mindfulness

Mechanisms Underlying Mindfulness-Based Addiction Treatment versus Cognitive Behavioral Therapy and Usual Care for Smoking Cessation

Claire Adams Spears1, Donald Hedeker2, Liang Li3, Cai Wu3, Natalie K. Anderson4, Sean C. Houchins4, Christine Vinci5, Diana Stewart Hoover3, Jennifer Irvin Vidrine6, Paul M. Cinciripini3, Andrew J. Waters7, and David W. Wetter8

1Georgia State University School of Public Health, Atlanta, GA

2The University of Chicago, Chicago, IL

3The University of Texas MD Anderson Cancer Center, Houston, TX

4The Catholic University of America, Washington, DC

5Rice University, Houston, TX

6Stephenson Cancer Center and The University of Oklahoma Health Sciences Center, Oklahoma City, OK

7Uniformed Services University of the Health Sciences, Washington, DC

8University of Utah and the Huntsman Cancer Institute, Salt Lake City, UT

Abstract

Objective—To examine cognitive and affective mechanisms underlying Mindfulness-Based Addiction Treatment (MBAT) versus Cognitive Behavioral Therapy (CBT) and Usual Care (UC)

for smoking cessation.

Method—Participants in the parent study from which data were drawn (N = 412; 54.9% female; 48.2% African-American, 41.5% non-Latino White, 5.4% Latino, 4.9% other; 57.6% annual

income < $30,000) were randomized to MBAT (n = 154), CBT (n = 155), or UC (n = 103). From quit date through 26 weeks post-quit, participants completed measures of emotions, craving,

dependence, withdrawal, self-efficacy, and attentional bias. Biochemically-confirmed 7-day

smoking abstinence was assessed at 4 and 26 weeks post-quit. Although the parent study did not

find a significant treatment effect on abstinence, mixed-effects regression models were conducted

to examine treatment effects on hypothesized mechanisms, and indirect effects of treatments on

abstinence were tested.

Results—Participants receiving MBAT perceived greater volitional control over smoking and evidenced lower volatility of anger than participants in both other treatments. However, there were

no other significant differences between MBAT and CBT. Compared to those receiving UC,

MBAT participants reported lower anxiety, concentration difficulties, craving, and dependence, as

Corresponding Author: Claire Adams Spears, Ph.D., Assistant Professor, Division of Health Promotion & Behavior, School of Public Health, Georgia State University; [email protected]; Phone: 404.413.9335.

HHS Public Access Author manuscript J Consult Clin Psychol. Author manuscript; available in PMC 2018 November 01.

Published in final edited form as: J Consult Clin Psychol. 2017 November ; 85(11): 1029–1040. doi:10.1037/ccp0000229.

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well as higher self-efficacy for managing negative affect without smoking. Indirect effects of

MBAT versus UC on abstinence occurred through each of these mechanisms.

Conclusions—Whereas several differences emerged between MBAT and UC, MBAT and CBT had similar effects on several of the psychosocial mechanisms implicated in tobacco dependence.

Results help to shed light on similarities and differences between mindfulness-based and other

active smoking cessation treatments.

Keywords

mindfulness; mechanisms; smoking cessation; nicotine dependence

Tobacco use is the leading preventable cause of illness and premature mortality in the U.S.

(USDHHS, 2014), and quitting smoking significantly increases life expectancy (Jha et al.,

2013). Although most smokers express interest in quitting, the vast majority are

unsuccessful in their quit attempts (CDC, 2011). Mindfulness (defined as “paying attention

in a particular way: on purpose, in the present moment, and nonjudgmentally;” Kabat-Zinn,

1994, p. 4) shows promise for improving psychological health (e.g., Gotink et al., 2015;

Khoury et al., 2013) and has been incorporated into smoking cessation treatments with

initial success (e.g., Brewer et al., 2011). However, the mechanisms underlying mindfulness-

based interventions versus more traditional approaches for smoking cessation are unclear.

The purpose of the present study was to investigate cognitive and affective mechanisms

underlying mindfulness-based versus cognitive-behavioral and usual care smoking cessation

treatments in a racially/ethnically diverse sample.

Mindfulness-based Treatments for Smoking Cessation

At least six trials support the use of in-person, multi-session mindfulness-based

interventions for smoking cessation (Brewer et al., 2011; Davis, Fleming, Bonus, & Baker,

2007; Davis, Goldberg, et al., 2014; Davis, Manley, Goldberg, Smith, & Jorenby, 2014;

Davis et al., 2013; Vidrine et al., 2016). Mindfulness-based programs have produced

significantly higher abstinence rates than standard treatment (Brewer et al., 2011) and

quitline-delivered treatment (Davis, Goldberg, et al., 2014). In the largest known trial of

mindfulness treatment for smoking cessation, Vidrine et al. (2016) compared mindfulness-

based addiction treatment (MBAT) to cognitive-behavioral therapy (CBT) and usual care

(UC). MBAT did not differ significantly from CBT or UC in terms of post-treatment

abstinence rates. However, mindfulness was superior in promoting lapse recovery. That is,

among participants who were not abstinent at the end of treatment, those who received

mindfulness-based treatment were more likely to regain abstinence at later time points

(versus CBT or UC). The potential for mindfulness to promote lapse recovery is critical

given that most smokers lapse early in the quit attempt (Hughes et al., 1992), and the

majority of these lapses lead to full-blown relapse (Kenford et al., 1994).

Although mindfulness-based smoking cessation treatments show promise, the underlying

mechanisms are yet to be well delineated. Investigating why and how treatments for

addictive behaviors work is a critical question (Witkiewitz & Marlatt, 2008), and a number

of researchers have called for studies to elucidate the mechanisms through which

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mindfulness-based treatments impact clinical outcomes (e.g., Davidson, 2016; Witkiewitz,

Bowen, et al., 2014). Understanding mechanisms could lead to the development of more

efficacious and cost-effective treatments. For example, interventions could have greater

impact by intensifying the focus on key mechanisms and/or removing treatment aspects that

do not directly target these mechanisms.

Potential Underlying Mechanisms

Emotions and Stress

Quitting smoking is associated with increased negative affect (Leventhal, Waters, Moolchan,

Heishman, & Pickworth, 2010), and negative affect and stress are strong predictors of

difficulty quitting (Baker, Piper, McCarthy, Majeskie, & Fiore, 2004). Positive emotions, on

the other hand, may protect against relapse (Levine, Marcus, Kalarchian, Houck, & Cheng,

2010). Randomized controlled trials indicate that mindfulness training reduces negative

emotions and stress (across healthy and clinical populations; Gotink et al., 2015), and

increases positive emotions (among adults in partial remission from depression; Garland,

Geschwind, Peeters, & Wichers, 2015) to a greater extent than wait-list controls or treatment

as usual. Goyal et al.’s (2014) meta-analysis in adult clinical populations indicated that

mindfulness meditation programs reduced anxiety and depression, but did not affect positive

emotions when compared to nonspecific active controls. However, there was insufficient

evidence regarding the effects of mindfulness vs. more specific active controls (e.g., CBT).

Although there is a relative dearth of trials comparing mindfulness to CBT, some research

suggests that mindfulness training and CBT are equally efficacious for reducing depression

and anxiety (Sundquist et al., 2015; Tovote et al., 2014).

