Meaningful digital biomarkers derived from wearable sensors to predict daily fatigue in multiple sclerosis patients and healthy controls
Max Moebus,
Shkurta Gashi,
Marc Hilty,
Pietro Oldrati,
Christian Holz
Affiliations
Max Moebus
Department of Computer Science, ETH Zürich, Stampfenbachstrasse 48, 8092 Zürich, Switzerland; Competence Center for Rehabilitation Engineering and Science, ETH Zürich, Gloriastrasse 37/39, 8092 Zürich, Switzerland
Shkurta Gashi
Department of Computer Science, ETH Zürich, Stampfenbachstrasse 48, 8092 Zürich, Switzerland; ETH AI Center, ETH Zürich, OAS J17, Binzmühlestrasse 13, 8092 Zürich, Switzerland
Marc Hilty
Neuroimmunology Department, University Hospital Zürich, Frauenklinikstrasse 26, 8091 Zürich, Switzerland
Pietro Oldrati
Neuroimmunology Department, University Hospital Zürich, Frauenklinikstrasse 26, 8091 Zürich, Switzerland
Christian Holz
Department of Computer Science, ETH Zürich, Stampfenbachstrasse 48, 8092 Zürich, Switzerland; ETH AI Center, ETH Zürich, OAS J17, Binzmühlestrasse 13, 8092 Zürich, Switzerland; Competence Center for Rehabilitation Engineering and Science, ETH Zürich, Gloriastrasse 37/39, 8092 Zürich, Switzerland; Corresponding author
Summary: Fatigue is the most common symptom among multiple sclerosis (MS) patients and severely affects the quality of life. We investigate how perceived fatigue can be predicted using biomarkers collected from an arm-worn wearable sensor for MS patients (n = 51) and a healthy control group (n = 23) at an unprecedented time resolution of more than five times per day. On average, during our two-week study, participants reported their level of fatigue 51 times totaling more than 3,700 data points. Using interpretable generalized additive models, we find that increased physical activity, heart rate, sympathetic activity, and parasympathetic activity while awake and asleep relate to perceived fatigue throughout the day—partly affected by dysfunction of the ANS. We believe our analysis opens up new research opportunities for fine-grained modeling of perceived fatigue based on passively collected physiological signals using wearables—for MS patients and healthy controls alike.
