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. Author manuscript; available in PMC: 2018 Nov 1.
Published in final edited form as: Health Psychol. 2017 Jul 20;36(11):1038–1046. doi: 10.1037/hea0000535

The Association of Positive Emotion and First Smoking Lapse: An Ecological Momentary Assessment Study

Christine Vinci 1, Liang Li 2, Cai Wu 2, Cho Y Lam 3, Lin Guo 4, Virmarie Correa-Fernández 5, Claire A Spears 6, Diana S Hoover 2, Paul E Etcheverry 7, David W Wetter 3
PMCID: PMC5653435  NIHMSID: NIHMS893491  PMID: 28726478

Abstract

Objective

Individuals attempting to quit smoking typically have poor success rates, and the majority fail to maintain long-term abstinence. Although a large body of evidence documents the impact of negative affect on reducing abstinence, there is a much smaller body of research on positive emotions, which could be an important mechanism that is associated with successful cessation. As such, this study examined positive emotions in real-time via ecological momentary assessment (EMA) in order to determine whether discrete positive emotions were uniquely related to two cessation milestones: quit day lapse and first lapse.

Method

Participants were 391 smokers who received tobacco cessation treatment. EMAs were completed pre- and post-quit, and positive emotion was assessed with three items (enthusiastic, happy, and relaxed) rated on 5-point Likert scales. Analyses examined the associations of the means and slopes of each emotion on the current day with the likelihood of lapse on the following day.

Results

When controlling for relevant covariates, pre-quit positive emotions were not related to quit day lapse. However, post-quit positive emotions were associated with first lapse. Specifically, high levels of happiness and relaxation, as well as increasing levels of enthusiasm, happiness, and relaxation were related to a lower likelihood of next day lapse.

Conclusions

These are some of the first real-time, real-world data to demonstrate that distinct positive emotions are associated with a lower risk of lapse during the post-quit period among smokers attempting to quit.

Keywords: smoking cessation, positive emotions, ecological momentary assessment

Despite significant progress in reducing the prevalence of cigarette smoking, it remains the leading preventable cause of morbidity and mortality in the U.S. (U.S. Department of Health and Human Services [USDHHS], 2014). Although approximately 70% of smokers indicate that they want to quit and over 50% actually make a quit attempt each year, only 6.2% of smokers successfully quit each year (CDC, 2011). Thus, identifying specific factors that are associated with successful cessation could inform cessation treatments and help these individuals become tobacco free.

Positive emotions are one potential, albeit understudied, mechanism that may be associated with successful cessation. A positive emotion can be defined as a subjectively pleasant experience that functions as an internal signal to approach or continue (Doran et al. 2006, Fredrickson, 2001). To date, many theoretical models of addiction focus on the role of negative affect (e.g., Affective Model of Drug Motivation, Baker, Piper, McCarthy, Majiskie, & Fiore, 2004; Tension Reduction Model, Conger, 1956; Motivational Model of Alcohol Use, Cooper, Frone, Russel, & Mudar, 1995; Cox & Klinger, 1988). Although many models note the positive reinforcing effects of drugs (e.g., Baker, Morse, & Sherman, 1987; Cooper et al., 1995; Cox & Klinger, 1988; Niaura, Rohsenow, Binkoff, Monti, Pedraza, & Abrams, 1988), the role of positive emotions as potential protective factors that promote reduced substance use has received much less attention.

Nonetheless, several models of health behavior have linked positive emotions to resiliency and the management of stress, which may be particularly relevant for someone attempting to quit (McCarthy, Piasecki, Fiore, & Baker, 2006). For instance, Fredrickson’s (1998) broaden and build theory posits that positive emotions are associated with resiliency, which is linked to better health and health outcomes (Fredrickson, 1998; Fredrickson, 2004). Zautra and colleagues’ (2001) dynamic model of affect proposes that positive affect (PA) is a unique resource that when present, may aid in the management of stressful situations. Multiple studies have found that high positive emotions and general PA are associated with behavior change, including the management of chronic pain (Zautra, Johnson, & Davis, 2005), binge eating behaviors (Castonguay, Pincus, Agras, & Hines, 1998), self-control (Winterich & Haws, 2011), and engagement in prosocial behaviors (DeSteno, 2009).