Affective Volatility

In addition to severity of negative affect, greater volatility (i.e., lability/scatter over time) of

negative affect over the course of smoking cessation predicts lower likelihood of abstinence

(Piasecki, Jorenby, Smith, Fiore, & Baker, 2003a, 2003b). Conceptually, mindfulness is

integrally related to affective volatility, particularly with regard to negative emotions. That

is, nonjudgmental observation of unpleasant internal and external stimuli is thought to lessen

the tendency for extreme mood fluctuations in reaction to those stimuli (Chambers, Gullone,

& Allen, 2009; Teasdale et al., 2002). Hill and Updegraff (2012) found that among college

students, dispositional mindfulness predicted lower volatility of both negative and positive

emotions. Adams et al. (2014) found that dispositional mindfulness predicted lower

volatility of negative emotions and depressive symptoms among smokers attempting to quit,

indicating higher stability in negative (but not positive) emotions. No study, to our

knowledge, has examined the effect of mindfulness-based treatment (or CBT) on affective

volatility.

Tobacco Dependence, Withdrawal, and Craving

Greater tobacco dependence and withdrawal symptoms predict more difficulty quitting

(Kenford et al., 2002; Piasecki et al., 2003a). However, smokers with greater dispositional

mindfulness tend to have lower levels of dependence (Vidrine et al., 2009). By promoting

nonjudgmental awareness and purposeful (rather than impulsive) action, mindfulness

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training might reduce the likelihood of smoking as an automatic reaction to internal and

external triggers (Brewer, Elwafi, & Davis, 2013). Given that automatic processes are

thought to play a critical role in addictive behavior (Tiffany, 1990), mindfulness could be a

useful strategy for reducing dependence. Mindfulness training might also help to attenuate

craving (Davis, Manley, et al., 2014; Ruscio, Muench, Brede, & Waters, 2015) and lessen

the negative emotional experience of withdrawal. Although the differential effects of

mindfulness vs. CBT are unclear, CBT might also reduce tobacco dependence (Raja et al.,

2014).

Agency

In terms of quitting smoking, a sense of “agency” can include self-efficacy for refraining

from smoking in high-risk situations as well as expectations about one’s ability to regulate

emotions without smoking (Vidrine et al., 2009). Smokers with greater agency are more

likely to successfully quit (Businelle et al., 2010). By increasing awareness of present-

moment experience, mindfulness interventions may help smokers to broaden their perceived

array of possible coping strategies and resources, which could increase agency. Indeed,

Vidrine et al. (2009) found that smokers with greater dispositional mindfulness indicated

greater self-efficacy for abstaining from smoking and stronger expectations that they could

regulate their emotions without smoking. Self-efficacy may also be a key mechanism

through which CBT influences smoking cessation (Hendricks, Delucchi, & Hall, 2010).

Attentional Bias

Among smokers, abstinence increases attentional bias toward smoking-related cues

(Leventhal et al., 2010), and attentional bias is associated with higher risk for early lapses to

smoking (Waters et al., 2003). Greater ability to focus and redirect attention is hypothesized

to be a key mechanism of mindfulness interventions (and has even been termed “attentional

control training” in some early work; Teasdale, Segal, & Williams, 1995). Mindfulness

meditation involves continual redirecting of attention to present-moment experience. In daily

life, mindfulness practice can involve noticing when one’s attention is captured by

problematic stimuli (e.g., smoking triggers) and disengaging from these cues. Through this

practice over time, smokers might experience greater purposeful control over their attention

and find that their attention is less automatically captured by smoking-related cues.

Although no known research has examined the effect of mindfulness training on attentional

bias specifically toward cigarettes, Davis, Goldberg, et al. (2014) found that a mindfulness-

based smoking cessation intervention led to greater self-reported attentional control.

Current Study

Data were collected as part of a randomized controlled trial (Vidrine et al., 2016) comparing

the efficacy of MBAT to CBT and UC for smoking cessation. In this parent trial, 7-day

abstinence rates at 4 weeks post-quit were 34.4% in MBAT, 32.3% in CBT, and 24.3% in

UC. At 26 weeks post-quit, rates were 13.0% in MBAT, 15.5% in CBT, and 11.7% in UC.

There were no significant differences between treatment groups on overall abstinence rates

over time. However, among participants who were smoking at the end of treatment, those in

MBAT were more likely to recover abstinence by the following week (26.8%) and 26 weeks

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post-quit (7.1%) compared to participants in either CBT (7.0% and 3.5% by the following

week and 26 weeks, respectively) or UC (13.2% and 0%). Despite the lack of significant

effects on overall abstinence, it is possible that the treatments operate via different

mechanisms. Mediation can occur in the absence of an overall effect of treatment on the

outcome (Mackinnon & Fairchild, 2009) and can provide important information about

mechanisms through which interventions might influence outcomes. Thus, the current study

examined mechanisms underlying MBAT vs. CBT and UC. First, we sought to examine

whether the three treatments had differential effects on proposed mechanisms. Second, we

investigated indirect effects of treatments through hypothesized mechanisms.

We hypothesized that both MBAT and CBT would lead to greater improvements in most

mechanisms compared to UC. In terms of differential effects of MBAT vs. CBT, we had

three specific predictions. First, given that mindful observation of unpleasant experiences is

thought to attenuate emotional reactivity, we expected that MBAT would more selectively

impact affective volatility. Second, because mindfulness practice involves conscious,

purposeful action (rather than “auto-pilot”), we hypothesized that MBAT would be more

likely to reduce certain aspects of dependence (i.e., automaticity, sense of loss of control).

Third, because a core component of mindfulness is attentional control training, we

hypothesized that MBAT would reduce attentional bias relative to CBT. Given past research

suggesting that both mindfulness and CBT might improve various mechanisms (e.g.,

emotions, agency), we did not have specific hypotheses about how MBAT vs. CBT might

differentially influence other variables.

Method

Participants

Participants for the parent study were recruited in the greater Houston, Texas area using print

media. Eligible participants were at least 18 years old, currently smoked cigarettes (at least 5

cigarettes/day for the past year), were motivated to quit smoking in the next month, had a

viable home address and phone number, were able to read and write in English, produced an

expired carbon monoxide (CO) level of ≥ 8 parts per million (ppm), and provided collateral

contact information. Exclusion criteria were: contraindication for the nicotine patch, regular

use of tobacco products other than cigarettes, use of bupropion or nicotine replacement other

than the patches provided in the study, pregnancy or lactation, another household member

enrolled in the study, active substance dependence, current psychiatric disorder or currently

used psychotropic medications, or having received smoking cessation treatment in the

previous three months. The study was approved by the institutional review board, and all

participants provided written informed consent.

Procedures

Participants were randomized to MBAT (n = 154), CBT (n = 155), or UC (n = 103) using adaptive minimization. See Vidrine et al. (2016) for more details on participant flow through

the study and session-by-session treatment outlines. The three treatments included some

common elements. All participants were given self-help materials based on the Treating Tobacco Use and Dependence Clinical Practice Guideline (Fiore et al., 2008),

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psychoeducation about tobacco dependence/lapse/relapse, and nicotine patch therapy. In

addition, all treatments incorporated cognitive-behavioral strategies, but only MBAT

specifically taught mindfulness.

Mindfulness-Based Addiction Therapy (MBAT)—The MBAT manual (Wetter et al., 2009) created for this trial closely follows the content of Mindfulness-Based Cognitive

Therapy (MBCT; Segal, Williams, & Teasdale, 2002), but replaces depression-related

material with material pertinent to smoking cessation. The three primary aims of MBAT

(based on MBCT) are to: 1) increase attention to present-moment experience (e.g., thoughts,

feelings, physical sensations); 2) encourage nonjudgmental awareness of mental events (i.e.,

noticing thoughts as “just thoughts” without becoming caught up in their content); and 3)

foster the ability to acknowledge difficult sensations (e.g., cravings, maladaptive thoughts),

refocus attention on the present moment, and purposefully choose how to respond (rather

than impulsively react). MBAT sessions involved 30-45 minutes of formal mindfulness

practice per session (e.g., sitting meditation, yoga), as well as discussion. Participants were

encouraged to practice mindfulness formally (e.g., body scan, sitting meditation) six days

per week in addition to informal practice (e.g., mindfulness of routine activities) several

times each day. MBAT was delivered in eight two-hour in-person group counseling sessions,

and session 5 occurred on the quit date.

