ArticleSensors (Basel, Switzerland)2023
Definition of High-Risk Motion Patterns for Female ACL Injury Based on Football-Specific Field Data: A Wearable Sensors Plus Data Mining Approach.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Development and Validation of a Kinetics Prediction Model for Football Cutting Using a Single Trunk-Mounted IMU.Sensors (Basel, Switzerland) · 2026Article
- On-Field Assessment of Joint Load in Football Using Machine Learning (Part II).Sensors (Basel, Switzerland) · 2026Article
- Testing ACL-Reconstructed Football Players on the Field: An Algorithm to Assess Cutting Biomechanics Injury Risk Through Wearable Sensors.Sports (Basel, Switzerland) · 2025Article
- Correlation of Biomechanical Variables of Lower Extremity Movement During Functional Tests and Tasks in Youth League Football Players: Cross-Sectional Correlation Study.JMIR formative research · 2025Article
- Wearable sensor systems measure differences in knee laxity between healthy and affected knees during dynamic exercise activities: A systematic review.Journal of experimental orthopaedics · 2024Review
- Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I.Sensors (Basel, Switzerland) · 2024Article
- Development of a Cutting Technique Modification Training Program and Evaluation of its Effects on Movement Quality and Cutting Performance in Male Adolescent American Football Players.Sports (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the present study was to investigate if the presence of anterior cruciate ligament (ACL) injury risk factors depicted in the laboratory would reflect at-risk patterns in football-specific field data. Twenty-four female footballers (14.9 ± 0.9 year) performed unanticipated cutting maneuvers in a laboratory setting and on the football pitch during football-specific exercises (F-EX) and games (F-GAME). Knee joint moments were collected in the laboratory and grouped using hierarchical agglomerative clustering. The clusters were used to investigate the kinematics collected on field through wearable sensors. Three clusters emerged: Cluster 1 presented the lowest knee moments; Cluster 2 presented high knee extension but low knee abduction and rotation moments; Cluster 3 presented the highest knee abduction, extension, and external rotation moments. In F-EX, greater knee abduction angles were found in Cluster 2 and 3 compared to Cluster 1 (
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Registered trials
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.