跳至主要内容
临床试验/NCT06044584
NCT06044584招募中不适用

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

ETH Zurich4 个研究点 分布在 2 个国家目标入组 150 人开始时间: 2023年9月29日最近更新:
适应症

试验速览

阶段
不适用
状态
招募中
发起方
入组人数
150
试验地点
4
主要终点
Physiological components of pain and pain attacks in the physiological signals

研究概览

简要总结

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.

详细描述

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.

研究设计

研究类型
Observational
观察模型
Cohort
时间视角
Prospective

入排标准

年龄范围
18 Years 至 80 Years(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

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

排除标准

  • 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

结局指标

主要结局

Physiological components of pain and pain attacks in the physiological signals

时间窗: 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.

Pain level

时间窗: 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.

Psychological and clinical factors affecting pain

时间窗: 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.

Psychosocial components of pain experience through questionnaires

时间窗: 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

Medication intake (rate and times per day)

时间窗: 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.

次要结局

  • Responsiveness to medication(Up to one month)
  • Predictors of chronification from acute phase(Up to one month)
  • Quality of Life and pain interference(Up to one month)
  • Rehabilitation, physiotherapy and their effect(Up to one month)
  • Sleep, activity and other daily factors and their correlation with pain(Up to one month)

研究者

发起方
ETH Zurich
申办方类型
Other
责任方
Principal Investigator
主要研究者

Stanisa Raspopovic

Principal Investigator

ETH Zurich

研究点 (4)

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