Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Feb 15.
Published in final edited form as: J Affect Disord. 2023 Nov 23;347:445–452. doi: 10.1016/j.jad.2023.11.038

Subscales of alexithymia show unique pathways through reappraisal and suppression to anxiety, depression and stress

Kristen P Morie 1, Kayla A Lord 1,2, Gretchen J Diefenbach 1,2, Oishani Basuchoudhary 3, Stephanie Lewis 3, Michal Assaf 1,3
PMCID: PMC10842914  NIHMSID: NIHMS1950309  PMID: 38007105

1. Introduction

Understanding contributors to emotional distress is a key component of our understanding of mental health symptoms and disorders, such as anxiety and depression. One key characteristic that may be associated with distress is alexithymia, defined as difficulty understanding and verbalizing one’s own emotions (1). Alexithymia is seen in approximately 10% of the general population (2). Identified in the 1970’s, alexithymia has recently been a focus of research given its prevalence among those with mental health disorders, with rates as high as 32.0% in adults with depression (3) and 76.5% in adolescents with depression (4), 79.1% in those with anxiety (5), and 30–50% in those with substance use disorders (6, 7). Given these findings, alexithymia is of significant clinical interest. Thus, the goal of this work was to examine the associations between facets of alexithymia, emotion regulation, and distress in a large sample of individuals recruited from an online crowdsourcing platform.

As alexithymia is so common among those with depression and anxiety, it may serve as a transdiagnostic vulnerability factor for mental illness (8) or for increased distress when an individual experiences trauma or a large-scale stressor, such as the COVID-19 pandemic (4, 9). Key features of alexithymia are poor emotion identification and decreased ability to communicate emotions to others, such as peers or therapists potentially contributing to increased distress. This interpretation of alexithymia as a potent construct that may worsen distress is supported by findings in both general and clinical populations that indicate alexithymia is associated with suicidality (10, 11), poorer interpersonal relationships (12, 13), and poorer quality of life (14, 15). Clearly, alexithymia is a powerful transdiagnostic risk factor, but mechanisms of action remain understudied. Examining the associations between different components of the alexithymic phenotype (e.g., identifying and verbalizing) and different types of distress (e.g., depression, anxiety, and stress) may help to clarify its potential role as a vulnerability factor for clinically significant symptoms. Furthermore, exploring potential mediators of these associations, such as emotion regulation, may help to delineate potential mechanisms.

Emotion regulation refers to the ability to manage one’s emotional state and how one responds to their emotions (16). Commonly used measures of emotion regulation include the assessment of suppression (a maladaptive response that involves ignoring or suppressing emotions) and reappraisal (the beneficial ability to re-frame one’s thoughts about what one is feeling) (17), and are commonly measured with the Emotion Regulation Questionnaire (ERQ), (18) which was used here. Given the recent model from our laboratory that states that an individual must be able to identify the emotion they are feeling before they can properly make use of these emotion regulation strategies (19), the presence of alexithymia would logically impair emotion regulation. The literature on alexithymia so far supports this assertion, with strong associations seen between alexithymia and measures of emotion regulation (20). Further, reappraisal is commonly negatively correlated with alexithymia, while suppression is commonly positively associated with alexithymia (21), and alexithymia is associated with generally maladaptive emotion regulation strategies (22)

A recent model of emotional identification by our laboratory illustrates the importance of different factors associated with intact emotional identification, and different patterns of deficits may contribute to different presentations of mental health difficulties (19). It is possible that different contributions of different alexithymia factors or certain factors over others may play an important role in different types of distress, even among individuals in the general population. This is supported by findings from Alkan et. al (23), which suggest that different combinations of alexithymia factors are associated with higher or lower distress in non-clinical adults. Other work that used cluster analysis of alexithymia has indicated that different combinations of individual factors of alexithymia are associated with different patterns of emotion regulation and different levels of depression and anxiety among the general population (24). Given significant mental health and physical health co-morbidities associated with distress (25), it is therefore essential to further examine how different factors of alexithymia may contribute to different kinds of distress, including depression, anxiety, and stress, in the general population.

Alexithymia is typically measured with either the 20-item Toronto Alexithymia Scale (TAS-20;26) or the 40-item Bermond Vost Alexithymia Questionnaire (BVAQ;27). The BVAQ was used for this study, as its constituent factors were identified as being preferable to the TAS-20 with regard to being separable from the general concept of distress (28). These questionnaires can be divided into individual factors, splitting the concept into three (TAS-20) or five (BVAQ) major factors. Common to both is identifying and verbalizing emotion, which respectively address the ability to identify what one is feeling and verbally describe that feeling. The TAS-20 also examines externalization, while the BVAQ splits alexithymia further into subscales describing analyzing emotion (examining explanations for experienced emotions) emotionalizing (intensity of experienced emotion) and fantasizing (ability to fantasize about virtual events). While comparatively little literature has examined these factors independently, doing so may offer additional insight into how alexithymia contributes to distress. The Depression Anxiety Stress scales (DASS), also used here, is a reliable trans-diagnostic measure of distress severity in clinical and non-clinical populations (29–32).

The goals of this project were twofold. The first goal was to determine the extent of the association between alexithymia and distress in the general population, with a focus on the specific contributions of each factor of alexithymia and the potential mediation through reappraisal and suppression. The second goal was to determine if combinations of specific factors of alexithymia were directly associated with specific aspects of distress, including depression, anxiety, and stress. Findings may help to further define the trans-diagnostic role of alexithymia and its constituent factors on distress in healthy and clinical populations.

