Similar Personality Changes among Younger and Older Adults: Findings from a Multi-Method Intervention Study on Socio-Emotional States and Traits

Similar Personality Changes among Younger and Older Adults: Findings from a Multi-Method Intervention Study on Socio-Emotional States and Traits | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Similar Personality Changes among Younger and Older Adults: Findings from a Multi-Method Intervention Study on Socio-Emotional States and Traits Cornelia Wrzus, Gabriela Küchler, Kira Borgdorf, Corina Aguilar-Raab, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6206183/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2025 Read the published version in Communications Psychology → Version 1 posted You are reading this latest preprint version Abstract Past research showed that personality traits develop less strongly after younger adulthood, though the underlying processes remain poorly understood, and personality intervention studies scarcely investigated age differences. Also, existing findings are mostly limited to explicit assessments of personality traits (i.e., questionnaires). In this preregistered, multi-method study, we examined associations between changes in personality states and explicit and implicit trait self-concepts in younger and older adults ( N = 165, age range = 19–78 years) after an eight-weeks socio-emotional intervention, three and 12 months later. Findings indicate changes in personality states, explicit self-concepts for both traits, and the implicit self-concept of extraversion. Only state changes in emotional stability predicted changes in the corresponding explicit but not implicit trait self-concept. Importantly, the effects were consistent across age groups, and exploratory analyses showed higher engagement among older adults throughout the intervention. The findings emphasize that older adults might benefit as much from socio-emotional interventions as younger adults, and potential age differences in skill acquisition might be set off through engagement. Scientific community and society/Social sciences/Psychology/Human behaviour Scientific community and society/Social sciences/Education intervention Big Five personality development explicit and implicit self-concepts socio-emotional traits and states age differences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Many people desire to change some of their personality characteristics in one way or another, and for example, want to be generally more outgoing or better able to handle stress (i.e., increase extraversion or emotional stability, Hudson et al., 2020; Stieger et al., 2021). While many aspire to change their traits, the goal to change is generally not sufficient (Haehner et al., 2024; Hudson et al., 2019; Lücke et al., 2020), emphasizing the need for effective interventions. Currently, evidence is accumulating that personality interventions can elicit targeted changes in self-reported traits, yet other trait manifestations as well as the processes underlying trait changes are scarcely examined (Haehner et al., 2024; Stieger et al., 2021; Wrzus & Roberts, 2017). Theoretical models highlight gradual shifts in personality states as well as self-reflections as potential mechanisms of long-term personality trait change (Baumert et al., 2017; Jackson & Wright, 2024; Wrzus & Roberts, 2017). Also, while the majority of personality intervention studies focused on young adults (Haehner et al., 2024), meta-analytic work demonstrates that personality development occurs throughout the entire adult lifespan, yet is more pronounced in young compared to later adulthood (Bleidorn et al., 2023; Bühler et al., 2024; Roberts et al., 2006). These age differences in normative personality development raise the question, whether personality traits are less malleable in late adulthood (Wrzus & Roberts, 2017) leading to less pronounced changes when older adults participate in personality change interventions. In the present research, we addressed these gaps and examined (a) whether short-term personality state changes lead to lasting trait changes during and beyond a socio-emotional intervention and (b) how these changes differ between younger and older adults. Furthermore, we expanded past operationalizations of personality, which primarily relied on self-report questionnaires that assess explicit self-concepts, through indirect measures that aim at implicit representations of self-concepts—focusing on the Big Five traits of emotional stability and extraversion, which are most often desired to change (Stieger et al., 2021). Processes of Personality Development in Adulthood Processes theories on personality development highlight so-called pre-action, action, and post-action factors that may facilitate or hinder change in personality traits (Geukes et al., 2018; Hennecke et al., 2014; Jackson & Wright, 2024; Wrzus & Roberts, 2017). Pre-action factors include the desire and belief in the feasibility of personality change, while action factors primarily involve engaging in behaviors relevant for trait changes, such as reserved individuals acting more outgoing than usual. Post-action factors include self-reflections and attributions of changed behaviors. In the current research, we focus on personality states as action factors and examine how their changes during an intervention might subsequently influence explicit and implicit trait self-concepts of emotional stability and extraversion. We address pre- and post-action factors during the intervention through goal setting, discrepancy awareness, and reflection (see Method section). Personality states represent momentary feelings, thoughts, and behaviors related to trait-domains (Fleeson & Gallagher, 2009). States that are congruent with existing trait levels are thought to reinforce stability, while incongruent states should promote trait changes (Wrzus & Roberts, 2017). On a state level emotional stability is represented by how individuals react emotionally to difficult situations and how intense and stable their emotions are (Soto & John, 2017; Suls & Martin, 2005). State extraversion is characterized by social and assertive behavior, positive affect and high levels of energy in social situations (Smillie et al., 2015; Soto & John, 2017). In this research, we follow dual-processes models of personality (Back et al., 2009; Rauthmann, 2024; Wrzus & Roberts, 2017) suggesting that personality traits are more than the averages of states and are also mentally represented explicitly, thus directly accessible in questionnaires, as well as implicitly, thus accessible with less direct measures such as word categorization tasks (e.g., IAT; Greenwald et al., 1998). Reflective processes (e.g., self-reflection) are assumed to translate changes in personality states primarily into the explicit self-concept, while associative processes (e.g., associative learning) are assumed to contribute to changes in the implicit self-concept (Gawronski & LeBel, 2008; Wrzus & Roberts, 2017). Empirical studies showed that both explicit and implicit self-concepts predict momentary behavior/states, especially for extraversion and emotional stability (Back et al., 2009; Quintus et al., 2021). Regarding the reversed effect of momentary states predicting changes in trait self-concepts, longitudinal studies found that repeated extraverted states predicted changes in explicit self-concepts of extraversion (Quintus et al., 2021; Van Zalk et al., 2020). Similarly, repeated stress-related negative affect predicted decreases in explicit self-concepts of emotional stability (Borghuis, et al., 2019; Quintus et al., 2021; Wrzus, 2021). Yet so far, only one study has linked personality states to changes in implicit self-concepts, showing effects for extraversion but no other Big Five traits (Quintus et al., 2021). Volitional Personality Development Even though many people desire to increase their emotional stability and extraversion, this desire does often not result in trait changes (for review, Haehner et al., 2024). A perhaps obvious reason for these findings may be that changing one’s personality traits might be difficult, particularly in adulthood, when traits are on average comparatively stable and are thought to undergo little change (Bleidorn et al., 2022; Roberts et al., 2006). For those aiming to alter daily behaviors and achieve long-term personality trait changes, it appears essential to focus on strategies that support to initiate and sustain changes in trait-relevant states and related self-concepts over time (Hennecke et al., 2014; Jackson & Wright, 2024; Wrzus & Roberts, 2017). Recent meta-analytic findings on volitional personality development further support the effectiveness of interventions in achieving personality trait changes (Haehner et al., 2024). These interventions have used a multitude of different strategies (e.g., see Haehner et al., 2024; Wright et al., 2025), which can be organized into three broad categories (Allemand & Flückiger, 2017; Wright et al., 2025): (a) motivators such as discrepancy awareness and goal setting, (b) behavioral practice, (c) self-reflection to target beliefs and insight as well as reinforcement of new behaviors. Strategies in these three domains operate across the (a) pre-action, (b) action, and (c) post-action stages, with reflective processes shaping explicit self-concepts and practicing new behaviors influencing implicit ones through associative processes, such as reinforcement and feedback learning (Wrzus & Roberts, 2017), Wright et al., 2025. Despite these theoretical insights and interventions targeting motivation, behavior, and reflection (Haehner et al., 2024; Wright et al., 2025), empirical studies rarely examined state changes and reflection directly. Also, research on how personality states and traits may change jointly during interventions remains limited (e.g., Olaru et al., 2025). Initial evidence suggests that state changes precede trait changes for resilience, a construct related to emotional stability (Stieger et al., 2022), and other traits such as mindfulness (Kiken et al., 2015). However, most studies have relied on self-report questionnaires of explicit self-concepts (but see Olaru et al., 2025). These measures capture self-evaluations of personality traits but are not suited to assess more indirect representations, that is, implicit self-concepts of traits (Back et al., 2009). Moreover, compared to indirect measures, explicit self-reports tend to be more susceptible to social desirability and demand effects (Krämer et al., 2024). Such effects are particularly relevant to account for when studying personality interventions, when participants desire (to observe) trait changes. Addressing these gaps is crucial to understanding the nuanced processes that enable personality change and developing more effective interventions. Age Differences in Personality Development and the Underlying Processes Previous studies on volitional personality development have predominantly focused on younger samples, limiting the generalizability of their results to older individuals. Moreover, intervention studies with older adults could help to understand whether the slower pace of typical personality development in older age (Bleidorn et al., 2022; Roberts et al., 2006) relates to changes in processes of personality development. We propose that older adults may exhibit less pronounced changes in personality-relevant states (i.e., thoughts, feelings, behaviors) due to slower learning processes, such as associative learning (Mutter et al., 2019) and reinforcement learning (Cutler et al., 2021). Furthermore, with age, changes in these states could affect personality self-concepts less strongly over time. This diminished influence may stem from reduced engagement in reflective processes (Küchler et al. 2025; reference blinded ) and from reflection focused on integrating experiences into existing self-concepts (Sneed & Whitbourne, 2003). For example, older adults are less likely to compare themselves to others or their past selves regarding personality traits and other characteristics ( reference blinded ). Also, older adults favor identity assimilation, thus preserving consistency in their self-views (Sneed & Whitbourne, 2003). Regarding implicit self-concepts, disruptions in learning processes critical for state changes may similarly hinder modifications in less conscious associations crucial for implicit self-concepts (Mutter et al., 2019). So far, only few studies examined age differences in state-trait links directly and observed mixed findings. Whereas one observational study found that changes in repeated stress reactivity in daily life were most strongly associated with changes in trait emotional stability among young adults compared to older adults (Wrzus et al., 2021), another study observed no significant age differences in state-trait links over 2 years (Quintus et al., 2021). Yet, personality intervention studies have not examined age differences in state-trait links likely because the majority focused on young adults only and were therefore not able to test age differences (Haehner et al., 2024). Findings from clinical interventions—although not fully generalizable to healthy populations—have also provided mixed evidence regarding age differences in the effects of interventions on changes in traits, some studies suggesting older samples to benefit less from interventions targeting socio-emotional domains than younger samples (e.g., Covin et al., 2008; Wetherell et al., 2013), and others reporting no age-differences in intervention effects (e.g., Cuijpers et al., 2009; Mewton et al., 2013). A meta-analysis of 207 intervention studies observed no substantial age differences in changes after clinical interventions, yet these studies did not assess state-trait associations (Roberts et al., 2017). Thus, multiple factors such as age differences and similarities in learning, motivation, or intervention adherence could explain this finding. Overall, theory and some observational evidence suggest that younger samples may benefit more from personality change interventions. However, to the best of our knowledge, no study to date has examined age differences in state-trait links in a controlled intervention design. Present Research The present research has two aims: First, expanding the scarce literature on personality state-trait associations, we aimed to investigate whether, during a targeted and evidence-based intervention, state changes in emotional stability and extraversion would predict changes in trait self-concepts. In addition to measuring personality via self-reports (i.e., explicit self-concepts), we innovatively employed indirect measures (i.e., implicit self-concepts) to provide a more comprehensive assessment of personality trait change. We hypothesized that throughout the intervention, state emotional stability (H1a) and state extraversion (H1b) would improve continuously. We further expected that the improvement of state extraversion would be more pronounced during the second half of the intervention, which focused on social competencies. Further, we expected that the intervention would lead to increases in the explicit self-concept of emotional stability (H2a) and extraversion (H2b) as well as the implicit self-concepts of both traits (H3a and H3b, respectively), with more pronounced increases in explicit than implicit trait self-concepts (H4). Consistent with process theories of personality change (ref), we expected that more pronounced changes in state emotional well-being would be associated with stronger changes in trait emotional stability (H5a) and more pronounced changes in state social behavior with stronger changes in trait extraversion (H5b). Second, we aimed to provide new insights into age differences in state and trait changes as well as their associations. We hypothesized that the increases in state emotional stability (H6a) and extraversion (H6b) would be more pronounced among younger compared to older adults. Also, we expected increases in explicit self-concepts and implicit self-concepts to be more pronounced among younger adults compared to older adults (H7a, H7b). Lastly, we hypothesized that the association between changes in state emotional well-being and trait emotional stability (H8a), and between social behavior and extraversion (H8b) would be stronger in younger than older adults. Exploratorily, we investigated the long-term effects of the intervention on explicit and implicit self-concepts of emotional stability and extraversion over 3 and 12 months after the intervention. Additionally, we examined whether younger and older adults differed in engaging in the intervention. Results To examine our research questions, we implemented an 8-week in-person intervention grounded in personality development principles (Jackson & Wright, 2024; Wrzus & Roberts, 2017) and