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Keogh A, Mc Ardle R, Diaconu MG, Ammour N, Arnera V, Balzani F, Brittain G, Buckley E, Buttery S, Cantu A, Corriol-Rohou S, Delgado-Ortiz L, Duysens J, Forman-Hardy T, Gur-Arieh T, Hamerlijnck D, Linnell J, Leocani L, McQuillan T, Neatrour I, Polhemus A, Remmele W, Saraiva I, Scott K, Sutton N, van den Brande K, Vereijken B, Wohlrab M, Rochester L. Mobilizing Patient and Public Involvement in the Development of Real-World Digital Technology Solutions: Tutorial. J Med Internet Res 2023; 25:e44206. [PMID: 37889531 PMCID: PMC10638632 DOI: 10.2196/44206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 08/09/2023] [Accepted: 08/31/2023] [Indexed: 10/28/2023] Open
Abstract
Although the value of patient and public involvement and engagement (PPIE) activities in the development of new interventions and tools is well known, little guidance exists on how to perform these activities in a meaningful way. This is particularly true within large research consortia that target multiple objectives, include multiple patient groups, and work across many countries. Without clear guidance, there is a risk that PPIE may not capture patient opinions and needs correctly, thereby reducing the usefulness and effectiveness of new tools. Mobilise-D is an example of a large research consortium that aims to develop new digital outcome measures for real-world walking in 4 patient cohorts. Mobility is an important indicator of physical health. As such, there is potential clinical value in being able to accurately measure a person's mobility in their daily life environment to help researchers and clinicians better track changes and patterns in a person's daily life and activities. To achieve this, there is a need to create new ways of measuring walking. Recent advancements in digital technology help researchers meet this need. However, before any new measure can be used, researchers, health care professionals, and regulators need to know that the digital method is accurate and both accepted by and produces meaningful outcomes for patients and clinicians. Therefore, this paper outlines how PPIE structures were developed in the Mobilise-D consortium, providing details about the steps taken to implement PPIE, the experiences PPIE contributors had within this process, the lessons learned from the experiences, and recommendations for others who may want to do similar work in the future. The work outlined in this paper provided the Mobilise-D consortium with a foundation from which future PPIE tasks can be created and managed with clearly defined collaboration between researchers and patient representatives across Europe. This paper provides guidance on the work required to set up PPIE structures within a large consortium to promote and support the creation of meaningful and efficient PPIE related to the development of digital mobility outcomes.
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Affiliation(s)
- Alison Keogh
- Insight Centre Data Analytics, University College Dublin, Dublin4, Ireland
- School of Medicine, Trinity College Dublin, Dublin2, Ireland
| | - Ríona Mc Ardle
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
| | - Mara Gabriela Diaconu
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nadir Ammour
- Clinical Science and Operations, Global Development, Sanofi Research & Development, Chilly-Mazarin, France
| | - Valdo Arnera
- Clario, Clario Holdings Inc, Geneva, Switzerland
| | - Federica Balzani
- Mobilise-D Patient and Public Advisory Group, Newcastle, United Kingdom
| | - Gavin Brittain
- Department of Clinical Neurology, Sheffield Teaching Hospitals National Health Service, Foundation Trust, Sheffield, United Kingdom
- Sheffield Institute for Translational Neuroscience, The University of Sheffield, Sheffield, United Kingdom
| | - Ellen Buckley
- Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom
- Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Sara Buttery
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Alma Cantu
- School of Computer Science, Newcastle University, Newcastle, United Kingdom
| | | | - Laura Delgado-Ortiz
- Non-Communicable Diseases and Environment Programme, ISGlobal, Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Centro de Investigación Biomedical en Red Epidemiologia y Salud Publica, Barcelona, Spain
| | - Jacques Duysens
- Mobilise-D Patient and Public Advisory Group, Newcastle, United Kingdom
| | - Tom Forman-Hardy
- Mobilise-D Patient and Public Advisory Group, Newcastle, United Kingdom
| | - Tova Gur-Arieh
- Mobilise-D Patient and Public Advisory Group, Newcastle, United Kingdom
