Emotion regulation monitoring in daily life: The roles of event intensity, emotion intensity, perceived regulation success and psychopathology

doi:10.21203/rs.3.rs-4962739/v1

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Research Article

Emotion regulation monitoring in daily life: The roles of event intensity, emotion intensity, perceived regulation success and psychopathology

https://doi.org/10.21203/rs.3.rs-4962739/v1

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Purpose

During emotional events, people monitor the effectiveness of their emotion regulation (ER) to decide whether to keep using their current ER strategy, switch to a new strategy, or stop the regulation process. However, there is little empirical research on the monitoring phase of ER, particularly on what and how situational and individual factors influence this process.

Methods

We tested situational and individual factors as predictors of real-life ER monitoring decisions. 155 young adults responded to 5 prompts per day in a 7-day experience sampling protocol. At each prompt they described an emotional event, rated the intensity of event, their current emotions and perceived success in ER, and reported their decision to simply stop using their current ER strategy or to switch to a new strategy during the event.

Results

Multilevel modeling results showed the decision to stop using the current ER strategy was predicted by perceived success in ER after both positive and negative events, and by depressive and anxiety symptoms after positive events. The decision to switch to a new strategy was more likely when there were high negative emotions after a negative event, and less likely when there was perceived success after a positive event.

Conclusions

These findings suggest that situational and individual factors affect people’s decisions about how to regulate emotion. Interventions addressing excessive emotion regulation issues in response to positive events and their underlying causes may benefit individuals with depression or anxiety.

emotion regulation monitoring

strategy switching

perceived regulation success

depression

anxiety

Emotion Regulation (ER) refers to the process by which individuals attempt to influence their subjective emotional experiences, physiological states, and behavioral expressions by using various strategies, to adapt to a constantly changing environment (Gross, 1998). The extended process model of ER (Gross, 2015a) originally proposed that ER occurs in three stages. The person experiences an emotion and determines whether to regulate it or not (identification); evaluates available strategies and chooses the most appropriate one (selection); and applies the chosen strategy (implementation). However, because the ER process is shaped by situational and individual factors and is therefore dynamic, Gross (2015b) introduced a fourth stage (monitoring). In the monitoring stage, the person evaluates whether regulation is still needed, based on their current emotional state. If regulation is needed, the person then decides whether to keep using the strategy they have been using or to switch to a different strategy. On the other hand, if regulation is no longer needed, the person might stop the regulation process. Therefore, the monitoring phase operates at a higher level than the other three processes (Bartolomeo et al., 2022).

Researchers have examined several situational and individual factors that influence the identification, selection, and implementation stages of the ER process. Situational factors are proximal and would influence the ER process from moment to moment. Examples include intensity of the emotional event, emotion intensity, and perceived success in regulating the emotion. For example, the ER process has been shown to be influenced by the intensity and valence of the emotion being regulated (Blanke et al., 2021; Dixon-Gordon et al., 2015). Individual factors, such as depressive and anxiety symptoms, are more distal but can also influence one's preferences for particular ER strategies. For example, inflexibility in the use of ER strategies has been associated with the development and maintenance of depressive and anxiety disorders (For a review, see Lincoln et al., 2022). In the current study we examined which and how different situational and individual factors influence the monitoring phase of the ER process in everyday life contexts.

Situational factors: event intensity, emotion intensity, and perceived regulation success

Event intensity

The intensity of emotional events or stimuli has been identified as an important influence on ER processes (for a review, see Sheppes et al., 2015). During the identification process, there appears to be a greater need for regulation for events with high intensity than those with low intensity (Barrett et al., 2001). During the selection process, individuals are more likely to use distraction as the ER strategy after more intense negative events, but more likely to use reappraisal after less intense negative events (Shafir et al., 2016; Sheppes et al., 2011). Studies using experience sampling have shown that during the implementation process, more intense events were associated with the use of more strategies (Hiekkaranta et al., 2021; Lennarz et al., 2019). Event intensity may also impact people’s effort in using these ER strategies during ER implementation, with higher effort exerted in response to more intense events (Blanke et al., 2021; Shafir et al., 2016). Notably, there has been no research on the effect of event intensity on the monitoring phase of the ER process.

