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Relations between trait anxiety and depression: A mediated moderation model
Journal of Affective Disorders 244 (2019) 217–222
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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Relations between trait anxiety and depression: A mediated moderation model
T
Tao Wanga, Min Lia, , Song Xua, Botao Liua, Tong Wua, Fang Lub, Junpeng Xiea, Li Penga, Jia Wangc ⁎
a
Department of Military Psychology, School of Psychology, Army Medical University, Chongqing, 400038, China School of Nursing, Army Medical University, Chongqing, 400038, China c School of Psychology, Army Medical University, Chongqing, 400038, China b
ARTICLE INFO
ABSTRACT
Keywords: Trait anxiety Depression Rumination Cognitive flexibility
Background: This study proposed a mediated moderation model based on the cognitive theory and susceptibility phenotype theory for depression to explore the moderating role of cognitive flexibility in the relationship between trait anxiety and depression and the mediating role of rumination in this interactive effect. Methods: The subjects were selected via the cluster sampling method. A cross-sectional survey of 1619 Chinese adults of Han nationality was recruited from three cities in central and western China. The trait anxiety subscale of the State-Trait Anxiety Inventory (STAI-Trait scale), Short Ruminative Response Scale (SRRS), Cognitive Flexibility Inventory (CFI) and Beck Depression Inventory (BDI-II) were used for a paper-and-pencil evaluation. The data were statistically processed using correlation analysis, multiple linear regression analysis, and a test of mediated moderation. Results: (1) Trait anxiety was significantly positively correlated with rumination and depression. There was a significant negative correlation between cognitive flexibility and trait anxiety, rumination, and depression. (2) The moderating effect of cognition flexibility on the relationship between trait anxiety and depression was mediated by rumination, and the indirect moderating effect accounted for 79.25% of the total variance. Limitations: Cross-sectional studies do not reveal the causal relationships between observed variables well. Selfreport questionnaires lack objective indicators. The applicability of the current results in the clinical depression population requires further experimental tests. Conclusions: The results support the important role of trait anxiety in the development of depression. Cognitive flexibility moderates the relationship between trait anxiety and depression and has a risk-buffering effect on this relationship. Rumination plays a mediating role in the moderating effects of cognitive flexibility on the relationship between trait anxiety and depression. The findings enrich the research on the relationship between trait anxiety and depression and have important practical implications for the early prevention and intervention of depression, which can increase individual cognitive flexibility and address rumination to regulate depression.
1. Introduction Trait anxiety reflects an individual's predisposition to worry and anxiety when facing dangerous or uncertain situations. In recent years, studies in cognitive neuroscience have suggested that high trait anxiety is an important vulnerable phenotype for stress-induced depression (Weger and Sandi, 2018). Individuals with high anxiety traits not only are more susceptible to stress but also have specific neurocognitive characteristics (Sandi and Richter-Levin, 2009). In the management of threats or ambiguous situations, people with high trait anxiety have a unique cognitive style. Specifically, there is a selective attention bias ⁎
toward threat-related stimuli, and it is easy to interpret ambiguous emotional stimuli as negative information, and as a result, the corresponding conditioned fear response increases. Sandi and RichterLevin (2009) proposed the hypothesis of a "neurocognitive model" from high anxiety traits to depression and considered that the neurocognitive style of trait anxiety (neurocognitive maladaptations) plays a central role in the pathological development of depression. The abnormalities in neurocognitive function often lead to individuals exhibiting a moodcongruent bias, a more conscientious consolidation of negative memory, and further enhancement of negative thoughts (Sandi and Richter-Levin, 2009). This shows that differences in cognitive factors
Correspondence to: Department of Military Psychology, School of Psychology, Army Medical University, Chongqing, 400038, China E-mail address: [email protected] (M. Li).
https://doi.org/10.1016/j.jad.2018.09.074 Received 11 June 2018; Received in revised form 29 August 2018; Accepted 19 September 2018 Available online 20 September 2018 0165-0327/ © 2018 Published by Elsevier B.V.
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play a particularly important role in the relationship between trait anxiety and depression. The cognitive model of depression posits that individual cognitive factors (belief systems, cognitive processes, etc.) are important psychopathological foundations for the development of depression. According to Beck (1987), an individual's underlying dysfunctional maladaptive self-schemata is relatively stable and systematically present in an individual's cognitive structure, and encountering a negative stress event activates these negative patterns about individuals and the world, which makes individuals perform cognitive process (encoding, interpreting, retrieval, etc.) in an automatic and distorted manner and ultimately leads to the occurrence, maintenance, enhancement, or deterioration of depression. Among the increasing body of research in recent years, Beck (2008) and Clark and Beck (2010) further extended the model to genetic and neurochemical pathways that interact with or are parallel to cognitive variables and claimed that in addition to cognitive patterns, genetic or personality factors, the cognitive control ability of the frontal cortical areas is an important component in the development of depression (Clark and Beck, 2010; Disner et al., 2011). Cognitive deficits are frequently observed in major depression (Beck, 1987; Austin et al., 2001). One of the most important indices that reflect individual cognitive control and executive function is cognitive flexibility (Alike, 2014). In general, cognitive flexibility refers to the ability to adapt cognitive processing strategies to face changing or unpredictable situations (Jacques and Zelazo, 2005). Based on the different domains or contexts of cognitive flexibility within the current literature, the instruments used to assess cognitive flexibility are diverse. Among them, numerous neuropsychological performance tests have been used to assess the cognitive process of cognitive flexibility, such as attention shifting, conflict monitoring, and task switching (Dajani, 2015), and a limited number of self-report psychological questionnaires have centered on cognitive awareness, willingness or self-efficacy of the communication competence, such as the Cognitive Flexibility Scale (CFS, Martin and Rubin, 1995), the Cognitive Flexibility Inventory (CFI, Dennis and Wal, 2010) and the Attributional Style Questionnaire (ASQ, Peterson et al.,1982). Current evidence indicates that both types of cognitive flexibility are closely associated with depression, although the operational definitions and measurement methods differ. Studies have demonstrated that impaired cognitive processes exist in depressive individuals (Deveney and Deldin, 2006), with varying degrees of dysfunction in multiple cognitive domains such as attention and memory and executive function (Carvalho et al., 2014). The cognitively significant characteristics of depression patients are stereotyped as rigid and inflexible (Moore, 1996). Cognitive flexibility shows a significant negative correlation with depression (Gunduz, 2013; Soltani et al., 2013; Başaran et al., 2016). Nolen-Hoeksema (1987) further found that depressive individuals had ruminative response styles that interfere with attention, concentration and the initiation of instrumental behaviors. Numerous studies have suggested that rumination is a manifestation of insufficient cognitive flexibility (Nolen-Hoeksema, 1991;Davis and NolenHoeksema, 2000) and a core process of depression development and maintenance (Watkins, 2007, 2009). Thus, on the one hand, rumination is a special kind of self-focus that increases the accessibility of negative cognitive patterns and leads to more negative interpretations of events. On the other hand, rumination can also inhibit problem-solving and cognitive flexibility, making people difficult to disengage from negative stimuli and eventually form a vicious cycle of emotion-cognition, which increases depression severity (Koster et al., 2011). Given that rumination has such negative consequences, why are some people so persistently ruminative (Davis and NolenHoeksema, 2000)? What kinds of people have a long-lasting contemplation of negative mood? Some empirical studies have found that rumination is closely related to certain personality factors, such as neuroticism (Spasojević and Alloy, 2001). It has also been noted that rumination may reflect some important cognitive features of the
