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Prigent G, Aminian K, Gonzenbach RR, April R, Paraschiv-Ionescu A. Effects of multidisciplinary inpatient rehabilitation on everyday life physical activity and gait in patients with multiple sclerosis. J Neuroeng Rehabil 2024; 21:88. [PMID: 38807215 PMCID: PMC11131212 DOI: 10.1186/s12984-024-01383-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 05/14/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND Multiple sclerosis is a progressive neurological disease that affects the central nervous system, resulting in various symptoms. Among these, impaired mobility and fatigue stand out as the most prevalent. The progressive worsening of symptoms adversely alters quality of life, social interactions and participation in activities of daily living. The main objective of this study is to bring new insights into the impact of a multidisciplinary inpatient rehabilitation on supervised walking tests, physical activity (PA) behavior and everyday gait patterns. METHODS A total of 52 patients, diagnosed with multiple sclerosis, were evaluated before and after 3 weeks of inpatient rehabilitation. Each measurement period consisted of clinical assessments and 7 days home monitoring using foot-mounted sensors. In addition, we considered two subgroups based on the Expanded Disability Status Scale (EDSS) scores: 'mild' (EDSS < 5) and 'severe' (EDSS ≥ 5) disability levels. RESULTS Significant improvements in fatigue, quality of life and perceived mobility were reported. In addition, walking capacity, as assessed by the 10-m walking test, two-minute walk test and timed-up-and-go test, improved significantly after rehabilitation. Regarding the home assessment, mildly disabled patients significantly increased their locomotion per day and complexity of daily PA pattern after rehabilitation, while severely disabled patients did not significantly change. There were distinct and significant differences in gait metrics (i.e., gait speed, stride length, cadence) between mildly and severely disabled patients, but the statistical models did not show a significant overall rehabilitation effect on these gait metrics. CONCLUSION Inpatient rehabilitation showed beneficial effects on self-reported mobility, self-rated health questionnaires, and walking capacity in both mildly and severely disabled patients. However, these improvements do not necessarily translate to home performance in severely disabled patients, or only marginally in mildly disabled patients. Motivational and behavioral factors should also be considered and incorporated into treatment strategies.
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Affiliation(s)
- Gaëlle Prigent
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Roman Rudolf Gonzenbach
- Department of Neurological Rehabilitation, Rehabilitation Centre Valens, Valens, Switzerland
| | - Roger April
- Department of Neurological Rehabilitation, Rehabilitation Centre Valens, Valens, Switzerland
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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2
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Kluge F, Brand YE, Micó-Amigo ME, Bertuletti S, D'Ascanio I, Gazit E, Bonci T, Kirk C, Küderle A, Palmerini L, Paraschiv-Ionescu A, Salis F, Soltani A, Ullrich M, Alcock L, Aminian K, Becker C, Brown P, Buekers J, Carsin AE, Caruso M, Caulfield B, Cereatti A, Chiari L, Echevarria C, Eskofier B, Evers J, Garcia-Aymerich J, Hache T, Hansen C, Hausdorff JM, Hiden H, Hume E, Keogh A, Koch S, Maetzler W, Megaritis D, Niessen M, Perlman O, Schwickert L, Scott K, Sharrack B, Singleton D, Vereijken B, Vogiatzis I, Yarnall A, Rochester L, Mazzà C, Del Din S, Mueller A. Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study. JMIR Form Res 2024; 8:e50035. [PMID: 38691395 PMCID: PMC11097052 DOI: 10.2196/50035] [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: 07/25/2023] [Revised: 12/18/2023] [Accepted: 12/21/2023] [Indexed: 05/03/2024] Open
Abstract
BACKGROUND Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. OBJECTIVE The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back-worn inertial sensors. METHODS Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back-worn inertial sensors. RESULTS The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. CONCLUSIONS Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. TRIAL REGISTRATION ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1136/bmjopen-2021-050785.
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Affiliation(s)
- Felix Kluge
- Novartis Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Yonatan E Brand
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Stefano Bertuletti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Ilaria D'Ascanio
- Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Tecla Bonci
- Department of Mechanical Engineering and Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Francesca Salis
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Abolfazl Soltani
- 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
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- 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, United Kingdom
| | - 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
- Unit Digitale Geriatrie, Universitätsklinikum Heidelberg, Heidelberg, Germany
| | - Philip Brown
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Joren Buekers
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Anne-Elie Carsin
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, 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, University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Carlos Echevarria
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Bjoern Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | - Judith Garcia-Aymerich
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Tilo Hache
- Novartis Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - 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, Tel Aviv University, Tel Aviv, Israel
- Department of Physical Therapy, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States
- Department of Orthopaedic Surgery, Rush Medical College, Chicago, IL, United States
| | - Hugo Hiden
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Emily Hume
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, United Kingdom
| | - 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
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, 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, United Kingdom
| | | | - Or Perlman
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - 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, United Kingdom
| | - Basil Sharrack
- Department of Neuroscience, The University of Sheffield, Sheffield, United Kingdom
- Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - 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, United Kingdom
| | - Alison Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- 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, United Kingdom
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- 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, United Kingdom
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- 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, United Kingdom
| | - Arne Mueller
- Novartis Biomedical Research, Novartis Pharma AG, Basel, Switzerland
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Delgado-Ortiz L, Ranciati S, Arbillaga-Etxarri A, Balcells E, Buekers J, Demeyer H, Frei A, Gimeno-Santos E, Hopkinson NS, de Jong C, Karlsson N, Louvaris Z, Palmerini L, Polkey MI, Puhan MA, Rabinovich RA, Rodríguez Chiaradia DA, Rodriguez-Roisin R, Toran-Montserrat P, Vogiatzis I, Watz H, Troosters T, Garcia-Aymerich J. Real-world walking cadence in people with COPD. ERJ Open Res 2024; 10:00673-2023. [PMID: 38444656 PMCID: PMC10910309 DOI: 10.1183/23120541.00673-2023] [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: 09/11/2023] [Accepted: 12/05/2023] [Indexed: 03/07/2024] Open
Abstract
Introduction The clinical validity of real-world walking cadence in people with COPD is unsettled. Our objective was to assess the levels, variability and association with clinically relevant COPD characteristics and outcomes of real-world walking cadence. Methods We assessed walking cadence (steps per minute during walking bouts longer than 10 s) from 7 days' accelerometer data in 593 individuals with COPD from five European countries, and clinical and functional characteristics from validated questionnaires and standardised tests. Severe exacerbations during a 12-month follow-up were recorded from patient reports and medical registries. Results Participants were mostly male (80%) and had mean±sd age of 68±8 years, post-bronchodilator forced expiratory volume in 1 s (FEV1) of 57±19% predicted and walked 6880±3926 steps·day-1. Mean walking cadence was 88±9 steps·min-1, followed a normal distribution and was highly stable within-person (intraclass correlation coefficient 0.92, 95% CI 0.90-0.93). After adjusting for age, sex, height and number of walking bouts in fractional polynomial or linear regressions, walking cadence was positively associated with FEV1, 6-min walk distance, physical activity (steps·day-1, time in moderate-to-vigorous physical activity, vector magnitude units, walking time, intensity during locomotion), physical activity experience and health-related quality of life and negatively associated with breathlessness and depression (all p<0.05). These associations remained after further adjustment for daily steps. In negative binomial regression adjusted for multiple confounders, walking cadence related to lower number of severe exacerbations during follow-up (incidence rate ratio 0.94 per step·min-1, 95% CI 0.91-0.99, p=0.009). Conclusions Higher real-world walking cadence is associated with better COPD status and lower severe exacerbations risk, which makes it attractive as a future prognostic marker and clinical outcome.
