Review in Journal of functional morphology and kinesiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing 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.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
16 authors.
Michail TsenosDepartment of Informatics, Athens University of Economics and Business, 10434 Athens, Greece.ORCID 0009-0005-0874-7410
Christos KokkotisDepartment of Occupational, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, Greece.
Dimitrios DraganidisDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Dietetics, University of Thessaly, 43100 Trikala, Greece.
Nikos AlibertisNovelcore, 10436 Athens, Greece.
Dimitrios PantazisDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, Greece.ORCID 0009-0002-9398-790X
Panagiotis TsimeasDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Dietetics, University of Thessaly, 43100 Trikala, Greece.
Athanasios PouliosDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Dietetics, University of Thessaly, 43100 Trikala, Greece.
Nikolaos ZarasDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, Greece.ORCID 0000-0001-8933-723X
Paraskevi MalliouDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, Greece.
Ilias TsaousidisDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Dietetics, University of Thessaly, 43100 Trikala, Greece.ORCID 0009-0000-8037-785X
Maria MichalopoulouDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, Greece.ORCID 0009-0001-8136-9090
Alexandra AvlonitiDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, Greece.ORCID 0000-0003-1406-039X
Ioannis G FatourosDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Dietetics, University of Thessaly, 43100 Trikala, Greece.ORCID 0000-0002-8475-8411
Athanasios ChatzinikolaouDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, Greece.ORCID 0000-0002-4238-7632
IRONMAN Consortium
Funding
European Union - NextGenerationEU through the Recovery and Resilience Facility (RRF) for the period 2022-2025, with the contribution of the National Public Investment Programme (PIP) and national resources covering VAT under grant agreement 8238 IRONMAN- ΥΠ3ΤΑ-0561410
6 · The paper itself
Abstract
Sports-related injuries remain a major challenge in team sports, with important consequences for athlete health, performance, and team success. Recent advances in artificial intelligence (AI) and sensor-based monitoring technologies have enabled the integration of large volumes of training, competition, and physiological data to support injury prediction and risk modelling. However, the literature is characterised by substantial methodological diversity, limiting the ability to draw consistent conclusions. Hence, this scoping review aimed to map the existing evidence on the use of AI and sensor-based monitoring technologies for injury prediction and risk modelling in team sports, and to identify key methodological trends and research gaps. The scoping review was conducted in accordance with the PRISMA-ScR guidelines. Systematic searches were performed in PubMed and Scopus. Eligible studies included team-sport athletes and applied AI or machine learning approaches to predict injury occurrence, injury risk, or related outcomes using data derived from wearable or monitoring systems. Data were charted on study characteristics, sports and competition level, data sources, modelling techniques, validation strategies, and performance metrics. The database search yielded 123 records (PubMed:
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.
Injury Prediction and Risk Modelling in Team Sports Using Artificial Intelligence and Sensor-Based Monitoring: A Scoping Review. · full record | OpenQuestion