Rescuing Cognitive and Emotional Regulatory Processes to Aid Smoking Prevention
Phase: NA
Conditions: Tobacco Smoking
Sponsor: Boston University Charles River Campus

 
 
 
 
 
Study Protocol For 
R21DA041531 - Rescuing Cognitive and Emotional Regulatory 
Processes to Aid Smoking Prevention 
Version Date: June 15, 2015 
 
 
 

SIGNIFICANCE 
Despite declines in cigarette use during the past several decades, smoking remains highly prevalent 
(19.3%) among U.S. adults, resulting in an estimated 443,000 premature deaths annually. Moreover, 
adolescents from low socio-economic status (SES) families have higher rates of smoking than their higher SES 
peers,25,26 and have greater increases in smoking over time.27 Individuals from low SES also have been found 
to benefit less from standard informational smoking prevention,1,9 exacerbating health disparities across SES 
levels. One possible explanation for this may be that cumulative stressors associated with low SES disrupt 
cognitive and emotion regulatory processes, which may increase the likelihood of smoking initiation and 
undermine the efficacy of prevention programs. 
Adolescence and Smoking 
Adolescence is characterized by heightened exposure to life stressors and negative affect,28 as well as 
experimentation, novelty and sensation seeking,29 which all increase the likelihood of smoking initiation. In 
2010, most new smokers were younger than 18 when they first smoked (CDC, 2012). Around 70% of current 
smokers begin smoking before the age of 18 years; of those who do not begin to smoking by this age, few are 
likely to become smokers as adults.2 Moreover, research shows that performance of adolescents on tasks 
including inhibitory control,30,31 working memory (WM) and decision-making improve during adolescence.32,33 
These marked changes in self-regulatory capacity make this developmental period a time both of vulnerability 
and opportunity for training and intervention.34 
Poverty, Working Memory Capacity, and Smoking 
Stress negatively influences executive functioning, including working memory.35-36 Recent research has 
linked WM capacity to a wide range of health behaviors, including drug use.17 Specifically, greater WM capacity 
is hypothesized to allow for greater use of strategies to resist maladaptive urges. As argued by Grenard et 
al.37: “For those higher in WM capacity, more top-down, goal-directed attentional resources are available to (a) 
suppress the influence of associative tendencies when they interfere with other active goal-states, (b) maintain 
conflicting goals in active memory, (c) draw on more knowledge concerning potential short vs. long-term 
outcomes, and (d) apply one of several cognitive processing strategies to resolve the goal conflict.” WM 
capacity has been found to be a strong predictor of the resumption of smoking in untreated individuals, and 
efficacious interventions for smoking modify this risk.38,39 WM has also been linked to smoking initiation among 
adolescents15 and is further linked with addiction potential through strong associations with delay 
discounting.40,41 Indeed, WM training in addicted individuals improves delay discounting.42 The validity of WM 
capacity as a treatment target in adolescents is supported by findings of reliable improvements in WM from a 
number of verbal and non-verbal training tasks (N = 40, 9–11 years of age; 10 sessions of training).43-45 
Although transfer of training effects to other cognitive tasks or academic performance can be small,46-48 this is 
not an issue for the current protocol, in which improved WM capacity and associated delay discounting level 
are the targeted acute outcomes for training. Because poverty has been shown to correlate with lower 
executive function abilities,35 adolescents from disadvantaged backgrounds, in particular, may benefit from 
these interventions. 
Poverty, Distress Tolerance, and Smoking 
Adolescents who experience affective distress appear to be at a heightened risk to take up smoking and 
progress to nicotine dependence.49,50 Furthermore, negative affectivity has been linked to maladaptive coping51 
and greater smoking dependency specifically among low-income African American smokers.52 Other work 
suggests negative affect (in the form of anhedonia) among adolescents who had never had a single puff of a 
cigarette is related to greater curiosity about trying smoking.53 Moreover, teens with higher anhedonia reported 
greater expectancies that smoking causes pleasure, despite never smoking a cigarette, suggesting that 
anticipated effects of smoking in nicotine-naive anhedonic youths may confer initiation risk.53 Another body of 
work indicates that low distress tolerance (DT), a perceived or behavioral tendency to not tolerate affective and 
physical distress, is related to both negative mood vulnerability and smoking.54 Additionally, our work has 
demonstrated that poverty is associated with decreased DT, as measured by task persistence.55 More 
generally, an abundance of evidence indicates that it is not just negative affect that drives maladaptive or 
impulsive behavior, but the relative intolerance of this affect. Specifically, the degree of DT is an effective 
predictor of maladaptive coping across a wide range of disorders and health-related behaviors. For example, 
our group has shown that anxiety sensitivity (one effective measure of DT)56,57 predicts the degree of eating in 
response to negative affective episodes,58 negative affect during and avoidance of exercise,58,59 illicit drug use 
patterns,60,61 treatment dropout among inner city drug users,62 and impulsive behavior more generally.63 DT 
has been linked to the duration of abstinence from smoking in a variety of studies,64-68 and treatment that 