Only two studies have assessed the effect of event intensity on the ER process when the event was positive. Hiekkaranta et al. (2021) used experience sampling to examine people’s ER strategies after positive events. They found that the intensity of the positive event was associated with the implementation of savoring, emotional expression, and sharing as ER strategies. In a laboratory experiment, Hay et al. (2015) asked participants to choose whether to use distraction or reappraisal when viewing emotional images of different valence (negative vs. positive) and intensity (high vs. low). Participants were more likely to choose distraction in response to high-intensity negative images than in response to high-intensity positive images. However, the choice of ER strategy did not differ for negative and positive low-intensity emotional images conditions, suggesting that both the valence and intensity of the emotional stimuli influenced the ER decision.

Emotion intensity

Emotion intensity—the subjective evaluation of the intensity of experienced emotions following an emotional event—is partially independent of event intensity, with only small to moderate correlations between them (Blanke et al., 2021). Emotion intensity influences the choice of particular strategies and the effort involved in implementing these strategies (Dixon-Gordon et al., 2015), with more intense emotions requiring greater effort to invest. In addition, intense emotions following regulation may indicate inadequate regulation and the need for further effort, and have therefore been found to be related to ER monitoring choices (Birk and Bonanno, 2016; Dorman Ilan et al., 2019; Murphy and Young, 2020). For example, Birk and Bonanno (2016) found in an experiment that the more intense the negative emotions were after an initial regulation attempt, the more often participants chose to switch to a different strategy rather than maintain the current one. The effect of emotion intensity on ER strategy choice during monitoring has not yet been studied in research outside the laboratory.

Perceived success in regulating emotions

Perceived success in regulating emotions refers to individuals’ subjective evaluation of whether they achieved their specific ER goals. This construct is closely associated with the ER process, as it reflects the outcome of the implemented ER strategies (for a review, see Springstein & English, 2024). Previous research has linked perceived regulation success to the different strategies used during the implementation stage of the ER process. For instance, studies involving either young or older adults found that individuals reported higher perceived success when using acceptance as an ER strategy compared to reappraisal (Rompilla et al., 2022; Troy et al., 2018).

The perception of success in achieving ER goals after initial ER efforts has also been shown to predict decisions made during the monitoring process (Springstein and English, 2024). Specifically, individuals are more likely to stop the regulation process when they perceive success in their regulatory efforts, while they may be more inclined to continue using the same strategy or change to a different one if they perceive their efforts as unsuccessful (Murphy and Young, 2020).

Individual factors: psychopathology

Aberrant ER functions have long been linked with emotional disorders (for reviews, see Gross & Jazaieri, 2014; Sheppes et al., 2015), such as depression (Liu and Thompson, 2017) and anxiety (Decker et al., 2008; Mathews et al., 2014). For instance, higher symptoms of depression and social anxiety have been shown to be associated with fewer ER attempts, greater difficulties in selecting appropriate strategies, and lower regulation motivation (Daniel et al., 2024; Millgram et al., 2020). In addition, the habitual use of maladaptive strategies, such as rumination and suppression, along with difficulties in employing adaptive strategies, may further contribute to the maintenance and worsening of depressive and anxiety symptoms (Lincoln et al., 2022). Similarly, associations between the rigid and inflexible use of ER strategies and depressive and anxiety symptoms have been reported (Hiekkaranta et al., 2021; Tng and Yang, 2023).

These studies on the relationship between psychopathology and ER focused on difficulties in the identification, selection, and implementation stages of the ER process. Only one study has examined the role of psychopathology symptoms in the monitoring stage of the ER process. Using an ER choice task in the laboratory, Toh and Yang (2024) found that while more intense negative stimuli were associated with a higher frequency of switching from reappraisal to distraction, the associations between depressive symptoms and strategy switching frequency were not significant. It remains unclear whether and how depressive and anxiety symptoms impact the ER monitoring process in the context of daily life.

The present study

The current study used the experience sampling method (ESM) to examine situational and individual factors associated with ER strategy decisions during the monitoring process. ESM is a longitudinal approach that employs repeated sampling over relatively short intervals to assess behavior, thoughts, and feelings as they occurred, thereby reducing recall bias (Myin-Germeys et al., 2018). Frequent sampling should capture dynamic change during the ER monitoring phase, when individuals can stop using or adjust their strategies. Because the participants provide data during their typical activities throughout the day, we can gain insights into the dynamic ER process in everyday life. Based on the extended process model of ER and extant research, we expected that the situational factors of event intensity and emotion intensity would be correlated with more strategy switching and less ER stopping, while the situational factor of perceived success in regulating emotions would be correlated with less strategy switching and more stopping. Based on extant evidence of the associations between depressive and anxiety symptoms and ER strategy selection and implementation stages, we expected that the individual factors of depressive and anxiety symptoms would be associated with more strategy switching and stopping during the monitoring phase.