Cognitive Behavioral Treatment (CBT)—CBT taught problem-solving/coping skills for smoking cessation based on relapse prevention theory (Marlatt & Gordon, 1985) and the

Guideline (Fiore et al., 2008). Primary topics included: 1) planning to quit smoking (e.g., recognizing triggers); 2) learning about nicotine addiction; 3) practicing stress management

techniques; 4) preparing for quit day and using the nicotine patch; 5) learning skills to cope

with cravings and negative emotions; 6) enlisting social support; 7) managing nutrition and

exercise; and 8) reviewing skills and planning to maintain abstinence. Like MBAT, CBT

involved eight two-hour in-person group counseling sessions, and session 5 occurred on the

quit date.

Usual Care (UC) Intervention—The UC intervention taught coping and problem-solving strategies based on the Guideline (Fiore et al., 2008). UC was delivered in four 5- to 10- minute individual counseling sessions, with session 3 occurring on the quit date (i.e., week 5

of the protocol as in MBAT and CBT). UC, which was less intensive in terms of both time

and attention, was designed to be consistent with what smokers requesting help with

cessation might receive in a healthcare setting.

Measures

(See Figure 1 for timeline of study procedures and assessments).

Covariates—Demographic variables (assessed at baseline) included age, gender, race/ ethnicity, partner status, and education. The Heaviness of Smoking Index (HSI; Kozlowski,

Porter, Orleans, Pope, & Heatherton, 1994) was administered at baseline to assess pre-

treatment smoking behavior. The two HSI items are cigarettes per day and time to first

cigarette after waking.

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Potential Mechanisms

Emotions and stress: The 20-item Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988) yields subscale scores for positive affect (PA; e.g., enthusiastic,

proud) and negative affect (NA; e.g., upset, irritable). The current study utilized PANAS

scores from the quit date and 1, 2, 3, 4, and 26 weeks post-quit, with Cronbach’s alphas

ranging from .92–.95 for the PA and NA subscales. The Center for Epidemiological Studies

- Depression (CES-D; Radloff, 1977), a 20-item measure of past-week depressive

symptoms, was administered on the quit date and at 4 and 26 weeks post-quit (α = .91–.93 in the current sample). The 4-item Perceived Stress Scale - Short Form (PSS-SF; Warttig,

Forshaw, South, & White, 2013) was administered on the quit date and 1, 2, 3, 4, and 26

weeks post-quit (α = .71–.81).

Dependence, withdrawal, and craving: The 68-item Wisconsin Inventory of Smoking Dependence Motives (WISDM; Piper et al., 2004) yields total and subscale scores for

“primary dependence” (i.e., automaticity, craving, tolerance, loss of control) and “secondary

dependence” (e.g., cognitive enhancement, positive and negative reinforcement). The

WISDM was administered at 4 and 26 weeks post-quit (α = .86–.99). The Wisconsin Smoking Withdrawal Scale (WSWS; Welsch et al., 1999) is a 28-item measure yielding a

total score and 7 subscale scores (i.e., anger, anxiety, concentration difficulty, craving,

hunger, sadness, sleep problems). It was administered on the quit date and 1, 2, 3, 4, and 26

weeks post-quit (α = .72–.94).

Agency: Two components of agency, self-efficacy and affect regulation expectancies (Vidrine et al., 2009), were assessed. The 9-item version of the Self-Efficacy Scale (SES;

Velicer, Diclemente, Rossi, & Prochaska, 1990), administered on the quit date and 1, 2, 3, 4,

and 26 weeks post-quit, assesses self-efficacy for avoiding smoking in positive affect/social,

negative affect, and habitual/craving situations (α = 80–.97). The Affective Information Processing Questionnaire (AIPQ: Wetter, Brandon, & Baker, 1992) assessed smokers’

expectations that they could regulate their emotions by smoking and by other means in

response to 10 vignettes. The AIPQ was administered on the quit date and 4 and 26 weeks

post-quit (α = .93–.97).

Subjective bias toward cigarettes: The Subjective Bias Questionnaire (SBQ) assesses attentional bias toward cigarettes. Three items, drawn from Leventhal et al. (2007), ask

participants to rate the extent to which their attention has been drawn to cigarettes, other

people smoking, and cigarette smoke on a 1 (not at all) to 5 (extreme amount) Likert scale

(α = .83–.86).

Outcome Variable

Abstinence: Seven-day point prevalence abstinence from smoking was assessed at 4 and 26 weeks post-quit and biochemically confirmed with CO level < 6 ppm (Vidrine et al., 2016).

Analyses

First, mixed-effects regression models were conducted using SAS PROC MIXED to

examine effects of treatments on hypothesized mechanisms from the quit date through 26

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weeks post-quit (6 time points total). Models specified an unstructured covariance matrix for

the vector of random intercept and slope of time for each participant. Group by time

interactions were not significant and were not retained in final models. Variables tested were

positive and negative affect (PANAS), depressive symptoms (CES-D), stress (PSS-SF),

withdrawal symptoms (WSWS), smoking dependence motives (WISDM), craving (WSWS,

WISDM), self-efficacy (SES), affect regulation expectancies (AIPQ), and subjective bias

toward cigarettes (SBQ). Analyses controlled for demographics, baseline smoking, and

abstinence at each time point.

Second, volatility indices were created for key affective variables expected to fluctuate over

the course of treatment. These variables were positive and negative affect (PANAS),

perceived stress (PSS-SF), and WSWS subscales for anger, anxiety, sadness, and craving.

The time points selected were those that all groups attended, beginning one week prior to the

quit date (week 1 pre-quit, quit day, and weeks 3, 4, and 26 post-quit). Volatility indices

were calculated using the mean square successive difference (MSSD) approach (Jahng,

Wood, & Trull, 2008), which captures both affective variability and temporal instability.

Given the unequal time intervals, an adjustment in the calculation of the successive

difference (lambda = 0.25) was used (Jahng et al., 2008). Analyses of covariance

(ANCOVAs) were conducted to examine group effects on volatility surrounding the quit date

and through follow-up, controlling for baseline demographic variables, baseline smoking,

and abstinence at each time point.

Third, indirect effects of MBAT vs. CBT vs. UC on abstinence were examined. Although

treatment did not directly impact overall abstinence rates, indirect effects analyses can

provide important information about underlying mechanisms (e.g., treatments could achieve

the same outcomes via different mechanisms). Further, there may be less statistical power to

detect overall effects than to detect other links within the mediation chain (Mackinnon &

Fairchild, 2009). Potential mechanisms that were identified in the first phase of analyses as

significantly differing between treatments were tested, controlling for baseline

demographics, number of cigarettes per day, and time to first cigarette. In predicting

abstinence at 4 weeks post-quit, mechanisms were assessed at 3 weeks post-quit. In

predicting abstinence at 26 weeks post-quit, mechanisms were assessed at 4 weeks post-quit.

Because the WISDM was only administered at 4 and 26 weeks post quit, week 4 WISDM

scores were examined as mechanisms predicting week 26 abstinence, but WISDM scores

were not used as mediators predicting 4-week abstinence.