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Affiliation(s)
- Laura Delgado-Ortiz
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Saverio Ranciati
- Department of Statistical Sciences, Università di Bologna, Bologna, Italy
| | - Ane Arbillaga-Etxarri
- Deusto Physical TherapIker, Physical Therapy Department, Faculty of Health Sciences, University of Deusto, San Sebastian, Spain
| | - Eva Balcells
- Universitat Pompeu Fabra, Barcelona, Spain
- Respiratory Medicine Department, Hospital del Mar, Barcelona, Spain
- Biomedical Research Networking Centre on Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Joren Buekers
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Heleen Demeyer
- Department of Rehabilitation Sciences, KULeuven, Leuven, Belgium
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium
| | - Anja Frei
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Elena Gimeno-Santos
- ISGlobal, Barcelona, Spain
- Biomedical Research Networking Centre on Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, Barcelona, Spain
- Hospital Clínic de Barcelona, Barcelona, Spain
| | | | - Corina de Jong
- Groningen Research Institute for Asthma and COPD, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | | | - Luca Palmerini
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, Università di Bologna, Bologna, Italy
| | - Michael I. Polkey
- National Heart and Lung Institute, Imperial College London, London, UK
- Respiratory Medicine, Royal Brompton and Harefield NHS Foundation Trust, London, UK
| | - Milo A. Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Roberto A. Rabinovich
- Respiratory Medicine Department, Royal Infirmary of Edinburgh, Centre for Inflammation Research, QMRI, The University of Edinburgh, Scotland, UK
| | - Diego A. Rodríguez Chiaradia
- Respiratory Medicine Department, Hospital del Mar, Barcelona, Spain
- Biomedical Research Networking Centre on Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Robert Rodriguez-Roisin
- Biomedical Research Networking Centre on Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, Barcelona, Spain
- Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Hospital Clínic of Barcelona, Barcelona, Spain
| | - Pere Toran-Montserrat
- Unitat de Suport a la Recerca Metropolitana Nord, Institut Universitari d'Investigació en Atenció Primària Jordi Gol, Mataró, Spain
- Germans Trias i Pujol Research Institute, Badalona, Spain
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Henrik Watz
- Pulmonary Research Institute at Lungen Clinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany
| | | | - Judith Garcia-Aymerich
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
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Taraldsen K, Polhemus A, Engdal M, Jansen CP, Becker C, Brenner N, Blain H, Johnsen LG, Vereijken B. Evaluation of mobility recovery after hip fracture: a scoping review of randomized controlled studies. Osteoporos Int 2024; 35:203-215. [PMID: 37801082 PMCID: PMC10837269 DOI: 10.1007/s00198-023-06922-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 09/11/2023] [Indexed: 10/07/2023]
Abstract
Few older adults regain their pre-fracture mobility after a hip fracture. Intervention studies evaluating effects on gait typically use short clinical tests or in-lab parameters that are often limited to gait speed only. Measurements of mobility in daily life settings exist and should be considered to a greater extent than today. Less than half of hip fracture patients regain their pre-fracture mobility. Mobility recovery is closely linked to health status and quality of life, but there is no comprehensive overview of how gait has been evaluated in intervention studies on hip fracture patients. The purpose was to identify what gait parameters have been used in randomized controlled trials to assess intervention effects on older people's mobility recovery after hip fracture. This scoping review is a secondary paper that identified relevant peer-reviewed and grey literature from 11 databases. After abstract and full-text screening, 24 papers from the original review and 8 from an updated search and manual screening were included. Records were eligible if they included gait parameters in RCTs on hip fracture patients. We included 32 papers from 29 trials (2754 unique participants). Gait parameters were primary endpoint in six studies only. Gait was predominantly evaluated as short walking, with gait speed being most frequently studied. Only five studies reported gait parameters from wearable sensors. Evidence on mobility improvement after interventions in hip fracture patients is largely limited to gait speed as assessed in a controlled setting. The transition from traditional clinical and in-lab to out-of-lab gait assessment is needed to assess effects of interventions on mobility recovery after hip fracture at higher granularity in all aspects of patients' lives, so that optimal care pathways can be defined.
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Affiliation(s)
- K Taraldsen
- Department of Rehabilitation Science and Health Technology, OsloMet, Oslo, Norway.
| | - A Polhemus
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - M Engdal
- Department of Neuromedicine and Movement Science, NTNU, Trondheim, Norway
| | - C-P Jansen
- Department of Clinical Gerontology, Robert Bosch Hospital, Stuttgart, Baden-Württemberg, Germany
| | - C Becker
- Department of Clinical Gerontology, Robert Bosch Hospital, Stuttgart, Baden-Württemberg, Germany
| | - N Brenner
- Department of Clinical Gerontology, Robert Bosch Hospital, Stuttgart, Baden-Württemberg, Germany
| | - H Blain
- Department of Geriatrics, Montpellier University Hospital and Montpellier University MUSE, Montpellier, France
| | - L G Johnsen
- Department of Neuromedicine and Movement Science, NTNU, Trondheim, Norway
- Department of Orthopaedic Surgery, St. Olav's Hospital HF, Trondheim, Norway
| | - B Vereijken
- Department of Neuromedicine and Movement Science, NTNU, Trondheim, Norway
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5
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Kirk C, Küderle A, Micó-Amigo ME, Bonci T, Paraschiv-Ionescu A, Ullrich M, Soltani 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, 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, Eskofier BM, Del Din S. Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device. Sci Rep 2024; 14:1754. [PMID: 38243008 PMCID: PMC10799009 DOI: 10.1038/s41598-024-51766-5] [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] [Received: 05/22/2023] [Accepted: 01/09/2024] [Indexed: 01/21/2024] Open
Abstract
This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987.
