ArticleNPJ digital medicine2024
Modeling multiple sclerosis using mobile and wearable sensor data.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07783074 (Fatigue Investigation Using Digital Outcomes), which is not on this map. Cited by 25 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Fatigue Investigation Using Digital Outcomes (FIDO) - An Exploratory Project
Who cites it
25 citing papers in PubMed.
- Digital signatures of fatigue and wearing-off symptoms in patients with multiple sclerosis under natalizumab.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026Article
- Advances and Challenges in Wearable Sensors for Health Monitoring.ACS applied materials & interfaces · 2026Review
- Artificial Intelligence Applications to Support Physical Activity, Mobility, and Fatigue Management in People with Multiple Sclerosis: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Rethinking EDSS-based ambulation assessment in multiple sclerosis using continuous variable monitoring.Neurological research and practice · 2026Article
- Patient Sharing of Digital Health Data in the Veterans Health Administration: Cross-Sectional Analysis.Journal of medical Internet research · 2026Article
- Deep learning in imaging analysis for multiple sclerosis: Diagnosis and monitoring.Caspian journal of internal medicine · 2026Review
- Trust and anxiety as primary drivers of digital health acceptance in multiple sclerosis: toward an extended disease-specific technology acceptance model.Frontiers in digital health · 2026Article
- Case-Based Lessons on Remote Patient Monitoring in Neurology Using Consumer-Grade Wearables.Journal of central nervous system disease · 2026Article
- User-centered design and formative evaluation of a gait dashboard for multidimensional walking assessment in multiple sclerosis.Frontiers in digital health · 2026Article
- Systematic review: digital biomarkers of fatigue in chronic diseases.NPJ digital medicine · 2025Article
- Digital Activity Markers in Chronic Inflammatory Demyelinating Polyneuropathy.Annals of clinical and translational neurology · 2025Observational
- Decreased Heart Rate Variability Is Associated with Increased Fatigue Across Different Medical Populations: A Systematic Review.Pathophysiology : the official journal of the International Society for Pathophysiology · 2025Review
- Enhancing wearable sensor data analysis for patient health monitoring using allied data disparity technique and multi instance ensemble perceptron learning.Scientific reports · 2025Article
- Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review.JAMIA open · 2025Review
- Breaking the Cycle: Can Vitamin D Bridge the Gap Between Gut Microbiota and Immune Dynamics in Multiple Sclerosis?International journal of molecular sciences · 2025Review
- Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation.Journal of medical Internet research · 2025Article
- Insights into endometriosis symptom trajectories and assessment of surgical intervention outcomes using longitudinal actigraphy.NPJ digital medicine · 2025Article
- Extensive benchmarking of a method that estimates external model performance from limited statistical characteristics.NPJ digital medicine · 2025Article
- From implementation to discontinuation: multi-year experience with the multiple sclerosis performance test as a digital monitoring tool.Frontiers in digital health · 2025Article
- Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple sclerosis (MS) is a neurological disease of the central nervous system that is the leading cause of non-traumatic disability in young adults. Clinical laboratory tests and neuroimaging studies are the standard methods to diagnose and monitor MS. However, due to infrequent clinic visits, it is fundamental to identify remote and frequent approaches for monitoring MS, which enable timely diagnosis, early access to treatment, and slowing down disease progression. In this work, we investigate the most reliable, clinically useful, and available features derived from mobile and wearable devices as well as their ability to distinguish people with MS (PwMS) from healthy controls, recognize MS disability and fatigue levels. To this end, we formalize clinical knowledge and derive behavioral markers to characterize MS. We evaluate our approach on a dataset we collected from 55 PwMS and 24 healthy controls for a total of 489 days conducted in free-living conditions. The dataset contains wearable sensor data - e.g., heart rate - collected using an arm-worn device, smartphone data - e.g., phone locks - collected through a mobile application, patient health records - e.g., MS type - obtained from the hospital, and self-reports - e.g., fatigue level - collected using validated questionnaires administered via the mobile application. Our results demonstrate the feasibility of using features derived from mobile and wearable sensors to monitor MS. Our findings open up opportunities for continuous monitoring of MS in free-living conditions and can be used to evaluate and guide the effectiveness of treatments, manage the disease, and identify participants for clinical trials.
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Registered trials
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