TY  - JOUR
AU  - Chikersal, Prerna
AU  - Venkatesh, Shruthi
AU  - Masown, Karman
AU  - Walker, Elizabeth
AU  - Quraishi, Danyal
AU  - Dey, Anind
AU  - Goel, Mayank
AU  - Xia, Zongqi
PY  - 2022
DA  - 2022/8/24
TI  - Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping
JO  - JMIR Ment Health
SP  - e38495
VL  - 9
IS  - 8
KW  - mobile sensing
KW  - sensor
KW  - sensing
KW  - mobile health
KW  - mHealth
KW  - algorithm
KW  - multiple sclerosis
KW  - disability
KW  - mental health
KW  - depression
KW  - sleep
KW  - fatigue
KW  - tiredness
KW  - predict
KW  - machine learning
KW  - feature selection
KW  - neurological disorder
KW  - COVID-19
KW  - isolation
KW  - behavior change
KW  - health outcome
KW  - fitness
KW  - movement
KW  - physical activity
KW  - exercise
KW  - tracker
KW  - digital phenotyping
AB  - 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. 
SN  - 2368-7959
UR  - https://mental.jmir.org/2022/8/e38495
UR  - https://doi.org/10.2196/38495
UR  - http://www.ncbi.nlm.nih.gov/pubmed/35849686
DO  - 10.2196/38495
ID  - info:doi/10.2196/38495
ER  -