This study aimed to identify emotion regulation (ER) profiles in two age-differentiated samples and examine their association with psychological functioning, while investigating the predictive role of attachment in profile membership.
Methods
One sample with 384 emerging adults (Mage = 22.66; SD = 3.00; 79% women) and one sample with 216 primarily middle-aged adults (Mage = 45.21; SD = 8.53; 74% women) were used.
Results
Three profiles were identified within each sample; two profiles were consistent, while one profile differed. In Sample 1, the profiles were (1) ‘Suppressive Regulators’, (2) ‘Mixed Regulators’, and (3) ‘Expressive Regulators’. In Sample 2, an ‘Adaptive Regulators’ profile replaced the ‘Suppressive Regulators’ profile. ‘Suppressive Regulators’ in Sample 1 and ‘Mixed Regulators’ in Sample 2 showed higher levels of depression, anxiety, stress, whereas ‘Expressive Regulators’ demonstrated lower levels of these dimensions. In Sample 1, both attachment significantly predicted profile membership, with higher insecure attachment increasing the likelihood of belonging to the ‘Suppressive Regulators’ profile. In Sample 2, only attachment avoidance emerged as a significant predictor.
Conclusions
These findings highlight the importance of identifying unique ER profiles due its impact on psychological functioning, considering developmental stages and underscore the role of attachment dimensions in shaping regulatory patterns.
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Introduction
Emotion Regulation and its Correlates
Emotion regulation (ER) involves the processes by which individuals influence their emotional experience, expression, and modulation in response to internal and external demands (Gross, 1998, 2015a). A central framework, the Process Model of Emotion Regulation (Gross, 1998, 2015b), distinguishes between antecedent-focused strategies (e.g., cognitive reappraisal), which occur before an emotion is fully generated, and response-focused strategies (e.g., expressive suppression), which occur afterward. Antecedent-focused strategies generally relate to better psychological outcomes (Gross, 2015a; Shum et al., 2024; Stover et al., 2024), while response-focused approaches, such as expressive suppression, have been linked to increased physiological arousal, reduced social connectedness, and poorer emotional health (Chervonsky & Hunt, 2017; Tyra et al., 2024).
While cognitive reappraisal is generally considered an adaptive strategy and suppression is often viewed as maladaptive one, emerging research challenges this simplistic classification. Previous studies suggest that the benefits of cognitive reappraisal may vary depending on individuals' regulation skills and the controllability or intensity of stressors (e.g., Troy et al., 2013; Ford & Troy, 2019). Similarly, research on expressive suppression suggests that it is not inherently maladaptive and can serve important functions depending on context, culture, and individual differences (e.g., Bonanno et al., 2004; Doulougeri et al., 2016; Wobeto et al., 2022). Thus, regulatory flexibility, frequency of use, personal goals, and context play crucial roles in determining their effectiveness (Bonanno & Burton, 2013; McRae et al., 2016; Tamir & Hu, 2024).
From a cognitive perspective, the Cognitive Emotion Regulation Model (CER; Garnefski & Kraaij, 2007) provides a framework for understanding how individuals use cognitive strategies to regulate emotions. Within this model, rumination and positive reappraisal are two important cognitive ER strategies. Rumination, characterized by repetitive and passive focus on negative emotions and their causes, is considered a maladaptive cognitive strategy associated with increased vulnerability to psychological disorders including depression or eating disorders (McLaughlin & Nolen-Hoeksema, 2011; Rickerby et al., 2024). Positive reappraisal, on the other hand, involves actively restructuring one’s interpretation of a situation to find meaning or benefit, contributing to emotional adaptation and resilience (Riepenhausen et al., 2022).
Beyond individual cognitive and behavioral strategies, ER also occurs in interpersonal contexts, where emotions are modulated through social interactions. Interpersonal ER models (Hofmann, 2014; Rimé, 2024; Zaki & Williams, 2013) focus on how individuals regulate emotions by engaging with others, a process that is particularly relevant to emotion communication. Communicating/expressing emotions to others serves various regulatory functions, such as venting, seeking social support and empathy, and/or gaining perspective and advice (see Rimé, 2024 for details). Although theories often assume that expressive suppression and emotion expression/communication are two opposing aspects of the same process, they likely function independently, each influencing personal and interpersonal emotional dynamics and outcomes (Cameron & Overall, 2018).
ER plays a fundamental role in shaping mental health outcomes, influencing how individuals manage emotional experiences and respond to stressors. Research consistently demonstrates that effective ER is associated with greater psychological well-being, whereas difficulties in regulating emotions contribute to a heightened risk of psychopathology, including depression, anxiety, and stress (e.g., Hu et al., 2014; Schäfer et al., 2017; Tamir et al., 2024).
Additionally, interpersonal ER strategies have been differentially associated with psychological outcomes. For instance, soothing and social modelling have been linked to self-reported psychopathology and greater difficulties in interpersonal ER, whereas perspective taking is positively associated with adaptive intra-personal ER strategies (Messina et al., 2022a). Furthermore, increased use of venting and reassurance-seeking has been associated with more psychopathological symptoms (Messina et al., 2022b). Interestingly, reassurance-seeking also emerged as a protective factor, positively predicting social support and indirectly reducing burnout, while venting was found to exacerbate burnout (Messina et al., 2025).
