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. Author manuscript; available in PMC: 2022 Apr 1.
Published in final edited form as: Pers Individ Dif. 2021 Jan 7;173:110607. doi: 10.1016/j.paid.2020.110607

Association of Positive Emotion Dysregulation to Resting Heart Rate Variability: The Influence of Positive Affect Intensity

Nicole H Weiss 1, Melissa R Schick 1, Elinor E Waite 2, Lauren A Haliczer 2, Katherine L Dixon-Gordon 2
PMCID: PMC7839974  NIHMSID: NIHMS1660831  PMID: 33518872

Abstract

Background:

A fast-growing body of research provides support for the role of positive emotion dysregulation in the etiology and maintenance of a wide range of psychiatric difficulties and clinically relevant behaviors. However, this work has exclusively relied on the subjective assessment of positive emotion dysregulation. Advancing research, the current study examined associations between physiological and subjective indices of positive emotional responding in the laboratory. Specifically, we explored the relation of the Difficulties in Emotion Regulation Scale – Positive (Weiss, Gratz, & Lavender, 2015) to resting heart rate variability (HRV) at high and low state positive affect intensity.

Methods:

Participants were 122 individuals recruited from college and community settings (Mage = 23.39, 84.4% female, 68.0% White).

Results:

Findings indicated a positive relation between positive emotion dysregulation and resting HRV at high state positive affect and a negative relation between positive emotion dysregulation and resting HRV at low state positive affect.

Conclusions:

Results extend our understanding of the associations among subjective and physiological indices of positive emotional processes. These findings have key implications for the conduct of research on positive emotion dysregulation.

Keywords: Positive emotion regulation, Difficulties in Emotion Regulation Scale – Positive, heart rate variability, concordance, objective, positive affect

1.1. Introduction

Research over the past two decades highlights the role of emotion dysregulation in psychopathology (Tull & Aldao, 2015). These investigations underscore the relevance of emotion dysregulation to the etiology and maintenance of a wide range of psychiatric difficulties and clinically relevant behaviors (Gratz & Tull, 2010a), including posttraumatic stress (Tull et al., 2007; Weiss et al., 2013), depression (Dixon-Gordon et al., 2015), anxiety (Roemer et al., 2009), borderline personality disorder (BPD; Gratz et al., 2006), substance use (Fox et al., 2007; Weiss et al., 2017), disordered eating (Lavender et al., 2014), nonsuicidal self-injury (Gratz & Tull, 2010b), HIV/sexual risk (Tull et al., 2012), and aggression (Gratz et al., 2009). Moreover, these studies suggest the utility of targeting emotion dysregulation as a target, outcome, and mechanism of psychological treatments (for a review, see Gratz et al., 2015).

While historically limited to dysregulation stemming from negative emotions, this research has recently expanded to include positive emotional experiences. Early studies in this area suggest that some individuals may be non-accepting of positive emotions or experience difficulties controlling impulsive behaviors and engaging in goal-directed behaviors in the context of positive emotional states (Weiss et al., 2015). For instance, positive emotions may elicit physiological arousal (e.g., increased heart rate or respiration; Litz et al., 2000) or maladaptive cognitions (e.g., "I don't deserve to be happy;" Norman et al., 2014) that result in negative affect interference (i.e., negative emotions in response to positive emotional stimuli; Frewen et al., 2012), such as fear (Beblo et al., 2012; 2013). Additionally, positive emotions may result in impairment in cognitive processes – such as narrowing of attention (Gable & Harmon-Jones, 2008) and increased distractibility (Dreisbach & Goschke, 2004) – that increase risk for behavioral dyscontrol (Slovic et al., 2004). Consistent with research on negative emotion dysregulation (see Gratz & Tull, 2010a), positive emotion dysregulation has been empirically linked to health outcomes, including posttraumatic stress (Weiss et al., 2018a), depression (Schick et al., 2019), substance use (Weiss et al., 2018b), and HIV (Weiss et al., 2019a).

One critical limitation of this work is its exclusive reliance on subjective assessments of positive emotion dysregulation (e.g., Difficulties in Emotion Regulation Scale – Positive [Weiss et al., 2015]; revised Regulatory Emotional Self-Efficacy Scale [Zou et al., 2019]; Perth Emotion Regulation Competency Inventory [Preece et al., 2018]). Subjective data are prone to numerous biases, including those related to social desirability, selective recall, or deficient awareness (see Althubaiti, 2016). Moreover, some populations may be unable to provide self-reports, including very young children and those with neurological limits, thereby precluding researcher’s ability to study positive emotion dysregulation in these populations. Identification of associated objective indices of positive emotional responding is imperative to advancing the field of mental health.

