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Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol
INTRODUCTION: Millions of people survive injuries to the central or peripheral nervous system for which neurorehabilitation is required. In addition to the physical and cognitive impairments, many neurorehabilitation patients experience pain, often not widely recognised and inadequately treated. Thi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668325/ https://www.ncbi.nlm.nih.gov/pubmed/37993169 http://dx.doi.org/10.1136/bmjopen-2023-073534 |
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author | Moscato, Serena Orlandi, Silvia Di Gregorio, Francesco Lullini, Giada Pozzi, Stefania Sabattini, Loredana Chiari, Lorenzo La Porta, Fabio |
author_facet | Moscato, Serena Orlandi, Silvia Di Gregorio, Francesco Lullini, Giada Pozzi, Stefania Sabattini, Loredana Chiari, Lorenzo La Porta, Fabio |
author_sort | Moscato, Serena |
collection | PubMed |
description | INTRODUCTION: Millions of people survive injuries to the central or peripheral nervous system for which neurorehabilitation is required. In addition to the physical and cognitive impairments, many neurorehabilitation patients experience pain, often not widely recognised and inadequately treated. This is particularly true for multiple sclerosis (MS) patients, for whom pain is one of the most common symptoms. In clinical practice, pain assessment is usually conducted based on a subjective estimate. This approach can lead to inaccurate evaluations due to the influence of numerous factors, including emotional or cognitive aspects. To date, no objective and simple to use clinical methods allow objective quantification of pain and the diagnostic differentiation between the two main types of pain (nociceptive vs neuropathic). Wearable technologies and artificial intelligence (AI) have the potential to bridge this gap by continuously monitoring patients’ health parameters and extracting meaningful information from them. Therefore, we propose to develop a new automatic AI-powered tool to assess pain and its characteristics during neurorehabilitation treatments using physiological signals collected by wearable sensors. METHODS AND ANALYSIS: We aim to recruit 15 participants suffering from MS undergoing physiotherapy treatment. During the study, participants will wear a wristband for three consecutive days and be monitored before and after their physiotherapy sessions. Measurement of traditionally used pain assessment questionnaires and scales (ie, painDETECT, Doleur Neuropathique 4 Questions, EuroQoL-5-dimension-3-level) and physiological signals (photoplethysmography, electrodermal activity, skin temperature, accelerometer data) will be collected. Relevant parameters from physiological signals will be identified, and AI algorithms will be used to develop automatic classification methods. ETHICS AND DISSEMINATION: The study has been approved by the local Ethical Committee (285-2022-SPER-AUSLBO). Participants are required to provide written informed consent. The results will be disseminated through contributions to international conferences and scientific journals, and they will also be included in a doctoral dissertation. TRIAL REGISTRATION NUMBER: NCT05747040. |
format | Online Article Text |
id | pubmed-10668325 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-106683252023-11-22 Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol Moscato, Serena Orlandi, Silvia Di Gregorio, Francesco Lullini, Giada Pozzi, Stefania Sabattini, Loredana Chiari, Lorenzo La Porta, Fabio BMJ Open Rehabilitation Medicine INTRODUCTION: Millions of people survive injuries to the central or peripheral nervous system for which neurorehabilitation is required. In addition to the physical and cognitive impairments, many neurorehabilitation patients experience pain, often not widely recognised and inadequately treated. This is particularly true for multiple sclerosis (MS) patients, for whom pain is one of the most common symptoms. In clinical practice, pain assessment is usually conducted based on a subjective estimate. This approach can lead to inaccurate evaluations due to the influence of numerous factors, including emotional or cognitive aspects. To date, no objective and simple to use clinical methods allow objective quantification of pain and the diagnostic differentiation between the two main types of pain (nociceptive vs neuropathic). Wearable technologies and artificial intelligence (AI) have the potential to bridge this gap by continuously monitoring patients’ health parameters and extracting meaningful information from them. Therefore, we propose to develop a new automatic AI-powered tool to assess pain and its characteristics during neurorehabilitation treatments using physiological signals collected by wearable sensors. METHODS AND ANALYSIS: We aim to recruit 15 participants suffering from MS undergoing physiotherapy treatment. During the study, participants will wear a wristband for three consecutive days and be monitored before and after their physiotherapy sessions. Measurement of traditionally used pain assessment questionnaires and scales (ie, painDETECT, Doleur Neuropathique 4 Questions, EuroQoL-5-dimension-3-level) and physiological signals (photoplethysmography, electrodermal activity, skin temperature, accelerometer data) will be collected. Relevant parameters from physiological signals will be identified, and AI algorithms will be used to develop automatic classification methods. ETHICS AND DISSEMINATION: The study has been approved by the local Ethical Committee (285-2022-SPER-AUSLBO). Participants are required to provide written informed consent. The results will be disseminated through contributions to international conferences and scientific journals, and they will also be included in a doctoral dissertation. TRIAL REGISTRATION NUMBER: NCT05747040. BMJ Publishing Group 2023-11-22 /pmc/articles/PMC10668325/ /pubmed/37993169 http://dx.doi.org/10.1136/bmjopen-2023-073534 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Rehabilitation Medicine Moscato, Serena Orlandi, Silvia Di Gregorio, Francesco Lullini, Giada Pozzi, Stefania Sabattini, Loredana Chiari, Lorenzo La Porta, Fabio Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol |
title | Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol |
title_full | Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol |
title_fullStr | Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol |
title_full_unstemmed | Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol |
title_short | Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol |
title_sort | feasibility interventional study investigating pain in neurorehabilitation through wearable sensors (painless): a study protocol |
topic | Rehabilitation Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668325/ https://www.ncbi.nlm.nih.gov/pubmed/37993169 http://dx.doi.org/10.1136/bmjopen-2023-073534 |
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