Individual Differences in Emotion Regulation
Attachment theory provides a well-established framework for understanding variability in ER (Mikulincer & Shaver, 2019). Early attachment experiences shape internal working models of self and others, which in turn influence how individuals manage emotional experiences throughout life (Collins, 1996; Hazan & Shaver, 1987). Secure attachment, which develops through consistent interactions with supportive and responsive attachment figures, is associated with greater flexibility and adaptability in ER; individuals with secure attachment are better able to process emotions constructively and effectively seek social support when needed (Domic-Siede et al., 2024; Eilert & Buchheim, 2023).
In contrast, insecure attachment, which results from interactions with attachment figures who are either unresponsive, unsupportive, or inconsistent in their responsiveness, is linked to maladaptive ER patterns; these individuals may struggle with emotional dysregulation, heightened reactivity, and difficulties in seeking or utilizing social support, which can contribute to increased psychological distress (Domic-Siede et al., 2024; Eilert & Buchheim, 2023). Specifically, individuals with anxious attachment are more likely to employ strategies aimed at drawing attention from others, such as intensified emotional expression, magnifying perceived threats, heightened focus on physiological sensations associated with emotions, and persistent rumination (e.g., Karreman & Vingerhoets, 2012; Mikulincer & Shaver, 2019; Winterheld, 2016). Differently, individuals with avoidant attachment tend to rely on emotion suppression strategies to keep their attachment needs deactivated and minimize the risk of rejection. These strategies often include expressive suppression and attentional diversion (e.g., Karreman & Vingerhoets, 2012; Mikulincer & Shaver, 2019; Winterheld, 2016). Additionally, attachment style significantly influences interpersonal ER, with higher levels of attachment anxiety being associated with greater use of interpersonal ER strategies, while attachment avoidance tends to relate to lower engagement in such strategies (e.g., Gökdağ, 2021; Messina et al., 2023, 2024).
Age Differences in Emotion Regulation
ER changes across different life stages due to cognitive maturation, shifting social roles, evolving goals, and variations in available resources (Mikkelsen & O'Toole, 2022; Schweizer et al., 2020). In the present study, emerging adulthood is defined as ages 18–29, in line with Arnett’s et al. (2014) theory, which conceptualizes this period as a distinct developmental stage marked by identity exploration, instability, and ongoing transition into adult roles, creating a heightened need for effective ER. This age range is also supported by neurodevelopmental research indicating that brain regions involved in impulse control and ER continue to mature throughout this phase (Hochberg & Konner, 2020).
Research shows that emerging adults often struggle with ER, displaying issues like anger dysregulation, fear suppression, and avoidant responses to sadness (Zimmermann & Iwanski, 2014). Late adolescents and young adults also tend to use fewer adaptive cognitive strategies (e.g., positive reappraisal) compared to adults (Garnefski & Kraaij, 2006), and they may rely more on self-blame and rumination than middle-aged adults (Bailly et al., 2022). Other studies highlight specific strategies that favor middle-aged adults, such as situation modification (e.g., seeking social support) and attentional deployment (e.g., religious coping), while reporting no substantial differences in response modulation strategies (Puente-Martínez et al., 2021). This pattern aligns with the idea that age-specific resources, such as greater social support in adulthood, can facilitate more adaptive ER (Urry & Gross, 2010). Livingstone and Isaacowitz (2021) further indicate that middle-aged adults often employ situation selection or modification to avoid negative experiences, whereas younger adults may be more likely to seek out or intensify such situations.
Despite evidence suggesting that ER generally improves with age, results remain mixed. Some studies show aging is tied to increased adaptive strategies due to accumulated experience and learning (English & Carstensen, 2013), as well as decreased reliance on costly strategies like expressive suppression (Eldesouky & English, 2018a, 2018b). However, other research suggests no significant differences or contradictory trends. For instance, Nolen-Hoeksema and Aldao (2011) found that most ER strategies decline with age, except for suppression and acceptance, which remain stable or increase. Likewise, Brummer et al. (2014) reported no significant differences in suppression between young and middle-aged adults.
While prior research on ER profiles has not examined age-related variations, a study by Livingstone et al. (2018) investigated ER beliefs profiles across different age groups. Their findings revealed that middle-aged and older adults were more likely to be classified as "Classically Adaptive Regulators," indicating a preference for a variety of situation-based and cognitive ER strategies. In contrast, younger adults were more likely to be classified as "Avoiders/Ruminators" or "Situation Modifiers”. Given that beliefs about emotion are likely to influence the selection and use of ER strategies (e.g., Ford & Gross, 2019), it is reasonable to expect that ER profiles may also vary across age groups.