Vagal-mediated heart rate variability (HRV) – the characteristic beat-to-beat variability in heart rate – is one objective index that has been linked to emotion dysregulation (Appelhans & Luecken, 2006; Thayer & Lane, 2000). The Theory of Neurovisceral Integration implicates the role of several brain structures in HRV, including the prefrontal cortex, which is also involved in emotion dysregulation (Thayer et al., 2012; Thayer & Lane, 2009). Specifically, the prefrontal cortex exerts an inhibitory influence on the amygdala, allowing for the organization of emotional responses in response to environmental demands (Thayer et al., 2012), including the regulation of autonomic nervous system (ANS) activity (Hansen et al., 2004). In turn, the ANS exerts inhibitory control over the heart via the vagal nerve, characterized by dominance of the parasympathetic nervous system relative to the sympathetic nervous system (Thayer et al., 2012; Thayer & Lane, 2009). This research posits that resting HRV may thus index the degree to which the brain is able to exhibit control over the periphery (Thayer et al., 2012). At rest, active cortical brain areas are indicative of lower emotion dysregulation, resulting in a low HRV, or more inflexible influence of the heart (Appelhans & Luecken, 2006). Consistent with theory, empirical data has linked emotion dysregulation to resting HRV. Specifically, greater negative emotion dysregulation predicts lower resting HRV among students (Visted et al., 2017; Williams et al., 2015). Research is needed to test whether these results extend to positive emotion dysregulation.

In examining the relation between positive emotion dysregulation and resting HRV, it is important to consider the potential influence of state positive affect intensity. In particular, existing literature indicates that an inverse association between positive emotion dysregulation and resting HRV may exist only at high levels of state positive affect intensity. Conceptual frameworks linking positive emotions to dysregulation underscore the role of intense emotion states (Cyders & Smith, 2008). These models purport that intense positive emotion states are more likely to result in behavioral dyscontrol including difficulties in both controlling impulsive behaviors and engaging in in goal-directed behaviors. Intense state positive emotions may impair executive functioning (Pessoa, 2009), reducing resources for exerting effortful control such as inhibition of a response (Tice et al., 2001). This, in turn, may result in urgency, or an elevated likelihood of rash action (for review, see Cyders & Smith, 2008), consistent with empirical evidence that more intense state positive emotions precipitate impulsive behavior (Jahng et al., 2011). Intense state positive emotions have also been shown to result in narrowing of attention (Gable & Harmon-Jones, 2008) and increased distractibility (Dreisbach & Goschke, 2004), which may interfere with task completion by shifting one’s focus toward improving the present moment, without consideration of the impact on one’s long-term goals. Finally, intense state positive emotions are more likely to elicit elevated physiological arousal, which may be negatively evaluated by people who experience such arousal as aversive (Taylor et al., 1992).

Advancing existing research, the present study examined the relation of positive emotion dysregulation to resting HRV and the moderating influence of state positive affect intensity. Consistent with past research on negative emotion dysregulation, we hypothesized that positive emotion dysregulation would be associated with lower levels of resting HRV. Moreover, given evidence to suggest that positive emotion dysregulation may be more salient in the context of high intensity positive emotions, we expected that state positive affect intensity would moderate the relation of positive emotion dysregulation to resting HRV, such that this relation would be significant at high (but not low) levels of state positive affect.

2.1. Methods

2.2. Participants

Participants were recruited both via flyers and website postings in the community and via the university’s psychology subject pool. Participants were screened for the larger study based upon three inclusionary criteria: (1) aged 18 through 55, (2) fluent in the English language, and (3) able to read and complete online questionnaires. In addition, in order to ensure adequate variability in emotion dysregulation, we extended additional invitations and offered more appointments times for participants with elevated BPD features (i.e., ≥ 38; Morey, 1991), who tend to exhibit heightened levels of emotion dysregulation (Gratz et al., 2006).

The final sample included 122 participants who attended the laboratory session and provided complete self-report and physiological data on the primary study variables. Participants ranged in age from 18 to 48 years (M = 23.39, SD = 6.71), and were predominately female (84.4%) and White (68.0%). See Table 1 for full demographic information.

Table 1.

Demographic Characteristics

Demographic Characteristics M (SD) or N (%)
Age 23.39 (6.71)
Sex
 Female 103 (84.4%)
 Male 18 (14.8%)
 Other/Prefer not to say 1 (0.8%)
Gender Identitya 78.72 (35.16)
Race/Ethnicity
 White 83 (68.0%)
 Black/African American 3 (2.5%)
 Asian 21 (17.2%)
 Hispanic/Latinx 1 (0.8%)
 Multiracial 13 (10.7%)
 Another category/Prefer not to say 1 (0.8%)
Highest Educational Attainment
 High school/GED 9 (7.3%)
 Some college/technical school 75 (61.5%)
 College graduate 27 (22.2%)
 Graduate/professional degree 11 (9.0%)
Employment
 Unemployed 13 (10.7%)
 Part-time student 5 (4.1%)
 Full-time student 60 (49.2%)
 Part-time employed 24 (19.7%)
 Full-time employed 20 (16.4%)
Relationship Status
 Single 105 (86.1%)
 Partnered 16 (13.1%)
 Separated 1 (0.8%)
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Note.

a

Higher scores on the gender identity measure indicate greater identification as a female; range was from 0 to 100.