Research examining the role of positive emotions in smoking cessation is fairly sparse and focused on general PA. This work has found that higher levels of PA are associated with better cessation outcomes (al’Absi, Hatsukami, Davis, & Wittmers, 2004; Doran et al., 2006; Leventhal, Ramsey, Brown, LaChance, & Kahler, 2008). For instance, 103 smokers enrolled in a cessation trial tracked their daily mood for three days pre-quit. Results indicated that although pre-quit levels of PA were not related to smoking status on quit day, higher levels of pre-quit PA were associated with abstinence through the 26 week post-quit period (Doran et al., 2006). Leventhal and colleagues (2008) examined PA from questionnaire data completed prior to participants beginning cessation treatment. Participants included 157 smokers who were also heavy drinkers. Results indicated that higher pre-quit PA was associated with abstinence from smoking above and beyond negative affect, smoking dependence, and depression level through the 26 week follow-up. Finally, among 72 smokers attempting to quit, individuals who maintained abstinence for the first week of quitting reported higher levels of PA within the first 24 hours of quitting when compared to participants who lapsed during the first week (data on PA collected at the end of quit day via retrospective self-report; al’Absi et al., 2004).

The current study builds on this foundation of work. First, studies examining PA and smoking cessation to date have generally used retrospective self-report methods for assessing PA (al’Absi et al., 2005; Leventhal et al., 2008). Retrospective recall is subject to numerous biases and errors (Hammersley, 1994; Schacter, 1999; Schacter, Chiao, & Mitchel, 2003), and the present study addresses this issue by collecting data in real-time in the real-world with ecological momentary assessment (EMA; Shiffman, Stone, & Hufford, 2008; Stone & Shiffman, 1994).

Second, the research on PA and cessation has examined PA as a general construct, but has not investigated specific discrete emotions that comprise PA (e.g., happiness, excitement, relaxation). Affective scientists have begun calling for the increased study of discrete emotions (Ferrer, Green, & Barrett, 2015; Pressman & Cohen, 2005), as data show that discrete emotions differentially influence behavior and decision making, cognition, and physiological responses (DeSteno et al., 2013; Ferrer et al., 2015; Levenson, Ekman, & Friesen, 1990; Smith & Ellsworth, 1985). Further, this literature has identified several individual emotions that comprise PA. For example, Watson and Clark (1994) found psychometric support for several positive affect scales that were comprised of individual positive emotions such as happy, confident, determined, relaxed, enthusiastic, surprised, and so on. When examining the literature on discrete emotions, happiness is the most studied aspect of PA with respect to health outcomes (Consedine & Moskowitz, 2007; Pressman & Cohen, 2005). Other emotions such as gratitude, pride, and hopefulness have also been examined as discrete positive emotions associated with non-health related behavior (DeSteno, 2009; McCullough, Shelley, Emmons, & Larson, 2001; Winterich & Haws, 2009). Although research examining the role of discrete positive emotions on health outcomes is limited, findings from these studies suggest that discrete emotions likely have differential effects on behavior and health (DeSteno et al., 2013; Ferrer et al., 2015).

Third, previous studies have typically examined how PA relates to long-term cessation outcomes, but positive emotions are likely to have acute, dynamic relationships with lapse risk during a quit attempt. Prior research has established that most individuals relapse early in the quitting process, with more than half relapsing within the first week (e.g., Shiffman et al., 2000; Zhu, Stretch, Balabanis, Rosbrook, Sadler, & Pierce, 1996). As noted by Shiffman et al. (2006), only examining long-term cessation outcomes can limit our ability to understand what is happening during the initial phases of a quit attempt and focusing on specific cessation milestones early in the quitting process is necessary. As such, we examined how positive emotions relate to two cessation milestones: lapse on the quit day (termed “quit day lapse”), and if able to maintain abstinence on quit day, avoiding first lapse during the first four weeks of a quit attempt (termed “first lapse”). Hence, we were able to study temporal associations in a more detailed manner.

