This document is dedicated the Adaptively Maladaptive research. The aim of this analysis is to investigate how the relationship between anxiety and wellbeing in elite sport populations is influenced by emotion regulation strategies. The analysis aims to test the hypothesis that “maladaptive” emotion regulation strategies would moderate the association between anxiety and mental wellbeing in this population. It is expected that the association between anxiety and mental wellbeing will weaken as the use of “maladaptive” emotion regulation strategies increase.
Please use the floating table of contents on the side of the screen to navigate between the different analysis sections. This provides you with the opportunity to explore the data from the data at the different levels of the analysis; ranging from the demographics, to the moderation analyses themselves.
If you have any questions, please ask away  either in person here at the poster presentation, or via email. I’m more than happy to discuss and talk about the research and the presentation.
Email: r.m.m.davies@sms.ed.ac.uk
Characteristic  Total  By Athlete Status  

N = 129^{1}  Elite Athlete, N = 55^{1}  Retired Athlete, N = 74^{1}  
Athlete Status  
Elite Athlete  55 (43%)  
Retired Athlete  74 (57%)  
Gender  
Female  48 (37%)  22 (40%)  26 (35%) 
Male  81 (63%)  33 (60%)  48 (65%) 
Education  
Bachelors or equivalent  48 (37%)  19 (35%)  29 (39%) 
Doctoral or equivalent  9 (7.0%)  1 (1.8%)  8 (11%) 
Masters or equivalent  37 (29%)  14 (25%)  23 (31%) 
No University Education  35 (27%)  21 (38%)  14 (19%) 
Age  27.28 (7.27)  24.71 (4.89)  29.19 (8.14) 
^{1} n (%); Mean (SD) 
Below is the summary tables of measures used for this analysis. This has been divided into the Cronbach Alpha’s and the measure summaries.
Measure  Cronbach alpha 

WEMWBS  0.83 
GAD7  0.86 
CERQ Self Blame  0.83 
CERQ Other Blame  0.80 
CERQ Rumination  0.59 
CERQ Catastrophising  0.84 
CERQ Acceptance  0.74 
CERQ Positive Reappraisal  0.80 
CERQ Positive Refocus  0.75 
CERQ Putting into Perspective  0.75 
CERQ Refocus on Planning  0.75 
Measure  N = 129 

WEMWBS  
Mean (SD)  47.98 (6.79) 
Median (IQR)  48.00 (43.00, 53.00) 
Range  31.00, 62.00 
GAD7  
Mean (SD)  7.18 (4.52) 
Median (IQR)  7.00 (4.00, 11.00) 
Range  0.00, 16.00 
CERQ Self Blame  
Mean (SD)  5.63 (2.17) 
Median (IQR)  5.00 (4.00, 7.00) 
Range  2.00, 10.00 
CERQ Other Blame  
Mean (SD)  4.62 (2.02) 
Median (IQR)  4.00 (3.00, 6.00) 
Range  2.00, 10.00 
CERQ Rumination  
Mean (SD)  6.44 (1.92) 
Median (IQR)  7.00 (5.00, 8.00) 
Range  3.00, 10.00 
CERQ Catastrophising  
Mean (SD)  4.84 (2.07) 
Median (IQR)  5.00 (3.00, 7.00) 
Range  2.00, 10.00 
CERQ Acceptance  
Mean (SD)  6.94 (1.78) 
Median (IQR)  7.00 (6.00, 8.00) 
Range  2.00, 10.00 
CERQ Positive Reappraisal  
Mean (SD)  7.18 (1.87) 
Median (IQR)  7.00 (6.00, 8.00) 
Range  3.00, 10.00 
CERQ Positive Refocus  
Mean (SD)  4.71 (2.13) 
Median (IQR)  5.00 (2.00, 6.00) 
Range  2.00, 10.00 
CERQ Putting into Perspective  
Mean (SD)  6.55 (2.07) 
Median (IQR)  7.00 (5.00, 8.00) 
Range  2.00, 10.00 
CERQ Refocus on Planning  
Mean (SD)  7.35 (1.92) 
Median (IQR)  8.00 (6.00, 9.00) 
Range  3.00, 10.00 
For ease of interpretation, the CERQ subscales have been divided into two groups for the correlation analyses. The first group of subscales to be featured are the “maladaptive” emotion regulation subscales. The second group contains the “adaptive” emotion regulation subscales.
Parameter  CERQ Catastrophising  CERQ Rumination  CERQ Other Blame  CERQ Self Blame  GAD7  WEMWBS 