| | | | - John Linnell
- Mobilise-D Patient and Public Advisory Group, Newcastle, United Kingdom
| | - Letizia Leocani
- Department of Neurology, San Raffele University, Milan, Italy
| | - Tom McQuillan
- Mobilise-D Patient and Public Advisory Group, Newcastle, United Kingdom
| | - Isabel Neatrour
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
| | - Ashley Polhemus
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Werner Remmele
- Mobilise-D Patient and Public Advisory Group, Newcastle, United Kingdom
| | - Isabel Saraiva
- Mobilise-D Patient and Public Advisory Group, Newcastle, United Kingdom
| | - Kirsty Scott
- Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom
- Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Norman Sutton
- Mobilise-D Patient and Public Advisory Group, Newcastle, United Kingdom
| | | | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Martin Wohlrab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tuebingen, Tuebingen, Germany
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
- Newcastle Upon Tyne Hospitals National Health Service Foundation Trust, Newcastle, United Kingdom
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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: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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3
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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4
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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: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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5
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Scott DL, Ibrahim F, Hill H, Tom B, Prothero L, Baggott RR, Bosworth A, Galloway JB, Georgopoulou S, Martin N, Neatrour I, Nikiphorou E, Sturt J, Wailoo A, Williams FMK, Williams R, Lempp H. Intensive therapy for moderate established rheumatoid arthritis: the TITRATE research programme. Programme Grants Appl Res 2021. [DOI: 10.3310/pgfar09080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background
Rheumatoid arthritis is a major inflammatory disorder and causes substantial disability. Treatment goals span minimising disease activity, achieving remission and decreasing disability. In active rheumatoid arthritis, intensive management achieves these goals. As many patients with established rheumatoid arthritis have moderate disease activity, the TITRATE (Treatment Intensities and Targets in Rheumatoid Arthritis ThErapy) programme assessed the benefits of intensive management.
Objectives
To (1) define how to deliver intensive therapy in moderate established rheumatoid arthritis; (2) establish its clinical effectiveness and cost-effectiveness in a trial; and (3) evaluate evidence supporting intensive management in observational studies and completed trials.
Design
Observational studies, secondary analyses of completed trials and systematic reviews assessed existing evidence about intensive management. Qualitative research, patient workshops and systematic reviews defined how to deliver it. The trial assessed its clinical effectiveness and cost-effectiveness in moderate established rheumatoid arthritis.
Setting
Observational studies (in three London centres) involved 3167 patients. These were supplemented by secondary analyses of three previously completed trials (in centres across all English regions), involving 668 patients. Qualitative studies assessed expectations (nine patients in four London centres) and experiences of intensive management (15 patients in 10 centres across England). The main clinical trial enrolled 335 patients with diverse socioeconomic deprivation and ethnicity (in 39 centres across all English regions).
Participants
Patients with established moderately active rheumatoid arthritis receiving conventional disease-modifying drugs.
Interventions
Intensive management used combinations of conventional disease-modifying drugs, biologics (particularly tumour necrosis factor inhibitors) and depot steroid injections; nurses saw patients monthly, adjusted treatment and provided supportive person-centred psychoeducation. Control patients received standard care.
Main outcome measures
Disease Activity Score for 28 joints based on the erythrocyte sedimentation rate (DAS28-ESR)-categorised patients (active to remission). Remission (DAS28-ESR < 2.60) was the treatment target. Other outcomes included fatigue (measured on a 100-mm visual analogue scale), disability (as measured on the Health Assessment Questionnaire), harms and resource use for economic assessments.