The current study extends prior research by (1) examining positive emotions in real-time via EMA, and (2) determining whether discrete positive emotions are uniquely related to two cessation milestones early in the quit attempt (quit day lapse and first lapse). We hypothesized that higher levels of discrete positive emotions would be associated with a lower likelihood of quit day lapse and first lapse among smokers making a quit attempt.

Method

Participants

Participants were part of a longitudinal cohort study that examined the effects of race/ethnicity and social/environmental influences on smoking cessation. Participants were recruited from the Houston, TX area through community outreach and media. All participants received smoking cessation treatment, which consisted of self-help materials, the nicotine patch, and counseling according to Treating Tobacco Use and Dependence Clinical Practice Guideline (Fiore et al., 2008). Additional details about treatment can be found in the procedures section. Participants were screened via phone and if eligible, were enrolled in the study.

Participants were eligible to participate if they were: (1) 21 years of age or older, (2) a current smoker with a history of smoking an average of at least five cigarettes per day for the past year, (3) motivated to quit smoking in the next 30 days, (4) able to provide a home address and functioning telephone number, and (5) able to speak, read, and write in English at or above the sixth grade level. Participants were excluded if: (1) the use of the nicotine patch was contraindicated, (2) they had an active substance use disorder or dependence, (3) they reported the regular use of tobacco products other than cigarettes, (4) they used bupropion or nicotine replacement products other than the patch, (5) another member of their household was enrolled in the study, or (6) they reported participation in a cessation program or study in the past 90 days. This study was approved by the University of Texas MD Anderson Cancer Center’s Institutional Review Board.

Procedures

Interested participants were screened via phone for inclusion/exclusion criteria. Eligible participants were then invited to an in-person screening and orientation session. At this session, a more detailed description of the study was provided and written informed consent was obtained. Next, participants completed self-report measures and expired carbon monoxide (CO) was assessed. Participants returned for their baseline visit, which occurred a week prior to their scheduled quit date. At this session they were trained in EMA procedures, received instructions for using the nicotine patch, and completed their first counseling session. Subsequent counseling sessions occurred on the quit date, and at weeks 1, 2, 3, and 4 post-quit. Counseling sessions lasted for 10–20 minutes each and were scheduled to occur the same day as assessment visits. All participants received a 6-week supply of nicotine patches and were instructed to begin use on the quit date. Participants were compensated for each assessment session with a $30 gift card.

Participants were tracked from one week prior to their quit date through the 26-week post-quit day follow-up. EMA data presented here were taken from 4 days pre-quit through 28 days post-quit day.

Measures

Demographic and Tobacco-related Variables

Demographics included gender, age, race/ethnicity, partner status, and education level. Tobacco-related variables were cigarettes smoked per day and time to first cigarette upon waking in the morning.

Ecological Momentary Assessment (EMA)

EMA data were collected via urge, slip, and random assessments on a palmtop personal computer. We utilized data collected from 4 days pre-quit through 28 days post-quit. EMA questions consisted of items assessing smoking-related constructs (e.g., affect, craving, smoking behavior) and took about 2.5 minutes to complete. Some examples included: I feel sad (response options from 1 [strongly agree] – 5 [strongly disagree]); I am confident in my ability not to smoke (response options from 1 [strongly agree] – 5 [strongly disagree]); Since the last assessment, did you drink any alcohol? (response option yes/no); Cigarettes are available to me (response options from 1 [not at all available] – 5 [easily]); Is smoking allowed where you are? (response options: forbidden, discouraged, allowed); I have an urge to smoke (response options from 1 [strongly agree] – 5 [strongly disagree]). For urge assessments, participants were instructed to complete an EMA whenever they experienced an urge to smoke. For slip assessments, participants were asked to complete an EMA whenever they smoked a cigarette. Random assessments consisted of random prompts sent to participants four times each day. If participants could not immediately complete the EMA when prompted, they could delay answering the questions up to four times, totaling 20 minutes. Data from the random assessments were used for the current analyses. Urge and slip assessment data were used only to aid in determining smoking lapse (see below).

