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Original investigation

Received June 18, 2018; Editorial Decision September 26, 2018; Accepted October 4, 2018

? The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved.
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Original investigation

Yoga as a Complementary Therapy for Smoking
Cessation: Results From BreathEasy, a
Randomized Clinical?Trial
Beth C.?Bock PhD1?3, Shira I.?Dunsiger PhD1?3, Rochelle K.?Rosen PhD1?3,
Herpreet?Thind PhD4, Ernestine?Jennings PhD1?3, Joseph L.?Fava PhD3,
Bruce M.?Becker MD1,2,5, James?Carmody PhD6, Bess H.?Marcus PhD2

1Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University, Providence, RI;
2Brown School of Public Health, Brown University, Providence, RI; 3Centers for Behavioral and Preventive Medicine,
The Miriam Hospital, Providence, RI; 4Department of Public Health, Zuckerberg College of Health Sciences, University
of Massachusetts Lowell, Lowell, MA; 5Rhode Island Hospital, Providence, RI; 6Department of Quantitative Health
Sciences, University of Massachusetts Medical School, Worcester, MA

Corresponding Author: Beth C.?Bock, PhD. The Miriam Hospital, Coro West, Suite 309, 164 Summit Ave, Providence, RI
02906, USA. E-mail: [email?protected]


Introduction: There is evidence that Yoga may be helpful as an aid for smoking cessation. Yoga has
been shown to reduce stress and negative mood and may aid weight control, all of which have
proven to be barriers to quitting smoking. This study is the first rigorous, randomized clinical trial
of Yoga as a complementary therapy for smokers attempting to quit.
Methods: Adult smokers (N? =? 227; 55.5% women) were randomized to an 8-week program of
cognitive-behavioral smoking cessation and either twice-weekly Iyengar Yoga or general Wellness
classes (control). Assessments included cotinine-verified 7-day point prevalence abstinence at
week 8, 3-month, and 6-month follow-ups.
Results: At baseline, participants? mean age was 46.2 (SD? =? 12.0) years and smoking rate was 17.3
(SD? =? 7.6) cigarettes/day. Longitudinally adjusted models of abstinence outcomes demonstrated sig-
nificant group effects favoring Yoga. Yoga participants had 37% greater odds of achieving abstinence
than Wellness participants at the end of treatment (EOT). Lower baseline smoking rates (=10 cigarettes/
day) were also associated with higher likelihood of quitting if given Yoga versus Wellness (OR?=?2.43,
95% CI?=?1.09% to 6.30%) classes at EOT. A?significant dose effect was observed for Yoga (OR?=?1.12, 95%
CI?=?1.09% to 1.26%), but not Wellness, such that each Yoga class attended increased quitting odds at EOT
by 12%. Latent Class?Modeling revealed a 4-class?model of distinct quitting patterns among participants.
Conclusions: Yoga appears to increase the odds of successful smoking abstinence, particularly
among light smokers. Additional work is needed to identify predictors of quitting patterns and
inform adjustments to therapy needed to achieve cessation and prevent relapse.
Implications: This study adds to our knowledge of the types of physical activity that aid smok-
ing cessation. Yoga increases the odds of successful smoking abstinence, and does so in a dose-
response manner. This study also revealed four distinct patterns of smoking behavior among
participants relevant to quitting smoking. Additional work is needed to determine whether vari-
ables that are predictive of these quitting patterns can be identified, which might suggest modifica-
tions to therapy for those who are unable to quit.