Participants

The sample size was determined based on similar ESM studies on strategy selection and ER motives (English et al., 2017; Ortner et al., 2022). One hundred and sixty-nine young adults were recruited by advertising on social media. However, seven participants did not respond to any of the ESM prompts to provide data, and seven participants responded to less than 30% of the prompts(Hiekkaranta et al., 2021); these 14 participants were excluded from the study. The final sample constituted 155 participants (136 women; mean age = 20.07, SD = 2.24, range 17 to 31 years). The study was conducted in accordance with the Declaration of Helsinki and was approved by the ethics review board of the Central China Normal University. Written informed content was obtained from all participants. Upon completion of the study, participants were compensated financially for their time and effort.

Procedure and measures

The study was carried out between November and December in 2022. Participants were invited to visit the laboratory to complete baseline questionnaires, receive information about the ESM procedure, and complete practice ESM questionnaires. Due to the COVID-19 pandemic, 32 of the 155 participants were unable to visit the laboratory. These participants completed an online version of the baseline questionnaires, participated in a briefing session by phone, and subsequently filled out the practice ESM questionnaires.

The ESM period lasted seven consequent days. Participants were semi-randomly prompted by a beep five times each day, from 10 a.m. to 10 p.m., with an interval of at least 90 minutes between two consecutive prompts. After receiving the weblink on their smartphone, participants were asked to complete the ESM questionnaires as soon as possible told the link would expire in 30 minutes. Participants were encouraged to respond to as many of the 35 total prompts as possible, and they were reimbursed 15–25 RMB depending on their compliance with the ESM protocol.

Baseline measures

Symptoms of depression Depressive symptoms were assessed using the Beck Depression Inventory-II (BDI-II; Beck et al., 1996). The BDI-II consists of 21 items. Each item has four response options that are scored from 0 to 3. Participants select the option that best describes their feelings. For instance: "I do not feel sad" (0); "I feel sad much of the time" (1); "I feel sad all the time" (2); "I am so sad or unhappy that I can't stand it" (3). Higher total scores indicate more severe depressive symptoms. The Chinese version of the BDI-II demonstrated good reliability and validity in a sample of college students (Yang et al., 2012). In this study, the Cronbach's alpha of BDI-II was .90.

Symptoms of anxiety Anxiety symptoms were measured using the State-Trait Anxiety Inventory-Trait Scale (STAI-T; Li & Qian, 1995; Spielberger, 1983). The STAI-T has 20 items, and each item is rated on a 4-point Likert scale from 1 (not at all) to 4 (very much). Higher scores indicate more severe anxiety symptoms. In this study, the Cronbach's alpha of STAI-T was .90.

ESM assessment

Event intensity The measure of event intensity was based on participants’ rating of how pleasant the event was (see Myin-Germeys et al., 2001). Participants were first asked to briefly describe the most important event that occurred since the last prompt, and then to rate its pleasantness from − 3 (very unpleasant) to 3 (very pleasant). Based on the pleasantness ratings, events were categorized as negative (rating < 0), neutral (rating 0), or positive (rating > 0), so that a lower pleasantness rating for negative events indicated a more intense negative event, while a higher pleasantness rating for positive events indicated a more intense positive event.

Emotion intensity Participants were asked "How do you feel right now?" to assess the intensity of their current emotional experience. Four items measured positive affect (PA; "happy," "relaxed," "enthusiastic," and "excited") and five items measured negative affect (NA; "anxious," "sad," "angry," "stressed," and "scared"). Each item was rated from 1 (not at all) to 5 (extremely). At each prompt, the mean rating on the four PA items was calculated as the indicator of PA intensity, and the mean rating of the five NA items was calculated as the indicator of NA intensity. Internal consistency coefficients for PA items were: within-person α = .77, ω = .80; between-person α = .91, ω = .92; for NA items were: within-person α = .78, ω = .79; between-person α = .94, ω = .94.

Decisions about ER The choice to attempt ER, the type of strategy chosen, and the decision to stop regulation or switch to a new strategy were assessed by the following ESM questionnaire items.

ER Identification "During the event, did you attempt to increase or decrease your emotions?" This item was scored as 0/1 (0 = No; 1 = Yes).

