Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Sep 30.
Published before final editing as: Subst Use Misuse. 2026 Sep 6:1–9. doi: 10.1080/10826084.2026.2727142

Emotion Dysregulation Modulates the Association Between Cigarette Use and HPA-Axis Functioning Among Women Currently Experiencing Intimate Partner Violence

Reagan E Fitzke 1,2, Alexa M Raudales 3,4, Jewelia J Ferguson 3, Alana Egan 3, Colin T Mahoney 1,2, Ceren Günsoy 3, Nicole H Weiss 3
PMCID: PMC13623208  NIHMSID: NIHMS2207944  PMID: 42702577

Abstract

Cigarette use is a salient public health concern, particularly among women experiencing intimate partner violence (IPV). While smoking cigarettes may attenuate acute stress levels, it also exacerbates long-term stress reactivity by disrupting hypothalamic-pituitary-adrenal (HPA)-axis functioning. However, little is known about how emotion dysregulation may influence the impact of cigarette use on stress reactivity among women experiencing ongoing IPV. This study examined whether emotion dysregulation moderates the association between cigarette use and HPA-axis functioning, measured by hair cortisol concentration, among 94 women currently experiencing IPV. Participants completed a baseline interview that included assessment of emotion dysregulation, followed by 30 days of thrice daily surveys that assessed cigarette use, and finally a follow-up interview where a hair sample was collected for cortisol analysis. Analyses revealed a significant moderating effect of emotion dysregulation on the association between cigarette use and HPA-axis functioning. Specifically, among women with low to moderate (but not high) emotion dysregulation, greater average cigarettes per day were associated with lower hair cortisol concentration during the 30-day sampling period. Findings suggest a potential stress-modulating function of cigarette use among women with low to moderate levels of emotion dysregulation, while elevated dysregulation may attenuate the stress-relieving effects of cigarette use. While the foremost priority is implementing safety-focused interventions and comprehensive domestic violence services to reduce ongoing relationship violence, our results also highlight the potential utility of tailoring smoking cessation interventions for women experiencing IPV to address emotion dysregulation and promote alternative stress-coping strategies.

Keywords: Cigarette use, Intimate partner violence, Emotion dysregulation, HPA-axis functioning, ecological momentary assessment, women’s health

Introduction

Nearly a quarter (22.3%) of adults currently use tobacco, creating a threat to mortality and morbidity across the globe (World Health Organization, 2020). Tobacco use has been attributed to approximately five million deaths per year, claiming more lives than the combination of tuberculosis, HIV/AIDS, and malaria. Tobacco use is also associated with a host of physical health morbidities – such as strokes, cardiovascular diseases, and a range of cancers – and detrimental downstream impacts for an affected individual’s wider family and community (World Health Organization, 2012, 2020). For example, parental tobacco use is correlated with adverse health outcomes for children (e.g., infant death syndrome, respiratory concerns) and the perpetuation of intergenerational poverty (World Health Organization, 2012, 2020). Cigarette use is the most common form of tobacco used in the United States (Cornelius, 2023), and is of particular salience for individuals experiencing intimate partner violence (IPV), which impacts approximately 50% of women in their lifetime (World Health Organization, 2005). Though it is of note that IPV affects all individuals, research has consistently found that women experience IPV at disproportionate rates as compared to men (Cunningham & Anderson, 2023; Tjaden & Thoennes, 2000). Women experiencing IPV are significantly more likely to engage in cigarette use (Crane et al., 2013) and experience worse tobacco use outcomes compared to those without a history of IPV (Ogden et al., 2022). Thus, there is a critical need to explore factors underlying cigarette use within a sample of women experiencing IPV.

Stress is an important factor underlying cigarette use (Lawless et al., 2015), which can be reliably assessed using hair cortisol as a biomarker (Stalder & Kirschbaum, 2012). Cortisol is a steroid hormone that is produced following activation of the hypothalamic-pituitary-adrenal (HPA) axis—an essential stress response system within the body (Stalder et al., 2017). Extensive research has established HPA-axis dysfunction, assessed via hair cortisol, within health risk behaviors (Koumantarou Malisiova et al., 2021), including cigarette use (Rohleder & Kirschbaum, 2006). Consistent with the self-medication hypothesis for substance use (Khantzian, 1985), a primary motive for cigarette use is to relieve stress (McEwen et al., 2008). Indeed, reductions in stress predict lower cravings for cigarettes in individuals’ daily lives (Selva Kumar et al., 2022). Likewise, cigarette use has been found to effectively reduce acute stress (Choi et al., 2015). Stress may be particularly pertinent to cigarette use among populations characterized by high levels of chronic stress, such as women experiencing IPV, who exhibit elevated HPA-axis dysfunction assessed via hair cortisol (Alhalal & Falatah, 2020). Further, one recent study reported that posttraumatic stress disorder (PTSD), which is a common consequence of chronic stress related to IPV, was associated with increased cigarette use both cross-sectionally and longitudinally among women veterans, underscoring the long-term effects of chronic stress and traumatic exposure on cigarette use as supported by this theoretical model (i.e., self-medication hypothesis; Mahoney et al., 2020). Research that deepens our understanding of cigarette use in the context of stress for those most impacted may be critical in improving outcomes for cigarette use.

