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

Official title

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

Overview

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:

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.

Interventions

• Other No intervention
  Observational study with no intervention - Monitoring

Primary outcome measures

• Pain level [Time frame: Up to one month]
• Psychosocial components of pain experience through questionnaires [Time frame: Up to one month]
• Physiological components of pain and pain attacks in the physiological signals [Time frame: Up to one month]
• Psychological and clinical factors affecting pain [Time frame: Up to one month]
• Medication intake (rate and times per day) [Time frame: Up to one month]

Secondary outcome measures (5)

• 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]

Eligibility 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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model

Cohort

Study locations

Switzerland · 3 centers

• Neuroengineering Lab — Zurich
• Balgrist University Hospital — Zurich
• CRR Suva (Clinique romande de réadaptation) — Sion

Italy · 1 center

• Unita Spinale ASL — Pietra Ligure

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. PMID 29371113
• 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
• 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: challe PMID 32541893
• 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
• 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
• 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
• 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

Identifiers

NCT: NCT06044584 · 2021-01814

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