, Ullrich M, Eskofier BM, Mazzà C. Consensus based framework for digital mobility monitoring. PLoS One 2021; 16:e0256541. [PMID: 34415959 PMCID: PMC8378707 DOI: 10.1371/journal.pone.0256541] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 08/09/2021] [Indexed: 12/31/2022] Open
Abstract
Digital mobility assessment using wearable sensor systems has the potential to capture walking performance in a patient's natural environment. It enables monitoring of health status and disease progression and evaluation of interventions in real-world situations. In contrast to laboratory settings, real-world walking occurs in non-conventional environments and under unconstrained and uncontrolled conditions. Despite the general understanding, there is a lack of agreed definitions about what constitutes real-world walking, impeding the comparison and interpretation of the acquired data across systems and studies. The goal of this study was to obtain expert-based consensus on specific aspects of real-world walking and to provide respective definitions in a common terminological framework. An adapted Delphi method was used to obtain agreed definitions related to real-world walking. In an online survey, 162 participants from a panel of academic, clinical and industrial experts with experience in the field of gait analysis were asked for agreement on previously specified definitions. Descriptive statistics was used to evaluate whether consent (> 75% agreement as defined a priori) was reached. Of 162 experts invited to participate, 51 completed all rounds (31.5% response rate). We obtained consensus on all definitions ("Walking" > 90%, "Purposeful" > 75%, "Real-world" > 90%, "Walking bout" > 80%, "Walking speed" > 75%, "Turning" > 90% agreement) after two rounds. The identification of a consented set of real-world walking definitions has important implications for the development of assessment and analysis protocols, as well as for the reporting and comparison of digital mobility outcomes across studies and systems. The definitions will serve as a common framework for implementing digital and mobile technologies for gait assessment and are an important link for the transition from supervised to unsupervised gait assessment.
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Affiliation(s)
- Felix Kluge
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Clint Hansen
- Department of Neurology, University of Kiel, Kiel, Germany
| | - Jorunn L. Helbostad
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | | | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- The Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Claudia Mazzà
- Department of Mechanical Engineering & Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
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28
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Montalban X, Graves J, Midaglia L, Mulero P, Julian L, Baker M, Schadrack J, Gossens C, Ganzetti M, Scotland A, Lipsmeier F, van Beek J, Bernasconi C, Belachew S, Lindemann M, Hauser SL. A smartphone sensor-based digital outcome assessment of multiple sclerosis. Mult Scler 2021; 28:654-664. [PMID: 34259588 PMCID: PMC8961252 DOI: 10.1177/13524585211028561] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Background: Sensor-based monitoring tools fill a critical gap in multiple sclerosis (MS)
research and clinical care. Objective: The aim of this study is to assess performance characteristics of the
Floodlight Proof-of-Concept (PoC) app. Methods: In a 24-week study (clinicaltrials.gov: NCT02952911), smartphone-based active
tests and passive monitoring assessed cognition (electronic Symbol Digit
Modalities Test), upper extremity function (Pinching Test, Draw a Shape
Test), and gait and balance (Static Balance Test, U-Turn Test, Walk Test,
Passive Monitoring). Intraclass correlation coefficients (ICCs) and age- or
sex-adjusted Spearman’s rank correlation determined test–retest reliability
and correlations with clinical and magnetic resonance imaging (MRI) outcome
measures, respectively. Results: Seventy-six people with MS (PwMS) and 25 healthy controls were enrolled. In
PwMS, ICCs were moderate-to-good (ICC(2,1) = 0.61–0.85) across tests.
Correlations with domain-specific standard clinical disability measures were
significant for all tests in the cognitive (r = 0.82,
p < 0.001), upper extremity function (|r|=
0.40–0.64, all p < 0.001), and gait and balance domains
(r = −0.25 to −0.52, all p < 0.05;
except for Static Balance Test: r = −0.20,
p > 0.05). Most tests also correlated with Expanded
Disability Status Scale, 29-item Multiple Sclerosis Impact Scale items or
subscales, and/or normalized brain volume. Conclusion: The Floodlight PoC app captures reliable and clinically relevant measures of
functional impairment in MS, supporting its potential use in clinical
research and practice.
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Affiliation(s)
- Xavier Montalban
- Department of Neurology-Neuroimmunology, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Jennifer Graves
- Department of Neurosciences, University of California San Diego, San Diego, CA, USA
| | - Luciana Midaglia
- Department of Neurology-Neuroimmunology, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Hospital Universitari Vall d'Hebron, Barcelona, Spain and Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Patricia Mulero
- Department of Neurology-Neuroimmunology, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | | | | | | | | | | | | | | | | | | | | | | | - Stephen L Hauser
- UCSF Weill Institute for Neurosciences and Department of Neurology, University of California San Francisco, San Francisco, CA, USA
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29
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Kawai H, Obuchi S, Hirayama R, Watanabe Y, Hirano H, Fujiwara Y, Ihara K, Kim H, Kobayashi Y, Mochimaru M, Tsushima E, Nakamura K. Intra-day variation in daily outdoor walking speed among community-dwelling older adults. BMC Geriatr 2021; 21:417. [PMID: 34238238 PMCID: PMC8268528 DOI: 10.1186/s12877-021-02349-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 06/15/2021] [Indexed: 12/30/2022] Open
Abstract
Background Walking speed is an important measure associated with health outcomes in older individuals, such as dependency and death. This study aimed to examine whether the walking speed of community-dwelling older adults varies between time periods within a day, as measured outdoors in daily life. We aimed to determine the types of walking speed variations and examine the factors associated with them. Methods Daily life outdoor walking speed was measured in 92 participants (average age 71.9 years±5.64) using a GPS smartphone app for 1 month. Average walking speeds for five time periods were analyzed with a linear mixed model. Intra-day walking speed variation patterns were classified by latent class analysis. Factors associated with the class were identified by logistic regression analysis. Results A statistically significant difference in average walking speed was found between early morning (1.33 m/s), and afternoon (1.27 m/s) and evening (1.26 m/s) (p < 0.01). The intra-day variation in walking speed was attributed to variation in cadence. Two classes were identified: (1) fast walking speed with large variation and (2) slow walking speed with little variation; hypertension and frailty level were associated with the class. Conclusion The results suggest that there is intra-day variation in walking speed in daily life, wherein the speed is the fastest early in the morning and slower in the afternoon and evening. A larger variation in the walking speed was related to the health status without hypertension or frailty. These results suggest that if a person shows less intra-day variation in walking speed, this could be a sign that they are susceptible to hypertension and an increased frailty level. Supplementary Information The online version contains supplementary material available at 10.1186/s12877-021-02349-w.