ER strategy use ER strategies were assessed using eight items (Hiekkaranta et al., 2021; Li et al., 2024): "I avoided the situation where the event occurred" (Avoidance), "I tried to change the situation" (Situation modification), "I thought of other ways to interpret the situation" (Reappraisal), "I engaged in activities to distract myself" (Distraction), "I have thought about it a lot" (Rumination), "I accepted that it happened" (Acceptance), "I tried not to show my emotions on the outside" (Expressive suppression), and "I talked about it with somebody" (Social sharing). Each item was rated from 1 (strongly disagree) to 7 (strongly agree). Internal consistency coefficients for these ER items were: within-person α = .57, ω = .62; between-person α = .84, ω = .87.

ER strategy switching "Did you switch strategies because the initial one was not working?" This item was scored as 0/1 (0 = No; 1 = Yes).

ER strategy stopping " Are you still trying to change your emotions?" This item was scored as 0/1 (0 = No; 1 = Yes).

Perceived ER success "I was able to regulate my emotions successfully." This item was scored as 0/1 (0 = No; 1 = Yes).

Data analysis

Data was prepared and analyzed in IBM SPSS Version 23 for Windows, and Mplus Version 7.4. Multilevel modeling was used to account for the multilevel nature of the data. Specifically, multilevel generalized linear mixed models (GLMM) were used because the outcome variables (i.e., ER switching and ER stopping) were binary. Only questionnaires in which a need for ER was identified—specifically, those with a score of 1 on the ER identification item—were included in the final analyses, as these questionnaires contained participants' responses regarding strategy switching, cessation, and success.

The situational factors of event intensity, emotion intensity and perceived regulation success, and the outcome measures of switching or stopping were level-1 variables. The individual factors of BDI-II and STAI-T scores, age, and gender were level-2 variables. First, four GLMMs were developed to assess the associations between the three situational factors and strategy switching and stopping respectively, with separate models for negative and positive events. Depressive and anxiety symptoms as individual factors were then added separately to the models to test their association with strategy switching and stopping. Age and gender (women = 0, men = 1) were entered into all the models as covariates. To control for possible between-person confounding, the level-1 continuous variables (i.e., event intensity and emotion intensity) were person-mean centered. The level 2 continuous variables (i.e., the BDI-II score, the STAI-T score and age) were grand mean centered (Brans et al., 2013).

Transparency and Openness

The full list of the ESM questionnaires is available at open science framework (https://osf.io/dqt9c/?view_only=cd98fbc03f3b46bc92205a9ac782a94c). We report how we determined our sample size, all inclusion and exclusion criteria, and all measures used in the study.

Descriptive statistics

Over the ESM period, the participants as a group responded to 75.4% of the 35 prompts, with a mean of 26.38 (SD = 6.03). There were 4,089 valid ESM observations in total. In 43.1% of the prompts, the participant reported they had attempted to regulate emotions after an event; of this subset, 38% of the events were identified as positive, 47% were negative, and the rest were neutral. Again in this subset (43.1%) of prompts, 65.2% were cases in which the participant decided to continue regulation but to switch their ER strategy, and 75.3% were cases in which participants decided to stop regulation. Descriptive statistics and the within- and between-person correlations are shown in Table 1.

Table 1

Descriptive statistics: Between-person means (M), within-person (SDw) and between-person (SDb) standard deviations, intraclass correlation coefficients (ICC) and between-person and within-person correlations
Variable	M	SD_w	SD_b	ICC	1	2	3	4	5	6	7	8	9	10
1 .PA	2.44	0.73	0.61	.38		.04	.14	.23**	.25**	.25**	.21**	.38***	.03	.24**
2. NA	1.76	0.55	0.57	.47	− .43***		.29***	.1	.15	.18*	.20*	− .18*	.28***	− .11
3. Avoidance	3.35	1.7	0.89	.17	− .20***	.19***		.46***	.50***	.60***	.54***	− .13	.32***	.27***
4. Situation Modification	3.86	1.48	1.05	.29	− .06***	.09***	.26***		.83***	.82***	.63***	.19*	.15	.47***
5. Reappraisal	4.3	1.42	1.05	.3	− .09***	.12***	.28***	.49***		.79***	.67***	.30***	.16*	.44***
6. Distraction	3.78	1.45	1.07	.3	− .09***	.12***	.32***	.36***	.40***		.63***	.09	.18*	.45***
7. Rumination	4	1.46	1.06	.3	.01	.12***	.20***	.23***	.26***	.26***		.1	.13	.46***
8. Acceptance	5.25	1.24	0.8	.23	.25***	− .25***	− .18***	.02	− .01	− .10***	.01		− .06	.22**
9. Suppression	3.79	1.49	1.01	.26	− .13***	.04**	.15***	.14***	.19***	.15***	− .01	.05**		− .41***
10. Social sharing	4.06	1.55	1.03	.27	.13***	− .02	.07***	.16***	.12***	.15***	.23***	.07***	− .34***
Note. * p < .05; p < .01; *p < .001. PA: positive emotions; NA: negative emotions. Between-person correlations are shown above the diagonal; average within-person correlations are below the diagonal.