2.3. Procedures

All procedures were reviewed and approved by the Institutional Review Board at [redacted]. Participants who were recruited from the community were compensated with $35 for the in-person sessions, and those who were recruited from the psychology subject pool were compensated with either pay or course credit. All participants completed: a brief telephone interview to assess for inclusion/exclusion criteria, an in-person interview for the larger study, and a laboratory session. Participants were instructed to abstain from nonprescribed medications and drugs the morning of their laboratory session. The laboratory session was scheduled in a sound-attenuated room that was set to a standard temperature (i.e., 70 degrees F). In the laboratory session, resting HRV was acquired during a five-minute neutral emotion induction. In this task, participants are presented with a series of colors and instructed to count how many times a particular color appears. This type of nondemanding cognitive task has been found to elicit neutral mood and serve as an appropriate baseline in past work (Jennings et al., 1992).

2.4. Measures

2.4.1. Objective Emotion Indicator

Electrocardiogram (ECG) was measured using Ag/AgCI Biopac hardware and software (Biopac Systems, Inc., Goleta, CA). Sequences of heart beat-to-beat intervals (RRI) were recorded via ECG and exported into AcqKnowledge 4.4 software to be used for analyses and calculation of resting HRV indices and mean heart rate. The RRI sequence was checked for artifacts and irregular beats and edited manually where necessary. Heart rate, expressed as beats per minute, was derived by calculating the average number of R-spikes in the ECG signal occurring each minute during the recording period. Resting HRV was calculated from edited sequential RR intervals derived from the ECG signal. Frequency domain HRV indices were calculated using Fourier analysis (Cooke et al., 1999; Taylor et al., 1998). Frequency domain indices of resting HRV provide information about how power distributed as a function of frequency (Malik, 1996). For the purposes of the present study, we calculated power spectral density (msec2/Hz) in the high frequency domain (Hf: 0.15-0.4Hz).

2.4.2. Subjective Emotion Measures

2.4.2.1. The Positive and Negative Affect Schedule

(PANAS; Watson et al., 1988) is a self-report measure of the intensity with which participants have experienced a range of 10 positive and 10 negative state emotions. Participants rate each item based on the extent to which they felt each of these emotional states at that moment on a 5-point Likert-type scale (1 = very slightly or not at all, 5 = extremely). Respective items are averaged for the positive and negative affect scales, resulting in possible total scores between 1 and 5, with higher scores indicating greater intensity of state affective experiences. We used the mean of each scale in our analyses to facilitate interpretation. The PANAS has good psychometric properties (Watson et al., 1988), and Cronbach’s α in the current sample was .83 and .65 for the state positive and negative affect scales, respectively.

2.4.2.2. The Difficulties in Emotion Regulation Scale – Positive

(DERS-P; Weiss et al., 2015) is a 13-item self-report measure that assesses difficulties regulating positive emotions including non-acceptance of positive emotions, difficulties engaging in goal-directed behaviors when experiencing positive emotions, and difficulties controlling impulsive behaviors when experiencing positive emotions. Participants rate each item using a 5-point Likert-type scale (1 = almost never, 5 = almost always). Items are summed for a possible total score between 13 and 65, with higher scores indicating greater difficulties regulating positive emotions. The DERS-P has good psychometric properties (Weiss et al., 2019b; Weiss et al., 2015), and Cronbach’s α in the current sample was .94.

2.4.3. Potential Covariates

Participants reported their demographic characteristics (e.g., age, sex), and other variables thought to affect resting HRV, including height, weight, and whether they currently take any medications (1 = yes, 0 = no). Given that many of these factors have documented associations with resting HRV (Antelmi et al., 2004), age, sex, body mass index, and medication status were examined as covariates. In addition, given that state negative affect may affect these relations, we examined whether these findings held when controlling for state negative affect.

2.5. Data Analytic Procedure

As recommended by Tabachnik and Fidell (2007), all study variables were assessed for assumptions of normality. Descriptive data and bivariate correlations were calculated. To address the question of whether positive emotion dysregulation, state positive affect, and their interaction are associated with resting HRV after controlling for the effects of covariates, moderation analyses were conducted with the PROCESS SPSS macro, as recommended by Hayes (2012). Continuous predictor variables were mean-centered prior to entry into the model and prior to construction of the interaction term to aid in interpretation of parameter estimates and to lessen the correlation between the interaction terms and its component variables. Following the methods described by Aiken et al. (1991), we plotted regression slopes of HRV on positive emotion dysregulation in participants with low (1 SD below mean) and high (1 SD above mean) state positive affect scores and conducted simple slopes analyses to examine whether the slopes of the regression lines differed significantly from zero. To further disentangle the nature of any interactions, we examined Johnson-Neyman regions of significance.

