Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom.
Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom.
JMIR Form Res. 2024 Oct 28;8:e55715. doi: 10.2196/55715.
Mobile health devices are increasingly available, presenting exciting opportunities to remotely collect high-frequency, electronic patient-generated health data (ePGHD). This novel data type may provide detailed insights into disease activity outside usual clinical settings. Assessing treatment responses, which can be hampered by the infrequency of appointments and recall bias, is a promising, novel application of ePGHD. Drugs with short treatment effects, such as intramuscular steroid injections, illustrate the challenge, as patients are unlikely to accurately recall treatment responses at follow-ups, which often occur several months later. Retrospective assessment means that responses may be over- or underestimated. High-frequency ePGHD, such as daily, app-collected, patient-reported symptoms between clinic appointments, may bridge this gap. However, the potential of ePGHD remains untapped due to the absence of established definitions for treatment response using ePGHD or established methodological approaches for analyzing this type of data.
This study aims to explore the feasibility of evaluating treatment responses to intramuscular steroid therapy in a case series of patients with rheumatoid arthritis tracking daily symptoms using a smartphone app.
We report a case series of patients who collected ePGHD through the REmote Monitoring Of Rheumatoid Arthritis (REMORA) smartphone app for daily remote symptom tracking. Symptoms were tracked on a 0-10 scale. We described the patients' longitudinal pain scores before and after intramuscular steroid injections. The baseline pain score was calculated as the mean pain score in the 10 days prior to the injection. This was compared to the pain scores in the days following the injection. "Response" was defined as any improvement from the baseline score on the first day following the injection. The response end time was defined as the first date when the pain score exceeded the pre-steroid baseline.
We included 6 patients who, between them, received 9 steroid injections. Average pre-injection pain scores ranged from 3.3 to 9.3. Using our definitions, 7 injections demonstrated a response. Among the responders, the duration of response ranged from 1 to 54 days (median 9, IQR 7-41), average pain score improvement ranged from 0.1 to 5.3 (median 3.3, IQR 2.2-4.0), and maximum pain score improvement ranged from 0.1 to 7.0 (median 4.3, IQR 1.7 to 6.0).
This case series demonstrates the feasibility of using ePGHD to evaluate treatment response and is an important exploratory step toward developing more robust methodological approaches for analysis of this novel data type. Issues highlighted by our analysis include the importance of accounting for one-off data points, varying response start times, and confounders such as other medications. Future analysis of ePGHD across a larger population is required to address issues highlighted by our analysis and to develop meaningful consensus definitions for treatment response in time-series data.
移动健康设备越来越普及，为远程收集高频、电子患者生成的健康数据（ePGHD）提供了令人兴奋的机会。这种新型数据类型可能提供疾病活动的详细信息，超出了通常的临床环境。评估治疗反应是一种很有前途的新型 ePGHD 应用，这种应用可能会受到就诊频率和回忆偏倚的阻碍。治疗效果短的药物，如肌肉内类固醇注射，说明了这一挑战，因为患者在后续就诊时不太可能准确回忆治疗反应，而后续就诊通常在几个月后进行。回顾性评估意味着反应可能被高估或低估。高频 ePGHD，如每天在诊所就诊之间使用智能手机应用程序收集的患者报告的症状，可能会弥补这一差距。然而，由于缺乏使用 ePGHD 评估治疗反应的既定定义或分析这种类型数据的既定方法，ePGHD 的潜力仍未得到开发。
本研究旨在探讨通过智能手机应用程序跟踪类风湿关节炎患者日常症状的 REMORA 智能手机应用程序，评估肌肉内类固醇治疗反应的可行性。
我们报告了一系列使用 REMORA 智能手机应用程序远程跟踪日常症状的类风湿关节炎患者的病例系列，该应用程序可用于每天收集 ePGHD。症状按 0-10 分进行评分。我们描述了肌肉内类固醇注射前后患者的纵向疼痛评分。基线疼痛评分是在注射前 10 天的平均疼痛评分。将其与注射后的疼痛评分进行比较。“反应”定义为注射后第一天的基线评分任何改善。反应结束时间定义为疼痛评分首次超过类固醇前基线的日期。
我们纳入了 6 名患者，他们总共接受了 9 次类固醇注射。平均注射前疼痛评分范围为 3.3 至 9.3。根据我们的定义，7 次注射显示出反应。在反应者中，反应持续时间从 1 天到 54 天不等（中位数 9，IQR 7-41），平均疼痛评分改善范围从 0.1 到 5.3（中位数 3.3，IQR 2.2-4.0），最大疼痛评分改善范围从 0.1 到 7.0（中位数 4.3，IQR 1.7-6.0）。
本病例系列证明了使用 ePGHD 评估治疗反应的可行性，这是朝着开发更强大的分析这种新型数据类型的方法迈出的重要探索性步骤。我们分析中突出的问题包括考虑一次性数据点、不同的反应起始时间以及其他药物等混杂因素的重要性。需要对更大人群的 ePGHD 进行进一步分析，以解决我们分析中突出的问题，并为时间序列数据中的治疗反应制定有意义的共识定义。