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Affiliation(s)
- Hisashi Kawai
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan.
| | - Shuichi Obuchi
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Ryo Hirayama
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan.,Osaka City University, Osaka, Japan
| | - Yutaka Watanabe
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan.,Gerodontology, Department of Oral Health Science, Faculty of Dental Medicine, Hokkaido University, Sapporo, Japan
| | - Hirohiko Hirano
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Yoshinori Fujiwara
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | | | - Hunkyung Kim
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Yoshiyuki Kobayashi
- Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
| | - Masaaki Mochimaru
- Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
| | - Eiki Tsushima
- Faculty of Medicine, Hirosaki University, Aomori, Japan
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30
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Short distance analysis of the 400-meter walk test of mobility in community-dwelling older adults. Gait Posture 2021; 88:60-65. [PMID: 34000486 DOI: 10.1016/j.gaitpost.2021.05.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 03/28/2021] [Accepted: 05/04/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND The 400-meter walk test (400MWT) is used to assess cardiovascular and pulmonary fitness or to predict adverse outcomes such as mobility disability. Additionally, short tests of walking such as the 4- or 8-meter walk test are administered to predict mortality, falls and other events. It remains uncertain if and how an integrated measurement of a short distance during 400MWT can replace an additional short distance measurement which would be clinically useful. RESEARCH QUESTION How do short distance (i.e. segment) measurements of gait speed and walk ratio during a 400MWT of mobility compare to those from an additional 8-meter walk test? METHODS A 400MWT and a separate 8-meter walk test were performed by 148 community-dwelling older adults (mean age 80.4 ± 4.4 years) using an instrumented walkway. RESULTS Gait speed and walk ratio (i.e. step length divided by step frequency) of single segments of the 400MWT were strongly associated with gait speed (r ≥ 0.91) and walk ratio (r ≥ 0.93) of an 8-meter walk test with best agreement in the middle part 20-meter walk during the 400MWT. Mean gait speed of all single walks on the instrumented walkway during the 400MWT was faster than the mean gait speed of the total 400MWT. SIGNIFICANCE A single walk of the 6th to 10th 20-meter walk during the 400MWT can be used as a substitute to an additional short distance trial. Furthermore, the awareness of being measured is higher on an instrumented walkway and possibly increases the motivation to overperform.
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31
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Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson's disease patients. J Neuroeng Rehabil 2021; 18:93. [PMID: 34082762 PMCID: PMC8173987 DOI: 10.1186/s12984-021-00883-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/20/2021] [Indexed: 12/28/2022] Open
Abstract
Background To objectively assess a patient’s gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing. Method To address this issue, we present a comprehensive free-living evaluation dataset, including 146.574 semi-automatic labeled strides of 28 Parkinson’s Disease patients. This dataset was used to evaluate the segmentation performance of a new Hidden Markov Model (HMM) based stride segmentation approach compared to an available dynamic time warping (DTW) based method. Results The proposed HMM achieved a mean F1-score of 92.1% and outperformed the DTW approach significantly. Further analysis revealed a dependency of segmentation performance to the number of strides within respective walking bouts. Shorter bouts (\documentclass[12pt]{minimal}
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\begin{document}$$< 30$$\end{document}<30 strides) resulted in worse performance, which could be related to more heterogeneous gait and an increased diversity of different stride types in short free-living walking bouts. In contrast, the HMM reached F1-scores of more than 96.2% for longer bouts (\documentclass[12pt]{minimal}
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\begin{document}$$> 50$$\end{document}>50 strides). Furthermore, we showed that an HMM, which was trained on at-lab data only, could be transferred to a free-living context with a negligible decrease in performance. Conclusion The generalizability of the proposed HMM is a promising feature, as fully labeled free-living training data might not be available for many applications. To the best of our knowledge, this is the first evaluation of stride segmentation performance on a large scale free-living dataset. Our proposed HMM-based approach was able to address the increased complexity of free-living gait data, and thus will help to enable a robust assessment of stride parameters in future free-living gait analysis applications. Supplementary Information The online version contains supplementary material available at 10.1186/s12984-021-00883-7.
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32
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Atrsaei A, Dadashi F, Mariani B, Gonzenbach R, Aminian K. Toward a remote assessment of walking bout and speed: application in patients with multiple sclerosis. IEEE J Biomed Health Inform 2021; 25:4217-4228. [PMID: 33914688 DOI: 10.1109/jbhi.2021.3076707] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Gait speed as a powerful biomarker of mobility is mostly assessed in controlled environments, e.g. in the clinic. With wearable inertial sensors, gait speed can be estimated in an objective manner. However, most of the previous works have validated the gait speed estimation algorithms in clinical settings which can be different than the home assessments in which the patients demonstrate their actual performance. Moreover, to provide comfort for the users, devising an algorithm based on a single sensor setup is essential. To this end, the goal of this study was to develop and validate a new gait speed estimation method based on a machine learning approach to predict gait speed in both clinical and home assessments by a sensor on the lower back. Moreover, two methods were introduced to detect walking bouts during daily activities at home. We have validated the algorithms in 35 patients with multiple sclerosis as it often presents with mobility difficulties. Therefore, the robustness of the algorithm can be shown in an impaired or slow gait. Against silver standard multi-sensor references, we achieved a bias close to zero and a precision of 0.15 m/s for gait speed estimation. Furthermore, the proposed machine learning-based locomotion detection method had a median of 96.8% specificity, 93.0% sensitivity, 96.4% accuracy, and 78.6% F1-score in detecting walking bouts at home. The high performance of the proposed algorithm showed the feasibility of the unsupervised mobility assessment introduced in this study.
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33
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Angelini L, Buckley E, Bonci T, Radford A, Sharrack B, Paling D, Nair KPS, Mazza C. A Multifactorial Model of Multiple Sclerosis Gait and Its Changes Across Different Disability Levels. IEEE Trans Biomed Eng 2021; 68:3196-3204. [PMID: 33625975 DOI: 10.1109/tbme.2021.3061998] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Mobility assessment is critical in the clinical management of people with Multiple Sclerosis (pwMS). Instrumented gait analysis provides a plethora of metrics for quantifying concurrent factors contributing to gait deterioration. However, a gait model discriminating underlying features contributing to this deterioration is lacking in pwMS. This study aimed at developing and validating such a model. METHODS The gait of 24 healthy controls and 114 pwMS with mild, moderate, or severe disability was measured with inertial sensors on the shanks and lower trunk while walking for 6 minutes along a hospital corridor. Twenty out of thirty-six initially explored metrics computed from the sensor data met the quality criteria for exploratory factor analysis. This analysis provided the sought model, which underwent a confirmatory factor analysis before being used to characterize gait impairment across the three disability groups. RESULTS A gait model consisting of five domains (rhythm/variability, pace, asymmetry, and forward and lateral dynamic balance) was revealed by the factor analysis, which was able to highlight gait abnormalities across the disability groups: significant alterations in rhythm/variability-, asymmetry-, and pace-based features were present in the mild group, but these were more profound in the moderate and severe groups. Deterioration in dynamic balance-based features was only noted in pwMS with a moderate and severe disability. CONCLUSION A conceptual model of gait for disease-specific mobility assessment in pwMS was successfully developed and tested. SIGNIFICANCE The new model, built with metrics that represent gait impairment in pwMS, highlighted clinically relevant changes across different disability levels, including those with no clinically observable walking disability. This shows the clear potential as a monitoring biomarker in pwMS.
