Mechanisms of Mindfulness for Smoking Cessation: Optimizing Quantity and Quality.
Phase: NA
Conditions: Smoking Cessation
Sponsor: Brown University

 
 
 
 
 
 
 
Mechanisms of Mindfulness for Smoking Cessation 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Registration Number - NCT02943499 
 
December 6, 2018   
 

Materials and Methods 
Study overview and timeline 
This was a parallel-group study with equal randomization and individuals were recruited 
using local flyers, and Facebook ads. The project director screened participants and 
eligibility criteria included: (1) smoking >10 cigarettes/day; (2) <3 months of smoking 
abstinence in the previous year; (3) 8+ of 10 on a “Readiness-to-change” scale [1]; (4) 
owning a smartphone; and (5) age between 21 – 65. Participants were excluded if they 
reported (1) a history of serious neurological or psychiatric conditions (beyond nicotine 
dependence; e.g. seizure disorder, history of a stroke, schizophrenia, bipolar disorder 
etc.); (2) changing dose of any psychoactive medication in the past three months; (3) 
previous experience with mindfulness-based stress reduction or equivalent; (4) current 
meditation or yoga practice (>30 minutes/day for >5 days); (5) current alcohol abuse; (6) 
claustrophobia; (7) pregnancy; (8) MRI incompatibility; or (9) tested positive for illegal 
psychoactive substances or cannabis. All participants underwent informed consent 
procedures prior to participation, and their participation was voluntary.  Participants 
were given $25 upon completion of the first functional Magnetic Resonance Imaging 
(fMRI) visit and $75 upon completion of the second visit. This study was approved by 
the Institutional Review Board of the University of Massachusetts Medical School 
(UMMS).   
 
At baseline, eligible participants performed a smoking cue-reactivity fMRI task at 
UMMS. They were then given a sealed envelope with their random assignment: an app-
based MT or active control app. This was generated in variable blocks of 4 and 6 by an 

independent statistician and the envelopes were prepared by an individual independent 
of data acquisition. An experimenter helped install the assigned app on their 
smartphone immediately following the first MRI visit, while making sure the participant 
understood the features of the app and could demonstrate how to use it.  Participants 
were instructed to use their app to help them quit smoking over the next 4-week period. 
At the post-treatment visit, participants completed the same cue-reactivity task as 
baseline. Smoking status was verified with a carbon monoxide breathalyzer test at each 
visit.  
 
Smartphone-based App Interventions 
The app-based MT program is a phone app designed to deliver core elements of a 
manualized MT program for smoking cessation with high fidelity [2,3]. The program has 
22 unique learning modules (5-15 minutes/module) consisting of daily training videos 
and in vivo on-demand exercises. Program features are designed to help users self-
monitor their smoking habits, identify triggers for smoking, learn methods to become 
more aware of cravings and use mindfulness practices to ride them out. The program 
calculates and encourages a gradual taper over a 3-week period based on baseline 
cigarette use. 
 
Participants in the control group used the National Cancer Institute’s QuitGuide app 
(NCI) (https://smokefree.gov/tools-tips/apps/quitguide). NCI is based on the design and 
principles of Smokefree.gov, the most accessed smoking cessation website in the US. It 
includes strategies for quitting and health outcomes information. 

Expectancy evaluation 
Expectancy was assessed at baseline [4]. Post-treatment, participants were asked, 
“How likely are you to recommend this app to a friend?” 
 
Study Outcomes 
The primary outcome of this study was to demonstrate that app-based MT reliably 
decreased PCC reactivity to smoking cues.  
 
Functional MRI Task 
At each MRI visit, participants completed a previously-developed cue-reactivity task 
(Figure 1) [5,6]. To reduce variability related to cravings during the MRI task, 
participants smoked immediately prior to scanning. Participants were shown 60 
smoking, 60 neutral, and 10 target images divided evenly across 5 scanning blocks 
lasting 5m18s each, for a total task time of 26.5 min. Images were presented for 4s 
each in a pseudorandom order. Participants were shown a fixation cross on a black 
screen during jittered inter-trial-intervals, which ranged from 6-14s (10s average). 
Smoking images included smoking-related content such as people smoking or holding 
cigarettes. Neutral images were matched for content. All images were novel in that no 
image shown on the pre-intervention visit was repeated during the second scan. Task 
instructions were to pay attention to all images, but to respond with a button-press any 
time they saw an image of an animal.  Animals were used as target images to ensure 
participants stayed awake and attended to the task, but were not included in analyses.  
Between each of the 5 task scans, participants were asked whether they had fallen 

asleep at any point during the scan and it was repeated if the participant reported falling 
asleep (n = 2, baseline).  
 
 
Figure 1. Study design. Yellow overlay on the anatomic brain image at the bottom 
represents the a priori defined PCC ROI derived from Janes et al (2015) for this 
analysis. 
 
