ArticleScientific reports2023
In-field assessment of change-of-direction ability with a single wearable sensor.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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.
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Who cites it
4 citing papers in PubMed.
- Validation of Insole Pressure Sensor Algorithms: Implications for In-Field Detection of Initial Contact and Hamstring Muscle Pre-Activity During Side-Cutting.Sensors (Basel, Switzerland) · 2026Article
- Development and Validation of a Kinetics Prediction Model for Football Cutting Using a Single Trunk-Mounted IMU.Sensors (Basel, Switzerland) · 2026Article
- Beyond the Disc: Positional Differences in Morphological and Physical Performance Characteristics Among Male Ultimate Frisbee Players.Journal of functional morphology and kinesiology · 2026Article
- Automated Detection of Change of Direction in Basketball Players Using Xsens Motion Tracking.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Agility T-test is a standardized method to measure the change-of-direction (COD) ability of athletes in the field. It is traditionally scored based on the total completion time, which does not provide information on the different CODs. Augmenting the T-test with wearable sensors provides the opportunity to explore new metrics. Towards this, data of 23 professional soccer players were recorded with a trunk-worn GNSS-IMU (Global Navigation Satellite System-Inertial Measurement Unit) device. A method for detecting the four CODs based on the wavelet-denoised antero-posterior acceleration signal was developed and validated using video data (60 Hz). Following this, completion time was estimated using GNSS ground speed and validated with the photocell data. The proposed method yields an error (mean ± standard deviation) of 0 ± 66 ms for the COD detection, - 0.16 ± 0.22 s for completion time, and a relative error for each COD duration and each sequential movement durations of less than 3.5 ± 16% and 7 ± 7%, respectively. The presented algorithm can highlight the asymmetric performance between the phases and CODs in the right and left direction. By providing a more comprehensive analysis in the field, this work can enable coaches to develop more personalized training and rehabilitation programs.
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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 Socratic graph.