2.6. Results

2.6.1. Preliminary Analyses

See Table 2 for descriptive data and correlations among the primary study variables. All primary study variables exhibited normal distributional properties, except resting HRV which exhibited substantial skew and kurtosis. A square-root transformation normalized HRV, and this transformed variable was used in all further analyses. Of note, resting HRV, state positive affect, and positive emotion dysregulation were not significantly associated at the bivariate level. Our recruitment strategies yielded an adequate distribution of BPD features (range = 5-67; 73.1% evidencing elevated BPD features on the PAI-BOR).

Table 2.

Descriptive Statistics and Bivariate Correlations among Variables of Interest

1 2 3 4 5 6 7
1. Age -
2. Female −.17 -
3. Body mass index .20* .04 -
4. Currently taking any medications −.17 −.19* .03 -
5. Resting HRV (msec^2) .02 −.12 −.08 −.15 -
6. State positive affect .31** −.11 −.09 −.23* −.10 -
7. Positive emotion dysregulation .07 −.13 −.08 −.13 .02 .002 -
Mean (SD) or N (%) 23.39 103 23.25 46 385.86 1.75 1.75
(6.71) (84.4%) (5.65) (37.7%) (782.70) (0.58) (0.77)
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Note. Sex coded with 1 = female, 0 = male. HRV = high frequency heart rate variability. HRV was square-root transformed, but untransformed means are presented for ease of interpretation.

*

p < .05

**

p < .001

2.6.2. Primary Analyses

Neither positive emotion dysregulation, b = 0.03, SE = 1.48, t = 0.02, p = .986, 95% CI [−2.91, 2.96], nor state positive affect, b = −3.93, SE = 2.07, t = −1.90, p = .060, 95% CI [−8.03, 0.17], were significantly associated with resting HRV in the model. However, the interaction between positive emotion dysregulation and state positive affect on resting HRV was significant, b = 7.05, SE = 2.56, t = 2.76, p = .007, 95% CI [1.98, 12.12]. As illustrated in Figure 1, analysis of simple slopes revealed that positive emotion dysregulation and resting HRV were significantly negatively associated for individuals with low state positive affect, b = −4.07, SE = 1.99, t = −2.04, p = .044, 95% CI [−8.02, −0.12], and non-significantly positively associated for individuals with high state positive affect, b = 4.13, SE = 2.20, t = 1.87,p = .063, 95% CI [−0.23, 8.49]. In terms of the regions of significance, the inverse link between positive emotion dysregulation and HRV was significant when state positive affect was 1.21 (i.e., 18.0% of the sample), whereas the positive association was evident when state positive affect was 2.31 (i.e., 18.9% of the sample). Of note, the significant interaction of positive emotion dysregulation and state positive affect remained without covariates in the model, and the same pattern of findings emerged. Likewise, the findings remained the same when controlling for state negative affect.

Figure 1. Positive Emotion Dysregulation by State Positive Affect Interaction for Heart Rate Variability.

Figure 1

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Note. Regression lines plotted 1 SD above and below the mean. Also in this model: age, sex (female vs. male), body mass index, medication status.

2.7. Discussion

There is growing evidence that positive emotion dysregulation is a transdiagnostic construct associated with a wide range of psychiatric and behavioral outcomes (Weiss et al., 2015). However, research is this area is limited through its exclusive reliance on the subjective measurement of positive emotion dysregulation. Recent research suggests that resting HRV is an objective index of negative emotion dysregulation (Visted et al., 2017; Williams et al., 2015). The current study advances research on positive emotion dysregulation by examining the relation of the Difficulties in Emotion Regulation Scale – Positive (Weiss et al., 2015) to resting HRV.

