Meaningful digital biomarkers derived from wearable sensors to predict daily fatigue in multiple sclerosis patients and healthy controls
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)...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S258900422400186X |
| _version_ | 1797317477151014912 |
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| author | Max Moebus Shkurta Gashi Marc Hilty Pietro Oldrati Christian Holz |
| author_facet | Max Moebus Shkurta Gashi Marc Hilty Pietro Oldrati Christian Holz |
| author_sort | Max Moebus |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T03:35:35Z |
| format | Article |
| id | doaj.art-bfceea9b0bbc4eedab32e5e1134d5cb8 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-08T03:35:35Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-bfceea9b0bbc4eedab32e5e1134d5cb82024-02-10T04:45:09ZengElsevieriScience2589-00422024-02-01272108965Meaningful digital biomarkers derived from wearable sensors to predict daily fatigue in multiple sclerosis patients and healthy controlsMax Moebus0Shkurta Gashi1Marc Hilty2Pietro Oldrati3Christian Holz4Department of Computer Science, ETH Zürich, Stampfenbachstrasse 48, 8092 Zürich, Switzerland; Competence Center for Rehabilitation Engineering and Science, ETH Zürich, Gloriastrasse 37/39, 8092 Zürich, SwitzerlandDepartment of Computer Science, ETH Zürich, Stampfenbachstrasse 48, 8092 Zürich, Switzerland; ETH AI Center, ETH Zürich, OAS J17, Binzmühlestrasse 13, 8092 Zürich, SwitzerlandNeuroimmunology Department, University Hospital Zürich, Frauenklinikstrasse 26, 8091 Zürich, SwitzerlandNeuroimmunology Department, University Hospital Zürich, Frauenklinikstrasse 26, 8091 Zürich, SwitzerlandDepartment 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 authorSummary: 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.http://www.sciencedirect.com/science/article/pii/S258900422400186XHealth sciencesNeuroscienceBioelectronics |
| spellingShingle | Max Moebus Shkurta Gashi Marc Hilty Pietro Oldrati Christian Holz Meaningful digital biomarkers derived from wearable sensors to predict daily fatigue in multiple sclerosis patients and healthy controls iScience Health sciences Neuroscience Bioelectronics |
| title | Meaningful digital biomarkers derived from wearable sensors to predict daily fatigue in multiple sclerosis patients and healthy controls |
| title_full | Meaningful digital biomarkers derived from wearable sensors to predict daily fatigue in multiple sclerosis patients and healthy controls |
| title_fullStr | Meaningful digital biomarkers derived from wearable sensors to predict daily fatigue in multiple sclerosis patients and healthy controls |
| title_full_unstemmed | Meaningful digital biomarkers derived from wearable sensors to predict daily fatigue in multiple sclerosis patients and healthy controls |
| title_short | Meaningful digital biomarkers derived from wearable sensors to predict daily fatigue in multiple sclerosis patients and healthy controls |
| title_sort | meaningful digital biomarkers derived from wearable sensors to predict daily fatigue in multiple sclerosis patients and healthy controls |
| topic | Health sciences Neuroscience Bioelectronics |
| url | http://www.sciencedirect.com/science/article/pii/S258900422400186X |
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