RESUMEN
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.
RESUMEN
OBJECTIVE: To report initial results of a planned multicenter year-long prospective study examining the risk and impact of COVID-19 among persons with neuroinflammatory disorders (NID), particularly multiple sclerosis (MS). METHODS: In April 2020, we deployed online questionnaires to individuals in their home environment to assess the prevalence and potential risk factors of suspected COVID-19 in persons with NID (PwNID) and change in their neurological care. RESULTS: Our cohort included 1115 participants (630 NID, 98% MS; 485 reference) as of 30 April 2020. 202 (18%) participants, residing in areas with high COVID-19 case prevalence, met the April 2020 CDC symptom criteria for suspected COVID-19, but only 4% of all participants received testing given testing shortages. Among all participants, those with suspected COVID-19 were younger, more racially diverse, and reported more depression and liver disease. PwNID had the same rate of suspected COVID-19 as the reference group. Early changes in disease management included telemedicine visits in 21% and treatment changes in 9% of PwNID. After adjusting for potential confounders, increasing neurological disability was associated with a greater likelihood of suspected COVID-19 (ORadj = 1.45, 1.17-1.84). INTERPRETATIONS: Our study of real-time, patient-reported experience during the COVID-19 pandemic complements physician-reported MS case registries which capture an excess of severe cases. Overall, PwNID seem to have a risk of suspected COVID-19 similar to the reference population.