Results
Evaluation of existing evidence for intensive rheumatoid arthritis management showed the following. First, in observational studies, DAS28-ESR scores decreased over 10–20 years, whereas remissions and treatment intensities increased. Second, in systematic reviews of published trials, all intensive management strategies increased remissions. Finally, patients with high disability scores had fewer remissions. Qualitative studies of rheumatoid arthritis patients, workshops and systematic reviews helped develop an intensive management pathway. A 2-day training session for rheumatology practitioners explained its use, including motivational interviewing techniques and patient handbooks. The trial screened 459 patients and randomised 335 patients (168 patients received intensive management and 167 patients received standard care). A total of 303 patients provided 12-month outcome data. Intention-to-treat analysis showed intensive management increased DAS28-ESR 12-month remissions, compared with standard care (32% vs. 18%, odds ratio 2.17, 95% confidence interval 1.28 to 3.68; p = 0.004), and reduced fatigue [mean difference –18, 95% confidence interval –24 to –11 (scale 0–100); p < 0.001]. Disability (as measured on the Health Assessment Questionnaire) decreased when intensive management patients achieved remission (difference –0.40, 95% confidence interval –0.57 to –0.22) and these differences were considered clinically relevant. However, in all intensive management patients reductions in the Health Assessment Questionnaire scores were less marked (difference –0.1, 95% confidence interval –0.2 to 0.0). The numbers of serious adverse events (intensive management n = 15 vs. standard care n = 11) and other adverse events (intensive management n = 114 vs. standard care n = 151) were similar. Economic analysis showed that the base-case incremental cost-effectiveness ratio was £43,972 from NHS and Personal Social Services cost perspectives. The probability of meeting a willingness-to-pay threshold of £30,000 was 17%. The incremental cost-effectiveness ratio decreased to £29,363 after including patients’ personal costs and lost working time, corresponding to a 50% probability that intensive management is cost-effective at English willingness-to-pay thresholds. Analysing trial baseline predictors showed that remission predictors comprised baseline DAS28-ESR, disability scores and body mass index. A 6-month extension study (involving 95 intensive management patients) showed fewer remissions by 18 months, although more sustained remissions were more likley to persist. Qualitative research in trial completers showed that intensive management was acceptable and treatment support from specialist nurses was beneficial.
Limitations
The main limitations comprised (1) using single time point remissions rather than sustained responses, (2) uncertainty about benefits of different aspects of intensive management and differences in its delivery across centres, (3) doubts about optimal treatment of patients unresponsive to intensive management and (4) the lack of formal international definitions of ‘intensive management’.
Conclusion
The benefits of intensive management need to be set against its additional costs. These were relatively high. Not all patients benefited. Patients with high pretreatment physical disability or who were substantially overweight usually did not achieve remission.
Future work
Further research should (1) identify the most effective components of the intervention, (2) consider its most cost-effective delivery and (3) identify alternative strategies for patients not responding to intensive management.
Trial registration
Current Controlled Trials ISRCTN70160382.
Funding
This project was funded by the National Institute for Health Research (NIHR) Programme Grants for Applied Research programme and will be published in full in Programme Grants for Applied Research; Vol. 9, No. 8. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- David L Scott
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, King’s College London, London, UK
| | - Fowzia Ibrahim
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, King’s College London, London, UK
| | - Harry Hill
- ScHARR Health Economics and Decision Science, The University of Sheffield, Sheffield, UK
| | - Brian Tom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Louise Prothero
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, King’s College London, London, UK
| | - Rhiannon R Baggott
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, King’s College London, London, UK
| | | | - James B Galloway
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, King’s College London, London, UK
| | - Sofia Georgopoulou
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, King’s College London, London, UK
| | - Naomi Martin
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, King’s College London, London, UK
| | - Isabel Neatrour
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, King’s College London, London, UK
| | - Elena Nikiphorou
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, King’s College London, London, UK
| | - Jackie Sturt
- Department of Adult Nursing, Florence Nightingale Faculty of Nursing, Midwifery & Palliative Care, King’s College London, London, UK
| | - Allan Wailoo
- ScHARR Health Economics and Decision Science, The University of Sheffield, Sheffield, UK
| | - Frances MK Williams
- Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Ruth Williams
