Evidence map›Paper›PMID 37511793›Full record

ArticleJournal of personalized medicine2023

Validation of Gait Measurements on Short-Distance Walkways Using Azure Kinect DK in Patients Receiving Chronic Hemodialysis.

Zhi-Ren Tsai, Chin-Chi Kuo, Cheng-Jui Wang, Jeffrey J P Tsai, Hsin-Hsu Chou

Open access · goldAbstract read
In one paragraph

Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.4field-weighted citation impact, top 17% of its field
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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. 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 at 2 institutions in 1 country.

Zhi-Ren TsaiDepartment of Computer Science & Information Engineering, Asia University, Taichung 41354, Taiwan.
Chin-Chi KuoDepartment of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan.
Cheng-Jui WangDepartment of Computer Science & Information Engineering, Asia University, Taichung 41354, Taiwan.
Jeffrey J P TsaiCenter for Precision Medicine Research, Asia University, Taichung 41354, Taiwan.
Hsin-Hsu ChouDepartment of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.ORCID 0000-0002-8725-6740
Asia University · TWChina Medical University · TW

Funding

Ministry of Science and Technology Taiwan MOST 109-2321-B-468-001, 110-2321-B-468-001, and 111-2321-B-468-001
6 · The paper itself

Abstract

Muscle dysfunction, skeletal muscle fibrosis, and disability are associated with weakness in patients with end-stage renal disease. The main purpose of this study was to validate the effectiveness of a proposed system for gait monitoring on short-distance 1.5 m walkways in a dialysis center. Gaits with reduced speed and stride length, long sit-to-stand time (SST), two forward angles, and two unbalanced gait regions are defined in the proposed Kinect v3 gait measurement and analysis system (K3S) and have been considered clinical features in end-stage renal disease (ESRD) associated with poor dialysis outcomes. The stride and pace calibrations of the Kinect v3 system are based on the Zeno Walkway. Its single rating intraclass correlation (ICC) for the stride is 0.990, and its single rating ICC for the pace is 0.920. The SST calibration of Kinect v3 is based on a pressure insole; its single rating ICC for the SST is 0.871. A total of 75 patients on chronic dialysis underwent gait measurement and analysis during walking and weighing actions. After dialysis, patients demonstrated a smaller stride (

Indexed as

chronic dialysisforward angleKinect v3 gait measurement and analysis system (K3S)

Identifiers

PMID37511793
PMCPMC10381698
OpenAlexW4385254961

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.