背景:面肩肱肌营养不良(FSHD)是一种导致严重残疾的进行性肌肉营养不良疾病。目前,FSHD症状的严重程度通过临床评估(例如FSHD临床评分和定时上行和上行测试)来评估。这些评估是有限的,在他们的能力,以捕捉不断变化和疾病对患者的生活质量的全面影响。与身体活动相关的真实世界数据,睡眠,和社会行为可能提供更多的洞察疾病的影响,并可能有助于评估对FSHD患者的功能和健康有重要影响的方面的治疗效果。
目的:本研究基于多个特征的连续收集,调查了使用智能手机和可穿戴设备捕获与FSHD相关的症状的可行性,比如步数,睡眠,和应用程序使用。我们还鉴定了可用于区分FSHD患者和非FSHD对照的特征。
方法:在这项探索性非干预性研究中,58名参与者(n=38,66%,FSHD患者,n=20,34%,非FSHD对照)使用智能手机监测应用程序监测6周。在学习期的第一天和最后一天,临床医生评估参与者的FSHD临床评分和定时Up-and-Go测试时间。参与者在他们的Android智能手机上安装了该应用程序,得到了一个智能手表,并被指示每周使用体重秤和血压监测仪测量他们的体重和血压。使用问卷在6周时评估了参与者智能手机上应用的用户体验和感知负担。收集到的数据,我们试图确定在区分2组(FSHD和非FSHD对照患者)方面最显著的行为特征以及进行分类的最佳时间窗.
结果:总体而言,参与者表示,该应用程序耐受性良好,但67%(39/58)注意到使用所有6周数据的电池寿命有差异,我们以93%的准确率对FSHD和非FSHD对照患者进行分类,100%灵敏度,和80%的特异性。我们发现分类的最佳时间窗口是数据收集的第一天和数据收集的第一周,这产生了准确性,灵敏度,特异性为95.8%,100%,94.4%,分别。与智能手机加速相关的功能,应用程序使用,location,身体活动,睡眠,呼叫行为是分类中最显著的特征。
结论:远程监测的数据收集允许收集FSHD和非FSHD对照患者6周的日常活动数据。我们展示了使用智能手机和可穿戴设备检测FSHD和非FSHD对照患者特征差异的初始能力,主要基于与身体和社会活动相关的数据。
背景:ClinicalTrials.govNCT04999735;https://www.clinicaltrials.gov/ct2/show/NCT04999735.
BACKGROUND: Facioscapulohumeral dystrophy (FSHD) is a progressive muscle dystrophy disorder leading to significant disability. Currently, FSHD symptom severity is assessed by clinical assessments such as the FSHD clinical score and the Timed Up-and-Go test. These assessments are limited in their ability to capture changes continuously and the full impact of the disease on patients\' quality of life. Real-world data related to physical activity, sleep, and social behavior could potentially provide additional insight into the impact of the disease and might be useful in assessing treatment effects on aspects that are important contributors to the functioning and well-being of patients with FSHD.
OBJECTIVE: This study investigated the feasibility of using smartphones and wearables to capture symptoms related to FSHD based on a continuous collection of multiple features, such as the number of steps, sleep, and app use. We also identified features that can be used to differentiate between patients with FSHD and non-FSHD controls.
METHODS: In this exploratory noninterventional study, 58 participants (n=38, 66%, patients with FSHD and n=20, 34%, non-FSHD controls) were monitored using a smartphone monitoring app for 6 weeks. On the first and last day of the study period, clinicians assessed the participants\' FSHD clinical score and Timed Up-and-Go test time. Participants installed the app on their Android smartphones, were given a smartwatch, and were instructed to measure their weight and blood pressure on a weekly basis using a scale and blood pressure monitor. The user experience and perceived burden of the app on participants\' smartphones were assessed at 6 weeks using a questionnaire. With the data collected, we sought to identify the behavioral features that were most salient in distinguishing the 2 groups (patients with FSHD and non-FSHD controls) and the optimal time window to perform the classification.
RESULTS: Overall, the participants stated that the app was well tolerated, but 67% (39/58) noticed a difference in battery life using all 6 weeks of data, we classified patients with FSHD and non-FSHD controls with 93% accuracy, 100% sensitivity, and 80% specificity. We found that the optimal time window for the classification is the first day of data collection and the first week of data collection, which yielded an accuracy, sensitivity, and specificity of 95.8%, 100%, and 94.4%, respectively. Features relating to smartphone acceleration, app use, location, physical activity, sleep, and call behavior were the most salient features for the classification.
CONCLUSIONS: Remotely monitored data collection allowed for the collection of daily activity data in patients with FSHD and non-FSHD controls for 6 weeks. We demonstrated the initial ability to detect differences in features in patients with FSHD and non-FSHD controls using smartphones and wearables, mainly based on data related to physical and social activity.
BACKGROUND: ClinicalTrials.gov NCT04999735; https://www.clinicaltrials.gov/ct2/show/NCT04999735.