Associations with ER switching

For negative events, there was a significant positive association between emotion intensity and ER switching, p = .028, OR = 1.38, 95% CI = [1.04, 1.84]. With every one-unit increase in emotion intensity, the probability for an individual to switch strategy increased by 1.38. Neither event intensity nor perceived success was significantly associated with ER switching. The above results did not change when the BDI-II score was entered by itself or when the STAI-T score was entered by itself, and neither variable was significantly associated with switching. The covariates (age or gender) were not significantly associated with switching.

For positive events, there was a significant negative association between perceived success and ER switching, p = .019, OR = 0.29, 95% CI = [0.10, 0.82]. This result indicates that following events in which individuals perceived their ER regulation to be successful, they were 0.29 times less likely to switch strategies than following events in which they perceived their regulations to be unsuccessful. Neither event intensity nor emotion intensity was significantly associated with switching. The above results did not change when the BDI-II and STAI-T scores were added to the model, and these variables were not significantly related to switching. The covariates (age or gender) were not significantly associated with switching.

Thus, there was a key difference between the results for switching after negative and positive events. After negative events, participants with higher emotion intensity were more likely to switch ER strategies; after positive events, participants with higher perceived success were less likely to switch ER strategies. Results of tests of the negative event model and the positive event model in predicting switching are presented in Table 2.

Table 2

Associations between ER strategy switching and event intensity, emotion intensity, perceived regulation success, depression symptoms, anxiety symptoms, age, and gender, following negative and positive events respectively
	ER strategy switching following negative events				ER strategy switching following positive events
	Coefficient	SE	p		Coefficient	SE	p
Model 1				Model 1
Intercept	− .82	.18	<.001	Intercept	− .59	.20	.003
Event intensity	− .18	.14	.204	Event intensity	− .21	.20	.295
Emotion intensity	.32	.15	.028	Emotion intensity	− .26	.16	0.107
Perceived regulation success	− .31	.26	.237	Perceived regulation success	-1.24	.53	.019
Age	− .14	.08	.070	Age	<.01	.10	.995
Gender	− .21	.57	.711	Gender	.68	.66	.303
Model 2				Model 2
Intercept	− .82	.18	<.001	Intercept	− .58	.20	.003
Event intensity	− .18	.14	.202	Event intensity	− .21	.20	.295
Emotion intensity	.32	.15	.027	Emotion intensity	− .26	.16	.107
Perceived regulation success	− .31	.26	.237	Perceived regulation success	-1.24	.53	.019
BDI-II	<-.01	.02	.817	BDI-II	− .01	.02	.639
Age	− .14	.08	.072	Age	<-.01	.10	.994
Gender	− .23	.58	.690	Gender	.65	.66	.324
Model 3				Model 3
Intercept	− .82	.18	<.001	Intercept	− .59	.20	.003
Event intensity	− .18	.14	.203	Event intensity	− .21	.20	.295
Emotion intensity	.32	.15	.028	Emotion intensity	− .26	.16	.107
Perceived regulation success	− .31	.26	.238	Perceived regulation success	-1.24	.53	.019
STAI-T	<-.01	.02	.974	STAI-T	<.01	.02	.936
Age	− .14	.08	.070	Age	<.01	.10	.990
Gender	− .22	.57	.707	Gender	.68	.66	.301
Note. SE = standard error; BDI-II: Beck Depression Inventory-II; STAI-T: State-Trait Anxiety Inventory-Trait Scale. Significant p-values bolded.

Associations with ER stopping

For negative events, perceived success was positively associated with stopping, p = .017, OR = 2.12, 95% CI = [1.15, 3.94]. The OR indicated that when participants perceived their regulation as successful following a negative event, they were 2.12 times more likely of stopping ER than the occasions that they perceived the regulation as unsuccessful. Neither event intensity nor emotion intensity was significantly associated with stopping. These results did not change when BDI-II and STAI-T were added to the model, and neither variable was significantly associated with stopping. The covariates of gender and age did not significantly predict stopping.

