Smartphone data during the COVID-19 pandemic can quantify behavioral changes in people with ALS

Abstract

Introduction: Passive data from smartphone sensors may be useful for health-care research. Our aim was to use the coronavirus disease-2019 (COVID-19) pandemic as a positive control to assess the ability to quantify behavioral changes in people with amyotrophic lateral sclerosis (ALS) from smartphone data. Methods: Eight participants used the Beiwe smartphone application, which passively measured their location during the COVID-19 outbreak. We used an interrupted time series to quantify the effect of the US state of emergency declaration on daily home time and daily distance traveled. Results: After the state of emergency declaration, median daily home time increased from 19.4 (interquartile range [IQR], 15.4-22.0) hours to 23.7 (IQR, 22.2-24.0) hours and median distance traveled decreased from 42 (IQR, 13-83) km to 3.7 (IQR, 1.5-10.3) km. The participant with the lowest functional ability changed behavior earlier. This participant stayed at home more and traveled less than the participant with highest functional ability, both before and after the state of emergency. Discussion: We provide evidence that smartphone-based digital phenotyping can quantify the behavior of people with ALS. Although participants spent large amounts of time at home at baseline, the COVID-19 state of emergency declaration reduced their mobility further. Given participants' high level of daily home time, it is possible that their exposure to COVID-19 could be less than that of the general population.