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. Author manuscript; available in PMC: 2023 Feb 1.
Published in final edited form as: Schizophr Res. 2022 Jan 10;240:135–142. doi: 10.1016/j.schres.2021.12.043

DECONSTRUCTING EMOTION REGULATION IN SCHIZOPHRENIA: THE NATURE AND CONSEQUENCES OF ABNORMALITIES IN MONITORING DYNAMICS

Lisa A Bartolomeo 1, Ian M Raugh 1, Gregory P Strauss 1,*
PMCID: PMC8917994  NIHMSID: NIHMS1770707  PMID: 35026598

Abstract

Prior studies implicate abnormalities at the identification, selection, and implementation stages of Gross’ extended process model of emotion regulation in schizophrenia. However, it is unclear whether monitoring dynamics (i.e., emotion regulation maintenance, switching, and stopping), another critical component of the model, are also abnormal or what predicts those abnormalities. The current study evaluated switching (i.e., switching to a different emotion regulation strategy because the initial strategy was not effective) and stopping dynamics (i.e., terminating the implementation of an emotion regulation strategy) and their associated mechanisms using 6 days of ecological momentary assessment in 47 outpatients with schizophrenia or schizoaffective disorder (SZ) and 52 healthy controls (CN). Results indicated that individuals with SZ exhibited excessive switching between emotion regulation strategies and delayed stopping compared to CN, self-efficacy moderated group differences in stopping abnormalities, and switching and stopping abnormalities were associated with different patterns of state-level positive and negative symptoms in SZ. Findings may inform psychosocial emotion regulation therapies for SZ that could incorporate elements for monitoring dynamics and associated mechanisms.

Keywords: Schizophrenia, Emotion Regulation, Ecological Momentary Assessment

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#EMA study by Bartolomeo et al. finds that individuals with #schizophrenia differ from healthy controls in key aspects of #emotionregulation in daily life, including excessive switching between strategies and prolonged emotion regulation attempts. In schizophrenia, these abnormalities were associated with different patterns of positive and negative symptoms, which may have important implications for #intervention. Results also revealed that #self-efficacy moderated group differences in rates of continuing versus stopping emotion regulation attempts, which may reflect impaired insight in schizophrenia and a key treatment target.

1. Introduction

Emotion regulation abnormalities are core to many forms of psychopathology and associated with illness liability and poor psychosocial functioning (Berking & Wupperman, 2012; Sheppes, Suri, & Gross, 2015). Mechanistic processes underlying emotion regulation abnormalities are therefore promising targets for prevention and intervention (Berking et al., 2008; Mennin & Fresco, 2010); however, for such efforts to be successful, it is necessary to clarify the nature of emotion regulation abnormalities within different psychological disorders so that treatments can be tailored accordingly. This is highly important for schizophrenia (SZ), a debilitating illness characterized by multiple affective disturbances, including processes related to emotion perception, expression, experience, and regulation (Kring & Elis, 2013; Kring & Moran, 2008; O’Driscoll, Laing, & Mason, 2014; Trémeau, 2006). Unfortunately, the link between emotion regulation and the development and maintenance of psychotic symptoms is not well understood. Facilitating this understanding necessitates a more granular approach that enables examination of multiple, distinct emotion regulation processes operating in tandem, such as James Gross’ Extended Process Model (Gross, 2015).

