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Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol

Authors :
Lorenzo Chiari
Giada Lullini
Serena Moscato
Silvia Orlandi
Francesco Di Gregorio
Stefania Pozzi
Loredana Sabattini
Fabio La Porta
Source :
BMJ Open, Vol 13, Iss 11 (2023)
Publication Year :
2023
Publisher :
BMJ Publishing Group, 2023.

Abstract

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.

Subjects

Subjects :
Medicine

Details

Language :
English
ISSN :
20446055
Volume :
13
Issue :
11
Database :
Directory of Open Access Journals
Journal :
BMJ Open
Publication Type :
Academic Journal
Accession number :
edsdoj.4461020592ba483084a2c01561b388b6
Document Type :
article
Full Text :
https://doi.org/10.1136/bmjopen-2023-073534