includes efforts to facilitate DT capacity produce better smoking cessation outcomes.69-71 Adolescents from 
disadvantaged backgrounds, in particular, may benefit from preventive interventions targeting low distress 
tolerance, because poverty is associated with the use of negative health behaviors to regulate stress,72 and 
negative affect/stress is a partial mediator of the effects of low SES on difficulties with smoking cessation.74 
Why Target Negative Affectivity and DT with a Mindfulness-Based Intervention? 
Mindfulness-based cognitive therapy interventions improve emotional regulation and reduce both 
depression and anxiety symptoms in clinical samples, and lower the likelihood of relapse of depressive 
symptoms, while also offering executive function benefits.75,76 Furthermore, mindfulness and DT are positively 
associated 77,78 and mindfulness training improves DT to a variety of stimuli (e.g., with pain).79,80 As one would 
expect, emotional regulation interventions (including mindfulness interventions) have shown initial successes 
for other substance use and impulse control disorders.79,81 The degree of DT appears to moderate the effects 
of mindfulness interventions on drug use disorders, with mindfulness having a greater benefit among those 
with lower DT.77 Mindfulness is also significantly associated with lower levels of nicotine dependence in 
smoking adults,82 and mindfulness interventions have shown specific success in reducing smoking.83-85 
Relevant to the potential for larger scale prevention projects, short-term mindfulness training can be offered 
online, with documented benefits for reductions in stress, anxiety, and depressive symptoms.86,87 Overall, the 
available evidence suggests that mindfulness-based cognitive therapy is an excellent candidate for offering 
preventive effects against the risks for smoking conferred by negative affectivity, DT, and other self-control 
challenges. 
 
INNOVATION 
Given converging evidence from developmental studies, psychopathology studies, intervention studies, 
and basic research on self-control abilities, we have identified WMC and DT as transdiagnostic, malleable risk 
variables in the fight against smoking initiation. Research provides overwhelming evidence that low-SES youth 
are at an increased risk for smoking,88,89 and this project is designed to address an existing intervention failure 
(informational smoking prevention approaches) for this particularly high-risk population. In addition to the novel 
targets for intervention matched to existing risk factors, this project utilizes novel methodologies, including both 
implicit and explicit propensities-to-smoke measures. This project has the potential to shift current paradigms 
for smoking prevention, attending to the rescuing of cognitive/affective self-regulation deficits to provide at-risk 
adolescents a greater capacity to utilize smoking prevention messaging and resist smoking urges and 
selfcontrol lapses more generally. Delivery of interventions at school and community center settings is relevant 
to ultimate dissemination in prevention intervention applications. Our analytic model examines crucial 
covariates (see below: parental smoking, peer smoking, and sensation seeking) of particular relevance to the 
study cohort in order to provide more precise estimates of the influence of the experimental variables, and to 
more fully characterize variables of influence for the population under study. The effects of the interventions 
are studied against a backdrop of a standard antismoking informational intervention, exactly the sort of 
intervention that has shown less success with low SES adolescents. 
 
