Nociceptive PainpAIn-senseNCT06044584
Unveiling Physiological and Psychosocial Pain Components with an Artificial Intelligence Based Telemonitoring Tool
An observational study of Nociceptive Pain and Neuropathic Pain, run by ETH Zurich at 4 sites in 2 countries.
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. Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Nociceptive Pain, Neuropathic Pain
- How many people
- 150 participants
- Where
- 4 sitesin 2 countries
- Study dates
- 29 Sep 2023to 31 Dec 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients with ongoing pain between 18 and 80 years old
Who may be able to join
- Ongoing nociceptive pain after an injury or Neuropathic pain (acute or chronic)
- Familiar with using electronic devices
Who cannot join
- 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 locations and contacts
4 sites in 2 countries. Contact the study team or a site near you to ask about taking part.
Noemi Gozzi contact
4 sites in 2 countries
Switzerland3 sites, 3 recruiting
- Recruiting
CRR Suva (Clinique romande de réadaptation)
Sion, Valais, 1950
Bertrand Leger, 79 293 88 76 ext. 0041, Bertrand.Leger@crr-suva.ch
- Recruiting
Neuroengineering Lab
Zurich, Canton of Zurich, 8001
Stanisa Raspopovic, PhD
- Recruiting
Balgrist University Hospital
Zurich, Canton of Zurich, 8008
Michele Hubli, PhD
Italy1 site, 1 recruiting
- Recruiting
Unita Spinale ASL
Pietra Ligure, Savona, 17027
Amy Bellito, +393404273393, amybellito@gmail.com
About this study
Official title: Unveiling Physiological and Psychosocial Pain Components with an Artificial Intelligence Based Telemonitoring Tool (pAIn-sense)
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.
Read the 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.
- Conditions
- Nociceptive Pain, Neuropathic Pain
- Being tested
- No intervention
- Keywords
- Pain, Telemonitoring, Artificial Intelligence, Physiological signals, Psychosocial
- Medical subject headings
- Nociceptive Pain, Neuralgia, Pain
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionOther
Observational study with no intervention - Monitoring
| Study group | Role | What they receive |
|---|---|---|
| Pain | Patients suffering from acute/chronic nociceptive and neuropathic pain Receives: Other: No intervention | |
| Control | Healthy controls Receives: Other: No intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Up to one month
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
Measured: Up to one month
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.
Measured: Up to one month
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.
Measured: Up to one month
Medication intake (rate and times per day)
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.
Measured: Up to one month
Secondary outcomes (5)
Rehabilitation, physiotherapy and their effect
Correlation between rehabilitation or physiotherapy attendance and pain
Measured: Up to one month
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)
Measured: Up to one month
Predictors of chronification from acute phase
Identification and classification of physiological and psychosocial markers, that characterize transition between acute pain and chronic pain
Measured: Up to one month
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.
Measured: Up to one month
Responsiveness to medication
Changes in physiological biomarkers and pain perception following the intake of medication
Measured: Up to one month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Sep 2023
- Study start29 Sep 2023
- Primary completion15 Dec 2028 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Nociceptive PainStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 150 participants (estimated)
Sponsor and investigators
- Lead sponsor
- ETH Zurich
- Sponsor type
- University, hospital or other
- Collaborators
- Balgrist University Hospital
- Responsible party
- Principal investigator, Stanisa Raspopovic, ETH Zurich
- Investigators
- Stanisa Raspopovic, PhD, Principal investigator, ETH Zurich
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06044584
- Sponsor's protocol ID
- 2021-01814
- Acronym
- pAIn-sense
- Status last verified
- Jan 2025
- First submitted
- 4 Sep 2023
- Last updated
- 20 Jan 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
May M, Junghaenel DU, Ono M, Stone AA, Schneider S. Ecological Momentary Assessment Methodology in Chronic Pain Research: A Systematic Review. J Pain. 2018 Jul;19(7):699-716. doi: 10.1016/j.jpain.2018.01.006. Epub 2018 Jan 31.
PubMed 29371113Kratz AL, Ehde DM, Bombardier CH, Kalpakjian CZ, Hanks RA. Pain Acceptance Decouples the Momentary Associations Between Pain, Pain Interference, and Physical Activity in the Daily Lives of People With Chronic Pain and Spinal Cord Injury. J Pain. 2017 Mar;18(3):319-331. doi: 10.1016/j.jpain.2016.11.006. Epub 2016 Dec 2.
PubMed 27919770Davis KD, Aghaeepour N, Ahn AH, Angst MS, Borsook D, Brenton A, Burczynski ME, Crean C, Edwards R, Gaudilliere B, Hergenroeder GW, Iadarola MJ, Iyengar S, Jiang Y, Kong JT, Mackey S, Saab CY, Sang CN, Scholz J, Segerdahl M, Tracey I, Veasley C, Wang J, Wager TD, Wasan AD, Pelleymounter MA. Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities. Nat Rev Neurol. 2020 Jul;16(7):381-400. doi: 10.1038/s41582-020-0362-2. Epub 2020 Jun 15.
PubMed 32541893Tracey I, Woolf CJ, Andrews NA. Composite Pain Biomarker Signatures for Objective Assessment and Effective Treatment. Neuron. 2019 Mar 6;101(5):783-800. doi: 10.1016/j.neuron.2019.02.019.