For positive events, the association between perceived success and ER stopping was significant, p = .036, OR = 4.65, 95% CI = [1.11, 19.56]. When individuals perceived their ER as successful in response to a positive event, they were 4.65 times more likely of stopping ER than the occasions that they perceived the regulation as unsuccessful. Neither event intensity nor emotion intensity was significantly associated with switching. In terms of psychopathology, depression was related to a higher probability of stopping, p = .027, OR = 1.07, 95% CI = [1.01, 1.13]. With every one-unit increase in depressive symptoms, the likelihood of stopping the ER process increased by 1.07. The positive association between anxiety symptoms and ER stopping was also significant, p = .025, OR = 1.05, 95% CI = [1.01, 1.09]. With every one-unit increase in the anxiety score, the likelihood of stopping ER increased by 1.05. Age as a covariate was not significantly associated with stopping, but men were less likely than women to stop the regulatory process, p = .043, OR = 0.25, 95% CI = [0.06, 0.96].

Thus, there were two key findings with regard to stopping the ER process. First, perceived success in ER was a significant predictor of regulation stopping, after both negative and positive events. Second, the individual factors of depressive symptoms and anxiety symptoms significantly predicted stopping the ER process after positive, but not negative, events. Results of the tests of the negative event model and the positive event model in predicting ER stopping are presented in Table 3.

Table 3

Associations between ER strategy stopping and event intensity, emotion intensity, perceived regulation success, depression symptoms, anxiety symptoms, age, and gender, following negative and positive events respectively
	ER strategy stopping following negative events				ER strategy stopping following positive events
	Coefficient	SE	p		Coefficient	SE	p
Model 1				Model 1
Intercept	1.43	.17	<.001	Intercept	1.37	.25	<.001
Event intensity	− .15	.15	.341	Event intensity	.36	.23	.112
Emotion intensity	− .20	.16	.210	Emotion intensity	.19	.16	.225
Perceived regulation success	.75	.32	.017	Perceived regulation success	1.54	.73	.036
Age	− .03	.08	.696	Age	− .02	.11	.872
Gender	.21	.47	.656	Gender	-1.41	.69	.043
Model 2				Model 2
Intercept	1.43	.17	<.001	Intercept	1.36	.24	<.001
Event intensity	− .14	.15	.350	Event intensity	.36	.23	.116
Emotion intensity	− .20	.16	.217	Emotion intensity	.20	.16	.221
Perceived regulation success	.76	.32	.017	Perceived regulation success	1.52	.72	.036
BDI-II	<.01	.02	.867	BDI-II	.06	.03	.027
Age	− .03	.08	.703	Age	<-.01	.11	.972
Gender	.21	.49	.661	Gender	-1.18	.67	.079
Model 3				Model 3
Intercept	1.44	.17	<.001	Intercept	1.36	.25	<.001
Event intensity	− .15	.15	.338	Event intensity	.36	.23	.116
Emotion intensity	− .20	.16	.207	Emotion intensity	.19	.16	.224
Perceived regulation success	.75	.32	.017	Perceived regulation success	1.52	.72	.036
STAI-T	.01	.02	.392	STAI-T	.05	.02	.025
Age	− .03	.08	.746	Age	<.01	.12	.942
Gender	.23	.48	.624	Gender	-1.35	.66	.041
Note. SE = standard error; BDI-II: Beck Depression Inventory-II; STAI-T: State-Trait Anxiety Inventory-Trait Scale. Significant p-values bolded.

Drawing from the extended process model of ER (Gross, 2015b), we investigated the influence of various situational and individual factors on the monitoring stage of the ER process. The results showed that greater emotion intensity, and in particular the intensity of NA, was associated with a higher likelihood of strategy switching. In addition, perceived success in regulating emotions was associated with a lower likelihood of strategy switching in response to positive events, and a higher likelihood of strategy stopping in response to both negative and positive events. Depressive and anxiety symptoms were associated with an increased probability of regulatory stopping in response to positive events exclusively.

Event intensity, emotion intensity and ER monitoring

Emotion intensity is a key contextual factor related to the first three stages of the ER process and in the current study more intense negative emotions were found to be related to a higher likelihood of strategy switching. As we measured ER retrospectively (since the last prompt) and NA at the current prompt, higher intense negative emotions could suggest less effective regulation. Our finding is consistent with a previous laboratory study which found that more intense negative emotions after initial regulation attempts predicted more frequent strategy switching (Murphy and Young, 2020). In another study (Birk and Bonanno, 2016), the greater the corrugator activity (an electrodermal indicator of psychological stress), the more likely individuals were to switch ER strategies. Therefore, experiencing high intense negative emotions after the initial regulatory effort, which may suggest a perceived failure or ineffectiveness of the previous ER strategy, may motivate the individual to continue engaging in the regulatory process and shift to a different strategy rather than terminate regulation altogether.

