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. Author manuscript; available in PMC: 2021 Apr 1.
Published in final edited form as: Drug Alcohol Depend. 2020 Jan 30;209:107840. doi: 10.1016/j.drugalcdep.2020.107840

Socioeconomic status, mindfulness, and momentary associations between stress and smoking lapse during a quit attempt

Christopher Cambron 1,2, Patricia Hopkins 2, Cassidy Burningham 1, Cho Lam 2, Paul Cinciripini 3, David W Wetter 2
PMCID: PMC7534963  NIHMSID: NIHMS1560119  PMID: 32058242

Abstract

Background.

Models of health disparities highlight stress among low socioeconomic status (SES) smokers as a barrier to cessation. Recent studies suggest that mindfulness may improve cessation outcomes by reducing stress during a quit attempt. The current study examined associations of SES and mindfulness with ecological momentary assessments (EMAs) of stress and smoking lapse during a quit attempt.

Methods.

EMAs (N=32,329) were gathered from 364 smokers engaged in a quit attempt. A multilevel structural equation model estimated within person paths from momentary stress to subsequent smoking lapse. Between person paths estimated paths from a latent variable for SES and mindfulness to stress and smoking lapse, the indirect effect of SES and mindfulness on lapse through stress, and moderation of within person stress-lapse associations by SES and mindfulness.

Results.

Within person estimates found that momentary increases in stress predicted increased risk of subsequent smoking lapse. Between person estimates found that lower SES was indirectly associated with greater risk for smoking lapse through increased stress; and, higher mindfulness was indirectly associated with lower risk for smoking lapse through reduced stress. Additionally, higher SES participants, who reported lower stress during the quit attempt, showed a stronger relationship between momentary increases in stress and risk for subsequent smoking lapse.

Conclusions.

Among low SES smokers engaged in a quit attempt, both SES and mindfulness uniquely influenced smoking lapse through their influence on stress. Findings support reports that mindfulness presents a promising intervention target to reduce stress and improve cessation outcomes among low SES smokers.

Keywords: SES-related smoking disparities, mindfulness, stress, ecological momentary assessment

1.1. Introduction

Rates of cigarette smoking in the United States have declined substantially in recent decades. Despite these reductions nearly 40 million people, or 15% of adults, still smoke (Drope et al., 2018). Most current smokers report a desire to quit, but only 6% of smokers successfully quit each year (Thomas et al., 2011). Low socioeconomic status (SES) groups continue to smoke at higher rates than the general population (Haskins, 2017; Hiscock et al., 2012) in part due to lower rates of smoking cessation success (Reid et al., 2010). As a result, SES-related smoking disparities have widened and low SES groups have borne a larger portion of the deleterious health consequences associated with cigarette smoking (Drope et al., 2018).

Theoretical models of drug use and dependence highlight the role that stress can play in derailing smoking cessation (Witkiewitz and Marlatt, 2004). Smokers regularly report smoking in response to stressful experiences (Kassel et al., 2003), and social cognitive models hypothesize that stress operates as a central influence on smoking lapse during a quit attempt (Witkiewitz and Marlatt, 2004). Cessation researchers define smoking lapse as a single smoking event during a quit attempt, while relapse is characterized by a return to consistent smoking (Shiffman, 2006). Importantly, it is the accumulated effects of multiple smoking lapses over days and weeks during a quit attempt often lead to relapse (Shiffman and Waters, 2004). Empirical studies have supported both theoretical models and anecdotal reports on the importance of stress during a quit attempt by demonstrating that increased stress temporally precedes a smoking lapse event (Cambron et al., 2019a; Oberleitner et al., 2018; Richards et al., 2011; Shiffman and Waters, 2004). Models of health disparities also implicate stress as a central factor impacting SES-related smoking disparities (Fagan et al., 2007; Hiscock et al., 2012; Matthews and Gallo, 2011). Lower SES smokers reliably report higher levels of psychological distress during a quit attempt over and above other relevant sociodemographic factors (Businelle et al., 2010; Businelle et al., 2013). Increased stress among low SES populations is thought to emanate from multiple sources including financial strain (Kendzor et al., 2010), disordered neighborhood environments (Steptoe and Feldman, 2001), unstable work environments (Hiscock et al., 2012), and other economically-related exposures (Santiago et al., 2011).