WEMWBS  0.23  0.03  0.07  0.20  0.44***  1.00*** 
GAD7  0.66***  0.33**  0.48***  0.34***  1.00***  
CERQ Self Blame  0.23  0.30**  0.04  1.00***  
CERQ Other Blame  0.59***  0.18  1.00***  
CERQ Rumination  0.39***  1.00***  
CERQ Catastrophising  1.00*** 
pvalue adjustment method: Holm (1979)
Parameter  CERQ Refocus on Planning  CERQ Putting into Perspective  CERQ Positive Refocus  CERQ Positive Reappraisal  CERQ Acceptance  GAD7  WEMWBS 

WEMWBS  0.23  0.46***  0.18  0.43***  0.19  0.44***  1.00*** 
GAD7  0.12  0.18  0.24  0.31**  0.13  1.00***  
CERQ Acceptance  0.19  0.14  0.02  0.33**  1.00***  
CERQ Positive Reappraisal  0.38***  0.44***  0.24  1.00***  
CERQ Positive Refocus  0.06  0.25  1.00***  
CERQ Putting into Perspective  0.12  1.00***  
CERQ Refocus on Planning  1.00*** 
pvalue adjustment method: Holm (1979)
The moderation analyses have been split into 2 sections. The first contains the analyses of the “maladaptive” CERQ subscales, whilst the second section contains the analyses of the “adaptive” CERQ subscales.
Each analysis features:
Please note that all the predictor variables have been standardised prior to analysis, and are thus displayed as Z scores.
The following section is dedicated to testing the main hypothesis  that the use of “maladaptive” cognitive emotion regulation strategies will moderate the association between anxiety severity (GAD  7) and mental well being (WEMWBS). Each of the “maladaptive emotion regulation strategies” subscales have been analysed separately.
The results below demonstrate a significant interaction between the Self Blame subscale of CERQ and GAD7 in predicting mental wellbeing ( B = 1.7, p = .002). The inclusion of the interaction term significantly improved the model fit (change in adj Rsq = .056, F (1, 123) = 10.02, p = .002).
The Johnson Neyman plot shows that as Self Blame increases from 1.68 and .91 Z scores, that the effect size of the influence of GAD7 in predicting WEMWBS decreases. As the Z score of Self Blame goes above .91, the effect size of GAD7 is no longer significant in predicting WEMWBS.
This is simplified further through the probing plot. The plot shows a decreasing slope size between GAD7 and WEMWBS as CERQ  Self Blame increases.
Thus the analysis for CERQ  Self Blame supports the hypothesis. This “maladaptive” emotion regulation strategy moderates the association between anxiety severity and mental wellbeing. For individuals with higher levels of anxiety severity, increased use of self blame is associated with improved mental wellbeing in comparison to decreased use of self blame. Yet for individuals with lower levels of anxiety severity, improved mental wellbeing is associated with decreased use of self blame.
Characteristic  Moderation  No moderation  

Beta  95% CI^{1}  pvalue  Beta  95% CI^{1}  pvalue  
CERQ_Self_Blame_Z  0.54  1.7, 0.57  0.3  0.39  1.5, 0.76  0.5 
GAD_Z  3.0  4.1, 1.9  <0.001  2.9  4.0, 1.7  <0.001 
Age_Z  0.03  1.0, 1.1  >0.9  0.09  1.2, 1.0  0.9 
Gender  
Female  —  —  —  —  
Male  0.78  1.4, 2.9  0.5  1.0  1.2, 3.2  0.4 
CERQ_Self_Blame_Z * GAD_Z  1.7  0.65, 2.8  0.002  
R²  0.265  0.205  
Adjusted R²  0.235  0.179  
Statistic  8.85  7.98  
Residual df  123  124  
pvalue  <0.001  <0.001  
^{1} CI = Confidence Interval 
Res.Df  RSS  Df  Sum of Sq  F  Pr(>F) 

124  4698.75 




123  4344.96  1  353.79  10.015  0.00196 
JOHNSONNEYMAN INTERVAL
When CERQ_Self_Blame_Z is OUTSIDE the interval [0.91, 4.70], the slope of GAD_Z is p < .05.
Note: The range of observed values of CERQ_Self_Blame_Z is [1.68, 2.02]
The results below demonstrate a significant interaction between the CERQ  Other Blame and GAD7 in predicting WEMWBS ( B = 2.8, p = .002). The inclusion of the interaction term significantly improved the model fit (change in adj Rsq = .143, F (1, 123) = 31.50, p < .001).
The Johnson Neyman plot shows that as Other Blame increases from .1.30 and .88 Z scores, that the effect size of the influence of GAD7 in predicting WEMWBS decreases. As the Z score of Other Blame increases from 2.34 to 2.67, the effect size of GAD7 increases in predicting WEMWBS. In between the Other Bla