Centre d'évaluation et de Traitement de la douleur, CHU Clermont-Ferrand, Clermont-Ferrand, France.
Centre d'évaluation et de Traitement de la douleur, CHU Toulouse, Toulouse, France.
JMIR Mhealth Uhealth. 2024 Jun 12;12:e54579. doi: 10.2196/54579.
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.
慢性疼痛影响大约 30%的普通人群,严重降低生活质量和职业生活质量,并导致额外的医疗保健费用。此外,慢性疼痛患者的医疗随访仍然复杂,仅提供关于日常疼痛体验的零碎数据。这种情况使得慢性疼痛患者的管理不尽如人意,部分原因可能是当前治疗方法的效果不佳。使用移动医疗 (mHealth) 程序实时监测慢性疼痛的主观和客观标志物,可以更好地描述患者、慢性疼痛、疼痛药物和日常影响,以帮助医疗管理。
本队列研究旨在评估我们的 mHealth 工具 (eDOL) 在使用 1 年后收集慢性疼痛患者广泛真实医疗数据的能力。通过这种方式收集的数据将提供关于慢性疼痛的新的流行病学和病理生理学数据。
建立了一个法国全国性的慢性疼痛患者队列,这些患者在 18 个疼痛诊所接受治疗,并使用 mHealth 工具进行随访。该队列可在真实环境中收集慢性疼痛的决定因素和后果及其演变情况,同时考虑到所有可能影响慢性疼痛的环境事件。患者被要求完成多项问卷、身体图表和每周量表,并能够与聊天机器人互动并使用慢性疼痛的教育模块。医生可以通过在线平台实时监测患者的进展情况。
队列研究包括 1427 名患者,分析了 1178 名患者。eDOL 工具能够收集各种社会人口统计学数据;用于描述疼痛障碍的特定数据,包括身体图表;与慢性疼痛及其对患者生活质量的心理和整体影响相关的合并症数据;药物和非药物治疗及其获益-风险比的数据;以及医疗或治疗史。在完成每周量表的患者中,49.4%(497/1007)在随访 3 个月后继续完成,12 个月随访后比例稳定在 39.3%(108/275)。总体而言,尽管在随访期间存在相当高的流失率,但 eDOL 工具仍收集了广泛的数据。随着时间的推移,这些数据量将会增加,并为涉及慢性疼痛患者的流行病学、护理途径、轨迹、医疗管理、社会人口统计学特征等方面的未来研究提供大量有意义的健康数据。
这项工作表明,mHealth 工具 eDOL 能够生成大量关于慢性疼痛的决定因素和后果及其在现实环境中的演变的数据。eDOL 工具可以纳入许多参数,以确保对慢性疼痛患者进行详细的特征描述,以用于未来的研究和疼痛管理。
ClinicalTrials.gov NCT04880096; https://clinicaltrials.gov/ct2/show/NCT04880096.