Cultivating mindfulness (i.e., a state of being both attentive to the present moment and non-judgmentally aware of current thoughts and emotions) offers a well-established method to protect against stressful experiences (Brown and Ryan, 2003; Gu et al., 2015). Increased mindfulness can alter affective and cognitive processes both by dampening responses to stressful events (i.e., stress reactivity) and by weakening the link between stressful experiences and drug use (i.e., improved self-regulatory capacity, Garland et al., 2017; Witkiewitz et al., 2014). Specific to smokers, higher trait mindfulness has been associated with reduced stress during a quit attempt (Adams et al., 2015; Spears et al., 2019). Higher trait mindfulness may also translate into important cessation outcomes including improved quit day abstinence (Spears et al., 2019), shorter and longer term sustained abstinence, and a hastened return to abstinence after a smoking lapse (Heppner et al., 2016). A systematic review of randomized controlled trials (RCTs) for mindfulness-based interventions with smokers indicated promising but inconclusive results for improved cessation outcomes (de Souza et al., 2015). However, a recent RCT among a low SES sample of smokers did not find that a mindfulness-based intervention improved overall cessation rates compared to either cognitive behavioral therapy or usual care (Vidrine et al., 2016). Taken together, the potential for mindfulness to reduce stress and support cessation attempts in conjunction with consistent reports that low SES smokers experience heightened stress and reduced cessation success have led to calls for further examination of the role of mindfulness in cessation processes among low SES populations (Spears, 2018).

Health disparities scholars highlight the importance of integrating SES as a meaningful predictor into psychosocial models of health behavior (Matthews and Gallo, 2011). In particular, the reserve capacity model identifies the processes through which increased stress can deplete coping resources needed to successfully maintain healthy behaviors or execute difficult behavioral changes. Given the consistent relationship of low SES with both increased stress and reduced smoking cessation success, SES represents an important component for reserve capacity perspectives on cessation (Matthews and Gallo, 2011). Conversely, mindfulness offers the potential to sustain reserve capacities by both reducing stress reactivity and enhancing self-regulatory capacity during a quit attempt (Garland et al., 2017). As such, simultaneous examination of the role of SES, mindfulness, stress, and smoking lapse during a quit attempt can provide important information for cessation efforts targeting low SES populations.

Few empirical studies have sought to explicitly test associations among SES, mindfulness, stress, and smoking lapse during a quit attempt. Measuring stress or smoking lapse in daily life outside of controlled laboratory settings has historically presented substantial challenges (Richards et al., 2011; Shiffman and Waters, 2004). Importantly, stress researchers have long recognized substantial heterogeneity in perceptions of stress across individuals after very similar events (Cohen et al., 1983). Thus, it is the perception of and reactivity to a stressor that illicits a negative response, degrades coping resources, and depletes reserve capacities (Matthews and Gallo, 2011; Witkiewitz and Marlatt, 2004). Retrospective reports of perceived stress and smoking lapse typically gathered by surveys are prone to substantial recall biases or errors and unlikely to accurately characterize experiences during a quit attempt (Shiffman, 2009). Ecological momentary assessments (EMAs) offer a well-established approach to collecting near real-time data on perceptions of stress and smoking lapse on multiple occasions throughout each day and produce highly-detailed, longitudinal data sets (Kamarck et al., 2011; Shiffman, 2009).

Drawing on the reserve capacity model (Matthews and Gallo, 2011), the current study examined the associations among SES, trait mindfulness, and EMAs of perceived stress and smoking lapse in diverse sample of smokers engaged in a quit attempt. First, we hypothesized that lower SES and higher stress would be associated with increased risk for lapse and that higher trait mindfulness would be associated with reduced risk for lapse. Second, we hypothesized momentary increases in stress would be associated with increased likelihood of subsequent smoking lapse. Third, we hypothesized that lower SES would be directly associated with increased stress and that higher trait mindfulness would be directly associated with reduced stress. We expected that both SES and mindfulness would be indirectly associated with smoking lapse through stress. It remained to be determined if both SES and mindfulness would uniquely predict stress among low SES smokers when included in the model simultaneously. Finally, we examined the potential that SES or trait mindfulness moderated moment to moment associations between perceived stress and smoking lapse.