Vigorous, aerobic exercise is an effective aid for smoking cessa-
tion as it reduces postcessation weight gain,1,2 improves mood, and
reduces cigarette cravings and withdrawal symptoms,3?7 offering sig-
nificant benefits for smokers trying to quit by directly addressing
fears of weight gain and ameliorating the physiological and affective
symptoms of nicotine withdrawal.7,8

Yoga may offer additional benefits beyond those seen for stand-
ard aerobic exercise. Yoga has been shown to improve mood and
reduce stress through the practice of asanas (Yoga postures), pra-
nayama (breathing exercises), and seated meditation.9,10 Yoga also
enhances mindfulness11?the purposeful direction of attention to
present-moment experiences (sensations, perceptions, and thoughts)
in a nonjudgmental way.12 Increases in mindfulness are associated
with reductions in perceived stress, psychological distress, and cogni-
tive reactivity.13,14 Mindfulness may also reduce symptoms of nicotine
withdrawal, improve coping with cravings, and increase the cogni-
tive deliberation needed to make effective choices to avoid smoking
in tempting situations.11,15,16 Thus, Yoga may have special relevance
for smokers who are trying to quit.9,15,16 Yoga practice also improves
weight control.17?22 By addressing barriers such as stress, cravings,
and weight concerns, and improving mindfulness, Yoga has some
potential as an effective complementary therapy for smoking cessa-
tion. This study investigates the efficacy of Yoga as a complementary
therapy for smoking cessation in a rigorously designed randomized
prospective clinical trial. We hypothesized that participants given the
Yoga intervention would show a significantly higher abstinence rates
compared to a control condition.


Study Design Overview
Adult smokers were randomly assigned in a 1:1 ratio to an 8-week
program of either (1) Yoga or (2) general Wellness, which controlled
for contact time and participant burden. In both programs, partici-
pants met for 1 hour, twice weekly. Participants also attended 1-hour
weekly of group-based smoking cessation counseling. Smoking
abstinence was measured at the end of treatment (EOT), and at 3-
and 6-month follow-ups. A?detailed description of the study design
and procedures has been published elsewhere.23 Recruitment and
follow-up retention are presented in the consort diagram (Figure?1).
All sessions were conducted at a university-affiliated hospital in New

Smoking Cessation Counseling
This intervention consisted of group-based cognitive-behavioral
therapy for smoking cessation delivered by PhD-level psychologists.23
Session content was similar to that used in our previous studies1,4,24
and included planning for the program?s targeted quit day (week
4), handling smoking triggers, coping with cravings, and managing
withdrawal. A? study manual was used to ensure that topics were
covered consistently across study arms and multiple cohorts. All ses-
sions were audio-recorded and coded to ensure treatment fidelity.

Yoga Intervention
This study used Iyengar Yoga25 because it emphasizes postural
alignment and the use of props (eg, blocks and straps) to facilitate
learning and reduce injury risk. Classes included 5 minutes of pra-
nayama, 45 minutes of dynamically linked asanas, and 5?10 minutes

of resting meditation. Classes were conducted by certified Iyengar
instructors with more than 15?years? experience. A?manual was used
to ensure consistent program delivery. Handouts showing the asanas
and sequence used for the current week?s practice were given to par-
ticipants weekly to encourage home practice.

Wellness Control
Group Wellness classes followed a format used in previous studies1,24
and consisted of videos, lectures, and demonstrations on a variety of
health topics (eg, cancer screenings, sleep hygiene, and healthy diet)
followed by a discussion guided by the study Wellness counselor or
other health care professional (eg, sleep expert).

Recruitment, Screening, and Randomization Procedures
Advertisements were placed on local radio stations and Web
sites. Flyers were posted at physician?s offices and retail outlets.
Advertisements emphasized the smoking cessation program and
explained that participants would also be randomized to a Wellness
or Yoga program. Callers were screened for eligibility by research
assistants and were excluded if they were pregnant, had a body mass
index of more than 40?kg/m2, smoked less than 5 cigarettes/day, were
currently enrolled in a quit-smoking program, were using medica-
tions to quit smoking, or had a medical condition that might make
participation in Yoga difficult or potentially hazardous (eg, heart
failure, ischemia, and hypertension).

Eligible individuals attended an orientation session to learn about
study details and expectations. After providing written consent and
completing baseline assessments, participants were randomized
using a permuted block randomization stratified on gender and level
of nicotine dependence (procedural details described elsewhere).23
All procedures and materials were approved by the Miriam Hospital
Clinical Research Review Board (IRB registration no.0000482).