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34
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Shah VV, McNames J, Harker G, Curtze C, Carlson-Kuhta P, Spain RI, El-Gohary M, Mancini M, Horak FB. Does gait bout definition influence the ability to discriminate gait quality between people with and without multiple sclerosis during daily life? Gait Posture 2021; 84:108-113. [PMID: 33302221 PMCID: PMC7946343 DOI: 10.1016/j.gaitpost.2020.11.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 10/21/2020] [Accepted: 11/24/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND There is currently no consensus about standardized gait bout definitions when passively monitoring walking during normal daily life activities. It is also not known how different definitions of a gait bout in daily life monitoring affects the ability to distinguish pathological gait quality. Specifically, how many seconds of a pause with no walking indicates an end to one gait bout and the start of another bout? In this study, we investigated the effect of 3 gait bout definitions on the discriminative ability to distinguish quality of walking in people with multiple sclerosis (MS) from healthy control subjects (HC) during a week of daily living. METHODS 15 subjects with MS and 16 HC wore instrumented socks on each foot and one Opal sensor over the lower lumbar area for a week of daily activities for at least 8 h/day. Three gait bout definitions were based on the length of the pause between the end of one gait bout and start of another bout (1.25 s, 2.50 s, and 5.0 s pause). Area under the curve (AUC) was used to compare gait quality measures in MS versus HC. RESULTS Total number of gait bouts over the week were statistically significantly different across bout definitions, as expected. However, AUCs of gait quality measures (such as gait speed, stride length, stride time) discriminating people with MS from HC were not different despite the 3 bout definitions. SIGNIFICANCE Quality of gait measures that discriminate MS from HC during daily life are not influenced by the length of a gait bout, despite large differences in quantity of gait across bout definitions. Thus, gait quality measures in people with MS versus controls can be compared across studies using different gait bout definitions with pause lengths ≤5 s.
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Affiliation(s)
- Vrutangkumar V. Shah
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA,Corresponding author at: Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA. (V.V. Shah)
| | - James McNames
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR, USA,APDM, Inc., Portland, OR, USA
| | - Graham Harker
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Carolin Curtze
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, USA
| | | | - Rebecca I. Spain
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA,Veterans Affairs Portland Health Care System, Portland, OR, USA
| | | | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Fay B. Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA,APDM, Inc., Portland, OR, USA
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35
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Chee JN, Ye B, Gregor S, Berbrayer D, Mihailidis A, Patterson KK. Influence of Multiple Sclerosis on Spatiotemporal Gait Parameters: A Systematic Review and Meta-Regression. Arch Phys Med Rehabil 2021; 102:1801-1815. [PMID: 33460576 DOI: 10.1016/j.apmr.2020.12.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 12/05/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To quantify the effect of multiple sclerosis (MS) on spatiotemporal gait characteristics accounting for disability severity and fall classification. DATA SOURCES MEDLINE (1946-August 2018), Allied and Complementary Medicine Database (1985-2018 August), and PsycINFO (1806-August 2018) were searched for terms on MS and gait. STUDY SELECTION Dual independent screening was conducted to identify observational, cross-sectional studies that compared adults with MS grouped according to Expanded Disability Status Scale (EDSS) level or fall history, reported on spatiotemporal gait characteristics, and were published in English. The search retrieved 5891 results, of which 12 studies satisfied the inclusion criteria. DATA EXTRACTION Two authors worked independently to extract and verify data on publication details, study methodology, participant characteristics, gait outcomes, conclusions, and limitations. Risk of bias was assessed using the QualSyst critical appraisal tool. A random-effects meta-regression and meta-analysis were conducted on pooled data. DATA SYNTHESIS All studies received quality ratings of very good to excellent and collectively examined 1513 individuals with MS. With every 1-point increase in EDSS, significant changes (P<.05) were observed in gait speed (-0.12 m/s; 95% confidence interval (CI), 0.08-0.15), step length (-0.04 m; 95% CI, 0.03-0.05), step time (+0.04 seconds; 95% CI, 0.02-0.06), step time variability (+0.009 seconds; 95% CI, 0.003-0.016), stride time (+0.08 seconds; 95% CI, 0.03-0.12), cadence (-4.4 steps per minute; 95% CI, 2.3-6.4), stance phase duration (+0.8% gait cycle; 95% CI, 0.1-1.5), and double support time (+3.5% gait cycle; 95% CI, 1.5-5.4). Recent fallers exhibited an 18% (95% CI, 13%-23%) reduction in gait speed compared with nonfallers (P<.001). CONCLUSIONS This review provides the most accurate reference values to-date that can be used to assess the effectiveness of MS gait training programs and therapeutic techniques for individuals who differ on disability severity and fall classification. Some gait adaptations could be part of adopting a more cautious gait strategy and should be factored into the design of future interventions.
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Affiliation(s)
- Justin N Chee
- Faculty of Medicine, University of Toronto, Rehabilitation Sciences Institute, Toronto, Ontario; Sunnybrook Health Sciences Centre, Hurvitz Brain Sciences Program, Sunnybrook Centre for Independent Living, Toronto, Ontario; KITE - Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada.
| | - Bing Ye
- Faculty of Medicine, University of Toronto, Rehabilitation Sciences Institute, Toronto, Ontario; KITE - Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada
| | - Sarah Gregor
- Faculty of Medicine, University of Toronto, Rehabilitation Sciences Institute, Toronto, Ontario; KITE - Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada
| | - David Berbrayer
- Faculty of Medicine, University of Toronto, Rehabilitation Sciences Institute, Toronto, Ontario; Sunnybrook Health Sciences Centre, Hurvitz Brain Sciences Program, Sunnybrook Centre for Independent Living, Toronto, Ontario
| | - Alex Mihailidis
- Faculty of Medicine, University of Toronto, Rehabilitation Sciences Institute, Toronto, Ontario; KITE - Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Department of Occupational Science & Occupational Therapy, Toronto, Ontario
| | - Kara K Patterson
- Faculty of Medicine, University of Toronto, Rehabilitation Sciences Institute, Toronto, Ontario; KITE - Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Department of Physical Therapy, Toronto, Ontario
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Krumpoch S, Lindemann U, Rappl A, Becker C, Sieber CC, Freiberger E. The effect of different test protocols and walking distances on gait speed in older persons. Aging Clin Exp Res 2021; 33:141-146. [PMID: 32930990 PMCID: PMC7897617 DOI: 10.1007/s40520-020-01703-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Accepted: 08/28/2020] [Indexed: 11/25/2022]
Abstract
Background and aims Walking is the core physical activity of older persons. The assessment of walking capacity is increasingly important for clinical purposes and clinical research. Differences between assessment tools and protocols for short walks to obtain gait characteristics can be responsible for changes, e.g., in gait speed from 0.1 to 0.2 m/s. The purpose of this study was to generate further knowledge for the harmonization and/or standardization of short walk-test protocols for assessing gait characteristics under supervised conditions. Methods For this cross-sectional study, 150 community-dwelling older adults (mean age 80.5 ± 4.5 years) were recruited. Participants performed eight walks differing in the distance (8-versus 4-m), static versus dynamic trials and comparing different test speed instructions (usual versus maximal) on an electronic walkway. Results A meaningful significant difference in mean usual gait speed was documented comparing the 4-m dynamic and static test protocol (0.12 m/s; p = 0.001). For the same comparison over an 8-m distance (dynamic versus static) and for the comparison between usual gait speed over 4-and 8-m, the differences in gait speed were smaller, but still statistically significant (p = 0.001). Conclusions Gait speed was faster, if the test protocol did not include a static start or stop. The differences were greater for a shorter walking distance. This aspect should be considered for the comparison of study results and is particularly relevant for systematic reviews and meta-analyses.