In contrast to the result for emotion intensity, the association between event intensity and ER monitoring (either switching or stopping) was not significant. The results on event intensity and emotion intensity are consistent with findings showing that emotion intensity was a stronger predictor of ER strategy use than event intensity was (Kuo et al., 2018). Together, these results suggest that intense negative emotions after the initial regulatory effort can explain why there was strategy switching in the monitoring phase of the ER process, especially when experience negative events.

Perceived regulation success and ER monitoring

Perceived success in regulating emotions emerged as the strongest predictor of ER monitoring decisions. Specifically, when individuals perceived they had successfully regulated their emotions during positive events, they were less likely to switch strategy. Strategy switching typically occurs when participants recognize that their current strategy is inadequate for achieving the desired emotional state (Gross, 2015a). Conversely, when a strategy is perceived as effective, individuals are more likely to persist with it. In a previous laboratory study (Dorman Ilan et al., 2019), participants were instructed to downregulate high- and low-intensity negative emotions using either reappraisal or distraction. The findings revealed that participants reported higher switching frequency when their initial strategy did not match the contextual demand. Moreover, participants reported greater perceived success when they switched to a strategy that did match the contextual demand. These results suggest a close relationship between perceived regulation success and strategy switching. However, it is important to note that while the aforementioned study was conducted in a controlled laboratory environment, our research focuses on real-life scenarios. This contextual difference may explain the discrepancy in results, as our study found no significant association between perceived regulation success and strategy switching during negative events. Conversely, perceived regulation success was associated with less strategy switching exclusively for positive events.

In addition, perceived regulation success was associated with a higher probability of stopping, for both negative and positive events. This is consistent with the extended ER process model, which holds that strategy stopping occurs when one perceives their ER efforts as successful in achieving goals (Gross, 2015b). More specifically, the model assumes that during the monitoring phase, individuals dynamically monitor the gap between the current emotional state and the target emotional state, and the ER process ends when they perceive that the desired emotional state has been achieved. The intensity of emotions after regulation or the change in emotion intensity from before to after regulation have been used as indicators of successful ER, but they are both indirect and inadequate because they do not consider how the individual evaluates the ER process (Springstein and English, 2024). Importantly, previous research has shown inconsistency between the effects of emotion intensity and perceived success in predicting phases of the ER process that occur earlier than monitoring, namely strategy selection and implementation (Rompilla et al., 2022; Troy et al., 2018). For example, Troy et al. (2018) demonstrated that while reappraisal was rated as more effective than acceptance in reducing NA and increasing PA, young adults often report greater perceived success when implementing acceptance instead of reappraisal. Similarly, Rompilla et al. (2022) found that older adults were most inclined to use acceptance and rated it as more successful than positive reappraisal or detachment, even though acceptance did not significantly alter their emotional experience. The current study extended this research by investigating how these situational factors (including, also, event intensity) simultaneously influence ER monitoring. These findings suggest that of the situational factors we examined, perceived regulation success represents the most proximal situational determinant of the final stage of the ER process.

Depression, anxiety, and ER monitoring

According to the Contrast Avoidance Model (Newman and Llera, 2011), individuals with depression and anxiety tend to avoid situations that elicit strong positive emotions due to their heightened sensitivity to emotional discrepancies. This may explain our findings that higher depressive or anxiety symptoms are associated with a greater likelihood of stopping the regulation when experiencing positive events. Individuals with higher depressive and anxiety symptoms, due to a lower preference for PA (Vanderlind et al., 2020), may be more likely to stop upregulating PA in response to positive events compared to individuals with lower depressive and anxiety symptoms. Similarly, patients with depression have been shown to be less likely to choose positive stimuli over negative stimuli (Millgram et al., 2015; Yoon et al., 2020) and to view positive stimuli for shorter durations (Kellough et al., 2008). Higher anxiety symptoms were found in one study to be significantly associated with higher motivation to downregulate PA rather than upregulate PA following positive events (Carl et al., 2014). Thus, individuals with higher depressive or anxiety symptoms may avoid the experience of high PA and have lower motivation to increase PA when experiencing positive events. This could explain why more severe depression and anxiety are associated with ER stopping.