Assessments were obtained by research assistants blind to random-
ization assignment. Participants were compensated $30 and $50 for
completing the 3- and 6-month follow-ups, respectively.

Smoking Outcomes
Research assistants collected data weekly on participant class attend-
ance, smoking rate (cigarettes per day), quit status, quit attempts,
and use of medications. Self-reports of 7?days of nonsmoking were
verified weekly by exhaled carbon monoxide (<10? ppm indicates
abstinence for the previous 12?24 hours)26 and by saliva cotinine
(<15? mg/mL) at EOT (week 8)? and all follow-ups.27 Participants
reporting 7-day point prevalence abstinence (7PPA) at EOT and
follow-up were considered continuously abstinent.

Smoking-Related Covariates
At baseline, participants completed a 25-item questionnaire con-
cerning their smoking history, including smoking rate, previous quit
attempts, and use of medications. At baseline and all follow-ups,
participants were assessed for nicotine dependence (Fagerstr?m Test
for Nicotine Dependence),28 and motivation to quit smoking, confi-
dence in quitting, and readiness to quit using three items answered
on a 10-point Likert scale from ?not at all? to ?extremely.?29

Other Covariates
Demographic information (eg, age, gender, and ethnicity) was col-
lected at baseline along with measurements of height and weight.

Nicotine & Tobacco Research, 2019, Vol. 21, No. 111518

Baseline measures were summarized using descriptive statistics, and
between-group differences in baseline characteristics were tested.30
Potential differences between cohorts in baseline characteristics were
explored using parametric (analysis of variance) and nonparametric
(chi-square) tests as appropriate.

Using a series of longitudinal models implemented with gener-
alized estimating equations with robust standard errors, we tested
the effect of randomization on binary smoking outcomes (7PPA and
continuous abstinence) over time (smoking status at week 5 through
follow-up) controlling for potential confounders including contam-
ination risk (defined as any Wellness participant who reported par-
ticipation in Yoga during the 8-week treatment). Models included
effects of intervention, time, time ? intervention, as well as potential
confounders. Data were clustered within participant within cohort,
and standard errors were adjusted accordingly. We explored poten-
tial moderating effects of smoking rate at baseline by including main
effects of smoking rate, and all two- and three-way interactions
between smoking rate, time, and treatment?group.

We tested the effects of randomization on mean smoking rate
over time using a longitudinal mixed effects model in which smok-
ing rates postquit date (week 5)? through follow-up were regressed

on time, group, and time ? group. Models adjusted for confound-
ers including contamination risk and baseline smoking rate, and
included a subject-specific intercept to adjust for repeated measures
of the outcome within participant. Potential cohort effects were also
explored. As with our primary outcomes, we explored potential
moderating effects of baseline smoking rate on treatment effects.

Using Latent Class?Models (LCMs), we sought to identify smok-
ing patterns during the treatment period (exploratory analysis). The
outcome of interest was self-reported 7PPA from quit date through
EOT, with cotinine validation at week 8.?LCMs assume the popula-
tion is made up of a finite number of patterns (smoking patterns in
this case). This technique reduces participant-level data from a vec-
tor of up to 8 weeks of data to a single class, corresponding to their
most likely pattern of smoking behavior. Class (pattern) provides
an objective grouping that can be used as a predictor or outcome
in subsequent analyses. To identify the number of classes supported
by the data, we fit a series of LCMs ranging from 2 to 6 classes
and identified the model that minimized the Bayesian information
criteria value (which maximizes fit). The optimal solution was a
4-class?model (significant model fit and significantly lowest Bayesian
information criteria), which is presented later. Classes were com-
pared based on baseline characteristics, randomized group, smoking

Figure?1. Consort diagram.

Nicotine & Tobacco Research, 2019, Vol. 21, No. 11 1519

rates at 3- and 6-month follow-up, and baseline motivational vari-
ables (ie, motivation, readiness, and confidence) using chi-square
tests and analysis of variance as appropriate. Finally, we explored
potential dose effects both within and across groups using a similar
modeling strategy.