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Ibrahim AA, Küderle A, Gaßner H, Klucken J, Eskofier BM, Kluge F. Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis. J Neuroeng Rehabil 2020; 17:165. [PMID: 33339530 PMCID: PMC7749504 DOI: 10.1186/s12984-020-00798-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/09/2020] [Indexed: 12/30/2022] Open
Abstract
Background Multiple sclerosis (MS) is a disabling disease affecting the central nervous system and consequently the whole body’s functional systems resulting in different gait disorders. Fatigue is the most common symptom in MS with a prevalence of 80%. Previous research studied the relation between fatigue and gait impairment using stationary gait analysis systems and short gait tests (e.g. timed 25 ft walk). However, wearable inertial sensors providing gait data from longer and continuous gait bouts have not been used to assess the relation between fatigue and gait parameters in MS. Therefore, the aim of this study was to evaluate the association between fatigue and spatio-temporal gait parameters extracted from wearable foot-worn sensors and to predict the degree of fatigue. Methods Forty-nine patients with MS (32 women; 17 men; aged 41.6 years, EDSS 1.0–6.5) were included where each participant was equipped with a small Inertial Measurement Unit (IMU) on each foot. Spatio-temporal gait parameters were obtained from the 6-min walking test, and the Borg scale of perceived exertion was used to represent fatigue. Gait parameters were normalized by taking the difference of averaged gait parameters between the beginning and end of the test to eliminate inter-individual differences. Afterwards, normalized parameters were transformed to principle components that were used as input to a Random Forest regression model to formulate the relationship between gait parameters and fatigue. Results Six principal components were used as input to our model explaining more than 90% of variance within our dataset. Random Forest regression was used to predict fatigue. The model was validated using 10-fold cross validation and the mean absolute error was 1.38 points. Principal components consisting mainly of stride time, maximum toe clearance, heel strike angle, and stride length had large contributions (67%) to the predictions made by the Random Forest. Conclusions The level of fatigue can be predicted based on spatio-temporal gait parameters obtained from an IMU based system. The results can help therapists to monitor fatigue before and after treatment and in rehabilitation programs to evaluate their efficacy. Furthermore, this can be used in home monitoring scenarios where therapists can monitor fatigue using IMUs reducing time and effort of patients and therapists.
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Affiliation(s)
- Alzhraa A Ibrahim
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany. .,Computer Science Department, Faculty of Computers and Information, Assiut University, Asyut, Egypt.
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Bavaria, Germany
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Bavaria, Germany.,Fraunhofer Institut for Integrated Circuits, Erlangen, Bavaria, Germany.,Medical Valley Digital Health Application Center, Bamberg, Bavaria, Germany
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
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Frost-Hunt A. Effects of Massage Therapy on Multiple Sclerosis: a Case Report. Int J Ther Massage Bodywork 2020; 13:35-41. [PMID: 33282034 PMCID: PMC7704040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND Multiple Sclerosis (MS) is characterized by degeneration of the myelin sheath of an axon resulting in decreased transmission of nerve impulses. It is an autoimmune disease with periods of exacerbation and remission. Types of MS include relapsing-remitting, acute progressive, chronic progressive attack-remitting, and benign. Symptoms vary from patient to patient. Common symptoms include fatigue, spasticity, swelling, and altered gait. MS is commonly treated with medications that help relieve symptoms and prolong disease progression. Massage Therapy (MT), specifically Swedish techniques, have been effective in treating MS. OBJECTIVE To examine the effects of MT on mobility, fatigue, and edema in a patient with MS. METHODS An MT student from MacEwan University's 2,200-hour Massage Therapy program administered five MT treatments over a six-week period to a 58-year-old female diagnosed with MS 11 years earlier. She presented with symptoms of decreased mobility, fatigue, and left ankle edema. Assessment included active and passive range of motion (ROM), myotomes, dermatomes, reflexes, and orthopedic tests. Goals for the treatment sessions were to increase mobility, decrease fatigue, and decrease edema. Assessment measures included the Timed-Up-and-Go (TUG) test for mobility, the Modified Fatigue Impact Scale (MFIS) to measure fatigue, and Figure-8 ankle measurement to measure edema. Techniques used included Swedish massage, passive ROM, manual lymphatic drainage (MLD), and home-care exercises. RESULTS Little change was noted in mobility. The patient's fatigue level and left ankle edema decreased. CONCLUSION The results suggest that MT is effective in reducing fatigue and edema in a patient with MS. Future studies are needed to evaluate the correlation between mobility and massage.
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Shah VV, McNames J, Mancini M, Carlson-Kuhta P, Spain RI, Nutt JG, El-Gohary M, Curtze C, Horak FB. Laboratory versus daily life gait characteristics in patients with multiple sclerosis, Parkinson's disease, and matched controls. J Neuroeng Rehabil 2020; 17:159. [PMID: 33261625 PMCID: PMC7708140 DOI: 10.1186/s12984-020-00781-4] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 10/25/2020] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND AND PURPOSE Recent findings suggest that a gait assessment at a discrete moment in a clinic or laboratory setting may not reflect functional, everyday mobility. As a step towards better understanding gait during daily life in neurological populations, we compared gait measures that best discriminated people with multiple sclerosis (MS) and people with Parkinson's Disease (PD) from their respective, age-matched, healthy control subjects (MS-Ctl, PD-Ctl) in laboratory tests versus a week of daily life monitoring. METHODS We recruited 15 people with MS (age mean ± SD: 49 ± 10 years), 16 MS-Ctl (45 ± 11 years), 16 people with idiopathic PD (71 ± 5 years), and 15 PD-Ctl (69 ± 7 years). Subjects wore 3 inertial sensors (one each foot and lower back) in the laboratory followed by 7 days during daily life. Mann-Whitney U test and area under the curve (AUC) compared differences between PD and PD-Ctl, and between MS and MS-Ctl in the laboratory and in daily life. RESULTS Participants wore sensors for 60-68 h in daily life. Measures that best discriminated gait characteristics in people with MS and PD from their respective control groups were different between the laboratory gait test and a week of daily life. Specifically, the toe-off angle best discriminated MS versus MS-Ctl in the laboratory (AUC [95% CI] = 0.80 [0.63-0.96]) whereas gait speed in daily life (AUC = 0.84 [0.69-1.00]). In contrast, the lumbar coronal range of motion best discriminated PD versus PD-Ctl in the laboratory (AUC = 0.78 [0.59-0.96]) whereas foot-strike angle in daily life (AUC = 0.84 [0.70-0.98]). AUCs were larger in daily life compared to the laboratory. CONCLUSIONS Larger AUC for daily life gait measures compared to the laboratory gait measures suggest that daily life monitoring may be more sensitive to impairments from neurological disease, but each neurological disease may require different gait outcome measures.