To achieve these goals, a path analysis was constructed to determine the extent to which our variables of interest (i.e., the five alexithymia factors and the two emotion regulation strategies) explained variance in reported depression, anxiety, and stress and examined the contributions of each individual factor through emotion regulation strategies. There were two major hypotheses. The first was that increased alexithymia would be associated with poorer emotion regulation strategies, with decreased reappraisal, increased suppression, and thus increased distress, with a large portion of the variance in distress explained by alexithymia and its associations with emotion regulation strategies. The second was that different combinations of alexithymia factors would be associated with depression, anxiety, and stress, suggesting that specific factors of alexithymia may be associated with different types of distress.

2. Methods

2.1. Participants and Procedures

Participants were 377 adults recruited using the secure crowdsourcing on-line data collection platform, Amazon Mechanical Turk (MTurk).Criteria for the current study were 18 years of age or older, U.S. resident status, functional English language fluency (per self-report), MTurk approval rating > 90%, IQ estimate > 5th percentile, no significant reported neurological disorder (e.g., epilepsy, brain tumor; per self-report), and that all participant’s data met criteria for quality assurance. In order to maximize statistical variability, participants were not excluded for self-reported mental health diagnoses or based on elevated self-report symptoms. In addition, as all data were self-report, we could not confirm accuracy of any reported diagnoses.

To avoid complications related to data quality often associated with online surveys, robust quality assurance (QA) protocols, described elsewhere (33), were carried out. In summary, data were only included from participants who completed all items, spent at least 36 minutes answering questions (~60% of predicted average time), and answered at least 75% of QA items correctly. Two types of QA items were included: repeated questions on which participants had to demonstrate consistency (5% of total items), and common knowledge questions (e.g., “When I look at it, the sun is very bright”), which, if read carefully, all participants should be able to answer correctly (5% of total items). Additionally, participants were excluded due to inconsistent or irregular reporting patterns, including inconsistent reporting of mental health diagnoses. In addition, the 9-item Raven’s Progressive Matrices Test (“Complete the Pattern”) (34) was administered as part of the battery. The Raven’s Progressive Matrices is a measure of abstract reasoning and is often used as a non-verbal estimate of fluid intelligence. A cut-off score of the 5th percentile was used to exclude participants with potential intellectual disability (33, 35). Figure 1 illustrates the procedure by which quality control was carried out. Study procedures were approved by Hartford HealthCare’s Institutional Review Board. All participants provided electronic informed consent. Participants were paid $5 if they completed the entire protocol and passed quality assurance. Participant enrollment outlining reasons for exclusion is presented in Figure 1.

Figure 1.

Figure 1.

Open in a new tab

Participant enrollment outlining reasons for exclusion.

2.2. Measures

Bermond Vorst Alexithymia Questionairre (BVAQ):

Alexithymia was assessed using the BVAQ (27). The BVAQ consists of 40 items that reflect how individuals consider their own emotions (for example, “I like to tell others how I feel,”) and asks participants to rate on a scale of 1 = “definitely applies to me” to 5 = “definitely does not apply to me.”. The questionnaire can be divided into five subscales, with eight items each, reflecting identifying, verbalizing, analyzing, fantasizing, and emotionalizing, in accordance with the five factor model of alexithymia (36). In brief, each factor can be described as the following. Identifying refers to one’s ability to identify one’s own feelings. Verbalizing refers to the ability to describe one’s emotions to others. Analyzing emotion consists of examining explanations or reasons for experienced emotions. Emotionalizing refers to the perceived intensity of experienced emotion. Fantasizing refers to the ability to fantasize about virtual events. Increased scores reflect greater alexithymia. Internal consistency in English-speaking samples has been reported as α = 0.85 for the total score (37)). In our sample, internal consistency (α) for each subscale was the following: analyzing = 0.67, emotionalizing = 0.83, fantasizing = 0.89, identifying = 0.88, verbalizing = 0.80.

Emotion Regulation Questionnaire (ERQ):

Cognitive reappraisal was assessed using the reappraisal subscale of the ERQ (38). The ERQ-Reappraisal subscale consists of six items describing the process of changing thoughts in order to change emotions [e.g., “When I want to feel less negative emotion (such as sadness or anger), I change what I’m thinking about”]. Participants rate the extent to which they agree or disagree with each statement on a 7-point scale (1 = strongly disagree to 7 = strongly agree). Scores range from 6–42 with higher scores indicating stronger tendencies to use cognitive reappraisal. The ERQ-reappraisal subscale has demonstrated excellent reliability and validity (39). The ERQ-Suppression subscale consists of four items describing the process of avoiding or suppressing emotional expression [e.g., “I keep my emotions to myself.”]. Scores range from 4–28 with higher scores indicating higher suppression. In our sample, α for each subscale was the following: reappraisal = 0.91, suppression = 0.83.

Depression Anxiety Stress Scale (DASS-21):

The DASS-21 is a 21-item self-report measure of depression, anxiety, and stress severity (40). Item responses range from 0 to 3 with higher scores reflecting more severe symptoms. Subscale item scores are summed and multiplied by two to arrive at a subscale score. Internal consistency for subscales range from good to excellent (30, 40–42) including in the current sample (current study αs: depression = 0.95, anxiety = 0.89, stress = 0.90).