The link between stress and cigarette use may be better understood by considering individual differences in emotion dysregulation. Broadly, emotion dysregulation is defined by an individual’s abilities to modulate their emotional arousal; recognize, understand, and accept emotions; and engage in goal-directed activities when experiencing emotions (Gratz & Roemer, 2004). Prior research demonstrates that emotion dysregulation plays a clinically relevant role in cigarette use outcomes (Rogers et al., 2018). It may be that individuals with greater emotion dysregulation are more likely to view their stress as overwhelming or intolerable and thus turn to cigarette use to cope with or alleviate elevated stress. Indeed, studies have shown that emotion dysregulation impacts cigarette use to reduce negative emotions (Johnson & McLeish, 2016; Short et al., 2015). Furthermore, research has detected influences of emotion on stress reactivity, assessed by heart rate variability, among individuals who use cigarettes (Borges et al., 2020). However, there is a need to clarify whether emotion dysregulation impacts how cigarette use may attenuate stress levels among high-risk populations, such as women IPV survivors. This information has the potential to elucidate unique vulnerability to deleterious cigarette use outcomes (e.g., cardiovascular and respiratory health conditions, long-term increases in mental health symptoms), which can inform smoking cessation interventions for these groups.

Addressing a critical gap in the literature, the goal of the present study was to examine whether emotion dysregulation moderated the relationship between HPA-axis functioning and cigarette use over a 30-day period among women experiencing IPV. Experience sampling methodology (ESM) was used to capture average number of cigarettes per day across a 30-day sampling period. ESM increases the accuracy and ecological validity of cigarette use by capturing experiences frequently, in near-real time, and as they occur naturally in women’s daily lives (Ebner-Priemer & Trull, 2009). To increase the accuracy of our measurement of stress during the 30-day sampling period, a hair sample was taken at a follow-up session to assess average cortisol during this same period. Hair cortisol reflects systemic concentrations of cortisol derived from the bloodstream through passive diffusion (Stalder & Kirschbaum, 2012), and thus provides an index of HPA-axis functioning occurring over longer periods of time (e.g., 30 days). Hair cortisol is more reliable as a marker of prolonged stress compared to alternative methods of cortisol collection (i.e., saliva, plasma, urine), which are affected by external factors, such as circadian variations, and reflect activation of the HPA-axis (Russell et al., 2012). We hypothesized that emotion dysregulation would moderate the association between HPA-axis functioning and average cigarette use per day, such that the positive association between hair cortisol concentration and average number of cigarettes per day would be stronger among women with high (versus low) emotion dysregulation. PTSD severity was included as a covariate given its robust relations with cortisol (Meewisse et al., 2007), cigarette use (Kearns et al., 2018), and emotion dysregulation (Seligowski et al., 2015). Accounting for PTSD severity enabled a more rigorous test of whether emotion dysregulation uniquely influences the relationship between chronic physiological stress and cigarette use beyond the effects of trauma-related psychopathology.

Method

Participants

Recruitment materials were posted in community establishments throughout Providence County, Rhode Island, as well as online via website postings. Participants were women who reported experiencing sexual and/or physical victimization in the past six months by their current male partner and past 30-day alcohol and/or drug use. Additional inclusion criteria were: (1) age 18 or older; (2) ability to read, write, and speak in English; and (3) current involvement in a relationship of at least six months’ duration with a male partner. Exclusion criteria were: (a) current mania/psychosis (which could interfere with participants’ ability to complete the study procedures; assessed by the Structured Clinical Interview for DSM-5 [SCID-5]; First et al., 2016), (b) current impairment in cognitive functioning (which could interfere with participants’ ability to complete the study procedures; assessed via the Mini-Mental Status Exam and requiring a score > 24; Folstein et al., 1975), (c) self-reported current pregnancy (because participants had to take part in an emotion induction that could elicit distress as part of the parent study), (d) colorblindness (because a lab task in the parent study required participants to identify colors), (e) cardiovascular disease (because participants’ cardiovascular functioning was captured as part of the parent study), and (f) residence in a shelter or group home (because structured living can reduce opportunities for substance use which was captured via assessment methods as part of the parent study; Kerman et al., 2023). Eligibility criteria were assessed through self-report during a phone screen except where otherwise noted. The final sample included 95 women who completed at least one survey during the ESM period and provided a hair sample. Additional demographic characteristics are summarized in Table 1.

Table 1.

Sample Demographics and Analytic Variable Descriptive Statistics (N = 94)

Variable Mean (SD) / N (%)
Age 40.80 (10.91)
Household income/month 1,283.38 (1595.42)
Years of school completed 12.42 (2.03)
Ethnicity
Hispanic/Latina 17 (18.1%)
Not Hispanic/Latina 72 (76.6%)
Not specified 5 (5.3%)
Race
Black or African
American 18 (19.1%)
White 52 (55.3%)
American
Indian/Alaska Native 7 (7.4%)
Hispanic/Latina 13 (13.8%)
Other 2 (2.1%)
Multiracial 2 (2.1%)
Employment status
Full time 5 (5.3%)
Part time 11 (11.7%)
Unemployed 57 (60.6%)
Not in labor force 15 (16.0%)
Not specified 6 (6.4%)
Mean (SD) Range Skewness (SE)
Average cigarettes per day (winsorized) 9.22 (8.11) 0.00 – 35.00 .90 (.02)
Emotion regulation composite score 90.50 (24.44) 42.00-160.00 0.19 (0.2)
Current PTSD severity 8.00 (5.86) 0.00 – 19.00 .36
Hair cortisol concentration (pg/mg) 84.56 (288.74) 1.38 – 2026.40 5.79 (.03)
Cigarettes per day x Emotion regulation - (206.04) - .08 (.02)
Open in a new tab

Procedures

All procedures were reviewed and approved by the Providence County, Rhode Island Institutional Review Board prior to beginning data collection. Data for the present secondary analysis were collected between 2018 and 2020 for a larger parent study examining the proximal role and temporal ordering of emotion dysregulation as well as the role of PTSD in substance use and HIV/sexual risk Providence County, Rhode Island. The parent study entailed (a) a baseline session, (b) an experimental session, (c) 30 days of ESM using interactive voice recording (IVR) technology, and (d) a follow-up session. The current study used data from the baseline session, ESM period, and the follow-up session. All participants were provided with a list of community resources, and assistance with referrals was provided upon request. The principal investigator, a licensed psychologist in the state of Providence County, Rhode Island, was available on-call if participants required additional trauma- and/or substance use-related support.