APPROACH 
Design Overview and Considerations 
We are targeting a high-risk (low SES adolescents) population at a highly relevant period (high school) for 
smoking onset. The study design calls for block randomization of 150 non-smoking adolescents to one of three 
intervention conditions: (1) a control condition offering health education combined with a smoking prevention 
informational intervention (C+SPII), (2) a WM intervention delivered prior to a smoking prevention informational 
intervention (WM+SPII), (3) a DT training intervention delivered prior to a smoking prevention informational 
intervention (DI+SPII). Smoking risk proximal outcomes are assessed by smoking propensity self-report, delay 
discounting, and implicit associations to smoking; Smoking behavior is assessed at one-month follow-up. 
• Why Utilize a SPII as a component common to all randomized conditions? Meta-analytic review 
indicates that smoking prevention informational interventions can offer benefits,1 but there is also evidence 
these benefits are limited for low-SES participants.1,9 Our interventions are designed to enhance the ability 
of individuals to utilize standard SPIIs (by enhancing cognitive and affective self-control skills), and hence, 
are designed to rescue prevention outcomes for these at-risk individuals. 
• Why block randomize by parental smoking? Parental smoking is associated with smoking attitudes 
among children and confers a significantly higher risk of smoking initiation.90-92 Crucially, research indicates 
that parental smoking is one significant mechanism by which low SES translates into youth smoking.93 
Ensuring balance of parental smoking status across the intervention conditions allows evaluation of potential 

moderating effects of this factor on interventions and outcomes. 
• Why covary parental smoking, peer smoking, and sensation seeking? As noted, we will ensure equal 
representation of parental smoking in the randomized conditions. Peer smoking 94 and sensation seeking 95 
are risk factors for smoking onset, that, at least in some studies,95 appear to be separate from each other and 
the independent variables under study. For example, in a longitudinal study of college students, high 
sensation seekers were more likely to initiate smoking and still identify themselves as smokers 20 years 
later.95 Statistical control of these variables should allow enhanced precision of independent variable effects. 
• Why focus on DT modification rather than affect itself in predictive models? As detailed above, DT 
has functioned as a transdiagnostic predictor of maladaptive coping responses. Affect is also a powerful 
predictor, but affective content and intensity may vary widely between assessment periods, and unlike 
smoking cessation/relapse studies, there is no specific several-week period where more intensive monitoring 
of affect would be expected to be useful for capturing new onset smoking under naturalistic conditions. For 
this reason, in data analysis we focus on the potentially powerful prediction offered by the more-stable DT 
measure. Nonetheless, negative affect at the assessment periods, and DT by affect interactions, will be the 
focus of exploratory analyses. 
• Why Utilize Proximal-- susceptibility to smoking, smoking IAT, delay discounting--Smoking Risk 
Outcomes? Proximal smoking risk outcomes are needed to allow testing of risk models outside a full 
longitudinal prevention study. We have selected three smoking risk outcome measures that include both 
well-established self-report measures as a well as measures that do not rely on self-report about intentions. 
First, susceptibility to smoking (defined as not being able to rule out the idea of smoking) is a wellestablished 
self-report measure that has repeatedly proven itself to be valuable in large scale (N > 4000) 
studies of smoking onset in adolescents.96-98 Second, the Implicit Association Test (IAT) has been 
successfully used to assess implicit associations toward smoking in children. This smoking IAT has shown 
itself to be valuable in identifying more favorable implicit attitudes in children from households with smoking 
parents.99 Also, among smokers, IAT responses are linked with measures of smoking motivation and 
dependence,100 and are predictive of smoking cessation outcomes.101 Third, concerning delay discounting, 
Bickel and associates102 have proposed that delay discounting may function as a behavioral marker of 
addiction potential by 1) identifying individuals who are drug-dependent, 2) identifying those at risk of 
developing drug dependence, 3) acting as a gauge of addiction severity, 4) correlating with all stages of 
addiction development, and 5) changing with effective treatment. Indeed, higher delay discounting rates are 
linked to smoking status103-106 and poorer response to smoking cessation treatment, specifically in low-SES107 
and adolescent samples.108 Delay discounting is also a mediator of the link between stress and cigarette 
smoking in adolescents,109 and in a large longitudinal study of adolescents across ages 15 to 21 found that 
delay discounting predicted both new-onset smoking and increased smoking rates. 110 Similar relationships 
exist outside of smoking, where delay discounting predicts a wide range of substance use disorders, 
consistent with the notion that delay discounting represents a trans-disease process placing individuals at 
risk for initial or continued substance use.40 
Participants. We propose to study 150 adolescents (expected mean age = 13) enrolled in high school as a 
freshman or sophomore. Reflecting the demographics of the community in which we are recruiting, we expect 
approximately 70% of the sample to be at or below the poverty level, balanced between males and females, 
with the majority from an ethnic minority background. Dr. Doan’s previous work among adolescents suggests 
that at this age, smoking rates are very low.3 Among of sample of 185 adolescents, only 3.8% reported 
smoking regularly and 87% reported having smoked once ever, or never at all. In sum, we do not believe that 
smoking status at this age is a problem, however, our targeted enrollment (N = 150) has accounted for the 
possibility of having to drop current smokers in the final analyses as well as attrition. 
Feasibility. Dr. Doan has experience working with low-income youth, and we plan to engage the following 
recommended best practices to ensure sufficient recruitment and retention.111 We attach letters of support from 
local high schools and community centers who serve individuals with characteristics suitable to our study. The 
headmasters of two local high schools have demonstrated enthusiasm for our study and plan to provide both 
institutional and social support in our endeavor. Additionally, we have budgeted for financial compensation for 
all components of the study (assessments and interventions), as well as snacks for the intervention sessions in 
order to improve enrollment and retention. The following projects and published studies demonstrate the 
productivity and preparedness of our research team for investigations in the impact of WM and mindfulness 
training on risk factors and smoking-related outcomes in at-risk youth. Dr. Doan is a developmental 
psychologist and expert in the acquisition and longitudinal study of low-income youth. Her work focuses on 
documentation of the effects of stress in this population. She has examined the impact of poverty on 