Positive emotions

Participants responded to three statements assessing positive emotions each time they completed an EMA. Participants were asked to rate the following three statements on a 5-point Likert scale (1 = strongly disagree through 5 = strongly agree): 1) I feel enthusiastic; 2) I feel happy; and 3) I feel relaxed. Part of the rationale for examining these three emotions is that they were derived from two higher-order constructs on the PANAS-X – joviality and serenity (Watson & Clark, 1994). Given that EMAs are meant to be brief, we were limited in the number of discrete positive emotion items to include. However, given recent advances in affective science that indicate differential effects of distinct emotions on various outcomes, future research in this area should include a variety of positive emotions including those positive emotions that have shown unique effects (e.g., authentic pride, gratitude).

Smoking Lapse

To determine lapse status, both on the quit day and during the post-quit period, data from EMA responses were utilized. If participants indicated smoking a cigarette via a random, slip, or urge assessment, they were considered to have lapsed on that day. Additionally, each morning participants were asked to complete a series of daily diary questions on their palmtop personal computer. If participants indicated having smoked on the previous day, they were coded as lapsed on that previous day.

Analytic Plan

Overview

The analyses consisted of both: (1) quit day lapse analysis examining whether pre-quit positive emotions were associated with lapse on quit day, and (2) post-quit, first lapse analysis examining whether post-quit positive emotions were associated with first lapse on the following day (for participants who did not lapse on quit day). For each participant, a trajectory was created for data from the pre-quit time period (Day -4 though Day -1) and for the post-quit time period (Day 0 [quit day] through first smoking lapse) on each positive emotion (enthusiastic, happy, relaxed). Each trajectory was estimated by the penalized spline model (Ruppert, Wand, & Carroll, 2003), which assumes that the trajectory is a smooth curve but with no other shape restrictions. The shape of the trajectory is completely data driven according to the maximum likelihood principle for penalized splines (Ruppert et al., 2003). Figure 1 presents the longitudinal trajectories of happiness that were created for six different participants during the post-quit period using EMA data between the quit day and the first lapse.

Figure 1.

Figure 1

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Post-quit positive emotions (happiness) trajectories for six study participants (a)–(f). The horizontal axis is the number of days since the quit day. The vertical axis is the happiness score (1–5). The vertical line indicates the day of the first lapse (solid line) or when data was censored (dashed line). The dots represent happiness measured at random assessments throughout the follow-up. The curved line is the estimated trajectory.

Quit day lapse analysis

Logistic regression was used to study the association between pre-quit positive emotions and risk of lapse on quit day. We utilized two approaches to analyze pre-quit positive emotions as predictors in the logistic regression. In Approach One, we were interested in whether positive emotions over the entire pre-quit period (Day -4 to Day -1) were related to quit day lapse on Day 0. This approach used the average mean (overall level of positive emotions on that day; higher values indicate higher positive emotions) and slope (overall temporal trend of positive emotions that day; positive slopes indicating increasing positive emotions and negative slopes indicating diminishing positive emotions) from the entire fitted trajectory over the four days prior to the quit day as predictors. In Approach Two, we used the entire 4-day trajectory, but examined only if the segment of the trajectory that occurred on the day prior to quit day (Day -1) was associated with lapse on the quit day itself (Day 0). Thus, the average mean and slope from Day -1 were the predictors pulled from this segment.