Baseline Session

Baseline sessions were conducted by female bachelor’s- or master’s-level clinical psychology doctoral students in a private office to protect participants’ safety and confidentiality. Participants provided informed consent, followed by a structured diagnostic interview, and then completed self-report measures on a computer. Participants were compensated with $40 for completing the baseline session.

ESM Period

During the ESM period, participants completed surveys through an IVR telephone system three times a day (i.e., morning, afternoon, and evening) for 30 days. Participants could elect to have the IVR system initiate reminder calls, or calls could be initiated by the participant. For each survey, participants were asked to report on experiences since the previous reporting interval. During the experimental session, participants were trained to use the IVR telephone system to record their information; training procedures were modeled after those detailed in Stone and Shiffman (2002). Participants were compensated $1 for each completed survey and received weekly bonuses of $5 if they completed > 80% of the ESM surveys.

Follow-Up Session

Follow-up sessions were conducted in a private office to protect participants’ safety and confidentiality by a trained female research assistant. Participants were interviewed about their experiences over the 30-day ESM period, provided a hair sample, and completed self-report measures on a computer. Participants were compensated $40 for completing the session.

Measures

Baseline Measures

Demographic Information.

Self-reported sociodemographic information regarding age, gender, ethnicity, race, income, educational level, and employment status were collected.

Emotion Dysregulation.

The Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004) is a 36-item self-report measure of emotion dysregulation. Respondents indicated how often each item applied to them using a 5-point Likert-type scale ranging from 1 (almost never) to 5 (almost always). Items were summed to produce a total score, with higher scores indicative of greater emotion dysregulation. The DERS is a widely used measure with sound psychometric properties, demonstrating good test–retest reliability, convergent validity, and discriminant validity (Gratz & Roemer, 2004). Internal consistency in the current sample was excellent (McDonald’s ω = .93).

PTSD Severity.

A computerized version of the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (SCID-5) was administered to establish current PTSD severity (First et al., 2016). PTSD severity was calculated by total number of symptoms that were present at clinically significant levels. The SCID-5 shows moderate to excellent inter-rater reliability, including sensitivity of 1.00, specificity of 0.96, and a κ of 0.80 for PTSD (Osório et al., 2019). The SCID-5 was administered by clinical psychology doctoral students trained to reliability with the principal investigator. All data were reviewed by the principal investigator. In the case of ambiguous responses, data were discussed by the principal investigator and interviewer until a consensus was reached.

ESM Measure

Cigarette Use.

Cigarette use was measured with a one-item question that assessed the number of cigarettes the participant used since the previous survey (i.e., “How many cigarettes did you smoke?”). Participants provided a numeric response for daily cigarette use; study variables were calculated based on these values, which are further described in the Analytic Plan.

Follow-up Session Measure

HPA-axis Functioning.

In a private office, research assistants cut a lock of hair from the back of the participant’s head (i.e., posterior vertex region) with fine scissors as close as possible to the scalp. Hair samples were taped with painter’s tape to aluminum foil for protection and stored in paper envelopes at room temperature until they were mailed to the Behavioral Immunology and Endocrinology Lab at the Anschutz Medical Campus – University of Colorado Denver for processing and analyses. Hair cortisol concentration was assessed using procedures described in Hoffman et al. (2017). Each hair sample was weighed, washed three times with 100% isopropanol alcohol, and re-weighed on a high-sensitivity electronic balance. Following freezing, the hair was ground for 4 to 5 min with one 3/16-in. stainless steel ball bearing, and the powdered hair was extracted in HPLC-grade methanol overnight while gently shaking. The hair was pelleted by spinning the cryovial for three minutes in a centrifuge at 1700 g, and the supernatant was removed and dried under a stream of nitrogen. The extracts were reconstituted with assay diluent based on hair weight (3–43 mg). Steroid levels were determined using a commercial high-sensitivity EIA kit (Salimetrics LLC, State College, PA) per manufacturer’s protocol. Results are reported as pg/mg, which corrects for the weight of hair extracted.

Analytic Plan

Data cleaning identified one individual whose hair cortisol values were more than three standard deviations above the mean. This outlier was removed from the dataset to produce a final analytic sample of n = 94. As some participants reported high, likely unrealistic, numbers of cigarettes since the last assessment (e.g., 99 cigarettes), we winsorized all outliers above the 95% level, where any value >15 was recoded to 15 (3.9% of total responses).1 We then used the survey-level cigarette data to develop a composite variable for cigarette use reflecting average cigarette use per day: total number of cigarettes reported by each participant across the ESM period, divided by the number of days that the participant completed at least one survey.