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Affiliation(s)
- Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, UK
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, 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 Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Abolfazl Soltani
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - 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
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, 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 Biomedical Sciences, University of Sassari, Sassari, 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
| | - 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
- Department of Physical Therapy, Sagol School of Neuroscience, 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
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, 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 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, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, 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, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, 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
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, 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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6
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Romijnders R, Salis F, Hansen C, Küderle A, Paraschiv-Ionescu A, Cereatti A, Alcock L, Aminian K, Becker C, Bertuletti S, Bonci T, Brown P, Buckley E, Cantu A, Carsin AE, Caruso M, Caulfield B, Chiari L, D'Ascanio I, Del Din S, Eskofier B, Fernstad SJ, Fröhlich MS, Garcia Aymerich J, Gazit E, Hausdorff JM, Hiden H, Hume E, Keogh A, Kirk C, Kluge F, Koch S, Mazzà C, Megaritis D, Micó-Amigo E, Müller A, Palmerini L, Rochester L, Schwickert L, Scott K, Sharrack B, Singleton D, Soltani A, Ullrich M, Vereijken B, Vogiatzis I, Yarnall A, Schmidt G, Maetzler W. Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases. Front Neurol 2023; 14:1247532. [PMID: 37909030 PMCID: PMC10615212 DOI: 10.3389/fneur.2023.1247532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/18/2023] [Indexed: 11/02/2023] Open
Abstract
Introduction The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings. Methods Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data. Results and discussion The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of -0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, -0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.
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Affiliation(s)
- Robbin Romijnders
- Digital Signal Processing and System Theory, Electrical and Information Engineering, Faculty of Engineering, Kiel University, Kiel, Germany
- Arbeitsgruppe Neurogeriatrie, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Clint Hansen
- Arbeitsgruppe Neurogeriatrie, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Arne Küderle
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy
| | - Lisa Alcock
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Clemens Becker
- Gesellschaft für Medizinische Forschung, Robert-Bosch Foundation GmbH, Stuttgart, Germany
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Tecla Bonci
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Philip Brown
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Ellen Buckley
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Alma Cantu
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Anne-Elie Carsin
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Marco Caruso
- Department of Electronics and Telecommunications, Polytechnic of Turin, 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
| | - 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 (CIRISDV), University of Bologna, Bologna, Italy
| | - Ilaria D'Ascanio
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
| | - Silvia Del Din
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Björn Eskofier
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | | | - Judith Garcia Aymerich
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Hugo Hiden
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Emily Hume
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - 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
| | - Cameron Kirk
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Novartis Institute of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Claudia Mazzà
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Dimitrios Megaritis
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Encarna Micó-Amigo
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Arne Müller
- Novartis Institute 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 (CIRISDV), University of Bologna, Bologna, Italy
| | - Lynn Rochester
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Lars Schwickert
- Gesellschaft für Medizinische Forschung, Robert-Bosch Foundation GmbH, Stuttgart, Germany
| | - Kirsty Scott
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - 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
| | - Abolfazl Soltani
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Digital Health Department, CSEM SA, Neuchâtel, Switzerland
| | - Martin Ullrich
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - 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 upon Tyne, United Kingdom
| | - Alison Yarnall
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Gerhard Schmidt
- Digital Signal Processing and System Theory, Electrical and Information Engineering, Faculty of Engineering, Kiel University, Kiel, Germany
| | - Walter Maetzler
- Arbeitsgruppe Neurogeriatrie, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
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7
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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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8
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Prigent G, Aminian K, Cereatti A, Salis F, Bonci T, Scott K, Mazzà C, Alcock L, Del Din S, Gazit E, Hansen C, Paraschiv-Ionescu A. A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings. Med Biol Eng Comput 2023; 61:2341-2352. [PMID: 37069465 PMCID: PMC10412496 DOI: 10.1007/s11517-023-02826-x] [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] [Received: 09/26/2022] [Accepted: 03/08/2023] [Indexed: 04/19/2023]
Abstract
Walking activity and gait parameters are considered among the most relevant mobility-related parameters. Currently, gait assessments have been mainly analyzed in laboratory or hospital settings, which only partially reflect usual performance (i.e., real world behavior). In this study, we aim to validate a robust walking detection algorithm using a single foot-worn inertial measurement unit (IMU) in real-life settings. We used a challenging dataset including 18 individuals performing free-living activities. A multi-sensor wearable system including pressure insoles, multiple IMUs, and infrared distance sensors (INDIP) was used as reference. Accurate walking detection was obtained, with sensitivity and specificity of 98 and 91% respectively. As robust walking detection is needed for ambulatory monitoring to complete the processing pipeline from raw recorded data to walking/mobility outcomes, a validated algorithm would pave the way for assessing patient performance and gait quality in real-world conditions.
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Affiliation(s)
- Gaëlle Prigent
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico Di Torino, Turin, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy
| | - Tecla Bonci
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Kirsty Scott
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - for the Mobilise-D consortium
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Electronics and Telecommunications, Politecnico Di Torino, Turin, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
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9
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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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10
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Salis F, Bertuletti S, Bonci T, Caruso M, Scott K, Alcock L, Buckley E, Gazit E, Hansen C, Schwickert L, Aminian K, Becker C, Brown P, Carsin AE, Caulfield B, Chiari L, D’Ascanio I, Del Din S, Eskofier BM, Garcia-Aymerich J, Hausdorff JM, Hume EC, Kirk C, Kluge F, Koch S, Kuederle A, Maetzler W, Micó-Amigo EM, Mueller A, Neatrour I, Paraschiv-Ionescu A, Palmerini L, Yarnall AJ, Rochester L, Sharrack B, Singleton D, Vereijken B, Vogiatzis I, Della Croce U, Mazzà C, Cereatti A. A multi-sensor wearable system for the assessment of diseased gait in real-world conditions. Front Bioeng Biotechnol 2023; 11:1143248. [PMID: 37214281 PMCID: PMC10194657 DOI: 10.3389/fbioe.2023.1143248] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 03/30/2023] [Indexed: 05/24/2023] Open
Abstract
Introduction: Accurately assessing people's gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72-4.87 steps/min, stride length 0.04-0.06 m, walking speed 0.03-0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.
