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Digital health technologies to strengthen patient-centred outcome assessment in clinical trials in inflammatory arthritis.
McGagh, Dylan; Song, Kaiyang; Yuan, Hang; Creagh, Andrew P; Fenton, Sally; Ng, Wan-Fai; Goldsack, Jennifer C; Dixon, William G; Doherty, Aiden; Coates, Laura C.
Afiliação
  • McGagh D; Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK; Big Data Institute, University of Oxford, Oxford, UK; Nuffield Department of Population Health, University of Oxford, Oxford, UK. Electronic address: dylan.mcgagh@ndorms.ox.ac.uk.
  • Song K; Oxford Medical School, Medical Sciences Division, University of Oxford, Oxford, UK.
  • Yuan H; Big Data Institute, University of Oxford, Oxford, UK; Nuffield Department of Population Health, University of Oxford, Oxford, UK.
  • Creagh AP; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
  • Fenton S; School of Sport, Exercise, and Rehabilitation Science, University of Birmingham, Birmingham, UK; National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK.
  • Ng WF; Health Research Board Clinical Research Facility, University College Cork, Cork, Ireland; Translational and Clinical Research Institute, Newcastle University Faculty of Medical Sciences, Newcastle upon Tyne, UK; NIHR Newcastle Biomedical Research Centre and NIHR Newcastle Clinical Research Facility,
  • Goldsack JC; The Digital Medicine Society, Boston, MA, USA.
  • Dixon WG; Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK; NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK; Department of Rheumatology,
  • Doherty A; Big Data Institute, University of Oxford, Oxford, UK; Nuffield Department of Population Health, University of Oxford, Oxford, UK.
  • Coates LC; Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
Lancet Rheumatol ; 2024 Jul 29.
Article em En | MEDLINE | ID: mdl-39089297
ABSTRACT
Common to all inflammatory arthritides, namely rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, and juvenile idiopathic arthritis, is a potential for reduced mobility that manifests through joint pain, swelling, stiffness, and ultimately joint damage. Across these conditions, consensus has been reached on the need to capture outcomes related to mobility, such as functional capacity and physical activity, as core domains in randomised controlled trials. Existing endpoints within these core domains rely wholly on self-reported questionnaires that capture patients' perceptions of their symptoms and activities. These questionnaires are subjective, inherently vulnerable to recall bias, and do not capture the granularity of fluctuations over time. Several early adopters have integrated sensor-based digital health technology (DHT)-derived endpoints to measure physical function and activity in randomised controlled trials for conditions including Parkinson's disease, Duchenne's muscular dystrophy, chronic obstructive pulmonary disease, and heart failure. Despite these applications, there have been no sensor-based DHT-derived endpoints in clinical trials recruiting patients with inflammatory arthritis. Borrowing from case studies across medicine, we outline the opportunities and challenges in developing novel sensor-based DHT-derived endpoints that capture the symptoms and disease manifestations most relevant to patients with inflammatory arthritis.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Lancet Rheumatol Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Lancet Rheumatol Ano de publicação: 2024 Tipo de documento: Article