susceptible personality and may mediate the potential influence of personality on depression (Roberts et al., 1998). Some studies also suggest that an individual's rumination reaction may be related to a lack of cognitive flexibility. Individuals with cognitive inflexibility are more likely to ruminate when they feel sad and find it difficult to disengage from negative emotions (Davis and Nolen-Hoeksema, 2000). In summary, trait anxiety makes an individual susceptible to depression. Some preliminary studies have demonstrated the relationship between trait anxiety and depression, rumination and cognitive flexibility (Roberts et al., 1998; Williams et al., 2017). However, few studies have focused on the relationship between personality and depression from the perspective of cognitive flexibility awareness (e.g., CFI), and the specific role of cognitive flexibility and rumination in the relationship between trait anxiety and depression still remains unclear. Cognitive flexibility is relatively stable (Rende, 2000), and rumination is a reactive style. Therefore, we speculated that cognitive flexibility may affect the direction and strength of the relationship between trait anxiety and depression and could be a cognitive moderator of depression, whereas rumination could be a mediator of depression. This study selected Chinese urban adults as the study subjects to investigate the relationship between trait anxiety and depression and the moderating role of cognitive flexibility and the mediating role of rumination. The study proposed a mediated moderation model, with the following hypotheses: (1) trait anxiety is an important contributor to depression; (2) the relationship between trait anxiety and depression is moderated by cognitive flexibility; and (3) the moderating effect of cognitive flexibility on trait anxiety and depression is mediated by rumination. 2. Methods 2.1. Sample and procedure The subjects were selected by the cluster sampling method. Employees of Han nationality from three cities (Chongqing, Xiangfan, and Jiuquan) in central and western China constituted the survey samples. None of the subjects had a history of mental illness or personality disorder. A total of 1,670 people participated in the crosssectional survey. Since the subjects lived in different cities, the whole population was sampled in batches from April to May 2017 for psychological assessment. After invalid or uncompleted questionnaires were eliminated, the final sample numbered 1,619, and the response rate was 96.95%. Table 1 shows the demographic characteristics of the subjects. The study was approved by the ethics committee of the Third Military Medical University of China. Written informed consent was obtained. To ensure the validity of the psychological assessment, all subjects were guided and tested on site by two or three graduate students or teachers majoring in psychology, and psychological reports were returned to all the subjects through email.
Table 1 Demographic characteristics of the subjects.
Sex Male Female Age Education (years) Marital status Single Married Divorced
No.
%
679 940
41.94 58.06
714 864 41
44.10 53.37 2.53
Note: No., number; SD, standard deviations. 218
Mean
SD
Range
29.81 15.44
9.16 2.61
18–55 9–21
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2.2. Measures
2.2.4. Short ruminative response scale (SRRS) The SRRS contains 10 items, which were simplified and revised by Treynor et al. (2003). Using 4-point Likert scales (1 = almost never, 4 = almost always) on the SRRS, participants were asked to indicate how they generally respond to their sad, depressive mood. Higher scores on the SRRS predict the worse ruminative response. Cronbach's alpha coefficient is 0.883 in the present sample.
Whether the data set has a normal distribution is important for the choice of statistical analysis. We used kurtosis and skewness values to examine whether all the observed variables have a normal distribution. Spearman correlations were computed for pairs of these continuous variables. According to Frazier et al. (2004), the other variables, which are the components of the interaction terms (i.e., rumination, trait anxiety, cognitive flexibility), were centered. The moderating effect of cognitive flexibility on the association between trait anxiety and BDI scores was tested using hierarchical regression models while controlling for demographic characteristics (i.e., gender, age). At the same time, a mediated moderation model was used to explore the moderating role of cognitive flexibility in the relationship between trait anxiety and depression and used to verify whether this moderating effect was mediated by rumination. This model is tested based on the following conditions to be fulfilled (Muller et al., 2005; Ye and Wen, 2013). First, a moderating effect must be present. That is, the effect of the interaction (c3) between trait anxiety and cognitive flexibility is significant. Second, the mediating effect of rumination is also significant. The impact of the interaction term on BDI scores is at least partly mediated by rumination. Either or both of the following conditions may be met: (a) the effect of trait anxiety and cognitive flexibility on rumination is significant (a3), and the effect of rumination and BDI scores is also significant (b1). Or (b) the effect of trait anxiety on rumination is significant (a1), and the effect of the interaction between cognitive flexibility and rumination on BDI scores is also significant (b2). That is, if a3 b1 is significant or a3 b2 is significant, the effect of the interaction between trait anxiety and cognitive flexibility on depression is mediated by rumination. If a1 b2 is significant, cognitive flexibility indirectly moderates the effect of trait anxiety on BDI scores when we adjust for the effect of rumination on the dependent variable. According to the above procedure, a bootstrap test was performed using Mplus 7.0 software to calculate the 95% confidence interval. Third, when the mediated moderation model is established, the indirect effect of moderation (c3-c3′) is calculated. Usually, the interactive effect between the independent variable and moderator on the dependent variable should be reduced or lose significance in magnitude compared with the overall effect of moderation.
2.3. Statistical analysis
3. Results
All data were analyzed using SPSS 21.0 and Mplus7.0 software. All statistical tests were two-sided, and p-values 〈 0.05 were considered statistically significant. First, Harman's single-factor test (Podsakoff et al., 2003) was used to test for common method bias. The factor analysis extracted 10 factors (eigenvalue 〉 1), of which the most important factor accounted for only 29.89% of the variance (less than 40%). Consequently, as a result of this test, common method variance is not a serious concern in the present study. Next, a multicollinearity analysis was performed to check whether the predictor variables were related to each other, and the variance inflation factor (VIF) was computed as an indicator. If the VIF values are higher than 5, there is multicollinearity between the independent variables. In the present study, the VIF values for all predictors were 1.174∼1.803 (less than 5), indicating no serious multicollinearity problem.
3.1. Descriptive analysis and Spearman's correlations for all variables
2.2.1. Spielberger state-trait anxiety inventory (STAI) Trait anxiety was tested using the trait anxiety subscale of the STAI developed by Spielberger and Gorsuch (1983). The scale has 20 items, with each item assessed on a four-point Likert scale ranging from 1 (completely absent) to 4 (very obvious). It describes a relatively stable personality trait and anxiety bias with individual differences to assess people's regular emotional experience. Items 1, 3, 4, 6, 7, 10, 13, 14, 16, and 19 are scored in reverse. The sum of the 20 items is the total score, with a higher score indicating a higher trait anxiety level. Cronbach's alpha coefficient is 0.87 in this study. 2.2.2. Beck depression inventory (BDI) The BDI-II was used to assess the severity of depressive symptoms over the last two weeks (Beck et al., 1996). The inventory consists of 21 items with response options from 0 to 3. A total score of 14 points or more indicates mild depression or worse. In this study, Cronbach's alpha coefficient is 0.931. 2.2.3. Cognitive flexibility inventory (CFI) The CFI is a 20-item self-report scale developed by Dennis and Vander Wal in 2010. This scale is designed to measure flexibility in terms of understanding and responding to the world and aims to measure an individual's ability to engage in alternative, balanced thinking in difficult situations (Dennis and Wal, 2010). The questionnaire was scored on a scale of 1 (strongly disagree) to 7 (very strongly agree), with items 2, 4, 7, 9, 11, and 17 being scored in reverse. Cronbach's alpha is 0.891 in this study.
