JMIR Publications

ABSTRACT

Background:

Chronic pain affects approximately 30% of the general population, severely degrades quality of life and professional life, and leads to additional health care costs amounting to billions of euros. Moreover, the medical follow-up of patients with chronic pain remains complex and provides only fragmentary data on painful daily experiences. This situation makes the management of patients suffering from chronic pain less than optimal, and may partly explain the lack of effectiveness of current therapies. Real-life monitoring of subjective and objective markers of chronic pain using mobile health (mHealth) programs could better characterize patients, chronic pain, pain medications and daily impact to help the medical management.

Objective:

The aim of this study was to assess the ability of our mHealth tool (eDOL) to collect extensive real-life medical data from chronic pain patients after 1-year of use. The data collected in this way would provide new epidemiological and pathophysiological data on chronic pain.

Methods:

A French national cohort of patients with chronic pain treated at 18 pain clinics has been set-up and followed up using mHealth tools. This cohort makes it possible to collect the determinants and repercussions of chronic pain, and their evolution in a real-life context, taking into account all the environmental events likely to influence chronic pain. The patients were asked to complete several questionnaires, body schemes and weekly meters, and were able to interact with a chatbot and use educational modules on chronic pain. Physicians were able to monitor their patients' progress in real time via an online platform.

Results:

1,427 patients were included and 1,178 patients were analyzed. The eDOL tool was able to collect various socio-demographic data, specific data for characterizing pain disorders including body scheme, data on comorbidities related to chronic pain and its psychological and overall impact on patients' quality of life, drug and non-drug therapeutics and their benefit/risk ratio, and medical/treatment history. Among the patients included, 50% continued to use the eDOL application after 3 months’ follow-up, then to stabilize at around 40% after 12 months' follow-up. Overall, despite a fairly high attrition rate over the follow-up time, eDOL has collected tens of thousands of data records. This amount of data will increase over time, and provide a significant mass of health data of interest for future research into the epidemiology, care pathways, trajectories, medical management, socio-demographic characteristics, etc. of patients suffering from chronic pain.

Conclusions:

This work demonstrates that mHealth eDOL is able to generate a considerable mass of data concerning the determinants and repercussions of chronic pain, and their evolution in a real-life context. The eDOL can incorporate numerous parameters to ensure the detailed characterization of patients with chronic pain for future research works and pain management. Clinical Trial: ClinicalTrial.gov NCT04880096; https://clinicaltrials.gov/ct2/show/NCT04880096