Background: The COVID-19 pandemic has broad negative impact on the physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS). Objective: We presented a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict their health outcomes in a natural experiment during a state-mandated stay-at-home period due to a global pandemic. Methods: First, we extracted features that capture behavior changes due to the stay-at-home order. Then, we adapted and applied an existing algorithm to these behavior-change features to predict the presence of depression, high global MS symptom burden, severe fatigue, and poor sleep quality during the stay-at-home period. Results: Using data collected between November 2019 and May 2020, the algorithm detected depression with an accuracy of 82.5% (65% improvement over baseline; F₁-score: 0.84), high global MS symptom burden with an accuracy of 90% (39% improvement over baseline; F₁-score: 0.93), severe fatigue with an accuracy of 75.5% (22% improvement over baseline; F₁-score: 0.80), and poor sleep quality with an accuracy of 84% (28% improvement over baseline; F₁-score: 0.84). Conclusions: Our approach could help clinicians better triage patients with MS and potentially other chronic neurological disorders for interventions and aid patient self-monitoring in their own environment, particularly during extraordinarily stressful circumstances such as pandemics, which would cause drastic behavior changes.

中文翻译：

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预测 COVID-19 居家期间的多发性硬化症结果：使用被动感知行为和数字表型的观察研究

背景： COVID-19 大流行对多发性硬化症 (MS) 等慢性神经系统疾病患者的身心健康产生了广泛的负面影响。目标：我们提出了一种机器学习方法，该方法利用来自智能手机的被动传感器数据和 MS 患者的健身追踪器，在国家规定的居家期间因全球大流行而进行的自然实验中预测他们的健康结果。方法：首先，我们提取了捕捉由于居家令引起的行为变化的特征。然后，我们对这些行为改变特征进行了调整并应用了现有算法，以预测在家期间是否存在抑郁症、高全球 MS 症状负担、严重疲劳和睡眠质量差。结果：使用 2019 年 11 月至 2020 年 5 月期间收集的数据，该算法检测到抑郁症的准确率为 82.5%（比基线改善 65%；F ₁分数：0.84），全球 MS 症状负担高，准确率为 90%（39比基线改善 %；F ₁ - 得分：0.93），严重疲劳，准确率为 75.5%（比基线改善 22%；F ₁ - 得分：0.80），睡眠质量差，准确率为 84%（改善 28%）超过基线；F ₁ - 分数：0.84）。结论：我们的方法可以帮助临床医生更好地对患有 MS 和潜在的其他慢性神经系统疾病的患者进行干预，并帮助患者在自己的环境中进行自我监测，特别是在大流行等压力异常大的情况下，这会导致行为发生剧烈变化。