PubMed 30844399Cohen SP, Vase L, Hooten WM. Chronic pain: an update on burden, best practices, and new advances. Lancet. 2021 May 29;397(10289):2082-2097. doi: 10.1016/S0140-6736(21)00393-7.
PubMed 34062143Volkow ND, McLellan AT. Opioid Abuse in Chronic Pain--Misconceptions and Mitigation Strategies. N Engl J Med. 2016 Mar 31;374(13):1253-63. doi: 10.1056/NEJMra1507771. No abstract available.
PubMed 27028915Lotsch J, Ultsch A. Machine learning in pain research. Pain. 2018 Apr;159(4):623-630. doi: 10.1097/j.pain.0000000000001118. No abstract available.
PubMed 29194126
Study at a glance
From the registry record, last updated by the sponsor on 20 Jan 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Unveiling Physiological and Psychosocial Pain Components with an Artificial Intelligence Based Telemonitoring Tool (pAIn-sense)
- ClinicalTrials.gov ID
- NCT06044584
- Acronym / sponsor ID
- pAIn-sense / 2021-01814
- Conditions
- Nociceptive Pain, Neuropathic Pain
- Keywords
- Pain, Telemonitoring, Artificial Intelligence, Physiological signals, Psychosocial
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 150 participants (estimated)
- Lead sponsor
- ETH ZurichUniversity, hospital or other
- Collaborators
- Balgrist University Hospital
- Study start
- 29 Sep 2023 (actual)
- Primary completion
- 15 Dec 2028 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 21 Sep 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- 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
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Pain | – | Other: No interventionPatients suffering from acute/chronic nociceptive and neuropathic pain |
| Control | – | Other: No interventionHealthy controls |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with ongoing pain between 18 and 80 years old
Key inclusion criteria (2 listed)
- Ongoing nociceptive pain after an injury or Neuropathic pain (acute or chronic)
- Familiar with using electronic devices
Key exclusion criteria (2 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No interventionObservational study with no intervention - Monitoring | Other | Control, Pain |
Primary outcome
Pain level
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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)
Time frame: 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.
5 secondary outcomes
Rehabilitation, physiotherapy and their effect
Time frame: Up to one month
Sleep, activity and other daily factors and their correlation with pain
Time frame: Up to one month
Predictors of chronification from acute phase
Time frame: Up to one month
Quality of Life and pain interference
Time frame: Up to one month
Responsiveness to medication
Time frame: Up to one month
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Jan 2025.
- Background: PubMed 29371113May M, Junghaenel DU, Ono M, Stone AA, Schneider S. Ecological Momentary Assessment Methodology in Chronic Pain Research: A Systematic Review. J Pain. 2018 Jul;19(7):699-716. doi: 10.1016/j.jpain.2018.01.006. Epub 2018 Jan 31.
- Background: PubMed 27919770Kratz AL, Ehde DM, Bombardier CH, Kalpakjian CZ, Hanks RA. Pain Acceptance Decouples the Momentary Associations Between Pain, Pain Interference, and Physical Activity in the Daily Lives of People With Chronic Pain and Spinal Cord Injury. J Pain. 2017 Mar;18(3):319-331. doi: 10.1016/j.jpain.2016.11.006. Epub 2016 Dec 2.
- Background: PubMed 32541893Davis KD, Aghaeepour N, Ahn AH, Angst MS, Borsook D, Brenton A, Burczynski ME, Crean C, Edwards R, Gaudilliere B, Hergenroeder GW, Iadarola MJ, Iyengar S, Jiang Y, Kong JT, Mackey S, Saab CY, Sang CN, Scholz J, Segerdahl M, Tracey I, Veasley C, Wang J, Wager TD, Wasan AD, Pelleymounter MA. Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities. Nat Rev Neurol. 2020 Jul;16(7):381-400. doi: 10.1038/s41582-020-0362-2. Epub 2020 Jun 15.
- Background: PubMed 30844399Tracey I, Woolf CJ, Andrews NA. Composite Pain Biomarker Signatures for Objective Assessment and Effective Treatment. Neuron. 2019 Mar 6;101(5):783-800. doi: 10.1016/j.neuron.2019.02.019.
- Background: PubMed 34062143Cohen SP, Vase L, Hooten WM. Chronic pain: an update on burden, best practices, and new advances. Lancet. 2021 May 29;397(10289):2082-2097. doi: 10.1016/S0140-6736(21)00393-7.
- Background: PubMed 27028915Volkow ND, McLellan AT. Opioid Abuse in Chronic Pain--Misconceptions and Mitigation Strategies. N Engl J Med. 2016 Mar 31;374(13):1253-63. doi: 10.1056/NEJMra1507771. No abstract available.
- Background: PubMed 29194126Lotsch J, Ultsch A. Machine learning in pain research. Pain. 2018 Apr;159(4):623-630. doi: 10.1097/j.pain.0000000000001118. No abstract available.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Noemi Gozzi contact
Stanisa Raspopovic, PhD principal investigator
ETH Zurich
4 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Nociceptive Pain and Neuropathic Pain, run by ETH Zurich at 4 sites in 2 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 20 Jan 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in Switzerland, Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Andrea Cimolato, PhD (772466601 ext. +41, andrea.cimolato@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Pain level (time frame: Up to one month). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06044584, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Jan 2025; AzTrials last refreshed its copy on 26 Oct 2026.