Athlete Burnout Symptoms Are Increasing: A Cross-Temporal Meta-Analysis of Average Levels From 1997 to 2019

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Abstract

With the increasing prevalence of mental health difficulties in sport, athletes may be at greater risk of burnout than ever before. In the present study, we tested this possibility by examining whether average athlete burnout levels have changed over the past 2 decades, from 1997 to 2019. A literature search returned 91 studies ( N = 21,012) and 396 effect sizes. Findings from cross-temporal meta-analysis suggested that burnout symptoms have increased over the past 2 decades. Specifically, we found that athletes’ mean levels of reduced sense of athletic accomplishment and sport devaluation have increased. As burnout symptoms are now typically higher among athletes than in the past, we can expect more athletes to be prone to the negative effects of burnout. Sport is therefore in urgent need of prevention and intervention strategies to stop and reverse this trend.

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A power primer.

Jacob Cohen (1992)

One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.

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Interrater reliability: the kappa statistic

Mary L McHugh (2012)

The kappa statistic is frequently used to test interrater reliability. The importance of rater reliability lies in the fact that it represents the extent to which the data collected in the study are correct representations of the variables measured. Measurement of the extent to which data collectors (raters) assign the same score to the same variable is called interrater reliability. While there have been a variety of methods to measure interrater reliability, traditionally it was measured as percent agreement, calculated as the number of agreement scores divided by the total number of scores. In 1960, Jacob Cohen critiqued use of percent agreement due to its inability to account for chance agreement. He introduced the Cohen’s kappa, developed to account for the possibility that raters actually guess on at least some variables due to uncertainty. Like most correlation statistics, the kappa can range from −1 to +1. While the kappa is one of the most commonly used statistics to test interrater reliability, it has limitations. Judgments about what level of kappa should be acceptable for health research are questioned. Cohen’s suggested interpretation may be too lenient for health related studies because it implies that a score as low as 0.41 might be acceptable. Kappa and percent agreement are compared, and levels for both kappa and percent agreement that should be demanded in healthcare studies are suggested.