2.1. Method

Data were drawn from a longitudinal cohort study examining racial/ethnic differences in smoking cessation processes in the Houston, TX area. Potential participants were required to be at least 21 years old with a home address and working telephone number, demonstrate proficiency in English at a 6th grade level, have smoked an average of five or more cigarettes daily for the past year, and be motivated to quit smoking in the next 30 days. Participants were excluded for contraindication for a nicotine patch, use of tobacco products other than cigarettes, an active substance use disorder, use of nicotine replacement products other than the patch, participation in a cessation program in the past 90 days, or having another household member currently enrolled in the study. Recruitment was conducted via media advertisements from 2005 to 2007. The study was approved by the Institutional Review Board of the University of Texas MD Anderson Cancer Center.

Participants meeting initial criteria were screened via phone (N=944), and those meeting all eligibility criteria (n=837) were invited to in-person orientation sessions. A total of 424 individuals attending orientation sessions met eligibility criteria, completed informed consent and baseline measures, and were enrolled in the smoking cessation program. Prior to enrollment participants smoked an average of 21 cigarettes per day. Only participants with post-quit EMAs were included in the current study (n=370) and six participants identifying as Asian American, Native American, or Pacific Islander were excluded given insufficient group size to model a separate racial/ethnic category (n=364). Additional sample details have been published elsewhere (Businelle et al., 2010). All smokers received identical cessation treatment based on the Treating Tobacco Use and Dependence Clinical Practice Guideline (Fiore et al., 2008) including six weeks of nicotine patch therapy, and six brief smoking cessation counseling sessions (five in-person sessions and one telephone session). Counseling sessions were conducted 1 week prior to quit day, on quit day, and at 1, 2, 3, and 4 weeks post-quit. Participants completed five assessments during in-person visits 1 week prior to quit day, on quit day, and at 1, 2, and 4 weeks post-quit. Participants received a $30 gift card for each completed in-person assessment and could receive up to $50 per week for completed EMAs.

2.2. Measures

Palmtop personal computers gathered EMAs from participants beginning one week prior to quit day and ending 28 days after quit day. Days were segmented into 4-hour blocks, one random EMA prompt was audibly and visually cued by the palmtop within each block, and consecutive EMA prompts were separated by at least 30 minutes. Over 28 days of post-quit monitoring, participants completed 32,329 random EMAs (77% of randomly issued prompts) for an average of 87 EMAs per person and approximately 3 EMAs per person per day. Participants were instructed to self-initiate a non-random, event-based EMA when experiencing a smoking lapse (i.e., one puff of a cigarette or more) or an urge to smoke (i.e., current urge of at least three on scale from one to five). Data from non-random EMAs (n=25,065) were used only to assess smoking lapses that occurred between two random EMAs. The interval between two completed random EMAs averaged six hours. Stress measures were drawn from random EMAs only in order to capture the most ecologically valid reports.

Smoking lapse was measured by both random and non-random EMAs. For random EMAs, participants responded to a single item asking if they had smoked any cigarettes that they had not already recorded in the computer. If lapse was indicated, participants responded to a second item “How long ago did you smoke the most recent cigarette that you did not record?” For non-random lapse EMAs, participants responded to two items “How many cigarettes did you smoke during this slip?” and “How long ago did you smoke your last cigarette?” Random and non-random smoking lapse items assessed the time that a smoking lapse occurred with response options ranging from 1 = 0–15 minutes to 7 = 8 hours or more. Time of lapse was measured by midpoint of the response option interval (e.g., 0–15 minutes = 7.5 minutes) subtracted from the time of prompt. Lapse times of 8 hours or more were set to missing given that a specific lapse time could not be identified. Intervals between two random EMAs that included one or more lapses were coded as 1. Intervals between two random EMAs that indicated no smoking lapse were coded as zero.