adolescents’ behavioral problems and self-regulatory ability,35 as well as the impact of poverty on WM (Doan & 
Evans, 2012), and health outcomes including smoking,3 and body mass index.112 Dr. Otto has extensive 
experience with proof-of-concept intervention studies113,114 and subsequent large-scale trials in specialty 
psychiatric as well as substance abusing populations, 115,116 including smokers (ongoing: 128; ongoing R34 
DA034658; recently completed R21 DA030808). Dr. Otto is also experience with conducting successful 
cognitive training interventions,117 as well as neuropsychological assessment studies for specialty populations 
ranging from those with depression118 to schizophrenia119 and illicit substance abuse.120 The co-PIs will also 
receive help from the consultant team (1) Dr. Zvolensky: leading expert in the study of negative affectivity and 
DT on smoking outcomes,121-123 (2) Dr. Bickel, a foremost expert on the association between delay discounting, 
WM, WM training, and addictive behavior including smoking,40,105,107 and Dr. Rosenfield, an expert statistician 
who has been a regular collaborator with both Drs. Otto and Zvolensky. 
Interventions. Participants will be randomized to 1 of 3 study conditions according to two block 
randomization factors: sex and parental smoking status. Each intervention is to be delivered over eight 
consecutive weeks. Interventions are to be led by the Project Director, with ongoing supervision from Drs. Otto 
and Doan. Although online training is available for mindfulness, and provides an excellent potential for aiding 
dissemination in a subsequent prevention study, we are conducting in-person mindfulness training to help 
reduce the impact of differential computer skills on the uptake of skills. For WM training, a computer program 
is used, but the Project Director will be present to facilitate use of the computer and work through the module. 
SPII: Smoking Prevention Informational Intervention. This intervention is common to all randomized 
conditions in the study. We selected the intervention from brief primary-care based interventions (for a 
metaanalysis see1) which followed guidelines from the National Institute of Health the US Public Health Service 
Tobacco Use and Dependence Clinical Practice Guideline.124 Youth will be provided with age-appropriate 
education on the norms and health consequences of smoking, affirmation of their non-smoking status, and 
help in developing a personalized strategy to maintain abstinence.125 Additionally, we will incorporate a 
motivational interviewing component.126 The SPII will be delivered during the last of the randomized 
intervention sessions described below. 
C: Control Condition Informational Interventions. This control informational intervention has been used 
in our and other’s previous studies.127,128 In the current application, it will match the session time of the DT 
intervention and will omit a focus on smoking (that focus is specific to the SPII intervention provided across all 
interventions) and consists of discussions of a variety of healthy lifestyle topics, such as healthy eating, 
stress/time management, and recommended health screenings. 
WM: Working Memory Intervention. For the working memory training, we will use the Cogmed RM 
program. Participants will be asked to use the program, while supervised twice a week, each time for an hour, 
for 8 weeks. Participants will also be asked to use the program on the other days for 25-35 minutes. For this 
additional use, a computer lab will be provided at the participating site to ensure all students have adequate 
access to the program. The CogMed Rm program has been shown to produce higher effect sizes than other 
programs including noncommercial programs developed by researchers for the purposes of their studies.129 It 
resembles a video game, and comprises several different “games” that require visuo-spatial working memory 
(remembering the position of objects) and a combination of verbal and visual working memory (remembering 
phonemes, letters, and digits). The program adapts to the user’s performance. If the trainee is doing well, the 
to-be-remembered list will increase by one item. If the trainee is struggling, the to-be-remembered list will 
decrease by one item. Accordingly, trainees are able to perform at the limit of their ability, stimulating WM 
capacity adaptation.130 
DT: Distress Tolerance (Mindfulness) Intervention. To enhance DT, we will use a Mindfulness Based 
Stress Reduction (MBSR) program that has been adapted for use with adolescents.131 This version of MBSR 
follows closely the original conceptualization developed by Kabat-Zinn.132 The focus is on formal and informal 
mindfulness practices, which encourage participants to foster intention, attention and attitude.133 We will make 
slight modifications to the delivery of the MBSR intervention to take into account the developmental period of 
our participants (e.g., their attention span) to encourage retention and increase relevancy. These changes will 
also allow us to match the duration with our WM intervention. 
Assessments. See Table 1 for a summary of assessments; clinician-rated assessments will be completed by 
the Independent Evaluator. 
Parental and Peer smoking. We will assess four categories of exposure to parent smoking: (1) both 
parents nonsmokers: (2) both parents former smokers or, one former smoker and one nonsmoker: (3) one 
parent a current smoker: the other a nonsmoker or a former smoker: (4) both parents current smokers.134 
Affiliation with peers who smoke is a strong psychosocial predictor of smoking initiation and current smoking,94 

