Evidence map›Paper›PMID 39219328›Full record

Trial reportJournal of musculoskeletal & neuronal interactions2024

Effect of Upper Limb Repetitive Facilitative Exercise on Gait of Stroke Patients based on Artificial Intelligence and Computer Vision Evaluation.

Caiping Song, Lingcong Wang, Jianhui Ding, Cui Xu, Hang Yang, Yajun Mao

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of musculoskeletal & neuronal interactions, 2024. 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
–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

2 citing papers in PubMed.

  1. Redefining Stroke Care with Artificial Intelligence: Recent Advances.Current neurology and neuroscience reports · 2026
    Review
  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

6 authors.

Caiping SongDepartment of Rehabilitation, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang, China.
Lingcong WangDepartment of Rehabilitation, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang, China.
Jianhui DingBeijing Fushu Health Technology Co., LTD, Beijing, China.
Cui XuDepartment of Rehabilitation, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang, China.
Hang YangDepartment of Rehabilitation, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang, China.
Yajun MaoDepartment of Rehabilitation, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aims to assess how enhancing upper limb function on the affected side of stroke influences the gait of the lower limb.

methodsForty eligible stroke patients were randomly assigned to either a control group or a treatment group, with 20 patients in each group. Both groups underwent dynamic evaluation using artificial intelligence and computer vision before treatment. This evaluation focused on analyzing the range of motion of the shoulder and elbow during the gait cycle, as well as various gait parameters (such as step length, step speed, and percentage of stance phase) on the affected side. Following evaluation, the control group received routine rehabilitation treatment.

resultsThe results indicated that there was no significant difference between the two groups before treatment. However, following treatment, there was a notable improvement in the motion of the shoulder and elbow joints on the affected side among patients in the treatment group (p<0.05), whereas the control group showed only slight improvement, which was not statistically significant (p>0.05).

conclusionThe improvement in upper limb function on the affected side also appears to positively influence gait recovery. However, it's important to note that the observation period was relatively short. Further studies are needed to confirm whether this effect is sustained over the long term.

Indexed as

Artificial IntelligenceExercise TherapyStrokeStroke RehabilitationUpper ExtremityAdultAgedFemaleGaitGait Disorders, NeurologicHumansMaleMiddle AgedRange of Motion, ArticularRecovery of FunctionArtificial IntelligenceComputer VisionGaitRepetitive Facilitative ExerciseStroke

Identifiers

PMID39219328
PMCPMC11367173

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.