Stress was measured at each random EMA by two items adapted from the daily inventory of stressful events (Almeida et al., 2002). One item asked if participants had experienced or thought about an ongoing stressor since their last completed assessment, and the second item asked if participants had experienced or thought about a new stressor. Response options were Definitely No, Mostly No, Mostly Yes, or Definitely Yes. Combined current stress was measured as the mean of responses to ongoing and new stressor items (r = .89). Similar EMA measures of stress have been associated with biophysical stress responses (Kamarck et al., 2011) and have been employed by other cessation studies (Bandiera et al., 2016; Businelle et al., 2016; Shiffman and Waters, 2004).

Socioeconomic status.

Health scholars have recognized that typical indicators of SES such as income or education may fail to capture the multidimensional nature of SES (Braveman et al., 2005). Latent variables offer an established method of modeling the shared variance among multiple theoretically-connected indicators (Muthen and Muthen, 2017). At baseline, participants responded to four questions that were used to estimate a latent variable for SES. Annual family income was indicated by 11 categories ranging from less than $10,000 to greater than $100,000 broken into $10,000 increments. Twenty eight percent of participants reported an income of less than $10,000, 24% reported $10,000 to $29,999, 19% reported $30,000 to $59,999 income, and 19% reported greater than $60,000. Education was indicated by 1 = college degree and 0 = less than college degree. Health insurance status was indicated by 1 = privately insured and 0 = not privately insured. Employment status was indicated by 1= currently employed in full or part-time work and 0 = currently unemployed.

Mindfulness.

At baseline, participants completed the 15-item Mindful Awareness and Attention Scale (MAAS). The MAAS is a validated measure of dispositional or trait mindfulness defined as open and receptive awareness and attention to the present moment. All items provided six response options (1=Almost Always to 6=Almost Never) and example MAAS items included “I could be experiencing some emotion and not be conscious of it until sometime later” and “I find myself doing things without paying attention” (Brown and Ryan, 2003).

Demographics and controls.

At baseline, participants answered questions providing demographic data on age, sex, and race/ethnicity. At each random EMA, participants rated cigarette availability in that moment on a 5-point scale from 1 = Not at all to 5 = Easily.

2.3. Analytic Approach

Idiographic scholars have noted that intensive longitudinal datasets produced by EMA allow researchers to disaggregate within person, momentary estimates from between person, average estimates to better understand the unfolding of person level processes (Curran and Bauer, 2011; Shiffman and Waters, 2004). This approach is particularly relevant for models of stress given potential variability in perceptions of stress across individuals following similar events (Cohen et al., 1983). For example, a within person (or person mean centered) estimate for the association of stress and smoking lapse evaluates how deviations from a person’s average levels of stress across the study period are associated with changes in the momentary likelihood of lapse. Multilevel structural equation models (MSEMs) offer one approach to this type of disaggregation and the advantages of MSEMs relative to other approaches are described in detail elsewhere (MacKinnon and Pirlott, 2015; Preacher et al., 2011; Preacher et al., 2010). MSEMs were estimated with Mplus 8.3 (Muthen and Muthen, 2017) using the Bayes estimator and probit link to evaluate the effects of covariates on the continuous latent response underlying a binary measure of smoking lapse (Muthén and Asparouhov, 2012). At the within person level, the effect of person mean centered measures of each covariate are evaluated in terms of the likelihood of smoking lapse in the subsequent moment; at the between person level, the effect of average levels of each covariate are evaluated on the likelihood of smoking lapse at any given moment across the study period. The Bayes estimator with non-informative priors provides similar estimates to a maximum likelihood estimator and allow for the computation of asymmetric credibility intervals (CI). Models were estimated with a minimum of 20,000 iterations. To determine significance, the 95% highest probability density interval (HPD) was calculated from the posterior distribution of the model and CIs that did not include zero were considered statistically different from zero (Muthén and Asparouhov, 2012). The Bayes estimator in Mplus is a full information estimator and handled missing data (Muthen and Muthen, 2017). Data were missing for less than 1% of data points. Missingness was largely by design due to left censoring for intervals at the start of the study period and uncollected information after the study period.

