Unveiling Physiological and Psychosocial Pain Components with an Artificial Intelligence Based Telemonitoring Tool
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-80
SponsorETH Zurich
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
Ongoing nociceptive pain after an injury or Neuropathic pain (acute or chronic)
Familiar with using electronic devices
Disqualifiers
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
Trial population
Patients with ongoing pain between 18 and 80 years old
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
No intervention
Other interventionObservational study with no intervention - Monitoring
Treatment groups
150 Participants
are divided into 2 treatment groupsGroup A: Pain1 intervention
Group B: Control1 intervention
Trial outcomes
Primary outcomes
1
Pain level
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.
Time frame
Up to one month2
Psychosocial components of pain experience through questionnaires
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
Time frame
Up to one month3
Physiological components of pain and pain attacks in the physiological signals
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.
Time frame
Up to one month4
Psychological and clinical factors affecting pain
Identified using questionnaires. Scales are usually represented with values from 0 to 10 with 0 best outcome and 10 worst outcome.
Time frame
Up to one monthSecondary outcomes
1
Rehabilitation, physiotherapy and their effect
Correlation between rehabilitation or physiotherapy attendance and pain
Time frame
Up to one month2
Sleep, activity and other daily factors and their correlation with pain
Correlation between sleep, activity and other daily factors with pain (measured both from wearable technology and from patients report)
Time frame
Up to one month3
Predictors of chronification from acute phase
Identification and classification of physiological and psychosocial markers, that characterize transition between acute pain and chronic pain
Time frame
Up to one month4
Quality of Life and pain interference
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.
Time frame
Up to one monthSponsors and contacts
Click on the lead sponsor to view all of their trials.
ETH Zurich
Lead sponsor
Balgrist University Hospital
Collaborator
Source ClinicalTrials.gov