Latent Profile Analysis (LPA) of Emotion Regulation
Traditionally, ER has been examined through variable-centered approaches, treating strategies (e.g., cognitive reappraisal, suppression) as independent constructs linked to mental health outcomes (Aldao et al., 2010; Cameron & Overall, 2018). However, this fails to capture the complexity of real-world ER, where individuals often use multiple strategies simultaneously in context-dependent ways (Aldao & Tull, 2015; Bonanno & Burton, 2013).
A growing body of research now advocates for person-centered approaches, recognizing that ER is a flexible system varying across individuals and contexts (Bonanno & Burton, 2013; Eldesouky & English, 2018a, 2018b; Ford et al., 2019; Puente-Martínez et al., 2021). This shift has led to the increased use of cluster and profile analyses for identifying ER profiles (Chesney et al., 2019; Moreira et al., 2024), which reveal naturally occurring strategy combinations. Such analyses also capture mixed profiles, where individuals exhibit both adaptive and maladaptive tendencies (Bonanno & Burton, 2013; Eldesouky & English, 2018a, 2018b).
Profile-based studies highlight considerable heterogeneity in ER patterns, identifying two or three profiles in some cases (Chen et al., 2018; Guérin-Marion et al., 2021 and four or more in others (Baziliansky & Cohen, 2021; Moumne et al., 2021; Pinto et al., 2022; Specker & Nickerson, 2019; Weiss et al., 2018). They also show that profiles high in adaptive strategies or low in dysregulation tend to be associated with better outcomes (e.g., resilience, psychological well-being), whereas maladaptive or high-dysregulation profiles are linked to greater distress (Baziliansky & Cohen, 2021; Moumne et al., 2021; Pinto et al., 2022).
Study Objectives
This study aims to (1) identify latent ER profiles based on four strategies (expressive suppression, rumination, emotion communication, and positive reappraisal) across two age cohorts (emerging adults aged 18–30 and adults aged 30 +); (2) investigate how these profiles relate to psychological outcomes (depression, anxiety, stress, and burnout), and (3) examine how attachment orientation predicts profile membership, controlling for age and sex.
We hypothesized that: (1) distinct ER profiles will emerge based on individuals’ use of expressive suppression, rumination, emotion communication, and positive reappraisal (Baziliansky & Cohen, 2021; Moumne et al., 2021; Pinto et al., 2022; Specker & Nickerson, 2019; Weiss et al., 2018); (2) the ER profiles emerged will differ across the two age cohorts, with emerging adults more likely to exhibit profiles characterized by greater emotional variability or maladaptive strategies, and older adults more likely to show adaptive profiles (Livingstone et al., 2018); (3) ER profiles will be significantly associated with psychological outcomes, such that individuals in maladaptive ER profiles will report higher levels of depression, anxiety, stress, and burnout compared to those in more adaptive profiles (Baziliansky & Cohen, 2021; Moumne et al., 2021; Pinto et al., 2022); and (4) attachment orientations (anxiety and avoidance) will significantly predict ER profile membership, with anxious attachment associated with profiles marked by high rumination and emotional expression, and avoidant attachment linked to higher suppression and lower emotional communication (Gökdağ, 2021; Messina et al., 2023, 2024).
A priori sample size calculations were not conducted. A total of 384 emerging adults participated in this study. No exclusions were made, and there were no missing data in the dataset. Most were women (78.9%; n = 303) with a mean age of 22.66 years (SD = 3.00; Min = 18, Max = 30; Mdn = 22). Most were single (90.5%) but around 55% were involved in a romantic relationship. From these, 66.5% (n = 254) are university students.
Sample 2
A priori sample size calculations were not conducted. A total of 216 middle-aged adults participated in this study. No exclusions were made, and there were no missing data in the dataset. Most were women (74.1%; n = 160) with a mean age of 45.21 years (SD = 8.53; Min = 31, Max = 70; Mdn = 45). Most were married (61.3%), around 73% were involved in a romantic relationship. Most hold a university degree (62%).
Measures
Emotion regulation
Expressive suppression was measured using The Emotion Regulation Questionnaire (ERQ; Gross & John, 2003). The ERQ is a 10-item self-report instrument designed to assess individual differences in ER strategies, particularly cognitive reappraisal (six items) and expressive suppression (4 items). Each item is rated on a 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). For this study, only the expressive suppression subscale was used. The Cronbach’s alpha for expressive suppression was 0.78 for Sample 1 and 0.81 for Sample 2.
Emotion communication was measured using the Stanford Emotional Self-Efficacy Scale (SESES; Giese-Davis et al., 2004). The SESES assesses an individual's perceived ability to regulate, express, and manage emotions effectively in different contexts. It has 15 items covering three dimensions. For this study, only the Emotion Communication subscale was used. It measures self-efficacy in expressing emotions to others in a clear and socially appropriate manner (5 items). Items are rated on a Likert scale, ranging from 1 (not at all confident) to 7 (completely confident). The Cronbach’s alpha for emotion communication was 0.71 for both samples.