personality change interventions (Allemand & Flückiger, 2017): Changes in state emotional stability and extraversion were facilitated through psychoeducation, behavioral instructions and coping strategies. Self-reflection exercises targeting changes in explicit self-concepts were encouraged through exercises of awareness of discrepancies between current and desired trait levels as well as reflections on state changes (see detailed intervention overview in Supplementary Table S1). The first part of the intervention, sessions 1-4, focused on emotional stability, while the second part, sessions 4-8, addressed extraversion and interpersonal competencies. In the following, we refer to these as part 1 and part 2. Participants selected a training buddy in the first session and received daily tasks at each session, including emotion regulation exercises, written self-reflections, and planned social interactions, to facilitate engagement with the intervention in-between weekly meetings. Younger ( n = 80, 18–37 years) and older adults ( n = 85, 50+ years) attended weekly two-hour sessions, conducted by two trainers in groups of five to twelve participants. Participants eligible for participation were randomly assigned into the intervention or waitlist control groups. More details on the sample, the recruitment, and asessments are provided in the Method section, a detailed session outline is available in Supplementary Table S1, and a validation of the intervention reported in ( reference blinded ). Personality traits of emotional stability and extraversion were assessed via questionnaires and an Implicit Association Test(Schmukle et al., 2008) before, midway (week 4), and after the intervention. Weekly assessments tracked state emotional stability and extraversion throughout. Follow-up personality assessments were conducted at three and 12 months post-intervention (see Figure 1). Changes in Personality States and Trait Self-Concepts Results on changes in personality states and traits before and throughout the intervention are displayed in Figure 2. State changes were analyzed using multilevel analyses. As predicted (H1a&b), state emotional stability and extraversion increased ( b = 0.09, 95% CI [0.06, 0.13] and b = 0.06, 95% CI [0.03, 0.08] ). Other than expected, state extraversion did not increase more during part 2 of the intervention ( b = 0.03, 95% CI [-0.30, 0.35]). More detailed statistics are provided in Supplementary Tables S2 and S3. Focusing on trait changes, we employed latent neighborhood change analyses to model trait changes from T1 to T2 (Part 1) and T2 to T3 (Part 2) separately. Results indicated that, in line with our predictions (H2a and H2b), the explicit self-concepts of both emotional stability (Part 1: b = 0.321, 95% CI [0.229, 0.412]; Part 2: b = 0.319, 95% CI [0.222, 0.416]) and extraversion (Part 1: b = 0.161, 95% CI [0.098, 0.225]; Part 2: b = 0.072, 95% CI [0.004, 0.140]) increased during the intervention (also see Table S4). Contrary to H3a, the implicit self-concept of emotional stability did not increase during the intervention (Part 1: b = 0.002, 95% CI [-0.059, 0.063]; Part 2: b = -0.049, 95% CI [-0.105, 0.006]). In line with H3b, the implicit self-concept of extraversion increased significantly during Part 1 ( b = 0.098, 95% CI [0.024, 0.172]) and showed a similar trend during Part 2 ( b = 0.062, 95% CI [0.000, 0.124]), without reaching conventional levels of significance. Results partially supported H4: Changes in the explicit self-concept were more pronounced regarding emotional stability. However, regarding extraversion, overlapping confidence intervals of change estimates indicate comparable changes for both types of self-concepts (also see Table S4). Model fit indices of all latent neighborhood change models were good and are displayed in Supplementary Table S5. Associations in State-Trait Changes Results partially supported H5a regarding the associations between states and traits: The more state emotional stability increased during the intervention, the more the explicit trait self-concept of emotional stability increased ( b = 0.367, 95% CI [0.191, 0.674]), but not the implicit trait self-concept ( b = 0.018, 95% CI [-0.059, 0.117]). Contrary to our assumptions (H5b), changes in state extraversion were not associated with changes in explicit ( b = 0.010, 95% CI [-0.239, 0.290]) or implicit ( b = 0.010, 95% CI [-0.942, 0.299]) trait self-concepts of extraversion throughout the intervention. For complete model results see Supplementary Table S6. Age Differences in Changes of Personality States, Trait Self-Concepts, and State-Trait Associations Contrary to our predictions of more pronounced intervention effects in young adulthood (H6a and H6b), age did not moderate state changes in emotional stability ( b = -0.01, 95% CI [-0.08, 0.05]) and extraversion ( b = -0.01, 95% CI [-0.06, 0.03]). Also, the analyses did not support age differences in changes in explicit self-concepts of emotional stability (H7a; Part 1: b = 0.042, 95% CI [-0.109, 0.192]; Part 2: b = 0.092, 95% CI [-0.037, 0.221]) or implicit trait self-concepts of emotional stability (H7b; Part 1: b = 0.033, 95% CI [-0.068, 0.134], b = -0.008, 95% CI [-0.116, 0.100]). Similarly, regarding extraversion, the analyses did not support age differences in changes in explicit self-concepts (H7a; Part 1: b = -0.004, 95% CI [-0.123, 0.116]; Part 2: b = 0.049, 95% CI [-0.056, 0.154]) or implicit trait self-concepts (H7b; Part 1: b = -0.090, 95% CI [-0.220, 0.040]; Part 2: b = 0.024, 95% CI [-0.083, 0.132]). Contrary to H8a and H8b, associations in state-trait changes were not stronger in younger adults compared to older adults. Specifically, the age group did not moderate the relationship between state and explicit trait changes in emotional stability ( b = -0.148, 95% CI [-0.414, 0.092]) or extraversion ( b = -0.029, 95% CI [-0.392, 0.322]). Similarly, the age group did not moderate the relationship between state and implicit trait changes in emotional stability ( b = 0.039, 95% CI [-0.139, 0.224]) or extraversion ( b = -0.116, 95% CI [-2.033, 0.450]). All non-significant age effects were examined with Bayesian estimation to obtain information on the evidence supporting the null hypotheses (see Supplementary Tables S6, S7, and S8). Exploratory Analyses of Long-term Trait Changes over 3 and 12 Months We used piecewise growth curve models to examine whether trait changes during the intervention sustained after 3 and 12 months and whether these long-term trajectories differed between younger and older adults. Figure 3 illustrates changes in standard deviations from T1 across the assessment period (see Table S9 for all parameter estimates). The results showed that the explicit self-concept of emotional stability remained stable after the intervention, with no significant increase or decrease, while the explicit trait self-concept of extraversion demonstrated a small but significant decrease. Both implicit trait self-concepts showed non-significant increases during follow-up assessments. Although explicit self-concept changes did not differ by age, older adults exhibited a significant increase in the implicit self-concept of extraversion. A similar, but nonsignificant trend was observed for age differences in the implicit self-concept of emotional stability. To examine the evidence supporting the null hypothesis—meaning the true absence of age differences in the observed effects—we compared differences in the Bayesian Information Criterion (BIC) between models with and without age group as a predictor (see Table S10 for details). Most models provided positive evidence in favor of the null hypothesis (BIC difference = 2–6; Bayes factor = 3–20; Kaplan & Depaoli, 2012). In addition, we found strong evidence supporting the null hypothesis for trait changes in the explicit trait self-concept of emotional stability across the intervention and follow-up period (BIC difference = 6–10; Bayes factor = 20–150). Exploratory Analyses of Engagement During the Intervention Participants attended the 8-week training regularly ( M = 6.94 sessions, SD = 0.06), with a moderate level of accomplished weekly tasks (scaled 1-7; M = 4.12, SD = 1.17), and engagement in practice with audio materials (scaled 1-7; M = 3.75, SD = 1.56). Yet, they had relatively few exchanges with their training buddies (scaled 1-5; M = 2.21, SD = 0.71). Participants reported that their weeks during the training were moderately hectic (scaled 1-7; M = 3.83, SD = 0.97), somewhat exhausting (scaled 1-7; M = 3.76, SD = 1.00), and moderately typical (scaled 1-7; M = 3.91, SD = 1.11). To better understand the lack of age differences in state and trait changes, we explored age differences in these variables of engagement and daily life demands among younger and older adults. Although younger and older adults did not differ in their desires to improve emotional stability (Cohen’s d = -0.19, p = .199), older adults even had a significantly lower desire to improve their extraversion (Cohen’s d = -0.36, p = .026). Importantly, older adults reported more engagement with the intervention (see Figure 4, Panel A): they were more engaged in weekly tasks and audio files. Yet, younger and older adults reported a similar amount of contact with their training buddies as well as practicing acquired skills in their daily lives. Regarding context factors (Figure 4, Panel B), younger adults reported more hectic and atypical weeks during the intervention than older adults, and there were no age difference in weekly exhaustion (see Supplementary Table S11). Discussion This longitudinal study uncovered substantial and lasting increases in personality traits of emotional stability and extraversion among younger and older adults following from an 8-week in-person intervention focusing on socio-emotional aspects of daily thoughts, feelings, and behavior. The findings provide novel theoretical contributions regarding (a) changes beyond trait self-reports, (b) processes underlying personality development, and (c) age similarities in personality development. We address all three aspects next. Following the intervention, changes were observed in both explicit and implicit self-concepts of trait extraversion, that is, how people describe themselves in questionnaires and which characteristics people associate themselves with more indirectly (Back et al., 2009; Wrzus & Roberts, 2017), as well as in weekly reports of social behavior (i.e., states), which confirmed hypotheses H1b, H2b, and H3b. Regarding emotional stability, explicit trait self-concepts and weekly reports of states, but not implicit trait self-concepts changed, confirming H1a and H2a, but not H3a. Thus, the current study addressed critical shortcomings of earlier intervention studies on personality development that focused on self-reports to assess personality traits (e.g., Hudson et al., 2020; Stieger et al., 2021). Self-report measures are commonly criticized for being prone to demand effects and report biases, which could result in people reporting increases in personality traits after participating in interventions without increases in other manifestations of personality traits (e.g., implicit self-concepts, behavior). We hypothesized that changes in explicit self-concepts would be more pronounced than changes in implicit self-concepts (H4). Descriptively changes in implicit self-concept of extraversion were weaker, yet overlapping confidence intervals limit strong conclusions. Together with the non-substantial change in implicit self-concepts of emotional stability, one could speculate that explicit and implicit self-concepts change to different extents or at different time scales. Earlier research postulated that implicit self-concepts might take more time to change (Gawronski & Bodenhausen, 2006; Wrzus & Roberts, 2017), because behavioral changes may need to be repeated more often and internalized through implicit learning. Despite some criticism regarding the reliability and interpretation of implicit measures such as the IAT (Dentale et al., 2016), its extensive usage and insightful findings (Greenwald et al., 2015; Nosek et al., 2007) support its complementary use for personality research. Supporting the theoretical propositions that both explicit and implicit self-concepts can change, a recent longitudinal study also observed meaningful changes in people’s implicit self-concept of extraversion (Quintus et al.). Still, specific reflective and associative processes, which explain individual differences in long-term changes of explicit and implicit trait self-concepts, are still poorly understood (Forscher et al., 2019; Küchler et al, 2025; Wrzus & Roberts, 2017). Nonetheless, the current study offers a more comprehensive understanding of “action” processes underlying personality change, demonstrating that state emotional stability and state extraversion increased during the intervention. Furthermore, individual differences in state increases predicted increases in explicit self-concepts of emotional stability. This is in line with earlier intervention and observational studies (Stieger et al., 2022; Quintus et al., 2021; Wrzus et al., 2022), as well as conceptual frameworks understanding state changes as the building blocks for trait changes (Baumert et al., 2017; Geukes et al., 2018; Wrzus & Roberts, 2017). Recently, an experimental study demonstrated that a single, brief increase in extraverted and emotionally stable behavior led to (temporal) increases in presumably stable trait representations of extraversion and emotional stability (Küchler et al., 2025). At the same time, earlier research and the current study indicate that motivation to change and behavioral changes are not sufficient to elicit or explain trait changes—as emphasized in the TESSERA framework (Figure 2, Wrzus & Roberts, 2017). Behavioral changes often explain small or no portions of individual differences in trait changes (see also Olaru et al., 2025, Quintus et al., 2021; Wrzus et al., 2022). Reflective and associative processes are considered key factors in explaining for whom stronger trait self-concept changes occur (Jackson & Wright, 2023; Wrzus & Roberts, 2017). Most previous intervention studies did not examine the theoretically proposed state-trait links and therefore miss that behavioral changes might not be sufficient to explain changes in people’s explicit or implicit trait self-concepts. The current intervention addressed both reflective processes such as noticing behavioral changes and associative processes such as practicing emotion regulation and functional interpersonal behavior repeatedly during daily assignments. At the moment it remains speculative, whether both pathways of personality development were addressed equally in the intervention and the extent and time courses of explicit and implicit trait changes truly differ due to different underlying processes (Gawronski & Bodenhausen, 2006; Wrzus & Roberts, 2017). In summary, intervention effects might be more lasting when not only behavioral changes occur but self-concepts change as well (Wrzus & Roberts, 2017). As one of few studies, we examined age differences in the change processes of state and traits and found similar changes for younger and older adults in personality states, explicit and implicit traits throughout the intervention. This finding agrees with recent experimental and meta-analytic work that found no significant age differences in trait changes after experimentally induced behavioral changes (Küchler et al., 2025) or psychotherapy (Cuijpers, 2024; Roberts et al., 2017). These and the current findings offer further indication that smaller normative personality changes with older age (Bleidorn et al., 2023; Roberts et al., 2006) might not be attributable to personality changes are generally diminished or impossible with age. Instead less pronounced normative trait changes (i.e., without intervention) might be attributed to older adults' reduced desire for change (Hudson & Fraley, 2016), and fewer life events triggering changes (Bühler et al.,2023). This reasoning extends predictions from the TESSERA framework (Wrzus & Roberts, 2017), which proposes that fewer contextual changes and dampened learning processes contribute to smaller normative trait changes with older age. The current results indicate that when older adults want to change and contextual changes occur for example through an (psychotherapeutic) intervention, they might compensate for potential cognitive drawbacks slowing learning (Cutler et al., 2021; Mutter et al., 2019) with higher motivation. Such an interpretation is well in line with lifespan psychology that repeatedly demonstrated continuous improvement in socio-emotional functioning as people get older (Charles & Carstensen, 2010). We deliberately chose to carry out the intervention in weekly in-person meetings together with tasks for