Figure 1 presents reports of ongoing and new stressors at differing levels of SES and trait mindfulness. Separate analyses for ongoing stressors, new stressors, and combined stressors produced substantively identical findings. This was unsurprising given the strong correlation between the two stress measures (r=.89). To avoid redundancy, the model for combined stress is presented below. Figure 2 presents the conceptual model that was tested for ease of interpretation. A preliminary structural equation model estimated the latent variable for SES and concurrent paths from both SES and mindfulness to smoking lapse after controlling for age, gender, and race/ethnicity at the between person level only. Results indicated that higher SES (Est.=−.080, Std.=−.140, p=.004) and higher MAAS scores (Est.=−.099, Std.=−.093, p=.009) were significantly associated with reduced likelihood of smoking lapse during the quit attempt and confirmed adequate fit for the model (χ2=59.46, df=28, p=.001; RMSEA=.006; CFI=.93).

Figure 1.

Figure 1.

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Percentage of EMAs during which participants reported “yes” to experiencing an ongoing or new stressor since the previous EMA. SES was measured by a latent variable of income, education, health insurance, and employment and mindfulness was measured by the MAAS. Lo = 1 SD below mean; Mid = mean; Hi = 1 SD above mean.

Figure 2.

Figure 2.

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Multilevel structural equation model of socioeconomic status (SES), mindfulness, and momentary measures of stressors and smoking lapse during a quit attempt. Black dots on the within level paths indicate a latent random slope. Subscript i indexes 364 participants and subscript j indexes 32,329 ecological momentary assessments. Within level paths control for the passage of time and width of intervals; between level paths control for race/ethnicity, age, sex, and cigarette availability.

In the final model, within level paths were estimated with random intercepts and slopes across participants such that each random slope was decomposed into an average slope and a variance component representing the distribution of slopes across participants (Bolger and Laurenceau, 2013). The within level paths controlled for the passage of time and the width of intervals and between level paths controlled for age, gender, and race/ethnicity. Between level paths used the Model Constraint command to decompose the direct effects of SES and mindfulness on smoking lapse into indirect effects through stress. An additional control for cigarette availability was included in the final model given that previous studies have noted an indirect effect of SES through cigarette availability to smoking lapse (Cambron et al., 2019b). A cross-level interaction was estimated by regressing SES and mindfulness on the within level path from stress to lapse. To test the robustness of our findings, we conducted a series of sensitivity tests including excluding both participants with lower and higher than average counts of EMAs. A model excluding participants with fewer than 30 EMAs and a model excluding participants with greater than 120 EMAs produced substantively identical results to those reported in the results section.

3.1. Results

The analytic sample was 44% male with a mean age of 42 and split across three racial/ethnic groups (Whites [n=123], African-Americans, [n=122], and Latinos [n=119]). The average income was approximately $30,000 to $39,999, 15% had a college degree, 41% had private health insurance, and 58% were employed. Table 1 provides descriptive statistics and correlations among variables of interest.

Table 1.

Descriptive statistics and correlations for time-varying and time-fixed variables

Variable M (%) SD Min Max Correlations
1 2 3 4 5 6 7
Smoking lapse 8.0% - 0 1 1 - .05 * .06 *  
Ongoing stressor 1.68 .71 1 4 2 .28 * - .49 *
New stressor 1.54 .64 1 4 3 .33 * .89 * -
Mindfulness (MAAS) 4.20 .95 1 6 4 − .17 * − .27 * − .28 * -
Income 3.82 2.85 1 11 5 − .15 * − .14 * − .15 * .05 -
College 14.7% - 0 1 6 − .21 * .06 .02 .01 .49 * -
Employed 57.5% - 0 1 7 − .24 * − .16 * − .17 * .01 .47 * .31 * -
Health insurance 41.3% - 0 1 8 − .04 − .10 − .13 .08 .65 * .51 * .66 *
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Note.N=364 for participants and N=32,329 for EMAs; within person correlations shown above the diagonal and reflect person mean centered estimates; between person correlations shown below the diagnonal.