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Affiliation(s)
- Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
| | - Tecla Bonci
- Department of Mechanical Engineering, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Marco Caruso
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico Di Torino, Torino, Italy
| | - Kirsty Scott
- Department of Mechanical Engineering, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Ellen Buckley
- Department of Mechanical Engineering, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Eran Gazit
- Centre for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel
| | - Clint Hansen
- Department of Neurology, University Medical Centre Schleswig-Holstein Campus Kiel and Kiel University, Kiel, Germany
| | - Lars Schwickert
- Department for Geriatric Rehabilitation, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Clemens Becker
- Department for Geriatric Rehabilitation, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Philip Brown
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Anne-Elie Carsin
- Instituto de Salud Global Barcelona, Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Centre 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
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Judith Garcia-Aymerich
- Instituto de Salud Global Barcelona, Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Jeffrey M. Hausdorff
- Centre for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel
| | - Emily C. Hume
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Northumbia, United Kingdom
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Sarah Koch
- Instituto de Salud Global Barcelona, Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Arne Kuederle
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Walter Maetzler
- Department of Neurology, University Medical Centre Schleswig-Holstein Campus Kiel and Kiel University, Kiel, Germany
| | - Encarna M. Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Arne Mueller
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Isabel Neatrour
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Centre for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Alison J. Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - David Singleton
- Insight Centre for Data Analytics, 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, Faculty of Health and Life Sciences, Northumbria University, Northumbia, United Kingdom
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
| | - Claudia Mazzà
- Department of Mechanical Engineering, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Andrea Cereatti
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico Di Torino, Torino, Italy
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Popham S, Burq M, Rainaldi EE, Shin S, Dunn J, Kapur R. An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study. JMIR BIOMEDICAL ENGINEERING 2023; 8:e43726. [PMID: 38875664 PMCID: PMC11041455 DOI: 10.2196/43726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/05/2022] [Accepted: 01/19/2023] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Measuring the amount of physical activity and its patterns using wearable sensor technology in real-world settings can provide critical insights into health status. OBJECTIVE This study's aim was to develop and evaluate the analytical validity and transdemographic generalizability of an algorithm that classifies binary ambulatory status (yes or no) on the accelerometer signal from wrist-worn biometric monitoring technology. METHODS Biometric monitoring technology algorithm validation traditionally relies on large numbers of self-reported labels or on periods of high-resolution monitoring with reference devices. We used both methods on data collected from 2 distinct studies for algorithm training and testing, one with precise ground-truth labels from a reference device (n=75) and the second with participant-reported ground-truth labels from a more diverse, larger sample (n=1691); in total, we collected data from 16.7 million 10-second epochs. We trained a neural network on a combined data set and measured performance in multiple held-out testing data sets, overall and in demographically stratified subgroups. RESULTS The algorithm was accurate at classifying ambulatory status in 10-second epochs (area under the curve 0.938; 95% CI 0.921-0.958) and on daily aggregate metrics (daily mean absolute percentage error 18%; 95% CI 15%-20%) without significant performance differences across subgroups. CONCLUSIONS Our algorithm can accurately classify ambulatory status with a wrist-worn device in real-world settings with generalizability across demographic subgroups. The validated algorithm can effectively quantify users' walking activity and help researchers gain insights on users' health status.
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Affiliation(s)
- Sara Popham
- Verily Life Sciences, South San Francisco, CA, United States
| | - Maximilien Burq
- Verily Life Sciences, South San Francisco, CA, United States
| | - Erin E Rainaldi
- Verily Life Sciences, South San Francisco, CA, United States
| | - Sooyoon Shin
- Verily Life Sciences, South San Francisco, CA, United States
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, United States
- Duke Clinical Research Institute, Durham, NC, United States
| | - Ritu Kapur
- Verily Life Sciences, South San Francisco, CA, United States
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12
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Delgado-Ortiz L, Polhemus A, Keogh A, Sutton N, Remmele W, Hansen C, Kluge F, Sharrack B, Becker C, Troosters T, Maetzler W, Rochester L, Frei A, Puhan MA, Garcia-Aymerich J. Listening to the patients' voice: a conceptual framework of the walking experience. Age Ageing 2023; 52:7008636. [PMID: 36729471 PMCID: PMC9894103 DOI: 10.1093/ageing/afac233] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 06/27/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND walking is crucial for an active and healthy ageing, but the perspectives of individuals living with walking impairment are still poorly understood. OBJECTIVES to identify and synthesise evidence describing walking as experienced by adults living with mobility-impairing health conditions and to propose an empirical conceptual framework of walking experience. METHODS we performed a systematic review and meta-ethnography of qualitative evidence, searching seven electronic databases for records that explored personal experiences of walking in individuals living with conditions of diverse aetiology. Conditions included Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, hip fracture, heart failure, frailty and sarcopenia. Data were extracted, critically appraised using the NICE quality checklist and synthesised using standardised best practices. RESULTS from 2,552 unique records, 117 were eligible. Walking experience was similar across conditions and described by seven themes: (i) becoming aware of the personal walking experience, (ii) the walking experience as a link between individuals' activities and sense of self, (iii) the physical walking experience, (iv) the mental and emotional walking experience, (v) the social walking experience, (vi) the context of the walking experience and (vii) behavioural and attitudinal adaptations resulting from the walking experience. We propose a novel conceptual framework that visually represents the walking experience, informed by the interplay between these themes. CONCLUSION a multi-faceted and dynamic experience of walking was common across health conditions. Our conceptual framework of the walking experience provides a novel theoretical structure for patient-centred clinical practice, research and public health.
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Affiliation(s)
| | | | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | | | | | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, UK
| | - Clemens Becker
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Thierry Troosters
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium,Department of Respiratory Diseases, University Hospitals Leuven, Leuven, Belgium
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Anja Frei
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Milo A Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Judith Garcia-Aymerich
- Address correspondence to: J. Garcia-Aymerich, ISGlobal, Dr. Aiguader 88, PRBB. Barcelona, Spain. Tel: (+34) 93 214 73 80;
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13
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Palmerini L, Reggi L, Bonci T, Del Din S, Micó-Amigo ME, Salis F, Bertuletti S, Caruso M, Cereatti A, Gazit E, Paraschiv-Ionescu A, Soltani A, Kluge F, Küderle A, Ullrich M, Kirk C, Hiden H, D’Ascanio I, Hansen C, Rochester L, Mazzà C, Chiari L. Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization. Sci Data 2023; 10:38. [PMID: 36658136 PMCID: PMC9852581 DOI: 10.1038/s41597-023-01930-9] [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: 04/12/2022] [Accepted: 01/03/2023] [Indexed: 01/21/2023] Open
Abstract
Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced.