For indirect effects analyses, sensitivity analyses were conducted to examine the robustness

of effects under different assumptions regarding missing data. Although a common practice

in smoking cessation research is to assume that participants with missing data are smoking,

this “missing = smoking” approach can lead to biased estimates (Blankers et al., 2016;

Hedeker, Mermelstein, & Demirtas, 2007). The statistical analyses using the available data

are based on full-likelihood estimation, and hence are valid under the missing at random

assumption (Laird, 1988). In order to study the sensitivity of results to the missing at random

assumption, we performed additional sensitivity analyses based on the multiple-model

multiple imputation approach outlined by Siddique, Harel, Crespi, and Hedeker (2014). The

abstinence outcomes at weeks 4 and 26 were imputed from a number of assumptions varying

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from missing at random (k = 1) to missing = smoking (k = .01). The mediator variables were imputed from a conditional multivariate normal distribution given the imputed abstinence

outcomes. This two-step approach was repeated five times to generate five imputed data sets.

The final sensitivity analyses combined the results from the five imputed data sets using the

multiple imputation formula.

Among participants who lapsed early in treatment in the parent trial, those who received

MBAT were more likely to recover abstinence than those who received CBT or UC (Vidrine

et al., 2016). Thus, we planned to conduct indirect effects analyses specifically among early

lapsers. However, these analyses were not conducted because of the small sample size and

highly unbalanced outcome division (4.2% abstinent at 26 weeks [n = 4], 65.3% smoking [n = 62], and 30.5% missing data [n = 29]).

Results

Participants

Participants were 412 smokers (48.2% African-American, 41.5% non-Latino White, 5.4%

Latino, 4.9% other). Over half (54.9%) were female, most (57.6%) reported a total annual

household income of less than $30,000, and one third had ≤ high school education/GED.

Participants smoked an average of 19.9 (SD = 10.1) cigarettes per day.

Effects of Treatments on Hypothesized Mechanisms over Time

Compared to those receiving CBT, MBAT participants scored lower on WISDM – Loss of

Control (β = .21, p = .046). The effects of MBAT vs. CBT on WISDM – Primary Dependence (β = .19, p = .059), WISDM – Craving (β = .19, p = .066), and WISDM – Tolerance (β = .19, p = .059) each trended as expected, with MBAT participants indicating less primary dependence, craving, and tolerance than those receiving CBT. In addition, the

effect of MBAT vs. CBT on subjective bias trended as expected, β = .16, p = .059, with participants in MBAT reporting less attentional bias toward cigarettes than those receiving

CBT (See Table 1).

Compared to participants receiving UC, participants receiving MBAT reported lower levels

of anger, anxiety, sadness, craving, concentration difficulties, craving, dependence motives

(all subscales of the WISDM except social-environmental goads), subjective bias toward

cigarettes, and higher self-efficacy for avoiding smoking when experiencing negative affect

(ps < .05; see Table 1). Compared to UC participants, those who received CBT reported lower negative affect, stress, anger, anxiety, sadness, concentration difficulties, dependence

motives (most subscales), expectations of regulating affect by smoking, and higher self-

efficacy for avoiding smoking when experiencing negative affect (ps < .05; see Table 1).

The ANCOVA predicting volatility of anger from treatment group was significant (see Table

2). Post-hoc tests indicated that participants receiving MBAT evidenced lower volatility of

anger than participants in CBT (p = .015) and UC (p = .005). The ANCOVA predicting volatility of craving trended as expected (p = .056), with MBAT participants evidencing the lowest volatility. However, MBAT did not differ significantly from CBT (p = .349) or UC (p = .109).

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Indirect Effects of Treatment on Abstinence through Hypothesized Mechanisms

The indirect effect of MBAT vs. CBT through WISDM – Loss of Control was not significant

under any of the missing data assumptions (for k = 1, βind. effect = .02 [−.03, .11]).

Indirect effects of MBAT vs. UC through hypothesized mechanisms are shown in Table 3. In

predicting abstinence at 4 weeks post-quit, indirect effects were significant for WSWS

Anxiety and Self-efficacy-Negative Affect. Compared to UC, MBAT predicted lower anxiety

and higher self-efficacy for managing negative affect, which predicted higher odds of

abstinence at week 4. Results were consistent across all three missing data assumptions. In

predicting week 26 abstinence, indirect effects for MBAT vs. UC were significant for

WSWS Concentration, WSWS Craving, and WISDM Total, Secondary Dependence

Motives, Cognitive Enhancement, Craving, Cue Exposure – Associative Processes, Negative

Reinforcement, Positive Reinforcement, and Taste-Sensory. Compared to UC, MBAT

predicted lower concentration difficulties, craving, and dependence, which predicted higher

odds of abstinence at 26 weeks post-quit. The above results were consistent across all three

missing data mechanisms.

For indirect effects of CBT vs. UC on week 4 abstinence, WSWS Anxiety was the only

significant mechanism across all three missing data assumptions (under k = 1, βind. effect = . 15 [.03, .31]). Compared to UC, CBT predicted lower anxiety, which predicted higher odds

of abstinence at 4 weeks post-quit. In predicting week 26 abstinence, indirect effects for

CBT vs. UC were significant across all three missing data mechanisms for PSS-SF

Perceived Stress (βind. effect = .09 [.01, .24]), WSWS Concentration (βind. effect = .09 [.01, . 24]), and Self-Efficacy-Negative Affect (βind. effect = .11 [.01, .28]). Compared to UC, CBT predicted lower stress and concentration difficulties, and higher self-efficacy for managing

negative affect without smoking, which predicted higher odds of abstinence at 26 weeks

post-quit. See online supplemental table for estimates of all of the component paths for the

CBT vs. UC indirect effects analysis.

Discussion

Although mindfulness-based treatments show promise for smoking cessation, there are few

data addressing the differential effects of mindfulness versus other active treatments on

specific hypothesized mechanisms. In the parent trial (Vidrine et al., 2016), there was no

effect of treatment type on overall abstinence rates, but MBAT did significantly increase

lapse recovery rates relative to CBT and UC. The current study examined potential cognitive

and affective mechanisms underlying MBAT versus CBT and UC for smoking cessation. As

hypothesized, participants receiving MBAT perceived greater volitional control over

smoking and evidenced lower volatility of anger than participants in both other treatments.

However, there were no other significant differences between MBAT and CBT, nor were

there significant indirect effects of MBAT vs. CBT, suggesting that mindfulness and

cognitive-behavioral approaches may similarly influence several of the psychosocial

mechanisms implicated in tobacco dependence. Compared to those receiving UC, however,

MBAT participants reported lower anxiety, attentional bias toward cigarettes, concentration

difficulties, craving, and smoking dependence motives, as well as higher self-efficacy for

managing negative affect. Indirect effects of MBAT versus UC occurred through lower

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anxiety, concentration difficulties, craving, and dependence motives, as well as greater self-

efficacy for managing negative affect without smoking.

Although a number of differences emerged between MBAT and UC, this study did not reveal

many differences between MBAT and CBT. MBAT and CBT were matched in intensity and

included common elements (e.g., MBAT incorporated cognitive-behavioral strategies in

addition to its unique focus on mindfulness), which could partially explain the overall lack

of differences. The lack of differences between MBAT and CBT is consistent with Goyal et

al.’s (2014) meta-analysis, which found that although mindfulness-based therapies were

superior to nonspecific active controls, there was insufficient evidence for differences

between mindfulness training and specific active controls (including CBT). Similarly,

Khoury et al.’s (2013) meta-analysis concluded that mindfulness-based therapy was superior

to some other active treatments (e.g., supportive therapy) but not CBT. Recently, Goldin et

al. (2016) reported that mindfulness training and CBT were equally efficacious for treating

social anxiety disorder and that there were more similarities than differences in terms of

underlying mechanisms.