Table 2 shows the means, standard deviations and ranges for all variables included in the mediated moderation analysis. In addition, Spearman correlations were computed. The results showed that trait anxiety and rumination were significantly positively correlated with depression (p < 0.001) and that cognitive flexibility had a significant negative correlation with trait anxiety, rumination, and depression (p < 0.001). 3.2. Relationship between trait anxiety and depression: a mediated moderation model Mediated moderation occurs when a moderating effect is mediated
Table 2 Spearman's correlation analysis of each variable.
1. 2. 3. 4.
Trait anxiety SRRS CFI BDI
Skewness
Kurtosis
Mean
SD
Range
1
2
3
4
0.073 0.610 0.206 1.588
−0.127 0.752 −0.459 2.292
40.86 17.75 95.79 7.52
8.79 4.88 17.72 8.95
20–77 10–40 44–140 0–47
1 0.470⁎⁎⁎ −0.537⁎⁎⁎ 0.536⁎⁎⁎
1 −0.307⁎⁎⁎ 0.559⁎⁎⁎
1 −0.353⁎⁎⁎
1
Note:
⁎⁎⁎ P < 0.001; SD, standard deviations; IQR, interquartile range; SRRS, Short Ruminative Response Scale; CFI, cognitive flexibility inventory; BDI, beck depression inventory.
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Fig. 1. The moderation model of CFI. Note: *p < 0.05, ⁎⁎p < 0.01. TA, trait anxiety; CFI, Cognitive Flexibility Inventory; BDI, Beck Depression Inventory.
by another variable (Mackinnon and Fairchild, 2009). First, according to the statistical procedure (Muller et al., 2005; Ye and Wen, 2013), all the predictors were centered, and the moderating effect of cognitive flexibility was tested. The moderation model is illustrated in Fig. 1. The results showed that the moderating effect of the interactive effect on depression was significant (c3 = 0.053, t = 2.454, p < 0.05), which indicates that the effect of trait anxiety on depression is moderated by cognitive flexibility. Second, the mediating role of rumination was detected in the hypothesized model. The hypothesized mediator, rumination, was regressed on the control variables, trait anxiety, cognitive flexibility, and the interaction term. As the results indicate (see Fig. 2), cognitive flexibility had no significant effect on rumination (a1 = 0.092, t = 0.176, p = 0.860), and the effect of the interaction term on depression was close to being significant (a3 = 0.795, t = 0.421, p = 0.059). The results showed that both trait anxiety and cognitive flexibility were not significantly related to rumination, but the interaction term was statistically marginal. Next, BDI scores were regressed on the control variables, trait anxiety, cognitive flexibility, the interaction term, the hypothesized mediator, rumination, and the interaction between cognitive flexibility and rumination. The results indicate that rumination was a significant predictor of BDI scores (b1 = 0.01, t = 7.703, p < 0.001) when all other variables and interactions were controlled. In addition, the interaction between cognitive flexibility and rumination had a significant effect on BDI scores (b2 = 0.134, t = 5.56, p < 0.001) (see Fig. 2). In brief, the interaction between trait anxiety and cognitive flexibility is significantly related to BDI scores. This result fulfilled the first condition for the mediated moderation model. Moreover, the effect of rumination on BDI scores was significant. However, the interaction between trait anxiety and cognitive flexibility did not have a significant effect on rumination. In addition, the coefficients of a3b1, a3b2, and a1b2 were not completely significant; thus, it could not be determined whether a mediated moderation model could be established. Based on the procedure for testing mediated moderation models (Ye and Wen, 2013), the 95% confidence intervals of a3b1 (H1), a3b2 (H2), and a1b2 (H3) were computed using the bias-corrected percentile bootstrap
method. If at least one of the confidence intervals of a3b1, a3b2, and a1b2 does not contain 0, the effect of the mediator is significant. The results showed that the 95% CI for a1b2 (H3) was [0.002, 0.015], the 95% CI for a3b2 (H2) was [0.03, 0.206], and the 95% CI for a1b2 (H3) was [−0.088, 0.108]. Since the confidence intervals for H1 and H2 do not contain 0, the relationship between trait anxiety and BDI scores can be established in a mediated moderation model. Therefore, the second condition of the test of mediated moderation is also met. In this case, the moderated effect is completely mediated if the coefficient (c3′) from the interaction term to BDI scores is not significant or partially mediated if the coefficient (c3′) from the interaction term to BDI scores is significant. Our results showed that c3′ from the interaction term to BDI scores was not significant (c3′ = 0.011, t = 0.474, p = 0.635), and the moderating effect of the interaction term on BDI scores was completely mediated by rumination. In addition, the indirect moderating effect (c3 - c3′) was 0.042, and the indirect effect accounted for 79.25% of the variance. Hierarchical regression analysis was used to examine the moderating effects. The results showed that the regression coefficient of the interaction item was significant (β = −0.01, t = −9.214, p < 0.001), and the moderating effect of cognitive flexibility additionally explained 3.4% of the variance (△R2 = 0.034, ΔF (1, 1613) = 84.90, p < 0.001). A simple slope analysis was conducted to test the moderating effect at high and low levels (one SD above and below the mean) of the moderator. The results showed that the associations of trait anxiety with depression were weaker in participants with a higher level of cognitive flexibility (mean + 1SD) (βHigh = 0.192, p < 0.001) than in those with a lower level of cognitive flexibility (mean - 1SD) (βLow = 0.85, p < 0.001). Thus, cognitive flexibility buffered the effects of trait anxiety on depression. 4. Discussion The results of this study show that trait anxiety is moderately positively correlated with depression, suggesting that high trait anxiety could be an important contributor to depression. Although high trait Fig. 2. The mediated moderation model of CFI and SRRS in the relationships between TA and BDI. Note: △p = 0.059, ⁎⁎⁎p < 0.001.TA, trait anxiety (X, independent variable); CFI, Cognitive Flexibility Inventory (U, moderator); SRRS, Short Ruminative Response Scale (W, mediator); BDI, Beck Depression Inventory (Y, dependent variable). X × U, interaction term between TA and CFI; U × W, interaction term between CFI and SRRS.