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Affiliation(s)
- Luca Palmerini
- grid.6292.f0000 0004 1757 1758University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy ,grid.6292.f0000 0004 1757 1758University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy
| | - Luca Reggi
- grid.6292.f0000 0004 1757 1758University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy
| | - Tecla Bonci
- grid.11835.3e0000 0004 1936 9262The University of Sheffield, INSIGNEO Institute for in silico Medicine, Sheffield, UK ,grid.11835.3e0000 0004 1936 9262The University of Sheffield, Department of Mechanical Engineering, Sheffield, UK
| | - Silvia Del Din
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK
| | - M. Encarna Micó-Amigo
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK
| | - Francesca Salis
- grid.11450.310000 0001 2097 9138University of Sassari, Department of Biomedical Sciences, Sassari, Italy
| | - Stefano Bertuletti
- grid.11450.310000 0001 2097 9138University of Sassari, Department of Biomedical Sciences, Sassari, Italy
| | - Marco Caruso
- grid.4800.c0000 0004 1937 0343Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy ,grid.4800.c0000 0004 1937 0343Politecnico di Torino, PolitoBIOMed Lab – Biomedical Engineering Lab, Torino, Italy
| | - Andrea Cereatti
- grid.4800.c0000 0004 1937 0343Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy
| | - Eran Gazit
- grid.413449.f0000 0001 0518 6922Tel Aviv Sourasky Medical Center, Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv-Yafo, Israel
| | - Anisoara Paraschiv-Ionescu
- grid.5333.60000000121839049Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Abolfazl Soltani
- grid.5333.60000000121839049Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Felix Kluge
- grid.5330.50000 0001 2107 3311Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Arne Küderle
- grid.5330.50000 0001 2107 3311Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Martin Ullrich
- grid.5330.50000 0001 2107 3311Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Cameron Kirk
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK
| | - Hugo Hiden
- grid.1006.70000 0001 0462 7212Newcastle University, School of Computing, Newcastle, UK
| | - Ilaria D’Ascanio
- grid.6292.f0000 0004 1757 1758University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy
| | - Clint Hansen
- grid.412468.d0000 0004 0646 2097Neurogeriatrics Kiel, Department of Neurology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Lynn Rochester
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK ,The Newcastle upon Tyne NHS Foundation Trust, Newcastle, UK
| | - Claudia Mazzà
- grid.11835.3e0000 0004 1936 9262The University of Sheffield, INSIGNEO Institute for in silico Medicine, Sheffield, UK ,grid.11835.3e0000 0004 1936 9262The University of Sheffield, Department of Mechanical Engineering, Sheffield, UK
| | - Lorenzo Chiari
- grid.6292.f0000 0004 1757 1758University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy ,grid.6292.f0000 0004 1757 1758University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy
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14
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Clay I, Peerenboom N, Connors DE, Bourke S, Keogh A, Wac K, Gur-Arie T, Baker J, Bull C, Cereatti A, Cormack F, Eggenspieler D, Foschini L, Ganea R, Groenen PM, Gusset N, Izmailova E, Kanzler CM, Leyens L, Lyden K, Mueller A, Nam J, Ng WF, Nobbs D, Orfaniotou F, Perumal TM, Piwko W, Ries A, Scotland A, Taptiklis N, Torous J, Vereijken B, Xu S, Baltzer L, Vetter T, Goldhahn J, Hoffmann SC. Reverse Engineering of Digital Measures: Inviting Patients to the Conversation. Digit Biomark 2023; 7:28-44. [PMID: 37206894 PMCID: PMC10189241 DOI: 10.1159/000530413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 03/07/2023] [Indexed: 05/21/2023] Open
Abstract
Background Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures. Summary In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled "Reverse Engineering of Digital Measures," was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools. Key Messages In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.
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Affiliation(s)
| | | | | | | | - Alison Keogh
- Insight Centre for Data Analytics, UC Dublin, Dublin, Ireland
- Mobilise-D, Newcastle University, Newcastle upon Tyne, UK
| | - Katarzyna Wac
- Quality of Life Lab, University of Geneva, Geneva, Switzerland
| | - Tova Gur-Arie
- Mobilise-D, Newcastle University, Newcastle upon Tyne, UK
| | | | - Christopher Bull
- Newcastle University, Newcastle, UK
- IDEA-FAST, Newcastle University, Newcastle upon Tyne, UK
| | - Andrea Cereatti
- Mobilise-D, Newcastle University, Newcastle upon Tyne, UK
- Polytechnic University of Torino, Torino, Italy
| | - Francesca Cormack
- IDEA-FAST, Newcastle University, Newcastle upon Tyne, UK
- Cambridge Cognition Ltd, Cambridge, UK
| | | | | | | | | | | | | | | | | | | | - Arne Mueller
- Mobilise-D, Newcastle University, Newcastle upon Tyne, UK
- Novartis, Basel, Switzerland
| | - Julian Nam
- F. Hoffmann-La Roche, Basel, Switzerland
| | - Wan-Fai Ng
- Newcastle University, Newcastle, UK
- IDEA-FAST, Newcastle University, Newcastle upon Tyne, UK
| | - David Nobbs
- IDEA-FAST, Newcastle University, Newcastle upon Tyne, UK
- F. Hoffmann-La Roche, Basel, Switzerland
| | | | | | - Wojciech Piwko
- Takeda Pharmaceuticals International, Zurich, Switzerland
| | - Anja Ries
- F. Hoffmann-La Roche, Basel, Switzerland
| | - Alf Scotland
- Biogen Digital Health International GmbH, Baar, Switzerland
| | - Nick Taptiklis
- IDEA-FAST, Newcastle University, Newcastle upon Tyne, UK
- Cambridge Cognition Ltd, Cambridge, UK
| | | | - Beatrix Vereijken
- Mobilise-D, Newcastle University, Newcastle upon Tyne, UK
- Norwegian University of Science and Technology, Trondheim, Norway
| | | | | | | | - Jörg Goldhahn
- Swiss Federal Institute of Technology, Zurich, Switzerland
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15
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The value of walking: a systematic review on mobility and healthcare costs. Eur Rev Aging Phys Act 2022; 19:31. [PMID: 36581809 PMCID: PMC9798720 DOI: 10.1186/s11556-022-00310-3] [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: 07/15/2022] [Accepted: 12/03/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The ability to walk is an important indicator of general health and mobility deficits have wide-ranging economic implications. We undertook a systematic review to elucidate the impact of walking parameters on health care costs. METHODS: Publications reporting on associations between health care costs and walking parameters were identified by a systematic literature search in MEDLINE, Embase, and manual reference screening, following the PRISMA reporting guidelines. First, titles and abstracts were screened by two independent reviewers followed by a review of the full articles if they met the inclusion criteria. Costs were converted to US-Dollars with inflation adjustment for 2021. A narrative synthesis was performed. RESULTS: Ten studies conducted between 2001 and 2021 fulfilled the inclusion criteria. Assessment of walking ability was carried out via patient reported outcomes, performance tests, or using wearable digital devices. Walking more than one hour per day, a faster walking speed and the ability to walk without impairments are associated with significant lower health care costs. A higher number of steps per day is associated with significant lower costs in two simulation studies, while in the study using a digital device, taking more than 10,000 steps per day is not significantly associated with lower direct costs. The heterogeneity of mobility assessments and of economic analyses both precluded a quantitative synthesis. CONCLUSION Cross-sectional and observational studies from this systematic review suggest a significant association of better walking performance with lower health care costs. Future health economic research and health technology assessments should use quantifiable mobility outcomes when evaluating new drugs or non-pharmacological interventions.