daily practice and a buddy system to enhance adherence to the intervention. The current study herein differs from other recent personality interventions that provided tasks digitally through mail or apps (see Haehner et al., 2024 for a review). Notably, we obtained substantially larger effect sizes regarding increases in emotional stability and similar effect sizes in extraversion compared to digital interventions, which obtained average effect sizes of d = 0.33 for self-reports of emotional stability and d = 0.38 for extraversion (Haehner et al., 2024). The current intervention targeted extraversion only in the second half and thus for only four weeks. We speculate that we would have seen even larger changes in extraversion with durations similar to the ones of digital interventions (i.e., 12-16 weeks) because the in-person meetings allowed to train interpersonal behavior directly in interpersonal exercises during the meetings with direct feedback from trainers instead of relying on the participant’s adherence to the exercises in their daily lives. This points to a potential benefit of in-person interventions, although the effectiveness and effect sizes of digital interventions are still impressive and allow the inclusion of many people simultaneously (Haehner et al., 2024), which is a disadvantage of in-person interventions. In addition to the already mentioned limitations of the study, that is, shortcomings of the implicit personality assessment and the relatively small number of participants, two further aspects seem relevant for future studies: First, it is still an open question, which processes and aspects are most relevant for altering personality traits in the direction that people desire (Haehner et al, 2024; Wright et al., 2024). Future intervention studies could compare different treatments to gauge the differential contributions of intervention parts (e.g., Haehner et al., 2025). At the same time, differences between such different treatment conditions might not be large, which has been demonstrated repeatedly for psychotherapy and mental well-being interventions (Roberts et al., 2017; van Agteren et al., 2021). Potential reasons are (a) common change factors that are included to most interventions and most powerful, while other factors play only a minor role and (b) limiting people to specific aspects of the treatment (e.g., social feedback, reflection) might be impossible in psychological treatments compared to pharmacological intervention, where provided substances can be strictly controlled (see Küchler et al., 2025 for similar arguments). Second, we measured states and daily life processes only in weekly assessments to reduce participant burden. More fine-grained assessments of behavioral, cognitive, and emotional changes in people’s daily lives would be highly desirable to get an even better understanding of the temporal dynamics of personality changes. Ultimately, the understanding of the daily life processes would greatly facilitate explaining individual differences in the strength and sustainability of behavioral and self-concept changes, which overlap with urgent questions of psychotherapy (Roberts et al., 2017; van Agteren et al., 2021). In sum, our multimethod study offers a detailed view of how younger and older adults’ socio-emotional personality traits can change through an in-person intervention. Over the course of 8 weeks, state extraversion and emotional stability increased, which partly explained the increases in trait extraversion and emotional stability. The changes were largely robust over the following 12 months. The current study offers exciting findings for aging societies regarding socio-emotional functioning, where lifelong learning is beneficial and needed both for the individual and society. Method Transparency and Openness We report how we determined our sample size, data exclusions, and all measures in this study. The study design, sampling rationale, hypotheses, and data analyses were preregistered on January 16, 2023 OSF: 1) https://osf.io/c7p8y/?view_only=4d3c2f310b394dd790b0192f06aa4c71 2) https://osf.io/c7p8y/?view_only=4d3c2f310b394dd790b0192f06aa4c71 At the time of the preregistration, several participants were screened for eligibility (see below for the criteria) and had answered the initial baseline questionnaire (T1). The intervention had not started, and no longitudinal data, which are the only relevant for this manuscript, had been collected. We did not conduct any data inspection before the preregistration. Minor deviations from the preregistration are detailed in Supplementary Table S12. The hypotheses were rephrased to improve grammatical clarity and consistency. The anonymized data, code, and materials are publicly accessible on OSF https://osf.io/ac4b2/?view_only=e4f6d2ef76c842bd8abe7a72ebdbb75b. The study was approved by the Ethics Committee of institution blinded , and all participants provided informed consent before participation. The study was performed in line with the principles of the Declaration of Helsinki and we followed JARS (Appelbaum et al., 2018) for reporting standards. This is the second manuscript submitted from the data of this project. The first manuscript ( reference blinded) focused on the validation of the intervention and changes on mindfulness, self-compassion, and related constructs. The Socio-emotional Intervention The intervention was designed as an in-person group intervention aimed at promoting emotional stability, extraversion, and mental health in healthy adults. It was conducted over eight weeks, with sessions lasting two hours each. At the end of each session, trainers assigned weekly tasks to participants to help them incorporate new strategies into their daily lives and engage in self-reflection. We provided materials for the weekly tasks in both paper-pencil format and online for audio-based resources. Participants who missed a session had online access to session materials and weekly tasks and were also offered catch -up sessions . Each intervention group was composed by the same participants across the intervention (five to twelve participants) and guided by two trainers to facilitate a familiar and secure social environment. The training was implemented with different groups meeting on separate weekdays. Sessions took place in course rooms at the ( institutions blinded) . Part 1 of the training focused on stress, resilience, attention, and emotion regulation and intended to primarily increase state and trait emotional stability (see Supplementary Table S1). Part 2 emphasized interpersonal socio-emotional competencies, covering topics such as social dynamics, systemic perspectives on social interactions, practicing social skills with video feedback, and summarizing the training content (Table S1). Thus, the last four sessions aimed to increase state and trait extraversion. Nevertheless, excercises for emotional stability were still encouraged in weekly tasks. The rationale behind this concept was that participants first learn to regulate their own attention and emotions before addressing interpersonal conflicts. This approach is also taken by validated trainings such as the Cognitively-Based Compassion Training (Ash et al., 2021). Overall, the intervention design integrated methods from evidence-based programs, such as Mindfulness-Based Stress Reduction (Kabat-Zinn, 1990), Cognitively-Based Compassion Training (Ash et al., 2021), the Social Emotional Ethical Learning program (Center for Contemplative Science and Compassion-Based Ethics [CCSCBE], 2019), Acceptance and Commitment Therapy (Beck, 1979; Hayes et al., 1999), systemic counseling (Schlippe & Schweitzer, 2016), and Group Training of Social Competences (GSK, Hinsch & Pfingsten, 2015). The intervention was tailored from these approaches for the specific purpose of this study by the project leader -- blinded --, who is a licensed and experienced psychotherapist, mindfulness, and compassion trainer, and – blinded ---, who is a licensed systemic counselor. Additionally, the training was discussed and modified within the project group, who are experts on research of personality development. The trainers were 27 graduate students in psychology and educational sciences, who were trained themselves over three months and then served as trainers within the intervention, earning course credits. Their training involved participating for one semester in a train-the-trainer seminar, which combined their own participation in the intervention with subsequent instruction in learning didactical principles, such as maintaining a trainer’s attitude and managing challenging participants or situations. This initial trial also served as a pilot test of the intervention, allowing for minor adjustments to each session (e.g., modifying the duration of exercises). Like the participants, the trainers were informed about the study’s objectives but remained blind to specific hypotheses (e.g., age differences). Each trainer participated in at least two supervision sessions per training cohort. These supervision sessions and the train-the-trainer seminar were led by the two project principals, (blinded), who have extensive experience in teaching and training facilitation. Trainers reported adhering closely to the training manual ( M = 5.80, SD = 1.14 on a 7-point scale, where 7 indicated complete adherence). An overview of the intervention and assessments is displayed in Figure 1. We provide a detailed outline of each session in Supplementary Table S1. Materials for each session and the study protocol have not yet been published but are available upon request. Recruitment of Participants and Screening Procedure Based on power analysis, we aimed to recruit 220 participants divided into two age groups n = 110 younger adults (18–35 years), and n = 110 older adults (aged 50+ years). The power estimation settings were 1–ß = .80, α = .05 for medium and large effect sizes and practical considerations related to the feasibility of the in-person intervention study. The intervention was designed as a randomized controlled trial (RCT) with a waitlist control group. A separate publication by the project team validated the effectiveness of the training ( reference blinded ). Since comparisons between the intervention group and the waitlist control group are limited to pre- and post-intervention changes and do not extend to the within-person processes examined in this manuscript, the RCT design is not further analyzed in the present study. Recruitment occurred using various online and offline channels (e.g., public lectures and advertising, flyers, social media) to reach a diverse sample. All advertisements informed about the training and the study, including their content, procedure, timeline, costs, and compensation. Also, a weblink led to the online screening. After providing informed consent for screening (i.e., asking about demographics and health information), participants underwent eligibility screening. Criteria included: (1) age ≥18 years, between 18–35 or 55+ (±2 years), (2) internet access and suitable hardware, (3) no concurrent socio-emotional, compassion, or mindfulness training, or psychotherapy, (4) sufficient language skills, and (5) values below depression and anxiety cut-offs (PHQ-9, GAD-7; Löwe et al., 2002, 2008). People reporting suicide ideation, scoring above the cut-offs for depression or anxiety were provided with online resources for therapeutic and counseling support. Eligible participants received a study ID and an information package on data privacy, study details, procedures (e.g., random group assignment), timeline, costs, and compensation. Enrollment required a fee of 80 EUR (50 EUR for students/seniors), with potential reimbursement of up to 110 EUR + 50% of the fee based on study engagement (e.g., completing questionnaires, attending ≥5 sessions). Those attending at least five of eight sessions could receive a 50% fee refund. Participants were informed about random assignment to an intervention or waitlist control group and enrolled online after providing informed consent A total of 1150 screenings were conducted and 203 individuals eventually enrolled in the intervention, of which 38 dropped out before the training started, and 20 did not complete the training (i.e., ≤ 4 training sessions; n = 18). Enrollment occurred in three cohorts, which were conducted in January 2023, April 2023, and June 2023. Drop-out analyses displayed in Table S13 show that completers and dropouts did not differ substantially in the variables of interest. Supplementary Figure S1 details the study and attrition flow, and reasons for exclusion. Final Sample of Participants in the Personality Intervention The final sample ( N = 165) that participated in the intervention consisted of 80 younger adults ( M Age = 28.33, SD Age = 4.92, 75 % female, 65 % university degree) and 85 older adults ( M Age = 63.55, SD Age = 7.20, 75 % female, 60% university degree). More detailed sociodemographic information is displayed in Table S14. Individuals who participated at T4 ( N = 123) and/or T5 ( N = 100) did not differ significantly from participants, who did left the study after T3, in any variable of interest or demographically. Table S13 provides a detailed overview. Measures Personality trait were measured before the start of the training (T1), four weeks into the training (T2), and during the week following the final training session (T3). Additional data collection occurred three months (T4) and twelve months (T5) after T3, with implicit measures being assessed only at T1 to T3 and at T5. Personality states were assessed 8 times, 5 days after the preceding training session. Personality Traits Explicit Self-Concepts. Explicit personality self-concepts were assessed using the Big Five Inventory-2 (Danner et al., 2019.; Soto & John, 2017). Emotional stability and Extraversion were measured with twelve items each. Participants rated each item on a 5-point Likert scale ranging from 1 ( disagree strongly ) to 5 ( agree strongly ). Since the original scoring key (Soto & John, 2017) was designed for negative emotionality/neuroticism, we reversed the coding direction. Both scales had very good reliability: Emotional stability ω = 85–.89, and extraversion ω = 86–.88. Descriptives for each assessment are displayed in Table 1. Implicit Self-Concepts. The Implicit Association Test (IAT) was used to assess implicit self-concepts of emotional stability and extraversion (Schmukle et al., 2008). The IAT is a reaction-time-based measure used to assess the strength of less conscious associations between self-concepts and attributes. Using a Computer, participants quickly sort words related to high versus low trait levels while associating them with either themselves or others. Faster reaction times when pairing a trait level with themselves compared to others represent a stronger implicit association with that specific trait level (Schmukle et al., 2008). The IAT has been validated in prior research for measuring implicit concepts of attitudes and personality traits (Back et al., 2009; Greenwald et al., 2003). Specifically, it involves word-sorting tasks comprising three practice blocks of 20 trials each (blocks 1, 2, 4) and two test blocks (blocks 3 and 5), with 60 trials per block for each trait(Greenwald et al., 2003; Richetin et al., 2015). Target categories ( me and others ) each included five stimuli (e.g., I, myself, their, your ). Attribute categories (traits) also consisted of 5 stimuli each, focusing on anxiety versus calmness (e.g., calm ) for emotional stability, and extraversion versus introversion (e.g., talkative ) for extraversion. In test blocks 3 and 5, target and attribute stimuli were interchanged. The word order was randomized across blocks, and stimuli within a block were repeated without replacement until the specified number of trials was completed. Implicit self-concept values were calculated using built-in error penalties and winsorized reaction times (i.e., 10,000 ms; (Greenwald et al., 2003; Richetin et al., 2015)). Split-half reliabilities were acceptable, with an internal consistency of .70–.76 for emotional stability, and .89–.95 for extraversion. Descriptives for each assessment are displayed in Table 1. Correlations between all measurements of personality traits are displayed in Table S15. Table 1 Descriptive Statistics of Explicit and Implicit Self-Concepts of Emotional Stability and Extraversion Variable M ( SD ) n T1 T2 T3 T4 T5 Emotional Stability Explicit 2.86 (0.60) 161 3.07 (0.64) 151 3.25 (0.62) 141 3.32 (0.61) 123 3.30 (0.67) 100 Implicit 0.33 (0.37) 143 0.34 (0.36) 141 0.28 (0.41) 135 - 0.35 (0.42) 94 Extraversion Explicit 3.21 (0.64) 161 3.33 (0.64) 151 3.40 (0.59) 141 3.36 (0.51) 123 3.29 (0.54) 100 Implicit -0.21 (0.56) 142 -0.09(0.56) 141 -0.08 (0.56) 135 - -0.02 (0.63) 93 Personality States We measured personality states using six