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anxiety itself did not directly lead to depression, individuals with high anxiety traits presented inadequate attention control, cognitive inhibition, task-switching difficulties, and reduced renewal capacity (Ansari et al., 2008). Therefore, in response to stressful events, individuals with high trait anxiety are more likely to stimulate the body's dysfunctional neurocognitive cascade, increase that individual's susceptibility to stress, and subsequently cause a series of behavioral and cognitive changes eventually leading to depression (Sandi and RichterLevin, 2009; Weger and Sandi, 2018). Consistent with our hypothesis, we found that the relationship between trait anxiety and depression was moderated by cognitive flexibility and that the amount of variation was 3.4%. More precisely, the BDI scores in the group with high cognitive flexibility were significantly lower than those in the group with low cognitive flexibility. In other words, cognitive flexibility “weakens” the risk effect of trait anxiety on depression; that is, cognitive flexibility has a “buffering” effect on the relationship between trait anxiety and depression. This finding suggests that improving cognitive flexibility can reduce the risk of depression in individuals with high trait anxiety. Related studies have found that cognitive behavior training can significantly enhance the cognitive flexibility of perfectionist individuals (Nazarzadeh et al., 2015), and attention control training, e.g., mindfulness training (Chiesa et al., 2011), can also increase individual cognitive flexibility, which eventually improves depressive symptoms (Teasdale et al., 1995; Dennis and Wal, 2010). Thus, our findings suggest that high trait anxiety is an important contributor to depression, whose strength is moderated by the level of cognitive flexibility, and that increased cognitive flexibility could be the potential mechanism by which cognitive therapy contributes to the outcomes of active intervention for depression. The study further explored the mediated moderation model to examine the underlying mechanisms of trait anxiety and cognitive flexibility interactions predicting depression and to determine why individuals with low cognitive flexibility are more likely to develop depression when they present with high trait anxiety tendencies. By including rumination as a mediator of the moderating effect, we found that people with low cognitive flexibility are more likely to ruminate given the interaction with trait anxiety, further leading to depression symptoms. The findings again confirm that rumination is a key mediator influencing the development and maintenance of depressive symptoms (Spasojević and Alloy, 2001; Watkins, 2009). The impaired disengagement hypothesis (Koster et al., 2011) argued that the impaired ability of individuals to escape from passively negative self-referenced information was the underlying cause of depressive thinking, and rumination was taken as a potential common proximal mechanism linking risk factors with depression (Spasojević and Alloy, 2001). Therefore, some scholars have proposed a variant of the CBT treatment method, namely, rumination-focused CBT (Watkins et al., 2007) using specific techniques such as functional analysis, behavioral activation or experiential/imagery exercise (Watkins, 2009) for targeting rumination and that such efforts appear to yield generalized improvement in chronic depression and treatment-resistant depression. Previous studies have found that rumination plays an important mediating role in the relationship between neurotic personality and depression and can also predict the effects of antidepressant treatment (Nolan et al., 1998; Watkins, 2009). Our study further found that, through the mediating role of rumination, the moderating effect of cognitive flexibility on the relationship between trait anxiety and depression accounted for 79.25% of the overall variance in depression, suggesting that the proportion of the mediation effect of rumination on the interactive function is prominent and that rumination could thus be a key mediator. In summary, this study proposed a mediated moderation model based on the cognitive model (Disner et al., 2011) and susceptibility phenotype theory for depression (Weger and Sandi, 2018), demonstrating the link between trait anxiety and depression in depth and identifying the specific role of cognitive flexibility and rumination in
these relationships. The study also identified that trait anxiety could be an important contributor to depression among Chinese adults. This research also clarified the conditions under which trait anxiety works and further reveals important reasons for the differences in the risk effects of trait anxiety under different conditions. The results enrich the study of the relationship between trait anxiety and depression and have important practical implications for the early prevention and intervention of depression. First, one should emphasize the role of vulnerable personality (trait anxiety) in depression, understand the anxiety trait levels of individuals, enhance early psychological screening, and improve individuals’ high trait anxiety levels through psychological training or intervention (Gupta et al., 2006). Second, studies have found that a high level of cognitive flexibility decreases the impact of trait anxiety on depression, such that interventions for individuals with high trait anxiety should also focus on improving individual cognitive flexibility by applying cognitive reconstruction techniques to clinical practice, such as attention training and mindfulness training (Chiesa et al., 2011; Johnco et al., 2014), thus reducing the risk of depression. Third, the mediating effect of rumination on the moderation function should also be strongly considered. Special cognitive techniques and methods focusing on rumination can be widely used to improve depression. 5. Limitations The current study also has several limitations. First, the data were collected using cluster sampling in a cross-sectional survey, and the representativeness of the study population was limited. In future research, stratified sampling and a longitudinal perspective study design could be used to strengthen the sample representativeness and better reveal the causal relationship between variables. Second, all observation variables were measured by self-report questionnaires. Combined with an objective index of cognitive flexibility measured by neuropsychological tests, such as the standardized experimental paradigms of the Wisconsin Card Sorting Test (WCST) and the Stroop Color and Word Test (Ionescu, 2012;Dajani and Uddin, 2015), such work could help us to deepen our understanding of the links between these two different types of cognitive flexibility and further explore their potential effects on the relationship between trait anxiety and depression (Lahr et al., 2007; Dennis and Wal, 2010). Third, the subjects in the study came from the general population, and the question of whether the results are applicable to people with clinical depression requires further testing. In addition, the combination of targeted cognitive interventions or training can better explore and verify the specific role of cognitive flexibility and rumination in trait anxiety and depression. 6. Conclusions The study finds that trait anxiety is moderately positively associated with depression and is an important contributor to depression. Cognitive flexibility plays an important moderating role in the relationship between trait anxiety and depression, explaining 3.4% of the variance. Cognitive flexibility has a “buffering” effect on the risk of trait anxiety associated with depression. Individuals with high cognitive flexibility are less susceptible to depression. In addition, rumination is a key mediator, and the moderating effect of cognitive flexibility on the relationship between trait anxiety and depression accounts for 79.25% of the total variance in depression through the mediating role of rumination. Author statement No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under 221
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consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.
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Author contributions TW, ML and SX designed the research; Tao W, SX, Tong W, JX, and LP conducted the psychological assessments; TW, FL, BL and JW analyzed the data; and TW, ML, and SX wrote and revised the paper. Conflict of interest There is no conflict of interest to declare. Acknowledgments The authors would like to thank the participants who filled out the psychological assessments and the invaluable assistance in statistical analysis from Associate Professor Wanchun Luo from the Third Military Medical University. Funding This work was supported by projects at the Military Research Foundation of the Chinese P.L.A. (No. AWS16J025), the Advance Research Fund of the Third Military Medical University (No. 2016XYY06), the Humanities and Social Sciences Fund of Army Medical University (No. 2017XRW19) and the Technology Innovation and Application Demonstration Project of Chongqing (No. cstc2018jscx-msybX0063). Conflict of interest There is no conflict of interest to declare. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2018.09.074. References Ansari, T.L., Derakshan, N., Richards, A., 2008. Effects of anxiety on task switching: evidence from the mixed antisaccade task. Cogn. Affect. Behav. Neurosci. 8, 229–238. Austin, M.P., Mitchell, P., Goodwin, G.M., 2001. Cognitive deficits in depression: possible implications for functional neuropathology. Br. J. Psychiat. 178, 200–206. Başaran, S.K., Yıldırım, Z.E., Gökdağ, C., 2016. The relationship between emotion regulation and problem solving: the mediating role of cognitive flexibility. In: European Association of Behavioural and Cognitive Therapies Congress. Beck, A.T., Steer, R.A., Brown, G.K., 1996. Manual For the BDI-II. Psychological Corporation, San Antonio, TX. Beck, A.T., 1987. Cognitive models of depression. Cognit. Psychother. Intern. Quarter. 1, 5–37. Beck, A.T., 2008. The evolution of the cognitive model of depression and its neurobiological correlates. Am. J. Psychiat. 165, 969–977. Carvalho, A.F., Fountoulakis, K.N., Mcintyre, R.S., 2014. cognitive dysfunction in major depressive disorder - pathophysiology, clinical implications and treatment opportunities. CNS Neurol. Disord. 13, 1637–1639. Chiesa, A., Calati, R., Serretti, A., 2011. Does mindfulness training improve cognitive abilities? a systematic review of neuropsychological findings. Clin. Psychol. Rev. 31, 449–464. Clark, D.A., Beck, A.T., 2010. Cognitive theory and therapy of anxiety and depression. Trends Cogn. Sci. 14, 418–424. Dajani, D.R., Uddin, L.Q., 2015. Demystifying cognitive flexibility: implications for clinical and developmental neuroscience. Trends Neurosci. 38, 571–578. Davis, R.N., Nolen-Hoeksema, S., 2000. Cognitive inflexibility among ruminators and nonruminators. Cognitive Ther. Res. 24, 699–711. Dennis, J.P., Wal, J.S.V., 2010. The cognitive flexibility inventory: instrument development and estimates of reliability and validity. Cognitive Ther. Res. 34, 241–253.