According to Gross’ model, emotion regulation consists of three stages (identification, selection, and implementation) driven by valuation systems that receive input of one’s current and desired emotional states, contextual factors, and past experiences (Gross, 2015). Monitoring dynamics underlie these regulatory valuation systems, which are responsible for tracking the effectiveness of emotion regulation strategies and guiding the decision to maintain, switch, or stop implementing a strategy. Prior evidence from laboratory and ecological momentary assessment (EMA) studies indicates that schizophrenia is characterized by abnormalities at each stage of emotion regulation, including: 1) inefficient identification characterized by overvaluing the goal to regulate at low levels of negative affect and undervaluing the goal to regulate at high levels of negative affect (Raugh & Strauss, in press); 2) a limited repertoire of emotion regulation strategies and a tendency to select maladaptive strategies (Green, Hellemann, Horan, Lee, & Wynn, 2012; Horan, Hajcak, Wynn, & Green, 2013; Kee et al., 2009; Kimhy et al., 2012; Livingstone, Harper, & Gillanders, 2009); and 3) impaired implementation of numerous emotion regulation strategies, likely resulting from dysfunctional cognitive and neural processes (e.g., aberrant emotion-attention interactions, low cognitive effort) (Bartolomeo, Culbreth, Ossenfort, & Strauss, 2020; Morris, Sparks, Mitchell, Weickert, & Green, 2012; Ursu et al., 2011; van der Meer et al., 2014; Visser, Esfahlani, Sayama, & Strauss, 2018). To our knowledge, no studies have directly examined monitoring dynamics in SZ, which is important for understanding how interactions between identification, selection, and implementation contribute to emotion regulation abnormalities.

Sheppes et al. (2015) proposed potential sources of dysfunction within monitoring dynamics that may contribute to emotion dysregulation in psychopathology. Switching involves changing the current regulatory tactic to a different tactic that is more positively valued. At least two patterns of ineffective switching are possible, including a failure to settle on one strategy (leading to repetitive switching), or a failure to switch to a new strategy despite evidence that the current strategy is ineffective. Stopping is defined as terminating an emotion regulation strategy or tactic when the desired emotional change has occurred, or when repeated emotion regulation efforts have been unsuccessful and the goal to regulate is no longer positively valued. At least two problems with stopping may also be present: terminating a strategy before it has an effect, or terminating too late, even when it is clear that a strategy is ineffective. An EMA study by Visser et al. (2018) found that individuals with SZ reported using more emotion regulation strategies than controls at each daily survey prompt and reported exerting high levels of emotion regulation effort even when negative emotion intensity was absent. These findings provide indirect support for the hypothesis that abnormal emotion regulation monitoring dynamics occur in SZ and are characterized by a failure to settle and delayed stopping.

To directly assess the nature of emotion regulation monitoring dynamic abnormalities in SZ, the current study used EMA to examine switching and stopping dynamics during emotion regulation (specifically the down-regulation of negative emotion) in daily life. Based on prior EMA evidence (Visser et al., 2018), It was hypothesized that individuals with SZ would exhibit increased switching and delayed stopping relative to controls. In addition, the study aimed to identify moderators of switching and stopping abnormalities by examining psychological processes that have been hypothesized to be involved with monitoring dynamic abnormalities, including emotional awareness, emotion regulation self-efficacy, and emotion regulation effort (Sheppes et al., 2015). We hypothesized that increased rates of switching and delayed stopping would be associated with reductions in all of these processes in SZ. Lastly, associations between monitoring dynamic abnormalities and clinical symptoms were examined. Based on prior studies (Badcock, Paulik, & Maybery, 2011; Henry, Rendell, Green, McDonald, & O’Donnell, 2008; Nittel et al., 2018), it was hypothesized that increased rates of switching and delayed stopping would be associated with more severe positive and negative symptoms measured via EMA.

2. Material and methods

This study was preregistered using Open Science Framework (https://osf.io/yd9t3) and is a subset of a larger study (Raugh et al., 2021).

2.1. Participants

Fifty individuals with DSM-5 (American Psychiatric Association, 2013) diagnoses of schizophrenia or schizoaffective disorder (SZ) and 55 healthy controls (CN) participated in the study. SZ were recruited from local community outpatient mental health centers, flyers, and electronic advertisements. Clinical diagnosis was determined via the Structured Clinical Interview for DSM-5 (SCID-5; First, 2015). CN were recruited from the local community using flyers and electronic advertisements. CN had no current psychiatric diagnoses as established by the SCID-5, no current SZ-spectrum personality disorders as determined via the SCID-PD (First, Williams, Benjamin, & Spitzer, 2015), no family history of psychosis, and were not taking psychotropic medications. All participants denied lifetime neurological disorders and substance use disorders within the last 6 months. Subjects provided written informed consent for a protocol approved by the University of Georgia Institutional Review Board and were compensated $20 per hour for laboratory testing, $1 per EMA survey completed, and $80 for returning the phone at the end of the study.