bipolar items for emotional stability (e.g., stressed versus relaxed ) and four items for extraversion (e.g., shy versus talkative ). The items were adapted from the Multidimensional Mood Questionnaire (Hinz et al., 2012; Steyer et al., 1994). Participants responded on a scale from scale ranging from 1 to 7. Internal consistency was excellent, with range of ω = .92 for state emotional stability and range of ω = .88 for state extraversion. The intraclass correlation (ICC) shows substantial variation in states within individuals across the intervention. Descriptives for each measuring point are presented in Table 2. Table 2 Descriptive Statistics of Weekly States Emotional Stability and Extraversion Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 M ( SD ) n M ( SD ) n M ( SD ) n M ( SD ) n M ( SD ) n M ( SD ) n M ( SD ) n M ( SD ) n ICC Emotional Stability 4.15 (1.17) 154 4.24 (1.17) 154 4.28 (1.26) 151 4.30 (1.28) 147 4.48 (1.31) 149 4.62 (1.28) 144 4.74 (1.34) 143 4.80 (1.35) 134 .38 Extra-version 4.69 (1.09) 154 4.75 (1.08) 154 4.87 (1.07) 151 4.81 (1.15) 147 4.97 (1.08) 149 5.00 (1.11) 143 5.07 (1.06) 143 5.08 (1.05) 135 .47 Control Variables Engagement in the intervention was assessed with 5 items in each weekly protocol. Participants reported the extent to which they completed weekly tasks on a scale from 1 ( not at all ) to 7 ( completely ) and rated their use of audio material. Additionally, they reported how often they applied knowledge and skills from the training in daily life, both on a scale from 1 ( not at all ) to 7 ( daily ). Finally, participants rated (5) the extent of interaction with their training buddy on a scale from 1 ( not at all ) to 5 ( intensively ). Context factors were measured by 3 items. Participants rated on a bipolar scale how hectic (1 = very hectic to 7 = very calm; reversed ), atypical (1 = very untypical to 7 = very norma; reversedl ) and how exhausting ( 1= very exhausting to 3 = very relaxed; reversed ) their week was. Analytic Strategy We winsorized the values of all variables ( M ± 3 SD ) in cases of outliers ( n = 7). We observed a small number of missing values, with an average of 2.3 % for data from weekly protocols and 8.9 % for trait measures. Age Group was coded as 0 = younger adults (18–37 years) and 1 = older adults (50+ years), based on the bimodal distribution of the variable, and grand-mean centered. We used RStudio Version 1.4.1106 for data preparation and control analyses (RStudio Team, 2021). We tested measurement invariance for each trait and type of self-concept and strong measurement invariance held in each measurement model (Chen, 2007; see Table S16 for details). For hypothesis testing, we conducted multilevel analyses to test our hypotheses regarding state changes (H1a, H1b, H6a, and H6b). At Level 1, we modeled time (a continuous variable coded from 0 to 7) as a random within-person effect. At Level 2, we included the age group as a fixed between-person effect. Further, we included cross-level interaction terms between time and age group as predictors. Additionally, to examine whether changes in extraversion were more pronounced in Part 2 of the training, we specified multilevel models incorporating a discontinuous dummy-coded time variable (Part 1 coded as 0, Part 2 as 1) and an interaction term between time (i.e., week) and intervention part (i.e., dummy variable). To test hypotheses involving trait change, we applied latent neighbor change (H2a-H4, H7a, and H7b; as used in Wrzus et al., 2021) and latent growth analyses (H5a, H5b, H8a, and H8b; as used in Geiser, 2011) in Mplus Version 8.6 (Muthén & Muthén, 1998–2017). Personality traits were modeled as latent variables. Latent explicit self-concepts were represented using three content-based parcels with averages of four items each, capturing the three facets of neuroticism/emotional stability anxiety , depression , emotional volatility and extraversion sociability, assertiveness, energy level , respectively (Matsunaga, 2008; Soto & John, 2017). Latent implicit self-concepts were modeled with two parcels based on split-half D2 scores (Schmukle et al., 2008). Neuroticism scores were reversed to represent emotional stability. Further, all models included indicator-specific method factors for latent traits (Figure 5), which offer more parsimonious and psychometrically robust solutions than correlated residuals (Geiser & Lockhart, 2012). We modeled three distinct model types for each trait self-concept. In Model A Latent neighbor change , we modeled change across two neighboring time intervals, from T1 to T2 and from T2 to T3, separately, to investigate whether the trait changes differed in the first and second intervention phases (Figure 5A). The age group served as a predictor of trait change during each phase. We used the maximum likelihood estimator (MLR) with robust standard errors. In Model B Latent growth , we applied bivariate latent growth models to investigate whether state changes during the intervention predicted trait changes (see Figure 5B; Muthén & Muthén, 1998–2017). Each model included an intercept and growth factor. The intercept was fixed to 1 across time . We fixed loadings of the latent trait and state slopes, where each unit increment corresponded to one week. For the trait slope, loadings were set to 0, 4, and 8 to reflect the approximate 4-week intervals between the 3 assessments at T1, T2, and T3. Similarly, for the state slope, loadings were set to 0, 1, 2, 3, 4, 5, 6, and 7 across the eight weekly assessments. The trait slope was predicted by the state slope, age group, and their interaction. In Model C Piecewise latent growth , we specified one intercept factor and two latent growth factors to examine trait trajectories across the intervention and the 12-month follow-up (see Figure 5C): Slope 1 modeled growth across the intervention and a plateau at later time points, while Slope 2 modeled potential separate trajectories following the intervention. Loadings were specified as 3 and 12 to represent intervals of 3 and 12 months post-intervention. Both slopes were predicted by age group. We used the Bayes estimator with default, non-informative priors (Muthén & Asparouhov, 2012) for Model B and C, which did not converge with MLR estimation likely due to the complexity. 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Test of the role of errors, extreme latencies treatment, scoring formula, and practice trials on reliability and validity. PLOS ONE , 10 (6), e0129601. https://doi.org/10.1371/journal.pone.0129601 Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R. (2007). The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological Science , 2 (4), 313–345. https://doi.org/10.1111/j.1745-6916.2007.00047.x Roberts, B. W., Luo, J., Briley, D. A., Chow, P. I., Su, R., & Hill, P. L. (2017). A systematic review of personality trait change through intervention. Psychological Bulletin , 143 (2), 117–141. https://doi.org/10.1037/bul0000088 Roberts, B. W., Walton, K. E., & Viechtbauer, W. (2006). Personality traits change in adulthood: Reply to Costa and McCrae (2006). Psychological Bulletin , 132 (1), 29–32. https://doi.org/10.1037/0033-2909.132.1.29 Schlippe, A. V., & Schweitzer, J. (2016). Lehrbuch der systemischen Therapie und Beratung I: Das Grundlagenwissen (3. Aufl.). Vandenhoeck & Ruprecht. https://doi.org/10.13109/9783666401855 Schmukle, S. C., Back, M. D., & Egloff, B. (2008). Validity of the Five-Factor Model for the Implicit Self-Concept of Personality. European Journal of Psychological Assessment , 24 (4), 263–272. https://doi.org/10.1027/1015-5759.24.4.263 Smillie, L. D., Wilt, J., Kabbani, R., Garratt, C., & Revelle, W. (2015). Quality of social experience explains the relation between extraversion and positive affect. Emotion , 15 (3), 339–349. https://doi.org/10.1037/emo0000047 Sneed, J. R., & Whitbourne, S. K. (2003). Identity processing and self-consciousness in middle and later adulthood. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences , 58 (6), P313–P319. https://doi.org/10.1093/geronb/58.6.P313 Soto, C. J., & John, O. P. (2017). 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(2016). Personality trait development at the end of life: Antecedents and correlates of mean-level trajectories. Journal of Personality and Social Psychology , 111 (3), 411–429. https://doi.org/10.1037/pspp0000071 Wetherell, J. L., Petkus, A. J., Thorp, S. R., Stein, M. B., Chavira, D. A., Campbell-Sills, L., Craske, M. G., Sherbourne, C., Bystritsky, A., Sullivan, G., & Roy-Byrne, P. (2013). Age differences in treatment response to a collaborative care intervention for anxiety disorders. British Journal of Psychiatry , 203 (1), 65–72. https://doi.org/10.1192/bjp.bp.112.118547 Wright, A., Haehner, P., Hopwood, C., & Bleidorn, W. (2024). A systematic review and taxonomy of neuroticism interventions for the general public. Preprint at https://doi.org/10.31234/osf.io/jy3eb Wrzus, C. (2021). Processes of personality development: An update of the TESSERA framework. In The Handbook of Personality Dynamics and Processes (S. 101–123). Elsevier. https://doi.org/10.1016/B978-0-12-813995-0.00005-4 Wrzus, C., Luong, G., Wagner, G. G., & Riediger, M. (2021). Longitudinal coupling of momentary stress reactivity and trait neuroticism: Specificity of states, traits, and age period. Journal of Personality and Social Psychology , 121 (3), 691–706. https://doi.org/10.1037/pspp0000308 Wrzus, C., & Roberts, B. W. (2017). Processes of personality development in adulthood: The TESSERA framework. Personality and Social Psychology Review , 21 (3), 253–277. https://doi.org/10.1177/1088868316652279 Additional Declarations There is NO Competing Interest. 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Implicit measures \u0026nbsp;\u0026nbsp;were only assessed at T1-T3 and T5.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6206183/v1/0466499829fcd8f891ee8752.png"},{"id":80634675,"identity":"56180c30-a6d0-4760-9d72-4a1d4b9a2a6b","added_by":"auto","created_at":"2025-04-15 12:15:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":138778,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAge \u0026nbsp;\u0026nbsp;Differences in Engagement with the Intervention and Context Factors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. All variables were scaled 1-7, except buddy \u0026nbsp;\u0026nbsp;exchange, which was scaled 1-5. Error bars represent standard errors.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6206183/v1/88d8c36f21c9d3fc4857f43c.png"},{"id":80635411,"identity":"b11bf4cd-d69a-4f84-87d8-28733b295146","added_by":"auto","created_at":"2025-04-15 12:23:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":137435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLatent Neighbor Change and Growth Models\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. \u003c/em\u003eA Latent neighbor change model: Estimates latent trait change separately for each intervention phase and predicts trait change by age group. B Latent Growth Model: Examines trait changes across the intervention: Latent slopes of trait changes from T1 to T3 are predicted by the latent slope of state changes across the eight-week intervention, age group, and their interaction. C Piecewise Latent Growth Model: Estimates latent growth in personality traits across the intervention (S\u003csub\u003e1\u003c/sub\u003e) and follow-up asssesments (S\u003csub\u003e2\u003c/sub\u003e). I = Intercept, S = Slope. Latent variables are represented in rounded forms. Latent traits were estimated with three indicators P (i.e., parcels) for explicit trait measures and two indicators for implicit trait measures at each measurement point. Measurement invariance was established by constraining intercepts and factor loadings to be equal for each measurement. Method effects over time were addressed using indicator-specific method factors (IS2, IS3).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6206183/v1/e8d05c3fedf3ee3cb49490dd.png"},{"id":96798732,"identity":"e21fec9d-bf7e-4334-b438-01b3ca95efb0","added_by":"auto","created_at":"2025-11-26 08:11:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1879416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6206183/v1/9c8ce395-a565-439c-990f-162ca94ac6cd.pdf"},{"id":80634674,"identity":"73c99630-08d1-4c27-b484-1b5fcaaff4d4","added_by":"auto","created_at":"2025-04-15 12:15:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":502008,"visible":true,"origin":"","legend":"supplementary materials","description":"","filename":"Paper3PersDevIntervSupp250310.docx","url":"https://assets-eu.researchsquare.com/files/rs-6206183/v1/2109c674292734c58743032b.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Similar Personality Changes among Younger and Older Adults: Findings from a Multi-Method Intervention Study on Socio-Emotional States and Traits","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMany people desire to change some of their personality characteristics in one way or another, and for example, want to be generally more outgoing or better able to handle stress (i.e., increase extraversion or emotional stability, Hudson et al., 2020; Stieger et al., 2021).\u0026nbsp;While many aspire to change their traits, the goal to change is generally not sufficient (Haehner et al., 2024; Hudson et al., 2019; L\u0026uuml;cke et al., 2020), emphasizing the need for effective interventions. Currently, evidence is accumulating that personality interventions can elicit targeted changes in self-reported traits, yet other trait manifestations as well as the processes underlying trait changes are scarcely examined (Haehner et al., 2024; Stieger et al., 2021; Wrzus \u0026amp; Roberts, 2017). Theoretical models highlight gradual shifts in personality states as well as self-reflections as potential mechanisms of long-term personality trait change\u0026nbsp;(Baumert et al., 2017; Jackson \u0026amp; Wright, 2024; Wrzus \u0026amp; Roberts, 2017). Also, while the majority of personality intervention studies focused on young adults (Haehner et al., 2024), meta-analytic work demonstrates that personality development occurs throughout the entire adult lifespan, yet is more pronounced in young compared to later adulthood (Bleidorn et al., 2023; B\u0026uuml;hler et al., 2024; Roberts et al., 2006). These age differences in normative personality development raise the question, whether personality traits are less malleable in late adulthood (Wrzus \u0026amp; Roberts, 2017) leading to less pronounced changes when older adults participate in personality change interventions.\u003c/p\u003e\n\u003cp\u003eIn the present research, we addressed these gaps and examined (a) whether short-term personality state changes lead to lasting trait changes during and beyond a socio-emotional intervention and (b) how these changes differ between younger and older adults. Furthermore, we expanded past operationalizations of personality, which primarily relied on self-report questionnaires that assess explicit self-concepts, through indirect measures that aim at implicit representations of self-concepts\u0026mdash;focusing on the Big Five traits of emotional stability and extraversion, which are most often desired to change (Stieger et al., 2021).\u003c/p\u003e\n\u003ch3\u003eProcesses of Personality Development in Adulthood\u003c/h3\u003e\n\u003cp\u003eProcesses theories on personality development highlight so-called pre-action, action, and post-action factors that may facilitate or hinder change in personality traits (Geukes et al., 2018; Hennecke et al., 2014; Jackson \u0026amp; Wright, 2024; Wrzus \u0026amp; Roberts, 2017). Pre-action factors include the desire and belief in the feasibility of personality change, while action factors primarily involve engaging in behaviors relevant for trait changes, such as reserved individuals acting more outgoing than usual. Post-action factors include self-reflections and attributions of changed behaviors. In the current research, we focus on personality states as action factors and examine how their changes during an intervention might subsequently influence explicit and implicit trait self-concepts of emotional stability and extraversion. We address pre- and post-action factors during the intervention through goal setting, discrepancy awareness, and reflection (see \u003cem\u003eMethod\u003c/em\u003e section).