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16
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Scott K, Bonci T, Salis F, Alcock L, Buckley E, Gazit E, Hansen C, Schwickert L, Aminian K, Bertuletti S, Caruso M, Chiari L, Sharrack B, Maetzler W, Becker C, Hausdorff JM, Vogiatzis I, Brown P, Del Din S, Eskofier B, Paraschiv-Ionescu A, Keogh A, Kirk C, Kluge F, Micó-Amigo EM, Mueller A, Neatrour I, Niessen M, Palmerini L, Sillen H, Singleton D, Ullrich M, Vereijken B, Froehlich M, Brittain G, Caulfield B, Koch S, Carsin AE, Garcia-Aymerich J, Kuederle A, Yarnall A, Rochester L, Cereatti A, Mazzà C. Design and validation of a multi-task, multi-context protocol for real-world gait simulation. J Neuroeng Rehabil 2022; 19:141. [PMID: 36522646 PMCID: PMC9754996 DOI: 10.1186/s12984-022-01116-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/23/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. METHODS The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants' strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. RESULTS The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. CONCLUSIONS The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. TRIAL REGISTRATION ISRCTN-12246987.
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Affiliation(s)
- Kirsty Scott
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK. .,Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK.
| | - Tecla Bonci
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK.,Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Ellen Buckley
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK.,Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Lars Schwickert
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Marco Caruso
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy.,Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.,PolitoBIOMed Lab, Biomedical Engineering Lab, 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
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Clemens Becker
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Philip Brown
- 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
| | - Björn Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - 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
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - 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
| | - Encarna M Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Arne Mueller
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Isabel Neatrour
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | | | - 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
| | | | - 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
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Gavin Brittain
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - 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
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - 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
| | - 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
| | - Arne Kuederle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Alison Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.,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.,Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Andrea Cereatti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy.,Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.,PolitoBIOMed Lab, Biomedical Engineering Lab, Politecnico di Torino, Turin, Italy
| | - Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK.,Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
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17
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Jahn K. Ganganalyse bei neurologischen Erkrankungen. KLIN NEUROPHYSIOL 2022. [DOI: 10.1055/a-1822-3227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Gangunsicherheit und Mobilitätseinschränkungen sind bei
neurologischen Patienten ein häufiges Leitsymptom. Gerade bei
älteren Patienten wird oft nicht gut differenziert, welche
Funktionsstörungen im Einzelnen vorliegen. Qualitative und quantitative
Analysen helfen, Teilursachen des Defizits zu differenzieren und den Verlauf
nachvollziehbar zu dokumentieren. Die Messung kann die Therapie steuern, deren
Erfolg dann erneut aufgezeichnet werden kann.
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18
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Hamilton RI, Williams J, Holt C. Biomechanics beyond the lab: Remote technology for osteoarthritis patient data-A scoping review. FRONTIERS IN REHABILITATION SCIENCES 2022; 3:1005000. [PMID: 36451804 PMCID: PMC9701737 DOI: 10.3389/fresc.2022.1005000] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 10/05/2022] [Indexed: 01/14/2024]
Abstract
The objective of this project is to produce a review of available and validated technologies suitable for gathering biomechanical and functional research data in patients with osteoarthritis (OA), outside of a traditionally fixed laboratory setting. A scoping review was conducted using defined search terms across three databases (Scopus, Ovid MEDLINE, and PEDro), and additional sources of information from grey literature were added. One author carried out an initial title and abstract review, and two authors independently completed full-text screenings. Out of the total 5,164 articles screened, 75 were included based on inclusion criteria covering a range of technologies in articles published from 2015. These were subsequently categorised by technology type, parameters measured, level of remoteness, and a separate table of commercially available systems. The results concluded that from the growing number of available and emerging technologies, there is a well-established range in use and further in development. Of particular note are the wide-ranging available inertial measurement unit systems and the breadth of technology available to record basic gait spatiotemporal measures with highly beneficial and informative functional outputs. With the majority of technologies categorised as suitable for part-remote use, the number of technologies that are usable and fully remote is rare and they usually employ smartphone software to enable this. With many systems being developed for camera-based technology, such technology is likely to increase in usability and availability as computational models are being developed with increased sensitivities to recognise patterns of movement, enabling data collection in the wider environment and reducing costs and creating a better understanding of OA patient biomechanical and functional movement data.
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Affiliation(s)
- Rebecca I. Hamilton
- Musculoskeletal Biomechanics Research Facility, School of Engineering, Cardiff University, Cardiff, United Kingdom
| | - Jenny Williams
- Musculoskeletal Biomechanics Research Facility, School of Engineering, Cardiff University, Cardiff, United Kingdom
| | | | - Cathy Holt
- Musculoskeletal Biomechanics Research Facility, School of Engineering, Cardiff University, Cardiff, United Kingdom
- Osteoarthritis Technology NetworkPlus (OATech+), EPSRC UK-Wide Research Network+, United Kingdom
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19
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Braun T, Wiegard A, Geritz J, Hansen C, Tan KE, Hildesheim H, Kudelka J, Maetzler C, Welzel J, Romijnders R, Maetzler W, Bergmann P. Association between heart failure severity and mobility in geriatric patients: an in-clinic study with wearable sensors. J Geriatr Cardiol 2022; 19:660-674. [PMID: 36284678 PMCID: PMC9548060 DOI: 10.11909/j.issn.1671-5411.2022.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023] Open
Abstract
BACKGROUND Individuals with heart failure (HF) frequently experience limitations in mobility, but specific aspects of these limitations are not well understood. This study investigated the association of HF severity, based on the New York Heart Association (NYHA) classes, with digital mobility outcomes (DMOs) and handgrip strength in older inpatients with HF. METHODS For this explorative analysis, hospital admission and discharge data from an ongoing, prospective cohort study were used. The sample included older participants with HF and a sub-sample of heart-healthy individuals. Participants were equipped with a wearable inertial measurement unit (IMU) system during mobility performance (balancing, sit-to-stand transfer, walking). We analyzed the association between 17 DMOs and HF severity with multiple linear regression models. RESULTS The total sample included 61 older participants (65-97 years of age, 55.7% female). Of all DMOs, only sway path in a semi-tandem stance position (m/s²) showed a relevant association with NYHA classes (admission: β = -0.28, P = 0.09; discharge: β = -0.39, P = 0.02). Handgrip strength showed a trend towards a significant association (admission: β = -0.15, P = 0.10; discharge: β = -0.15, P = 0.19). CONCLUSIONS This is to our best knowledge the first analysis on the association of HF severity and IMU-based DMOs. Sway path and handgrip strength may be the most promising parameters for monitoring mobility aspects in treatment of HF.