2.3. Data Analysis

Descriptive statistics and correlations among variables were obtained using SPSS, version 29.0. Multi-collinearity was assessed with the variance inflation factor (VIF) and tolerance calculated in SPSS. Path analysis was conducted using AMOS, version 26.0. The five BVAQ subscales were modeled (i.e., verbalizing, fantasizing, identifying, analyzing, and emotionalizing) as predictors of the ERQ suppression and reappraisal subscales as well as of the DASS-21 outcomes (i.e., depression, anxiety, stress). BVAQ subscale scores were allowed to correlate (see Figure 2).

Figure 2.

Figure 2.

Open in a new tab

Path model testing direct and indirect effects from alexithymia to mental health symptoms through emotion regulation strategies

DASS = Depression, Anxiety, Stress Scale.

Note: The correlations between the five subscales representing alexithymia are not presented here but were included in the models.

There were three models tested with separate DASS outcomes as indicators: depression, anxiety, and stress.

Model fit was assessed using the following fit indices: the χ2 value, goodness-of-fit index (GFI), comparative fit index (CFI), normed fit index (NFI), Tucker-Lewis index (TLI), and root mean square error of approximation (RMSEA). Fit is generally considered acceptable when the GFI, CFI, NFI, and TLI ≥ 0.90, the RMSEA ≤ 0.08, and the χ2 p-value > 0.05 (Bae, 2017; Hair et al., 2010). Ideally, GFI, CFI, NFI, and TLI ≥ 0.95 and RMSEA ≤ 0.05 (Lance et al., 2016). Given that the models included multiple mediation effects, we presented: total effects, direct effects, and specific indirect effects through suppression and appraisal (i.e., equal to the product of the regression weights of the paths from the predictor to the mediator and from the mediator to the outcome). The bias-corrected bootstrap confidence interval (CI) was set to 95% and the number of bootstrap samples was 1,000. If the 95% CI did not contain zero, it indicated a significant indirect effect.

3. Results

3.1. Demographics

Average age of all individuals was 38.4 (sd = 11.7; range: 20–78). 57.3% of the sample identified as female, with one participant opting not to report gender. 84.8% of the sample were white, 8.2% were black/African American, 5.8% were Asian, 0.3% were Native American or Alaska Native, and 0.6% chose not to answer. 7.2% were Hispanic or Latino and the remaining 91.8% were non-Hispanic or Latino or chose not to answer (1.1%).

Descriptive statistics and correlations between all study variables are presented in Table 1. Depression, anxiety, and stress subscales were all negatively correlated with fantasizing and emotionalizing, while positively associated with verbalizing and identifying. The analyzing subscale was not correlated with any mental health measure.

Table 1.

Descriptive statistics and correlations for all study variables

1 2 3 4 5 6 7 8 9 10 M SD
1 Verbalizing - 20.06 6.86
2 Fantasizing −.11* - 18.81 7.64
3 Identifying .62* −.15* - 15.81 6.81
4 Emotionalizing −.04 .37* −.21* - 21.28 6.69
5 Analyzing .44* .30* .32* .43* - 17.55 4.76
6 BVAQ total .64* .51* .53* .52* .79* - 93.50 19.08
7 Reappraisal −.35* .03 −.34* .09 −.20* −.25* - 30.19 7.53
8 Suppression .64* .07 .38* .24* .49* .60* −.24* 14.35 6.04
9 Depression .44* −.28* .41* −.28* .07 .11* −.40* .32* - 3.89 5.27
10 Anxiety .35* −.26* .37* −.29* −.002 .05 −.25* .21* .68* - 2.59 3.82
11 Stress .41* −.34* .43* −.33* .01 .06 −.30* .23* .69* .76* 4.58 4.55
Open in a new tab

Note:

* =

p < .05. BVAQ, Bermond-Vorst Alexithymia Questionnaire total score. M, mean. SD, standard deviation.

Reappraisal was negatively associated with verbalizing, identifying, and analyzing. Suppression, however, was positively associated with verbalizing, identifying, emotionalizing and analyzing.

3.2. Fitness of the Path Model

VIFs ranged from 1.19 to 2.60, which is below the standard value of 10, and tolerance values ranged from 0.38 to 0.84, which is above the standard value of 0.10, indicating no issues with multi-collinearity. The path model depicted in Figure 1 exhibited good fit with each outcome: χ2 (1, 36) = .65, p = .421, GFI = 1.00, CFI = 1.00, TLI = 1.01, NFI = 1.00, RMSEA < .001. Squared multiple correlations indicated that 37.3% of the variance in depression, 25.2% of the variance in anxiety, and 35.3% of the variance in stress was explained by the model.

3.3. Direct Effects of Alexithymia on Emotion Regulation

In each model, 49.5% of variance in suppression and 15.6% of the variance in reappraisal was explained by the five BVAQ subscales. Verbalizing (b = −.22 [−.35, −.09], SE = .07, p = .001, β = −.20) and identifying (b = −.19 [−.34, −.05], SE = .07, p = .004, β = −.17) negatively predicted reappraisal. Verbalizing (b = .51 [.42, .60], SE = .04, p = .003, β = .58), emotionalizing (b = .19 [.11, .27], SE = .04, p = .001, β = .21) and analyzing (b = .17 [.05, .29], SE = .06, p = .003, β = .13) positively predicted suppression. There were no other significant paths from alexithymia subscales to emotion regulation indicators

3.4. Direct Effects on Depression, Anxiety, and Stress

Reappraisal negatively predicted depression and stress, but did not significantly predict anxiety. Suppression positively predicted depression and anxiety, but did not significantly predict stress. Regarding direct effects of alexithymia subscales, fantasizing and emotionalizing negatively predicted depression, anxiety, and stress. Verbalizing positively predicted depression and stress. Finally, identifying positively predicted anxiety and stress. Analyzing was not significantly, directly associated with any outcome.