Positive reappraisal and rumination were measured using the Cognitive Emotion Regulation Questionnaire (CERQ; Garnefski et al., 2002). The CERQ is a 36-item self-report scale designed to assess nine cognitive strategies individuals use to regulate their emotions following negative experiences. For this study, only two dimensions were measured: positive reappraisal that refers to the ability to reinterpret a negative event in a way that increases its perceived meaning, promotes growth, and fosters a positive perspective (4 items); and rumination that refers to the repetitive and passive focus on the negative aspects of a distressing event, leading to prolonged emotional distress and difficulty moving forward (4 items). Items are rated on a Likert scale, ranging from 1 (‘almost’ never) to 5 (‘almost’ always). The Cronbach’s alpha for Positive Reappraisal was 0.86 in Sample 1 and 0.88 in Sample 2. Similarly, for Rumination, the reliability coefficients were 0.86 in Sample 1 and 0.88 in Sample 2.
Depression, Anxiety, and Stress
Depression, anxiety, and stress were measured using the Depression, Anxiety, and Stress Scale– 21 Items (DASS-21; Lovibond & Lovibond, 1995). It is a 21-item self-report questionnaire designed to assess three negative emotional states: Depression (7 items), Anxiety (7 items), and Stress (7 items). Each item is rated on a 4-point Likert scale, reflecting the severity/frequency of symptoms over the past week 1 (did not apply to me) to 4 (apply to me very much). The Cronbach’s alpha values for the Depression scale were 0.92 in Sample 1 and 0.88 in Sample 2. For Anxiety, the reliability coefficients were 0.86 in Sample 1 and 0.91 in Sample 2. For Stress, Cronbach’s alpha was 0.89 in Sample 1 and 0.90 in Sample 2.
Burnout
Personal and school-related burnout were measured using the Copenhagen Burnout Inventory (CBI; Kristensen et al., 2005). The CBI consists of 19 items, divided into three subscales, each measuring burnout in a different context: personal burnout (6 items); work-related burnout (7 items); and client-related burnout (6 items). Items are rated on a 5-point Likert scale, typically ranging from 0 (never/almost never) to 5 (always). For this study, participants were asked to rate only their personal burnout and their work-related burnout (in this case adjusted to measure school-related burnout—applied only to university students). The Cronbach’s alpha was 0.85 for personal burnout and 0.80 for academic-related burnout (only measured in Sample 1).
Attachment Orientations
Attachment orientations were measured using the Experiences in Close Relationships– Relationship Structures (ECR-RS; Fraley et al., 2011). The ECR-RS consists of 9 items and assesses two key dimensions of attachment across various relationship types: attachment anxiety (6 items) and attachment avoidance (3 items). Participants were instructed to identify a person with whom they have a strong emotional bond and whom they typically seek out for support, and to respond to the ECR-RS items based on that relationship. Items are rated on a 7-point Likert scale, ranging from 1 (Strongly Disagree) to 7 (Strongly Agree). The Cronbach alpha for attachment anxiety was 0.83 for both samples. The Cronbach alpha for attachment avoidance was 0.81 for Sample 1 and 0.83 for Sample 2.
Procedure
The present study utilized two independent samples. These samples were drawn from two larger studies investigating individual differences in day-to-day ER and ER in relation to burnout among academic students. Both larger studies were approved by the Ethics Committee of [removed for blind review] (References: Reference: 9-2021 and Reference 13/2021).
For both studies, data were collected between the years 2022 and 2023, online, with participants accessing the study via a web-based survey platform. Upon clicking the study link, participants were first directed to a landing page displaying the informed consent form. After providing informed consent, participants proceeded to complete the survey, which included demographic questions and measures of ER, psychological functioning, and attachment. The estimated completion time was approximately 15 min for both studies.
To recruit participants, both studies employed a convenience and snowball sampling approach. The survey link was distributed via social media platforms and email invitations targeting relevant populations. No financial compensation was offered.
Data Analytic Plan
LPA was performed using the tidyLPA package (Rosenberg et al., 2021) of snowRMM (Version 5.8.6) (Seol, 2024) in JAMOVI (Version 2.6) (R Core Team, 2024). Various model solutions, ranging from two to five profiles, were tested and evaluated. Several criteria informed the selection of the optimal LPA solution, following established guidelines (Ferguson et al., 2020; Nylund-Gibson & Choi, 2018; Spurk et al., 2020). After establishing the optimal profile solution in each sample, we explored how membership in each latent profile related to various outcome measures (e.g., depression, anxiety, stress) using Analyses of Variance (ANOVAs).
Finally, given the role of attachment orientation in individual differences in ER, we examined whether attachment anxiety and attachment avoidance predicted latent profile membership across the different samples. To do so, multinomial logistic regression was employed. The detailed analytic procedure is described in supplemental data.
Results
Latent Profiles Analysis
Descriptive statistics among study variables for Sample 1 and Sample 2 are shown in Table S1 in supplemental file. Skewness and kurtosis values suggest no severe deviation from normality.