CBT and mindfulness-based treatments certainly have commonalities. Both promote

awareness of and exposure to internal sensations, which might lessen tendencies toward

automatic and avoidant responses (Baer, 2003). However, whereas CBT involves efforts to

change irrational thinking, mindfulness involves non-evaluative observation of thoughts (i.e.,

changing the way one relates to his/her thoughts rather than directly attempting to change

the content of thoughts; Baer, 2003; Teasdale et al., 2002). Some studies have revealed

meaningful differences between these two approaches. For example, mindfulness-based

relapse prevention predicted fewer days of substance use than cognitive-behavioral relapse

prevention (Witkiewitz, Warner, et al., 2014), especially at 12-month follow-up (Bowen et

al., 2014). More research is needed to examine potential differences between mindfulness

and CBT for various outcomes and populations, their underlying mechanisms, and with

longer follow-up periods.

Two significant differences between MBAT and CBT did emerge. First, MBAT participants

perceived greater volitional control over smoking than those receiving CBT or UC.

Mindfulness involves purposeful, present-focused attention, which might increase the

tendency for purposeful action and help people to feel more in control of their behavior.

Second, MBAT predicted lower volatility of anger than both CBT and UC. Although no

known research has examined the effects of mindfulness treatment on volatility,

dispositional mindfulness predicts lower volatility of negative affect (Adams et al., 2014),

and mindfulness training shows promise for reducing anger (e.g., Amutio et al., 2014).

Mindfulness meditation may promote “metacognitive awareness,” or “decentering,”

whereby individuals learn to view thoughts and feelings as mental events rather than facts

(Bishop et al., 2004; Teasdale et al., 2002). This decentered perspective may reduce the

likelihood of automatic reactions to experiences of anger, thus preventing its escalation. By

helping smokers to observe the cognitive, emotional and physiological sensations of anger

without reacting or becoming “stuck” in them, mindfulness training might prevent anger

from cycling out of control (and thus reduce volatility of anger). Although we were not able

to examine mediators of the effect of MBAT vs. CBT on lapse recovery because of small

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sub-samples, greater perceived volitional control over smoking and lower affective volatility

could be mechanisms explaining why MBAT was superior to both CBT and usual care for

promoting lapse recovery in the parent trial. For example, among participants who lapse

early in a quit attempt, those learning to practice mindfulness may perceive greater control

over their smoking behavior and maintain more emotional equilibrium in the context of this

slip-up, which could help them to regain abstinence. Future research with larger samples

might examine mediators of the effect of MBAT vs. other active treatments specifically on

lapse recovery.

A side-by-side comparison of the MBAT vs. UC and CBT vs. UC analyses further sheds

light on similarities and differences between MBAT and CBT. Effect sizes for the MBAT vs.

UC and CBT vs. UC comparisons are similar overall, with a few notable differences. MBAT

seems to have produced greater reductions in several aspects of dependence (including

automaticity), craving, and subjective bias toward cigarettes. On the other hand, CBT

appears to have produced greater reductions in negative affect and stress. Similarly, whereas

indirect effects of MBAT vs. UC on week 26 abstinence occurred through reduced craving

and dependence, indirect effects of CBT vs. UC occurred through reduced stress and higher

self-efficacy for managing negative affect without smoking. Given the relatively large array

of mechanisms examined and the general lack of significant differences between MBAT and

CBT, these findings should be interpreted with caution and require replication. In addition,

UC was less intensive than both MBAT and CBT, involved individual rather than group

counseling, and was not balanced for time/attention; differences between UC and

MBAT/CBT could be partially due to dose effects.

Results revealed a number of differences between MBAT and UC. MBAT both impacted the

following variables to a greater extent than did UC, and there were significant indirect

effects through these mechanisms for either week 4 or week 26 abstinence: 1) anxiety, 2)

concentration difficulties, 3) craving, 4) self-efficacy for managing negative emotions, and 5)

smoking dependence motives. First, consistent with extant research comparing mindfulness-

based treatment to nonspecific active controls (Goyal et al., 2014; Khoury et al., 2013),

MBAT was associated with lower anxiety compared to UC, and MBAT produced an indirect

effect on week 4 abstinence via anxiety. Second, MBAT resulted in less difficulty

concentrating compared to UC, which predicted a higher likelihood of abstinence 26 weeks

post-quit. By improving present-focused attention, mindfulness meditation might minimize

difficulties with concentration, thus lessening withdrawal symptoms.

Third, in line with the findings of Davis, Manley et al. (2014), MBAT (vs. UC) was

associated with lower craving, which predicted 26-week abstinence. “Urge surfing,” a core

MBAT practice (which has also been incorporated into other mindfulness-based

interventions and cognitive-behavioral relapse prevention), teaches people to bring present-

focused, nonjudgmental awareness to cravings (e.g., Bowen & Marlatt, 2009). Participants

are asked to imagine their cravings as waves in the ocean that naturally rise but also dissipate

with time. This way of observing cravings, without smoking or fighting against them, may

prevent further escalations in craving, which could support prolonged abstinence. Fourth,

MBAT participants indicated greater self-efficacy for coping with negative emotions without

smoking than those in UC, which predicted abstinence at 4 weeks post-quit. This is

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consistent with Vidrine et al.’s (2009) finding that smokers with greater dispositional

mindfulness were more confident in their ability to regulate emotions without smoking, and

extends this finding to mindfulness treatment.

Finally, MBAT (vs. UC) reduced a variety of aspects of tobacco dependence, which

predicted greater 26-week abstinence. MBAT influenced several smoking dependence

motives that are consistent with the conceptual underpinnings of mindfulness-based

treatment for addictive behaviors. Mindfulness is thought to foster awareness and purposeful

responding rather than impulsive, automatic reactions to cues associated with addictive

behavior (Brewer et al., 2013). Thus, it makes sense that mindfulness training would reduce

the following particular subscales: Automaticity (smoking without awareness or purposeful

choice), Loss of Control (the feeling that cigarettes control one’s life, rather than the sense

of personal control over smoking), Cue Exposure-Associative Processes (strong links

between external cues and smoking behavior), and Negative Reinforcement (smoking in

attempt to reduce unpleasant feelings).

This study is limited by a reliance on repeated administration of self-report questionnaires

with significant time lapses between assessments. Future research could utilize ecological

momentary assessment (Shiffman, Stone, & Hufford, 2008) to examine how mindfulness

might modulate cognitive and affective variables on a real-time, real-world basis. In

addition, it is possible that MBAT might differentially impact other mechanisms (e.g.,

interpersonal or physiological indicators) not measured in the current study. Furthermore,

adherence to between-session formal meditation practice recommendations in this study was

low (Vidrine et al., 2016), and more informal mindfulness practices (e.g., urge surfing) were

not assessed. More differences between MBAT and other active treatments could emerge

with greater adherence to regular mindfulness practice. In a similar vein, continued

mindfulness practice over a longer time period may be necessary for unique effects of

mindfulness practice to develop. Finally, because this is the first known study to examine

mechanisms underlying mindfulness-based versus cognitive-behavioral and usual care

treatments for smoking cessation, a relatively large number of analyses were conducted to

examine multiple potential mechanisms. Given the potential for this approach to inflate Type

I error, we suggest that readers examine standardized estimates in addition to p-values. In addition, abstinence rates were higher at 4-weeks post-quit than at 26-weeks post-quit,

which could have limited statistical power for analyses predicting the later time point.

Replication will be needed to increase confidence in the findings.