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Affiliation(s)
- Tobias Braun
- Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany
- Department of Applied Health Sciences, Division of Physiotherapy, Hochschule für Gesundheit (University of Applied Sciences), Gesundheitscampus 6-8, 44801 Bochum, Germany
- HSD Hochschule Döpfer (University of Applied Sciences), Waidmarkt 3 und 9, 50676 Cologne, Germany
| | - Anna Wiegard
- Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Johanna Geritz
- Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Clint Hansen
- Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Kim Eng Tan
- Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Hanna Hildesheim
- Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Jennifer Kudelka
- Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Corina Maetzler
- Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Julius Welzel
- Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Robbin Romijnders
- Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Walter Maetzler
- Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Philipp Bergmann
- Department of Internal Medicine I, Christian-Albrechts-University of Kiel, Arnold-Heller-Straße 3, 24105 Kiel, Germany
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20
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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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21
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Roth N, Ullrich M, Kuderle A, Gladow T, Marxreiter F, Gassner H, Kluge F, Klucken J, Eskofier BM. Real-World Stair Ambulation Characteristics Differ Between Prospective Fallers and Non-Fallers in Parkinson's Disease. IEEE J Biomed Health Inform 2022; 26:4733-4742. [PMID: 35759602 DOI: 10.1109/jbhi.2022.3186766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Falls are among the leading causes of injuries or death for individuals from the age of 65 and the prevalence of falls is especially high for patients suffering from neurological diseases like Parkinson's disease (PD). Due to advancements in wearable sensor technology, inertial measurement units (IMUs) can be integrated unobtrusively into patients' everyday lives to monitor various mobility and gait parameters, which are related to common risk factors like reduced balance and reduced lower-limb muscle strength, or lower range of joints. Although stair ambulation is a fundamental part of our daily lives and is known for its unique challenges for the gait and balance system, long-term gait analysis studies have not investigated real-world stair ambulation parameters yet. Therefore, we applied a recently published gait analysis pipeline on real-world foot-worn IMU data of 40 PD patients over a recording period of two weeks to extract objective gait parameters from level walking but also from stair ascending and stair descending gait. In combination with fall records from a prospective three-month follow-up phase, we investigated group differences in gait parameters of future fallers compared to non-fallers for each individual gait activity. We found significant differences in stair ascending and descending parameters. Stance time was increased by up to 20% and gait speed reduced by up to 16% for fallers compared to non-fallers during stair walking. These differences were not present in level walking parameters. Hence, these results suggest that real-world stair ambulation provides sensitive parameters for mobility and fall risk due to the unique challenges stairs add to the balance and control system. Our work complements existing gait analysis studies by adding new insights into mobility and gait performance during real-world gait.
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22
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Bonci T, Salis F, Scott K, Alcock L, Becker C, Bertuletti S, Buckley E, Caruso M, Cereatti A, Del Din S, Gazit E, Hansen C, Hausdorff JM, Maetzler W, Palmerini L, Rochester L, Schwickert L, Sharrack B, Vogiatzis I, Mazzà C. An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks. Front Bioeng Biotechnol 2022; 10:868928. [PMID: 35721859 PMCID: PMC9201978 DOI: 10.3389/fbioe.2022.868928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, and accuracy were calculated. Absolute agreement [intraclass correlation coefficient (ICC2,1)] between marker-based and pressure insole stride parameters was also tested. In the YA cohort, the proposed method outperformed the existing ones, with sensitivity, PPV, and F1 scores ≥ 99% for both GEs and conditions, with a virtually null bias (<10 ms). Overall, temporal inaccuracies minimally impacted stride duration, length, and speed (median absolute errors ≤1%). Similar algorithm performances were obtained for all the other five cohorts in GE detection and propagation to the stride parameters, where an excellent absolute agreement with the pressure insoles was also found (ICC2,1=0.817− 0.999). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments.
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Affiliation(s)
- Tecla Bonci
- Department of Mechanical Engineering, Insigno Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- *Correspondence: Tecla Bonci,
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kirsty Scott
- Department of Mechanical Engineering, Insigno Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Clemens Becker
- Department for Geriatric Rehabilitation, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Ellen Buckley
- Department of Mechanical Engineering, Insigno Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Marco Caruso
- Department of Electronics and Telecommunications, Politecnico Di Torino, Torino, Italy
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico Di Torino, Torino, Italy
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Eran Gazit
- Centre for the Study of Movement, Cognition and Mobility, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel
| | - Clint Hansen
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel University, Kiel, Germany
| | - Jeffrey M. Hausdorff
- Centre for the Study of Movement, Cognition and Mobility, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine, Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Orthopaedic Surgery, Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States
| | - Walter Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel University, Kiel, Germany
| | - 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
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Lars Schwickert
- Department for Geriatric Rehabilitation, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Basil Sharrack
- Department of Neuroscience, Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle Upon Tyne, United Kingdom
| | - Claudia Mazzà
- Department of Mechanical Engineering, Insigno Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
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23
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Mellema M, Gjøvaag T. Reported Outcome Measures in Studies of Real-World Ambulation in People with a Lower Limb Amputation: A Scoping Review. SENSORS 2022; 22:s22062243. [PMID: 35336412 PMCID: PMC8955603 DOI: 10.3390/s22062243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 12/02/2022]
Abstract
Background: The rapidly increasing use of wearable technology to monitor free-living ambulatory behavior demands to address to what extent the chosen outcome measures are representative for real-world situations. This scoping review aims to provide an overview of the purpose of use of wearable activity monitors in people with a Lower Limb Amputation (LLA) in the real world, to identify the reported outcome measures, and to evaluate to what extent the reported outcome measures capture essential information from real-world ambulation of people with LLA. Methods: The literature search included a search in three databases (MEDLINE, CINAHL, and EMBASE) for articles published between January 1999 and January 2022, and a hand-search. Results and conclusions: 98 articles met the inclusion criteria. According to the included studies’ main objective, the articles were classified into observational (n = 46), interventional (n = 34), algorithm/method development (n = 12), and validity/feasibility studies (n = 6). Reported outcome measures were grouped into eight categories: step count (reported in 73% of the articles), intensity of activity/fitness (31%), type of activity/body posture (27%), commercial scores (15%), prosthetic use and fit (11%), gait quality (7%), GPS (5%), and accuracy (4%). We argue that researchers should be more careful with choosing reliable outcome measures, in particular, regarding the frequently used category step count. However, the contemporary technology is limited in providing a comprehensive picture of real-world ambulation. The novel knowledge from this review should encourage researchers and developers to engage in debating and defining the framework of ecological validity in rehabilitation sciences, and how this framework can be utilized in the development of wearable technologies and future studies of real-world ambulation in people with LLA.
