ArticleiScience2024
Meaningful digital biomarkers derived from wearable sensors to predict daily fatigue in multiple sclerosis patients and healthy controls.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- Targeting progressive multiple sclerosis: Toward mechanism-informed precision medicine.Journal of internal medicine · 2026Review
- Artificial Intelligence Applications to Support Physical Activity, Mobility, and Fatigue Management in People with Multiple Sclerosis: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Feasibility of Privacy-Preserving LiDAR-Based Continuous Gait and Activity Monitoring in Three People with Multiple Sclerosis: A Technical Proof-of-Concept Case Study.Sensors (Basel, Switzerland) · 2026Article
- Deep learning in imaging analysis for multiple sclerosis: Diagnosis and monitoring.Caspian journal of internal medicine · 2026Review
- Exploring the feasibility of modeling next-day fatigue and sleepiness using digital sleep tracker data in neurodegenerative and immune-mediated inflammatory diseases.Frontiers in digital health · 2026Article
- Beyond the hours slept: inconsistent sleep routines threaten mental health in 100,000 UK Biobank participants.BMC public health · 2025Article
- Systematic review: digital biomarkers of fatigue in chronic diseases.NPJ digital medicine · 2025Article
- Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation.Journal of medical Internet research · 2025Article
- Application of telemedicine in fatigue management for patients with multiple sclerosis: A scoping review.PloS one · 2025Article
- miRNAs involved in the regulation of exercise fatigue.Frontiers in physiology · 2025Review
- Assessing the Role of the Autonomic Nervous System as a Driver of Sleep Quality in Patients With Multiple Sclerosis: Observation Study.JMIR neurotechnology · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.