Six participants (3 SZ and 3 CN) were excluded for not reaching a priori EMA compliance standards (responding to <20% of momentary surveys), resulting in a final sample of 47 SZ and 52 CN. Groups did not differ on age, sex, ethnicity, or parental education; however, SZ had lower personal education (see Table 1). Mean adherence rates were 66% in SZ and 74% in CN (F(1, 96) = 3.51, p = .06).

Table 1.

Participant Demographic and Clinical Characteristics

SZ (n=47) CN (n=52) Test Statistic p-value
Age 39.53 (12.31) 38.94 (10.26) F = 0.07 p = 0.80
Parental Education 14.05 (2.80) 13.59 (2.88) F = 0.59 p = 0.45
Participant Education 13.04 (2.25) 15.56 (2.81) F = 23.87 p < .001
% Female 66 69.2 χ2 = 0.12 p = 0.73
% Race χ2 = 7.29 p = 0.20
 White 59.6 44.2
 Black 29.8 28.8
 Asian-American 0 5.8
 Latinx 4.3 11.5
 Biracial 6.4 5.8
 Other 0 3.8
MCCB T-score 41.51 (15.49) 52.17 (10.05) F = 18.40 p < 0.001
Symptom Ratings
 PANSS Total 64.07 (14.09) -- -- --
 PANSS Positive 17.61 (5.99) -- -- --
 PANSS Disorganized 6.89 (2.85) -- -- --
 BNSS Total 15.74 (13.36) -- -- --
 BNSS Alogia 0.76 (1.95) -- -- --
 BNSS Anhedonia 4.33 (4.46) -- -- --
 BNSS Asociality 2.83 (2.62) -- -- --
 BNSS Avolition 3.98 (3.37) -- -- --
 BNSS Blunted Affect 2.43 (3.59) -- -- --
 LOF Total 2.54 (0.88) -- -- --
 LOF Work 1.70 (1.55) -- -- --
 LOF Social 2.94 (1.25) -- -- --
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Note. SZ = schizophrenia group; CN = control group; MCCB = MATRICS Cognitive Consensus Battery; PANSS = Positive and Negative Syndrome Scale; BNSS = Brief Negative Symptom Scale; LOF = Level of Function Scale (mean LOF scores displayed). Values reflect Mean (SD) unless otherwise indicated.

2.2. Procedures

The study consisted of an initial laboratory session, six consecutive days of EMA, and a final post-EMA laboratory session.

During the initial laboratory visit, participants provided informed consent and completed a series of diagnostic and clinical symptom interviews, including the SCID-5 (First, 2015), Brief Negative Symptom Scale (BNSS; Kirkpatrick et al., 2010), Positive and Negative Symptom Scale (PANSS; Kay, Fiszbein, & Opler, 1987), and Level of Function Scale (LOF; Hawk, Carpenter, & Strauss, 1975). All interviews were conducted by either Dr. Strauss (a licensed clinical psychologist) or lab personnel trained to reliability standards (inter-rater reliability of alpha > 0.80) who sought consultation with Dr. Strauss to establish consensus for diagnoses. Participants were provided with a Blu Vivo 5R smartphone running Android operating system 7.0 that was programmed with the mEMA app from ilumivu to collect EMA data. Trained lab personnel instructed participants in the use of the phone and mobile app, including a guided demonstration of survey notifications and completion of a practice survey with an overview and explanation of the types of questions that would be asked. Additionally, participants were provided with written instructions and encouraged to contact the researchers in the event of any technical problems. Lab personnel conducted follow-up calls on the subsequent day to ensure that the phone and app were functioning properly and that there were no issues responding to surveys.