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Affiliation(s)
- Vrutangkumar V Shah
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA.
| | - James McNames
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR, USA
- APDM Wearable Technologies, Portland, OR, USA
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | - Patricia Carlson-Kuhta
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | - Rebecca I Spain
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
- Veterans Affairs Portland Health Care System, Portland, OR, USA
| | - John G Nutt
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | | | - Carolin Curtze
- Department of Biomechanics, University of Nebraska At Omaha, Omaha, NE, USA
| | - Fay B Horak
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
- APDM Wearable Technologies, Portland, OR, USA
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An Objective Methodology for the Selection of a Device for Continuous Mobility Assessment. SENSORS 2020; 20:s20226509. [PMID: 33202608 PMCID: PMC7696193 DOI: 10.3390/s20226509] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 11/05/2020] [Accepted: 11/10/2020] [Indexed: 01/11/2023]
Abstract
Continuous monitoring by wearable technology is ideal for quantifying mobility outcomes in “real-world” conditions. Concurrent factors such as validity, usability, and acceptability of such technology need to be accounted for when choosing a monitoring device. This study proposes a bespoke methodology focused on defining a decision matrix to allow for effective decision making. A weighting system based on responses (n = 69) from a purpose-built questionnaire circulated within the IMI Mobilise-D consortium and its external collaborators was established, accounting for respondents’ background and level of expertise in using wearables in clinical practice. Four domains (concurrent validity, CV; human factors, HF; wearability and usability, WU; and data capture process, CP), associated evaluation criteria, and scores were established through literature research and group discussions. While the CV was perceived as the most relevant domain (37%), the others were also considered highly relevant (WU: 30%, HF: 17%, CP: 16%). Respondents (~90%) preferred a hidden fixation and identified the lower back as an ideal sensor location for mobility outcomes. Overall, this study provides a novel, holistic, objective, as well as a standardized approach accounting for complementary aspects that should be considered by professionals and researchers when selecting a solution for continuous mobility monitoring.
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Toward a Regulatory Qualification of Real-World Mobility Performance Biomarkers in Parkinson's Patients Using Digital Mobility Outcomes. SENSORS 2020; 20:s20205920. [PMID: 33092143 PMCID: PMC7589106 DOI: 10.3390/s20205920] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/12/2020] [Accepted: 10/17/2020] [Indexed: 02/06/2023]
Abstract
Wearable inertial sensors can be used to monitor mobility in real-world settings over extended periods. Although these technologies are widely used in human movement research, they have not yet been qualified by drug regulatory agencies for their use in regulatory drug trials. This is because the first generation of these sensors was unreliable when used on slow-walking subjects. However, intense research in this area is now offering a new generation of algorithms to quantify Digital Mobility Outcomes so accurate they may be considered as biomarkers in regulatory drug trials. This perspective paper summarises the work in the Mobilise-D consortium around the regulatory qualification of the use of wearable sensors to quantify real-world mobility performance in patients affected by Parkinson's Disease. The paper describes the qualification strategy and both the technical and clinical validation plans, which have recently received highly supportive qualification advice from the European Medicines Agency. The scope is to provide detailed guidance for the preparation of similar qualification submissions to broaden the use of real-world mobility assessment in regulatory drug trials.
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Bourke AK, Scotland A, Lipsmeier F, Gossens C, Lindemann M. Gait Characteristics Harvested During a Smartphone-Based Self-Administered 2-Minute Walk Test in People with Multiple Sclerosis: Test-Retest Reliability and Minimum Detectable Change. SENSORS 2020; 20:s20205906. [PMID: 33086734 PMCID: PMC7589972 DOI: 10.3390/s20205906] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 10/09/2020] [Accepted: 10/13/2020] [Indexed: 01/26/2023]
Abstract
The measurement of gait characteristics during a self-administered 2-minute walk test (2MWT), in persons with multiple sclerosis (PwMS), using a single body-worn device, has the potential to provide high-density longitudinal information on disease progression, beyond what is currently measured in the clinician-administered 2MWT. The purpose of this study is to determine the test-retest reliability, standard error of measurement (SEM) and minimum detectable change (MDC) of features calculated on gait characteristics, harvested during a self-administered 2MWT in a home environment, in 51 PwMS and 11 healthy control (HC) subjects over 24 weeks, using a single waist-worn inertial sensor-based smartphone. Excellent, or good to excellent test-retest reliability were observed in 58 of the 92 temporal, spatial and spatiotemporal gait features in PwMS. However, these were less reliable for HCs. Low SEM% and MDC% values were observed for most of the distribution measures for all gait characteristics for PwMS and HCs. This study demonstrates the inter-session test-retest reliability and provides an indication of clinically important change estimates, for interpreting the outcomes of gait characteristics measured using a body-worn smartphone, during a self-administered 2MWT. This system thus provides a reliable measure of gait characteristics in PwMS, supporting its application for the longitudinal assessment of gait deficits in this population.
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Affiliation(s)
- Alan K. Bourke
- Roche Pharma Research and Early Development, pRED Informatics, Roche Innovation Center Basel, F Hoffmann–La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland; (A.K.B.); (F.L.); (M.L.)
| | - Alf Scotland
- Inovigate, Aeschenvorstadt 55, 4051 Basel, Switzerland;
| | - Florian Lipsmeier
- Roche Pharma Research and Early Development, pRED Informatics, Roche Innovation Center Basel, F Hoffmann–La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland; (A.K.B.); (F.L.); (M.L.)
| | - Christian Gossens
- Roche Pharma Research and Early Development, pRED Informatics, Roche Innovation Center Basel, F Hoffmann–La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland; (A.K.B.); (F.L.); (M.L.)
- Correspondence: ; Tel.: +41-61-687-5113
| | - Michael Lindemann
- Roche Pharma Research and Early Development, pRED Informatics, Roche Innovation Center Basel, F Hoffmann–La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland; (A.K.B.); (F.L.); (M.L.)
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Shah VV, McNames J, Harker G, Mancini M, Carlson-Kuhta P, Nutt JG, El-Gohary M, Curtze C, Horak FB. Effect of Bout Length on Gait Measures in People with and without Parkinson's Disease during Daily Life. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5769. [PMID: 33053703 PMCID: PMC7601493 DOI: 10.3390/s20205769] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/30/2020] [Accepted: 10/09/2020] [Indexed: 01/06/2023]
Abstract
Although the use of wearable technology to characterize gait disorders in daily life is increasing, there is no consensus on which specific gait bout length should be used to characterize gait. Clinical trialists using daily life gait quality as study outcomes need to understand how gait bout length affects the sensitivity and specificity of measures to discriminate pathological gait as well as the reliability of gait measures across gait bout lengths. We investigated whether Parkinson's disease (PD) affects how gait characteristics change as bout length changes, and how gait bout length affects the reliability and discriminative ability of gait measures to identify gait impairments in people with PD compared to neurotypical Old Adults (OA). We recruited 29 people with PD and 20 neurotypical OA of similar age for this study. Subjects wore 3 inertial sensors, one on each foot and one over the lumbar spine all day, for 7 days. To investigate which gait bout lengths should be included to extract gait measures, we determined the range of gait bout lengths available across all subjects. To investigate if the effect of bout length on each gait measure is similar or not between subjects with PD and OA, we used a growth curve analysis. For reliability and discriminative ability of each gait measure as a function of gait bout length, we used the intraclass correlation coefficient (ICC) and area under the curve (AUC), respectively. Ninety percent of subjects walked with a bout length of less than 53 strides during the week, and the majority (>50%) of gait bouts consisted of less than 12 strides. Although bout length affected all gait measures, the effects depended on the specific measure and sometimes differed for PD versus OA. Specifically, people with PD did not increase/decrease cadence and swing duration with bout length in the same way as OA. ICC and AUC characteristics tended to be larger for shorter than longer gait bouts. Our findings suggest that PD interferes with the scaling of cadence and swing duration with gait bout length. Whereas control subjects gradually increased cadence and decreased swing duration as bout length increased, participants with PD started with higher than normal cadence and shorter than normal stride duration for the smallest bouts, and cadence and stride duration changed little as bout length increased, so differences between PD and OA disappeared for the longer bout lengths. Gait measures extracted from shorter bouts are more common, more reliable, and more discriminative, suggesting that shorter gait bouts should be used to extract potential digital biomarkers for people with PD.
