Blinding and Sham Control Methods in Trials of Physical, Psychological, and Self-Management Interventions for Pain (Article I): a Systematic Review and Description of Methods

Blinding is challenging in randomised controlled trials (RCTs) of physical, psychological, and self-management therapies (PPS) for pain, mainly due to their complex and participatory nature. To develop standards for the design, implementation, and reporting of control interventions in efficacy and mechanistic trials, a systematic overview of currently employed sham interventions and other blinding methods was required. Twelve databases were searched for placebo or sham controlled RCTs of PPS treatments in a clinical pain population. Screening and data extraction were performed in duplicate, and trial features, description of control methods and their similarity to the active intervention under investigation were extracted (protocol registration ID: CRD42020206590). The review included 198 unique control interventions, published between 2008 and December 2021. Most trials studied people with chronic pain, and more than half were manual therapy trials. The described control interventions ranged from clearly modelled based on the active treatment, to largely dissimilar control interventions. Similarity between control and active interventions was more frequent for certain aspects (e.g., duration and frequency of treatments) than others (e.g., physical treatment procedures and patient sensory experiences). We also provide an overview of additional, potentially useful methods to enhance blinding, as well as the reporting of processes involved in developing control interventions. A comprehensive picture of prevalent blinding methods is provided, including a detailed assessment of the resemblance between active and control interventions. These findings can inform future developments of control interventions in efficacy and mechanistic trials and best-practice recommendations

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

International audienceBackground: 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