and will be measured by summating responses to three items asking whether the adolescent’s best friend 
smokes and how many of his or her other four best male and four best female friends smoke (range 0 to 9 
friends smoking).110,135 
Sensation Seeking (SS). SS is positively associated with smoking onset,95 as well as heavier smoking and 
poorer cessation adherence and success.136-138 We will use the Sensation Seeking Scale,139 as it has been 
used with acceptable reliability with low-income minority youth.140 
Smoking Susceptibility. Consistent with previous work141 we will measure smoking susceptibility with two 
items (e.g., “Have you ever been curious about smoking a cigarette: 0 = No, 1 = Yes”) from the pan-Canadian 
Youth Smoking Survey, and three items repeatedly used in the literature (e.g., “If one of your best friends were 
to offer you a cigarette, would you smoke it: 0 = Definitely no, 3 = Definitely yes”; e.g.,)97,98 to create a single 
composite score that is the sum of these five items providing a continuous score ranging from 0 (no 
susceptibility) to 11 (highest susceptibility). 
Smoking IAT: We will use a smoking-specific implicit association test (IAT)142 that assesses implicit 
cognitive associations between smoking and negative vs. positive social consequences.101 This version of the 
IAT successfully predicted odds of smoking abstinence over and above the effects of other relevant predictors. 
Delay Discounting. Identical to methods employed by Reynolds & Fields143 for adolescents 
experimenting with smoking, we will assess delay discounting by presenting participants with computer-based 
choices between $10 available after a specified delay (i.e. 1, 2, 30, 180 or 365 days) and a smaller amount 
available immediately (e.g. ‘would you rather have $10 in 30 days or $2 now?’). This computerized task uses 
an adjusting amount procedure (adjusting the immediate amount in increments of ±$0.50) to derive 
indifference points. At the end of the session one question would be selected and honored—resulting in either 
immediate or delayed money. 
Smoking Status. Self-reports of smoking status will be collected using time line follow-back methodology 
to determine the time frame of smoking initiation if it occurred. Self-reports of abstinence at each assessment 
visit will be verified by expired carbon monoxide (4 ppm cutoff).144 
 WM Capacity. WM 
assessment includes these three 
tasks plus a warm-up task, the 
sequence recall of digits—
auditory (SRD-A). The 
Sequenced Recall Reversed 
Digits—Auditory (SRRD-A) 
program is identical to the SRD-
A, except that participants are 
required to recall the digits in the 
reverse order of presentation. 
The Sequenced Recall of 
Words—Visual (SRW-V) task 
presents a list of four-letter 
words on a computer screen. 
The Verbal Memory—
Categorizing (VM-C) presents 
participants with 20 words falling 
into four categories (e.g., colors, 
vegetables). . The WM capacity 
data submitted for analysis will 
be the z-score (using BL means 
and SDs) average of the SRRD-
A, SRW-V, and VM-C as 
specified below.   
Positive and Negative Affect 
Schedule (PANAS). The 
negative affective score will be used as additional exploratory predictor of smoking status alone and in 
interaction with DT. 
Distress Tolerance. Because self-report and behavioral measures of DT are only partially correlated,56 
we will use both assessment strategies in the current study, with the variable submitted for analysis 
Table 1. Time Line and Schedule of Assessments for Study Implementation
Measures 
 