Table 1 presents the results of the LPA. For Sample 1, based on model fit indices and interpretability, the three-class model appears to be the most balanced solution (see supplemental file for a detailed interpretation). In the three‐profile solution (see Fig. 1 and Table 2), Profile 1 (in red) was labeled “Suppressive Regulators” and shows moderately elevated suppression, suggesting that individuals in this group are more likely to regulate emotions by suppressing their emotional expressions. Their levels of rumination, positive reappraisal and emotion communication are near average, indicating a somewhat mixed regulatory approach. Profile 2 (in blue) was labeled “Mixed Regulators” as they maintain relatively stable and balanced/moderate levels across all four strategies. This suggests that individuals in this group do not rely heavily on any one strategy but instead use a more even distribution of ER techniques. Finally, Profile 3 (in green) was designated “Expressive Regulators,” characterized by the tendency to engage in emotional expression while relying less on suppression. This group sows a strong preference for openly expressing emotions rather than suppressing them, with low rumination and low positive reappraisal.
Table 1
Fit indices for the LPA models across the samples
LogLik
AIC
BIC
SABIC
CAIC
AWE
BLRT p value
ENTROPY
Proportion of each profile
Sample EA (N = 384)
Two
-2294.42
4622.83
4689.99
4636.06
4706.99
4840.51
<.01
0.82
77.6%|| 22.4%
Three
-2256.58
4565.17
4667.88
4585.39
4693.88
4899.09
<.01
0.75
19.3%|| 65.9%|| 14.8%
Four
-2234.47
4538.94
4677.21
4566.16
4712.21
4989.10
<.01
0.69
19.5%|| 19%|| 47.1%|| 14.3%
Five
-2215.55
4519.09
4692.92
4553.32
4736.92
5085.36
<.01
0.70
12.8%|| 16.4%|| 40.9%|| 12.5%|| 17.4%
Sample AD (N = 216)
Two
-1336.44
2706.88
2764.26
2710.39
2781.26
2905.88
<.01
0.71
49.5%|| 50.5%
Three
-1314.87
2681.74
2769.50
2687.11
2795.50
2985.78
<.01
0.74
40.7%|| 42.6%|| 16.7%
Four
-1272.22
2662.45
2861.59
2674.63
2920.59
3354.28
n.s
0.73
12.5%|| 11.6%|| 29.2%|| 46.8%
Five
-1283.02
2654.03
2802.54
2663.11
2846.54
3169.44
n.s
.081
30.6%|| 3.7%|| 14.8%|| 38.4%|| 12.5%
LogLik = Log-Likelihood; AIC = Akaike information criterion; BIC = Bayesian information criterion; SABIC = Sample Adjusted BIC; CAIC = Consistent Akaike Information Criterion; AWE = Approximate Weight of Evidence Criterion.; BLRT = Bootstrapped Likelihood Ratio Test
Fig. 1
Score of each profile on each dimension for Sample 1. Note. PR = positive reappraisal; ES = expressive suppression; EC = emotion communication
For Sample 2, considering model fit indices, classification quality, and interpretability, the three-class model emerges as the most balanced solution. In the three‐profile solution (see Fig. 2 and Table 2). Profile 1 (in red) was labeled “Mixed Regulators” (like profile 2 in sample 1) and is characterized by mixed/moderate levels across all four strategies. Their levels of rumination and positive reappraisal are near average, indicating a somewhat mixed regulatory approach. While they do not engage in extreme suppression or reappraisal, they may rely on a mixed but not necessarily optimal strategy for managing emotions.
Fig. 2
Score of each profile on each dimension for Sample 2. Note. PR = positive reappraisal; ES = expressive suppression; EC = emotion communication
Profile 2 (in blue) was labeled “Engaged Regulators” as these individuals show low emotion suppression, moderate rumination but high emotion communication and positive reappraisal. Their positive reappraisal is relatively high, implying that they communicate their emotions and attempt to reframe experiences positively.
Profile 3 (in green) was designated “Expressive Regulators”. This group, like profile 3 in sample 1, is characterized by low emotion suppression, high emotion communication, and low levels of rumination and positive reappraisal.
Relationship Between Profiles and Psychological Functioning
For Sample 1, the comparison tests revealed that Profile 1 (Suppressive Regulators) exhibited higher levels of depression, anxiety, and stress symptoms compared to Profile 3 (Expressive Regulators), which reported the lowest symptom levels. Similarly, Profile 1 (Suppressive Regulators) demonstrated greater burnout. Additionally, significant differences emerged between Profile 2 (Mixed regulators) and Profile 3 (Expressive Regulators), particularly in attachment anxiety, attachment avoidance, depression, and work-related burnout, with Profile 2 exhibiting more negative outcomes (see Table 3).
Table 3
Means, SD, and test-comparisons for profiles on outcomes (sample 1 and 2)
For sample 2, significant differences were found only for attachment avoidance, depression, and stress. Profile 1 (Mixed Regulators) reported more attachment avoidance, depression, and stress in comparison to Profile 2 (Engaged Regulators) and Profile 3 (Expressive Regulators) (only for attachment avoidance and depression) (see Table 3).
