Mobile Health App and Web Platform (eDOL) for Medical Follow-Up of Patients With Chronic Pain: Cohort Study Involving the French eDOL National Cohort After 1 Year.


Journal

JMIR mHealth and uHealth
ISSN: 2291-5222
Titre abrégé: JMIR Mhealth Uhealth
Pays: Canada
ID NLM: 101624439

Informations de publication

Date de publication:
12 Jun 2024
Historique:
received: 27 11 2023
accepted: 27 03 2024
revised: 13 03 2024
medline: 12 6 2024
pubmed: 12 6 2024
entrez: 12 6 2024
Statut: epublish

Résumé

Chronic pain affects approximately 30% of the general population, severely degrades quality of life and professional life, and leads to additional health care costs. 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 with 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 medical management. This cohort study aimed 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. A French national cohort of patients with chronic pain treated at 18 pain clinics has been established and followed up using mHealth tools. This cohort makes it possible to collect the determinants and repercussions of chronic pain and their evolutions in a real-life context, taking into account all 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 could monitor their patients' progress in real time via an online platform. The cohort study included 1427 patients and analyzed 1178 patients. The eDOL tool was able to collect various sociodemographic 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; data on drug and nondrug therapeutics and their benefit-to-risk ratio; and medical or treatment history. Among the patients completing weekly meters, 49.4% (497/1007) continued to complete them after 3 months of follow-up, and the proportion stabilized at 39.3% (108/275) after 12 months of follow-up. Overall, despite a fairly high attrition rate over the follow-up period, the eDOL tool collected extensive data. This amount of data will increase over time and provide a significant volume of health data of interest for future research involving the epidemiology, care pathways, trajectories, medical management, sociodemographic characteristics, and other aspects of patients with chronic pain. This work demonstrates that the mHealth tool eDOL is able to generate a considerable volume of data concerning the determinants and repercussions of chronic pain and their evolutions in a real-life context. The eDOL tool can incorporate numerous parameters to ensure the detailed characterization of patients with chronic pain for future research and pain management. ClinicalTrials.gov NCT04880096; https://clinicaltrials.gov/ct2/show/NCT04880096.

Sections du résumé

BACKGROUND 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. 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 with 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 medical management.
OBJECTIVE OBJECTIVE
This cohort study aimed 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 METHODS
A French national cohort of patients with chronic pain treated at 18 pain clinics has been established and followed up using mHealth tools. This cohort makes it possible to collect the determinants and repercussions of chronic pain and their evolutions in a real-life context, taking into account all 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 could monitor their patients' progress in real time via an online platform.
RESULTS RESULTS
The cohort study included 1427 patients and analyzed 1178 patients. The eDOL tool was able to collect various sociodemographic 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; data on drug and nondrug therapeutics and their benefit-to-risk ratio; and medical or treatment history. Among the patients completing weekly meters, 49.4% (497/1007) continued to complete them after 3 months of follow-up, and the proportion stabilized at 39.3% (108/275) after 12 months of follow-up. Overall, despite a fairly high attrition rate over the follow-up period, the eDOL tool collected extensive data. This amount of data will increase over time and provide a significant volume of health data of interest for future research involving the epidemiology, care pathways, trajectories, medical management, sociodemographic characteristics, and other aspects of patients with chronic pain.
CONCLUSIONS CONCLUSIONS
This work demonstrates that the mHealth tool eDOL is able to generate a considerable volume of data concerning the determinants and repercussions of chronic pain and their evolutions in a real-life context. The eDOL tool can incorporate numerous parameters to ensure the detailed characterization of patients with chronic pain for future research and pain management.
TRIAL REGISTRATION BACKGROUND
ClinicalTrials.gov NCT04880096; https://clinicaltrials.gov/ct2/show/NCT04880096.

Identifiants

pubmed: 38865173
pii: v12i1e54579
doi: 10.2196/54579
doi:

Banques de données

ClinicalTrials.gov
['NCT04880096']

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e54579

Informations de copyright

©Noémie Delage, Nathalie Cantagrel, Sandrine Soriot-Thomas, Marie Frost, Rodrigue Deleens, Patrick Ginies, Alain Eschalier, Alice Corteval, Alicia Laveyssière, Jules Phalip, Célian Bertin, Bruno Pereira, Chouki Chenaf, Bastien Doreau, Nicolas Authier, ePAIN, Nicolas Kerckhove. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 12.06.2024.

Auteurs

Noémie Delage (N)

Centre d'évaluation et de Traitement de la douleur, CHU Clermont-Ferrand, Clermont-Ferrand, France.

Nathalie Cantagrel (N)

Centre d'évaluation et de Traitement de la douleur, CHU Toulouse, Toulouse, France.

Sandrine Soriot-Thomas (S)

Centre de Recherche Clinique, CHU Amiens Picardie, Amiens, France.

Marie Frost (M)

Centre d'évaluation et de Traitement de la douleur, CHU Grenoble, Grenoble, France.

Rodrigue Deleens (R)

Centre d'évaluation et de Traitement de la douleur, CHU Rouen, Rouen, France.

Patrick Ginies (P)

Centre d'évaluation et de Traitement de la douleur, CHU Montpellier, Montpellier, France.

Alain Eschalier (A)

Analgesia Institute, Clermont-Ferrand, France.

Alice Corteval (A)

Analgesia Institute, Clermont-Ferrand, France.

Alicia Laveyssière (A)

Analgesia Institute, Clermont-Ferrand, France.

Jules Phalip (J)

Analgesia Institute, Clermont-Ferrand, France.
Service de pharmacologie médicale, CHU Clermont-Ferrand, Clermont-Ferrand, France.

Célian Bertin (C)

Service de pharmacologie médicale, CHU Clermont-Ferrand, Clermont-Ferrand, France.

Bruno Pereira (B)

Direction de la recherche clinique et de l'innovation, CHU Clermont-Ferrand, Clermont-Ferrand, France.

Chouki Chenaf (C)

Service de pharmacologie médicale, CHU Clermont-Ferrand, Clermont-Ferrand, France.

Bastien Doreau (B)

Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes, Université Clermont Auvergne, Clermont-Ferrand, France.

Nicolas Authier (N)

Service de pharmacologie médicale, CHU Clermont-Ferrand, Clermont-Ferrand, France.
See Acknowledgments, , France.

Nicolas Kerckhove (N)

Service de pharmacologie médicale, CHU Clermont-Ferrand, Clermont-Ferrand, France.

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Classifications MeSH