Another possibility is that the excessive strategy stopping related to depression and anxiety starts with problems in selecting and implementing contextually appropriate ER strategies. For example, depression and anxiety have been found to be related to the use of strategies that dampen PA rather than strategies that amplify PA (Abasi et al., 2023). Beblo et al. (2012) found that compared to healthy volunteers, patients with depression were morely like to use suppression strategies when experiencing positive emotions. Blalock et al. (2016) found that compared to healthy controls, patients with social anxiety disorder reported less use of reappraisal to increase positive feelings. Thus, difficulties in selecting and implementing effective and active strategies to upregulate PA may lead to the high rate of stopping during ER monitoring.

Lastly, these results may be related to fixed beliefs about emotion associated with depression and anxiety. Individuals who believe emotions are malleable are more likely to exert effort to influence and change their emotions (Kneeland et al., 2016), and they usually report higher regulatory self-efficacy (Tamir et al., 2007). By contrast, individuals who believe that emotional states are stable and cannot be changed may prematurely stop ER attempts (Aldao et al., 2015). There is evidence that more severe depressive or anxiety symptoms are associated with stronger fixed beliefs about emotion (Daniel et al., 2020), which may lead to reduced effort in regulating emotions and a tendency to discontinue such efforts prematurely. However, the mediating role of emotion malleability belief in the association between psychopathology symptoms and ER monitoring needs to be empirically tested.

Limitations

This study has several limitations. First, we recruited a healthy sample with varying levels of depressive and anxiety symptoms, limiting the generalization of the findings to clinical samples that meet the relevant diagnostic criteria. Second, this study was correlational in nature, which means we cannot infer causal relationships between the situational and personal factors we studied and decisions made during ER monitoring phase. Third, we did not ask the participants why they decided to stop or continue regulating; it is possible that participants would not have insight about the choices they made, but it is worth pursuing the possibility that they do. Moreover, we found that some participants reported using more than one strategy following an event but also reported no switching, and we need to further explore the simultaneous and successive use of ER strategies.

Lastly, this study measured perceived regulation success but did not test the relationships between specific ER goals and monitoring decisions. Previous research found that different goals may influence an individual's strategy selection, implementation decisions, and perceived regulation success. For example, pro-hedonic goals have been found to be positively associated with antecedent-focused strategies, such as reappraisal and distraction (Eldesouky and English, 2019), whereas social goals were positively associated with greater use of expressive suppression (Wilms et al., 2020). Thus, future research can further investigate the relationship between regulation goals and ER monitoring decisions.

The current study is the first to examine the factors influencing strategy switching and stopping in the ER monitoring process in the context of daily life. The results indicated that among the situational factors we studied, perceived regulation success was the most relevant, as it influenced strategy switching and stopping in response to both negative and positive events. Higher depressive and anxiety symptoms were related to a higher likelihood of regulation stopping when responding to positive events. These findings provide new insight into the distinctive roles of situational and personal factors in the final stage of the ER process. Interventions aimed at reducing excessive ER efforts related to positive events and their underlying causes may benefit individuals suffering from depression or anxiety.

Funding statement

This work has been supported by the National Natural Science Foundation of China (31700957), the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (17YJC190014), the Key Laboratory of Adolescent Cyberpsychology and Behavior Central China Normal University (CCNU), Ministry of Education (2019B06), and the Fundamental Research Funds for the Central Universities of Central China Normal University (CCNU24ZZ044).

Author Contribution

X-h.L., Y-t.Y.,and X.L. drafted the main text of the manuscript and prepared Tables 1-3. M.P., M. H., T.V., and I.M. critically revised the manuscript. All authors contributed to writing and editing of the manuscript and approved its final version.

Data Availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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Emotion regulation monitoring in daily life: The roles of event intensity, emotion intensity, perceived regulation success and psychopathology

Status:

Version 1

Abstract

Purpose

Methods

Results

Conclusions

Introduction

Situational factors: event intensity, emotion intensity, and perceived regulation success

Event intensity

Emotion intensity

Perceived success in regulating emotions

Individual factors: psychopathology

The present study

Methods

Participants

Procedure and measures

Baseline measures

ESM assessment

Data analysis

Transparency and Openness

Results

Descriptive statistics

Associations with ER switching

Associations with ER stopping

Discussion

Event intensity, emotion intensity and ER monitoring

Perceived regulation success and ER monitoring

Depression, anxiety, and ER monitoring

Limitations

Conclusions

Declarations

Funding statement

Author Contribution

Data Availability

References

Additional Declarations

Status:

Version 1