Real-time Pain Monitoring in Fibromyalgia Patients

Pain Clinical Trial

Official title: Utilization of Real-time Pain Monitoring System (PAMS), ANAPA System, in Patients With Rheumatic Disease

Status Completed

Clinical Trial Summary

This study aims to examine whether the pain of fibromyalgia patients can be reduced with utilization of real-time pain monitoring system (PAAS). In this pilot study, adult fibromyalgia patients were randomly assigned to use or to not use PAAS. Changes in the visual analogue scale (VAS) were examined by rheumatologists at baseline and after three months, and correlations between conventional pain VAS or PAAS VAS and clinical parameters (patient global assessment, physician global assessment, fibromyalgia impact questionnaire) were investigated. We also examined if the utilization of PAAS can affect health related quality of life and depression.

Clinical Trial Description

Chronic pain is difficult to treat and is debilitating to patients in various ways, including wide spread suffering, disability, social displacement, and expense. For effective pain management, the first step should be to assess the exact status of the pain. This process may include characterizing the pain, quantifying it as accurately as possible, and analyzing influencing factors. However, it is hard to objectify pain because it is an invisible and subjective. The visual analogue scale (VAS) is widely used to assess the severity of pain. Moreover, current management of chronic pain is based on a patient's recall, which may be inaccurate. It would be ideal if we could manage pain by recording its status on a real-time basis, reflecting the impact of environmental factors. The Pain Assessment and Analysis System (PAAS) has been developed to monitor and record real-time pain. Users are asked to report the type of experienced pain and its severity. Therefore, for user convenience, PAAS can be accessed using a wearable device (Painmeter, LST, Seoul, Korea) that is interlinked with a smartphone mobile application (DrKooB-PAAS, iKooB Inc. Seoul, South Korea). The system records the frequency and severity of pain and can create reports summarizing the pain over the course of various time intervals. In addition, this system also records the time, temperature, humidity, and weather, along with the reported pain, to determine any correlations between the pain and these environmental factors. Fibromyalgia (FMS) is a complex disorder characterized by intractable, widespread pain and somatic symptoms such as insomnia, constipation, diarrhea, and cognitive dysfunction. The exact pathogenesis has yet to be elucidated, but current understandings suggest that disturbances in pain-regulating neurotransmitters are involved. The diagnosis and management of FMS are a challenge for physicians. Its management should combine pharmacological and non-pharmacological approaches, which suggests there are various factors that influence treatment outcomes. To date, tricyclic agents, serotonin norepinephrine reuptake inhibitors, and pregabalin are known to have beneficial effects; however, a substantial number of patients are still suffering from the uncontrolled pain of FMS.

To address the pain in these patients, an appropriate pain monitoring system that can reflect real-time pain severity and frequency and can analyze the pattern of impacting factors can be used to provide effective treatment and to eventually improve patient pain. Therefore, we aimed to investigate the feasibility of a real-time pain monitoring system, the Pain Assessment and Analysis System (PAAS), in patients with FMS in order to evaluate its effect on pain reduction. In addition, we evaluated the correlations between the VAS measured by PAAS and clinical parameters reflecting the disease activity of FMS. ;

Related Conditions & MeSH terms

• Fibromyalgia
• Myofascial Pain Syndromes
• Pain

• NCT number: NCT03571009
• Study type: Interventional
• Source: Seoul St. Mary's Hospital
• Status: Completed
• Phase: N/A
• Start date: December 22, 2015
• Completion date: September 21, 2017