\u003c/p\u003e\n\u003cp\u003ePersonality states represent momentary feelings, thoughts, and behaviors related to trait-domains (Fleeson \u0026amp; Gallagher, 2009). States that are congruent with existing trait levels are thought to reinforce stability, while incongruent states should promote trait changes\u0026nbsp;(Wrzus \u0026amp; Roberts, 2017). On a state level emotional stability is represented by how individuals react emotionally to difficult situations and how intense and stable their emotions are (Soto \u0026amp; John, 2017; Suls \u0026amp; Martin, 2005). State extraversion is characterized by social and assertive behavior, positive affect and high levels of energy in social situations (Smillie et al., 2015; Soto \u0026amp; John, 2017). In this research, we follow dual-processes models of personality (Back et al., 2009; Rauthmann, 2024; Wrzus \u0026amp; Roberts, 2017) suggesting that personality traits are more than the averages of states and are also mentally represented explicitly, thus directly accessible in questionnaires, as well as implicitly, thus accessible with less direct measures such as word categorization tasks (e.g., IAT; Greenwald et al., 1998). Reflective processes (e.g., self-reflection) are assumed to translate changes in personality states primarily into the explicit self-concept, while associative processes (e.g., associative learning) are assumed to contribute to changes in the implicit self-concept (Gawronski \u0026amp; LeBel, 2008; Wrzus \u0026amp; Roberts, 2017).\u003c/p\u003e\n\u003cp\u003eEmpirical studies showed that both explicit and implicit self-concepts predict momentary behavior/states, especially for extraversion and emotional stability\u0026nbsp;(Back et al., 2009; Quintus et al., 2021). Regarding the reversed effect of momentary states predicting changes in trait self-concepts, longitudinal studies found that repeated extraverted states predicted changes in explicit self-concepts of extraversion (Quintus et al., 2021; Van Zalk et al., 2020). Similarly, repeated stress-related negative affect predicted decreases in explicit self-concepts of emotional stability (Borghuis, et al., 2019; Quintus et al., 2021; Wrzus, 2021). Yet so far,\u0026nbsp;only one study has linked personality states to changes in implicit self-concepts, showing effects for extraversion but no other\u0026nbsp;Big Five traits\u0026nbsp;(Quintus et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eVolitional Personality Development\u003c/h3\u003e\n\u003cp\u003eEven though many people desire to increase their emotional stability and extraversion, this desire does often not result in trait changes (for review, Haehner et al., 2024). A perhaps obvious reason for these findings may be that changing one\u0026rsquo;s personality traits might be difficult, particularly in adulthood, when traits are on average comparatively stable and are thought to undergo little change (Bleidorn et al., 2022; Roberts et al., 2006). For those aiming to alter daily behaviors and achieve long-term personality trait changes, it appears essential to focus on strategies that support to initiate and sustain changes in trait-relevant states and related self-concepts over time (Hennecke et al., 2014; Jackson \u0026amp; Wright, 2024; Wrzus \u0026amp; Roberts, 2017).\u003c/p\u003e\n\u003cp\u003eRecent meta-analytic findings on volitional personality development further support the effectiveness of interventions in achieving personality trait changes (Haehner et al., 2024). These interventions have used a multitude of different strategies (e.g., see Haehner et al., 2024; Wright et al., 2025), which can be organized into three broad categories (Allemand \u0026amp; Fl\u0026uuml;ckiger, 2017; Wright et al., 2025): (a) motivators such as discrepancy awareness and goal setting, (b) behavioral practice, (c) self-reflection to target beliefs and insight as well as reinforcement of new behaviors. Strategies in these three domains operate across the (a) pre-action, (b) action, and (c) post-action stages, with reflective processes shaping explicit self-concepts and practicing new behaviors influencing implicit ones through associative processes, such as reinforcement and feedback learning (Wrzus \u0026amp; Roberts, 2017), Wright et al., 2025.\u003c/p\u003e\n\u003cp\u003eDespite these theoretical insights and interventions targeting motivation, behavior, and reflection (Haehner et al., 2024; Wright et al., 2025), empirical studies rarely examined state changes and reflection directly. Also, research on how personality states and traits may change jointly during interventions remains limited (e.g., Olaru et al., 2025). Initial evidence suggests that state changes precede trait changes for resilience, a construct related to emotional stability (Stieger et al., 2022), and other traits such as mindfulness (Kiken et al., 2015). However, most studies have relied on self-report questionnaires of explicit self-concepts (but see Olaru et al., 2025). These measures capture self-evaluations of personality traits but are not suited to assess more indirect representations, that is, implicit self-concepts of traits (Back et al., 2009). Moreover, compared to indirect measures, explicit self-reports tend to be more susceptible to social desirability and demand effects (Kr\u0026auml;mer et al., 2024). Such effects are particularly relevant to account for when studying personality interventions, when participants desire (to observe) trait changes. Addressing these gaps is crucial to understanding the nuanced processes that enable personality change and developing more effective interventions.\u003c/p\u003e\n\u003ch3\u003eAge Differences in Personality Development and the Underlying Processes\u003c/h3\u003e\n\u003cp\u003ePrevious studies on volitional personality development have predominantly focused on younger samples, limiting the generalizability of their results to older individuals. Moreover, intervention studies with older adults could help to understand whether the slower pace of typical personality development in older age (Bleidorn et al., 2022; Roberts et al., 2006) relates to changes in processes of personality development. We propose that older adults may exhibit less pronounced changes in personality-relevant states (i.e., thoughts, feelings, behaviors) due to slower learning processes, such as associative learning (Mutter et al., 2019) and reinforcement learning (Cutler et al., 2021). Furthermore, with age, changes in these states could affect personality self-concepts less strongly over time. This diminished influence may stem from reduced engagement in reflective processes (K\u0026uuml;chler et al. 2025; \u003cem\u003ereference blinded\u003c/em\u003e) and from reflection focused on integrating experiences into existing self-concepts (Sneed \u0026amp; Whitbourne, 2003). For example, older adults are less likely to compare themselves to others or their past selves regarding personality traits and other characteristics (\u003cem\u003ereference blinded\u003c/em\u003e). Also, older adults favor identity assimilation, thus preserving consistency in their self-views (Sneed \u0026amp; Whitbourne, 2003). Regarding implicit self-concepts, disruptions in learning processes critical for state changes may similarly hinder modifications in less conscious associations crucial for implicit self-concepts (Mutter et al., 2019).\u003c/p\u003e\n\u003cp\u003eSo far, only few studies examined age differences in state-trait links directly and observed mixed findings. Whereas one observational study found that changes in repeated stress reactivity in daily life were most strongly associated with changes in trait emotional stability among young adults compared to older adults (Wrzus et al., 2021), another study observed no significant age differences in state-trait links over 2 years (Quintus et al., 2021). Yet, personality intervention studies have not examined age differences in state-trait links likely because the majority focused on young adults only and were therefore not able to test age differences (Haehner et al., 2024). Findings from clinical interventions\u0026mdash;although not fully generalizable to healthy populations\u0026mdash;have also provided mixed evidence regarding age differences in the effects of interventions on changes in traits, some studies suggesting older samples to benefit less from interventions targeting socio-emotional domains than younger samples (e.g., Covin et al., 2008; Wetherell et al., 2013), and others reporting no age-differences in intervention effects (e.g., Cuijpers et al., 2009; Mewton et al., 2013). A meta-analysis of 207 intervention studies observed no substantial age differences in changes after clinical interventions, yet these studies did not assess state-trait associations (Roberts et al., 2017). Thus, multiple factors such as age differences and similarities in learning, motivation, or intervention adherence could explain this finding. Overall, theory and some observational evidence suggest that younger samples may benefit more from personality change interventions. However, to the best of our knowledge, no study to date has examined age differences in state-trait links in a controlled intervention design.\u003c/p\u003e\n\u003ch2\u003ePresent Research\u003c/h2\u003e\n\u003cp\u003eThe present research has two aims: First, expanding the scarce literature on personality state-trait associations, we aimed to investigate whether, during a targeted and evidence-based intervention, state changes in emotional stability and extraversion would predict changes in trait self-concepts. In addition to measuring personality via self-reports (i.e., explicit self-concepts), we innovatively employed indirect measures (i.e., implicit self-concepts) to provide a more comprehensive assessment of personality trait change. We hypothesized that throughout the intervention, state emotional stability (H1a) and state extraversion (H1b) would improve continuously. We further expected that the improvement of state extraversion would be more pronounced during the second half of the intervention, which focused on social competencies. Further, we expected that the intervention would lead to increases in the explicit self-concept of emotional stability (H2a) and extraversion (H2b) as well as the implicit self-concepts of both traits (H3a and H3b, respectively), with more pronounced increases in explicit than implicit trait self-concepts (H4). Consistent with process theories of personality change (ref), we expected that more pronounced changes in state emotional well-being would be associated with stronger changes in trait emotional stability (H5a) and more pronounced changes in state social behavior with stronger changes in trait extraversion (H5b).\u003c/p\u003e\n\u003cp\u003eSecond, we aimed to provide new insights into age differences in state and trait changes as well as their associations. We hypothesized that the increases in state emotional stability (H6a) and extraversion (H6b) would be more pronounced among younger compared to older adults. Also, we expected increases in explicit self-concepts and implicit self-concepts to be more pronounced among younger adults compared to older adults (H7a, H7b). Lastly, we hypothesized that the association between changes in state emotional well-being and trait emotional stability (H8a), and between social behavior and extraversion (H8b) would be stronger in younger than older adults.\u003c/p\u003e\n\u003cp\u003eExploratorily, we investigated the long-term effects of the intervention on explicit and implicit self-concepts of emotional stability and extraversion over 3 and 12 months after the intervention. Additionally, we examined whether younger and older adults differed in engaging in the intervention.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo examine our research questions, we implemented an 8-week in-person intervention grounded in personality development principles (Jackson \u0026amp; Wright, 2024; Wrzus \u0026amp; Roberts, 2017) and personality change interventions (Allemand \u0026amp; Fl\u0026uuml;ckiger, 2017): Changes in state emotional stability and extraversion were facilitated through \u003cstrong\u003epsychoeducation, behavioral instructions\u0026nbsp;\u003c/strong\u003eand coping strategies. Self-reflection exercises targeting changes in explicit self-concepts were encouraged through exercises of awareness of discrepancies between current and desired trait levels as well as reflections on state changes (see detailed intervention overview in Supplementary Table S1). The first part of the intervention, sessions 1-4, focused on emotional stability, while the second part, sessions 4-8, addressed extraversion and interpersonal competencies. In the following, we refer to these as part 1 and part 2. Participants selected a training buddy in the first session and received daily tasks at each session, including emotion regulation exercises, written self-reflections, and planned social interactions, to facilitate engagement with the intervention in-between weekly meetings.\u003c/p\u003e\n\u003cp\u003eYounger (\u003cem\u003en\u003c/em\u003e = 80, 18\u0026ndash;37 years) and older adults (\u003cem\u003en\u003c/em\u003e = 85, 50+ years) attended weekly two-hour sessions, conducted by two trainers in groups of five to twelve participants. Participants eligible for participation were randomly assigned into the intervention or waitlist control groups. More details on the sample, the recruitment, and asessments are provided in the Method section, a detailed session outline is available in Supplementary Table S1, and a validation of the intervention reported in (\u003cem\u003ereference blinded\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003ePersonality traits of emotional stability and extraversion were assessed via questionnaires and an Implicit Association Test(Schmukle et al., 2008) before, midway (week 4), and after the intervention. Weekly assessments tracked state emotional stability and extraversion throughout. Follow-up personality assessments were conducted at three and 12 months post-intervention (see Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eChanges in Personality States and Trait Self-Concepts\u003c/h3\u003e\n\u003cp\u003eResults on changes in personality states and traits before and throughout the intervention are displayed in Figure 2. State changes were analyzed using multilevel analyses. As predicted (H1a\u0026amp;b), state emotional stability and extraversion increased (\u003cem\u003eb\u003c/em\u003e = 0.09, 95% CI [0.06, 0.13] and \u003cem\u003eb\u003c/em\u003e = 0.06, 95% CI [0.03, 0.08] ). Other than expected, state extraversion did not increase more during part 2 of the intervention (\u003cem\u003eb\u003c/em\u003e = 0.03, 95% CI [-0.30, 0.35]). More detailed statistics are provided in Supplementary Tables S2 and S3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFocusing on trait changes, we employed latent neighborhood change analyses to model trait changes from T1 to T2 (Part 1) and T2 to T3 (Part 2) separately. Results indicated that, in line with our predictions (H2a and H2b), the explicit self-concepts of both emotional stability (Part 1: \u003cem\u003eb\u003c/em\u003e = 0.321, 95% CI [0.229, 0.412]; Part 2: \u003cem\u003eb\u003c/em\u003e = 0.319, 95% CI [0.222, 0.416]) and extraversion (Part 1: \u003cem\u003eb\u003c/em\u003e = 0.161, 95% CI [0.098, 0.225]; Part 2: \u003cem\u003eb\u003c/em\u003e = 0.072, 95% CI [0.004, 0.140]) increased during the intervention (also see Table S4).\u003c/p\u003e\n\u003cp\u003eContrary to H3a, the implicit self-concept of emotional stability did not increase during the intervention (Part 1: \u003cem\u003eb\u0026nbsp;\u003c/em\u003e= 0.002, 95% CI [-0.059, 0.063]; Part 2: \u003cem\u003eb\u0026nbsp;\u003c/em\u003e= -0.049, 95% CI [-0.105, 0.006]). In line with H3b, the implicit self-concept of extraversion increased significantly during Part 1 (\u003cem\u003eb\u003c/em\u003e = 0.098, 95% CI [0.024, 0.172]) and showed a similar trend during Part 2 ( \u003cem\u003eb\u003c/em\u003e = 0.062, 95% CI [0.000, 0.124]), without reaching conventional levels of significance.\u003c/p\u003e\n\u003cp\u003eResults partially supported H4: Changes in the explicit self-concept were more pronounced regarding emotional stability. However, regarding extraversion, overlapping confidence intervals of change estimates indicate comparable changes for both types of self-concepts (also see Table S4). Model fit indices of all latent neighborhood change models were good and are displayed in Supplementary Table S5.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eAssociations in State-Trait Changes\u003c/h3\u003e\n\u003cp\u003eResults partially supported H5a regarding the associations between states and traits: The more state emotional stability increased during the intervention, the more the explicit trait self-concept of emotional stability increased (\u003cem\u003eb\u003c/em\u003e = 0.367, 95% CI [0.191, 0.674]), but not the implicit trait self-concept (\u003cem\u003eb\u003c/em\u003e = 0.018, 95% CI [-0.059, 0.117]). Contrary to our assumptions (H5b), changes in state extraversion were not associated with changes in explicit (\u003cem\u003eb\u003c/em\u003e = 0.010, 95% CI [-0.239, 0.290]) or implicit (\u003cem\u003eb\u003c/em\u003e = 0.010, 95% CI [-0.942, 0.299]) trait self-concepts of extraversion throughout the intervention. For complete model results see Supplementary Table S6.