Clinical trial NCT06044584: Unveiling Physiological and Psychosocial Pain Components with an Artificial Intelligence Based Telemonitoring Tool
Official title: Unveiling Physiological and Psychosocial Pain Components with an Artificial Intelligence Based Telemonitoring Tool (pAIn-sense)
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Nociceptive Pain
Neuropathic Pain
Keywords: Pain
Telemonitoring
Artificial Intelligence
Physiological signals
Psychosocial
Lead sponsor: ETH Zurich
Sponsor class: OTHER
Collaborators: name: Balgrist University Hospital
First posted: 2023-09-21
Enrollment: 150
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with ongoing pain between 18 and 80 years old

Brief 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.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

* Ongoing nociceptive pain after an injury or Neuropathic pain (acute or chronic)
* Familiar with using electronic devices

Exclusion Criteria:

* Inability to follow the procedures of the study, e.g. due to language problems, psychological disorders, dementia, etc.
* Unable or not willing to give informed consent

Primary outcomes:
measure: Pain level
timeFrame: Up to one month
measure: Psychosocial components of pain experience through questionnaires
timeFrame: Up to one month
measure: Physiological components of pain and pain attacks in the physiological signals
timeFrame: Up to one month
measure: Psychological and clinical factors affecting pain
timeFrame: Up to one month
measure: Medication intake (rate and times per day)
timeFrame: Up to one month
Secondary outcomes:
measure: Rehabilitation, physiotherapy and their effect
timeFrame: Up to one month
measure: Sleep, activity and other daily factors and their correlation with pain
timeFrame: Up to one month
measure: Predictors of chronification from acute phase
timeFrame: Up to one month
measure: Quality of Life and pain interference
timeFrame: Up to one month
measure: Responsiveness to medication
timeFrame: Up to one month
Other outcomes:
Locations:
city: Pietra Ligure
country: Italy
facility: Unita Spinale ASL
state: Savona
city: Sion
country: Switzerland
facility: CRR Suva (Clinique romande de réadaptation)
state: Valais
city: Zürich
country: Switzerland
facility: Neuroengineering Lab
state: Zurich
city: Zurich
country: Switzerland
facility: Balgrist University Hospital
Study officials:
affiliation: ETH Zurich
name: Stanisa Raspopovic, PhD
role: PRINCIPAL_INVESTIGATOR
References:
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