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Contents lists available at ScienceDirect
Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Relations between trait anxiety and depression: A mediated moderation model
T
Tao Wanga, Min Lia, , Song Xua, Botao Liua, Tong Wua, Fang Lub, Junpeng Xiea, Li Penga, Jia Wangc ⁎
a
Department of Military Psychology, School of Psychology, Army Medical University, Chongqing, 400038, China School of Nursing, Army Medical University, Chongqing, 400038, China c School of Psychology, Army Medical University, Chongqing, 400038, China b
ARTICLE INFO
ABSTRACT
Keywords: Trait anxiety Depression Rumination Cognitive flexibility
Background: This study proposed a mediated moderation model based on the cognitive theory and susceptibility phenotype theory for depression to explore the moderating role of cognitive flexibility in the relationship between trait anxiety and depression and the mediating role of rumination in this interactive effect. Methods: The subjects were selected via the cluster sampling method. A cross-sectional survey of 1619 Chinese adults of Han nationality was recruited from three cities in central and western China. The trait anxiety subscale of the State-Trait Anxiety Inventory (STAI-Trait scale), Short Ruminative Response Scale (SRRS), Cognitive Flexibility Inventory (CFI) and Beck Depression Inventory (BDI-II) were used for a paper-and-pencil evaluation. The data were statistically processed using correlation analysis, multiple linear regression analysis, and a test of mediated moderation. Results: (1) Trait anxiety was significantly positively correlated with rumination and depression. There was a significant negative correlation between cognitive flexibility and trait anxiety, rumination, and depression. (2) The moderating effect of cognition flexibility on the relationship between trait anxiety and depression was mediated by rumination, and the indirect moderating effect accounted for 79.25% of the total variance. Limitations: Cross-sectional studies do not reveal the causal relationships between observed variables well. Selfreport questionnaires lack objective indicators. The applicability of the current results in the clinical depression population requires further experimental tests. Conclusions: The results support the important role of trait anxiety in the development of depression. Cognitive flexibility moderates the relationship between trait anxiety and depression and has a risk-buffering effect on this relationship. Rumination plays a mediating role in the moderating effects of cognitive flexibility on the relationship between trait anxiety and depression. The findings enrich the research on the relationship between trait anxiety and depression and have important practical implications for the early prevention and intervention of depression, which can increase individual cognitive flexibility and address rumination to regulate depression.
1. Introduction Trait anxiety reflects an individual's predisposition to worry and anxiety when facing dangerous or uncertain situations. In recent years, studies in cognitive neuroscience have suggested that high trait anxiety is an important vulnerable phenotype for stress-induced depression (Weger and Sandi, 2018). Individuals with high anxiety traits not only are more susceptible to stress but also have specific neurocognitive characteristics (Sandi and Richter-Levin, 2009). In the management of threats or ambiguous situations, people with high trait anxiety have a unique cognitive style. Specifically, there is a selective attention bias ⁎
toward threat-related stimuli, and it is easy to interpret ambiguous emotional stimuli as negative information, and as a result, the corresponding conditioned fear response increases. Sandi and RichterLevin (2009) proposed the hypothesis of a "neurocognitive model" from high anxiety traits to depression and considered that the neurocognitive style of trait anxiety (neurocognitive maladaptations) plays a central role in the pathological development of depression. The abnormalities in neurocognitive function often lead to individuals exhibiting a moodcongruent bias, a more conscientious consolidation of negative memory, and further enhancement of negative thoughts (Sandi and Richter-Levin, 2009). This shows that differences in cognitive factors
Correspondence to: Department of Military Psychology, School of Psychology, Army Medical University, Chongqing, 400038, China E-mail address: [email protected] (M. Li).
https://doi.org/10.1016/j.jad.2018.09.074 Received 11 June 2018; Received in revised form 29 August 2018; Accepted 19 September 2018 Available online 20 September 2018 0165-0327/ © 2018 Published by Elsevier B.V.
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play a particularly important role in the relationship between trait anxiety and depression. The cognitive model of depression posits that individual cognitive factors (belief systems, cognitive processes, etc.) are important psychopathological foundations for the development of depression. According to Beck (1987), an individual's underlying dysfunctional maladaptive self-schemata is relatively stable and systematically present in an individual's cognitive structure, and encountering a negative stress event activates these negative patterns about individuals and the world, which makes individuals perform cognitive process (encoding, interpreting, retrieval, etc.) in an automatic and distorted manner and ultimately leads to the occurrence, maintenance, enhancement, or deterioration of depression. Among the increasing body of research in recent years, Beck (2008) and Clark and Beck (2010) further extended the model to genetic and neurochemical pathways that interact with or are parallel to cognitive variables and claimed that in addition to cognitive patterns, genetic or personality factors, the cognitive control ability of the frontal cortical areas is an important component in the development of depression (Clark and Beck, 2010; Disner et al., 2011). Cognitive deficits are frequently observed in major depression (Beck, 1987; Austin et al., 2001). One of the most important indices that reflect individual cognitive control and executive function is cognitive flexibility (Alike, 2014). In general, cognitive flexibility refers to the ability to adapt cognitive processing strategies to face changing or unpredictable situations (Jacques and Zelazo, 2005). Based on the different domains or contexts of cognitive flexibility within the current literature, the instruments used to assess cognitive flexibility are diverse. Among them, numerous neuropsychological performance tests have been used to assess the cognitive process of cognitive flexibility, such as attention shifting, conflict monitoring, and task switching (Dajani, 2015), and a limited number of self-report psychological questionnaires have centered on cognitive awareness, willingness or self-efficacy of the communication competence, such as the Cognitive Flexibility Scale (CFS, Martin and Rubin, 1995), the Cognitive Flexibility Inventory (CFI, Dennis and Wal, 2010) and the Attributional Style Questionnaire (ASQ, Peterson et al.,1982). Current evidence indicates that both types of cognitive flexibility are closely associated with depression, although the operational definitions and measurement methods differ. Studies have demonstrated that impaired cognitive processes exist in depressive individuals (Deveney and Deldin, 2006), with varying degrees of dysfunction in multiple cognitive domains such as attention and memory and executive function (Carvalho et al., 2014). The cognitively significant characteristics of depression patients are stereotyped as rigid and inflexible (Moore, 1996). Cognitive flexibility shows a significant negative correlation with depression (Gunduz, 2013; Soltani et al., 2013; Başaran et al., 2016). Nolen-Hoeksema (1987) further found that depressive individuals had ruminative response styles that interfere with attention, concentration and the initiation of instrumental behaviors. Numerous studies have suggested that rumination is a manifestation of insufficient cognitive flexibility (Nolen-Hoeksema, 1991;Davis and NolenHoeksema, 2000) and a core process of depression development and maintenance (Watkins, 2007, 2009). Thus, on the one hand, rumination is a special kind of self-focus that increases the accessibility of negative cognitive patterns and leads to more negative interpretations of events. On the other hand, rumination can also inhibit problem-solving and cognitive flexibility, making people difficult to disengage from negative stimuli and eventually form a vicious cycle of emotion-cognition, which increases depression severity (Koster et al., 2011). Given that rumination has such negative consequences, why are some people so persistently ruminative (Davis and NolenHoeksema, 2000)? What kinds of people have a long-lasting contemplation of negative mood? Some empirical studies have found that rumination is closely related to certain personality factors, such as neuroticism (Spasojević and Alloy, 2001). It has also been noted that rumination may reflect some important cognitive features of the
susceptible personality and may mediate the potential influence of personality on depression (Roberts et al., 1998). Some studies also suggest that an individual's rumination reaction may be related to a lack of cognitive flexibility. Individuals with cognitive inflexibility are more likely to ruminate when they feel sad and find it difficult to disengage from negative emotions (Davis and Nolen-Hoeksema, 2000). In summary, trait anxiety makes an individual susceptible to depression. Some preliminary studies have demonstrated the relationship between trait anxiety and depression, rumination and cognitive flexibility (Roberts et al., 1998; Williams et al., 2017). However, few studies have focused on the relationship between personality and depression from the perspective of cognitive flexibility awareness (e.g., CFI), and the specific role of cognitive flexibility and rumination in the relationship between trait anxiety and depression still remains unclear. Cognitive flexibility is relatively stable (Rende, 2000), and rumination is a reactive style. Therefore, we speculated that cognitive flexibility may affect the direction and strength of the relationship between trait anxiety and depression and could be a cognitive moderator of depression, whereas rumination could be a mediator of depression. This study selected Chinese urban adults as the study subjects to investigate the relationship between trait anxiety and depression and the moderating role of cognitive flexibility and the mediating role of rumination. The study proposed a mediated moderation model, with the following hypotheses: (1) trait anxiety is an important contributor to depression; (2) the relationship between trait anxiety and depression is moderated by cognitive flexibility; and (3) the moderating effect of cognitive flexibility on trait anxiety and depression is mediated by rumination. 2. Methods 2.1. Sample and procedure The subjects were selected by the cluster sampling method. Employees of Han nationality from three cities (Chongqing, Xiangfan, and Jiuquan) in central and western China constituted the survey samples. None of the subjects had a history of mental illness or personality disorder. A total of 1,670 people participated in the crosssectional survey. Since the subjects lived in different cities, the whole population was sampled in batches from April to May 2017 for psychological assessment. After invalid or uncompleted questionnaires were eliminated, the final sample numbered 1,619, and the response rate was 96.95%. Table 1 shows the demographic characteristics of the subjects. The study was approved by the ethics committee of the Third Military Medical University of China. Written informed consent was obtained. To ensure the validity of the psychological assessment, all subjects were guided and tested on site by two or three graduate students or teachers majoring in psychology, and psychological reports were returned to all the subjects through email.
