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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.

infections is higher for people with serious underlying medical conditions, 2 such as amyotrophic lateral sclerosis (ALS).
Insight into the behavioral change of people with ALS is useful for future studies of the risk of contracting COVID-19, as well as the consequences for social support, social withdrawal, and quality of life of patients. However, behavioral change can be difficult to quantify using traditional research methods such as surveys, which require participant effort and are subject to recall bias.
Smartphone sensors provide an opportunity to measure behavior passively, including mobility, and construct digital phenotypes. 3 The COVID-19 measures, with recommendations to reduce mobility, are a useful positive control to test the feasibility of using smartphones to quantify behavioral change in neurologic populations. We therefore used personal smartphone data to identify behavioral changes in people with ALS due to the COVID-19 outbreak.

| METHODS
For this analysis, participants in an ongoing study were selected if passive mobility data were available between February 13 and April 13, 2020. These participants had been recruited from the ALS Multidisciplinary Clinics at Massachusetts General Hospital (Boston, Massachusetts) and Washington University (St. Louis, Missouri).
Participation required informed consent. The study was approved by the local institutional review boards.

| Smartphone data collection
Participants installed the Beiwe smartphone app on their personal smartphones. Beiwe is an open-source, end-to-end encrypted digital phenotyping platform that consists of Android and iOS smartphone applications, a web-based platform for study setup, HIPAA-compliant cloud-based data storage, and a data analysis back-end. 4 The smartphone app was configured to collect location data using the GPS sensor for 60 seconds every 10 minutes, as described elsewhere. 5 All data were collected and stored in compliance with local, state, and national laws, and all regulations and policies.

| Calculating mobility metrics from location data
To calculate mobility metrics from location data, we imputed the missing location data caused by the intermittent location sampling scheme.
Latitude-longitude coordinate pairs were projected on a sphere and converted into a temporal sequence of flights (periods of linear movement) and pauses (stationary periods). Missing data were imputed using a method described elsewhere. 6 From the complete location trajectories, we calculated daily home time (in hours) and distance traveled (in kilometers) each day for each participant.
Home location was inferred by selecting the location where the participant spent most of their time between 7:00 PM and 9:00 AM. Distances over 150 km traveled were recorded as 150 km, as differences in distance traveled would otherwise be driven by few less-relevant long-distance trips.

| Statistical methods
We used an interrupted time series to analyze pre-pandemic (February 13 to March 12 2020) and pandemic phase (after the government's state of emergency declaration: March 13 to April 13, 2020) behavior. 7 We used mixed effects models to investigate how the declaration of a national emergency impacted on daily home time and daily distance traveled, each with a fixed effect for time since February 13, an indicator for whether a time was pre-or post-pandemic, and an interaction between these two effects, and a random intercept and slope for each participant.
Within-subject covariance was unstructured. As our participants live in different states, it is possible that local declarations of emergency had a more profound effect on behavior. We did a secondary analysis, using the state of emergency declaration in participants' state of residence.

| RESULTS
Eight participants contributed data (  Figure 1). The secondary analysis, individualizing each participant's estimate of behavior change to the date of the local state of emergency declaration, rather than the national state of emergency declaration, showed a more gradual change that remained significant (data not shown).
We compared the mobility of an ambulatory participant with nearnormal function (ALSFRS-R: 46 of 48; Figure 2A) and a nonambulatory participant with low function (ALSFRS-R: 23 of 48; Figure 2B). The ambu-  Figure 2B). In addition, this participant changed behavior earlier in the COVID pandemic, beginning to spend more time at home and traveling less distance in February, a trend that continued into the pandemic phase.

| DISCUSSION
People with ALS spent more time at home than the general population both before and during the COVID-19 pandemic. US mobility research based on smartphone data show that the average daily home time for the general population in March and April increased from 10 hours pre-pandemic to 14 hours during the pandemic (varying by state; range, <5 to 17 hours). 9 In people with ALS, the absolute change in daily home time was similar, yet they were less mobile both before and during the pandemic.
We demonstrated differences in mobility and behavior between participants with low and high function according to the ALSFRS-R.
This finding, although based on limited sample size, supports the clinical meaningfulness of the ALSFRS-R and opens a pathway for using smartphone-based digital phenotyping to quantify the impact of ALS on people's lives. The association between digital phenotypes and disease progression should be further investigated in larger cohorts.
Our finding of high home isolation of people with ALS is relevant for researchers investigating the impact of COVID-19 on people with neurologic disorders. These researchers should investigate the generalizability of our findings in larger samples.
Compliance with stay-at-home-orders results in a lower exposure to community spread of the virus. Whether this results in a lower total exposure to the virus for a given individual ultimately depends on the number of cohabitants, caregivers, and visitors who come into close contact with the person, and their behavior. Further investigating the extent of social isolation, which has its own negative health implica-