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Exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology()
Osteoarthritis is a degenerative joint disease that affects millions of people worldwide. Current guidelines emphasize the importance of regular physical activity as a preventive measure against disease progression and as a valuable strategy for pain and functionality management. Despite this, most...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889133/ https://www.ncbi.nlm.nih.gov/pubmed/35252602 http://dx.doi.org/10.1016/j.heliyon.2022.e08991 |
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author | Ravalli, Silvia Roggio, Federico Lauretta, Giovanni Di Rosa, Michelino D'Amico, Agata Grazia D'agata, Velia Maugeri, Grazia Musumeci, Giuseppe |
author_facet | Ravalli, Silvia Roggio, Federico Lauretta, Giovanni Di Rosa, Michelino D'Amico, Agata Grazia D'agata, Velia Maugeri, Grazia Musumeci, Giuseppe |
author_sort | Ravalli, Silvia |
collection | PubMed |
description | Osteoarthritis is a degenerative joint disease that affects millions of people worldwide. Current guidelines emphasize the importance of regular physical activity as a preventive measure against disease progression and as a valuable strategy for pain and functionality management. Despite this, most patients with osteoarthritis are inactive. Modern technological advances have led to the implementation of digital devices, such as wearables and smartphones, showing new opportunities for healthcare professionals and researchers to monitor physical activity and therefore engage patients in daily exercising. Additionally, digital devices have emerged as a promising tool for improving frequent health data collection, disease monitoring, and supporting public health surveillance. The leveraging of digital data has laid the foundation for developing a new concept of epidemiological study, known as "Digital Epidemiology". Analyzing real-world data can change the way we observe human behavior and suggest health interventions, as in the case of physical exercise and osteoarthritic patients. Furthermore, large-scale data could contribute to personalized and precision medicine in the future. Herein, an overview of recent clinical applications of wearables for monitoring physical activity in patients with osteoarthritis and the benefits of exploiting real-world data in the context of digital epidemiology are discussed. |
format | Online Article Text |
id | pubmed-8889133 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-88891332022-03-03 Exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology() Ravalli, Silvia Roggio, Federico Lauretta, Giovanni Di Rosa, Michelino D'Amico, Agata Grazia D'agata, Velia Maugeri, Grazia Musumeci, Giuseppe Heliyon Review Article Osteoarthritis is a degenerative joint disease that affects millions of people worldwide. Current guidelines emphasize the importance of regular physical activity as a preventive measure against disease progression and as a valuable strategy for pain and functionality management. Despite this, most patients with osteoarthritis are inactive. Modern technological advances have led to the implementation of digital devices, such as wearables and smartphones, showing new opportunities for healthcare professionals and researchers to monitor physical activity and therefore engage patients in daily exercising. Additionally, digital devices have emerged as a promising tool for improving frequent health data collection, disease monitoring, and supporting public health surveillance. The leveraging of digital data has laid the foundation for developing a new concept of epidemiological study, known as "Digital Epidemiology". Analyzing real-world data can change the way we observe human behavior and suggest health interventions, as in the case of physical exercise and osteoarthritic patients. Furthermore, large-scale data could contribute to personalized and precision medicine in the future. Herein, an overview of recent clinical applications of wearables for monitoring physical activity in patients with osteoarthritis and the benefits of exploiting real-world data in the context of digital epidemiology are discussed. Elsevier 2022-02-22 /pmc/articles/PMC8889133/ /pubmed/35252602 http://dx.doi.org/10.1016/j.heliyon.2022.e08991 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Article Ravalli, Silvia Roggio, Federico Lauretta, Giovanni Di Rosa, Michelino D'Amico, Agata Grazia D'agata, Velia Maugeri, Grazia Musumeci, Giuseppe Exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology() |
title | Exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology() |
title_full | Exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology() |
title_fullStr | Exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology() |
title_full_unstemmed | Exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology() |
title_short | Exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology() |
title_sort | exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology() |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889133/ https://www.ncbi.nlm.nih.gov/pubmed/35252602 http://dx.doi.org/10.1016/j.heliyon.2022.e08991 |
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