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

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ClinicalTrials.gov Identifier: NCT06044584
Recruitment Status : Recruiting
First Posted : September 21, 2023
Last Update Posted : September 21, 2023
Sponsor:
Collaborator:
Balgrist University Hospital
Information provided by (Responsible Party):
Stanisa Raspopovic, ETH Zurich

Study Description
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Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information
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
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:

  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.

Study Design
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Study Type : Observational
Estimated 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 : August 29, 2023
Estimated Primary Completion Date : December 15, 2028
Estimated Study Completion Date : December 31, 2028
Groups and Cohorts
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Group/Cohort 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
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Primary Outcome Measures :
  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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.


Secondary Outcome Measures :
  1. Rehabilitation, physiotherapy and their effect [ Time Frame: Up to one month ]
    Correlation between rehabilitation or physiotherapy attendance and pain

  2. Sleep, activity and other daily factors and their correlation with pain [ Time Frame: Up to one month ]
    Correlation between sleep, activity and other daily factors with pain (measured both from wearable technology and from patients report)

  3. Predictors of chronification from acute phase [ Time Frame: Up to one month ]
    Identification and classification of physiological and psychosocial markers, that characterize transition between acute pain and chronic pain

  4. Quality of Life and pain interference [ Time Frame: 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.

  5. Responsiveness to medication [ Time Frame: Up to one month ]
    Changes in physiological biomarkers and pain perception following the intake of medication


Eligibility Criteria
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Ages Eligible for Study:   18 Years to 80 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
Patients with ongoing pain between 18 and 80 years old
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
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT06044584


Contacts
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Contact: Andrea Cimolato, PhD 772466601 ext +41 andrea.cimolato@gmail.com
Contact: Noemi Gozzi noemi.gozzi@gmail.com

Locations
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Switzerland
Neuroengineering Lab Recruiting
Zürich, Zurich, Switzerland, 8001
Contact: Stanisa Raspopovic, PhD         
Balgrist University Hospital Recruiting
Zurich, Switzerland, 8008
Contact: Michele Hubli, PhD         
Sponsors and Collaborators
ETH Zurich
Balgrist University Hospital
Investigators
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Principal Investigator: Stanisa Raspopovic, PhD ETH Zurich
More Information
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Publications:

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Responsible Party: Stanisa Raspopovic, Principal Investigator, ETH Zurich
ClinicalTrials.gov Identifier: NCT06044584    
Other Study ID Numbers: 2021-01814
First Posted: September 21, 2023    Key Record Dates
Last Update Posted: September 21, 2023
Last Verified: September 2023
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Stanisa Raspopovic, ETH Zurich:
Pain
Telemonitoring
Artificial Intelligence
Physiological signals
Psychosocial
Additional relevant MeSH terms:
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Neuralgia
Nociceptive Pain
Peripheral Nervous System Diseases
Neuromuscular Diseases
 Nervous System Diseases
Pain
Neurologic Manifestations