We used the PROCESS SPSS macro (Model 1; Hayes, 2022) to conduct moderation analyses assessing the interaction effects of average number of cigarettes per day and emotion dysregulation on hair cortisol concentration. The SPSS PROCESS macro examines the conditional effect of the independent variable (i.e., average number of cigarettes per day) on the dependent variable (i.e., hair cortisol concentration) at different levels (16th percentile [low], 50th percentile [medium], 84th percentile [high]) of the moderator variable (i.e., emotion dysregulation) using the entire sample. PROCESS employs ordinary least squares (OLS) regression and bootstrapping, providing greater statistical power than traditional inference methods while avoiding reliance on distributional assumptions. Bootstrapping was implemented with 5,000 random samples generated from the observed covariance matrix to estimate bias-corrected 95% confidence intervals (CIs) and significance values. For interactions found to be significant, following the methods described by Aiken and West (1991), we plotted regression slopes of differences in hair cortisol concentration and average number of cigarettes per day at high (1 SD above the mean) and low (1 SD below the mean) levels of emotion dysregulation and conducted follow-up analyses to examine whether the slopes of the regression lines differed significantly from zero. We also examined Johnson-Neyman regions of significance to further disentangle interaction effects in the model. The model controlled for PTSD severity.

Results

Preliminary Results

Analytic sample demographics and basic correlations between study variables are reported in Table 1. Bivariate correlation analyses indicated a small to moderate positive correlation between average number of cigarettes per day and emotion dysregulation (r = .21, p < .001), a small positive association between average number of cigarettes per day and hair cortisol concentration (r = .02, p = .03), and a small negative relation between hair cortisol concentration and emotion dysregulation (r = −.18, p < .001).

Primary Results

We first conducted a stepwise linear regression to evaluate the incremental effect of the interaction term. The first model with cigarette use and emotion dysregulation as individual predictors of hair cortisol with PTSD severity as a covariate exhibited acceptable model fit (F = 113.47; p < 0.001; R2 = 0.048). The second model with the interaction term was also significant and accounted for a slightly greater proportion of the variance in hair cortisol (F = 92.92, p < 0.001; R2 = 0.052). Thus, we report main and interaction effects from the full, original moderation model.

Table 2 reports final model fit and moderation findings. There was a significant main effect of average number of cigarettes per day on hair cortisol concentration, such that higher average use of cigarettes per day predicted lower levels of hair cortisol overall (b = −11.15, SE = 1.68, t = −6.64, p < 0.001, 95%CI [−14.44, −7.86]). There was also a significant main effect of emotion dysregulation on hair cortisol, with higher emotion dysregulation similarly predicting lower levels of hair cortisol (b = −1.02, SE = 0.25, t = −4.12, p < 0.001, 95%CI [−1.50, −0.53]). More importantly, the interaction between average number of cigarettes per day and emotion dysregulation was significant (b = −1.02, SE = 0.25, t = -4.12, p < 0.001, 95%CI [−1.50, −0.53]). Specifically, at low (b = −5.28, SE = 0.71, t = −7.42, p = <.001, 95% CI [−6.68, −3.39]) and moderate (b = −2.81, SE = 0.47, t = −5.95, p = <.001, 95% CI [−3.74, −1.89]) levels of emotion dysregulation, higher average number of cigarettes per day was associated with lower levels of hair cortisol. At high levels of emotion dysregulation, however, the effect of average number of cigarettes per day on hair cortisol was no longer significant (i.e., hair cortisol remained consistent at any level of cigarette use; b = −0.44, SE = 0.58, t = −0.76, p = .45, 95% CI [−1.58, 0.70]). Figure 1 illustrates simple slope tests of the interaction. For model regions of significance, the inverse link between average number of cigarettes per day and hair cortisol concentration was significant when emotion dysregulation was at a value of 108.69 or lower (i.e., 79.7% of the sample).

Table 2.

Interaction Effects of Average Number of Cigarettes per Day and Emotion Dysregulation on Hair Cortisol Concentration

Model Fit Summary
R R-sq MSE F Df1 Df2 p
Value .23 .05 97108.23 92.92 4.00 6655.00 <.001
Model Results
b β SE t p 95% CI
Cigarette Use −11.15 −0.07 1.68 −6.64 <.001 [−14.44, −7.86]
Emotion Dysregulation −1.02 −0.01 0.25 −4.13 <.001 [−1.50, −0.53]
Cigarette Use x Emotion Dysregulation 0.09 0.07 0.02 5.45 <.001 [0.06, 0.13]
PTSD Severity (Control) 12.53 0.23 0.68 18.43 <.001 [11.20, 13.87]
Conditional Effects of Cigarette Use at Value(s) of Emotion Dysregulation
Effect SE t p 95% CI
Low (63.00) −5.28 0.71 −7.42 <.001 [−6.68, −3.89]
Medium (89.50) −2.81 0.47 −5.95 <.001 [−3.74, −1.89]
High (115.00) −0.44 0.58 −0.76 0.44 [−1.58, 0.70]
Open in a new tab

Note. PROCESS MACRO does not supply standardized betas (β). Standardized betas reported were manually calculated using the following formula: β=b*(SDY​/SDX). Unstandardized beta values are reported in the table above; SDs for all analytic variables are reported in Table 1.

Figure 1. Interaction Plot of Average Number of Cigarettes Per Day x Emotion Dysregulation Predicting Hair Cortisol Concentration (pg/mg).