Post-quit first lapse analysis

For the post-quit, first lapse analysis, we used a Cox proportional hazard model with time-dependent covariates to determine whether positive emotions experienced each day were associated with a higher likelihood of smoking on the next day. For each participant, a positive emotion trajectory was created that began on the quit day (Day 0) and ended when they made their first lapse. Therefore, all available data were used leading up to the first lapse (Day 0 – Day 28), but, there were a different number of days leading up to a lapse for each participant. If a participant never lapsed, his/her data were censored at Day 28. From the trajectory, we specifically extracted the mean and slope of each positive emotion on each day in order to determine whether these values were related to lapsing on the following day. For instance, we examined whether or not positive emotions on Day 0 were associated with lapsing on Day 1; on Day 1, we examined if positive emotions were associated with lapsing on Day 2, etc. We repeated this for all days post-quit.

In order to evaluate the goodness of fit, or the relative importance of the individual positive emotions (i.e., enthusiastic, happy, relaxed) on their association with first lapse, we used the generalized R-square (GRS) statistic for the Cox model (Allison, 1995). This statistic ranges between 0 and 1 and is larger when the predictors are more strongly associated with the dependent variable (Allison, 1995). Therefore, it can be used as a measure of variable importance and is compared across different models (Hosmer & Lemeshow 1999). However, similar to other goodness of fit metrics (e.g., AIC, BIC), there is no statistical test for GRS and we are not able to determine whether the differences in GRS are statistically significant. Note that the GRS statistic does not have the interpretation of being the “proportion” of the variation in the data explained by the predictors (Allison 1995; Hosmer & Lemeshow, 1999).

Additional details

Each positive emotion was entered in separate models because our preliminary analyses showed collinearity (rs ranging from .39 – .65) among these three positive emotions. However, in addition to entering the mean and slope separately into the models (as shown in Model 1), we also present results that include both the mean and slope simultaneously in the same model, Model 2, (e.g., mean and slope of happy were entered in the same model) in order to determine whether the mean and slope are unique predictors of cessation, or, whether they account for similar effects. In all models, we adjusted for participants’ gender, age, partner status, education level, and race/ethnicity.

Results

Characteristics of Study Participants

A total of 434 individuals were eligible and consented to participate in the study. Of these 434 participants, 396 returned for their baseline visit one week prior to their quit date. Of these, 5 individuals did not complete any EMAs during the entire study, and were therefore eliminated from the subsequent analyses, resulting in 391 participants.

Participants in quit day lapse analysis

Of the 391 participants, 36 had no pre-quit EMA data during the 4 days prior to quit day. Thus, the total number of participants included in the analyses examining the association of pre-quit positive emotions with quit day lapse was 355.

Participants in post-quit first lapse analysis

Of the 391 participants, 14 had no post-quit EMA data and an additional 4 participants were dropped from the analyses due to having no follow-up in-person visits (and therefore smoking status was unable to be biochemically confirmed). Of the remaining 373 participants, 133 individuals lapsed on quit day. These 133 individuals were included in the quit day lapse analyses, but were dropped from the first lapse analysis. Thus, the total number of participants included in the first lapse, post-quit analyses were the remaining 240 individuals who lapsed beyond quit day.

Table 1 presents information for the pre- and post-quit participants who were included in the primary analyses on the variables of age, gender, income, employment, education, race/ethnicity, cigarettes smoked per day, and time to first cigarette upon waking in the morning.

Table 1.

Sample Characteristics for Quit Day and Post-quit First Lapse Participants.