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Affiliation(s)
- Vrutangkumar V. Shah
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA; (G.H.); (M.M.); (P.C.-K.); (J.G.N.); (F.B.H.)
| | - James McNames
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR 97207, USA;
| | - Graham Harker
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA; (G.H.); (M.M.); (P.C.-K.); (J.G.N.); (F.B.H.)
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA; (G.H.); (M.M.); (P.C.-K.); (J.G.N.); (F.B.H.)
| | - Patricia Carlson-Kuhta
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA; (G.H.); (M.M.); (P.C.-K.); (J.G.N.); (F.B.H.)
| | - John G. Nutt
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA; (G.H.); (M.M.); (P.C.-K.); (J.G.N.); (F.B.H.)
| | | | - Carolin Curtze
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE 68182, USA;
| | - Fay B. Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA; (G.H.); (M.M.); (P.C.-K.); (J.G.N.); (F.B.H.)
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Motti Ader LG, Greene BR, McManus K, Tubridy N, Caulfield B. Short Bouts of Gait Data and Body-Worn Inertial Sensors Can Provide Reliable Measures of Spatiotemporal Gait Parameters from Bilateral Gait Data for Persons with Multiple Sclerosis. BIOSENSORS 2020; 10:E128. [PMID: 32962269 PMCID: PMC7558375 DOI: 10.3390/bios10090128] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 09/11/2020] [Accepted: 09/17/2020] [Indexed: 11/17/2022]
Abstract
Wearable devices equipped with inertial sensors enable objective gait assessment for persons with multiple sclerosis (MS), with potential use in ambulatory care or home and community-based assessments. However, gait data collected in non-controlled settings are often fragmented and may not provide enough information for reliable measures. This paper evaluates a novel approach to (1) determine the effects of the length of the walking task on the reliability of calculated measures and (2) identify digital biomarkers for gait assessments from fragmented data. Thirty-seven participants (37) diagnosed with relapsing-remitting MS (EDSS range 0 to 4.5) executed two trials, walking 20 m each, with inertial sensors attached to their right and left shanks. Gait events were identified from the medio-lateral angular velocity, and short bouts of gait data were extracted from each trial, with lengths varying from 3 to 9 gait cycles. Intraclass correlation coefficients (ICCs) evaluate the degree of agreement between the two trials of each participant, according to the number of gait cycles included in the analysis. Results show that short bouts of gait data, including at least six gait cycles of bilateral data, can provide reliable gait measurements for persons with MS, opening new perspectives for gait assessment using fragmented data (e.g., wearable devices, community assessments). Stride time variability and asymmetry, as well as stride velocity variability and asymmetry, should be further explored as digital biomarkers to support the monitoring of symptoms of persons with neurological diseases.
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Affiliation(s)
- Lilian Genaro Motti Ader
- CeADAR—Centre for Applied Data Analytics, University College Dublin, Dublin D04 V2N9, Ireland
- Kinesis Health Technologies Ltd., Belfield Office Park, Clonskeagh, Dublin D04 V2N9, Ireland; (B.R.G.); (K.M.)
- School of Public Health, Physiotherapy and Sport Sciences, University College Dublin, Dublin D04 V1W8, Ireland;
| | - Barry R. Greene
- Kinesis Health Technologies Ltd., Belfield Office Park, Clonskeagh, Dublin D04 V2N9, Ireland; (B.R.G.); (K.M.)
| | - Killian McManus
- Kinesis Health Technologies Ltd., Belfield Office Park, Clonskeagh, Dublin D04 V2N9, Ireland; (B.R.G.); (K.M.)
- Insight Centre for Data Analytics, University College Dublin, Dublin D04 V1W8, Ireland
| | - Niall Tubridy
- Department of Neurology, St. Vincent’s University Hospital, Dublin D04 T6F4, Ireland;
| | - Brian Caulfield
- School of Public Health, Physiotherapy and Sport Sciences, University College Dublin, Dublin D04 V1W8, Ireland;
- Insight Centre for Data Analytics, University College Dublin, Dublin D04 V1W8, Ireland
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The feasibility and validity of a wearable sensor system to assess the stability of high-functioning lower-limb prosthesis users. ACTA ACUST UNITED AC 2020; Online first. [PMID: 33510564 DOI: 10.1097/jpo.0000000000000332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Introduction Lower-limb prosthesis users (LLPUs) experience increased fall risk due to gait and balance impairments. Clinical outcome measures are useful for measuring balance impairment and fall risk screening but suffer from limited resolution and ceiling effects. Recent advances in wearable sensors that can measure different components of gait stability may address these limitations. This study assessed feasibility and construct validity of a wearable sensor system (APDM Mobility Lab) to measure postural control and gait stability. Materials and Methods Lower-limb prosthesis users (n=22) and able-bodied controls (n=24) completed an Instrumented Stand-and-Walk Test (ISAW) while wearing the wearable sensors. Known-groups analysis (prosthesis versus controls) and convergence analysis (Prosthetic Limb Users Survey of Mobility [PLUS-M] and Activity-specific Balance Confidence [ABC] Scale) were performed on 20 stability-related measures. Results The system was applied without complications; however missing anticipatory postural adjustment data points for nine subjects affected the analysis. Of the 20 analyzed measures output by the sensors, only three significantly differed (p≤.05) between cohorts, and two demonstrated statistically significant correlations with the self-report measures. Conclusions The results of this study suggest the clinical feasibility but only partial construct validity of the wearable sensor system in conjunction with the ISAW test to measure LLPU stability and balance. The sample consisted of high-functioning LLPUs, so further research should evaluate a more representative sample with additional outcome measures and tasks.