*

p < .05

Table 2 provides results from the final MSEM. At the within person level, higher stress in the interval j−1→j relative to each participant’s average stress across the study period was associated with increased risk for smoking lapse in the subsequent interval j→j+1 after controlling for smoking lapse in the prior interval j−1→j. Additionally, the strength of this path varied significantly across participants. The model accounted for 17% of the variance in subsequent smoking lapse at the within person level. At the between person level, lower SES was indirectly associated with increased risk for lapse through higher stress across the study period. Higher MAAS scores were indirectly associated with reduced risk for lapse through lower stress. Neither SES nor MAAS scores were directly associated with smoking lapse after stress was included in the model. The model explained 16% of the variance in smoking lapse at the between person level. A cross-level interaction showed that higher SES was associated with a stronger association between stress and subsequent smoking lapse at the within person level.

Table 2.

Within and between estimates from a multilevel structural equation models for socioeconomic status (SES), mindfulness (MAAS), stress, and smoking lapse (SMK) during a quit attempt

Variables path Est. (Std.) SD pa [95% CI]b
Within Direct
    Stressi(j−1 → j) → SMKi(j → j+1) (bw) .099 (.053) .031 .001 [.036, .156] *
SMKi(j−1 → j) → SMKi(j → j+1) (ar) .137 (.108) .030 .000 [.077, .194] *
Between Direct
SESi → Stressi (a1) −.044 (−.127) .021 .016 [−.085, −.004] *
→ SMKi (c’1) −.041 (−.113) .027 .070 [−.094, .013]
MAASi → Stressi (a2) −.193 (−.275) .035 .000 [−.261, −.124] *
→ SMKi (c’2) −.048 (−.065) .044 .136 [−.136, .039]
SESi with MAASi (cov) .111 (.061) .115 .162 [−.114, .346]
Stressori → SMKi (bb) .206 (.196) .066 .001 [.078, .332] *
Latent Variable
SESi → Incomei 1.000 (.687) .000 .000 [1.000, 1.000]
Collegei .399 (.609) .075 .000 [.269, .562] *
Insuredi 1.560 (.948) .567 .000 [.848, 3.013] *
Employedi .497 (.692) .086 .000 [.348, .682] *
Between Indirect
SESi → Stressi → SMKi a1*(bw+bb) −.013 (−.004) .007 .016 [−.029, −.001] *
MAASi → Stressi → SMKi a2*(bw+bb) −.057 (−.017) .018 .000 [−.098, −.027] *
Crosslevel Interaction
SESi → bw .035 (.264) .017 .018 [.002, .067] *
MAASi → bw .030 (.115) .032 .162 [−.032, .096]
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Notes. i indexes participants (N=364) and j indexes EMAs (N=32,329); Est. = undstandardized estimate (probit); Std. = standardized estimate; SD = posterior standard deviation; p = Bayesian one-tailed p-value; within paths control for the passage of time and width of interval; between paths controlled for race/ethnicity, age, sex, and cigarette availability.

a

= for positive values, the one-tailed p-value is the proportion of posterior distribution above 0;

b

= the 2.5 and 97.5 percentiles in the asymmetric posterior distribution, credibility intervals (CI) that do not include 0 noted with an asterisk (*).

4.1. Discussion and Conclusions

Despite consistent reductions in cigarette smoking at the national level, SES-related smoking disparities have continued to widen (Drope et al., 2018). As a result, cessation and health equity researchers alike have sought to identify strategies to support smoking cessation among low SES populations. Interventions to increase mindfulness have been identified as a potential strategy to combat SES-related smoking disparities (de Souza et al., 2015; Spears, 2018). Inconsistent findings from recent mindfulness-based RCTs among low SES smokers (Vidrine et al., 2016) suggest that further etiological research is needed. The current study examined associations among a multidimensional measure of SES, trait mindfulness, momentary experiences of stress, and smoking lapse among a low SES sample of smokers engaged in a quit attempt. The findings identified unique paths from lower SES to increased risk for smoking lapse and from higher trait mindfulness to reduced risk for lapse above and beyond other sociodemographic factors. A multilevel structural equation model decomposed paths from SES and mindfulness to smoking lapse through their effect on perceived stress. Results indicated that lower SES was indirectly associated with increased lapse risk through greater perceived stress. Higher trait mindfulness was indirectly associated with reduced lapse risk through lower perceived stress (see Table 2). These findings suggest that, even among a sample of smokers with a reduced range of SES levels, unique gradients of stress related to both SES and mindfulness were important for understanding smoking lapse during a quit attempt. From a reserve capacity perspective, lower SES can be thought of as depleting resources important for executing a positive behavioral change, while higher trait mindfulness uniquely sustained those resources (Matthews and Gallo, 2011). These findings support reports of mindfulness as a promising intervention target to improve cessation outcomes among low SES smokers (de Souza et al., 2015; Spears, 2018).