Quit Day Lapse (n = 355) Post-quit First Lapse (n = 240)
Mean (SD) or % Mean (SD) or %
Age 41.76 (11.23) 42.04 (11.48)
Gender (% female) 55.21% 52.50%
Annual income less than $20,000 41.85% 37.56%
Employed 56.98% 61.51%
Education (high school graduate or less) 40.34% 40.42%
Partner Status (% with partner) 36.93% 36.67%
Race/Ethnicity
 Caucasian 32.68% 32.92%
 African American 33.24% 35.00%
 Hispanic 32.11% 29.58%
 Other 1.97% 2.50%
Cigarettes smoked per day (collected at baseline) 20.79 (9.68) 20.65 (9.16)
Time to first cigarette in the morning
 5 minutes or less 47.89% 46.25%
 6–30 minutes 29.58% 30.42%
 31–60 minutes 11.55% 11.25%
 60 minutes or greater 10.99% 12.08%
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Note. SD = standard deviation

Compliance with EMA

When examining the pre-quit phase only, participants completed 77.7% of random assessments over the monitoring period. When examining the post-quit phase only, participants completed 76.17% of random assessments. Out of all completed random assessments, 88% were completed immediately upon receiving the prompt.

Primary Findings

Quit day lapse

Results from the logistic regressions examining quit day lapse using the pre-quit positive emotions trajectories (both Approaches One and Two) showed no significant associations.

Post-quit first lapse

Table 2 presents the hazard ratios of the mean and slope of positive emotions on each day as they related to first lapse on the next day. Higher mean levels of happiness and relaxation were significantly associated with a lower risk of lapsing on the next day in both Models 1 and 2. For example, for each unit of increase in happiness (range from 1 to 5), the hazard of lapse on the next day is reduced by 35% (hazard ratio = 0.65 in Table 2, Model 1). Hence, a participant with the lowest happiness score of 1 would have an approximately 5.6 times ((1/0.65)(5−1) = 5.6) higher risk of lapsing on the next day than a participant with the highest happiness score of 5. Steeper, increasing slopes for enthusiastic, happy, and relaxed were significantly associated with a lower risk of lapse on the next day in both Models 1 and 2, indicating that more rapid increases in positive emotions are associated with lower risk of next day lapse. Results from the GRS should also be noted. For example, in Model 1, when both the mean and slope are significant, the mean appears to be more important for happy than relaxed, as indicated by a higher GRS. In Model 2, all three positive emotions have comparable GRS, though happiness has a slightly higher value. Of note, the hazard ratios are similar between the results of Models 1 and 2, suggesting that the effects of the mean and slope are largely unrelated. In fact, the correlations between mean and slope are very low and non-significant for all three positive emotions (enthusiastic = 0.02; happy = 0.12; relaxed =−0.03). Thus, the mean and slope capture different aspects of the effects of positive emotions on next day lapse.

Table 2.

Cox proportional hazard models with time-dependent covariates associated with next day lapse from positive emotions trajectories on the day before.

Hazard Ratio 95% Confidence Interval p-value Generalized R-square
Model 1
Enthusiastic Mean 0.83 0.67 1.03 0.090 .012
Slope 0.21 0.09 0.49 <0.001 .049
 Happy Mean 0.65 0.51 0.82 <0.001 .052
Slope 0.32 0.12 0.80 0.016 .024
 Relaxed Mean 0.70 0.55 0.90 0.005 .032
Slope 0.33 0.13 0.83 0.018 .024
Model 2
Enthusiastic Mean 0.82 0.66 1.02 0.080 .062
Slope 0.20 0.08 0.47 <0.001
 Happy Mean 0.64 0.50 0.81 <0.001 .076
Slope 0.35 0.14 0.87 0.024
 Relaxed Mean 0.64 0.49 0.83 0.001 .071
Slope 0.28 0.11 0.68 0.005
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Note. Model 1 = mean and slope entered separately into model; Model 2 = mean and slope entered together into model. Covariates: gender, age, partner status, education level, and race.

Additional considerations

Thirty-seven participants did not report lapsing during an EMA, but were considered lapsed at a subsequent visit due to either self-reported lapse during the visit (n = 19), CO level >10ppm (n = 9), or both self-reported lapse and CO > 10ppm (n = 9). Thus, a sensitivity analysis was conducted to determine whether excluding these 37 participants would impact the findings. When excluded, the results were largely consistent with our findings in Table 2. The only exceptions were that the slope of relaxed in Table 2 Model 1, and the slope of happiness in Table 2 Model 2, were no longer significant in the sensitivity results.

