Evidence map›Paper›PMID 39680265›Full record

ArticleSports medicine - open2024

Critical Fluctuations as an Early Warning Signal of Sports Injuries? A Proof of Concept Using Football Monitoring Data.

Niklas D Neumann, Jur J Brauers, Nico W van Yperen, Mees van der Linde, Koen A P M Lemmink, Michel S Brink, Fred Hasselman, Ruud J R den Hartigh

Abstract read
In one paragraph

Article in Sports medicine - open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

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

8 authors.

Niklas D NeumannDepartment of Psychology, Faculty of Behavioral and Social Sciences, University of Groningen, Groningen, The Netherlands. n.d.neumann@rug.nl.ORCID http://orcid.org/0000-0003-2062-1958
Jur J BrauersDepartment of Human Movement Sciences, Faculty of Medical Sciences, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Nico W van YperenDepartment of Psychology, Faculty of Behavioral and Social Sciences, University of Groningen, Groningen, The Netherlands.
Mees van der LindeFootball Club Groningen, Groningen, The Netherlands.
Koen A P M LemminkDepartment of Human Movement Sciences, Faculty of Medical Sciences, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Michel S BrinkDepartment of Human Movement Sciences, Faculty of Medical Sciences, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Fred HasselmanBehavioral Science Institute, Radboud University, Nijmegen, The Netherlands.
Ruud J R den HartighDepartment of Psychology, Faculty of Behavioral and Social Sciences, University of Groningen, Groningen, The Netherlands.

Funding

ZonMw 546003004
6 · The paper itself

Abstract

backgroundThere has been an increasing interest in the development and prevention of sports injuries from a complex dynamic systems perspective. From this perspective, injuries may occur following critical fluctuations in the psychophysiological state of an athlete. Our objective was to quantify these so-called Early Warning Signals (EWS) as a proof of concept to determine their explanatory performance for injuries. The sample consisted of 23 professional youth football (soccer) players. Self-reports of psychological and physiological factors as well as data from heart rate and GPS sensors were gathered on every training and match day over two competitive seasons, which resulted in an average of 339 observations per player (range = 155-430). We calculated the Dynamic Complexity (DC) index of these data, representing a metric of critical fluctuations. Next, we used this EWS to predict injuries (traumatic and overuse).

resultsResults showed a significant peak of DC in 30% of the incurred injuries, in the six data points (roughly one and a half weeks) before the injury. The warning signal exhibited a specificity of 95%, that is, correctly classifying non-injury instances. We followed up on this promising result with additional calculations to account for the naturally imbalanced data (fewer injuries than non-injuries). The relatively low F

conclusionBy detecting critical fluctuations preceding one-third of the injuries, this study provided support for the complex systems theory of injuries. Furthermore, it suggests that increasing critical fluctuations may be seen as an EWS on which practitioners can intervene. Yet, the relatively high false positive rate on the entire data set, including periods without injuries, suggests critical fluctuations may also precede transitions to other (e.g., stronger) states. Future research should therefore dig deeper into the meaning of critical fluctuations in the psychophysiological states of athletes. KEY POINTS: Complex Systems Theory suggests that sports injuries may be preceded by a warning signal characterized by a short window of increased critical fluctuations. Results of the current study showed such increased critical fluctuations before 30% of the injuries. Across the entire data set, we also found a considerable number of critical fluctuations that were not followed by an injury, suggesting that the warning signal may also precede transitions to other (e.g., healthier) states. Increased critical fluctuations may be interpreted as a window of opportunity for the practitioner to launch timely and targeted interventions, and researchers should dig deeper into the meaning of such fluctuations.

Indexed as

Complex dynamic systemsCritical fluctuationsDynamic complexityEarly warning signalsFootballInjury predictionMultidisciplinarityNonlinear time series analysisPersonalized approachProcess monitoring

Identifiers

PMID39680265
PMCPMC11649608

What Socratic holds

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Registered trials

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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.