Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ARUdAarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ARUdA
Part of book or chapter of book . 2020
Data sources: ARUdA
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
versions View all 2 versions
addClaim

Emotion regulation

Authors: Ruiz M. C.; Robazza C.;

Emotion regulation

Abstract

How do athletes feel when they perform at their best? How can they reach and maintain optimal feeling states? How do athletes feel when they perform poorly? How can they avoid or regulate their dysfunctional feelings? How can they optimize their performance? These are critical questions for athletes, coaches, and practitioners that have also attracted the attention of researchers. Indeed, athletes’ ability to regulate their emotional states is crucial for a successful performance. For decades, researchers have examined the relationships between emotions and performance (Hanin, 2000; Jones, Lane, Bray, Uphill, & Catlin, 2005; Lane et al., 2016; Ruiz, Raglin, & Hanin, 2017; Turner & Jones, 2018). Anxiety, as the most common emotion that athletes experience prior to competition, was the focus of initial research, which aimed at understanding how such emotion could influence performance (Hackfort & Schwenkmezger, 1993; Hanton, Mellalieu, & Williams, 2015; Marchant, Maher, & Wang, 2014; Turner & Jones, 2018). Beyond anxiety, however, athletes experience an array of emotions, which can be functional or dysfunctional for their performance. There is, therefore, a need for a more holistic approach to the study of a variety of unpleasant and pleasant emotions and other non-emotion components of athletes’ experiences, which form the so-called psychobiosocial states. Because of the acknowledged impact of emotions on performance, emotion regulation strategies have attracted research attention in recent years (Friesen et al., 2013; Lane, Beedie, Jones, Uphill, & Devonport, 2012). Although emotion-centred strategies are useful to improve performance, a combination of strategies focused on emotional states as well as action or task-execution patterns are deemed most effective (Bortoli, Bertollo, Hanin, & Robazza, 2012; Robazza, Bertollo, Filho, Hanin, & Bortoli, 2016).

peerReviewed

Countries
Finland, Italy
Keywords

suorituskyky, urheilu, liikuntapsykologia, tunteet, Liikuntapsykologia, emotion, emotion regulation, psychobiosocial states, IZOF model, MAP model, Sport and Exercise Psychology, urheilijat

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Related to Research communities
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!