用于慢性疼痛患者医疗随访的移动健康应用和网络平台 (eDOL):法国 eDOL 国家队列 1 年后的队列研究。

IF 5.4 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES JMIR mHealth and uHealth Pub Date : 2024-06-12 DOI:10.2196/54579
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, Nicolas Kerckhove
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引用次数: 0

摘要

背景:慢性疼痛影响着约 30% 的普通人群,严重降低生活质量和职业生活,并导致额外的医疗费用。此外,对慢性疼痛患者的医学随访仍然很复杂,只能提供有关日常疼痛经历的零碎数据。这种情况使得对慢性疼痛患者的管理不尽如人意,这也是当前疗法缺乏有效性的部分原因。利用移动医疗(mHealth)程序对慢性疼痛的主观和客观指标进行实时监测,可以更好地描述患者、慢性疼痛、止痛药物和日常影响,从而帮助医疗管理:这项队列研究旨在评估我们的移动医疗工具(eDOL)在使用一年后收集慢性疼痛患者大量真实医疗数据的能力。通过这种方式收集的数据将为慢性疼痛提供新的流行病学和病理生理学数据:方法:建立了一个法国全国慢性疼痛患者队列,这些患者在 18 家疼痛诊所接受治疗,并使用移动医疗工具进行随访。通过该队列,可以收集慢性疼痛的决定因素和反作用力及其在现实生活中的演变,同时考虑到可能影响慢性疼痛的所有环境事件。患者被要求填写几份问卷、身体计划和每周测量表,并能与聊天机器人互动,使用有关慢性疼痛的教育模块。医生可通过在线平台实时监控患者的病情进展:这项队列研究包括 1427 名患者,分析了 1178 名患者。eDOL 工具能够收集各种社会人口学数据;描述疼痛疾病特点的具体数据,包括身体方案;与慢性疼痛相关的合并症数据及其对患者生活质量的心理和整体影响;药物和非药物疗法及其效益风险比数据;以及医疗或治疗史。在完成每周测量的患者中,49.4%(497/1007)在随访 3 个月后继续完成测量,这一比例在随访 12 个月后稳定在 39.3%(108/275)。总体而言,尽管随访期间的流失率相当高,但 eDOL 工具收集了大量数据。随着时间的推移,这些数据量将会增加,并为未来涉及慢性疼痛患者的流行病学、护理路径、轨迹、医疗管理、社会人口特征和其他方面的研究提供大量有价值的健康数据:这项工作表明,移动医疗工具 eDOL 能够生成大量数据,这些数据涉及慢性疼痛的决定因素和影响,以及它们在现实生活中的演变。eDOL 工具可纳入大量参数,确保为未来研究和疼痛管理详细描述慢性疼痛患者的特征:试验注册:ClinicalTrials.gov NCT04880096;https://clinicaltrials.gov/ct2/show/NCT04880096。
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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.

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: 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: 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: 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: 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: ClinicalTrials.gov NCT04880096; https://clinicaltrials.gov/ct2/show/NCT04880096.

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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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