Table 1 Demographic characteristics of the subjects.
Sex Male Female Age Education (years) Marital status Single Married Divorced
No.
%
679 940
41.94 58.06
714 864 41
44.10 53.37 2.53
Note: No., number; SD, standard deviations. 218
Mean
SD
Range
29.81 15.44
9.16 2.61
18–55 9–21
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2.2. Measures
2.2.4. Short ruminative response scale (SRRS) The SRRS contains 10 items, which were simplified and revised by Treynor et al. (2003). Using 4-point Likert scales (1 = almost never, 4 = almost always) on the SRRS, participants were asked to indicate how they generally respond to their sad, depressive mood. Higher scores on the SRRS predict the worse ruminative response. Cronbach's alpha coefficient is 0.883 in the present sample.
Whether the data set has a normal distribution is important for the choice of statistical analysis. We used kurtosis and skewness values to examine whether all the observed variables have a normal distribution. Spearman correlations were computed for pairs of these continuous variables. According to Frazier et al. (2004), the other variables, which are the components of the interaction terms (i.e., rumination, trait anxiety, cognitive flexibility), were centered. The moderating effect of cognitive flexibility on the association between trait anxiety and BDI scores was tested using hierarchical regression models while controlling for demographic characteristics (i.e., gender, age). At the same time, a mediated moderation model was used to explore the moderating role of cognitive flexibility in the relationship between trait anxiety and depression and used to verify whether this moderating effect was mediated by rumination. This model is tested based on the following conditions to be fulfilled (Muller et al., 2005; Ye and Wen, 2013). First, a moderating effect must be present. That is, the effect of the interaction (c3) between trait anxiety and cognitive flexibility is significant. Second, the mediating effect of rumination is also significant. The impact of the interaction term on BDI scores is at least partly mediated by rumination. Either or both of the following conditions may be met: (a) the effect of trait anxiety and cognitive flexibility on rumination is significant (a3), and the effect of rumination and BDI scores is also significant (b1). Or (b) the effect of trait anxiety on rumination is significant (a1), and the effect of the interaction between cognitive flexibility and rumination on BDI scores is also significant (b2). That is, if a3 b1 is significant or a3 b2 is significant, the effect of the interaction between trait anxiety and cognitive flexibility on depression is mediated by rumination. If a1 b2 is significant, cognitive flexibility indirectly moderates the effect of trait anxiety on BDI scores when we adjust for the effect of rumination on the dependent variable. According to the above procedure, a bootstrap test was performed using Mplus 7.0 software to calculate the 95% confidence interval. Third, when the mediated moderation model is established, the indirect effect of moderation (c3-c3′) is calculated. Usually, the interactive effect between the independent variable and moderator on the dependent variable should be reduced or lose significance in magnitude compared with the overall effect of moderation.
2.3. Statistical analysis
3. Results
All data were analyzed using SPSS 21.0 and Mplus7.0 software. All statistical tests were two-sided, and p-values 〈 0.05 were considered statistically significant. First, Harman's single-factor test (Podsakoff et al., 2003) was used to test for common method bias. The factor analysis extracted 10 factors (eigenvalue 〉 1), of which the most important factor accounted for only 29.89% of the variance (less than 40%). Consequently, as a result of this test, common method variance is not a serious concern in the present study. Next, a multicollinearity analysis was performed to check whether the predictor variables were related to each other, and the variance inflation factor (VIF) was computed as an indicator. If the VIF values are higher than 5, there is multicollinearity between the independent variables. In the present study, the VIF values for all predictors were 1.174∼1.803 (less than 5), indicating no serious multicollinearity problem.
3.1. Descriptive analysis and Spearman's correlations for all variables
2.2.1. Spielberger state-trait anxiety inventory (STAI) Trait anxiety was tested using the trait anxiety subscale of the STAI developed by Spielberger and Gorsuch (1983). The scale has 20 items, with each item assessed on a four-point Likert scale ranging from 1 (completely absent) to 4 (very obvious). It describes a relatively stable personality trait and anxiety bias with individual differences to assess people's regular emotional experience. Items 1, 3, 4, 6, 7, 10, 13, 14, 16, and 19 are scored in reverse. The sum of the 20 items is the total score, with a higher score indicating a higher trait anxiety level. Cronbach's alpha coefficient is 0.87 in this study. 2.2.2. Beck depression inventory (BDI) The BDI-II was used to assess the severity of depressive symptoms over the last two weeks (Beck et al., 1996). The inventory consists of 21 items with response options from 0 to 3. A total score of 14 points or more indicates mild depression or worse. In this study, Cronbach's alpha coefficient is 0.931. 2.2.3. Cognitive flexibility inventory (CFI) The CFI is a 20-item self-report scale developed by Dennis and Vander Wal in 2010. This scale is designed to measure flexibility in terms of understanding and responding to the world and aims to measure an individual's ability to engage in alternative, balanced thinking in difficult situations (Dennis and Wal, 2010). The questionnaire was scored on a scale of 1 (strongly disagree) to 7 (very strongly agree), with items 2, 4, 7, 9, 11, and 17 being scored in reverse. Cronbach's alpha is 0.891 in this study.