3.5. Indirect Effects of Alexithymia on Mental Health through Reappraisal and Suppression

Higher levels of verbalizing resulted in higher levels of depression and anxiety, respectively, due to the indirect effects through reappraisal and suppression, but the magnitude of the associations were larger through suppression. Only reappraisal mediated the positive association between verbalizing and stress. Reappraisal also mediated the positive associations between identifying and anxiety and stress, respectively. Notably, reappraisal also mediated the association between identifying and depression, such that higher identifying was associated with higher depression due to the indirect effect, in the context of no significant direct effect of identifying. Additionally, suppression mediated the associations between emotionalizing and depression and anxiety, respectively. Due to the indirect effect, higher levels of emotionalizing were associated with higher levels of depression and anxiety. Finally, suppression mediated the associations between analyzing and depression and anxiety, respectively, in the context of no direct effect of analyzing in either model. In sum, the associations between domains of alexithymia and depression, anxiety, and stress are complex. Indirect effects through emotion regulation, particularly suppression, suggest higher levels of alexithymia are associated with higher levels of depressive symptoms.

4. Discussion

Our hypotheses for this work were that alexithymia would influence distress through emotion regulation strategies, and that combinations of different factors of alexithymia would have different ramifications for the type of distress experienced. We constructed a model to test the direct and indirect effects of alexithymia factors through emotion regulation strategies on depression, anxiety, and stress, and findings revealed that both hypotheses were largely supported.

The overall models which included alexithymia subscales and reappraisal and suppression as mediators were significant, and explained a sizeable proportion of variance in each distress outcome (approximately one fourth to one third). This is largely consistent with previous studies that have found associations between alexithymia and emotion regulation in various populations (43–45), including those with clinically significant depression (46), anxiety (47) and stress (48). This is also consistent with previous literature indicating the direction of effects of reappraisal and suppression, with increased alexithymia associated with increased suppression and decreased reappraisal capability (21). This lends evidence to the interpretation that increased alexithymia is associated with poorer emotion regulation (20), and potentially an increased use of maladaptive strategies, such as suppression (49). One key aspect of our model is that it included individual subscales rather than a total alexithymia score. The inclusion of these subscales allowed for detailed investigations into pathways that may underlie types of distress, including depression, anxiety, and stress.

Correlations and direct effects revealed that alexithymia factors showed nuanced associations with types of distress, as predicted by our previously published model (19). Depression, anxiety, and stress were positively associated with more difficulty verbalizing and identifying emotion, consistent with previous findings in distress and alexithymia (9, 28). However, certain subscales were negatively associated, including emotionalizing and fantasizing. The negative association with emotionalizing may suggest a tendency to avoid emotion or recognition of negative emotion in distressed individuals, similar to work in individuals with alexithymia who demonstrated increased incidence of displacement behaviors, such as self-grooming and scratching, during stressful interviews (50). The negative association with fantasizing may speak to alterations in patterns of rumination on negative emotion, which has been seen previously in evaluations of alexithymia and effects on emotional responding (51). Future work should further explore these subscales and their associations with symptomatology in clinical populations.

Further, direct effects within the model revealed slightly different patterns of associations between alexithymia subscales and depression, anxiety, and stress. All three subscales of the DASS were negatively associated with emotionalizing and fantasizing, perhaps suggesting that reduced rumination associated with fantasizing and reduced emotional intensity are associated with distress as described above. Depression was not associated with identifying directly but was associated with verbalizing, consistent with observations in previous samples of individuals with depression (52). Other work has also demonstrated that the describing emotion subscale of the TAS-20 was associated with depression specifically (8), consistent with our findings. Anxiety was not associated with verbalizing directly but was associated with worse identifying, which is consistent with work that has found that difficulty identifying emotion may worsen anxiety about bodily sensations, as suggested by De Berardis et al., (53). It is also consistent with work that found that difficulty identifying emotion may preclude regulation of anxiety (47). Finally, stress was associated with both identifying and verbalizing emotion. As it has been seen that alexithymia in multiple disorders or during stressful life events has deleterious effects on quality of life (4, 9, 54), it is possible that difficulty identifying and verbalizing emotions may worsen stress experience during high-stress events. Indeed, alexithymia has previously been associated with higher reported stress (55).

Proper emotion regulation may rely on intact ability to identify and describe one’s emotions, as well as the ability to accurately determine what may have triggered the emotion itself, as described in previous models of emotional identification and regulation (19, 56). Gross’s process model of emotion regulation also emphasizes the importance of emotion identification as well as proper selection of regulation strategies, such as reappraisal or suppression (16). Specific factors of alexithymia may impair emotional regulation via impairment of the identification processes or by impairing selection of regulation strategies (20). The statistical model examined here allowed for the examination of direct associations between subscales of alexithymia with reappraisal and suppression, as well as the indirect associations of subscales of alexithymia through reappraisal and suppression on depression, anxiety, and stress. The negative relationships observed between alexithymia subscales and reappraisal are consistent with models that hypothesize that poorer ability to identify emotion impairs reappraisal. Suppression, however, was positively associated with most alexithymia subscales with the exception of fantasizing, reinforcing that this strategy is maladaptive (57). This finding also reinforces the idea that alexithymia is associated with poorer selection of emotional regulation strategies, a key factor in the process model (16).