Figure 1

Open in a new tab

Discussion

The purpose of the present study was to further elucidate the role of stress and emotion dysregulation in cigarette use among women experiencing IPV. To do so, we examined the moderating role of emotion dysregulation in the relation between average cigarette use per day from ESM and HPA-axis functioning from a hair sample during a 30-day reporting period. While we found that emotion dysregulation moderated the association between average cigarette use per day and HPA axis-functioning, findings were in a direction contrary to our study hypothesis. Specifically, we found that the positive association between average number of cigarettes per day and HPA-axis functioning was significant only for women with low to moderate (but not high) levels of emotion dysregulation. These findings challenge our previous understanding of stress and emotion dysregulation among women experiencing IPV, in that the role of cigarette or nicotine/tobacco use in regulating stress among this population may be more complex in its impact on HPA-axis-functioning than previous research has suggested.

Contrary to our hypothesis, women experiencing ongoing IPV with low, but not high, levels of emotion dysregulation who reported smoking more cigarettes per day demonstrated lower hair cortisol concentration. Prior models of substance use stress-coping (Wills & Shiffman, 1985) and self-medication (Khantzian, 1985) posit that individuals engage in substance use, such as cigarette use, to modulate (i.e., cope with) stress (for a review, see Kassel et al., 2003). Indeed, past research has found that individuals expect cigarette use to reduce stress (Barroso-Hurtado et al., 2023) and thus tend to smoke more when they are experiencing stress (Cambron et al., 2019). Moreover, physiological investigations have shown that cigarette smoking in an unpleasant state (e.g., anxiety, depression, fatigue) was linked to a decrease in arousal, suggesting that cigarette smoking may relieve stress subjectively (Choi et al., 2015). Our study’s findings suggest a more complex role of cigarette use in modulating stress than simply in reductions in HPA-axis functioning broadly.

One possible interpretation for this study’s findings could be that smoking cigarettes may be used to modulate stress among individuals with low emotion dysregulation. Notably, however, although our findings for women with low and moderate levels of emotion dysregulation alone do not support short-term disruptions in HPA-axis functioning, previous research indicates that cigarette use is likely to have paradoxical consequences in the long-term, as cigarette use as a coping strategy for managing stress can cyclically reinforce motivations for continued cigarette use through negative reinforcement processes (Baker et al., 2004). This, in turn, can exacerbate the negative impacts of cigarette use among women experiencing IPV (Parmar et al., 2023). Specifically, relying on cigarettes to cope with IPV-related stress may reinforce nicotine dependence over time, increasing barriers to smoking cessation while contributing to poorer physical and mental health outcomes. As a result, smoking may perpetuate a cycle in which temporary relief from distress ultimately intensifies overall health burden and vulnerability among women experiencing IPV. Thus, this study’s findings may be most useful to consider in the context of cigarette use being particularly impactful on HPA-axis functioning for women with high emotion dysregulation.

Existing evidence suggests that emotion regulatory efforts (e.g., cigarette use) among individuals who experience high levels of emotion dysregulation may be less effective. One explanation for this finding is that emotional experiences are more intense among individuals with high levels of emotion dysregulation (Veilleux et al., 2014), and intense emotions are more difficult to regulate (Dixon-Gordon et al., 2015). This may be compounded among women experiencing IPV who experience significant and chronic stress (Yim & Kofman, 2019), including threats of violence in their relationships (Spencer et al., 2019), as well as other major life areas tied to IPV such as financial strain, (single-) parenting, and involvement in legal proceedings (Anderson & Saunders, 2003). Collectively, these findings suggest that women experiencing IPV with high levels of emotion dysregulation may experience stress more intensely, and as such, may be less effective at attenuating it. Future research in this area can examine how these processes unfold during moments of stress, including through laboratory or real-world stress paradigms that capture momentary changes in HPA-axis functioning and emotion dysregulation pre- and post-cigarette use.

While the current study has many strengths, including recruitment of a community sample of women experiencing IPV and use of ESM and hair cortisol methods to more reliably capture cigarette use and HPA-axis functioning across the 30-day sampling period, several limitations warrant consideration. First, the study design limits conclusions regarding the temporal ordering of proposed pathways. Future research using experimental and longitudinal designs is needed to clarify temporal precedence. For instance, investigations that involve the real-time collection of biological data—such as saliva samples to capture cortisol—alongside ESM will capture fluctuations in both cigarette use and cortisol, thereby speaking to their proximal associations. Second, self-reports of cigarette use and emotion dysregulation may be prone to an individual’s willingness and ability to accurately report on their experiences. Future research may benefit from objective assessments of these constructs, such as using a Bluetooth-enabled e-cigarette and physiological data from wearable biosensors (Kleiman et al., 2023). Finally, findings were examined in a sample of community women experiencing IPV, thus findings may not generalize to other trauma-exposed samples (e.g., non-interpersonal trauma) and settings (e.g., inpatient hospitalization). Further, the sample was comprised of individuals who identified as women and were in heterosexual relationships and thus may not reflect experiences for men and those in same-gender or gender-expansive and -diverse relationships.

Implications

Despite these limitations, findings of the current study advance research on the role of stress regulation in cigarette use in a clinically relevant sample of community women experiencing IPV. We found that the association between average number of cigarettes per day and HPA-axis functioning across a 30-day sampling period was significant and positive, but only for women with low to moderate levels of emotion dysregulation. These findings suggest that women experiencing IPV ranging from low to moderate levels of emotion dysregulation may use more cigarettes in the context of high stress, perhaps because they are trying to modulate that stress, consistent with theoretical models of coping and substance use (Khantzian, 1985; Wills & Shiffman, 1985). Conversely, for women with high levels of emotion dysregulation, cigarette use may not be as effective at modulating stress, leading to stress maintaining its salience regardless of cigarette use. These findings highlight key roles of stress and emotion dysregulation in cigarette use among women experiencing IPV.

