Evidence map›Paper›PMID 33816062›Full record

ArticleCurrent geriatrics reports2021

Walking speed measurement technology: A review.

Yohanna MejiaCruz, Jean Franco, Garret Hainline, Stacy Fritz, Zhaoshuo Jiang, Juan M Caicedo, Benjamin Davis, Victor Hirth

Abstract read
In one paragraph

Article in Current geriatrics reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Probabilistic Estimation of Cadence and Walking Speed From Floor Vibrations.IEEE journal of translational engineering in health and medicine · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Measuring Gait Parameters from Structural Vibrations.Measurement : journal of the International Measurement Confederation · 2022
    Article
  14. Article
  15. Article
  16. Article
  17. 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

8 authors.

Yohanna MejiaCruzSan Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132.
Jean FrancoUniversity of South Carolina, 300 Main St, Columbia SC, 29201.
Garret HainlineUniversity of South Carolina, 300 Main St, Columbia SC, 29201.
Stacy FritzUniversity of South Carolina, 300 Main St, Columbia SC, 29201.
Zhaoshuo JiangSan Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132.
Juan M CaicedoUniversity of South Carolina, 300 Main St, Columbia SC, 29201.
Benjamin DavisAdvanced Smart Systems and Evaluation Technologies (ASSET), LLC, 1400 Laurel Street, Suite 1B, Columbia, South Carolina 29201.
Victor HirthGeriatric Health and Wellness, LTD, One Still Hopes Drive, West Columbia, SC 29169.

Funding

SCH: INT: Inferring at home gait parameters of older adults using floor vibrations R01AG067395 · NIA · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI CAICEDO, JUAN MARTIN, JIANG, ZHAOSHUO · 2019 to 2022
$1.2M
NIA NIH HHS R01 AG067395
6 · The paper itself

Abstract

purpose of reviewThis article presents an overview of the main technologies used to estimate gait parameters, focusing on walking speed (WS). RECENT

findingsNew wearable and environmental technologies to estimate WS have been developed in the last five years. Wearable technologies refer to sensors attached to parts of the patient's body that capture the kinematics during walking. Alternatively, environmental technologies capture walking patterns using external instrumentation. In this review, wearable and external technologies have been included.From the different works reviewed, external technologies face the challenge of implementation outside controlled facilities; an advantage that wearable technologies have, but have not been fully explored. Additionally, systems that can track WS changes in daily activities, especially at-home assessments, have not been developed. SUMMARY: Walking speed is a gait parameter that can provide insight into an individual's health status. Image-based, walkways, wearable, and floor-vibrations technologies are the most current used technologies for estimating WS. In this paper, research from the last five years that explore each technology's capabilities on WS estimation and an evaluation of their technical and clinical aspects is presented.

Indexed as

floor vibrationsgait analysisimage-basedsensor technologywalking speedwearable sensors

Identifiers

PMID33816062
PMCPMC8014958

What OpenQuestion holds

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Registered trials

None linked

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.