This is the first known study to directly compare mechanisms underlying the effects of

mindfulness-based versus cognitive-behavioral and usual care treatments for smoking

cessation. Whereas numerous differences emerged between MBAT and UC, the effects of

MBAT vs. CBT on the psychosocial mechanisms implicated in tobacco dependence appear

very similar. Future research on the mechanisms underlying MBAT vs. other active

treatments may need to utilize a wider array of assessments and methodologies in order to

uncover (or not) differential effects on specific mechanisms. Finally, even in the absence of

many indirect effects of MBAT vs. another intensive active treatment on abstinence, future

research might investigate whether mindfulness-based treatments are more effective for

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specific subgroups of individuals, whereas other treatment approaches such as CBT may be

more appropriate with other groups.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

Acknowledgments

Funding Statement: This work was supported by the National Institute on Drug Abuse (R01DA018875), the National Center for Complementary and Integrative Health (K23AT008442); the National Cancer Institute (P30CA016672), and the National Institute on Minority Health and Health Disparities (K99MD010468). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Witkiewitz K, Bowen S, Harrop EN, Douglas H, Enkema M, Sedgwick C. Mindfulness-based treatment to prevent addictive behavior relapse: theoretical models and hypothesized mechanisms of change. Subst Use Misuse. 2014; 49(5):513–524. DOI: 10.3109/10826084.2014.891845 [PubMed: 24611847]

Witkiewitz K, Marlatt GA. Why and how do substance abuse treatments work? Investigating mediated change. Addiction. 2008; 103(4):649–650. DOI: 10.1111/j.1360-0443.2008.02193.x [PubMed: 18339109]

Witkiewitz K, Warner K, Sully B, Barricks A, Stauffer C, Thompson BL, Luoma JB. Randomized trial comparing mindfulness-based relapse prevention with relapse prevention for women offenders at a residential addiction treatment center. Substand Use and Misuse. 2014; 49(5):536–546. DOI: 10.3109/10826084.2013.856922

Spears et al. Page 17

J Consult Clin Psychol. Author manuscript; available in PMC 2018 November 01.

A u th

o r M

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t A

u th

o r M

a n u scrip

t A

u th

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t A

u th

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t

Public Health Statement

Compared to standard smoking cessation treatment, mindfulness-based interventions may

produce more favorable cognitive and emotional outcomes, which could improve chances

of quitting. Mindfulness and cognitive-behavioral treatments appear to have similar

effects on several of the psychosocial mechanisms implicated in tobacco dependence.

Spears et al. Page 18

J Consult Clin Psychol. Author manuscript; available in PMC 2018 November 01.

A u th

o r M

a n u scrip

t A

u th

o r M

a n u scrip

t A

u th

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a n u scrip

t A

u th

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t

Figure 1. Timeline of study procedures and assessments.

Notes:

*Mechanisms for indirect effects analyses predicting Week 4 abstinence were assessed at

Week 3 post-quit.

**Mechanisms for indirect effects analyses predicting Week 26 abstinence were assessed at

Week 4 post-quit.

Spears et al. Page 19

J Consult Clin Psychol. Author manuscript; available in PMC 2018 November 01.

A u th

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t A

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A u th

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Spears et al. Page 20

Ta b

le 1

D if

fe re

nt ia

l E

ff ec

ts o

f M

B A

T , C

B T

, a nd

U C

o n

hy po

th es

iz ed

m ec

ha ni

sm s

ov er

t im

e.

M B

A T

v s.

C B

T (

M B

A T

c od

ed a

s 1,

C B

T a

s 0)

M B

A T

v s.

U C

( M

B A

T c

od ed

a s

1, U

C a

s 0)

C B

T v

s. U

C (

C B

T c

od ed

a s

1, U

C a

s 0)

V ar

ia b

le β

(9 5%

C I)

p β

(9 5%

C I)

p β

(9 5%

C I)

p

E m

ot io

ns

P

os it

iv e

A ff

ec t

(P A

N A

S P

A )

− .0

9 (−

.2 9,

.1 0)

.3 37

.0 9

(− .1

3, .3

0) .4

44 .1

8 (−

.0 4,

.4 0)

.1 09

N

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iv e

A ff

ec t

(P A

N A

S N

A )

.0 8

(− .1

1, .2

7) .4

03 −

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(− .4

0, .0

3) .0

86 −

.2 7

(− .4

8, −

.0 5)

.0 15

D

ep re

ss iv

e S

ym pt

om s

(C E

S −

D )

.0 3

(− .1

6, .2

2) .7

62 −

.1 8

(− .4

0, .0

3) .0

99 −

.2 1

(− .4

3, .0

05 )

.0 56

P er

ce iv

ed S

tr es

s (P

S S

− S

F )

.0 9

(− .1

0, .2

8) .3

62 −

.1 6

(− .3

8, .0

5) .1

41 −

.2 5

(− .4

7, −

.0 3)

.0 24

W it

hd ra

w al

a nd

C ra

vi ng

( W

S W

S )

A

ng er

.0 2

(− .1

5, .2

0) .7

79 −

.2 2

(. 42

, . 02

) .0

31 −

.2 4

(− .4

5, −

.0 4)

.0 17

A

nx ie

ty −

.0 6

(− .2

3, .1

2) .5

26 −

.2 8

(. 48

, . 07

) .0

07 −

.2 5

(− .4

3, −

.0 7)

.0 08

S

ad ne

ss −

.0 03

( −

.1 8,

.1 8)

.9 73

− .2

3 (.

44 , .

02 )

.0 32

− .2

3 (−

.4 4,

− .0

2) .0

35

C

on ce

nt ra

ti on

D if

fi cu

lt ie

s .0

03 (

− .1

8, .1

9) .9

76 −

.2 6

(. 48

, . 05

) .0

14 −

.2 7

(− .4

8, −

.0 6)

.0 13

C

ra vi

ng −

.0 9

(− .2

6, .0

8) .2

94 −

.2 4

(. 44

, . 05

) .0

16 −

.1 5

(− .3

5, .0

5) .1

38

H

un ge

r .0

5 (−

.1 4,

.2 3)

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.0 02

( −

.2 1,

.2 2)

.9 86

− .0

4 (−

.2 6,

.1 7)

.6 92

S

le ep

− .1

2 (−

.3 0,

.0 7)

.2 27

− .0

09 (

− .2

2, .2

1) .9

34 .1

1 (−

.1 1,

.3 2)

.3 31

S m

ok in

g D

ep en

de nc

e M

ot iv

es (

W IS

D M

)

T

ot al

− .1

4 (−

.3 4,

.0 6)

.1 72

− .4

6 (−

.6 9,

− .2

3) .0

00 1

− .3

2 (−

.5 5,

− .0

8) .0

08

P

ri m

ar y

− .1

9 (−

.3 9,

.0 07

) .0

59 −

.4 3

(− .6

6, −

.2 1)

.0 00

2 −

.2 4

(− .4

7, −

.0 1)

.0 39

S

ec on

da ry

− .1

1 (−

.3 1,

.1 0)

.3 07

− .4

6 (−

.6 9,

− .2

2) .0

00 1

− .3

5 (−

.5 9,

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2) .0

04

A

ff il

ia ti

ve A

tt ac

hm en

t −

.1 3

(− .3

4, .0

8) .2

37 −

.3 6

(− .6

0, −

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− .2

3 (−

.4 7,

.0 1)

.0 60

A

ut om

at ic

it y

− .1

2 (−

.3 2,

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7 (−

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4) .0

21 −

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8) .2

03

L

os s

of C

on tr

ol −

.2 1

(− .4

1, −

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) .0

46 −

.4 3

(− .6

6, −

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3 −

.2 3

(− .4

6, .0

04 )

.0 53

B

eh av

io ra

l C

ho ic

e –

M el

io ra

ti on

− .1

4 (−

.3 4,

.0 7)

