Evidence map›Paper›PMID 39473802›Full record

ArticleDigital biomarkers

Spatial-Temporal Analysis of Gait in Amyotrophic Lateral Sclerosis Using Foot-Worn Inertial Sensors: An Observational Study.

Meghan Lukac, Hannah Luben, Anne E Martin, Zachary Simmons, Andrew Geronimo

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. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Observational
  3. Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026
    Review
  4. Article
  5. Review
  6. Article
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

5 authors.

Meghan LukacDepartment of Mechanical Engineering, Pennsylvania State University, University Park, PA, USA.
Hannah LubenDepartment of Mechanical Engineering, Pennsylvania State University, University Park, PA, USA.
Anne E MartinDepartment of Mechanical Engineering, Pennsylvania State University, University Park, PA, USA.
Zachary SimmonsDepartment of Neurology, Penn State College of Medicine, Hershey, PA, USA.
Andrew GeronimoDepartment of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA.

Funding

Penn State Clinical and Translational Science InstituteUL1TR002014 · NCATS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI KRASCHNEWSKI, JENNIFER L. · 2016 to 2025
$33.7M
A novel use of web-based software to efficiently triage pre-surgical patients basR44TR000045 · NCATS · MEDSLEUTH, INC. · PI KALAMAS, ALICIA GRUBER · 2012 to 2012
$546k
NCATS NIH HHS R44 TR000045NCATS NIH HHS UL1 TR002014
6 · The paper itself

Abstract

Introduction: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that alters gait and increases the risk of falls. The current model of care involves in-person multidisciplinary clinic visits to, in part, assess alterations in gait, evaluate safety, and make recommendations for management. Clinic visits, however, are relatively infrequent, and multidisciplinary evaluations can be physically demanding for patients. To better understand how gait changes over time in those with ALS and enable healthcare providers to properly respond to these changes, remote monitoring of functional mobility would be advantageous. Methods: The objective of this study was to remotely track long-term changes in walking speed using wearable inertial measurement units (IMUs). Nine ALS patients and 6 healthy controls submitted twice-weekly home walking recordings for 24 and 4 weeks, respectively. An IMU data processing method was developed and validated against laboratory-measured walking speed. Results: For both ALS patients and healthy controls, home walking speed was less than clinic walking speed by an average of 0.19 m/s ( Conclusions: Remote longitudinal gait monitoring of ALS patients is feasible with the use of an IMU. Decreases in walking speed were detected in the majority of patients, most strongly in those who eventually transitioned to an AD. Home walking speed may more accurately represent the walking abilities of ALS patients in their real-life environments, a finding which further supports the case for remote monitoring in ALS.

Indexed as

Amyotrophic lateral sclerosisGaitWearable sensors

Identifiers

PMID39473802
PMCPMC11521409

What OpenQuestion holds

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Registered trials

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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.