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Network Analysis of Competitive State Anxiety.

Richard Mullen1, Eleri Sian Jones2

  • 1Division of Sport, Health and Exercise Sciences, Brunel University London, London, United Kingdom.

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PubMed
Summary
This summary is machine-generated.

This study re-examined competitive state anxiety in athletes using a network approach. Findings suggest network analysis effectively maps anxiety symptoms, aiding assessment and intervention strategies for sports performance.

Keywords:
anxietycommunity detectiongraph theorynetwork analysispredictabilitystate anxiety

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Area of Science:

  • Sports Psychology
  • Psychometric Measurement
  • Network Analysis

Background:

  • Competitive state anxiety is crucial for sports performance, yet its measurement remains debated.
  • Existing methods may not fully capture the complex interplay of anxiety symptoms.
  • A network approach offers a novel perspective on understanding anxiety as a system.

Purpose of the Study:

  • To apply network analysis to competitive state anxiety measurement in athletes.
  • To explore the structure and interconnections of anxiety symptoms.
  • To identify potential targets for improved assessment and intervention.

Main Methods:

  • Utilized a dataset of 485 competitive athletes from the United Kingdom.
  • Employed network analysis, including graphical LASSO, node predictability, and exploratory graph analysis.
  • Reduced data to 15 items from the modified Three Factor Anxiety Inventory.

Main Results:

  • A network structure of 15 anxiety-related items was estimated.
  • Community detection identified three distinct symptom clusters.
  • Several network nodes were found to bridge these communities, indicating interconnectedness.

Conclusions:

  • Network analysis provides a valuable framework for understanding competitive state anxiety.
  • The identified network structure and bridging nodes can inform more nuanced assessment strategies.
  • This approach may lead to more effective, targeted interventions for athletes experiencing anxiety.