Neuroimaging data collection 
Imaging data were collected on two Philips 3 Tesla MRI scanners. High-resolution 
anatomical MRI scans were acquired using the MPRAGE protocol with the following 
parameters: 181 (sagittal) slices, repetition time (TR)/echo time (TE): 7.0/3.2ms, shot 
interval: 3000ms, field of view (FOV) 240 x 240 x 181, matrix = 240x240; 1 mm isotropic 

voxels. Cue-reactivity task scans were collected with the following parameters: 139 
scans, 37 (transverse) slices; TR/TE: 2000/30ms, FOV 216 x 216 x 130, matrix = 
80x80; 2.7x2.7x3.5mm voxels.   
 
fMRI preprocessing 
fMRI data analysis was conducted using tools from the fMRI of the Brain Software 
Library (FSL; www.fmrib. ox.ac.uk/fsl). The first five volumes for each run were removed 
to allow for signal stabilization. Functional data were pre-processed using the following: 
(1) motion correction with MCFLIRT, (2) brain extraction using BET, (3) slice timing 
correction, (4) spatial smoothing with a Gaussian kernel of full-width half-maximum 6 
mm, and (5) high-pass temporal filter with Gaussian-weighted least-squares straight-line 
fitting with  =100s. A script was also used to detect and adjust for artifacts due to 
motion and intensity spiking prior to conducting preprocessing within FSL [7]. 
Participants’ structural images were registered to the MNI152 2mm standard space 
template; corresponding functional datasets were transformed into standard space at 
2mm3 resolution using the resulting registration transformation matrices. 
 
Cue-Reactivity Analysis 
First-level analysis was conducted on each of the participant’s five cue-reactivity runs 
separately. First-level general linear model included three regressors, convolved with 
the gamma hemodynamic response function, corresponding to smoking, neutral, and 
target image presentation. Confound regressors based on rigid-body head-motion 
parameters were included to model motion effects. Consistent with our prior work [6,8], 

another regressor was included that represented motion/intensity artifacts identified and 
removed prior to preprocessing. Contrasts between smoking and neutral image 
conditions were created for each run. First-level results were then combined across task 
scans using a second-level fixed effects analysis to obtain the average contrast-related 
brain reactivity for each participant. To determine the Group by Treatment interaction on 
PCC reactivity to smoking > neutral cues, a repeated measures ANOVA was conducted 
on beta-weights for smoking > neutral contrasts that were extracted from the PCC 
region-of-interest (ROI), which was based on an independent dataset evaluating the 
smoking > neutral contrast at the whole brain level (Figure 1, bottom) [6].  To examine 
between-group differences in pre- vs. post-treatment cue reactivity, the post-treatment 
PCC BOLD response for the smoking vs neutral contrast was subtracted from the 
baseline response. The groups were then compared directly on this measure using a t-
test. 
 
To confirm that the task activated the PCC at baseline, a group level voxel-wise 
analysis was conducted, restricting the analysis to the PCC. Cluster-level correction 
was applied using randomise, FSL’s tool for nonparametric permutation inference, z = 
3.1, p < 0.05.  
 
Study blinding 
Participants were blinded to group and team membership. Team members who 
randomized participants and performed scans did not perform study analyses. The 

principal investigator and team members who conducted fMRI and statistical analysis 
were pseudo-blinded to group until all analyses were complete.  
 
Sample Size 
Our prior work with experienced meditators showed a percent signal change in PCC 
activity of -0.3 (SD = 0.2), while novices showed a percent signal change of 0.0 (SD = 
0.2) [9], which yielded an effect size (ES) of 1.5. To be conservative we used an ES of 
0.9 for power calculation; a sample size of 20 subjects/group provided 80% power to 
detect an ES of 0.9 with 2-sided 5% type I error using a 2-sample t-test.     
 
Correlation analysis 
The relationship between treatment-induced changes in PCC reactivity to smoking > 
neutral cues and the change in number of cigarettes smoked per day was evaluated 
using Spearman’s correlation coefficient. Correlation comparison was performed using 
a standard z-test of Fisher’s z-transformed correlations. Cigarette use change scores 
and change in PCC reactivity () were calculated by subtracting baseline from post-
treatment values. To examine the role of app modules in the change in smoking, a 
linear regression model was created for each group with change in smoking as the 
dependent variable and, as independent variables,  PCC, the number of modules 
completed, and the baseline number of cigarettes smoked. To determine whether 
effects were specific to the PCC, the medial prefrontal cortex (mPFC) and bilateral 
anterior insula (dAI) were evaluated. The mPFC (8mm sphere centered on x = -4, y = 
54, z = 4) was chosen given that it typically co-activates with the PCC to smoking cues 

and shows decreased activation in experienced meditators [9]. In contrast, the dAI ROI, 
which has been used in our prior work [6] does not show decreased activation in 
meditators, yet has been linked to nicotine dependence. 
 
 

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