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, King’s College London, London, UK
| | - Heidi Lempp
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, King’s College London, London, UK
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6
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Scott D, Ibrahim F, Hill H, Tom B, Prothero L, Baggott RR, Bosworth A, Galloway JB, Georgopoulou S, Martin N, Neatrour I, Nikiphorou E, Sturt J, Wailoo A, Williams FMK, Williams R, Lempp H. The clinical effectiveness of intensive management in moderate established rheumatoid arthritis: The titrate trial. Semin Arthritis Rheum 2020; 50:1182-1190. [PMID: 32931984 PMCID: PMC7390769 DOI: 10.1016/j.semarthrit.2020.07.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 07/21/2020] [Accepted: 07/28/2020] [Indexed: 12/31/2022]
Abstract
OBJECTIVES Many trials have shown that intensive management is effective in patients with early active rheumatoid arthritis (RA). But its benefits are unproven for the large number of RA patients seen in routine care who have established, moderately active RA and are already taking conventional synthetic disease modifying anti-rheumatic drugs (csDMARDs). The TITRATE trial studied whether these patients also benefit from intensive management and, in particular, achieve more remissions. METHODS A 12-month multicentre individually randomised trial compared standard care with monthly intensive management appointments which was delivered by specially trained healthcare professionals and incorporated monthly clinical assessments, medication titration and psychosocial support. The primary outcome was 12-month remission assessed using the Disease Activity Score for 28 joints using ESR (DAS28-ESR). Secondary outcomes included fatigue, disability, harms and healthcare costs. Intention-to-treat multivariable logistic- and linear regression analyses compared treatment arms with multiple imputation used for missing data. RESULTS 459 patients were screened and 335 were randomised (168 intensive management; 167 standard care); 303 (90%) patients provided 12-month outcomes. Intensive management increased DAS28-ESR 12-month remissions compared to standard care (32% vs 18%, p = 0.004). Intensive management also significantly increased remissions using a range of alternative remission criteria and increased patients with DAS28-ESR low disease activity scores. (48% vs 32%, p = 0.005). In addition it substantially reduced fatigue (mean difference -18; 95% CI: -24, -11, p<0.001). There was no evidence that serious adverse events (intensive management =15 vs standard care =11) or other adverse events (114 vs 151) significantly increase with intensive management. INTERPRETATION The trial shows that intensive management incorporating psychosocial support delivered by specially trained healthcare professions is effective in moderately active established RA. More patients achieve remissions, there were greater improvements in fatigue, and there were no more harms.
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Affiliation(s)
- David Scott
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London Cutcombe Road, London, SE5 9RJ, United Kingdom
| | - Fowzia Ibrahim
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London Cutcombe Road, London, SE5 9RJ, United Kingdom.
| | - Harry Hill
- ScHARR Health Economics and Decision Science, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, United Kingdom
| | - Brian Tom
- MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, United Kingdom
| | - Louise Prothero
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London Cutcombe Road, London, SE5 9RJ, United Kingdom
| | - Rhiannon R Baggott
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London Cutcombe Road, London, SE5 9RJ, United Kingdom
| | - Ailsa Bosworth
- National Rheumatoid Arthritis Society (NRAS), Switchback Office Park, Gardner Rd, Maidenhead, SL6 7RJ, United Kingdom
| | - James B Galloway
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London Cutcombe Road, London, SE5 9RJ, United Kingdom
| | - Sofia Georgopoulou
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London Cutcombe Road, London, SE5 9RJ, United Kingdom
| | - Naomi Martin
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London Cutcombe Road, London, SE5 9RJ, United Kingdom
| | - Isabel Neatrour
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London Cutcombe Road, London, SE5 9RJ, United Kingdom
| | - Elena Nikiphorou
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London Cutcombe Road, London, SE5 9RJ, United Kingdom
| | - Jackie Sturt
- Department Of Adult Nursing, Florence Nightingale Faculty of Nursing, Midwifery & Palliative Care, King's College London, James Clerk Maxwell Building, 57 Waterloo Road, London SE1 8WA, United Kingdom
| | - Allan Wailoo
- ScHARR Health Economics and Decision Science, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, United Kingdom
| | - Frances M K Williams
- Twin Research & Genetic Epidemiology, School of Life Course Sciences, King's College London, St Thomas' Hospital, London SE1 7EH, United Kingdom
| | - Ruth Williams
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London Cutcombe Road, London, SE5 9RJ, United Kingdom
| | - Heidi Lempp
- Centre for Rheumatic Diseases, Department of Inflammation Biology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London Cutcombe Road, London, SE5 9RJ, United Kingdom
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