ArticleNPJ digital medicine2019
Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Remote Monitoring in Myasthenia Gravis: Exploring Symptom Variability.Annals of clinical and translational neurology · 2026Trial
- A smartphone sensor-based digital outcome assessment of multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2022Trial
- Sensors in Multiple Sclerosis.Current neurology and neuroscience reports · 2025Review
- A Novel Whole-Body Wearable Technology for Motor Assessment in Multiple Sclerosis: Feasibility and Usability Pilot Study.Sensors (Basel, Switzerland) · 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
- Investigating Measurement Equivalence of Smartphone Sensor-Based Assessments: Remote, Digital, Bring-Your-Own-Device Study.Journal of medical Internet research · 2025Article
- Digital remote monitoring of people with multiple sclerosis.Frontiers in immunology · 2025Review
- Current Analytical Methods and Challenges for the Clinical Diagnosis of Invasive Pulmonary Aspergillosis Infection.Journal of fungi (Basel, Switzerland) · 2024Review
- Modeling multiple sclerosis using mobile and wearable sensor data.NPJ digital medicine · 2024Article
- Digital Phenotyping for Monitoring and Disease Trajectory Prediction of Patients With Cancer: Protocol for a Prospective Observational Cohort Study.JMIR research protocols · 2023Article
- Bringing it all together: wearable data fusion.NPJ digital medicine · 2023Article
- Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective.Journal of medical Internet research · 2023Article
- Feasibility and scalability of a fitness tracker study: Results from a longitudinal analysis of persons with multiple sclerosis.Frontiers in digital health · 2023Article
- Biopolymer Composites with Sensors for Environmental and Medical Applications.Materials (Basel, Switzerland) · 2022Review
- Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping.JMIR mental health · 2022Article
- Free-Living Motor Activity Monitoring in Ataxia-Telangiectasia.Cerebellum (London, England) · 2022Article
- Transparent Quality Optimization for Machine Learning-Based Regression in Neurology.Journal of personalized medicine · 2022Article
- Evaluation of upper extremity ataxia through image processing in individuals with multiple sclerosis.Arquivos de neuro-psiquiatria · 2022Article
- Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
43 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Technological advances in passive digital phenotyping present the opportunity to quantify neurological diseases using new approaches that may complement clinical assessments. Here, we studied multiple sclerosis (MS) as a model neurological disease for investigating physiometric and environmental signals. The objective of this study was to assess the feasibility and correlation of wearable biosensors with traditional clinical measures of disability both in clinic and in free-living in MS patients. This is a single site observational cohort study conducted at an academic neurological center specializing in MS. A cohort of 25 MS patients with varying disability scores were recruited. Patients were monitored in clinic while wearing biosensors at nine body locations at three separate visits. Biosensor-derived features including aspects of gait (stance time, turn angle, mean turn velocity) and balance were collected, along with standardized disability scores assessed by a neurologist. Participants also wore up to three sensors on the wrist, ankle, and sternum for 8 weeks as they went about their daily lives. The primary outcomes were feasibility, adherence, as well as correlation of biosensor-derived metrics with traditional neurologist-assessed clinical measures of disability. We used machine-learning algorithms to extract multiple features of motion and dexterity and correlated these measures with more traditional measures of neurological disability, including the expanded disability status scale (EDSS) and the MS functional composite-4 (MSFC-4). In free-living, sleep measures were additionally collected. Twenty-three subjects completed the first two of three in-clinic study visits and the 8-week free-living biosensor period. Several biosensor-derived features significantly correlated with EDSS and MSFC-4 scores derived at visit two, including mobility stance time with MSFC-4 z-score (Spearman correlation -0.546;
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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.