Indirect effects of the BVAQ subscales through reappraisal and suppression also revealed slightly different patterns of significance for depression, anxiety, and stress notably that associations between subscales and stress were not mediated by suppression. The indirect pathway through reappraisal was significant for all DASS subscales , suggesting that a potential mechanism of these elements of distress is failure to reappraise emotion due to difficulty identifying it as well as difficulty verbalizing it. This is again consistent with the model that intact emotional identification of the self is necessary for regulation (19), and with models of emotion regulation that emphasize identification of one’s own emotions as an important step (16). In addition, there were similar patterns of indirect effects through suppression for verbalizing, emotionalizing and analyzing for anxiety and depression, suggesting that suppression of emotions mediates the effect of these subscales on these measures of distress. Given noted associations, it is possible that individuals with poorer ability to describe emotions, who do not ruminate on them, and experience less emotional intensity are also more likely to suppress emotion, contributing to more severe depression and anxiety, though this indirect association was not seen for stress.

Strengths of this work include the large sample size and the use of a theory-informed statistical model to examine alexithymia and subscales of alexithymia and associations with emotion regulation and distress, notably depression, anxiety, and stress. Weaknesses include wholly online survey methodology, although we followed a careful analysis plan to ensure responses were valid, as described in previous published work by our group (33, 35). Another weakness was the low internal consistency for emotionalizing, and future work should further investigate these factors in larger samples. The sample also spanned a very large age range, and effects of age should be examined in larger samples.

In conclusion, it is clear that pre-existing models of alexithymia are accurate in describing its role in emotion regulation and distress, and aspects of alexithymia, identified via subscales of the construct, may play specific roles in anxiety, depression, and stress, either directly or through associations with emotion regulation strategies. This supports models of emotional identification that emphasize the importance of different factors and processes that ultimately allow individuals to identify emotions and regulate them in a healthy way. It also sets the stage for future work that further examines alexithymia and constituent subscales in clinical populations, and may help to identify specific treatment plans for different mental health difficulties.

Table 2.

Direct Effects
95% CI
b Lower Upper SE p β
Predictor Outcome
Verbalizing Depression 0.12 0.03 0.23 0.05 0.012 0.16
Fantasizing Depression −0.12 −0.19 −0.06 0.03 0.001 −0.17
Identifying Depression 0.08 −0.04 0.17 0.05 0.118 0.11
Emotionalizing Depression −0.15 −0.24 −0.07 0.04 0.001 −0.19
Analyzing Depression −0.05 −0.18 0.10 0.07 0.502 −0.05
Verbalizing Anxiety 0.08 −0.01 0.17 0.04 0.074 0.14
Fantasizing Anxiety −0.07 −0.11 −0.02 0.02 0.002 −0.14
Identifying Anxiety 0.09 0.02 0.16 0.04 0.008 0.16
Emotionalizing Anxiety −0.11 −0.18 −0.05 0.03 0.001 −0.19
Analyzing Anxiety −0.06 −0.16 0.04 0.05 0.248 −0.08
Verbalizing Stress 0.13 0.03 0.24 0.05 0.017 0.19
Fantasizing Stress −0.12 −0.18 −0.06 0.03 0.002 −0.21
Identifying Stress 0.12 0.03 0.21 0.05 0.010 0.18
Emotionalizing Stress −0.13 −0.20 −0.05 0.04 0.002 −0.19
Analyzing Stress −0.07 −0.17 0.05 0.06 0.307 −0.07
Open in a new tab

Table 3.

Indirect Effects
95% CI
b Lower Upper SE p
Predictor Mediator Outcome
Verbalizing Reappraisal Depression 0.04 0.02 0.07 0.01 0.001
Fantasizing Reappraisal Depression 0.004 −0.01 0.03 0.01 0.636
Identifying Reappraisal Depression 0.03 0.01 0.07 0.02 0.003
Emotionalizing Reappraisal Depression −0.02 −0.06 0.01 0.02 0.10
Analyzing Reappraisal Depression 0.03 −0.01 0.07 0.02 0.11
Verbalizing Suppression Depression 0.09 0.03 0.15 0.03 0.003
Fantasizing Suppression Depression 0.002 −0.01 0.02 0.01 0.643
Identifying Suppression Depression 0.002 −0.02 0.02 0.01 0.674
Emotionalizing Suppression Depression 0.03 0.01 0.07 0.01 0.001
Analyzing Suppression Depression 0.03 0.01 0.07 0.01 0.003
Verbalizing Reappraisal Anxiety 0.01 0.001 0.03 0.01 0.025
Fantasizing Reappraisal Anxiety 0.001 −0.004 0.01 0.004 0.526
Identifying Reappraisal Anxiety 0.01 0.001 0.03 0.01 0.029
Emotionalizing Reappraisal Anxiety −0.01 −0.02 0.001 0.01 0.080
Analyzing Reappraisal Anxiety 0.01 −0.002 0.03 0.01 0.119
Verbalizing Suppression Anxiety 0.04 0.001 0.09 0.02 0.043
Fantasizing Suppression Anxiety 0.001 −0.004 0.01 0.003 0.466
Identifying Suppression Anxiety 0.001 −0.01 0.01 0.004 0.512
Emotionalizing Suppression Anxiety 0.02 0.001 0.04 0.01 0.029
Analyzing Suppression Anxiety 0.01 0.001 0.05 0.01 0.028
Verbalizing Reappraisal Stress 0.02 0.01 0.05 0.01 0.002
Fantasizing Reappraisal Stress 0.002 −0.01 0.02 0.01 0.619
Identifying Reappraisal Stress 0.02 0.003 0.04 0.01 0.006
Emotionalizing Reappraisal Stress −0.01 −0.03 0.002 0.01 0.076
Analyzing Reappraisal Stress 0.01 −0.002 0.04 0.01 0.097
Verbalizing Suppression Stress 0.04 −0.01 0.08 0.02 0.104
Fantasizing Suppression Stress 0.001 −0.003 0.01 0.003 0.415
Identifying Suppression Stress 0.001 −0.01 0.01 0.004 0.460
Emotionalizing Suppression Stress 0.01 −0.001 0.04 0.01 0.080
Analyzing Suppression Stress 0.01 <0.001 0.04 0.01 0.061
Open in a new tab