In contrast to the study hypothesis, our findings provided support for a positive relation between positive emotion dysregulation and resting HRV at high state positive affect and a negative relation between positive emotion dysregulation and resting HRV at low state positive affect. In particular, in the context of low state positive affect, greater positive emotion dysregulation was associated with lower resting HRV, consistent with extant literature suggesting that lower resting HRV reflects greater dysregulation and psychological distress (Williams et al., 2015). Yet, at high levels of state positive affect, greater positive emotion dysregulation was associated with greater resting HRV. Although unexpected, this link between elevated resting HRV and greater psychological distress under some conditions is not without support in the literature. For instance, one study revealed that the highest levels of HRV were seen among those high in generalized anxiety when they were more mindful or aware (Mankus et al., 2013). One possible explanation is that the measurement of resting HRV was taken during a nondemanding cognitive task. Given that attention to a task can be associated with decrements in HRV (Laborde et al., 2015), it is conceivable that those individuals with greater positive emotion dysregulation were also paying less attention to the nondemanding task, thereby leading to higher HRV levels relative to participants who were paying greater attention. Given the association between attentional impairment (e.g., in attention deficit hyperactivity disorder) and emotion dysregulation (Martel, 2009), it is certainly possible that those higher in positive emotion dysregulation were also less likely to attend to the task. Another possible explanation for these unexpected results is that those with elevated positive emotion dysregulation may be particularly averse to high State positive affect. This may lead to more efforts to down-regulate positive emotions (Weiss et al., 2020), and later higher resting HRV. It’s also conceivable that low state positive affect reflects greater psychopathology, and that high positive emotion dysregulation is linked to resting HRV more so in this context. Finally, given evidence that dissociation is related to high HRV in BPD (Krause-Utz et al., 2019), perhaps more positive emotion dysregulation is linked to high resting HRV at high state positive affect via dissociation.

Findings of the current study have important implications for future research. Namely, our results indicate that resting HRV – in the context of state positive affect – may provide an objective index of positive emotion dysregulation. Investigations that integrate objective assessment of positive emotion dysregulation – such as resting HRV – may enhance reliability and validity of existing findings using subjective assessment alone. Moreover, they may facilitate research on positive emotion dysregulation within populations that are unable to provide subjective reports. Additionally, because lack of coherence is common to affect response systems (Ginsberg et al., 2010), subjective assessment alone almost certainly provides an incomplete picture of positive emotion dysregulation. A multi-method approach that includes both subjective and objective assessment of positive emotion dysregulation is a crucial next step that may improve understanding of the role of positive emotion dysregulation in health behavior. Future studies are needed to identify other objective indices of positive emotion dysregulation, including exploring behavioral tasks and other psychophysiological measures that have been found to be related to negative emotion dysregulation.

Results should be considered within the context of study limitations. First, this study utilized a laboratory-based design that may have limited ecological validity. Research that utilizes ambulatory data, such as physiological output from wearable biosensors and subjective report of positive emotion dysregulation from ecological momentary assessment, is needed to better understand the concordance of HRV and positive emotion dysregulation. Second, a trait measure of positive emotion dysregulation was administered. There is evidence to suggest the utility of measuring state emotion dysregulation in the laboratory (Lavender et al., 2015); future investigations in this area are warranted. Third, positive emotion dysregulation can involve both the down-regulation of positive emotions (i.e., efforts to avoid or diminish positive emotional experiences) and the up-regulation of positive emotions (i.e., efforts to elicit or increase positive emotional experiences; Zou et al., 2019); these are distinct processes (Kim & Hamann, 2007). However, the measure of positive emotion dysregulation used in the current study (i.e., the DERS-P) does not distinguish between up- and down-regulatory functions. Thus, future investigations are needed to examine the relationship between positive emotion dysregulation characterized by the down- versus up-regulation of positive emotions and HRV. Lastly, while use of a community/college sample overrecruited for high BPD features is a strength of the current study, findings may not generalize to other populations. Replication among other samples (e.g., individuals receiving clinical services) is warranted. Despite these limitations, results advance knowledge on positive emotion dysregulation. Specifically, findings suggest that resting HRV – in the context of state positive affect – may be an index of positive emotion dysregulation.

Acknowledgments

Role of Funding Sources

Work on this paper by the first author (NHW) was supported by National Institutes of Health Grants K23DA039327 and P20GM125507. The opinions, findings, and conclusions or recommendations in this manuscript are those of the authors and do not necessarily reflect those of the National Institutes of Health.

Footnotes

Conflict of Interest

None to report.

