Evidence map›Paper›PMID 32300324›Full record

ArticleFrontiers in psychology2020

Psychological Predictors for Depression and Burnout Among German Junior Elite Athletes.

Insa Nixdorf, Jürgen Beckmann, Raphael Nixdorf

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In one paragraph

Article in Frontiers in psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline 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

3 authors.

Insa NixdorfChair of Sport Psychology, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany.
Jürgen BeckmannChair of Sport Psychology, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany.
Raphael NixdorfChair of Sport Psychology, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There exists a strong need for research in clinical sport psychology which does not merely gather information on prevalence rates for psychological disorders and case studies of affected athletes. Rather, research should also uncover the underlying psychological variables which increase the risk for depression and burnout in elite athletes. Many studies gather general factors (e.g., gender, injury, sport discipline) and stay on a more descriptive level. Both constructs (burnout and depression) are based on a temporal, stress-related process model assuming the development of either syndrome results from unfavorable personal (e.g., dysfunctional attitudes, perfectionism, negative coping strategies) or environmental (e.g., cohesion) factors coexisting with severe stressors (i.e., chronic stress). Integrating this knowledge, we propose a shared model for depression and burnout in athletes: a sport specific diathesis-stress model. The present longitudinal study assesses data throughout one sporting season to analyze predictors for both constructs in junior elite athletes. Hierarchical multiple linear regression analyses resulted in six predictors for best model fit. The following factors demonstrated a significant impact on predicting (a) burnout or (b) depression scores at the end of the season: dysfunctional attitudes (a and b), coping strategies (a and b), perfectionism (a), recovery (b), stress (a) and the level of depression at onset (b). Variables such as cohesion or attributional style did not significantly predict depression or burnout. The study supports the structure of a process model (diathesis-stress model) for burnout and depression with the assumption of temporal progression. With some vulnerabilities and their temporal, developmental link identified, prevention can become athlete-specific, effective and economical.

Indexed as

athlete burnoutdepressiondiathesislongitudinal studystressvulnerability

Identifiers

PMID32300324
PMCPMC7142210

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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 OpenQuestion graph.