Highlights.

Mediation models were constructed for depression, anxiety and stress, with alexithymia factors as predictors and suppression and reappraisal tested as mediators.

37.3% of the variance in depression, 25.2% variance in anxiety, and 35.3% variance in stress was explained by each model.

Direct associations revealed different associations of alexithymia factors with depression, anxiety and stress

Indirect effects indicated different patterns of alexithymia factors through reappraisal and suppression on depression, anxiety and stress.

Findings support the importance of examining multiple factors of alexithymia

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  • 1.Taylor GJ. Alexithymia: concept, measurement, and implications for treatment. Am J Psychiatry. 1984. Jun;141(6):725–32. [DOI] [PubMed] [Google Scholar]
  • 2.Tolmunen T, Heliste M, Lehto SM, Hintikka J, Honkalampi K, Kauhanen J. Stability of alexithymia in the general population: an 11-year follow-up. Compr Psychiatry. 2011. Sep-Oct;52(5):536–41. [DOI] [PubMed] [Google Scholar]
  • 3.Honkalampi K, Hintikka J, Tanskanen A, Lehtonen J, Viinamaki H. Depression is strongly associated with alexithymia in the general population. J Psychosom Res. 2000. Jan;48(1):99–104. [DOI] [PubMed] [Google Scholar]
  • 4.Wang X, Li X, Guo C, Hu Y, Xia L, Geng F, et al. Prevalence and Correlates of Alexithymia and Its Relationship With Life Events in Chinese Adolescents With Depression During the COVID-19 Pandemic. Front Psychiatry. 2021;12:774952. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Karukivi M, Hautala L, Kaleva O, Haapasalo-Pesu KM, Liuksila PR, Joukamaa M, et al. Alexithymia is associated with anxiety among adolescents. J Affect Disorders. 2010. Sep;125(1–3):383–7. [DOI] [PubMed] [Google Scholar]
  • 6.Morie KP, Yip SW, Nich C, Hunkele K, Carroll KM, Potenza MN. Alexithymia and Addiction: A Review and Preliminary Data Suggesting Neurobiological Links to Reward/Loss Processing. Curr Addict Rep. 2016. Jun;3(2):239–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Haviland MG, Hendryx MS, Shaw DG, Henry JP. Alexithymia in women and men hospitalized for psychoactive substance dependence. Compr Psychiat. 1994. Mar-Apr;35(2):124–8. [DOI] [PubMed] [Google Scholar]
  • 8.Leweke F, Leichsenring F, Kruse J, Hermes S. Is alexithymia associated with specific mental disorders? Psychopathology. 2012;45(1):22–8. [DOI] [PubMed] [Google Scholar]
  • 9.La Rosa VL, Gori A, Faraci P, Vicario CM, Craparo G. Traumatic Distress, Alexithymia, Dissociation, and Risk of Addiction During the First Wave of COVID-19 in Italy: Results from a Cross-sectional Online Survey on a Non-clinical Adult Sample. Int J Ment Health Addict. 2022;20(5):3128–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Davey S, Halberstadt J, Bell E, Collings S. A scoping review of suicidality and alexithymia: The need to consider interoception. J Affect Disord. 2018. Oct 1;238:424–41. [DOI] [PubMed] [Google Scholar]
  • 11.Hemming L, Haddock G, Shaw J, Pratt D. Alexithymia and Its Associations With Depression, Suicidality, and Aggression: An Overview of the Literature. Front Psychiatry. 2019;10:203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Vanheule S, Vandenbergen J, Verhaeghe P, Desmet M. Interpersonal problems in alexithymia: A study in three primary care groups. Psychol Psychother. 2010. Feb 11. [DOI] [PubMed] [Google Scholar]
  • 13.Talebi Joybari M. Depression and interpersonal problems in adolescents: their relationship with alexithymia and coping styles. Iran J Psychiatry Behav Sci. 2014. Winter;8(4):38–45. [PMC free article] [PubMed] [Google Scholar]
  • 14.Shim EJ, Park A, Park SP. The relationship between alexithymia and headache impact: the role of somatization and pain catastrophizing. Qual Life Res. 2018. Sep;27(9):2283–94. [DOI] [PubMed] [Google Scholar]
  • 15.Vieira RV, Vieira DC, Gomes WB, Gauer G. Alexithymia and its impact on quality of life in a group of Brazilian women with migraine without aura. J Headache Pain. 2013. Feb 25;14(1):18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Gross JJ. Emotion Regulation: Current Status and Future Prospects. Psychol Inq. 2015. Jan 2;26(1):1–26. [Google Scholar]
  • 17.Goldin PR, McRae K, Ramel W, Gross JJ. The neural bases of emotion regulation: reappraisal and suppression of negative emotion. Biol Psychiatry. 2008. Mar 15;63(6):577–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Gross JJ, John OP. Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. J Pers Soc Psychol. 2003. Aug;85(2):348–62. [DOI] [PubMed] [Google Scholar]