Over six consecutive days, participants were prompted to respond to eight momentary surveys per day that were quasi-randomly scheduled within 90 minute epochs between 9 AM and 9 PM. Momentary surveys were scheduled between 18 minutes to 3 hours apart from each other. Survey availability lasted 25 minutes: 10 minutes prior to and 15 minutes after receiving a notification, which was signaled to the participant via a tone and vibration emitted by the smartphone. Attempts to respond to the survey after the 15-min window were not permitted, but participants were allotted unlimited time to complete the questions. Surveys assessed the following: 1) momentary emotion; 2) emotional events; 3) emotion regulation, including switching (“Did you switch strategies because it was not working?” with responses coded as Yes or No) and stopping (“Are you still trying to change your emotions?” with responses coded as Yes or No); 4) proposed moderators, including emotional awareness (“How well can you describe the feelings that you are having right now?”), emotion regulation self-efficacy (“How successful do you think you will be at changing your emotions the next time you are in this situation?”), and emotion regulation effort (“How much effort did you use to try to change your emotions by ___?”; 5) current context; and 6) momentary positive and negative symptoms. Survey questions and flow are displayed in Supplemental Materials.

The final phase occurred one week after the initial laboratory visit, at the end of the EMA phase. Participants returned the EMA equipment, completed neuropsychological testing (Wechsler Test of Adult Reading (Wechsler, 2001) and the MATRICS Consensus Cognitive Battery (Nuechterlein et al., 2008)), and other study procedures not germane to the current study.

2.3. Data Analysis

SPSS Statistics 26.0 (IBM Corporation, 2019) and R (R Core Team, 2020) were used to conduct the statistical analyses. Only survey instances where participants endorsed down-regulating negative emotion were included in the analysis. To examine the nature of monitoring dynamic abnormalities in SZ, separate multi-level binary logistic regressions were conducted to determine whether the probability of switching and stopping predicted diagnostic group (SZ, CN), with nested levels of day and survey instance; however, when including day resulted in singular fit, it was dropped from the model. Linear mixed modeling (LMM) with an AR1 covariance structure was used to evaluate group (SZ, CN) differences in stopping duration using a fixed model and random slope design. Maximum likelihood estimation was employed to account for missing data, and analyses were nested within day and survey instance. Tests for moderation were pre-registered as additional Group × Context (switching or stopping Yes or No) LMMs. However, it was determined that hierarchical logistic regressions of Context (switching or stopping) as an outcome of Group and Moderator (self-efficacy, etc) were more appropriate for evaluating the hypothesized moderation effects. Separate multi-level binary logistic regressions with a diagonal covariance structure were also conducted to determine whether emotional awareness, emotion regulation self-efficacy, and emotion regulation effort moderated group differences in switching and stopping rate. To assess the relationship between switching and stopping abnormalities with EMA state measures of positive and negative symptoms, point-biserial correlations were used for associations with switching/stopping frequency (dichotomous variables) and bivariate correlations were used for associations with emotion regulation duration (continuous variable). Exploratory correlations were conducted to examine the relationship between monitoring dynamic abnormalities and clinical rating scales.

Additional exploratory analyses that were not preregistered were conducted, including multi-level binary logistic regressions regarding the effects of negative affect, social context (i.e., whether participants reported being currently engaged in a social interaction), goal-directed context (i.e., whether participants reported being currently engaged in a goal-directed activity), and their interactions with Group on the probability of switching and stopping.

3. Results

3.1. Processing dynamic abnormalities

Consistent with hypotheses, multilevel binary logistic regression indicated that SZ had higher rates of switching and LMM indicated that SZ demonstrated delayed stopping relative to CN (see Table 2). These findings suggest that the nature of monitoring dynamic abnormalities in SZ is characterized by more frequent switching between emotion regulation strategies and delayed termination of emotion regulation attempts.

Table 2.

Results of Multilevel Models Examining Whether Groups Differ in Switching and Stopping

SZ (n=47) CN (n=52) Coefficient (SE) t p 95% CI Lower, Upper
Frequency of switching 42.1% 20.80% 1.02 (0.46) 2.24 .026 0.12, 1.93
Frequency of continuing to regulate 53.9% 34.0% 1.95 (0.40) 4.83 <.001 1.16, 2.75
Stopping Duration 82.81 (60.93) 59.53 (49.18) 65.8 (6.16) 10.67 <.001 53.4, 78.1
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Note. Switching rate = percentage of surveys where participants endorsed switching. Stopping rate = percentage of surveys where participants reported they had stopped regulating. SZ = schizophrenia group. CN = control group. Stopping duration = LMM, others regression. Values reflect mean (SD) unless otherwise indicated.