.1 98

− .4

5 (−

.6 8,

− .2

1) .0

00 2

− .3

1 (−

.5 5,

− .0

8) .0

10

C

og ni

ti ve

E nh

an ce

m en

t −

.1 1

(− .3

2, .0

9) .2

84 −

.4 1

(− .6

4, −

.1 7)

.0 00

7 −

.2 9

(− .5

3, −

.0 6)

.0 15

C

ra vi

ng −

.1 9

(− .3

9, .0

1) .0

66 −

.4 4

(− .6

8, −

.2 1)

.0 00

2 −

.2 5

(− .4

9, −

.0 2)

.0 32

C

ue E

xp os

ur e

– A

ss oc

ia ti

ve P

ro ce

ss es

− .1

5 (−

.3 5,

.0 6)

.1 59

− .4

3 (−

.6 7,

− .2

0) .0

00 3

− .2

8 (−

.5 2,

− .0

5) .0

18

N

eg at

iv e

R ei

nf or

ce m

en t

− .0

9 (−

.2 9,

.1 1)

.3 91

− .4

5 (−

.6 8,

− .2

2) .0

00 2

− .3

6 (−

.5 9,

− .1

3) .0

03

J Consult Clin Psychol. Author manuscript; available in PMC 2018 November 01.

A u th

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t A

u th

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u th

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Spears et al. Page 21

M B

A T

v s.

C B

T (

M B

A T

c od

ed a

s 1,

C B

T a

s 0)

M B

A T

v s.

U C

( M

B A

T c

od ed

a s

1, U

C a

s 0)

C B

T v

s. U

C (

C B

T c

od ed

a s

1, U

C a

s 0)

V ar

ia b

le β

(9 5%

C I)

p β

(9 5%

C I)

p β

(9 5%

C I)

p

P

os it

iv e

R ei

nf or

ce m

en t

− .0

5 (−

.2 6,

.1 5)

.6 29

− .4

9 (−

.7 3,

− .2

6) <

.0 00

1 −

.4 4

(− .6

8, −

.2 1)

.0 00

2

S

oc ia

l− E

nv ir

on m

en ta

l G

oa ds

.0 4

(− .1

7, .2

5) .6

91 −

.0 7

(− .3

0, .1

7) .5

85 −

.1 1

(− .3

5, .1

3) .3

73

S m

ok in

g D

ep en

de nc

e M

ot iv

es (

W IS

D M

), c

on ti

nu ed

T

as te

-S en

so ry

− .1

4 (−

.3 4,

.0 7)

.1 98

− .4

6 (−

.6 9,

− .2

2) .0

00 1

− .3

2 (−

.5 6,

− .0

9) .0

07

T

ol er

an ce

− .1

9 (−

.3 9,

.0 07

) .0

59 −

.4 4

(− .6

6, −

.2 1)

.0 00

2 −

.2 4

(− .4

7, −

.0 1)

.0 38

W

ei gh

t C

on tr

ol −

.0 5

(− .2

6, .1

7) .6

81 −

.2 9

(− .5

4, −

.0 4)

.0 22

− .2

5 (−

.5 0,

.0 04

) .0

54

S el

f- ef

fi ca

cy (

S E

S )

T

ot al

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2 (−

.1 9,

.1 6)

.8 64

.1 6

(− .0

5, .3

6) .1

28 .1

7 (−

.0 3,

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.0 96

P

os it

iv e

A ff

ec t/

S oc

ia l

S it

ua ti

on s

− .0

5 (−

.2 3,

.1 3)

.5 94

.1 0

(− .1

1, .3

1) .3

36 .1

5 (−

.0 6,

.3 6)

.1 55

N

eg at

iv e

A ff

ec t

S it

ua ti

on s

− .0

08 (

− .1

8, .1

7) .9

26 .2

0 (.

00 4,

.4 0)

.0 45

.2 1

(. 01

, . 41

) .0

38

H

ab it

ua l/

C ra

vi ng

S it

ua ti

on s

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( −

.1 7,

.1 8)

.9 35

.1 4

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6, .3

3) .1

74 .1

3 (−

.0 7,

.3 3)

.2 01

A ff

ec t

R eg

ul at

io n

E xp

ec ta

nc ie

s (A

IP Q

)

B

y N

ot S

m ok

in g

− .0

8 (−

.2 6,

.1 1)

.4 21

.1 1

(− .1

0, .3

1) .3

10 .1

8 (−

.0 3,

.3 9)

.0 85

B

y S

m ok

in g

.0 6

(− .1

2, .2

4) .5

14 −

.1 7

(− .3

8, .0

3) .0

90 −

.2 3

(− .4

4, −

.0 3)

.0 23

S ub

je ct

iv e

B ia

s to

w ar

d C

ig ar

et te

s (S

B Q

) −

.1 6

(− .3

3, .0

06 )

.0 59

− .2

3 (−

.4 2,

− .0

4) .0

16 −

.0 7

(− .2

7, .1

2) .4

48

N ot

es .

S ta

nd ar

di ze

d pa

ra m

et er

e st

im at

es a

re s

ho w

n (i

.e .,

co nt

in uo

us v

ar ia

bl es

w er

e re

sc al

ed f

or a

na ly

si s)

. H yp

ot he

si ze

d m

ec ha

ni sm

s w

er e

m ea

su re

d fr

om t

he q

ui t

da te

t hr

ou gh

2 6

w ee

ks p

os t-

qu it

.

M B

A T

– M

in df

ul ne

ss -B

as ed

A dd

ic ti

on T

re at

m en

t; C

B T

= C

og ni

ti ve

B eh

av io

ra l

T he

ra py

; U

C =

U su

al C

ar e.

PA N

A S

P A

= P

os it

iv e

A ff

ec t

su bs

ca le

o f

th e

P os

it iv

e an

d N

eg at

iv e

A ff

ec t

S ch

ed ul

e; P

A N

A S

N A

= N

eg at

iv e

A ff

ec t

su bs

ca le

o f

th e

P os

it iv

e an

d N

eg at

iv e

A ff

ec t

S ch

ed ul

e; C

E S

-D =

C en

te r

fo r

E pi

de m

io lo

gi ca

l S

tu di

es –

D ep

re ss

io n;

P S

S -S

F =

P er

ce iv

ed S

tr es

s S

ca le

- S

ho rt

F or

m ;

W S

W S

= W

is co

ns in

S m

ok in

g W

it hd

ra w

al S

ca le

; W

IS D

M =

W is

co ns

in I

nv en

to ry

o f

S m

ok in

g D

ep en

de nc

e M

ot iv

es ;

S E

S =

S el

f- E

ff ic

ac y

S ca

le ;

A IP

Q =

A ff

ec ti

ve I

nf or

m at

io n

P ro

ce ss

in g

Q ue

st io

nn ai

re ;

S B

Q =

S ub

je ct

iv e

B ia

s Q

ue st

io nn

ai re

.

A ll

m od

el s

co nt

ro ll

ed f

or d

em og

ra ph

ic s,

b as

el in

e sm

ok in

g (c

ig ar

et te

s pe

r da

y an

d ti

m e

to f

ir st

c ig

ar et

te ),

a nd

a bs

ti ne

nc e

at e

ac h

ti m

e po

in t.

R es

ul ts

s ho

w n

in b

ol d

re pr

es en

t si

gn if

ic an

t ef

fe ct

s ba

se d

on a

lp ha

=

.0 5.

J Consult Clin Psychol. Author manuscript; available in PMC 2018 November 01.

A u th

o r M

a n u scrip

t A

u th

o r M

a n u scrip

t A

u th

o r M

a n u scrip

t A

u th

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t

Spears et al. Page 22

Ta b

le 2

E ff

ec ts

o f

T re

at m

en t

G ro

up o

n V

ol at

il it

y of

E m

ot io

ns a

nd C

ra vi

ng .