Baseline
 
 
Intervention 
1-Week 
Post- 
Intervention
1-Month 
Post 
Intervention 
Initial Assessment 
 
 
 
 
  Demographics 
X 
 
 
 
Additional  Covariates 
 
 
 
 
   Parental Smoking 
X 
 
 
 
Peer Smoking 
X 
 
 
 
Sensation Seeking 
X
 
 
 
Mediator Variables
 
 
 
 
   Working Memory (WM)  
X 
 
X 
 
Distress Tolerance (DT) 
X 
 
X 
 
Proximal Smoking Risk 
Outcomes
 
 
 
 
    Smoking Susceptibility 
X 
 
X 
X 
Smoking IAT 
X 
 
X 
X 
Delay Discounting 
X 
 
X 
X 
Actual Smoking 
Outcomes
 
 
 
 
Timeline Follow-Back  
X 
 
 
X 
Carbon Monoxide 
X 
 
 
X 
Affect 
 
 
 
 
    PANAS 
X 
 
X 
X 
Intervention 
Integrity/Acceptance 
 
 
 
 
    Participant Adherence 
 
X 
 
 
    Therapist Adherence 
 
X 
 
 

representing the mean z-score (using the BL mean and SD) on the two assessment measures (follow-up 
analyses will consider the two measures individually). Perceived Distress Tolerance will be assessed with the 
10-Item Distress Intolerance Index (DII).145 Behavioral Distress Tolerance will be assessed with the 
computerized Mirror-Tracing Persistence Task (MTPT-C).146 
Participant Adherence. The number of sessions attended will be used to assess patient adherence. 
Interventionist Adherence. All mindfulness sessions will be audiotaped. Independent master-level raters 
(available to Dr. Otto) will rate 10% of sessions for therapist adherence. 
Benchmarks 
This study is being conducted to validate specific cognitive-affective targets for use in larger prevention 
studies. As such, documenting a significant effect of interventions on delay discounting and distress tolerance 
represents a go/no go criterion for future study (Aim 2). Also, to encourage study of the modification of these 
targets in a larger scale (R01) environment, documentation of significant benefit on proximal measures of 
smoking risk/behavior is needed (Aim 3). Success with these benchmarks, in a design with adequate 
feasibility/acceptability would document the need for R01 study in a prevention trial with longitudinal follow-up.