3.2. Moderators of processing dynamic abnormalities

Group differences in switching frequency were not moderated by emotional awareness, emotion regulation self-efficacy, or emotion regulation effort (see Table 3).

Table 3.

Results of Multilevel Models Examining Moderators of Switching and Stopping Rate

Coefficient (SE) z p 95% CI Lower, Upper
Switching rate
Emotional Awareness
 Group 2.06 (1.29) 1.6 0.11 −0.46, 4.59
 Emotional Awareness 0.01 (0.02) 0.83 0.41 −0.02, 0.04
 Group × Emotional Awareness −0.01 (0.02) −0.87 0.39 −0.05, 0.02
Self-efficacy
 Group 0.97 (1.51) 0.64 0.52 −1.99, 3.92
 Self-efficacy 0 (0.02) 0.15 0.88 −0.04, 0.04
 Group × Self-efficacy 0 (0.02) 0.07 0.95 −0.04, 0.05
Effort
 Group 0.59 (1.72) 0.35 0.73 −2.78, 3.97
 Effort 0.01 (0.02) 0.63 0.53 −0.03, 0.05
 Group × Effort 0.01 (0.02) 0.26 0.80 −0.04, 0.05
Stopping rate
Emotional Awareness
 Group 0.34 (0.89) 0.39 0.69 −1.39, 2.09
 Emotional Awareness 0 (0.01) −0.38 0.71 −0.02, 0.02
 Group × Emotional Awareness 0.01 (0.01) 0.84 0.40 −0.01, 0.03
Self-efficacy
 Group −2.32 (1.2) −1.94 0.05 −4.67, 0.02
 Self-efficacy −0.05 (0.02) −3.46 < .001 −0.09, −0.02
 Group × Self-efficacy 0.05 (0.02) 2.86 0.004 0.02, 0.09
Effort
 Group 1.07 (1.24) 0.86 0.39 −1.36, 3.5
 Effort 0.01 (0.01) 0.92 0.36 −0.01, 0.04
 Group × Effort 0 (0.02) −0.09 0.93 −0.03, 0.03
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Note. Switching rate = percentage of surveys where participants endorsed switching. Stopping rate = percentage of surveys where participants reported they had stopped regulating.

With regard to moderators of stopping rate, there was a significant Group × Emotion regulation self-efficacy interaction (see Table 3 and Figure 1). Results indicated that CN were more likely to stop regulation than SZ at lower levels of self-efficacy but less likely to stop regulation than SZ at higher levels of self-efficacy. The Group × Emotional awareness and emotion regulation effort interactions were nonsignificant, suggesting that neither variable moderated group differences in stopping rate.

Figure 1. Moderating Effect of Emotion Regulation Self-Efficacy on Probability of Stopping in Schizophrenia and Control Groups.

Figure 1.

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Note. CN = Control group; SZ = Schizophrenia group. Statistics colored in black reflect between-group contrasts; statistics colored corresponding to the group (CN or SZ) are the linear effect in that group. Shaded area reflects standard error.

*** = p < .001, ** = p < .01, * = p < .05

3.3. Associations with momentary symptoms

In SZ, reduced switching frequency was associated with more severe avolition and asociality in the moment as measured via EMA. These results suggest that negative symptoms are related to being less likely to switch strategies. Correlations with positive symptoms were nonsignificant (see Table 4).

Table 4.

Correlations Between Monitoring Dynamics and Momentary Symptoms

Switching: Frequency of Changing Strategies Stopping: Frequency of Continuing to Regulate Stopping: Emotion Regulation Duration
Anhedonia −0.15 0.04 −0.31**
Avolition −0.19* 0.03 −0.21*
Asociality −0.17* 0.02 −0.30**
Delusions −0.01 0.16* −0.17
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Note. Correlation results examining associations between monitoring dynamics and symptoms in the moment in SZ. Values reflect Pearson correlation coefficients.