\u003c/p\u003e\n\u003ch3\u003eAge Differences in Changes of Personality States, Trait Self-Concepts, and State-Trait Associations\u003c/h3\u003e\n\u003cp\u003eContrary to our predictions of more pronounced intervention effects in young adulthood (H6a and H6b), age did not moderate state changes in emotional stability (\u003cem\u003eb\u003c/em\u003e = -0.01, 95% CI [-0.08, 0.05]) and extraversion (\u003cem\u003eb\u003c/em\u003e = -0.01, 95% CI [-0.06, 0.03]). Also, the analyses did not support age differences in changes in explicit self-concepts of emotional stability (H7a; Part 1: \u003cem\u003eb\u003c/em\u003e = 0.042, 95% CI [-0.109, 0.192]; Part 2: \u003cem\u003eb\u003c/em\u003e = 0.092, 95% CI [-0.037, 0.221]) or implicit trait self-concepts of emotional stability (H7b; Part 1: \u003cem\u003eb\u003c/em\u003e = 0.033, 95% CI [-0.068, 0.134], \u003cem\u003eb\u003c/em\u003e = -0.008, 95% CI [-0.116, 0.100]).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly, regarding extraversion, the analyses did not support age differences in changes in explicit self-concepts (H7a; Part 1: \u003cem\u003eb\u003c/em\u003e = -0.004, 95% CI [-0.123, 0.116]; Part 2: \u003cem\u003eb\u003c/em\u003e = 0.049, 95% CI [-0.056, 0.154]) or implicit trait self-concepts (H7b; Part 1: \u003cem\u003eb\u003c/em\u003e = -0.090, 95% CI [-0.220, 0.040]; Part 2: \u003cem\u003eb\u003c/em\u003e = 0.024, 95% CI [-0.083, 0.132]).\u003c/p\u003e\n\u003cp\u003eContrary to H8a and H8b, associations in state-trait changes were not stronger in younger adults compared to older adults. Specifically, the age group did not moderate the relationship between state and explicit trait changes in emotional stability (\u003cem\u003eb\u003c/em\u003e = -0.148, 95% CI [-0.414, 0.092]) or extraversion (\u003cem\u003eb\u003c/em\u003e = -0.029, 95% CI [-0.392, 0.322]). Similarly, the age group did not moderate the relationship between state and implicit trait changes in emotional stability (\u003cem\u003eb\u003c/em\u003e = 0.039, 95% CI [-0.139, 0.224]) or extraversion (\u003cem\u003eb\u003c/em\u003e = -0.116, 95% CI [-2.033, 0.450]). All non-significant age effects were examined with Bayesian estimation to obtain information on the evidence supporting the null hypotheses (see Supplementary Tables S6, S7, and S8).\u003c/p\u003e\n\u003ch3\u003eExploratory Analyses of Long-term Trait Changes over 3 and 12 Months\u003c/h3\u003e\n\u003cp\u003eWe used piecewise growth curve models to examine whether trait changes during the intervention sustained after 3 and 12 months and whether these long-term trajectories differed between younger and older adults. Figure 3 illustrates changes in standard deviations from T1 across the assessment period (see Table S9 for all parameter estimates).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results showed that the explicit self-concept of emotional stability remained stable after the intervention, with no significant increase or decrease, while the explicit trait self-concept of extraversion demonstrated a small but significant decrease. Both implicit trait self-concepts showed non-significant increases during follow-up assessments. Although explicit self-concept changes did not differ by age, older adults exhibited a significant increase in the implicit self-concept of extraversion. A similar, but nonsignificant trend was observed for age differences in the implicit self-concept of emotional stability.\u003c/p\u003e\n\u003cp\u003eTo examine the evidence supporting the null hypothesis\u0026mdash;meaning the true absence of age differences in the observed effects\u0026mdash;we compared differences in the Bayesian Information Criterion (BIC) between models with and without age group as a predictor (see Table S10 for details). Most models provided positive evidence in favor of the null hypothesis (BIC difference = 2\u0026ndash;6; Bayes factor = 3\u0026ndash;20; Kaplan \u0026amp; Depaoli, 2012). In addition, we found strong evidence supporting the null hypothesis for trait changes in the explicit trait self-concept of emotional stability across the intervention and follow-up period (BIC difference = 6\u0026ndash;10; Bayes factor = 20\u0026ndash;150).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eExploratory Analyses of Engagement During the Intervention\u003c/h3\u003e\n\u003cp\u003eParticipants attended the 8-week training regularly (\u003cem\u003eM\u003c/em\u003e = 6.94 sessions, \u003cem\u003eSD\u003c/em\u003e = 0.06), with a moderate level of accomplished weekly tasks (scaled 1-7; \u003cem\u003eM\u003c/em\u003e = 4.12, \u003cem\u003eSD\u003c/em\u003e = 1.17), and engagement in practice with audio materials (scaled 1-7; \u003cem\u003eM\u003c/em\u003e = 3.75, \u003cem\u003eSD\u003c/em\u003e = 1.56). Yet, they had relatively few exchanges with their training buddies (scaled 1-5; \u003cem\u003eM\u003c/em\u003e = 2.21, \u003cem\u003eSD =\u0026nbsp;\u003c/em\u003e0.71). Participants reported that their weeks during the training were moderately hectic (scaled 1-7; \u003cem\u003eM\u003c/em\u003e = 3.83, \u003cem\u003eSD =\u0026nbsp;\u003c/em\u003e0.97), somewhat exhausting (scaled 1-7; \u003cem\u003eM\u003c/em\u003e = 3.76, \u003cem\u003eSD =\u0026nbsp;\u003c/em\u003e1.00), and moderately typical (scaled 1-7; \u003cem\u003eM\u003c/em\u003e = 3.91, \u003cem\u003eSD =\u0026nbsp;\u003c/em\u003e1.11).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo better understand the lack of age differences in state and trait changes, we explored age differences in these variables of engagement and daily life demands among younger and older adults. Although younger and older adults did not differ in their desires to improve emotional stability (Cohen\u0026rsquo;s d = -0.19, \u003cem\u003ep\u003c/em\u003e = .199), older adults even had a significantly lower desire to improve their extraversion (Cohen\u0026rsquo;s d = -0.36, \u003cem\u003ep\u003c/em\u003e = .026). Importantly, older adults reported more engagement with the intervention (see Figure 4, Panel A): they were more engaged in weekly tasks and audio files. Yet, younger and older adults reported a similar amount of contact with their training buddies as well as practicing acquired skills in their daily lives. Regarding context factors (Figure 4, Panel B), younger adults reported more hectic and atypical weeks during the intervention than older adults, and there were no age difference in weekly exhaustion (see Supplementary Table S11).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis longitudinal study uncovered substantial and lasting increases in personality traits of emotional stability and extraversion among younger and older adults following from an 8-week in-person intervention focusing on socio-emotional aspects of daily thoughts, feelings, and behavior. The findings provide novel theoretical contributions regarding (a) changes beyond trait self-reports, (b) processes underlying personality development, and (c) age similarities in personality development. We address all three aspects next.\u003c/p\u003e\n\u003cp\u003eFollowing the intervention, changes were observed in both explicit and implicit self-concepts of trait extraversion, that is, how people describe themselves in questionnaires and which characteristics people associate themselves with more indirectly (Back et al., 2009; Wrzus \u0026amp; Roberts, 2017), as well as in weekly reports of social behavior (i.e., states), which confirmed hypotheses H1b, H2b, and H3b. Regarding emotional stability, explicit trait self-concepts and weekly reports of states, but not implicit trait self-concepts changed, confirming H1a and H2a, but not H3a. Thus, the current study addressed critical shortcomings of earlier intervention studies on personality development that focused on self-reports to assess personality traits (e.g., Hudson et al., 2020; Stieger et al., 2021). Self-report measures are commonly criticized for being prone to demand effects and report biases, which could result in people reporting increases in personality traits after participating in interventions without increases in other manifestations of personality traits (e.g., implicit self-concepts, behavior).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe hypothesized that changes in explicit self-concepts would be more pronounced than changes in implicit self-concepts (H4). Descriptively changes in implicit self-concept of extraversion were weaker, yet overlapping confidence intervals limit strong conclusions. Together with the non-substantial change in implicit self-concepts of emotional stability, one could speculate that explicit and implicit self-concepts change to different extents or at different time scales. Earlier research postulated that implicit self-concepts might take more time to change (Gawronski \u0026amp; Bodenhausen, 2006; Wrzus \u0026amp; Roberts, 2017), because behavioral changes may need to be repeated more often and internalized through implicit learning. Despite some criticism regarding the reliability and interpretation of implicit measures such as the IAT (Dentale et al., 2016), its extensive usage and insightful findings (Greenwald et al., 2015; Nosek et al., 2007) support its complementary use for personality research. Supporting the theoretical propositions that both explicit and implicit self-concepts can change, a recent longitudinal study also observed meaningful changes in people\u0026rsquo;s implicit self-concept of extraversion (Quintus et al.). Still, specific reflective and associative processes, which explain individual differences in long-term changes of explicit and implicit trait self-concepts, are still poorly understood (Forscher et al., 2019; K\u0026uuml;chler et al, 2025; Wrzus \u0026amp; Roberts, 2017).\u003c/p\u003e\n\u003cp\u003eNonetheless, the current study offers a more comprehensive understanding of \u0026ldquo;action\u0026rdquo; processes underlying personality change, demonstrating that state emotional stability and state extraversion increased during the intervention. Furthermore, individual differences in state increases predicted increases in explicit self-concepts of emotional stability. This is in line with earlier intervention and observational studies (Stieger et al., 2022; Quintus et al., 2021; Wrzus et al., 2022), as well as conceptual frameworks understanding state changes as the building blocks for trait changes (Baumert et al., 2017; Geukes et al., 2018; Wrzus \u0026amp; Roberts, 2017). Recently, an experimental study demonstrated that a single, brief increase in extraverted and emotionally stable behavior led to (temporal) increases in presumably stable trait representations of extraversion and emotional stability (K\u0026uuml;chler et al., 2025). At the same time, earlier research and the current study indicate that motivation to change and behavioral changes are not sufficient to elicit or explain trait changes\u0026mdash;as emphasized in the TESSERA framework (Figure 2, Wrzus \u0026amp; Roberts, 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBehavioral changes often explain small or no portions of individual differences in trait changes (see also Olaru et al., 2025, Quintus et al., 2021; Wrzus et al., 2022). Reflective and associative processes are considered key factors in explaining for whom stronger trait self-concept changes occur (Jackson \u0026amp; Wright, 2023; Wrzus \u0026amp; Roberts, 2017). Most previous intervention studies did not examine the theoretically proposed state-trait links and therefore miss that behavioral changes might not be sufficient to explain changes in people\u0026rsquo;s explicit or implicit trait self-concepts. The current intervention addressed both reflective processes such as noticing behavioral changes and associative processes such as practicing emotion regulation and functional interpersonal behavior repeatedly during daily assignments. At the moment it remains speculative, whether both pathways of personality development were addressed equally in the intervention and the extent and time courses of explicit and implicit trait changes truly differ due to different underlying processes (Gawronski \u0026amp; Bodenhausen, 2006; Wrzus \u0026amp; Roberts, 2017). In summary, intervention effects might be more lasting when not only behavioral changes occur but self-concepts change as well (Wrzus \u0026amp; Roberts, 2017).\u003c/p\u003e\n\u003cp\u003eAs one of few studies, we examined age differences in the change processes of state and traits and found similar changes for younger and older adults in personality states, explicit and implicit traits throughout the intervention. This finding agrees with recent experimental and meta-analytic work that found no significant age differences in trait changes after experimentally induced behavioral changes (K\u0026uuml;chler et al., 2025) or psychotherapy (Cuijpers, 2024; Roberts et al., 2017). These and the current findings offer further indication that smaller normative personality changes with older age (Bleidorn et al., 2023; Roberts et al., 2006) might not be attributable to personality changes are generally diminished or impossible with age. Instead less pronounced normative trait changes (i.e., without intervention) might be attributed to older adults\u0026apos; reduced desire for change (Hudson \u0026amp; Fraley, 2016), and fewer life events triggering changes (B\u0026uuml;hler et al.,2023). This reasoning extends predictions from the TESSERA framework (Wrzus \u0026amp; Roberts, 2017), which proposes that fewer contextual changes and dampened learning processes contribute to smaller normative trait changes with older age. The current results indicate that when older adults want to change and contextual changes occur for example through an (psychotherapeutic) intervention, they might compensate for potential cognitive drawbacks slowing learning (Cutler et al., 2021; Mutter et al., 2019) with higher motivation. Such an interpretation is well in line with lifespan psychology that repeatedly demonstrated continuous improvement in socio-emotional functioning as people get older (Charles \u0026amp; Carstensen, 2010).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe deliberately chose to carry out the intervention in weekly in-person meetings together with tasks for daily practice and a buddy system to enhance adherence to the intervention. The current study herein differs from other recent personality interventions that provided tasks digitally through mail or apps (see Haehner et al., 2024 for a review). Notably, we obtained substantially larger effect sizes regarding increases in emotional stability and similar effect sizes in extraversion compared to digital interventions, which obtained average effect sizes of \u003cem\u003ed\u003c/em\u003e = 0.33 for self-reports of emotional stability and \u003cem\u003ed\u003c/em\u003e = 0.38 for extraversion (Haehner et al., 2024). The current intervention targeted extraversion only in the second half and thus for only four weeks. We speculate that we would have seen even larger changes in extraversion with durations similar to the ones of digital interventions (i.e., 12-16 weeks) because the in-person meetings allowed to train interpersonal behavior directly in interpersonal exercises during the meetings with direct feedback from trainers instead of relying on the participant\u0026rsquo;s adherence to the exercises in their daily lives. This points to a potential benefit of in-person interventions, although the effectiveness and effect sizes of digital interventions are still impressive and allow the inclusion of many people simultaneously (Haehner et al., 2024), which is a disadvantage of in-person interventions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to the already mentioned limitations of the study, that is, shortcomings of the implicit personality assessment and the relatively small number of participants, two further aspects seem relevant for future studies: First, it is still an open question, which processes and aspects are most relevant for altering personality traits in the direction that people desire (Haehner et al, 2024; Wright et al., 2024). Future intervention studies could compare different treatments to gauge the differential contributions of intervention parts (e.g., Haehner et al., 2025). At the same time, differences between such different treatment conditions might not be large, which has been demonstrated repeatedly for psychotherapy and mental well-being interventions (Roberts et al., 2017; van Agteren et al., 2021). Potential reasons are (a) common change factors that are included to most interventions and most powerful, while other factors play only a minor role and (b) limiting people to specific aspects of the treatment (e.g., social feedback, reflection) might be impossible in psychological treatments compared to pharmacological intervention, where provided substances can be strictly controlled (see K\u0026uuml;chler et al., 2025 for similar arguments). Second, we measured states and daily life processes only in weekly assessments to reduce participant burden. More fine-grained assessments of behavioral, cognitive, and emotional changes in people\u0026rsquo;s daily lives would be highly desirable to get an even better understanding of the temporal dynamics of personality changes. Ultimately, the understanding of the daily life processes would greatly facilitate explaining individual differences in the strength and sustainability of behavioral and self-concept changes, which overlap with urgent questions of psychotherapy (Roberts et al., 2017; van Agteren et al., 2021).