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Polhemus AM, Bergquist R, Bosch de Basea M, Brittain G, Buttery SC, Chynkiamis N, Dalla Costa G, Delgado Ortiz L, Demeyer H, Emmert K, Garcia Aymerich J, Gassner H, Hansen C, Hopkinson N, Klucken J, Kluge F, Koch S, Leocani L, Maetzler W, Micó-Amigo ME, Mikolaizak AS, Piraino P, Salis F, Schlenstedt C, Schwickert L, Scott K, Sharrack B, Taraldsen K, Troosters T, Vereijken B, Vogiatzis I, Yarnall A, Mazza C, Becker C, Rochester L, Puhan MA, Frei A. Walking-related digital mobility outcomes as clinical trial endpoint measures: protocol for a scoping review. BMJ Open 2020; 10:e038704. [PMID: 32690539 PMCID: PMC7371223 DOI: 10.1136/bmjopen-2020-038704] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 05/14/2020] [Accepted: 05/18/2020] [Indexed: 02/06/2023] Open
Abstract
INTRODUCTION Advances in wearable sensor technology now enable frequent, objective monitoring of real-world walking. Walking-related digital mobility outcomes (DMOs), such as real-world walking speed, have the potential to be more sensitive to mobility changes than traditional clinical assessments. However, it is not yet clear which DMOs are most suitable for formal validation. In this review, we will explore the evidence on discriminant ability, construct validity, prognostic value and responsiveness of walking-related DMOs in four disease areas: Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease and proximal femoral fracture. METHODS AND ANALYSIS Arksey and O'Malley's methodological framework for scoping reviews will guide study conduct. We will search seven databases (Medline, CINAHL, Scopus, Web of Science, EMBASE, IEEE Digital Library and Cochrane Library) and grey literature for studies which (1) measure differences in DMOs between healthy and pathological walking, (2) assess relationships between DMOs and traditional clinical measures, (3) assess the prognostic value of DMOs and (4) use DMOs as endpoints in interventional clinical trials. Two reviewers will screen each abstract and full-text manuscript according to predefined eligibility criteria. We will then chart extracted data, map the literature, perform a narrative synthesis and identify gaps. ETHICS AND DISSEMINATION As this review is limited to publicly available materials, it does not require ethical approval. This work is part of Mobilise-D, an Innovative Medicines Initiative Joint Undertaking which aims to deliver, validate and obtain regulatory approval for DMOs. Results will be shared with the scientific community and general public in cooperation with the Mobilise-D communication team. REGISTRATION Study materials and updates will be made available through the Center for Open Science's OSFRegistry (https://osf.io/k7395).
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Affiliation(s)
- Ashley Marie Polhemus
- Epidemiology, Biostatistics, and Prevention Institute, University of Zürich, Zürich, Switzerland
| | - Ronny Bergquist
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Magda Bosch de Basea
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Gavin Brittain
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield, UK
- Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, UK
| | | | - Nikolaos Chynkiamis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, Tyne and Wear, UK
| | | | - Laura Delgado Ortiz
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Heleen Demeyer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium
| | - Kirsten Emmert
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Schleswig-Holstein, Germany
| | - Judith Garcia Aymerich
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Heiko Gassner
- Department of Molecular Neurology, Erlangen University Hospital, Erlangen, Bayern, Germany
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Schleswig-Holstein, Germany
| | | | - Jochen Klucken
- Department of Molecular Neurology, Erlangen University Hospital, Erlangen, Bayern, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Bayern, Germany
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Letizia Leocani
- Department of Neurology, San Raffaele Hospital, Milan, Italy
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Schleswig-Holstein, Germany
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Newcastle University Faculty of Medical Sciences, Newcastle upon Tyne, Newcastle upon Tyne, UK
| | - A Stefanie Mikolaizak
- Department of Clinical Gerontology, Robert Bosch Hospital, Stuttgart, Baden-Württemberg, Germany
| | - Paolo Piraino
- Department of Research & Early Development Statistics, Bayer AG, Berlin, Germany
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Sardegna, Italy
| | - Christian Schlenstedt
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Schleswig-Holstein, Germany
| | - Lars Schwickert
- Department of Clinical Gerontology, Robert Bosch Hospital, Stuttgart, Baden-Württemberg, Germany
| | - Kirsty Scott
- INSIGNEO Institute for in Silico Medicine, The University of Sheffield, Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, Sheffield, UK
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield, UK
- Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, UK
| | - Kristin Taraldsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Thierry Troosters
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Flanders, Belgium
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, Tyne and Wear, UK
| | - Alison Yarnall
- Translational and Clinical Research Institute, Newcastle University Faculty of Medical Sciences, Newcastle upon Tyne, Newcastle upon Tyne, UK
| | - Claudia Mazza
- INSIGNEO Institute for in Silico Medicine, The University of Sheffield, Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, Sheffield, UK
| | - Clemens Becker
- Department of Clinical Gerontology, Robert Bosch Hospital, Stuttgart, Baden-Württemberg, Germany
| | - Lynn Rochester
- Translational and Clinical Research Institute, Newcastle University Faculty of Medical Sciences, Newcastle upon Tyne, Newcastle upon Tyne, UK
| | - Milo Alan Puhan
- Epidemiology, Biostatistics, and Prevention Institute, University of Zürich, Zürich, Switzerland
| | - Anja Frei
- Epidemiology, Biostatistics, and Prevention Institute, University of Zürich, Zürich, Switzerland
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Voss S, Joyce J, Biskis A, Parulekar M, Armijo N, Zampieri C, Tracy R, Palmer S, Fefferman M, Ouyang B, Liu Y, Berry-Kravis E, O’Keefe JA. Normative database of spatiotemporal gait parameters using inertial sensors in typically developing children and young adults. Gait Posture 2020; 80:206-213. [PMID: 32531757 PMCID: PMC7388584 DOI: 10.1016/j.gaitpost.2020.05.010] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 04/20/2020] [Accepted: 05/09/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND Inertial sensors are increasingly useful to clinicians and researchers to detect gait deficits. Reference values are necessary for comparison to children with gait abnormalities. OBJECTIVE To present a normative database of spatiotemporal gait and turning parameters in 164 typically developing children and young adults ages 5-30 utilizing the APDM Mobility Lab® system. METHODS Participants completed the i-WALK test at both self-selected (SS) and fast as possible (FAP) walking speeds. Spatiotemporal gait and turning parameters included stride length, stride length variability, gait speed, cadence, stance, swing, and double support times, and foot strike, toe-off, and toe-out angles, turn duration, peak turn velocity and number of steps to turn. RESULTS Absolute stride length and gait speed increased with age. Normalized gait speed, absolute and normalized cadence, and stride length variability decreased with age. Normalized stride length and all parameters of gait cycle phase and foot position remained unaffected by age except for greater FSA in children 7-8. Foot position parameters in children 5-6 were excluded due to aberrant values and high standard deviations. Turns were faster in children ages 5-13 and 7-13 in the SS and FAP conditions, respectively. There were no differences in number of steps to turn. Similar trends were observed in the FAP condition except: normalized gait speed did not demonstrate a relationship with age and children ages 5-8 demonstrated increased stance and double support times and decreased swing time compared to children 11-13 and young adults (ages 5-6 only). Females ages 5-6 demonstrated increased stride length variability in the SS condition; males ages 7-8 and 14-30 ha d increased absolute stride length in the FAP condition. Similarities and differences were found between our values and previous literature. SIGNIFICANCE This normative database can be used by clinicians and researchers to compare abnormal gait patterns and responses to interventions.