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References

  1. Aiken LS, West SG, & Reno RR (1991). Multiple regression: Testing and interpreting interactions. Sage. [Google Scholar]
  2. Althubaiti A (2016). Information bias in health research: definition, pitfalls, and adjustment methods. Journal of Multidisciplinary Healthcare, 9, 211–217. doi: 10.2147/JMDH.S104807 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Antelmi I, De Paula RS, Shinzato AR, Peres CA, Mansur AJ, & Grupi CJ (2004). Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease. The American Journal of Cardiology, 93(3), 381–385. doi: 10.1016/j.amjcard.2003.09.065 [DOI] [PubMed] [Google Scholar]
  4. Appelhans BM, & Luecken LJ (2006). Heart rate variability as an index of regulated emotional responding. Review of General Psychology, 10(3), 229–240. doi: 10.1037/1089-2680.10.3.229 [DOI] [Google Scholar]
  5. Beblo T, Fernando S, Kamper P, Griepenstroh J, Aschenbrenner S, Pastuszak A, Schlosser N, & Driessen M (2013). Increased attempts to suppress negative and positive emotions in borderline personality disorder. Psychiatry Research, 210(2), 505–509. doi: 10.1016/j.pyschres.2013.06.036 [DOI] [PubMed] [Google Scholar]
  6. Beblo T, Fernando S, Klocke S, Griepenstroh J, Aschenbrenner S, & Driessen M (2012). Increased suppression of negative and positive emotions in major depression. Journal of Affective Disorders, 141(2-3), 474–479. doi: 10.1016/j.jad.2012.03.019 [DOI] [PubMed] [Google Scholar]
  7. Cooke WH, Hoag JB, Crossman AA, Kuusela TA, Tahvanainen KU, & Eckberg DL (1999). Human responses to upright tilt: A window on central autonomic integration. The Journal of Physiology, 517(2), 617–628. doi: 10.1111/j.1469-7793.1999.0617t.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cyders MA, & Smith GT (2008). Emotion-based dispositions to rash action: Positive and negative urgency. Psychological Bulletin, 134(6), 807–828. doi: 10.1037/a0013341 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dixon-Gordon KL, Weiss NH, Tull MT, DiLillo D, Messman-Moore T, & Gratz KL (2015). Characterizing emotional dysfunction in borderline personality, major depression, and their co-occurrence. Comprehensive Psychiatry, 62, 187–203. doi: 10.1016/j.comppsych.2015.07.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Dreisbach G, & Goschke T (2004). How positive affect modulates cognitive control: Reduced perseveration at the cost of increased distractibility. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(2), 343–353. doi: 10.1037/0278-7393.30.2.343 [DOI] [PubMed] [Google Scholar]
  11. Faul F, Erdfelder E, Buchner A, & Lang A-G (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. [DOI] [PubMed] [Google Scholar]
  12. Faul F, Erdfelder E, Lang A-G, & Buchner A (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. [DOI] [PubMed] [Google Scholar]
  13. Fox HC, Hong KA, & Sinha R (2008). Difficulties in emotion regulation and impulse control in recently abstinent alcoholics compared with social drinkers. Addictive Behaviors, 33(2), 388–394. doi: 10.1016/j.addbeh.2007.10.002 [DOI] [PubMed] [Google Scholar]
  14. Frewen PA, Dean JA, & Lanius RA (2012). Assessment of anhedonia in psychological trauma: Development of the Hedonic Deficit and Interference Scale. European Journal of Psychotraumatology, 3I(1), 8585–8591. doi: 10.3402/ejpt.v3i0.8585 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gable PA, & Harmon-Jones E (2008). Approach-motivated positive affect reduces breadth of attention. Psychological Science, 19(5), 476–482. doi: 10.1111/j.1467-9280.2008.02112.x [DOI] [PubMed] [Google Scholar]
  16. Ginsberg JP, Berry ME, & Powell DA (2010). Cardiac coherence and posttraumatic stress disorder in combat veterans. Alternative Therapies in Health & Medicine, 16(4), 52–60. [PubMed] [Google Scholar]
  17. Gratz KL, Paulson A, Jakupcak M, & Tull MT (2009). Exploring the relationship between childhood maltreatment and intimate partner abuse: Gender differences in the mediating role of emotion dysregulation. Violence and Victims, 24(1), 68–82. doi: 19.1891/0886-6708.24.1.68 [DOI] [PubMed] [Google Scholar]
  18. Gratz KL, & Roemer L (2004). Multidimensional assessment of emotion regulation and dysregulation: Development, factor structure, and initial validation of the difficulties in emotion regulation scale. Journal of Psychopathology and Behavioral Assessment, 26(1), 41–54. doi: 10.1023/B:JOBA.0000007455.08539.94 [DOI] [Google Scholar]
  19. Gratz KL, Rosenthal MZ, Tull MT, Lejuez CW, & Gunderson JG (2006). An experimental investigation of emotion dysregulation in borderline personality disorder. Journal of Abnormal Psychology, 115(4), 850–855. doi: 10.1037/0021-843X.115.4.850 [DOI] [PubMed] [Google Scholar]