A d

ju st

ed f

or d

em og

ra p

h ic

s an

d b

as el

in e

sm ok

in g

A d

ju st

ed f

or d

em og

ra p

h ic

s, b

as el

in e

sm ok

in g,

a n

d

ab st

in en

ce E

st im

at ed

M ar

gi n

al M

ea n

s* (

S ta

n d

ar d

E rr

or )

V ar

ia b

le F

p F

p U

C C

B T

M B

A T

P os

it iv

e A

ff ec

t (P

A N

A S

P A

) .4

5 .6

38 .8

1 .4

45 45

.9 5

(1 0.

22 )

34 .4

4 (7

.8 3)

47 .9

8 (7

.7 5)

N eg

at iv

e A

ff ec

t (P

A N

A S

N A

) .9

2 .4

00 1.

24 .2

93 65

.1 3

(1 3.

15 )

38 .9

3 (1

0. 07

) 50

.0 4

(9 .9

7)

P er

ce iv

ed S

tr es

s (P

S S

-S F

) .9

5 .9

49 .6

5 .5

23 7.

22 (1

.1 9)

5. 67

(. 91

) 5.

67 (

.9 0)

A n

ge r

(W S

W S

) 5.

87 .0

03 6.

61 .0

02 .9

8a (.

12 )

.8 5a

(. 09

) .4

8b (.

09 )

A nx

ie ty

( W

S W

S )

.4 4

.6 47

.8 0

.4 51

.6 9

(. 10

) .5

8 (.

07 )

.5 4

(. 07

)

S ad

ne ss

( W

S W

S )

2. 97

.0 53

.6 8

.5 09

.5 2

(. 08

) .4

3 (.

06 )

.4 0

(. 06

)

C ra

vi ng

( W

S W

S )

2. 33

.0 99

2. 94

.0 56

1. 15

(. 18

) .9

7 (.

14 )

.6 4

(. 13

)

N ot

es .

M B

A T

– M

in df

ul ne

ss -B

as ed

A dd

ic ti

on T

re at

m en

t; C

B T

= C

og ni

ti ve

B eh

av io

ra l

T he

ra py

; U

C =

U su

al C

ar e.

PA N

A S

P A

= P

os it

iv e

A ff

ec t

su bs

ca le

o f

th e

P os

it iv

e an

d N

eg at

iv e

A ff

ec t

S ch

ed ul

e; P

A N

A S

N A

= N

eg at

iv e

A ff

ec t

su bs

ca le

o f

th e

P os

it iv

e an

d N

eg at

iv e

A ff

ec t

S ch

ed ul

e; P

S S

-S F

= P

er ce

iv ed

S tr

es s

S ca

le -

S

ho rt

F or

m ;

W S

W S

= W

is co

ns in

S m

ok in

g W

it hd

ra w

al S

ca le

.

E st

im at

ed m

ar gi

na l

m ea

ns a

re b

as ed

o n

m od

el s

co nt

ro ll

in g

fo r

de m

og ra

ph ic

s, b

as el

in e

sm ok

in g

(c ig

ar et

te s

pe r

da y

an d

ti m

e to

f ir

st c

ig ar

et te

), a

nd a

bs ti

ne nc

e at

e ac

h ti

m e

po in

t. S

ig ni

fi ca

nt d

if fe

re nc

es a

re

ba se

d on

T uk

ey H

S D

. R es

ul ts

s ho

w n

in b

ol d

re pr

es en

t si

gn if

ic an

t ef

fe ct

s ba

se d

on a

lp ha

= .0

5.

J Consult Clin Psychol. Author manuscript; available in PMC 2018 November 01.

A u th

o r M

a n u scrip

t A

u th

o r M

a n u scrip

t A

u th

o r M

a n u scrip

t A

u th

o r M

a n u scrip

t

Spears et al. Page 23

Table 3

Indirect Effects of MBAT vs. UC in Predicting Week 4 and 26 Abstinence.

Dependent Variable: Week 4 Abstinence

Variable a path b path Direct Effect (95% CI) Indirect Effect (95% CI)

Withdrawal (WSWS)

Anxiety −.29 −.26 .25 (−.33, .83) .08 (.001, .21)

Sadness −.21 −.35 .26 (−.29, .82) .08 (−.004, .22)

Concentration Difficulties −.18 .13 .36 (−.19, .93) −.02 (−.09, .03)

Craving −.23 −.70 .19 (−.39, .76) .16 (−.01, .39)

Self-efficacy for Negative Affect Situations (SES) .28 .50 .22 (−.32, .79) .14 (.03, .31)

Subjective Bias (SBQ) −.20 −.53 .26 (−.30, .86) .11 (−.01, .28)

Dependent Variable: Week 26 Abstinence

Variable a path b path Direct Effect (95% CI) Indirect Effect (95% CI)

Withdrawal and Craving (WSWS)

Anxiety −.25 −.07 −.14 (−.77, .49) .02 (−.06, .12)

Sadness −.19 −.36 −.20 (−.86, .43) .07 (−.02, .24)

Concentration Difficulties −.28 −.36 −.22 (−.87, .41) .10 (.002, .28)

Craving −.31 −.50 −.28 (−.93, .35) .16 (.03, .38)

Smoking Dependence Motives (WISDM)

Total −.42 −.37 −.28 (−.92, .33) .16 (.02, .38)

Primary −.39 −.31 −.24 (−.87, .37) .12 (−.008, .36)

Secondary −.41 −.39 −.28 (−.92, .33) .16 (.03, .37)

Affiliative Attachment −.33 −.16 −.17 (−.79, .44) .05 (−.06, .21)

Automaticity −.22 −.24 −.16 (−.78, .44) .06 (−.02, .20)

Loss of Control −.40 −.16 −.19 (−.81, .43) .07 (−.06, .26)

Behavioral Choice - Melioration −.42 −.18 −.20 (−.80, .42) .08 (−.04, .25)

Cognitive Enhancement −.41 −.40 −.28 (−.90, .33) .16 (.03, .38)

Craving −.37 −.51 −.30 (−.93, .31) .19 (.04, .45)

Cue Exposure – Associative Processes −.36 −.45 −.30 (−.94, .31) .17 (.04, .38)

Negative Reinforcement −.45 −.39 −.29 (−.94, .31) .18 (.05, .37)

Positive Reinforcement −.43 −.34 −.27 (−.90, .34) .15 (.03, .33)

Taste-Sensory −.34 −.42 −.25 (−.87, .37) .15 (.03, .34)

Tolerance −.43 −.22 −.21 (−.85, .41) .10 (−.05, .31)

Weight Control −.22 −.02 −.13 (−.76, .49) .01 (−.07, .12)

Self-efficacy for Negative Affect Situations (SES) .29 .21 −.18 (−.84, .46) .06 (−.03, .22)

Subjective Bias (SBQ) −.21 −.62 −.23 (−.87, .40) .13 (−.01, .36)

Notes. Standardized estimates are shown (i.e., using rescaled mediators). “a path” = effect of treatment on mediator; “b path” = effect of mediator on abstinence, controlling for treatment; “indirect effect” = indirect effect of treatment on abstinence through mediator; “direct effect” = effect of

J Consult Clin Psychol. Author manuscript; available in PMC 2018 November 01.

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Spears et al. Page 24

treatment on abstinence, controlling for mediator. Mindfulness-Based Addiction Treatment (MBAT) coded as 1, Usual Care (UC) as 0. Models controlled for demographics and baseline smoking. Variables in bold represent significant indirect effects based on alpha = .05.

J Consult Clin Psychol. Author manuscript; available in PMC 2018 November 01.