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.

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.

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.

NCT05747040.

Health information systems represent an opportunity to improve the care provided to people with multimorbidity. There is a pressing need to assess their impact on clinical outcomes to validate this intervention. Our study will determine whether using a digital platform (Multimorbidity Management Health Information System, METHIS) to manage multimorbidity improves health-related quality of life (HR-QoL).

A superiority, cluster randomised trial will be conducted at primary healthcare practices (1:1 allocation ratio). All public practices in the Lisbon and Tagus Valley (LVT) Region, Portugal, not involved in a previous pilot trial, will be eligible. At the participant level, eligible patients will be people with complex multimorbidity, aged 50 years or older, with access to an internet connection and a communication technology device. Participants who cannot sign/read/write and who do not have access to an email account will not be included in the study. The intervention combines a training programme and a customised information system (METHIS). Both are designed to help clinicians adopt a goal-oriented care model approach and to encourage patients and carers to play a more active role in autonomous healthcare. The primary outcome is HR-QoL, measured at 12 months with the physical component scale of the 12-item Short Form questionnaire (SF-12). Secondary outcomes will also be measured at 12 months and include mental health (mental component Scale SF-12, Hospital Anxiety and Depression Scale). We will also assess serious adverse events during the trial, including hospitalisation and emergency services. Finally, at 18 months, we will ask the general practitioners for any potentially missed diagnoses.

The Research and Ethics Committee (LVT Region) approved the trial protocol. Clinicians and patients will sign an informed consent. A data management officer will handle all data, and the publication of several scientific papers and presentations at relevant conferences/workshops is envisaged.

NCT05593835.