Importantly, we observed that a multidimensional measure of SES and trait mindfulness did not appear to covary significantly in preliminary or final models (Est.=.111, p=.162). In addition, each of the four indicators of SES – income, education, health insurance, and employment – showed very weak bivariate associations with mindfulness (see correlations in Table 1). This lack of clear association between SES and mindfulness requires further consideration. It is important to recognize that the paths from SES and mindfulness to perceived stress represent different aspects of how stress is relevant for cessation. The path from lower SES to increased stress likely emanates from external sources such as greater financial strain (Kendzor et al., 2010), disordered neighborhood environments (Steptoe and Feldman, 2001), unstable work environments (Hiscock et al., 2012), or other economically-related exposures known to degrade health (Santiago et al., 2011). On the other hand, the path from higher trait mindfulness to reduced perception of stress likely represents an improved internal response to a stressor (i.e., stress reactivity). Thus, the path from mindfulness to stress is comprised of a process shown by RCTs to be malleable via intervention (Garland et al., 2017). Future RCTs explicitly seeking to improve stress responses among low SES smokers likely exposed to heightened stress in daily life are needed to support cessation outcomes and to reduce SES-related smoking disparities, and mindfulness-based interventions may be a promising approach.

As expected, momentary increases in stress, above an individual’s average experience of stress during a quit attempt, were related to a higher lapse risk in the following moment, and the strength of that path varied significantly across smokers. Given the significant variation in this path, we explored the potential that SES and mindfulness were each associated with the strength of the stress-lapse path. Interestingly, those indicating higher scores on the latent variable for SES appeared to show a stronger association between stress and lapse. While it is not accurate to call these “high SES” smokers given the socioeconomic composition of this sample (mean income of approximately $30,000, 15% with a college degree), the findings do suggest that deviations from normal stress levels may be a more important determinant of lapse among smokers with “higher” than “lower” SES. One possibility might be that chronic stressors are so ubiquitous among the lowest SES individuals that momentary perturbations have less impact on lapse. We did not observe an association between mindfulness and the strength of the stress-lapse path. Further research is needed to confirm these exploratory findings.

The current study has some limitations that are important to consider. First, we did not assess the source of momentary stress (e.g., financial, neighborhood, work) and therefore could not identify specific types of stress that may be differentially related to SES, mindfulness, or smoking lapse. Future studies should gather precise information on sources of stress to provide a more detailed description of the stress-lapse association. Secondly, the MAAS scale is a unidimensional measure of dispositional mindfulness, and evidence suggests that mindfulness may be better understood as a multifaceted construct (Baer et al., 2006). Thus, we were unable to assess the potential differential impacts of facets of mindfulness on stress and lapse. Future smoking cessation studies should consider multidimensional measures of mindfulness.

In sum, this study provides important information for both SES-related smoking disparities and mindfulness-based health behavior research. We identified unique paths from both SES and mindfulness to subsequent stress, which in turn were both related to smoking lapse during a quit attempt. From a reserve capacity perspective, lower SES appeared to deplete resources important for smoking cessation, while increased mindfulness appeared to sustain those resources. We encourage future researchers to incorporate and expand upon these findings to propel knowledge on SES-related smoking disparities and mindfulness-based interventions.

Highlights.

  • Lower SES smokers experienced greater stress during a quit attempt

  • Higher mindfulness smokers experienced lower stress during a quit attempt

  • SES and mindfulness uniquely impacted smoking lapse through stress

  • Increasing mindfulness among low SES smokers may aid cessation outcomes

Acknowledgments

Funding

Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award numbers F32CA232796, R01DA014818, P30CA042014, and P30CA016672. Additional support was provided by the Huntsman Cancer Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Huntsman Cancer Foundation.

Footnotes

Conflicts of Interest

The authors have no conflicts of interest to report.

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