Foremost, this study emphasizes the salience of safety interventions among women experiencing ongoing relationship violence, particularly for women with deficits in emotion regulation. Results suggest that women who experience high levels of emotion dysregulation and use cigarettes to cope with ongoing stress are the most susceptible to experiencing HPA axis dysfunction. Removing ongoing stressors through safety interventions while providing comprehensive domestic violence resources are thus of imperative simultaneous processes that could circumvent use via stress coping with resultant long-term health effects.

Secondary to immediate violence and safety intervention efforts, our preliminary findings (if replicated in subsequent studies) may highlight the need for addressing stress and emotion dysregulation among women experiencing IPV in smoking cessation interventions. In particular, our initial findings suggest women experiencing IPV may use cigarettes to modulate stress in the short-term. Thus, women with high levels of emotion dysregulation may require a focus on addressing emotion dysregulation in smoking cessation interventions (Bradizza et al., 2017) for stress modulation strategies to be effective. Clinically, this may include integrating trauma-informed care and emotion regulation skills training into smoking cessation programs for women experiencing IPV. Specifically, clinicians may tailor interventions to address underlying emotional and interpersonal challenges associated with IPV. Moreover, additional screening for cigarette use and emotion regulation among women experiencing IPV may provide clinically useful information to further inform treatment. Though cigarette use may modulate stress in the short-term for some, our study highlights a need for integrative treatments for emotion regulation, stress modulation, and cigarette use among women experiencing IPV, particularly to reduce impacts on HPA axis functioning among those who experience high levels of emotion dysregulation. Taken together, these findings underscore the critical importance of addressing the complex interplay between stress, emotion regulation, and cigarette use among women experiencing IPV in order to inform violence intervention and smoking cessation or reduction efforts.

Acknowledgments

This research was supported by National Institute on Drug Abuse Grant K23 DA039327, awarded to NHW. Work on this paper by AMR was supported by National Institute on Alcohol Abuse and Alcoholism Grant R25AA028464. Work on this paper by AE was supported by National Institute on Drug Abuse Grant F31 DA060021. Work on this paper by RF was supported by National Institute on Alcohol Abuse andAlcoholism Grant F31 AA032741.

Footnotes

Declarations of Interest: The authors report there are no competing interests to declare.

1

Sensitivity analyses were conducted that excluded cases with high values (3.9% of cases; 367 out of 9409 non-missing cases; 15+ cigarettes used since last survey) to compare with analysis that winsorized these same high values; results remained the same in strength and direction, so analyses with winsorized values were retained in the final analyses.

Data Availability Statement:

Requests for deidentified data can be made to the corresponding author.

