ArticleFrontiers in physiology2022
Wellness Forecasting by External and Internal Workloads in Elite Soccer Players: A Machine Learning Approach.
Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- The 2026 Men's FIFA Football World Cup: Evidence-Based Guidelines to Protect Player Health and Performance from Environmental Challenges.Sports medicine (Auckland, N.Z.) · 2026Review
- Article
- Multi-sensor fusion outperforms single indicators for fatigue prediction in university soccer players: a machine learning approach.Frontiers in physiology · 2026Article
- Artificial Intelligence in Sports Biomechanics: A Scoping Review on Wearable Technology, Motion Analysis, and Injury Prevention.Bioengineering (Basel, Switzerland) · 2025Review
- Effect of match load on perceived wellness in highly trained female football players.PloS one · 2025Article
- Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review.Frontiers in sports and active living · 2024Article
- Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approach.Frontiers in psychology · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Training for success has increasingly become a balance between maintaining high performance standards and avoiding the negative consequences of accumulated fatigue. The aim of this study is to develop a big data analytics framework to predict players' wellness according to the external and internal workloads performed in previous days. Such a framework is useful for coaches and staff to simulate the players' response to scheduled training in order to adapt the training stimulus to the players' fatigue response. 17 players competing in the Italian championship (Serie A) were recruited for this study. Players' Global Position System (GPS) data was recorded during each training and match. Moreover, every morning each player has filled in a questionnaire about their perceived wellness (WI) that consists of a 7-point Likert scale for 4 items (fatigue, sleep, stress, and muscle soreness). Finally, the rate of perceived exertion (RPE) was used to assess the effort performed by the players after each training or match. The main findings of this study are that it is possible to accurately estimate players' WI considering their workload history as input. The machine learning framework proposed in this study is useful for sports scientists, athletic trainers, and coaches to maximise the periodization of the training based on the physiological requests of a specific period of the season.
Indexed as
Identifiers
What OpenQuestion holds
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.