\u003c/p\u003e\n\u003cp\u003eIn sum, our multimethod study offers a detailed view of how younger and older adults\u0026rsquo; socio-emotional personality traits can change through an in-person intervention. Over the course of 8 weeks, state extraversion and emotional stability increased, which partly explained the increases in trait extraversion and emotional stability. The changes were largely robust over the following 12 months. The current study offers exciting findings for aging societies regarding socio-emotional functioning, where lifelong learning is beneficial and needed both for the individual and society.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003eTransparency and Openness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe report how we determined our sample size, data exclusions, and all measures in this study. The study design, sampling rationale, hypotheses, and data analyses were preregistered on January 16, 2023 OSF:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1) https://osf.io/c7p8y/?view_only=4d3c2f310b394dd790b0192f06aa4c71\u003c/p\u003e\n\u003cp\u003e2) https://osf.io/c7p8y/?view_only=4d3c2f310b394dd790b0192f06aa4c71\u003c/p\u003e\n\u003cp\u003eAt the time of the preregistration, several participants were screened for eligibility (see below for the criteria) and had answered the initial baseline questionnaire (T1). The intervention had not started, and no longitudinal data, which are the only relevant for this manuscript, had been collected. We did not conduct any data inspection before the preregistration. Minor deviations from the preregistration are detailed in Supplementary Table S12. The hypotheses were rephrased to improve grammatical clarity and consistency. The anonymized data, code, and materials are publicly accessible on OSF https://osf.io/ac4b2/?view_only=e4f6d2ef76c842bd8abe7a72ebdbb75b. The study was approved by the Ethics Committee of \u003cem\u003einstitution blinded\u003c/em\u003e, and all participants provided informed consent before participation. The study was performed in line with the principles of the Declaration of Helsinki and we followed JARS (Appelbaum et al., 2018) for reporting standards.\u003c/p\u003e\n\u003cp\u003eThis is the second manuscript submitted from the data of this project. The first manuscript (\u003cem\u003ereference blinded)\u003c/em\u003e focused on the validation of the intervention and changes on mindfulness, self-compassion, and related constructs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Socio-emotional Intervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intervention was designed as an in-person group intervention aimed at promoting emotional stability, extraversion, and mental health in healthy adults. It was conducted over eight weeks, with sessions lasting two hours each. At the end of each session, trainers assigned weekly tasks to participants to help them incorporate new strategies into their daily lives and engage in self-reflection. We provided materials for the weekly tasks in both paper-pencil format and online for audio-based resources. Participants who missed a session had online access to session materials and weekly tasks and were also offered\u0026nbsp;\u003cstrong\u003ecatch\u003c/strong\u003e\u003cstrong\u003e-up sessions\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach intervention group was composed by the same participants across the intervention (five to twelve participants) and guided by two trainers to facilitate a familiar and secure social environment. The training was implemented with different groups meeting on separate weekdays. Sessions took place in course rooms at the (\u003cem\u003einstitutions blinded)\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003ePart 1 of the training focused on stress, resilience, attention, and emotion regulation and intended to primarily increase state and trait emotional stability (see Supplementary Table S1). Part 2 emphasized interpersonal socio-emotional competencies, covering topics such as social dynamics, systemic perspectives on social interactions, practicing social skills with video feedback, and summarizing the training content (Table S1). Thus, the last four sessions aimed to increase state and trait extraversion. Nevertheless, excercises for emotional stability were still encouraged in weekly tasks. The rationale behind this concept was that participants first learn to regulate their own attention and emotions before addressing interpersonal conflicts. This approach is also taken by validated trainings such as the Cognitively-Based Compassion Training\u0026nbsp;(Ash et al., 2021).\u003c/p\u003e\n\u003cp\u003eOverall, the intervention design integrated methods from evidence-based programs, such as Mindfulness-Based Stress Reduction \u0026nbsp;(Kabat-Zinn, 1990), Cognitively-Based Compassion Training (Ash et al., 2021), the Social Emotional Ethical Learning program (Center for Contemplative Science and Compassion-Based Ethics [CCSCBE], 2019), Acceptance and Commitment Therapy (Beck, 1979; Hayes et al., 1999), systemic counseling\u0026nbsp;(Schlippe \u0026amp; Schweitzer, 2016), and Group Training of Social Competences (GSK, Hinsch \u0026amp; Pfingsten, 2015). The intervention was tailored from these approaches for the specific purpose of this study by the project leader --\u003cem\u003eblinded\u003c/em\u003e--, who is a licensed and experienced psychotherapist, mindfulness, and compassion trainer, and \u0026ndash;\u003cem\u003eblinded\u003c/em\u003e---, who is a licensed systemic counselor. Additionally, the training was discussed and modified within the project group, who are experts on research of personality development.\u003c/p\u003e\n\u003cp\u003eThe trainers were 27 graduate students in psychology and educational sciences, who were trained themselves over three months and then served as trainers within the intervention, earning course credits. Their training involved participating for one semester in a train-the-trainer seminar, which combined their own participation in the intervention with subsequent instruction in learning didactical principles, such as maintaining a trainer\u0026rsquo;s attitude and managing challenging participants or situations. This initial trial also served as a pilot test of the intervention, allowing for minor adjustments to each session (e.g., modifying the duration of exercises). Like the participants, the trainers were informed about the study\u0026rsquo;s objectives but remained blind to specific hypotheses (e.g., age differences).\u003c/p\u003e\n\u003cp\u003eEach trainer participated in at least two supervision sessions per training cohort. These supervision sessions and the train-the-trainer seminar were led by the two project principals, (blinded), who have extensive experience in teaching and training facilitation. \u0026nbsp; Trainers reported adhering closely to the training manual (\u003cem\u003eM\u003c/em\u003e = 5.80, \u003cem\u003eSD\u003c/em\u003e = 1.14 on a 7-point scale, where 7 indicated complete adherence). An overview of the intervention and assessments is displayed in Figure 1. We provide a detailed outline of each session in Supplementary Table S1. Materials for each session and the study protocol have not yet been published but are available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRecruitment of Participants and Screening Procedure\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on power analysis, we aimed to recruit 220 participants divided into two age groups \u003cem\u003en\u003c/em\u003e = 110 younger adults (18\u0026ndash;35 years), and \u003cem\u003en\u003c/em\u003e = 110 older adults (aged 50+ years). The power estimation settings were 1\u0026ndash;\u0026szlig; = .80, \u0026alpha; = .05 for medium and large effect sizes and practical considerations related to the feasibility of the in-person intervention study. The intervention was designed as a randomized controlled trial (RCT) with a waitlist control group. A separate publication by the project team validated the effectiveness of the training (\u003cem\u003ereference blinded\u003c/em\u003e). Since comparisons between the intervention group and the waitlist control group are limited to pre- and post-intervention changes and do not extend to the within-person processes examined in this manuscript, the RCT design is not further analyzed in the present study.\u003c/p\u003e\n\u003cp\u003eRecruitment occurred using various online and offline channels (e.g., public lectures and advertising, flyers, social media) to reach a diverse sample. All advertisements informed about the training and the study, including their content, procedure, timeline, costs, and compensation. Also, a weblink led to the online screening.\u003c/p\u003e\n\u003cp\u003eAfter providing informed consent for screening (i.e., asking about demographics and health information), participants underwent eligibility screening. Criteria included: (1) age\u0026nbsp;\u0026ge;18 years, between 18\u0026ndash;35 or 55+ (\u0026plusmn;2 years), (2) internet access and suitable hardware, (3) no concurrent socio-emotional, compassion, or mindfulness training, or psychotherapy, (4) sufficient language skills, and (5) values below depression and anxiety cut-offs (PHQ-9, GAD-7; L\u0026ouml;we et al., 2002, 2008). People reporting suicide ideation, scoring above the cut-offs for depression or anxiety were provided with online resources for therapeutic and counseling support. Eligible participants received a study ID and an information package on data privacy, study details, procedures (e.g., random group assignment), timeline, costs, and compensation. Enrollment required a fee of 80 EUR (50 EUR for students/seniors), with potential reimbursement of up to 110 EUR + 50% of the fee based on study engagement (e.g., completing questionnaires, attending \u0026ge;5 sessions). Those attending at least five of eight sessions could receive a 50% fee refund. Participants were informed about random assignment to an intervention or waitlist control group and enrolled online after providing informed consent\u003c/p\u003e\n\u003cp\u003eA total of 1150 screenings were conducted and 203 individuals eventually enrolled in the intervention, of which 38 dropped out before the training started, and 20 did not complete the training\u0026nbsp;(i.e., \u0026le; 4 training sessions; \u003cem\u003en\u003c/em\u003e = 18). Enrollment occurred in three cohorts, which were conducted in January 2023, April 2023, and June 2023. Drop-out analyses displayed in Table S13 show that completers and dropouts did not differ substantially in the variables of interest. Supplementary Figure S1 details the study and attrition flow, and reasons for exclusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFinal Sample of Participants in the Personality Intervention\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final sample (\u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 165) that participated in the intervention consisted of 80 younger adults (\u003cem\u003eM\u003c/em\u003e\u003csub\u003eAge\u003c/sub\u003e= 28.33, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003eAge\u003c/sub\u003e = 4.92, 75 % female, 65 % university degree) and 85 older adults (\u003cem\u003eM\u003c/em\u003e\u003csub\u003eAge\u003c/sub\u003e= 63.55, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003eAge\u003c/sub\u003e = 7.20, 75 % female, 60% university degree). More detailed sociodemographic information is displayed in Table S14. Individuals who participated at T4 (\u003cem\u003eN\u003c/em\u003e = 123) and/or T5 (\u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 100) did not differ significantly from participants, who did left the study after T3,\u0026nbsp;in any variable of interest or demographically. Table S13 provides a detailed overview.\u003c/p\u003e\n\u003ch2\u003eMeasures\u003c/h2\u003e\n\u003cp\u003ePersonality trait were measured before the start of the training (T1), four weeks into the training (T2), and during the week following the final training session (T3). Additional data collection occurred three months (T4) and twelve months (T5) after T3, with implicit measures being assessed only at T1 to T3 and at T5. Personality states were assessed 8 times, 5 days after the preceding training session.\u003c/p\u003e\n\u003ch3\u003ePersonality Traits\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eExplicit Self-Concepts.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExplicit personality self-concepts were assessed using the Big Five Inventory-2\u0026nbsp;(Danner et al., 2019.; Soto \u0026amp; John, 2017). Emotional stability and Extraversion were measured with twelve items each. Participants rated each item on a 5-point Likert scale ranging from 1 (\u003cem\u003edisagree strongly\u003c/em\u003e) to 5 (\u003cem\u003eagree strongly\u003c/em\u003e). Since the original scoring key\u0026nbsp;(Soto \u0026amp; John, 2017)\u0026nbsp;was designed for negative emotionality/neuroticism, we reversed the coding direction. Both scales had very good reliability: Emotional stability \u0026omega; = 85\u0026ndash;.89, and extraversion \u0026omega; = \u0026nbsp;86\u0026ndash;.88. Descriptives for each assessment are displayed in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplicit Self-Concepts.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Implicit Association Test (IAT) was used to assess implicit self-concepts of emotional stability and extraversion\u0026nbsp;(Schmukle et al., 2008). The IAT is a reaction-time-based measure used to assess the strength of less conscious associations between self-concepts and attributes. Using a Computer, participants quickly sort words related to high versus low trait levels while associating them with either themselves or others. Faster reaction times when pairing a trait level with themselves compared to others represent a stronger implicit association with that specific trait level\u0026nbsp;(Schmukle et al., 2008). The IAT has been validated in prior research for measuring implicit concepts of attitudes and personality traits\u0026nbsp;(Back et al., 2009; Greenwald et al., 2003).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecifically, it involves word-sorting tasks comprising three practice blocks of 20 trials each (blocks 1, 2, 4) and two test blocks (blocks 3 and 5), with 60 trials per block for each trait(Greenwald et al., 2003; Richetin et al., 2015). Target categories (\u003cem\u003eme\u003c/em\u003e and \u003cem\u003eothers\u003c/em\u003e) each included five stimuli (e.g., \u003cem\u003eI, myself, their, your\u003c/em\u003e). Attribute categories (traits) also consisted of 5 stimuli each, focusing on anxiety versus calmness (e.g., \u003cem\u003ecalm\u003c/em\u003e) for emotional stability, \u0026nbsp;and extraversion versus introversion (e.g., \u003cem\u003etalkative\u003c/em\u003e) for extraversion. In test blocks 3 and 5, target and attribute stimuli were interchanged. The word order was randomized across blocks, and stimuli within a block were repeated without replacement until the specified number of trials was completed.\u003c/p\u003e\n\u003cp\u003eImplicit self-concept values were calculated using built-in error penalties and winsorized reaction times (i.e., \u0026lt; 300 ms and \u0026gt; 10,000 ms;\u0026nbsp;(Greenwald et al., 2003; Richetin et al., 2015)). Split-half reliabilities were acceptable, with an internal consistency of .70\u0026ndash;.76 \u0026nbsp;for emotional stability, and .89\u0026ndash;.95 for extraversion. Descriptives for each assessment are displayed in Table 1. Correlations between all measurements of personality traits are displayed in Table S15.