Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Mobilengine, Budapest, Hungary.
JMIR Mhealth Uhealth. 2021 Apr 16;9(4):e19564. doi: 10.2196/19564.
Although fatigue is one of the most debilitating symptoms in patients with multiple sclerosis (MS), its pathogenesis is not well understood. Neurogenic, inflammatory, endocrine, and metabolic mechanisms have been proposed. Taking into account the temporal dynamics and comorbid mood symptoms of fatigue may help differentiate fatigue phenotypes. These phenotypes may reflect different pathogeneses and may respond to different mechanism-specific treatments. Although several tools have been developed to assess various symptoms (including fatigue), monitor clinical status, or improve the perceived level of fatigue in patients with MS, options for a detailed, real-time assessment of MS-related fatigue and relevant comorbidities are still limited.
This study aims to present a novel mobile app specifically designed to differentiate fatigue phenotypes using circadian symptom monitoring and state-of-the-art characterization of MS-related fatigue and its related symptoms. We also aim to report the first findings regarding patient compliance and the relationship between compliance and patient characteristics, including MS disease severity.
After developing the app, we used it in a prospective study designed to investigate the brain magnetic resonance imaging correlates of MS-related fatigue. In total, 64 patients with MS were recruited into this study and asked to use the app over a 2-week period. The app features the following modules: Visual Analogue Scales (VASs) to assess circadian changes in fatigue, depression, anxiety, and pain; daily sleep diaries (SLDs) to assess sleep habits and quality; and 10 one-time questionnaires to assess fatigue, depression, anxiety, sleepiness, physical activity, and motivation, as well as several other one-time questionnaires that were created to assess those relevant aspects of fatigue that were not captured by existing fatigue questionnaires. The app prompts subjects to assess their symptoms multiple times a day and enables real-time symptom monitoring through a web-accessible portal.
Of 64 patients, 56 (88%) used the app, of which 51 (91%) completed all one-time questionnaires and 47 (84%) completed all one-time questionnaires, VASs, and SLDs. Patients reported no issues with the usage of the app, and there were no technical issues with our web-based data collection system. The relapsing-remitting MS to secondary-progressive MS ratio was significantly higher in patients who completed all one-time questionnaires, VASs, and SLDs than in those who completed all one-time questionnaires but not all VASs and SLDs (P=.01). No other significant differences in demographics, fatigue, or disease severity were observed between the degrees of compliance.
The app can be used with reasonable compliance across patients with relapsing-remitting and secondary-progressive MS irrespective of demographics, fatigue, or disease severity.
疲劳是多发性硬化症（MS）患者最具致残性的症状之一，但发病机制尚不清楚。神经源性、炎症性、内分泌和代谢机制都被提出来了。考虑到疲劳的时间动态和合并的情绪症状，可能有助于区分疲劳表型。这些表型可能反映了不同的发病机制，并可能对不同的针对特定机制的治疗有反应。尽管已经开发了几种工具来评估各种症状（包括疲劳）、监测临床状况或改善 MS 患者的疲劳感知水平，但对 MS 相关疲劳及其相关症状进行详细、实时评估的选择仍然有限。
本研究旨在介绍一种新的移动应用程序，该应用程序专门通过昼夜节律症状监测和对 MS 相关疲劳及其相关症状的最新特征描述来区分疲劳表型。我们还旨在报告关于患者依从性的第一个发现，以及依从性与患者特征（包括 MS 疾病严重程度）之间的关系。
在开发应用程序后，我们将其用于一项前瞻性研究，旨在调查 MS 相关疲劳的脑磁共振成像相关性。共有 64 名 MS 患者被招募到这项研究中，并要求他们在两周内使用该应用程序。该应用程序具有以下模块：视觉模拟量表（VAS）评估疲劳、抑郁、焦虑和疼痛的昼夜变化；每日睡眠日记（SLD）评估睡眠习惯和质量；以及 10 个一次性问卷评估疲劳、抑郁、焦虑、嗜睡、身体活动和动机，以及几个其他一次性问卷评估疲劳问卷未涵盖的那些与疲劳相关的方面。该应用程序提示患者每天多次评估自己的症状，并通过可访问网络的门户实现实时症状监测。
在 64 名患者中，56 名（88%）使用了该应用程序，其中 51 名（91%）完成了所有一次性问卷，47 名（84%）完成了所有一次性问卷、VAS 和 SLD。患者报告使用该应用程序没有问题，我们的基于网络的数据收集系统也没有出现技术问题。与仅完成所有一次性问卷但未完成所有 VAS 和 SLD 的患者相比，完成所有一次性问卷、VAS 和 SLD 的患者的复发缓解型 MS 到继发进展型 MS 的比例明显更高（P=.01）。在依从性程度方面，没有观察到患者在人口统计学、疲劳或疾病严重程度方面的其他显著差异。
该应用程序可在具有复发缓解型和继发进展型 MS 的患者中合理地使用，无论患者的人口统计学、疲劳或疾病严重程度如何。
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