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

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.

Study Overview

• Status: Recruiting
• Conditions: Neuropathic Pain; Nociceptive Pain
• Intervention / Treatment: 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 Type: Observational
• Enrollment (Estimated): 150

Contacts and Locations

• Study Contact: Name: Andrea Cimolato, PhD; Phone Number: +41 772466601; Email: andrea.cimolato@gmail.com
• Study Contact Backup: Name: Noemi Gozzi; Email: noemi.gozzi@gmail.com

Study Locations

• Italy
  • Savona
    • Pietra Ligure, Savona, Italy, 17027
      • Recruiting
      • Unita Spinale ASL
      • Contact: Amy Bellito; Phone Number: +393404273393; Email: amybellito@gmail.com
• Switzerland
  • Zurich, Switzerland, 8008
    • Recruiting
    • Balgrist University Hospital
    • Contact: Michele Hubli, PhD
  • Valais
    • Sion, Valais, Switzerland, 1950
      • Recruiting
      • CRR Suva (Clinique romande de réadaptation)
      • Contact: Bertrand Leger; Phone Number: 0041 79 293 88 76; Email: Bertrand.Leger@crr-suva.ch
  • Zurich
    • Zürich, Zurich, Switzerland, 8001
      • Recruiting
      • Neuroengineering Lab
      • Contact: Stanisa Raspopovic, PhD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Patients with ongoing pain between 18 and 80 years old

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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	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.	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.	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.	Up to one month

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Rehabilitation, physiotherapy and their effect	Correlation between rehabilitation or physiotherapy attendance and pain	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)	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	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.	Up to one month
Responsiveness to medication	Changes in physiological biomarkers and pain perception following the intake of medication	Up to one month

Collaborators and Investigators

• Sponsor: ETH Zurich
• Collaborators: Balgrist University Hospital
• Investigators: Principal Investigator: Stanisa Raspopovic, PhD, ETH Zurich

Publications and helpful links

General Publications

• Lotsch J, Ultsch A. Machine learning in pain research. Pain. 2018 Apr;159(4):623-630. doi: 10.1097/j.pain.0000000000001118. No abstract available.
• 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.
• Cohen 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.
• Tracey 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.
• Davis 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.
• Kratz 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.
• Volkow 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.

Study record dates

Study Major Dates

• Study Start (Actual): September 29, 2023
• Primary Completion (Estimated): December 15, 2028
• Study Completion (Estimated): December 31, 2028

Study Registration Dates

• First Submitted: September 4, 2023
• First Submitted That Met QC Criteria: September 13, 2023
• First Posted (Actual): September 21, 2023

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: January 17, 2025
• Last Verified: January 1, 2025

More Information

Terms related to this study

• Keywords: Pain; Telemonitoring; Artificial Intelligence; Psychosocial; Physiological signals
• Additional Relevant MeSH Terms: Pain; Neurologic Manifestations; Nervous System Diseases; Neuromuscular Diseases; Peripheral Nervous System Diseases; Neuralgia; Nociceptive Pain
• Other Study ID Numbers: 2021-01814

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No