**

Denotes significance at the 0.01 level (2-tailed).

*

Denotes significance at the 0.05 level (2-tailed).

Less time regulating was associated with more severe momentary anhedonia, avolition, and asociality. In contrast, higher rates of continuing to regulate were associated with more severe momentary delusions (see Table 4). These findings indicate that negative symptoms may be related to being less likely to persist in emotion regulation attempts, whereas positive symptoms are associated with being more likely to persist.

3.4. Exploratory analyses

Post-hoc exploratory correlations were conducted to examine the association between monitoring dynamics and clinical measures in SZ, including neurocognitive performance, clinically rated symptoms, and functional outcome. Results indicated that lower switching rate was associated with more severe total negative symptoms, anhedonia, and blunted affect. All other correlations were nonsignificant (see Table S2 in Supplemental Materials).

Exploratory analyses that examined negative affect as a predictor of group differences in switching were nonsignificant (B = 0.03, z = 1.79, χ2 = 3.22, p = .072); further, the relationship between negative affect and switching probability was not moderated by group (B = −0.02, z = −1.19, χ2 = 1.42, p = .233). For stopping, there was a significant Group × Negative affect interaction (B = −0.03, z = −2.01, χ2 = 4.04, p = .044), such the relationship between negative affect and the probability of persisting in regulation attempts was stronger in CN (B = 0.05; 95% CI [0.02 to 0.08]) than SZ (B = 0.02; 95% CI [0.001 to 0.04]) (see Figure 2). Specifically, SZ were significantly more likely to persist at lower levels of negative affect but less likely to persist at higher levels of negative affect compared to CN. The results of exploratory analyses examining group differences in the relationship between contextual variables (i.e. social and goal-directed activity) on switching and stopping probability yielded nonsignificant Group × Context interactions (p’s > 0.05), suggesting that social and goal-directed activities did not influence group differences in switching and stopping dynamics (see Table S3 Supplemental Materials).

Figure 2. The Relationship Between Negative Affect and Probability of Persisting in Emotion Regulation in Schizophrenia and Control Groups.

Figure 2.

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Note. CN = Control group; SZ = Schizophrenia group. Statistics colored in black reflect between-group contrasts; statistics colored corresponding to the group (CN or SZ) are the linear effect in that group. Shaded area reflects standard error.

*** = p < .001, ** = p < .01, * = p < .05

4. Discussion

The current study aimed to determine: 1) the nature of emotion regulation processing dynamic abnormalities in SZ, 2) moderators of these abnormalities, and 3) whether abnormalities in monitoring dynamics are associated with state-level clinical symptoms. Consistent with hypotheses, results indicated that the nature of emotion regulation monitoring dynamic abnormalities in SZ is characterized by a failure to settle and delayed stopping. Further, individuals with SZ demonstrated inefficient stopping dynamics, evidenced by patients being more likely to persist in emotion regulation at lower levels of negative affect but less likely to persist at higher levels of negative affect compared to CN. This extends prior laboratory-based research in healthy individuals indicating that greater levels of negative affect were associated with greater likelihood of switching (Murphy & Young, 2020) and that greater responsiveness to internal feedback predicted greater switching frequency (Birk & Bonanno, 2016).

Sheppes et al. (2015) proposed that a failure to settle could result from instability in thoughts and behavior, suggesting that disorganized symptoms or cognitive impairment may contribute to switching abnormalities in SZ. However, post-hoc exploratory correlations between switching rate and measures of disorganized symptoms and cognitive performance did not support this hypothesis (see Table S2 in Supplemental Materials). Regarding delayed stopping in SZ, one potential explanation for this finding is impaired time perception. Individuals with SZ have been shown to overestimate elapsed time compared to healthy controls, which has been attributed to generalized impairments in memory and attention (Bonnot et al., 2011). To explore this possibility, we conducted exploratory correlations between attention and memory subscores on the MCCB and rates of continuing to regulate and emotion regulation duration in SZ. None of the correlations were significant, suggesting that delayed stopping was not associated with attention or memory deficits in the current sample (see Table S2 in Supplemental Materials). Nonetheless, abnormal time perception may still reflect a general cognitive process contributing to longer self-reports of the duration of continuing to regulate.