  • 19.Morie KP, Crowley MJ, Mayes LC, Potenza MN. The process of emotion identification: Considerations for psychiatric disorders. J Psychiatr Res. 2022. Apr;148:264–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Preece DA, Mehta A, Becerra R, Chen W, Allan A, Robinson K, et al. Why is alexithymia a risk factor for affective disorder symptoms? The role of emotion regulation. J Affect Disord. 2022. Jan 1;296:337–41. [DOI] [PubMed] [Google Scholar]
  • 21.Laloyaux J, Fantini C, Lemaire M, Luminet O, Laroi F. Evidence of Contrasting Patterns for Suppression and Reappraisal Emotion Regulation Strategies in Alexithymia. The Journal of nervous and mental disease. 2015. Sep;203(9):709–17. [DOI] [PubMed] [Google Scholar]
  • 22.Preece DA, Mehta A, Petrova K, Sikka P, Bjureberg J, Becerra R, et al. Alexithymia and emotion regulation. J Affect Disord. 2023. Mar 1;324:232–8. [DOI] [PubMed] [Google Scholar]
  • 23.Alkan H E, Crayen C, Heuser I, Eid M. It’s in the mix: psychological distress differs between combinations of alexithymic facets. Front Psychol. 2014;5:1259. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Chen J, Xu T, Jing J, Chan RC. Alexithymia and emotional regulation: A cluster analytical approach. BMC Psychiatry. 2011. Feb 23;11:33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Arvidsdotter T, Marklund B, Kylen S, Taft C, Ekman I. Understanding persons with psychological distress in primary health care. Scand J Caring Sci. 2016. Dec;30(4):687–94. [DOI] [PubMed] [Google Scholar]
  • 26.Bagby RM, Parker JD, Taylor GJ. The twenty-item Toronto Alexithymia Scale--I. Item selection and cross-validation of the factor structure. J Psychosom Res. 1994. Jan;38(1):23–32. [DOI] [PubMed] [Google Scholar]
  • 27.Sauvage L, Loas G. Criterion validity of Bermond-Vorst Alexithymia Questionnaire-20 Form B: a study of 63 alcoholic subjects. Psychol Rep. 2006. Feb;98(1):234–6. [DOI] [PubMed] [Google Scholar]
  • 28.Preece DA, Becerra R, Boyes Mark E. , Chelsea Northcott, Lauren McGillivray, Penelope A. Hasking Do self-report measures of alexithymia measure alexithymia or general psychological distress? A factor analytic examination across five samples. Personality and Individual Differences. 2020;155. [Google Scholar]
  • 29.Gloster AT, Rhoades HM, Novy D, Klotsche J, Senior A, Kunik M, et al. Psychometric properties of the Depression Anxiety and Stress Scale-21 in older primary care patients. J Affect Disord. 2008. Oct;110(3):248–59. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Crawford JR, Henry JD. The Depression Anxiety Stress Scales (DASS): normative data and latent structure in a large non-clinical sample. Br J Clin Psychol. 2003. Jun;42(Pt 2):111–31. [DOI] [PubMed] [Google Scholar]
  • 31.Brown TA, Chorpita BF, Korotitsch W, Barlow DH. Psychometric properties of the Depression Anxiety Stress Scales (DASS) in clinical samples. Behaviour research and therapy. 1997. Jan;35(1):79–89. [DOI] [PubMed] [Google Scholar]
  • 32.Lovibond PF, Lovibond SH. The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour research and therapy. 1995. Mar;33(3):335–43. [DOI] [PubMed] [Google Scholar]
  • 33.Grella ON, Dunlap A, Nicholson AM, Stevens K, Pittman B, Corbera S, et al. Personality as a mediator of autistic traits and internalizing symptoms in two community samples. BMC Psychol. 2022. Mar 28;10(1):81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Bilker WB, Hansen JA, Brensinger CM, Richard J, Gur RE, Gur RC. Development of abbreviated nine-item forms of the Raven’s standard progressive matrices test. Assessment. 2012. Sep;19(3):354–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Diefenbach GJ, Stevens KT, Dunlap A, Nicholson AM, Grella ON, Pearlson G, et al. Autistic Traits Moderate Reappraisal Success for Depression and Anxiety Symptoms. Journal of autism and developmental disorders. 2022. Apr;52(4):1435–43. [DOI] [PubMed] [Google Scholar]
  • 36.Morera OF, Culhane SE, Watson PJ, Skewes MC. Assessing the reliability and validity of the Bermond-Vorst Alexithymia Questionnaire among U.S. Anglo and U.S. Hispanic samples. Journal of psychosomatic research. 2005. Mar;58(3):289–98. [DOI] [PubMed] [Google Scholar]
  • 37.Vorst C, Bermond B. Validity and reliability of the Bermond–Vorst Alexithymia Questionnaire. Personality and Individual Differences. 2001;30(3). [Google Scholar]
  • 38.Gross JJ, John OP. Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. Journal of Personality and Social Psychology. 2003;85:348–62. [DOI] [PubMed] [Google Scholar]