Attachment and Profile Membership
Results from multinomial regression analyses are presented in Table 4 and detailed on supplemental file. For sample 1, higher attachment anxiety and attachment avoidance significantly increased the likelihood of being in P1.1 (Suppressive Regulators) compared to P1.2 (Mixed Regulators). Higher attachment anxiety significantly decreased the likelihood of being in P1.3 (Expressive Regulators) compared to P1.2 (Mixed Regulators). Higher attachment avoidance strongly decreased the likelihood of being in P1.3 (Expressive Regulators). Age showed a marginal effect, suggesting that older individuals may be slightly more likely to be in P1.3 (Expressive Regulators) than P1.2 (Mixed Regulators).
Table 4
Multinomial logistic regression models with attachment as predictors, controlling for sex and age for sample 1 and 2
For sample 2, higher attachment avoidance strongly increased the likelihood of being in P2.1 (Mixed Regulators) compared to P2.2 (Engaged Regulators) and P2.3 (Expressive Regulators). Conversely, higher attachment avoidance showed a non-significant trend toward decreasing the likelihood of being in P2.3 (Expressive Regulators) compared to P2.2 (Engaged Regulators).
Discussion
The present study examined distinct ER profiles derived from four ER strategies, namely expressive suppression, emotion communication, rumination, and positive reappraisal, across two age-diverse samples (emerging adults and middle-aged adults). It further explored how these profiles related to psychological functioning, as well as the extent to which attachment orientation predicted profile membership.
In both Sample 1 and Sample 2, two common profiles were identified: the Mixed Regulators and the Expressive Regulators profiles. The Mixed Regulators demonstrated moderate use of all ER strategies, whereas the Expressive Regulators showed high emotion communication alongside lower reliance on other strategies. The Mixed Regulators aligns with a body of research documenting multi-strategy patterns, wherein individuals employ moderately a variety of ER strategies without a clear distinction between adaptive and maladaptive strategies (e.g., De France & Hollenstein, 2017; Grommisch et al., 2020; Lougheed & Hollenstein, 2012; McCullen et al., 2024; Shi et al., 2021). De France and Hollenstein (2017) identified a ‘Multi-strategy profile’ in a university sample, and Lougheed and Hollenstein (2012) reported an ‘Average Strategy profile’ among adolescents. Similarly, Grommisch et al. (2020) described a ‘Multi-ER (moderate level)’ profile in community-dwelling adults, and Shi et al. (2021) observed a ‘Medium Regulators profile’ in a comparable population. Moreover, McCullen et al. (2024) distinguished between ‘Low moderate’ and ‘High moderate’ regulators, both characterized by moderate use of different ER strategies.
Our findings suggest that the Mixed Regulators does not consistently align with either adaptive or maladaptive outcomes. In Sample 1, Mixed Regulators exhibited fewer psychopathological symptoms than Suppressive Regulators but reported more depressive symptoms and school burnout than Expressive Regulators. However, no significant differences were found between the groups in terms of anxiety, stress, or personal burnout symptoms. However, in Sample 2, they reported higher levels of depression and stress (but not anxiety) than both the Expressive Regulators and the Engaged Regulators. This pattern is broadly consistent with results from McCullen et al. (2024), in which profiles exhibiting moderate use of multiple strategies reported more depressive symptoms than the ‘Selective’ or the ‘High regulators’ profiles. In contrast, Shi et al. (2021) observed that when a moderate-use profile includes primarily maladaptive strategies, it is linked to higher depressive symptoms compared to profiles with more balanced (medium/moderate) regulation.
Together, these findings underscore that the adaptive value of a multi-strategy approach may rely on contextual factors—such as the characteristics of the sample or the specific interplay of strategies—highlighting the importance of considering both individual and environmental variability when evaluating ER profiles. For example, the common Mixed Regulators profile showed different characteristics in both samples. The higher levels of suppression observed in Sample 2, combined with a low use of other strategies, may reflect a less adaptive regulatory pattern, which could help explain the differences in outcomes. Indeed, the costs associated with the prevalent use of expressive suppression are widely recognized in the literature (English, 2024).
Also, the developmental differences observed between our two age-diverse samples may stem from shifts in regulatory goals and strategies that emerge across the lifespan. Livingstone and Isaacowitz (2021) reported that middle-aged adults more often engage in situation selection and modification strategies, aiming to lessen negative experiences, something that our findings confirm. In contrast, younger adults tend to expose themselves to negative situations—possibly reflecting elevated emotional reactivity or reliance on relatively immature regulatory strategies. These divergent patterns may highlight why a ‘mixed’ approach to ER may come at a higher cost for older adults, who face more complex life demands and often strive to minimize exposure to distress. Younger adults may benefit from exploring a wider repertoire of strategies without incurring substantial psychological distress.