According to Gross (2015), failure to settle and delayed stopping may be highly demanding on physiological and cognitive systems, thus taxing already limited resources in SZ. Consistent with this notion, past research has shown that inability to discontinue an ineffective emotion regulation strategy and implement a new strategy is associated with worse psychological outcomes, including increased depression, anxiety, and general distress (Kato, 2012). Thus, excessive switching and delayed stopping in SZ may be meaningfully related to the maintenance and exacerbation of psychiatric symptoms, possibly as a result of elevated negative affect, chronic stress, and insufficient resources. Importantly, some emotion regulation strategies are more taxing and require more resources than others (Strauss, Ossenfort, & Whearty, 2016). It is possible that individuals with SZ fail to select the most contextually appropriate strategies because they are less adept at identifying appropriate context for each strategy and when it is most appropriate to use available resources. Inappropriate selection could compound switching and stopping abnormalities, such that SZ may excessively switch between inappropriate strategies and spend more time regulating, leading to further resource depletion. Specific strategies that participants selected and switched between were not examined in the current study, but are important considerations for future studies examining abnormalities in emotion regulation monitoring dynamics in SZ.

Regarding moderators of monitoring dynamics, emotion regulation self-efficacy moderated group differences in rates of continuing versus stopping regulation attempts. CN were more likely to continue regulating at high levels of emotion regulation self-efficacy and more likely to stop at lower levels. This pattern supports Sheppes et al.’s (2015) theory that individuals with low self-efficacy are more likely to stop regulating prematurely, suggesting that self-efficacy is a normative process that guides stopping dynamics in psychiatrically healthy adults. In contrast, momentary levels of self-efficacy did not influence stopping dynamics in SZ. The lack of modulation of stopping rate based on self-efficacy observed in SZ may reflect impaired insight into the effectiveness of regulation attempts. Poor insight may lead to inefficient stopping behaviors, such as failing to stop when efforts are ineffective or stopping prematurely even when efforts are efficacious and may benefit from continued regulation.

The results of correlational analyses indicated that individual differences in positive and negative symptoms are associated with different patterns of monitoring dynamic abnormalities in SZ. Down-regulating negative emotion when positive symptoms are present may require more time, resulting in longer emotion regulation attempts. In contrast, impaired motivation associated with negative symptoms may lessen the likelihood of switching strategies and persisting in emotion regulation attempts that require significant effort. Thus, different clinical profiles within the SZ-spectrum may be differentially associated with specific monitoring dynamic abnormalities.

Certain limitations should be considered. First, the current study examined two monitoring dynamics: switching and stopping. An important direction for future research will be to also examine the third monitoring dynamic, maintenance. Maintenance occurs when a strategy or tactic is effective at changing the emotional response and contextually appropriate, leading to consistent action outputs of identification, selection, and implementation stages across consecutive emotion regulation cycles. Maintenance failures can arise from interference from other active goals (Gross, 2015). To our knowledge, no study has empirically examined emotion regulation maintenance in healthy or clinical populations. EMA is a valuable tool for studying maintenance because it allows researchers to examine the temporal sequence of emotion regulation cycles, including what strategies are implemented and their effectiveness over time. Novel analytic approaches may need to be developed for this purpose. Second, the current sample consisted of stable adult outpatients with chronic SZ. Thus, the results may not extend transphasically (i.e., to individuals in earlier phases of illness) or to inpatient populations. Third, it is unclear whether the observed abnormalities in switching and stopping dynamics are driven by psychosis versus general psychopathology. For example, Sheppes et al. (2015) proposed that manic states in bipolar disorder may be associated with excessive switching, suggesting a failure to settle could occur in both SZ and bipolar disorder. It is important for future studies to examine these processes in other clinical populations in order to determine what is unique or specific to SZ.

