ArticleJournal of neurology2020
Wearable sensors can reliably quantify gait alterations associated with disability in people with progressive multiple sclerosis in a clinical setting.
Article in Journal of neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 64 citations in OpenAlex.
- Characterizing gait in people with multiple sclerosis using digital data from smartphone sensors: A proposed framework.Multiple sclerosis (Houndmills, Basingstoke, England) · 2025Pooled it
- Concurrent Validity Evidence for Pressure-Sensing Walkways Measuring Spatiotemporal Features of Gait: A Systematic Review and Meta-Analysis.Sensors (Basel, Switzerland) · 2024Pooled it
- Cognitive-motor dual-task training improves dynamic stability during straight and curved gait in patients with multiple sclerosis: a randomized controlled trial.European journal of physical and rehabilitation medicine · 2024Trial
- A smartphone sensor-based digital outcome assessment of multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2022Trial
- Six-minute walk test instrumented with a single inertial sensor: dataset from healthy adults of different ages.Scientific data · 2026Article
- Lumbar Acceleration Gait Estimation: "Step-by-Step" Algorithm Updates and Improvements.Journal of medical Internet research · 2025Article
- Sensors in Multiple Sclerosis.Current neurology and neuroscience reports · 2025Review
- Investigating Measurement Equivalence of Smartphone Sensor-Based Assessments: Remote, Digital, Bring-Your-Own-Device Study.Journal of medical Internet research · 2025Article
- Predicting Real-World Physical Activity in Multiple Sclerosis: An Integrated Approach Using Clinical, Sensor-Based, and Self-Reported Measures.Sensors (Basel, Switzerland) · 2025Article
- Cognitive-motor interference in multiple sclerosis revisited: a dual-task paradigm using wearable inertial sensors and the Paced Auditory Serial Addition Test.Frontiers in neurology · 2025Article
- A Wearable Gait Monitoring System for 17 Gait Parameters Based on Computer Vision.IEEE transactions on instrumentation and measurement · 2025Article
- Walk Longer! Using Wearable Inertial Sensors to Uncover Which Gait Aspects Should Be Treated to Increase Walking Endurance in People with Multiple Sclerosis.Sensors (Basel, Switzerland) · 2024Article
- Convolutional neural network based detection of early stage Parkinson's disease using the six minute walk test.Scientific reports · 2024Article
- Physical Functioning, Physical Activity, and Variability in Gait Performance during the Six-Minute Walk Test.Sensors (Basel, Switzerland) · 2024Article
- Sufficiency for PSS tracking gait disorders in multiple sclerosis: A managerial perspective.Heliyon · 2024Article
- Dynamic Stability, Symmetry, and Smoothness of Gait in People with Neurological Health Conditions.Sensors (Basel, Switzerland) · 2024Article
- Characterization of Walking in Mild Parkinson's Disease: Reliability, Validity and Discriminant Ability of the Six-Minute Walk Test Instrumented with a Single Inertial Sensor.Sensors (Basel, Switzerland) · 2024Article
- Assessing Gait & Balance in Adults with Mild Balance Impairment: G&B App Reliability and Validity.Sensors (Basel, Switzerland) · 2023Article
- Uncovering Subtle Gait Deterioration in People with Early-Stage Multiple Sclerosis Using Inertial Sensors: A 2-Year Multicenter Longitudinal Study.Sensors (Basel, Switzerland) · 2023Article
- Monitoring and Predicting Health Status in Neurological Patients: The ALAMEDA Data Collection Protocol.Healthcare (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Gait disability in people with progressive multiple sclerosis (MS) is difficult to quantify using existing clinical tools. This study aims to identify reliable and objective gait-based biomarkers to monitor progressive multiple sclerosis (MS) in clinical settings. During routine clinical visits, 57 people with secondary progressive MS and 24 healthy controls walked for 6 minutes wearing three inertial motion sensors. Fifteen gait measures were computed from the sensor data and tested for between-session reliability, for differences between controls and people with moderate and severe MS disability, and for correlation with Expanded Disability Status Scale (EDSS) scores. The majority of gait measures showed good to excellent between-session reliability when assessed in a subgroup of 23 healthy controls and 25 people with MS. These measures showed that people with MS walked with significantly longer step and stride durations, reduced step and stride regularity, and experienced difficulties in controlling and maintaining a stable walk when compared to controls. These abnormalities significantly increased in people with a higher level of disability and correlated with their EDSS scores. Reliable and objective gait-based biomarkers using wearable sensors have been identified. These biomarkers may allow clinicians to quantify clinically relevant alterations in gait in people with progressive MS within the context of regular clinical visits.
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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.