Unveiling Physiological and Psychosocial Pain Components With an Artificial Intelligence Based Telemonitoring Tool — pAIn-sense  

Neuropathic Pain Clinical Trial

Official title: Unveiling Physiological and Psychosocial Pain Components With an Artificial Intelligence Based Telemonitoring Tool (pAIn-sense)

Status Recruiting

Clinical Trial Summary

The pAIn-sense study aims to revolutionize the monitoring and treatment of chronic pain, a major health concern that significantly impacts psychological well-being and quality of life. Traditional approaches to pain management face challenges like unspecific drug use and high healthcare costs, and they often leave patients dissatisfied. PAIn-sense aims at comprehensively understanding pain from both physical and emotional perspectives. To accomplish this, the study will employ advanced Artificial Intelligence (AI) techniques and wearable sensing technology. The study aims to monitor patients continuously, during both day and night activities, to gather a multidimensional set of data on their physiological, psychosocial, and pain conditions.

Clinical Trial Description

Chronic pain has long been known as one of the major health concerns, impacting psychological health, functioning, and quality of life. However, its treatment is complex and is challenged by a complex interplay between biological, psychological, and social factors. Common pain treatments present significant medical and technological limitations, reflected in unspecific drug usage and an extremely high number of medical examinations that patients face regularly, with a huge cost burden on the healthcare system. Furthermore, the overall efficacy of pain management is often limited (73% dissatisfaction with treatment), leaving the patient in poor life conditions. Designing individualized targeted therapies requires understanding each subject's multidimensional pain experience, taking into consideration both the physical and emotional aspects involved. However, today, the golden standard measurement for pain is self-reports, which inherently suffer from subjective differences in perception and reporting. Healthcare systems advocate for the discovery of biomarkers and reliable clinical trial endpoints for pain to foster diagnosis, monitor pain progression, assess new treatments, and personalized therapeutic response. Nevertheless, most of the evidence today comes from inpatient settings or controlled laboratory environments. The pAIn-sense study aims at providing a radically novel approach in the monitoring and treatment of pain patients: a novel telemonitoring system allowing to understand the real nature of the pain (emotional vs physical), leveraging the use of advanced Artificial Intelligence techniques and wearable sensing technology collecting biometric data, therefore enabling efficient personalized treatments.

To achieve this goal, the investigators will combine real patient data both from a physical and emotional perspective, to characterize the pain nature of patients and provide a tailored continuum-of-care.

The system will include:

1. Robotic wearable sensors (Hardware): wearable technology for physiological monitoring (e.g., skin conductance, blood volume pressure and heart rate, activity)

2. Digital platform (Software): a customized application that collects psychological assessments, psychological status, medication, subjective pain level and sleep quality.

3. AI-based engine: advanced AI models take all the previous physical and psychological information and model it to provide an outline of what is the nature of the pain level of the subject.

The system will be used to monitor the patient during normal activities (day and night) while collecting physiological, psychosocial, and pain information. ;

Related Conditions & MeSH terms

• Neuralgia
• Neuropathic Pain
• Nociceptive Pain

• NCT number: NCT06044584
• Study type: Observational
• Source: ETH Zurich
• Contact: Andrea Cimolato, PhD
• Phone: 772466601
• Email: andrea.cimolato@gmail.com
• Status: Recruiting
• Start date: August 29, 2023
• Completion date: December 31, 2028