Table 2 shows the means, standard deviations and ranges for all variables included in the mediated moderation analysis. In addition, Spearman correlations were computed. The results showed that trait anxiety and rumination were significantly positively correlated with depression (p < 0.001) and that cognitive flexibility had a significant negative correlation with trait anxiety, rumination, and depression (p < 0.001). 3.2. Relationship between trait anxiety and depression: a mediated moderation model Mediated moderation occurs when a moderating effect is mediated
Table 2 Spearman's correlation analysis of each variable.
1. 2. 3. 4.
Trait anxiety SRRS CFI BDI
Skewness
Kurtosis
Mean
SD
Range
1
2
3
4
0.073 0.610 0.206 1.588
−0.127 0.752 −0.459 2.292
40.86 17.75 95.79 7.52
8.79 4.88 17.72 8.95
20–77 10–40 44–140 0–47
1 0.470⁎⁎⁎ −0.537⁎⁎⁎ 0.536⁎⁎⁎
1 −0.307⁎⁎⁎ 0.559⁎⁎⁎
1 −0.353⁎⁎⁎
1
Note:
⁎⁎⁎ P < 0.001; SD, standard deviations; IQR, interquartile range; SRRS, Short Ruminative Response Scale; CFI, cognitive flexibility inventory; BDI, beck depression inventory.
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Fig. 1. The moderation model of CFI. Note: *p < 0.05, ⁎⁎p < 0.01. TA, trait anxiety; CFI, Cognitive Flexibility Inventory; BDI, Beck Depression Inventory.
by another variable (Mackinnon and Fairchild, 2009). First, according to the statistical procedure (Muller et al., 2005; Ye and Wen, 2013), all the predictors were centered, and the moderating effect of cognitive flexibility was tested. The moderation model is illustrated in Fig. 1. The results showed that the moderating effect of the interactive effect on depression was significant (c3 = 0.053, t = 2.454, p < 0.05), which indicates that the effect of trait anxiety on depression is moderated by cognitive flexibility. Second, the mediating role of rumination was detected in the hypothesized model. The hypothesized mediator, rumination, was regressed on the control variables, trait anxiety, cognitive flexibility, and the interaction term. As the results indicate (see Fig. 2), cognitive flexibility had no significant effect on rumination (a1 = 0.092, t = 0.176, p = 0.860), and the effect of the interaction term on depression was close to being significant (a3 = 0.795, t = 0.421, p = 0.059). The results showed that both trait anxiety and cognitive flexibility were not significantly related to rumination, but the interaction term was statistically marginal. Next, BDI scores were regressed on the control variables, trait anxiety, cognitive flexibility, the interaction term, the hypothesized mediator, rumination, and the interaction between cognitive flexibility and rumination. The results indicate that rumination was a significant predictor of BDI scores (b1 = 0.01, t = 7.703, p < 0.001) when all other variables and interactions were controlled. In addition, the interaction between cognitive flexibility and rumination had a significant effect on BDI scores (b2 = 0.134, t = 5.56, p < 0.001) (see Fig. 2). In brief, the interaction between trait anxiety and cognitive flexibility is significantly related to BDI scores. This result fulfilled the first condition for the mediated moderation model. Moreover, the effect of rumination on BDI scores was significant. However, the interaction between trait anxiety and cognitive flexibility did not have a significant effect on rumination. In addition, the coefficients of a3b1, a3b2, and a1b2 were not completely significant; thus, it could not be determined whether a mediated moderation model could be established. Based on the procedure for testing mediated moderation models (Ye and Wen, 2013), the 95% confidence intervals of a3b1 (H1), a3b2 (H2), and a1b2 (H3) were computed using the bias-corrected percentile bootstrap
method. If at least one of the confidence intervals of a3b1, a3b2, and a1b2 does not contain 0, the effect of the mediator is significant. The results showed that the 95% CI for a1b2 (H3) was [0.002, 0.015], the 95% CI for a3b2 (H2) was [0.03, 0.206], and the 95% CI for a1b2 (H3) was [−0.088, 0.108]. Since the confidence intervals for H1 and H2 do not contain 0, the relationship between trait anxiety and BDI scores can be established in a mediated moderation model. Therefore, the second condition of the test of mediated moderation is also met. In this case, the moderated effect is completely mediated if the coefficient (c3′) from the interaction term to BDI scores is not significant or partially mediated if the coefficient (c3′) from the interaction term to BDI scores is significant. Our results showed that c3′ from the interaction term to BDI scores was not significant (c3′ = 0.011, t = 0.474, p = 0.635), and the moderating effect of the interaction term on BDI scores was completely mediated by rumination. In addition, the indirect moderating effect (c3 - c3′) was 0.042, and the indirect effect accounted for 79.25% of the variance. Hierarchical regression analysis was used to examine the moderating effects. The results showed that the regression coefficient of the interaction item was significant (β = −0.01, t = −9.214, p < 0.001), and the moderating effect of cognitive flexibility additionally explained 3.4% of the variance (△R2 = 0.034, ΔF (1, 1613) = 84.90, p < 0.001). A simple slope analysis was conducted to test the moderating effect at high and low levels (one SD above and below the mean) of the moderator. The results showed that the associations of trait anxiety with depression were weaker in participants with a higher level of cognitive flexibility (mean + 1SD) (βHigh = 0.192, p < 0.001) than in those with a lower level of cognitive flexibility (mean - 1SD) (βLow = 0.85, p < 0.001). Thus, cognitive flexibility buffered the effects of trait anxiety on depression. 4. Discussion The results of this study show that trait anxiety is moderately positively correlated with depression, suggesting that high trait anxiety could be an important contributor to depression. Although high trait Fig. 2. The mediated moderation model of CFI and SRRS in the relationships between TA and BDI. Note: △p = 0.059, ⁎⁎⁎p < 0.001.TA, trait anxiety (X, independent variable); CFI, Cognitive Flexibility Inventory (U, moderator); SRRS, Short Ruminative Response Scale (W, mediator); BDI, Beck Depression Inventory (Y, dependent variable). X × U, interaction term between TA and CFI; U × W, interaction term between CFI and SRRS.
