Unveiling Physiological and Psychosocial Pain Components with an Artificial Intelligence Based Telemonitoring Tool

NCT06044584 | Recruiting

• 1 other identifier: 2021-01814
• Study Type: observational
• Target: 150
• Locations: 2 countries
• Sites: 4

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.

Conditions

Nociceptive Pain; Neuropathic Pain

Keywords

Pain; Telemonitoring; Artificial Intelligence; Physiological signals; Psychosocial

Outcome Measures

Primary Outcomes (5)

• 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 Outcomes (5)

• Rehabilitation, physiotherapy and their effect

  Up to one month

• Sleep, activity and other daily factors and their correlation with pain

  Up to one month

• Predictors of chronification from acute phase

  Up to one month

• Quality of Life and pain interference

  Up to one month

• Responsiveness to medication

  Up to one month

Study Arms (2)

Pain

Patients suffering from acute/chronic nociceptive and neuropathic pain

Other: No intervention

Control

Healthy controls

Other: No intervention

Interventions

No intervention OTHER

Observational study with no intervention - Monitoring

Control; Pain

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with ongoing pain between 18 and 80 years old

You may qualify if:

• Ongoing nociceptive pain after an injury or Neuropathic pain (acute or chronic)
• Familiar with using electronic devices

You may not qualify if:

• 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

Sponsors & Collaborators

• ETH Zurich: lead
• Balgrist University Hospital: collaborator

Study Sites (4)

Unita Spinale ASL

Pietra Ligure, Savona, 17027, Italy

RECRUITING

Neuroengineering Lab

Zurich, Canton of Zurich, 8001, Switzerland

RECRUITING

Balgrist University Hospital

Zurich, Canton of Zurich, 8008, Switzerland

RECRUITING

CRR Suva (Clinique romande de réadaptation)

Sion, Valais, 1950, Switzerland

RECRUITING

Related Publications (7)

• 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.

  PMID: 29371113 BACKGROUND

• 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.

  PMID: 27919770 BACKGROUND

• 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.

  PMID: 32541893 BACKGROUND

• 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.

  PMID: 30844399 BACKGROUND

• 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.

  PMID: 34062143 BACKGROUND

• 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.

  PMID: 27028915 BACKGROUND

• 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.

  PMID: 29194126 BACKGROUND

MeSH Terms

Conditions

Nociceptive Pain; Neuralgia; Pain

Condition Hierarchy (Ancestors)

Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Peripheral Nervous System Diseases; Neuromuscular Diseases; Nervous System Diseases

Study Officials

• Stanisa Raspopovic, PhD

  ETH Zurich

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Andrea Cimolato, PhD

CONTACT

772466601; andrea.cimolato@gmail.com

Noemi Gozzi

CONTACT

noemi.gozzi@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: September 4, 2023
• First Posted: September 21, 2023
• Study Start: September 29, 2023
• Primary Completion (Estimated): December 15, 2028
• Study Completion (Estimated): December 31, 2028
• Last Updated: January 20, 2025

Record last verified: 2025-01

Locations

CH (3); IT (1)