Discussion

The current study demonstrated that among a racially/ethnically diverse sample of smokers making a quit attempt, positive emotions during the pre-quit period were not related to quit day lapse. This finding is consistent with prior research (Businelle et al., 2014; Doran et al., 2006). However, after quit day, positive emotions were associated with first lapse on the following day. Specifically, having higher levels of happiness and relaxation, as well as increasing levels of enthusiasm, happiness, and relaxation, was linked to a lower likelihood of next day lapse. These findings highlight the importance of positive emotions in promoting resiliency in response to stressful situations such as quitting smoking, consistent with several conceptual models (Fredrickson, 1998; Fredrickson, 2004; Zautra et al., 2001), and are some of the first real-time, real-world data to demonstrate that distinct positive emotions, such as happiness, may be relevant for smokers attempting to quit.

Our findings begin to address calls for additional research directly comparing happiness to other discrete positive emotions (Pressman & Cohen, 2005), as well as for examining the associations of discrete emotions on cancer-related behaviors (Ferrer et al., 2015). Although our range of positive emotions was very limited, the results align with prior research indicating that positive emotions are associated with behavior change including smoking cessation (al’Absi et al., 2004; Castonguay et al., 1998; DeSteno, 2009; Doran et al., 2006; Leventhal et al., 2008; Winterich & Hawes, 2011; Zautra et al., 2005), and help move the field forward in several ways.

First, our results indicate that both the overall level of positive emotions (i.e., the mean), as well as changes in these emotions over the course of the day (i.e., the slope) are uniquely related to the likelihood of lapse. That is, both having higher mean levels of happiness and increases in happiness throughout the day are related to a lower risk of lapsing. Although this study presents some of the first research examining the role of discrete positive emotions during a quit attempt, prior work has investigated the role of discrete emotions on various behavioral outcomes outside the health behavior literature. For instance, when examining the role of gratitude, participants who felt grateful (as opposed to happy or neutral) were more likely to engage in prosocial behaviors (DeSteno et al., 2010). When participants felt pride, they were more likely to persevere on a task than those who did not feel pride (Williams & DeSteno, 2008). These findings suggest that distinct positive emotions have differential relationships with behavioral outcomes. Research on distinct emotions also indicates that the temporal focus of an emotion (past vs present vs future) can impact behavior. Through a series of studies, Winterich and Haws (2009) found that future-focused positive emotions such as hopefulness and anticipated pride were associated with higher self-control, when compared to past/present-focused emotions such as happiness and general pride. Future research in smoking cessation should explore not only other discrete emotions (e.g., gratitude, pride, hopefulness), but also consider how the temporal focus of particular emotions may be related to cessation. Considering that the presence of future-focused positive emotions may be related to self-control (Winterick & Haws, 2009), such emotions may be particularly relevant during the cessation process.

Second, our analytic approach allowed us to take advantage of all post-quit assessments of positive emotions for each participant, even though we focused on the associations of the current days’ emotions with next day lapse. Existing analytic approaches, such as using simple parametric models to fit linear or quadratic shapes are unlikely to fit every participants’ data well. To address this methodological challenge, we used penalized spline models to provide a data-driven, flexible fit approach. In order to compare amongst positive emotion items regarding their relative association with lapse, we used the GRS statistic as the metric. This statistic allows us to evaluate and compare the importance of variables across models, but we are unable to determine whether differences in GRS are statistically significant. From this perspective, our finding that happiness is related to a lower likelihood of lapse when compared to the other two emotions should be interpreted as exploratory.