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anxiety itself did not directly lead to depression, individuals with high anxiety traits presented inadequate attention control, cognitive inhibition, task-switching difficulties, and reduced renewal capacity (Ansari et al., 2008). Therefore, in response to stressful events, individuals with high trait anxiety are more likely to stimulate the body's dysfunctional neurocognitive cascade, increase that individual's susceptibility to stress, and subsequently cause a series of behavioral and cognitive changes eventually leading to depression (Sandi and RichterLevin, 2009; Weger and Sandi, 2018). Consistent with our hypothesis, we found that the relationship between trait anxiety and depression was moderated by cognitive flexibility and that the amount of variation was 3.4%. More precisely, the BDI scores in the group with high cognitive flexibility were significantly lower than those in the group with low cognitive flexibility. In other words, cognitive flexibility “weakens” the risk effect of trait anxiety on depression; that is, cognitive flexibility has a “buffering” effect on the relationship between trait anxiety and depression. This finding suggests that improving cognitive flexibility can reduce the risk of depression in individuals with high trait anxiety. Related studies have found that cognitive behavior training can significantly enhance the cognitive flexibility of perfectionist individuals (Nazarzadeh et al., 2015), and attention control training, e.g., mindfulness training (Chiesa et al., 2011), can also increase individual cognitive flexibility, which eventually improves depressive symptoms (Teasdale et al., 1995; Dennis and Wal, 2010). Thus, our findings suggest that high trait anxiety is an important contributor to depression, whose strength is moderated by the level of cognitive flexibility, and that increased cognitive flexibility could be the potential mechanism by which cognitive therapy contributes to the outcomes of active intervention for depression. The study further explored the mediated moderation model to examine the underlying mechanisms of trait anxiety and cognitive flexibility interactions predicting depression and to determine why individuals with low cognitive flexibility are more likely to develop depression when they present with high trait anxiety tendencies. By including rumination as a mediator of the moderating effect, we found that people with low cognitive flexibility are more likely to ruminate given the interaction with trait anxiety, further leading to depression symptoms. The findings again confirm that rumination is a key mediator influencing the development and maintenance of depressive symptoms (Spasojević and Alloy, 2001; Watkins, 2009). The impaired disengagement hypothesis (Koster et al., 2011) argued that the impaired ability of individuals to escape from passively negative self-referenced information was the underlying cause of depressive thinking, and rumination was taken as a potential common proximal mechanism linking risk factors with depression (Spasojević and Alloy, 2001). Therefore, some scholars have proposed a variant of the CBT treatment method, namely, rumination-focused CBT (Watkins et al., 2007) using specific techniques such as functional analysis, behavioral activation or experiential/imagery exercise (Watkins, 2009) for targeting rumination and that such efforts appear to yield generalized improvement in chronic depression and treatment-resistant depression. Previous studies have found that rumination plays an important mediating role in the relationship between neurotic personality and depression and can also predict the effects of antidepressant treatment (Nolan et al., 1998; Watkins, 2009). Our study further found that, through the mediating role of rumination, the moderating effect of cognitive flexibility on the relationship between trait anxiety and depression accounted for 79.25% of the overall variance in depression, suggesting that the proportion of the mediation effect of rumination on the interactive function is prominent and that rumination could thus be a key mediator. In summary, this study proposed a mediated moderation model based on the cognitive model (Disner et al., 2011) and susceptibility phenotype theory for depression (Weger and Sandi, 2018), demonstrating the link between trait anxiety and depression in depth and identifying the specific role of cognitive flexibility and rumination in
these relationships. The study also identified that trait anxiety could be an important contributor to depression among Chinese adults. This research also clarified the conditions under which trait anxiety works and further reveals important reasons for the differences in the risk effects of trait anxiety under different conditions. The results enrich the study of the relationship between trait anxiety and depression and have important practical implications for the early prevention and intervention of depression. First, one should emphasize the role of vulnerable personality (trait anxiety) in depression, understand the anxiety trait levels of individuals, enhance early psychological screening, and improve individuals’ high trait anxiety levels through psychological training or intervention (Gupta et al., 2006). Second, studies have found that a high level of cognitive flexibility decreases the impact of trait anxiety on depression, such that interventions for individuals with high trait anxiety should also focus on improving individual cognitive flexibility by applying cognitive reconstruction techniques to clinical practice, such as attention training and mindfulness training (Chiesa et al., 2011; Johnco et al., 2014), thus reducing the risk of depression. Third, the mediating effect of rumination on the moderation function should also be strongly considered. Special cognitive techniques and methods focusing on rumination can be widely used to improve depression. 5. Limitations The current study also has several limitations. First, the data were collected using cluster sampling in a cross-sectional survey, and the representativeness of the study population was limited. In future research, stratified sampling and a longitudinal perspective study design could be used to strengthen the sample representativeness and better reveal the causal relationship between variables. Second, all observation variables were measured by self-report questionnaires. Combined with an objective index of cognitive flexibility measured by neuropsychological tests, such as the standardized experimental paradigms of the Wisconsin Card Sorting Test (WCST) and the Stroop Color and Word Test (Ionescu, 2012;Dajani and Uddin, 2015), such work could help us to deepen our understanding of the links between these two different types of cognitive flexibility and further explore their potential effects on the relationship between trait anxiety and depression (Lahr et al., 2007; Dennis and Wal, 2010). Third, the subjects in the study came from the general population, and the question of whether the results are applicable to people with clinical depression requires further testing. In addition, the combination of targeted cognitive interventions or training can better explore and verify the specific role of cognitive flexibility and rumination in trait anxiety and depression. 6. Conclusions The study finds that trait anxiety is moderately positively associated with depression and is an important contributor to depression. Cognitive flexibility plays an important moderating role in the relationship between trait anxiety and depression, explaining 3.4% of the variance. Cognitive flexibility has a “buffering” effect on the risk of trait anxiety associated with depression. Individuals with high cognitive flexibility are less susceptible to depression. In addition, rumination is a key mediator, and the moderating effect of cognitive flexibility on the relationship between trait anxiety and depression accounts for 79.25% of the total variance in depression through the mediating role of rumination. Author statement No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under 221
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consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.
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Author contributions TW, ML and SX designed the research; Tao W, SX, Tong W, JX, and LP conducted the psychological assessments; TW, FL, BL and JW analyzed the data; and TW, ML, and SX wrote and revised the paper. Conflict of interest There is no conflict of interest to declare. Acknowledgments The authors would like to thank the participants who filled out the psychological assessments and the invaluable assistance in statistical analysis from Associate Professor Wanchun Luo from the Third Military Medical University. Funding This work was supported by projects at the Military Research Foundation of the Chinese P.L.A. (No. AWS16J025), the Advance Research Fund of the Third Military Medical University (No. 2016XYY06), the Humanities and Social Sciences Fund of Army Medical University (No. 2017XRW19) and the Technology Innovation and Application Demonstration Project of Chongqing (No. cstc2018jscx-msybX0063). Conflict of interest There is no conflict of interest to declare. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2018.09.074. References Ansari, T.L., Derakshan, N., Richards, A., 2008. Effects of anxiety on task switching: evidence from the mixed antisaccade task. Cogn. Affect. Behav. Neurosci. 8, 229–238. Austin, M.P., Mitchell, P., Goodwin, G.M., 2001. Cognitive deficits in depression: possible implications for functional neuropathology. Br. J. Psychiat. 178, 200–206. Başaran, S.K., Yıldırım, Z.E., Gökdağ, C., 2016. The relationship between emotion regulation and problem solving: the mediating role of cognitive flexibility. In: European Association of Behavioural and Cognitive Therapies Congress. Beck, A.T., Steer, R.A., Brown, G.K., 1996. Manual For the BDI-II. Psychological Corporation, San Antonio, TX. Beck, A.T., 1987. Cognitive models of depression. Cognit. Psychother. Intern. Quarter. 1, 5–37. Beck, A.T., 2008. The evolution of the cognitive model of depression and its neurobiological correlates. Am. J. Psychiat. 165, 969–977. Carvalho, A.F., Fountoulakis, K.N., Mcintyre, R.S., 2014. cognitive dysfunction in major depressive disorder - pathophysiology, clinical implications and treatment opportunities. CNS Neurol. Disord. 13, 1637–1639. Chiesa, A., Calati, R., Serretti, A., 2011. Does mindfulness training improve cognitive abilities? a systematic review of neuropsychological findings. Clin. Psychol. Rev. 31, 449–464. Clark, D.A., Beck, A.T., 2010. Cognitive theory and therapy of anxiety and depression. Trends Cogn. Sci. 14, 418–424. Dajani, D.R., Uddin, L.Q., 2015. Demystifying cognitive flexibility: implications for clinical and developmental neuroscience. Trends Neurosci. 38, 571–578. Davis, R.N., Nolen-Hoeksema, S., 2000. Cognitive inflexibility among ruminators and nonruminators. Cognitive Ther. Res. 24, 699–711. Dennis, J.P., Wal, J.S.V., 2010. The cognitive flexibility inventory: instrument development and estimates of reliability and validity. Cognitive Ther. Res. 34, 241–253.
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