The relation between emotion regulation choice and posttraumatic growth

ABSTRACT

Previous research has examined emotion regulation (ER) and trauma in the context of psychopathology, yet little research has examined ER in posttraumatic growth (PTG), the experience of positive psychological change following a traumatic event. ER typically involves decreasing negative affect by engaging (e.g. reappraisal) or disengaging (e.g. distraction) with emotional content. To investigate how ER may support PTG, participants who experienced a traumatic event in the past 6 months completed a PTG questionnaire and an ER choice task in which they down regulated their negative emotion in response to negative pictures of varying intensity by choosing to distract or reappraise. Latent growth curve analyses revealed that an increase in reappraisal choice from low to high subjective stimulus intensity predicted higher PTG, suggesting that individuals who chose reappraisal more as intensity increased reported higher PTG. Findings suggest that reappraisal of negative stimuli following a traumatic event may be a key component of PTG.

KEYWORDS:

• Emotion regulation
• trauma
• reappraisal
• posttraumatic growth

Author notes

The current manuscript is part of a larger umbrella study aimed at examining the role of emotion regulation in individual differences in stress management, mental health, and health behaviours. As such, participants complete various batteries of questionnaires that differ depending on their experiences, mental health, and physical health status. All measures are declared in the methods or supplementary materials sections of the present manuscript. In addition, a portion of the participants in the present study (50% of the sample) also form a subsample of the participant sample reported in Study One of a multi-study manuscript that is currently in preparation. All publications derived from the larger umbrella dataset declare all acquired measures and sample overlap.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Details on all measures collected can be found in the online supplemental materials.

2 When normative ratings were used, the measurement model was a good fit by all measures, χ²(5) = 6.21, p = .29, RMSEA = .047, CFI = .994. Similar to the subjective ratings model, the mean of the intercept was .74 (p < .001) and the mean of the slope was -.16 (p < .001). Variance estimates of the intercept (SD = .03) and slope (SD = .01) were also both significant (p < .01) indicating sufficient variability/individual differences in RCP across intensity. Similar to the subjective ratings model, results of the LGCM analyses predicting PTG revealed that the intercept (β = .23, p < .05) and slope (β = .22, p < .05) significantly predicted PTG. Regarding the covariates, however, depressive symptoms were not related to PTG, the intercept, or slope. Trait reappraisal use was not related to the intercept or slope, yet it significantly predicted PTG (β = .25, p < .01), indicating that greater trait reappraisal is related to greater PTG. Finally, perceived stress was not related to the intercept or slope, yet it significantly predicted PTG (β = .27, p < .05).

3 To confirm the consistency of the present pattern of findings we conducted additional LGCM analyses in which we included only one or none of the covariates. Analyses confirm that RCP across subjective intensities consistently predicts PTG. The non-covariate model predicting PTG from reappraisal choice proportion across intensity was a good fit by all measures: χ2(2) = 2.72, p = .26, RMSEA = .06, CFI = 1.0 with the intercept and slope each significantly predicting PTG, β = .213, p < .05 and β = .266, p < .05, respectively. Similarly, we modeled the relation between PTG and reappraisal choice proportion across intensity with only ERQ included as a covariate. This model too was a good fit (χ²(3) = 2.86, p = .41RMSEA = .000, CFI = 1.0) with both the intercept (β = .220, p < .05) and the slope (β = .227, p < .05) predicting PTG. In addition, in this model responses on the ERQ also predicted slope, β = .194, p < .05, with greater trait reappraisal use predicting a steeper increase in reappraisal choice proportion across intensity. Finally we modeled the relation between PTG and reappraisal choice proportion across intensity with only CESD included as a covariate; this too was a good fit (χ²(5) = 7.07, p = .22, RMSEA = .06, CFI = .99) with the intercept marginally predicting PTG (β = .209, p < .05) and the slope significantly predicting PTG (β = .272, p < .05).

Additional information

Funding

Sara Sagui-Henson was supported by a T32 award (T32 AT003997) from the National Center for Complementary and Integrative Health (NCCIH) of the National Institutes of Health.