References

  1. Aiken LS. (1991). Multiple regression: Testing and interpreting interactions. sage Newbury Park, CA. [Google Scholar]
  2. Alhalal E, & Falatah R. (2020). Intimate partner violence and hair cortisol concentration: A biomarker for HPA axis function. Psychoneuroendocrinology, 122, 104897. 10.1016/j.psyneuen.2020.104897 [DOI] [PubMed] [Google Scholar]
  3. Anderson DK, & Saunders DG. (2003). Leaving An Abusive Partner: An Empirical Review of Predictors, the Process of Leaving, and Psychological Well-Being. Trauma, Violence, & Abuse, 4(2), 163–191. 10.1177/1524838002250769 [DOI] [PubMed] [Google Scholar]
  4. Baker TB, Brandon TH, & Chassin L. (2004). Motivational Influences on Cigarette Smoking. Annual Review of Psychology, 55(1), 463–491. 10.1146/annurev.psych.55.090902.142054 [DOI] [PubMed] [Google Scholar]
  5. Barroso-Hurtado M, Suárez-Castro D, Martínez-Vispo C, Becoña E, & López-Durán A. (2023). Perceived Stress and Smoking Cessation: The Role of Smoking Urges. International Journal of Environmental Research and Public Health, 20(2), 1257. 10.3390/ijerph20021257 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Borges AM, Yang M-J, Farris SG, Zvolensky M, & Leyro TM. (2020). Examining the role of emotion regulation in the bidirectional relation between physiological and subjective stress response among daily cigarette smokers. Personality and Individual Differences, 155, 109740. 10.1016/j.paid.2019.109740 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bradizza CM, Stasiewicz PR, Zhuo Y, Ruszczyk M, Maisto SA, Lucke JF, Brandon TH, Eiden RD, Slosman KS, & Giarratano P. (2017). Smoking Cessation for Pregnant Smokers: Development and Pilot Test of an Emotion Regulation Treatment Supplement to Standard Smoking Cessation for Negative Affect Smokers. Nicotine & Tobacco Research, 19(5), 578–584. 10.1093/ntr/ntw398 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cambron C, Haslam AK, Baucom BRW, Lam C, Vinci C, Cinciripini P, Li L, & Wetter DW. (2019). Momentary precipitants connecting stress and smoking lapse during a quit attempt. Health Psychology, 38(12), 1049–1058. 10.1037/hea0000797 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Choi D, Ota S, & Watanuki S. (2015). Does cigarette smoking relieve stress? Evidence from the event-related potential (ERP). International Journal of Psychophysiology, 98(3), 470–476. 10.1016/j.ijpsycho.2015.10.005 [DOI] [PubMed] [Google Scholar]
  10. Cornelius ME. (2023). Tobacco product use among adults–United States, 2021. MMWR. Morbidity and Mortality Weekly Report, 72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Crane CA, Hawes SW, & Weinberger AH. (2013). Intimate Partner Violence Victimization and Cigarette Smoking: A Meta-Analytic Review. Trauma, Violence, & Abuse, 14(4), 305–315. 10.1177/1524838013495962 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cunningham M, & Anderson L, K. (2023). Women experience more intimate partner violence than men over the life course: Evidence for gender asymmetry at all ages in a national sample. Sex Roles, 89(11), 702–717. [Google Scholar]
  13. Dixon-Gordon KL, Aldao A, & De Los Reyes A. (2015). Emotion regulation in context: Examining the spontaneous use of strategies across emotional intensity and type of emotion. Personality and Individual Differences, 86, 271–276. 10.1016/j.paid.2015.06.011 [DOI] [Google Scholar]
  14. Ebner-Priemer UW, & Trull TJ. (2009). Ecological momentary assessment of mood disorders and mood dysregulation. Psychological Assessment, 21(4), 463–475. 10.1037/a0017075 [DOI] [PubMed] [Google Scholar]
  15. First MB, Williams JB, Karg RS, & Spitzer RL. (2016). SCID-5-CV: Structured clinical interview for DSM-5 disorders: Clinician version. (No Title). [Google Scholar]
  16. Folstein MF, Folstein SE, & McHugh PR. (1975). “Mini-mental state.” Journal of Psychiatric Research, 12(3), 189–198. 10.1016/0022-3956(75)90026-6 [DOI] [PubMed] [Google Scholar]
  17. 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. 10.1023/B:JOBA.0000007455.08539.94 [DOI] [Google Scholar]
  18. Hayes AF. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (Third edition). The Guilford Press. [Google Scholar]
  19. Hoffman MC, D’Anna-Hernandez K, Benitez P, Ross RG, & Laudenslager ML. (2017). Cortisol during human fetal life: Characterization of a method for processing small quantities of newborn hair from 26 to 42 weeks gestation. Developmental Psychobiology, 59(1), 123–127. 10.1002/dev.21433 [DOI] [PubMed] [Google Scholar]
  20. Johnson AL, & McLeish AC. (2016). The indirect effect of emotion dysregulation in terms of negative affect and smoking-related cognitive processes. Addictive Behaviors, 53, 187–192. 10.1016/j.addbeh.2015.10.023 [DOI] [PubMed] [Google Scholar]
  21. Kassel JD, Stroud LR, & Paronis CA. (2003). Smoking, stress, and negative affect: Correlation, causation, and context across stages of smoking. Psychological Bulletin, 129(2), 270–304. 10.1037/0033-2909.129.2.270 [DOI] [PubMed] [Google Scholar]
  22. Kearns NT, Carl E, Stein AT, Vujanovic AA, Zvolensky MJ, Smits JAJ, & Powers MB. (2018). Posttraumatic stress disorder and cigarette smoking: A systematic review. Depression and Anxiety, 35(11), 1056–1072. 10.1002/da.22828 [DOI] [PubMed] [Google Scholar]
  23. Kerman N, Kidd SA, Voronov J, Marshall CA, O'Shaughnessy B, Abramovich A, & Stergiopoulos V. (2023). Victimization, safety, and overdose in homeless shelters: A systematic review and narrative synthesis. Health & Place, 83, 103092. 10.1016/j.healthplace.2023.103092 [DOI] [PubMed] [Google Scholar]
  24. Khantzian EJ. (1985). The self-medication hypothesis of addictive disorders: Focus on heroin and cocaine dependence. American Journal of Psychiatry, 142(11), 1259–1264. 10.1176/ajp.142.11.1259 [DOI] [PubMed] [Google Scholar]
  25. Kleiman EM, Glenn CR, & Liu RT. (2023). The use of advanced technology and statistical methods to predict and prevent suicide. Nature Reviews Psychology, 2(6), 347–359. 10.1038/s44159-023-00175-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Koumantarou Malisiova E, Mourikis I, Darviri C, Nicolaides NC, Zervas IM, Papageorgiou C, & Chrousos GP. (2021). Hair cortisol concentrations in mental disorders: A systematic review. Physiology & Behavior, 229, 113244. 10.1016/j.physbeh.2020.113244 [DOI] [PubMed] [Google Scholar]