Third, our findings indicated that recent positive emotions (across either the entire four day pre-quit period or the day immediately preceding the quit day) were not associated with quit day lapse, suggesting that positive emotions experienced prior to making an attempt to quit are not as influential as positive emotions experienced during the quit attempt itself (i.e., positive emotions in response to the stressor of quitting appear more relevant). A possible explanation for this finding is related to the difference in smoking behavior in the pre- versus post-quit periods. In pre-quit, participants maintained regular smoking behavior and thus should not be experiencing withdrawal symptoms or general distress due to an active quit attempt. Therefore, pre-quit positive emotions may be less related to quit day lapse because smoking behavior has not changed. During the post-quit period, smokers are experiencing withdrawal. Thus, positive emotions may counteract some of the effects of emotional distress during this time period. Similar to our findings, some previous research has found that post-quit variables tend to be more highly associated with smoking outcomes than are pre-quit variables. For example, pre-quit self-efficacy has a weaker relationship with lapse than does post-quit self-efficacy (Gwaltney, Metrik, Kahler, & Shiffman, 2009), and our findings suggest that positive emotions may follow a similar pattern.

In light of the present findings, future research might focus on both testing strategies for increasing positive emotions and whether successfully increasing positive emotions improves the likelihood of remaining abstinent. For example, one useful strategy may be mindfulness. Mindfulness, the ability to purposefully attend to the present moment without judging thoughts/emotions/sensations as good or bad, has been associated with higher positive affect (Davidson et al., 2003; Jain et al., 2007; Tang et al., 2007), lower nicotine dependence (Vidrine et al., 2009; Vinci et al., 2016), and lower craving (Bowen & Marlatt, 2009; Elwafi, Witkiewtiz, Mallik, Thornhill, & Brewer, 2013; Westbrook, Creswell, Tabibnia, Julson, Kober, & Tindle, 2011). Although smoking cessation treatments have utilized mindfulness-based interventions to effectively reduce smoking behavior (e.g., Brewer et al., 2011; Davis, Goldberg, Anderson, Manley, Smith, & Baker, 2014; Davis, Fleming, Bonus, & Baker, 2007; Vidrine et al., 2016), continued research is needed to understand the role of positive emotions within these interventions, and to determine whether certain mindful activities directly target positive emotions.

Behavioral activation, a commonly used treatment for depression, might be another potential approach. In a randomized controlled trial for smokers reporting mild levels of depression, a behavioral activation treatment incorporated the engagement of positive activities during the cessation process (MacPherson et al., 2010). Results indicated that participants who underwent the behavioral activation treatment had higher rates of abstinence over the 26 week follow-up period and reported fewer depressive symptoms when compared to an active control treatment group. Unfortunately, ratings of positive emotions were not reported. Therefore, we do not know whether engagement in positive activities actually led to higher positive emotions, resulting in increased abstinence. Future research should determine whether the incorporation of positive activities into treatment actually increases positive emotions, leading to increased cessation.

Limitations of the present study should be noted. First, this study consisted of individuals interested in quitting smoking. As such, the results may not apply to individuals with lower levels of motivation to quit. Second, the current study only examined three discrete positive emotions – enthusiastic, happy, and relaxed. Other positive emotions (e.g., pride, gratitude, hopefulness) that have been differentially associated with behavior should be incorporated into future studies examining discrete positive emotions on health outcomes, including smoking cessation. Third, although a definite strength of the study, utilizing EMA has some limitations including the self-report nature of EMA, issues with reactivity (e.g., the act of self-monitoring changing behavior), and concerns about noncompliance (e.g., participants not responding to prompts). That said, our data suggest that participants were generally compliant with the EMA procedures.

In conclusion, this study presents some of the first findings demonstrating that discrete positive emotions (collected via EMA) were related to smoking lapse. Additionally, the examination of discrete positive emotions on cessation milestones (such as quit day lapse and first lapse) is practically non-existent, making these results innovative and useful to the tobacco literature. That said, additional research is needed to replicate these findings and extend the methodology to include other discrete emotions. Understanding how certain variables (e.g., discrete positive emotions) are related to lapse during the cessation process has the ability to inform treatment development. Future research should examine how to enhance these positive emotions through treatments such as behavioral activation and mindfulness.

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