Evidence map›Paper›PMID 39166022›Full record

ArticleHeliyon2024

Maximizing the performance of badminton athletes through core strength training: Unlocking their full potential using machine learning (ML) modeling.

Shuzhen Ma, Kim Geok Soh, Salimah Binti Japar, Simao Xu, Guo Zhicheng

RetractedErratum issuedAbstract read
In one paragraph

Article in Heliyon, 2024. 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 It has been retracted, and should not be counted.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 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. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Shuzhen MaDepartment of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Selangor, 43400, Malaysia.
Kim Geok SohDepartment of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Selangor, 43400, Malaysia.
Salimah Binti JaparDepartment of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Selangor, 43400, Malaysia.
Simao XuChengdu Sport University, Chengdu, 61004, China.
Guo ZhichengGuangxi Arts and Crafts School, Liuzhou, 545005, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Core strength training plays an essential role in maximizing performance for badminton athletes. The core muscles in the abdominal, back, and hip regions provide stability, enable efficient power transfer between the upper and lower body, and allow for rapid changes in direction - all crucial components for success in badminton. However, optimizing core training requires an understanding of its impact on sport-specific skills. A variety of exercises targeting the abdominal, back, and hip muscles are discussed. Incorporating core strength training into regular regimens can improve athletes' overall strength, endurance, balance, control, and prevent injuries. This study investigates the effects of various core exercises on stability, agility, and power in badminton players. A comprehensive literature review was conducted to explore the biomechanical demands of badminton and how core musculature contributes to movements like serving, smashing, and lunging. Studies assessing the effects of core training programs in related racquet sports were also examined. The results indicate that targeted core exercises significantly improve athletes' stability, agility, and power output. Exercises targeting the abdominal, back, and hip muscles enhance performance capabilities while reducing injury risk. Machine learning (ML) techniques are then applied to further analyze the relationship between core training and athletic performance. An Artificial Neural Network (ANN) is developed using a dataset of athletes' training histories, metrics, and injury profiles. The model predicts enhancements to stability, agility, and strength from optimized core strengthening routines. Validation confirms the network accurately captures the complex interactions between training variables and physical attributes. This integrated approach provides evidence-based guidelines for tailoring individualized training regimens to unleash players' full abilities. ANNs hold promise for analyzing large datasets on athletes' performance metrics, training variables, and injury histories to design personalized training programs. Linear regression analysis confirmed the ANN's accurate predictions. The findings emphasize integrating data-driven core strength training tailored for badminton into comprehensive programs can help optimize physical abilities and elevate performance levels.

Indexed as

Artificial neural networkAthlete potentialBadminton performanceCore strength trainingInjury preventionStability and agility

Identifiers

PMID39166022
PMCPMC11334617

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.