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Affiliation(s)
- Stephanie Voss
- Department of Occupational Therapy, Rush University, Chicago, IL, United States
| | - Jessica Joyce
- Department of Cell & Molecular Medicine, Rush University, Chicago, IL, United States
| | - Alexandras Biskis
- Department of Cell & Molecular Medicine, Rush University, Chicago, IL, United States
| | - Medha Parulekar
- Rush Medical College, Rush University, Chicago, IL, United States
| | - Nicholas Armijo
- Department of Cell & Molecular Medicine, Rush University, Chicago, IL, United States
| | - Cris Zampieri
- Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Rachel Tracy
- Department of Occupational Therapy, Rush University, Chicago, IL, United States
| | - Sasha Palmer
- Department of Occupational Therapy, Rush University, Chicago, IL, United States
| | - Marie Fefferman
- Rush Medical College, Rush University, Chicago, IL, United States
| | - Bichun Ouyang
- Department of Neurological Sciences, Rush University, Chicago, IL, United States
| | - Yuanqing Liu
- Department of Neurological Sciences, Rush University, Chicago, IL, United States
| | - Elizabeth Berry-Kravis
- Department of Neurological Sciences, Rush University, Chicago, IL, United States,Department of Pediatrics, Rush University, Chicago, IL, United States
| | - Joan A. O’Keefe
- Department of Occupational Therapy, Rush University, Chicago, IL, United States,Department of Cell & Molecular Medicine, Rush University, Chicago, IL, United States,Department of Neurological Sciences, Rush University, Chicago, IL, United States,Corresponding author: Joan A. O’Keefe, PhD, PT, Departments of Cell & Molecular Medicine and Neurological Sciences, Rush University, 600 South Paulina Street, Suite 507 Armour Academic Center, Chicago, IL 60612,
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Ilg W, Seemann J, Giese M, Traschütz A, Schöls L, Timmann D, Synofzik M. Real-life gait assessment in degenerative cerebellar ataxia. Neurology 2020; 95:e1199-e1210. [DOI: 10.1212/wnl.0000000000010176] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 03/02/2020] [Indexed: 01/01/2023] Open
Abstract
ObjectivesWith disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid motor biomarkers are highly warranted. In this observational study, we aimed to unravel and validate markers of ataxic gait in real life by using wearable sensors.MethodsWe assessed gait characteristics of 43 patients with degenerative cerebellar disease (Scale for the Assessment and Rating of Ataxia [SARA] 9.4 ± 3.9) compared with 35 controls by 3 body-worn inertial sensors in 3 conditions: (1) laboratory-based walking; (2) supervised free walking; (3) real-life walking during everyday living (subgroup n = 21). Movement analysis focused on measures of spatiotemporal step variability and movement smoothness.ResultsA set of gait variability measures was identified that allowed us to consistently identify ataxic gait changes in all 3 conditions. Lateral step deviation and a compound measure of spatial step variability categorized patients vs controls with a discrimination accuracy of 0.86 in real life. Both were highly correlated with clinical ataxia severity (effect size ρ = 0.76). These measures allowed detecting group differences even for patients who differed only 1 point in the clinical SARAposture&gait subscore, with highest effect sizes for real-life walking (d = 0.67).ConclusionsWe identified measures of ataxic gait that allowed us not only to capture the gait variability inherent in ataxic gait in real life, but also to demonstrate high sensitivity to small differences in disease severity, with the highest effect sizes in real-life walking. They thus represent promising candidates for motor markers for natural history and treatment trials in ecologically valid contexts.Classification of evidenceThis study provides Class I evidence that a set of gait variability measures, even if accessed in real life, correlated with the clinical severity of ataxia in patients with degenerative cerebellar disease.
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Bertuletti S, Salis F, Cereatti A, Angelini L, Buckley E, Nair KPS, Mazza C, Croce UD. Inter-leg Distance Measurement as a Tool for Accurate Step Counting in Patients with Multiple Sclerosis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:6413-6417. [PMID: 31947310 DOI: 10.1109/embc.2019.8857353] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Step detection is commonly performed using wearable inertial devices. However, methods based on the extraction of signals features may deteriorate their accuracy when applied to very slow walkers with abnormal gait patterns. The aim of this study is to test and validate an innovative step counter method (DiSC) based on the direct measurement of inter-leg distance. Data were recorded using an innovative wearable system which integrates a magneto-inertial unit and multiple distance sensors (DSs) attached to the shank. The method allowed for the detection of both left and right steps using a single device and was validated on thirteen people affected by multiple sclerosis (0 <; EDSS <; 6.5) while performing a six-minute walking test. Two different measurement ranges for the distance sensor were tested (DS200: 0-200 mm; DS400: 0-400 mm). Accuracy was evaluated by comparing the estimates of the DiSC method against video recordings used as gold standard. Preliminary results showed a good accuracy in detecting steps with half the errors in detecting the step of the instrumented side compared to the non-instrumented (mean absolute percentage error 2.4% vs 4.8% for DS200; mean absolute percentage error 2% vs 5.4% for DS400). When averaging errors across patients, over and under estimation errors were compensated, and very high accuracy was achieved (E%<; 1.2% for DS200; E%<; 0.7% for DS400). DS400 is the suggested configuration for patients walking with a large base of support.
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Angelini L, Hodgkinson W, Smith C, Dodd JM, Sharrack B, Mazzà C, Paling D. Wearable sensors can reliably quantify gait alterations associated with disability in people with progressive multiple sclerosis in a clinical setting. J Neurol 2020; 267:2897-2909. [PMID: 32468119 PMCID: PMC7501113 DOI: 10.1007/s00415-020-09928-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 05/15/2020] [Accepted: 05/16/2020] [Indexed: 12/11/2022]
Abstract
Gait disability in people with progressive multiple sclerosis (MS) is difficult to quantify using existing clinical tools. This study aims to identify reliable and objective gait-based biomarkers to monitor progressive multiple sclerosis (MS) in clinical settings. During routine clinical visits, 57 people with secondary progressive MS and 24 healthy controls walked for 6 minutes wearing three inertial motion sensors. Fifteen gait measures were computed from the sensor data and tested for between-session reliability, for differences between controls and people with moderate and severe MS disability, and for correlation with Expanded Disability Status Scale (EDSS) scores. The majority of gait measures showed good to excellent between-session reliability when assessed in a subgroup of 23 healthy controls and 25 people with MS. These measures showed that people with MS walked with significantly longer step and stride durations, reduced step and stride regularity, and experienced difficulties in controlling and maintaining a stable walk when compared to controls. These abnormalities significantly increased in people with a higher level of disability and correlated with their EDSS scores. Reliable and objective gait-based biomarkers using wearable sensors have been identified. These biomarkers may allow clinicians to quantify clinically relevant alterations in gait in people with progressive MS within the context of regular clinical visits.
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Affiliation(s)
- Lorenza Angelini
- Department of Mechanical Engineering and Insigneo Institute for in silico Medicine, University of Sheffield, Pam Liversidge Building, Mappin Street, Sheffield, S1 3JD, UK.
| | | | - Craig Smith
- Medical School, University of Sheffield, Sheffield, UK
| | | | - Basil Sharrack
- Academic Department of Neuroscience, Sheffield NIHR Neuroscience BRC, Sheffield Teaching Hospital NHS Foundation Trust, Sheffield, UK
| | - Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute for in silico Medicine, University of Sheffield, Pam Liversidge Building, Mappin Street, Sheffield, S1 3JD, UK
| | - David Paling
- Sheffield Institute of Translational Neuroscience, Sheffield Teaching Hospital NHS Foundation Trust, Sheffield, UK
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