  • 39.Preece DA, Becerra R, Robinson K, Gross JJ. The Emotion Regulation Questionnaire: Psychometric Properties in General Community Samples. Journal of Personality Assessment. 2020;102(3):348–56. [DOI] [PubMed] [Google Scholar]
  • 40.Lovibond SH, Lovibond PF. Manual for the Depression Anxiety Stress Scales. Sydney: The Psychology Foundation of Australia; 1995. [Google Scholar]
  • 41.Brown TA, Chorpita BF, Korotitsch W, Barlow DH. Psychometric properties of the Depression Anxiety Stress Scales (DASS) in clinical samples. Behaviour Research and Therapy. 1997;35:79–89. [DOI] [PubMed] [Google Scholar]
  • 42.Park SH, Song YJC, Demetriou EA, Pepper KL, Thomas EE, Hickie IB, et al. Validation of the 21-item Depression, Anxiety, and Stress Scales (DASS-21) in individuals with autism spectrum disorder. Psychiatry Res. 2020. Sep;291:113300. [DOI] [PubMed] [Google Scholar]
  • 43.Morie KP, Zhai ZW, Potenza MN, Mayes LC. Alexithymia, Emotion-Regulation Strategies, and Traumatic Experiences in Prenatally Cocaine-Exposed Young Adults. Am J Addict. 2020. May 20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Morie KP, Jackson S, Zhai ZW, Potenza MN, Dritschel B. Mood Disorders in High-Functioning Autism: The Importance of Alexithymia and Emotional Regulation. Journal of autism and developmental disorders. 2019. Apr 26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Venta A, Hart J, Sharp C. The relation between experiential avoidance, alexithymia and emotion regulation in inpatient adolescents. Clin Child Psychol P. 2013. Jul;18(3):398–410. [DOI] [PubMed] [Google Scholar]
  • 46.Lumley MA, Smith JA, Longo DJ. The relationship of alexithymia to pain severity and impairment among patients with chronic myofascial pain - Comparisons with self-efficacy, catastrophizing, and depression. Journal of psychosomatic research. 2002. Sep;53(3):823–30. [DOI] [PubMed] [Google Scholar]
  • 47.Oussi A, Hamid K, Bouvet C. Managing emotions in panic disorder: A systematic review of studies related to emotional intelligence, alexithymia, emotion regulation, and coping. J Behav Ther Exp Psychiatry. 2023. Jun;79:101835. [DOI] [PubMed] [Google Scholar]
  • 48.Mail Gurkan Z, Sengul Y, Guven Ekiz T, Tantik Pak A. Effect of alexithymia and difficulty of emotion regulation, neuroticism, low extraversion, and suicidality on quality of life in epilepsy. Epilepsy Behav. 2022. Oct;135:108887. [DOI] [PubMed] [Google Scholar]
  • 49.Sfarlea A, Dehning S, Keller LK, Schulte-Korne G. Alexithymia predicts maladaptive but not adaptive emotion regulation strategies in adolescent girls with anorexia nervosa or depression. J Eat Disord. 2019;7:41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Troisi A, Belsanti S, Bucci AR, Mosco C, Sinti F, Verucci M. Affect regulation in alexithymia: an ethological study of displacement behavior during psychiatric interviews. The Journal of nervous and mental disease. 2000. Jan;188(1):13–8. [DOI] [PubMed] [Google Scholar]
  • 51.Luminet O, Rime B, Bagy MR, Taylor G. A multimodal investigation of emotional responding in alexithymia. Cognition Emotion. 2010. [Google Scholar]
  • 52.Willroth EC, Flett JAM, Mauss IB. Depressive symptoms and deficits in stress-reactive negative, positive, and within-emotion-category differentiation: A daily diary study. Journal of personality. 2020. Apr;88(2):174–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.De Berardis D, Campanella D, Gambi F, La Rovere R, Sepede G, Core L, et al. Alexithymia, fear of bodily sensations, and somatosensory amplification in young outpatients with panic disorder. Psychosomatics. 2007. May-Jun;48(3):239–46. [DOI] [PubMed] [Google Scholar]
  • 54.Linn BK, Zhao J, Bradizza CM, Lucke JF, Ruszczyk MU, Stasiewicz PR. Alexithymia disrupts emotion regulation processes and is associated with greater negative affect and alcohol problems. J Clin Psychol. 2021. Dec;77(12):2915–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Connelly M, Denney DR. Regulation of emotions during experimental stress in alexithymia. Journal of psychosomatic research. 2007. Jun;62(6):649–56. [DOI] [PubMed] [Google Scholar]
  • 56.Scherer KR. Studying appraisal-driven emotion processes: taking stock and moving to the future. Cogn Emot. 2019. Feb;33(1):31–40. [DOI] [PubMed] [Google Scholar]
  • 57.Richmond S, Hasking P, Meaney R. Psychological Distress and Non-Suicidal Self-Injury: The Mediating Roles of Rumination, Cognitive Reappraisal, and Expressive Suppression. Arch Suicide Res. 2017. Jan 02;21(1):62–72. [DOI] [PubMed] [Google Scholar]

RESOURCES