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

Sponsor

ETH Zurich (Other)

Overall Status

Recruiting

CT.gov ID

NCT06044584

Collaborator

Balgrist University Hospital (Other)

150

Enrollment

Locations

64.1

Anticipated Duration (Months)

Patients Per Site

1.2

Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Nociceptive Pain Neuropathic Pain	Other: No intervention

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:

Robotic wearable sensors (Hardware): wearable technology for physiological monitoring (e.g., skin conductance, blood volume pressure and heart rate, activity)
Digital platform (Software): a customized application that collects psychological assessments, psychological status, medication, subjective pain level and sleep quality.
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.

Study Design

Study Type:

Observational

Anticipated Enrollment :

150 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

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

Actual Study Start Date :

Aug 29, 2023

Anticipated Primary Completion Date :

Dec 15, 2028

Anticipated Study Completion Date :

Dec 31, 2028

Arms and Interventions

Arm	Intervention/Treatment
Pain Patients suffering from acute/chronic nociceptive and neuropathic pain	Other: No intervention Observational study with no intervention - Monitoring
Control Healthy controls	Other: No intervention Observational study with no intervention - Monitoring

Arm

Intervention/Treatment

Pain

Patients suffering from acute/chronic nociceptive and neuropathic pain

Other: No intervention

Observational study with no intervention - Monitoring

Control

Healthy controls

Other: No intervention

Observational study with no intervention - Monitoring

Outcome Measures

Primary Outcome Measures

Pain level [Up to one month]
Reported trough a digital health platform by the patients. The level and its dynamic are monitored daily. The pain level is recorded through a score from 1 to 10 that is reported trough a digital health platform by the patients.
Psychosocial components of pain experience through questionnaires [Up to one month]
Monitored using the wearable technology and software digital platforms. Questionnaires will be presented to the patients and will include anxiety, depression, fatigue, pain catastrophizing, sleep, awareness, pain efficacy, treatment expectation
Physiological components of pain and pain attacks in the physiological signals [Up to one month]
Measured and extracted from wearable technology worn continuously. Physiological biomarkers will include Skin Conductance (SC), blood volume pulse (BVP), Heart rate (HR), Brain signals (functional magnetic resonance imaging, electroencephalogram), movements (accelerometer, IMU), temperature.
Psychological and clinical factors affecting pain [Up to one month]
Identified using questionnaires. Scales are usually represented with values from 0 to 10 with 0 best outcome and 10 worst outcome.
Medication intake (rate and times per day) [Up to one month]
As described in each patient's constant pain therapy or reported by the patient on request using the platform. Medication will be measure in terms of rate of medications and changes during the protocols, times per day of intake, number of times a on-request medication is taken.

Secondary Outcome Measures

Rehabilitation, physiotherapy and their effect [Up to one month]
Correlation between rehabilitation or physiotherapy attendance and pain
Sleep, activity and other daily factors and their correlation with pain [Up to one month]
Correlation between sleep, activity and other daily factors with pain (measured both from wearable technology and from patients report)
Predictors of chronification from acute phase [Up to one month]
Identification and classification of physiological and psychosocial markers, that characterize transition between acute pain and chronic pain
Quality of Life and pain interference [Up to one month]
QoL index done through questioners and how much pain interfere with the overall quality of life. Scales from 0 to 10, with 10 better outcome and 0 worst outcome.
Responsiveness to medication [Up to one month]
Changes in physiological biomarkers and pain perception following the intake of medication

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 80 Years

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Yes

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