Despite these limitations, the present study yielded several important findings that advance the field’s knowledge of emotion regulation abnormalities in SZ and potential targets for intervention. Pending replication and further study, our results indicate that the nature of processing dynamic abnormalities in SZ is characterized by excessive switching and delayed stopping. To target these abnormalities, experiential exposures may be helpful for enhancing contextual learning and expanding the behavioral repertoire of adaptive responding. Exposures provide the opportunity to practice emotion regulation skills, such as maintaining strategies (to target excessive switching), changing regulatory goals, and accepting or tolerating emotions (to target delayed stopping). Exposures are a component of Emotion Regulation Therapy (ERT), an intervention that incorporates principles from CBT, experiential therapy, and DBT (Mennin, 2004; Renna, Quintero, Fresco, & Mennin, 2017). The goals of ERT coincide with the three stages of emotion regulation in Gross’ extended process model, such that patients develop skills to overcome difficulties at the identification, selection, and implementation stages, as well as problems with monitoring dynamics. Although ERT has not been examined in individuals with SZ, there is evidence supporting its efficacy in other clinical populations, including patients with major depressive disorder and generalized anxiety disorder (Mennin, 2004; Mennin, Fresco, Ritter, & Heimberg, 2015).

In order to optimize a psychosocial intervention like ERT for use in psychotic populations, there is a need for additional research on the processes that moderate emotion regulation difficulties. The current findings indicated that self-efficacy moderated stopping abnormalities in SZ, which may stem from deficient insight. To target poor insight into emotion regulation effectiveness, patients may benefit from psychoeducation about emotions/emotion regulation to better understand their emotions and goals. It will be important for future studies to not only replicate and identify new moderators of identification, selection, implementation, and monitoring dynamics, but also to explore how moderators interact across emotion regulation stages and cycles. Different approaches may also be needed to target emotion regulation processes associated with positive versus negative symptoms. For example, patients with high positive symptoms may benefit from a skills-based approach where they learn how to select contextually appropriate strategies and determine when it is appropriate to stop regulating, whereas individuals with high negative symptoms may benefit from treatment targeting defeatist attitudes, which may contribute to being less likely to switch strategies and persist in emotion regulation attempts (Grant, Huh, Perivoliotis, Stolar, & Beck, 2012). These skills may lessen switching frequency and time spent regulating, which in turn may conserve cognitive and physiological resources. A deeper understanding of these processes may inform novel targets for intervention and the development of personalized treatment approaches that target difficulties at a specific stage or moderator.

5. Conclusions

Individuals with SZ demonstrated abnormal monitoring dynamics in daily life compared to CN, including a failure to settle and delayed stopping. Emotion regulation self-efficacy moderated group differences in stopping dynamics, evidenced by individuals with SZ endorsing greater self-efficacy when continuing to regulate and lower self-efficacy when they had stopped regulating, whereas the opposite was evident in CN. In SZ, lower switching frequency and shorter regulation attempts were associated with more severe negative symptoms, while greater frequency of continuing to regulate was associated with more severe positive symptoms. The current findings extend prior evidence for impaired emotion regulation in SZ by identifying novel abnormalities in monitoring dynamics that may have important implications for intervention.

Supplementary Material

1

Biographies

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Lisa Bartolomeo. Lisa Bartolomeo, M.S. is a Clinical Psychology Ph.D. student at the University of Georgia. Her research interests involve emotion regulation and mechanisms underlying negative symptoms in individuals with schizophrenia and youth at clinical high-risk for psychosis.

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Ian Raugh. Ian Raugh, B.A. is a Clinical Psychology Ph.D. student at the University of Georgia. His research interests involve examining mechanisms underlying transdiagnostic impairments in emotion regulation and acceptance, specifically using digital phenotyping technologies.

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Greg Strauss. Greg Strauss, Ph.D. is an Assistant Professor at the University of Georgia and director of the Clinical Affective Neuroscience Laboratory and Georgia Psychiatric Risk Evaluation Program (G-PREP). Dr. Strauss’ program of research examines the phenomenology, etiology, assessment, and treatment of negative symptoms in schizophrenia and youth at clinical high-risk for psychosis.

Footnotes

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