Evidence map›Paper›PMID 40740790›Full record

ArticleDigital biomarkers

Monitoring Mobility at Home: The GAIT-HUB Sensor-Based Protocol for Remote Gait Analysis.

Giuseppina Pilloni, Timothy Sung Hyuk Ko, Erica Kreisberg, Josh Geel, Josef Maxwell Gutman, Carrie Sammarco, Cheongeun Oh, Leigh Charvet

Abstract read
In one paragraph

Article in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Giuseppina PilloniDepartment of Neurology, New York University Grossman School of Medicine, New York, NY, USA.
Timothy Sung Hyuk KoDepartment of Neurology, New York University Grossman School of Medicine, New York, NY, USA.
Erica KreisbergDepartment of Neurology, New York University Grossman School of Medicine, New York, NY, USA.
Josh GeelDepartment of Neurology, New York University Grossman School of Medicine, New York, NY, USA.
Josef Maxwell GutmanDepartment of Neurology, New York University Grossman School of Medicine, New York, NY, USA.
Carrie SammarcoDepartment of Neurology, New York University Grossman School of Medicine, New York, NY, USA.
Cheongeun OhDepartment of Population Health, New York University Grossman School of Medicine, New York, NY, USA.
Leigh CharvetDepartment of Neurology, New York University Grossman School of Medicine, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Gait is a critical indicator of neurological health, with changes often signaling underlying decline. We developed a remote gait monitoring protocol using off-the-shelf shoe-based sensors (RunScribe) to assess gait parameters in real-world home settings. This protocol, known as Gait Assessment with Innovative Technologies - Home-based Use and Benefit (GAIT-HUB), was tested in individuals with multiple sclerosis (MS), a population at high risk for gait impairment due to the disease's variable progression. Methods: Participants with MS completed an in-clinic baseline gait assessment using a validated sensor (G-Sensor®) and three weekly, remotely supervised gait assessments at home using the RunScribe sensors. Gait parameters were compared across devices using intra-class correlation coefficients (ICCs) and Bland-Altman analyses. Longitudinal reliability of remote assessments and system usability score (SUS) were evaluated. Results: Twenty-nine participants (76% women, ages 19-67, PDDS range 0-5) successfully completed the home-based assessments. High agreement between devices was observed for gait speed, stride length, and cadence (ICCs >0.90), though phases like stance and swing showed more variability. Bland-Altman analyses indicated minimal bias in most parameters. Longitudinal assessments demonstrated strong reliability (ICCs >0.87) for key metrics, and SUS indicated good-to-excellent usability of the remote protocol. Conclusion: The GAIT-HUB protocol enables reliable and feasible home-based gait monitoring using wearable sensors that patients can easily self-apply. This approach provides valuable insights into daily mobility patterns beyond clinical visits, supporting more precise and timely assessments of functional status between appointments and offering the potential for seamless integration into telemedicine routine care.

Indexed as

Clinical studyData recordingGaitHome-based assessmentMobilityMonitoringMotor symptomsObjective dataWearablesWearable sensors

Identifiers

PMID40740790
PMCPMC12310191

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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.