  20. Gratz KL, & Tull MT (2010a). Emotion regulation as a mechanism of change in acceptance-and mindfulness-based treatments In Baer RA (Ed.), Assessing Mindfulness and Acceptance: Illuminating the Theory and Practice of Change (pp. 105–133). Oakland, CA: New Harbinger Publications. [Google Scholar]
  21. Gratz KL, & Tull MT (2010b). The relationship between emotion dysregulation and deliberate self-harm among inpatients with substance use disorders. Cognitive Therapy & Research, 34(6), 544–553. doi: 10.1007/s10608-009-9268-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Gratz KL, Weiss NH, & Tull MT (2015). Examining emotion regulation as an outcome, mechanism, or target of psychological treatments. Current Opinion in Psychology, 3, 85–90. doi: 10.1016/j.copsyc.2015.02.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hansen AL, Johnsen BH, Sollers JJ, Stenvik K, & Thayer JF (2004). Heart rate variability and its relation to prefrontal cognitive function: The effects of training and detraining. European Journal of Applied Physiology, 93(3), 263–272. doi: 10.1007/s00421-004-1208-0 [DOI] [PubMed] [Google Scholar]
  24. Hayes AF (2012). PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling. University of Kansas, KS. [Google Scholar]
  25. Jahng S, Solhan MB, Tomko RL, Wood PK, Piasecki TM, & Trull TJ (2011). Affect and alcohol use: An ecological momentary assessment study of outpatients with borderline personality disorder. Journal of Abnormal Psychology, 120(3), 572–584. doi: 10.1037/a0024686 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Jennings JR, Kamarck T, Stewart C, Eddy M, & Johnson P (1992). Alternate cardiovascular baseline assessment techniques: Vanilla or resting baseline. Psychophysiology, 29(6), 742–750. doi: 10.1111/j.1469-8986.1992.tb02052.x [DOI] [PubMed] [Google Scholar]
  27. Kim S, & Hamann SB (2007). Neural correlates of positive and negative emotion regulation. Journal of Cognitive Neuroscience, 19(5), 776–798. doi: 10.1162/jocn.2007.19.5.776 [DOI] [PubMed] [Google Scholar]
  28. Krause-Utz A, Walther J-C, Lis S, Schmahl C, & Bohus M (2019). Heart rate variability during a cognitive reappraisal task in female patients with borderline personality disorder: The role of comorbid posttraumatic stress disorder and dissociation. Psychological Medicine, 49(11), 1810–1821. doi: 10.1017/S0033291718002489 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Laborde S, Lautenbach F, & Allen MS (2015). The contribution of coping-related variables and heart rate variability to visual search performance under pressure. Physiology & Behavior, 139, 532–540. doi: 10.1016/j.physbeh.2014.12.003 [DOI] [PubMed] [Google Scholar]
  30. Lavender JM, Tull MT, DiLillo D, Messman-Moore T, & Gratz KL (2015). Development and validation of a state-based measure of emotion dysregulation: The State Difficulties in Emotion Regulation Scale (S-DERS). Assessment, 24(2), 197–209. doi: 10.1177/1073191115601218 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Lavender JM, Wonderlich SA, Peterson CB, Crosby RD, Engel SG, Mitchell JE, Crow SJ, Smith TL, Klein MH, Goldschmidt AB, & Berg KC (2014). Dimensions of emotion dysregulation in bulimia nervosa. European Eating Disorders Review, 22(3), 212–216. doi: 10.1002/erv.2288 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Litz BT, Orsillo SM, Kaloupek D, & Weathers F (2000). Emotional processing in posttraumatic stress disorder. Journal of Abnormal Psychology, 109(1), 26–39. doi: 10.1037/0021-843X.109.1.26 [DOI] [PubMed] [Google Scholar]
  33. Malik M (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use: Task force of the European Society of Cardiology and the North American Society for Pacing and Electrophysiology. Annals of Noninvasive Electrocardiology, 1(2), 151–181. doi: 10.1111/j.1542-474X.1996.tb00275.x [DOI] [Google Scholar]
  34. Mankus AM, Aldao A, Kerns C, Mayville EW, & Mennin DS (2013). Mindfulness and heart rate variability in individuals with high and low generalized anxiety symptoms. Behaviour Research and Therapy, 51(7), 386–391. doi: 10.1016/j.brat.2013.03.005 [DOI] [PubMed] [Google Scholar]
  35. Martel MM (2009). Research review: A new perspective on attention- deficit/hyperactivity disorder: Emotion dysregulation and trait models. Journal of Child Psychology and Psychiatry, 50(9), 1042–1051. doi: 10.1111/j.1469-7610.2009.02105.x [DOI] [PubMed] [Google Scholar]
  36. Norman SB, Wilkins KC, Myers US, & Allard CB (2014). Trauma informed guilt reduction therapy with combat veterans. Cognitive and Behavioral Practice, 21(1), 78–88. doi: 10.1016/j.cbpra.2013.08.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Pessoa L (2009). How do emotion and motivation direct executive control? Trends in Cognitive Sciences, 13(4), 160–166. doi: 10.1016/j.tics.2009.01.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Preece DA, Becerra R, Robinson K, Dandy J, & Allan A (2018). Measuring emotion regulation ability across negative and positive emotions: The Perth Emotion Regulation Competency Inventory (PERCI). Personality and Individual Differences, 135(1), 229–241. doi: 10.1016/j.paid.2018.07.025 [DOI] [Google Scholar]
