ArticleFrontiers in physiology2025
The impact of precompetition state on athletic performance among track and field athletes using machine learning.
Article in Frontiers in physiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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
3 citing papers in PubMed.
- Impact of Synbiotics Intake on Body-Composition-Related Indicators and Physical Performance in Judokas Before the Match.Nutrients · 2026Article
- Review
- Construction of the prediction model and analysis of key winning factors in world women's volleyball using gradient boosting decision tree.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aims to compare the differences in the precompetition status (nutritional, physiological, biochemical, psychological, and sleep statuses) among college track and field athletes with different competition performances and to screen for key indicators of differences affecting athletic performance. Methods: Multiple indicators, traditional methods, and machine learning methods are used to detect the exercise load, fatigue index, and precompetition state of athletes with different sports performances. Results: (1) Two weeks before the competition, the fat mass in the left upper limb in the BP group was significantly higher than that in the BnP group ( Conclusion: Precompetition absolute basophil, LDH, TG, white blood cells, creatine kinase, fat mass in the left upper limb, erythrocyte pressure (HCT), and individual failure anxiety can be used as training monitoring indicators that focus on tracking athlete status before the race.
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Registered trials
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