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 620px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eDescriptive Statistics of Explicit and Implicit Self-Concepts of Emotional Stability and Extraversion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eVariable \u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cem\u003eEmotional Stability\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eExplicit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2.86 (0.60)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e161\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.07 (0.64)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e151\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.25 (0.62)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e141\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.32 (0.61)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e123\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.30 (0.67)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e100\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eImplicit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.33 (0.37)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e143\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.34 (0.36)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e141\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.28 (0.41)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e135\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.35 (0.42)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e94\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cem\u003eExtraversion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eExplicit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3.21 (0.64)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e161\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.33 (0.64)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e151\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.40 (0.59)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e141\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.36 (0.51)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e123\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.29 (0.54)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e100\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eImplicit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.21 (0.56)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e142\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.09(0.56)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e141\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-0.08 (0.56)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e135\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.02 (0.63)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e93\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003ePersonality States\u003c/h3\u003e\n\u003cp\u003eWe measured personality states using six bipolar items for emotional stability\u0026nbsp;(e.g., \u003cem\u003estressed\u003c/em\u003e versus \u003cem\u003erelaxed\u003c/em\u003e) \u0026nbsp; and four items for extraversion (e.g., \u003cem\u003eshy\u0026nbsp;\u003c/em\u003eversus \u003cem\u003etalkative\u003c/em\u003e). The items were adapted from the Multidimensional Mood Questionnaire\u0026nbsp;(Hinz et al., 2012; Steyer et al., 1994).\u0026nbsp;Participants responded on a scale from scale ranging from 1 to 7. Internal consistency was excellent, with range of\u0026nbsp;\u0026omega;\u0026nbsp;= .92 for state emotional stability and range of\u0026nbsp;\u0026omega;\u0026nbsp;= .88 for state extraversion. The intraclass correlation (ICC) shows substantial variation in states within individuals across the intervention. Descriptives for each measuring point are presented in Table 2.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003eDescriptive Statistics of Weekly States Emotional Stability and Extraversion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeek 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeek 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeek 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeek 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeek 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeek 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeek 7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeek 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEmotional\u003c/p\u003e\n \u003cp\u003eStability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.15 (1.17)\u003c/p\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.24 (1.17)\u003c/p\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.28 (1.26)\u003c/p\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.30 (1.28)\u003c/p\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.48 (1.31)\u003c/p\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.62 (1.28)\u003c/p\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.74 (1.34)\u003c/p\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.80 (1.35)\u003c/p\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eExtra-version\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.69 (1.09)\u003c/p\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.75 (1.08)\u003c/p\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.87 (1.07)\u003c/p\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.81 (1.15)\u003c/p\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.97 (1.08)\u003c/p\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5.00 (1.11)\u003c/p\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5.07 (1.06)\u003c/p\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5.08 (1.05)\u003c/p\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eControl Variables\u003c/h3\u003e\n\u003cp\u003eEngagement in the intervention was assessed with 5 items in each weekly protocol. Participants reported the extent to which they completed weekly tasks on a scale from 1 (\u003cem\u003enot at all\u003c/em\u003e) to 7 (\u003cem\u003ecompletely\u003c/em\u003e) and rated \u0026nbsp;their use of audio material. Additionally, \u0026nbsp; they reported how often they applied knowledge and skills from the training in daily life, both on a scale from 1 (\u003cem\u003enot at all\u003c/em\u003e) to 7 (\u003cem\u003edaily\u003c/em\u003e). Finally, participants rated (5) the extent of interaction with their training buddy on a scale from 1 (\u003cem\u003enot at all\u003c/em\u003e) to 5 (\u003cem\u003eintensively\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eContext factors were measured by 3 items. Participants rated on a bipolar scale how hectic (1 = \u003cem\u003every hectic\u003c/em\u003e to 7 = \u003cem\u003every calm; reversed\u003c/em\u003e), atypical (1 = \u003cem\u003every untypical\u003c/em\u003e to 7 = \u003cem\u003every norma; reversedl\u003c/em\u003e) and how exhausting ( 1= \u003cem\u003every exhausting\u003c/em\u003e to 3 = \u003cem\u003every relaxed; reversed\u003c/em\u003e) their week was.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytic Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe winsorized the values of all variables (\u003cem\u003eM\u003c/em\u003e \u0026plusmn; 3 \u003cem\u003eSD\u003c/em\u003e) in cases of outliers (\u003cem\u003en\u003c/em\u003e = 7). \u0026nbsp;We observed a small number of missing values, with an average of 2.3 % for data from weekly protocols and 8.9 % for trait measures. Age Group was coded as 0 = younger adults (18\u0026ndash;37 years) and 1 = older adults (50+ years), based on the bimodal distribution of the variable, and grand-mean centered.\u0026nbsp;We used RStudio Version 1.4.1106 for data preparation and control analyses (RStudio Team, 2021). We tested measurement invariance for each trait and type of self-concept and strong measurement invariance held in each measurement model\u0026nbsp;(Chen, 2007; see Table S16 for details).\u003c/p\u003e\n\u003cp\u003eFor hypothesis testing, we conducted multilevel analyses to test our hypotheses regarding state changes (H1a, H1b, H6a, and H6b). At Level 1, we modeled time (a continuous variable coded from 0 to 7) as a random within-person effect. At Level 2, we included the age group as a fixed between-person effect. Further, we included cross-level interaction terms between time and age group as predictors. Additionally, to examine whether changes in extraversion were more pronounced in Part 2 of the training, we specified multilevel models incorporating a discontinuous dummy-coded time variable (Part 1 coded as 0, Part 2 as 1) and an interaction term between time (i.e., week) and intervention part (i.e., dummy variable).\u0026nbsp;To test hypotheses involving trait change, we applied latent neighbor change (H2a-H4, H7a, and H7b; as used in Wrzus et al., 2021) and latent growth analyses\u0026nbsp;(H5a, H5b, H8a, and H8b; as used in Geiser, 2011) in Mplus Version 8.6 (Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n, 1998\u0026ndash;2017). Personality traits were modeled as latent variables. Latent explicit self-concepts were represented using three content-based parcels with averages of four items each, capturing the three facets of neuroticism/emotional stability \u003cem\u003eanxiety\u003c/em\u003e, \u003cem\u003edepression\u003c/em\u003e, \u003cem\u003eemotional volatility\u003c/em\u003e and extraversion \u003cem\u003esociability, assertiveness, energy level\u003c/em\u003e, respectively\u0026nbsp;(Matsunaga, 2008; Soto \u0026amp; John, 2017). Latent implicit self-concepts were modeled with two parcels based on split-half D2 scores\u0026nbsp;(Schmukle et al., 2008). Neuroticism scores were reversed to represent emotional stability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther, all models included indicator-specific method factors for latent traits (Figure 5), which offer more parsimonious and psychometrically robust solutions than correlated residuals\u0026nbsp;(Geiser \u0026amp; Lockhart, 2012). We modeled three distinct model types for each trait self-concept.\u003c/p\u003e\n\u003cp\u003eIn \u003cem\u003eModel A\u003c/em\u003e \u003cem\u003eLatent neighbor change\u003c/em\u003e, we modeled change across two neighboring time intervals, from T1 to T2 and from T2 to T3, separately, to investigate whether the trait changes differed in the first and second intervention phases (Figure 5A). The age group served as a predictor of trait change during each phase.\u0026nbsp;We used the maximum likelihood estimator (MLR) with robust standard errors.\u003c/p\u003e\n\u003cp\u003eIn \u003cem\u003eModel B\u003c/em\u003e \u003cem\u003eLatent growth\u003c/em\u003e, we applied bivariate latent growth models to investigate whether state changes during the intervention predicted trait changes (see Figure 5B;\u0026nbsp;Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n, 1998\u0026ndash;2017). Each model included an intercept and growth factor. The intercept was fixed to 1 across time\u003cstrong\u003e. \u003cstrong\u003eWe fixed loadings of the latent trait and state slopes, where each unit increment corresponded to one week.\u0026nbsp;\u003c/strong\u003e\u003c/strong\u003eFor the trait slope, loadings were set to 0, 4, and 8 to reflect the approximate 4-week intervals between the 3 assessments at T1, T2, and T3. Similarly, for the state slope, loadings were set to 0, 1, 2, 3, 4, 5, 6, and 7 across the eight weekly assessments. The trait slope was predicted by the state slope, age group, and their interaction.\u003c/p\u003e\n\u003cp\u003eIn \u003cem\u003eModel C Piecewise latent growth\u003c/em\u003e, we specified one intercept factor and two latent growth factors to examine trait trajectories across the intervention and the 12-month follow-up (see Figure 5C): \u003cstrong\u003eSlope 1 modeled growth across the intervention and a plateau at later time points, while Slope 2 modeled potential separate trajectories following the intervention.\u003c/strong\u003e Loadings were specified as 3 and 12 to represent intervals of 3 and 12 months post-intervention. Both slopes were predicted by age group.\u003c/p\u003e\n\u003cp\u003eWe used the Bayes estimator with default, non-informative priors (Muth\u0026eacute;n \u0026amp; Asparouhov, 2012) for Model B and C, which did not converge with MLR estimation likely due to the complexity. For Bayes estimation, we employed 10,000 iterations per analysis for explicit self-concepts and 20,0000 for implicit self-concepts and models including latent interactions to achieve convergence with values below 1.1 of the Gelman\u0026ndash;Rubin diagnostic (Potential Scale Reduction Factor, PSRF (Gelman \u0026amp; Rubin, 1992; Muth\u0026eacute;n \u0026amp; Asparouhov, 2012). To verify estimation accuracy, we used the first half of iterations as a burn-in, to ensure that estimates and PSRF values remained consistent when doubling iterations. The analyses provided point estimates and 95% credibility intervals (CI) for the posterior distribution, with effects deemed significant if the CI excluded zero.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDisclosure and Competing Interests\u003c/h2\u003e\n\u003cp\u003eWe have no conflicts of interest to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAllemand, M., \u0026amp; Fl\u0026uuml;ckiger, C. (2017). Changing personality traits: Some considerations from psychotherapy process-outcome research for intervention efforts on intentional personality change. \u003cem\u003eJournal of Psychotherapy Integration\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(4), 476\u0026ndash;494. https://doi.org/10.1037/int0000094\u003c/li\u003e\n \u003cli\u003eAsh, M., Harrison, T., Pinto, M., DiClemente, R., \u0026amp; Negi, L. T. (2021). 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Processes of personality development in adulthood: The TESSERA framework. \u003cem\u003ePersonality and Social Psychology Review\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(3), 253\u0026ndash;277. https://doi.org/10.1177/1088868316652279\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"intervention, Big Five personality development, explicit and implicit self-concepts, socio-emotional traits and states, age differences","lastPublishedDoi":"10.21203/rs.3.rs-6206183/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6206183/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePast research showed that personality traits develop less strongly after younger adulthood, though the underlying processes remain poorly understood, and personality intervention studies scarcely investigated age differences. Also, existing findings are mostly limited to explicit assessments of personality traits (i.e., questionnaires). In this preregistered, multi-method study, we examined associations between changes in personality states and explicit and implicit trait self-concepts in younger and older adults (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;165, age range\u0026thinsp;=\u0026thinsp;19\u0026ndash;78 years) after an eight-weeks socio-emotional intervention, three and 12 months later. Findings indicate changes in personality states, explicit self-concepts for both traits, and the implicit self-concept of extraversion. Only state changes in emotional stability predicted changes in the corresponding explicit but not implicit trait self-concept. Importantly, the effects were consistent across age groups, and exploratory analyses showed higher engagement among older adults throughout the intervention. The findings emphasize that older adults might benefit as much from socio-emotional interventions as younger adults, and potential age differences in skill acquisition might be set off through engagement.\u003c/p\u003e","manuscriptTitle":"Similar Personality Changes among Younger and Older Adults: Findings from a Multi-Method Intervention Study on Socio-Emotional States and Traits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-15 11:58:56","doi":"10.21203/rs.3.rs-6206183/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-psychology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commspsychol","sideBox":"Learn more about [Communications Psychology](http://www.nature.com/commspsychol/)","snPcode":"44271","submissionUrl":"https://mts-commspsychol.nature.com/cgi-bin/main.plex","title":"Communications Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb189671-998b-4054-92de-23ee82ff783f","owner":[],"postedDate":"April 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46303737,"name":"Scientific community and society/Social sciences/Psychology/Human behaviour"},{"id":46303738,"name":"Scientific community and society/Social sciences/Education"}],"tags":[],"updatedAt":"2025-11-26T08:11:14+00:00","versionOfRecord":{"articleIdentity":"rs-6206183","link":"https://doi.org/10.1038/s44271-025-00350-2","journal":{"identity":"communications-psychology","isVorOnly":false,"title":"Communications Psychology"},"publishedOn":"2025-11-25 05:00:00","publishedOnDateReadable":"November 25th, 2025"},"versionCreatedAt":"2025-04-15 11:58:56","video":"","vorDoi":"10.1038/s44271-025-00350-2","vorDoiUrl":"https://doi.org/10.1038/s44271-025-00350-2","workflowStages":[]},"version":"v1","identity":"rs-6206183","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6206183","identity":"rs-6206183","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}