The second common profile identified was the Expressive Regulators, characterized by higher levels of emotion communication relative to other strategies. In Sample 1, this was accompanied by higher levels of positive reappraisal. While this specific combination of elevated emotional expression/positive reappraisal and lower engagement in alternative strategies has not been extensively documented in past research, Grommisch et al. (2020) highlighted a “social sharing” profile that similarly emphasizes sharing and discussing emotions with others. This similarity suggests that the active communication of emotional experiences may represent a distinct regulatory style, one that potentially fosters social support and facilitates the processing of difficult feelings. It is important to acknowledge that the effects of the'Expressive Regulators' profile can vary depending on the type of emotional communication employed, whether venting or sharing. For example, emotional venting, which often involves expressing frustration or distress without seeking resolution, is typically associated with negative outcomes (e.g., low perceived social support, burnout) (e.g., Messina et al., 2025). On the other hand, social sharing, which entails openly communicating emotions in a supportive context, is generally linked to more positive outcomes (e.g., Rimé, 2024). However, given the limited references to such a profile in the existing literature, further work is needed to clarify its nature.
In our emerging adulthood sample, the Expressive Regulators exhibited more favorable outcomes compared to the Suppressor Regulators, and,—specifically in terms of depressive symptoms and school-related burnout—also compared to the Mixed Regulators. This combination likely reflects a more adaptive regulatory style, as emotional expression and positive reappraisal are usually associated with better psychological adjustment. In contrast, in Sample 2, emotional communication was not accompanied by similarly high levels of positive reappraisal, which may help explain the differences in psychological outcomes observed across samples.
Other possible explanation for this finding lies in the developmental context of emerging adulthood. Because emerging adults frequently seek feedback, validation, and closeness through their social networks (e.g., Mitchell et al., 2025), openly sharing emotions can strengthen these relationships and provide robust protective factors against depression, stress, or burnout. It is important to note the nature of the comparison groups in each sample. In Sample 1, the Expressive Regulators are contrasted with a more suppressive profile, which carries well-documented costs (as described below). This contrast naturally accentuates the apparent advantages of emotional expression. In Sample 2, however, the Expressive Regulators are compared to a Mixed or an Engaged Regulators profile. These profiles may also confer relatively adaptive outcomes. As a result, the benefits of an expressive style do not stand out as strongly when compared against other profiles that are not characterized by problematic ER strategies (such as suppression).
Two distinct profiles– Suppressive Regulators profile (sample 1) and Engaged Regulators profile (sample 2)– are consistent with previous studies. Suppressive regulators have been identified across various age groups, including adolescents and university students (“suppression propensity” De France & Hollenstein, 2017; Lougheed & Hollenstein, 2012), as well as in adults (“suppressive regulators”; Churbaji et al., 2024). In some instances, this profile co-occurs with avoidance and is referred to as “avoiders” (Dixon-Gordon et al., 2015). This profile exhibited the most adverse outcomes, particularly when compared to the Expressive Regulators. This pattern aligns with previous research highlighting the detrimental effects of expressive suppression (Chervonsky & Hunt, 2017; Tyra et al., 2024).
Finally, an "Engaged Regulators" profile emerged exclusively in Sample 2. The positive outcomes observed in this profile can be attributed to the unique combination of ER strategies employed by individuals in this group. First, this profile is characterized by low expressive suppression, a strategy often linked to negative psychological outcomes (English, 2024). Instead, these individuals show a tendency to engage in high emotion communication. Additionally, this profile demonstrates a relatively moderate use of rumination and positive reappraisal. While rumination is generally associated with maladaptive outcomes when excessive, moderate use may allow for reflection without becoming overwhelming. The high positive reappraisal observed in this group may help mitigate the negative effects of rumination, promoting psychological resilience. This combination of diverse ER strategies may reflect individuals' ability to adjust their emotional responses according to specific contexts and goals, a flexible approach that supports better psychological adjustment (e.g., Bonanno et al., 2004).
Although this precise configuration has not been explicitly documented in the existing literature, it shares similarities with some previously identified patterns. For instance, McCullen et al. (2024) reported a “Selective Regulators” profile, characterized by high reappraisal, low suppression, moderate distraction, selective attention, and high situation selection in a community sample of adults. While not identical, the parallels suggest that actively engaging with emotions by reframing them positively and selectively addressing stressors may represent a distinct regulatory approach probably often observed in more mature or experienced populations.
Regarding the role of attachment orientations in predicting profile membership, our findings indicate that both attachment anxiety and attachment avoidance increased the likelihood of belonging to the Suppressive Regulators in Sample 1. This aligns with existing literature suggesting that attachment insecurity is generally associated with the use of less adaptive ER strategies and greater ER difficulties (e.g., Karreman & Vingerhoets, 2012; Messina et al., 2024; Winterheld, 2016). Notably, expressive suppression is particularly linked to attachment avoidance, as individuals with this attachment orientation tend to resist activating the attachment system, striving to maintain independence and avoid vulnerability (Mikulincer & Shaver, 2019). Since emotional expression and communication can expose individuals to perceived relational dependence, those high in attachment avoidance may prefer suppression as a means of self-protection and emotional detachment. Expressive suppression is also associated with attachment anxiety, albeit to a lesser extent than with attachment avoidance. This pattern is consistent with previous studies suggesting that anxiously attached individuals may suppress their emotions in situations where they are particularly concerned about the impression they make on others or when emotional expression is perceived as a potential barrier to achieving their relational or personal goals (Brandão et al., 2023).
