Evidence map›Paper›PMID 36101807›Full record

ArticleContrast media & molecular imaging2022

Application of Biomechanics Based on Intelligent Technology and Big Data in Physical Fitness Training of Athletes.

Kai Li, Jinqian Zhang, Qingling Qu, Bairan Li, Sukwon Kim

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.3field-weighted citation impact, top 16% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 2 countries.

Kai LiDepartment of Physical Education, Jeonbuk National University, Jeonju 54896, Jeollabuk, Republic of Korea.
Jinqian ZhangDepartment of Physical Education, Jeonbuk National University, Jeonju 54896, Jeollabuk, Republic of Korea.
Qingling QuDepartment of Physical Education, Jeonbuk National University, Jeonju 54896, Jeollabuk, Republic of Korea.
Bairan LiDepartment of Physical Education, Jeonbuk National University, Jeonju 54896, Jeollabuk, Republic of Korea.
Sukwon KimDepartment of Physical Education, Jeonbuk National University, Jeonju 54896, Jeollabuk, Republic of Korea.ORCID 0000-0003-3168-2692
Jeonju National University of Education · KRPingdingshan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Physical training has a high degree of participation all over the world. With the opening of the era of national fitness, physical training has become more popular from the original specialization, and the complex training methods and contents have gradually become simplified. The development and change of physical training has also brought many problems to the professional training of athletes, such as high training intensity but poor effect, insufficient training posture, and long-term physical injury. In order to help athletes achieve better results in physical training and reduce the probability of injury, taking sprint training as an example, this article adopted the sports and body data of elite athletes through intelligent technology and big data analysis, established a human motion model from the perspective of biomechanics, and then conducted a corresponding test run experiment for athletes. The experimental results suggested that drag resistance running could improve the specific strength quality of sprinting. At the same time, when using resistance load for training, the maximum speed should not exceed 90% of the maximum speed without resistance. The average horizontal maximum velocity decreased by approximately 9% when training under a resistance load, and the best training results were obtained by training athletes within this range.

Indexed as

AthletesBig DataBiomechanical PhenomenaHumansPhysical FitnessTechnology

Identifiers

PMID36101807
PMCPMC9439893
OpenAlexW4293149810

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.