  27. Lawless MH, Harrison KA, Grandits GA, Eberly LE, & Allen SS. (2015). Perceived stress and smoking-related behaviors and symptomatology in male and female smokers. Addictive Behaviors, 51, 80–83. 10.1016/j.addbeh.2015.07.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Mahoney CT, Zweig IR, Marx BP, & Keane TM. (2020). Cross-lagged effects of posttraumatic stress disorder symptom severity and cigarette smoking among OEF/OIF/OND veterans. Depression and Anxiety, 37(11), 1118–1126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. McEwen A, West R, & McRobbie H. (2008). Motives for smoking and their correlates in clients attending Stop Smoking treatment services. Nicotine & Tobacco Research, 10(5), 843–850. 10.1080/14622200802027248 [DOI] [PubMed] [Google Scholar]
  30. Meewisse M-L, Reitsma JB, De Vries G-J, Gersons BPR, & Olff M. (2007). Cortisol and post-traumatic stress disorder in adults: Systematic review and meta-analysis. British Journal of Psychiatry, 191(5), 387–392. 10.1192/bjp.bp.106.024877 [DOI] [PubMed] [Google Scholar]
  31. Ogden SN, Dichter ME, & Bazzi AR. (2022). Intimate partner violence as a predictor of substance use outcomes among women: A systematic review. Addictive Behaviors, 127, 107214. 10.1016/j.addbeh.2021.107214 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Osório FL, Loureiro SR, Hallak JEC, Machado-de-Sousa JP, Ushirohira JM, Baes CVW, Apolinario TD, Donadon MF, Bolsoni LM, Guimarães T, Fracon VS, Silva-Rodrigues APC, Pizeta FA, Souza RM, Sanches RF, Dos Santos RG, Martin-Santos R, & Crippa JAS. (2019). Clinical validity and intrarater and test–retest reliability of the Structured Clinical Interview for DSM-5 – Clinician Version (SCID-5-CV). Psychiatry and Clinical Neurosciences, 73(12), 754–760. 10.1111/pcn.12931 [DOI] [PubMed] [Google Scholar]
  33. Parmar MP, Kaur M, Bhavanam S, Mulaka GSR, Ishfaq L, Vempati R, C MF, Kandepi HV, Er R, Sahu S, & Davalgi S. (2023). A Systematic Review of the Effects of Smoking on the Cardiovascular System and General Health. Cureus. 10.7759/cureus.38073 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Rogers AH, Bakhshaie J, Viana AG, Manning K, Mayorga NA, Garey L, Raines AM, Schmidt NB, & Zvolensky MJ. (2018). Emotion dysregulation and smoking among treatment-seeking smokers. Addictive Behaviors, 79, 124–130. 10.1016/j.addbeh.2017.12.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Rohleder N, & Kirschbaum C. (2006). The hypothalamic–pituitary–adrenal (HPA) axis in habitual smokers. International Journal of Psychophysiology, 59(3), 236–243. 10.1016/j.ijpsycho.2005.10.012 [DOI] [PubMed] [Google Scholar]
  36. Russell E, Koren G, Rieder M, & Van Uum S. (2012). Hair cortisol as a biological marker of chronic stress: Current status, future directions and unanswered questions. Psychoneuroendocrinology, 37(5), 589–601. 10.1016/j.psyneuen.2011.09.009 [DOI] [PubMed] [Google Scholar]
  37. Seligowski AV, Lee DJ, Bardeen JR, & Orcutt HK. (2015). Emotion Regulation and Posttraumatic Stress Symptoms: A Meta-Analysis. Cognitive Behaviour Therapy, 44(2), 87–102. 10.1080/16506073.2014.980753 [DOI] [PubMed] [Google Scholar]
  38. Selva Kumar D, Nahvi S, Rivera-Mindt M, Arnsten J, & Minami H. (2022). The Impact of Coping With Stressful Events on Negative Affect and Cravings Among Smokers With Mood Disorders. Nicotine & Tobacco Research, 24(6), 881–889. 10.1093/ntr/ntab260 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Short NA, Oglesby ME, Raines AM, Zvolensky MJ, & Schmidt NB. (2015). Posttraumatic stress and emotion dysregulation: Relationships with smoking to reduce negative affect and barriers to smoking cessation. Comprehensive Psychiatry, 61, 15–22. 10.1016/j.comppsych.2015.05.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Spencer C, Mallory AB, Cafferky BM, Kimmes JG, Beck AR, & Stith SM. (2019). Mental health factors and intimate partner violence perpetration and victimization: A meta-analysis. Psychology of Violence, 9(1), 1–17. 10.1037/vio0000156 [DOI] [Google Scholar]
  41. Stalder T, & Kirschbaum C. (2012). Analysis of cortisol in hair – State of the art and future directions. Brain, Behavior, and Immunity, 26(7), 1019–1029. 10.1016/j.bbi.2012.02.002 [DOI] [PubMed] [Google Scholar]
  42. Stalder T, Steudte-Schmiedgen S, Alexander N, Klucken T, Vater A, Wichmann S, Kirschbaum C, & Miller R. (2017). Stress-related and basic determinants of hair cortisol in humans: A meta-analysis. Psychoneuroendocrinology, 77, 261–274. 10.1016/j.psyneuen.2016.12.017 [DOI] [PubMed] [Google Scholar]
  43. Stone AA, & Shiffman S. (2002). Capturing momentary, self-report data: A proposal for reporting guidelines. Annals of Behavioral Medicine, 24(3), 236–243. 10.1207/S15324796ABM2403_09 [DOI] [PubMed] [Google Scholar]
  44. Tjaden P, & Thoennes N. (2000). Prevalence and consequences of male-to-female and female-to-male intimate partner violence as measured by the National Violence Against Women Survey. Violence against Women, 6(2), 142–161. [Google Scholar]
  45. Veilleux JC, Skinner KD, Reese ED, & Shaver JA. (2014). Negative affect intensity influences drinking to cope through facets of emotion dysregulation. Personality and Individual Differences, 59, 96–101. 10.1016/j.paid.2013.11.012 [DOI] [Google Scholar]
  46. Wills TA, & Shiffman S. (1985). Coping and substance use. Academic Press. [Google Scholar]
  47. World Health Organization. (2005). WHO multi-country study study on women’s health and domestic violence against women: Summary report : initial results on prevalence, health outcomes and women’s responses. World Health Organization. [Google Scholar]
  48. World Health Organization. (2012). WHO global report on mortality attributable to tobacco. 392. https://iris.who.int/handle/10665/44815 [Google Scholar]
  49. World Health Organization. (2020). WHO global report on trends in prevalence of tobacco use 2000-2025. World Health Organization. [Google Scholar]
  50. Yim IS, & Kofman YB. (2019). The psychobiology of stress and intimate partner violence. Psychoneuroendocrinology, 105, 9–24. 10.1016/j.psyneuen.2018.08.017 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Requests for deidentified data can be made to the corresponding author.

RESOURCES