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      Is Perceived Autonomy Support Provided by a Coach Related to the Intention of Injury Preventative Behavior Among National and International Level Golfers?

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          Abstract

          The successful implementation of injury prevention programs is reliant on athletes and coaches accepting, adopting, and complying with behaviors that reduce injury risk. Exploring factors, such as motivation and planned behavior, that might increase the frequency of these behaviors warrants investigation. The aim of the study was to investigate the complex interaction between perceived autonomy support, self-determined motivation, planned behavior, and how this relates to golfers self-reported intention injury preventative behavior. A total of 60 golfers completed questions on psychological measures of perceived autonomy support from coaches, autonomous motivation, and intentions of injury preventative behavior. A neural network model analysis was performed to investigate the strength of connection between covariates and construct a network structure. Analysis of results was performed by assessing edge strengths and node centrality to guide inference of the network topology. The most central node was autonomous regulation and the results showed one cluster comprising positive interactions between perceived autonomy support, effort of injury preventative behavior, and frequency of injury preventative behavior. When aiming to encourage injury preventative behavior, coaches should consider giving feedback that supports autonomous motivation since it is positively associated with effort and frequency of injury preventative behavior among high-level golfers. Injury prevention programs should include strategies to improve the athlete's autonomous motivation to carry out preventive activities.

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          Most cited references41

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          The theory of planned behavior

          Icek Ajzen (1991)
          Organizational Behavior and Human Decision Processes, 50(2), 179-211
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            Intrinsic Motivation and Self-Determination in Human Behavior

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              Sparse inverse covariance estimation with the graphical lasso.

              We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.
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                Author and article information

                Contributors
                Journal
                Front Sports Act Living
                Front Sports Act Living
                Front. Sports Act. Living
                Frontiers in Sports and Active Living
                Frontiers Media S.A.
                2624-9367
                09 March 2021
                2021
                : 3
                : 569590
                Affiliations
                Center of Research on Welfare, Health and Sport, Halmstad University , Halmstad, Sweden
                Author notes

                Edited by: Behzad Behzadnia, University of Tabriz, Iran

                Reviewed by: Maurizio Bertollo, G. d'Annunzio University of Chieti–Pescara, Italy; Aurelio Olmedilla, University of Murcia, Spain

                *Correspondence: James Parker james.parker@ 123456hh.se

                This article was submitted to Movement Science and Sport Psychology, a section of the journal Frontiers in Sports and Active Living

                Article
                10.3389/fspor.2021.569590
                8006294
                33791597
                5bc1ce41-34d2-4632-b737-5e07c7449af4
                Copyright © 2021 Parker, Johnson and Ivarsson.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 05 June 2020
                : 12 February 2021
                Page count
                Figures: 2, Tables: 4, Equations: 0, References: 42, Pages: 9, Words: 6261
                Categories
                Sports and Active Living
                Original Research

                autonomy support,coaching,golf,injury prevention,network analysis

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