  39. Roemer L, Lee JK, Salters-Pedneault K, Erisman SM, Orsillo SM, & Mennin DS (2009). Mindfulness and emotion regulation difficulties in generalized anxiety disorder: Preliminary evidence for independent and overlapping contributions. Behavior Therapy, 40(2), 142–154. doi: 10.1016/j.beth.2008.04.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Schick MR, Weiss NH, Contractor A, Dixon-Gordon KL, & Spillane N (2019). Depression and risky alcohol use: An examination of the role of difficulties regulating positive emotions in trauma-exposed individuals. The American Journal of Drug and Alcohol Abuse,45(3), 323–332. doi: 10.1080/00952990.2019.1572759 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Slovic P, Finucane ML, Peters E, & MacGregor DG (2004). Risk as analysis and risk as feelings: Some thoughts about affect, reason, risk, and rationality. Risk analysis, 24, 311–322. doi: 10.1111/j.0272-4332.2004.00433.x [DOI] [PubMed] [Google Scholar]
  42. Tabachnick BG, & Fidell LS (2007). Using multivariate statistics, 5th Needham Height, MA: Allyn & Bacon. [Google Scholar]
  43. Taylor JA, Carr DL, Myers CW, & Eckberg DL (1998). Mechanisms underlying very-low-frequency RR-interval oscillations in humans. Circulation, 98(6), 547–555. doi: 10.1161/01.CIR.98.6.547 [DOI] [PubMed] [Google Scholar]
  44. Taylor S, Koch WJ, & McNally RJ (1992). How does anxiety sensitivity vary across the anxiety disorders? Journal of Anxiety Disorders, 6(3), 249–259. doi: 10.1016/0887-6185(92)90037-8 [DOI] [Google Scholar]
  45. Thayer JF, & Lane RD (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of Affective Disorders, 61(3), 201–216. doi: 10.1016/S0165-0327(00)00338-4 [DOI] [PubMed] [Google Scholar]
  46. Thayer JF, & Lane RD (2009). Claude Bernard and the heart-brain connection: Further elaboration of a model of neurovisceral integration. Neuroscience & Biobehavioral Reviews, 33(2), 81–88. doi: 10.1016/j.neubiorev.2008.08.004 [DOI] [PubMed] [Google Scholar]
  47. Tice DM, Bratslavsky E, & Baumeister RF (2001). Emotional distress regulation takes precedence over impulse control: If you feel bad, do it! Journal of Personality and Social Psychology, 80(1), 53–67. doi: 10.1037/0022-3514.80.1.53 [DOI] [PubMed] [Google Scholar]
  48. Tull MT, & Aldao A (2015). Editorial Overview: New directions in the science of emotion regulation. Current Opinion in Psychology, 3, iv–x. doi: 10.10.1016/j.copsyc.2015.03.009 [DOI] [Google Scholar]
  49. Tull MT, Barrett HM, McMillan ES, & Roemer L (2007). A preliminary investigation of the relationship between emotion regulation difficulties and posttraumatic stress symptoms. Behavior Therapy, 38(3), 303–313. doi: 10.1016/j.beth.2006.10.001 [DOI] [PubMed] [Google Scholar]
  50. Watson D, Clark LA, & Tellegen A (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063. doi: 10.1037/0022-3514.54.6.1063 [DOI] [PubMed] [Google Scholar]
  51. Weiss NH, Darosh A, Contractor A, Schick MR, & Dixon-Gordon KL (2019). Confirmatory validation of the factor structure and psychometric properties of the Difficulties in Emotion Regulation Scale – Positive. Journal of Clinical Psychology, 75(7), 1267–1287. doi: 10.1002/jclp.22768 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Weiss NH, Dixon-Gordon KL, Peasant C, & Sullivan TP (2018). An examination of the role of difficulties regulating positive emotions in posttraumatic stress disorder. Journal of Traumatic Stress, 31(5), 775–780. doi: 10.1002/jts.22330 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Weiss NH, Forkus SR, Contractor A, Darosh A, Goncharenko S, & Dixon-Gordon KL (2019). Do difficulties regulating positive emotions contribute to risky sexual behavior among trauma-exposed individuals?: A path analysis. Archives of Sexual Behavior, 48(7), 2075–2087. doi: 10.1007/s10508-019-1410-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Weiss NH, Forkus SR, Contractor AA, & Schick MR (2018). Difficulties regulating positive emotions and alcohol and drug misuse: A path analysis. Addictive Behaviors, 84, 45–52. doi: 10.1016/j.addbeh.2018.03.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Weiss NH, Gratz KL, & Lavender J (2015). Factor structure and initial validation of a multidimensional measure of difficulties in the regulation of positive emotions: The DERS-Positive. Behavior Modification, 39(3), 431–453. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Zou C, Plaks JE, & Peterson JB (2019). Don't get too excited: Assessing individual differences in the down-regulation of positive emotions. Journal of Personality Assessment, 101(1), 73–83. doi: 10.1080/00223891.2017.1339711 [DOI] [PubMed] [Google Scholar]

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