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Affiliation(s)
- Mirjam Mellema
- Department of Mechanical, Electronic and Chemical Engineering, Faculty of Technology, Art and Design, Oslo Metropolitan University, P.O. Box 4, St. Olavs Plass, 0130 Oslo, Norway
- Department of Occupational Therapy, Prosthetics and Orthotics, Faculty of Health Sciences, Oslo Metropolitan University, P.O. Box 4, St. Olavs Plass, 0130 Oslo, Norway;
- Correspondence:
| | - Terje Gjøvaag
- Department of Occupational Therapy, Prosthetics and Orthotics, Faculty of Health Sciences, Oslo Metropolitan University, P.O. Box 4, St. Olavs Plass, 0130 Oslo, Norway;
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24
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Mazzà C, Alcock L, Aminian K, Becker C, Bertuletti S, Bonci T, Brown P, Brozgol M, Buckley E, Carsin AE, Caruso M, Caulfield B, Cereatti A, Chiari L, Chynkiamis N, Ciravegna F, Del Din S, Eskofier B, Evers J, Garcia Aymerich J, Gazit E, Hansen C, Hausdorff JM, Helbostad JL, Hiden H, Hume E, Paraschiv-Ionescu A, Ireson N, Keogh A, Kirk C, Kluge F, Koch S, Küderle A, Lanfranchi V, Maetzler W, Micó-Amigo ME, Mueller A, Neatrour I, Niessen M, Palmerini L, Pluimgraaff L, Reggi L, Salis F, Schwickert L, Scott K, Sharrack B, Sillen H, Singleton D, Soltani A, Taraldsen K, Ullrich M, Van Gelder L, Vereijken B, Vogiatzis I, Warmerdam E, Yarnall A, Rochester L. Technical validation of real-world monitoring of gait: a multicentric observational study. BMJ Open 2021; 11:e050785. [PMID: 34857567 PMCID: PMC8640671 DOI: 10.1136/bmjopen-2021-050785] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users' perspective on the device. METHODS AND ANALYSIS This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs.After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson's disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users' perspective on the deployed technology and relevance of the mobility assessment. ETHICS AND DISSEMINATION The study has been granted ethics approval by the centre's committees (London-Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available. TRIAL REGISTRATION NUMBER ISRCTN (12246987).
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Affiliation(s)
- Claudia Mazzà
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, 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 Biomedical Sciences, University of Sassari, Sassari, Sardegna, Italy
| | - Tecla Bonci
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Philip Brown
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Marina Brozgol
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Ellen Buckley
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Anne-Elie Carsin
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Marco Caruso
- Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy
- PolitoBIOMed Lab - Biomedical Engineering Lab, Politecnico di Torino, Torino, Italy
| | - Brian Caulfield
- Insight Centre for Data Analytics, O'Brien Science Centre, University College Dublin, Dublin, Ireland
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Andrea Cereatti
- Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, 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
| | - Nikolaos Chynkiamis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Fabio Ciravegna
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Computer Science, The University of Sheffield, Sheffield, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Björn Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jordi Evers
- McRoberts BV, Den Haag, Zuid-Holland, Netherlands
| | - Judith Garcia Aymerich
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - 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
- Department of Physical Therapy, Sackler Faculty of Medicine & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jorunn L Helbostad
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - 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
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Neil Ireson
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Computer Science, The University of Sheffield, Sheffield, UK
| | - Alison Keogh
- Insight Centre for Data Analytics, O'Brien Science Centre, University College Dublin, Dublin, Ireland
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sarah Koch
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Vitaveska Lanfranchi
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Computer Science, The University of Sheffield, Sheffield, UK
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Arne Mueller
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Isabel Neatrour
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | | | - 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
| | | | - Luca Reggi
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Sardegna, Italy
| | - Lars Schwickert
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Kirsty Scott
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, 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
| | - Henrik Sillen
- Digital Health R&D, AstraZeneca Sweden, Sodertalje, Sweden
| | - David Singleton
- Insight Centre for Data Analytics, O'Brien Science Centre, University College Dublin, Dublin, Ireland
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Abolfazi Soltani
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Kristin Taraldsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Linda Van Gelder
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - 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
| | - Elke Warmerdam
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Alison Yarnall
- 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
| | - Lynn Rochester
- 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
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25
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Habets JGV, Herff C, Kubben PL, Kuijf ML, Temel Y, Evers LJW, Bloem BR, Starr PA, Gilron R, Little S. Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer. SENSORS 2021; 21:s21237876. [PMID: 34883886 PMCID: PMC8659489 DOI: 10.3390/s21237876] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/07/2023]
Abstract
Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.
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Affiliation(s)
- Jeroen G. V. Habets
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
- Correspondence: ; Tel.: +31-433-876-052
| | - Christian Herff
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Pieter L. Kubben
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Mark L. Kuijf
- Department of Neurology, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands;
| | - Yasin Temel
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Luc J. W. Evers
- Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GC Nijmegen, The Netherlands; (L.J.W.E.); (B.R.B.)
| | - Bastiaan R. Bloem
- Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GC Nijmegen, The Netherlands; (L.J.W.E.); (B.R.B.)
| | - Philip A. Starr
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
| | - Ro’ee Gilron
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
| | - Simon Little
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
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26
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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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27
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An Inertial Sensor-Based Gait Analysis Pipeline for the Assessment of Real-World Stair Ambulation Parameters. SENSORS 2021; 21:s21196559. [PMID: 34640878 PMCID: PMC8513040 DOI: 10.3390/s21196559] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 12/27/2022]
Abstract
Climbing stairs is a fundamental part of daily life, adding additional demands on the postural control system compared to level walking. Although real-world gait analysis studies likely contain stair ambulation sequences, algorithms dedicated to the analysis of such activities are still missing. Therefore, we propose a new gait analysis pipeline for foot-worn inertial sensors, which can segment, parametrize, and classify strides from continuous gait sequences that include level walking, stair ascending, and stair descending. For segmentation, an existing approach based on the hidden Markov model and a feature-based gait event detection were extended, reaching an average segmentation F1 score of 98.5% and gait event timing errors below ±10ms for all conditions. Stride types were classified with an accuracy of 98.2% using spatial features derived from a Kalman filter-based trajectory reconstruction. The evaluation was performed on a dataset of 20 healthy participants walking on three different staircases at different speeds. The entire pipeline was additionally validated end-to-end on an independent dataset of 13 Parkinson’s disease patients. The presented work aims to extend real-world gait analysis by including stair ambulation parameters in order to gain new insights into mobility impairments that can be linked to clinically relevant conditions such as a patient’s fall risk and disease state or progression.
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