In Sample 2, only attachment avoidance emerged as a significant predictor of profile membership, and as expected, it was associated with the profile linked to poorer outcomes—the Mixed Regulators. This finding suggests that, for individuals with higher attachment avoidance, a regulatory style characterized by a moderate use of multiple strategies may not be sufficient to buffer against emotional distress. Since attachment-avoidant individuals tend to minimize emotional dependence on others and suppress attachment-related distress (Mikulincer & Shaver, 2019), they may struggle to effectively integrate adaptive strategies, such as reappraisal or emotion communication, into their regulatory repertoire. As a result, even when they engage in a range of regulation strategies, the underlying avoidance of emotional vulnerability may limit their ability to derive full psychological benefits from them.
This study primarily focused on ER strategies used to manage negative emotions. The regulation of positive emotions was not directly addressed in this study, which is a significant gap in our understanding of ER profiles. Emerging research underscores that difficulties in managing positive emotional experiences, such as dampening, can have profound consequences for mental health, particularly in the development and maintenance of internalizing symptoms like depression (Rogier et al., 2019; Vogel et al., 2023).
Limitations and Future Research
Despite the contributions of the current study, some limitations should be noted. First, the use of self-report measures raises concerns about social desirability bias and the accuracy of retrospective accounts of emotional experiences. Future research may benefit from integrating multiple assessment methods. Second, the cross-sectional nature of this study limits our ability to draw causal inferences; longitudinal designs would be necessary to determine whether particular profiles lead to specific outcomes over time. Third, our focus on two age-diverse but distinct samples provide valuable insight into developmental differences, yet it may limit the generalizability of findings to other groups, such as adolescents or older adults over 65. Additionally, the broad age range within the middle-aged sample poses challenges in drawing precise conclusions about how ER strategies evolve within this stage of life.
Also, although our person-centered approach highlights important individual differences, it cannot fully capture the dynamic interplay of strategies used in real-world contexts. Incorporating momentary or daily diary studies could elucidate how people adapt their ER strategies in response to shifting situational demands. Finally, as this study is part of a larger project, we focused on a selected set of outcomes, while other relevant variables, such as substance use and behavioral addictions, were not assessed; future research should examine this type of outcomes to provide a more comprehensive understanding of the implications of ER profiles.
Clinical Implications
The identification of distinct ER profiles in this study provides clinically insights that can inform both assessment and intervention strategies. By recognizing that individuals regulate emotions in systematically different ways, mental health professionals can adopt a more personalized approach to treatment.
Different ER profiles exhibit varying levels of psychological distress, highlighting the need for targeted interventions rather than a one-size-fits-all approach.
The Suppressive Regulators profile was associated with elevated symptoms of depression, anxiety, and stress. Given this group's tendency to inhibit emotional expression and their stronger association with insecure attachment (particularly attachment anxiety and avoidance), therapeutic approaches such as emotion-focused therapy (EFT) (e.g., Greenberg, 2017) or attachment-based therapy (e.g., Steele & Steele, 2019) may be particularly beneficial. EFT emphasizes emotional awareness, expression, and relational attunement, aligning well with the needs of suppressive individuals, as its multidimensional therapeutic relationship, marked by emotional connection, validation, and support (Marren et al., 2022) can directly counteract the interpersonal and emotional avoidance often characteristic of this profile.
Mixed Regulators, despite their use of multiple ER strategies, still exhibited poor outcomes, suggesting that have a large repertoire of strategies alone is not always adaptive in middle-aged adults. Therapists may need to help these individuals refine how and when they apply regulation strategies, ensuring they are contextually appropriate and emotionally effective. For Mixed Regulators, Acceptance and Commitment Therapy (ACT) can be especially beneficial by enhancing psychological flexibility (Macri & Rogge, 2024), and specifically, ER flexibility, helping individuals develop greater awareness of their emotional states and contextually choose between acceptance and active regulation strategies for more adaptive emotional responding.
The finding that attachment orientations predict ER profile membership has important clinical implications. It suggests that individuals’ habitual patterns of regulating emotion are not merely situational but are deeply rooted in early relational experiences. Clinicians should therefore incorporate attachment-informed assessments into their intake and formulation processes, as understanding a client’s attachment style can provide valuable insight into their typical regulatory strategies, such as suppression in avoidant individuals or heightened emotional expression in anxiously attached individuals. This knowledge can guide the selection of therapeutic approaches that target the underlying attachment dynamics, such as Attachment-based Compassion Therapy (e.g., García-Campayo et al., 2023).
Acknowledgements
I would like to express my sincere gratitude to the reviewers for their valuable comments and suggestions, which significantly contributed to improving this manuscript.
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