All analyses were conducted on the intent-to-treat sample (N?=?227).
Models used likelihood-based approaches to estimation and thus made
use of all available data without directly imputing missing data. Results
were compared to the conservative assumption that missing equals
smoking (in the case of binary outcomes) and did not differ substan-
tially from the maximum likelihood estimation. All analyses were run
using SAS v.?9.3 and R, and significance value was set at a?=?.05 a priori.


Among participants (N?=?227) randomized at baseline, 55.5% were
women, 72.3% had attended at least some college, 42.3% were mar-
ried or partnered, and 55.9% were employed full time. Mean age
was 46.2 (SD?=?12.0) years. Mean smoking rate at baseline was 17.0
(SD?=?7.8) cigarettes/day. There were no significant between-group
differences in baseline characteristics (Table? 1), and no differences
between study cohorts. Overall, the study retention rate was 94.7%
through final follow-up with no difference between groups (p >?.05).

Smoking Outcomes
Longitudinal adjusted models indicate significant group effects
favoring Yoga with respect to 7PPA at EOT (odds ratio [OR]?=?1.37,
95% confidence interval [CI]?=?1.07% to 2.79%). The odds of 7PPA
at EOT were 37% higher for Yoga versus Wellness participants.
Effects were no longer significant at 3- and 6- month follow-up (ps >
.05). There were no significant effects of group on continuous abstin-
ence (p?=?.52). Overall, 11.2% of participants had prolonged abstin-
ence at 6?months, with no significant group effect (p?=?.92).

Moderating Effects
Exploration of the moderating effects of baseline smoking rate sug-
gests there were significant effects of group on 7PPA at EOT among
those with low smoking rates at baseline (=10 cigarettes/day?=?25th
percentile; OR? =? 2.43, 95% CI? =? 1.09% to 6.30%). Among light
smokers at baseline, the odds of 7PPA at EOT for Yoga were 2.43
times that of Wellness. There were no moderating effects of baseline
smoking rate on continuous abstinence (p >?.05).

Adjusted models indicate that Yoga participants were smoking
significantly fewer cigarettes per day at EOT compared to Wellness
(adjusting for baseline). Specifically, there was a 1.54 (standard
error?=?0.59, p?=?.01) difference in cigarettes per day favoring Yoga
at EOT. This effect was most pronounced among those with higher
smoking rates at baseline (=20 cigarettes/day? =? 75th percentile).
Among these individuals, those in Yoga smoked 2.66 fewer ciga-
rettes per day at EOT compared to Wellness (standard error?=?1.33,

Patterns of Quitting Behavior
LCM analysis suggests a 4-class?model is best supported by the data.
Patterns show that 16% of participants quit by week 4 and had
high probability of remaining quit through week 8 (Class 1: Quit
Date Quitters). Most participants (71%) were not able to achieve
abstinence (Class 2: Non-quitters), 5% of participants were slow
and steady quitters such that the slope increased over time with

high probability of being quit by EOT (Class 3: Slow Quitters), and
8% of participants had high probability of quitting on or before
week 4 with a decline in odds of remaining quit thereafter (Class 4:
Relapsers). These patterns are depicted in Figure?2.

Participants randomized to Yoga were significantly more likely to
be Quit Date Quitters or Slow Quitters compared to Wellness partic-
ipants (p?=?.04). Both Quit Date Quitters and Slow Quitters (67.6%
and 60%, respectively) were more likely to report 7PPA at 3?months
than Non-quitters and Relapsers (2.2% and 14.3%, respectively).
Although there were no significant between-class differences in base-
line demographics (Table?2), there was a significant effect of baseline
smoking rate on the distribution of classes. Specifically, light smok-
ers (=10 cigarettes/day) were significantly more likely to be Slow
Quitters compared to other classes (p?=?.04).

There were significant between-class differences in readiness
and confidence in quitting (and a trend for motivation to quit) at

Table?1. Participant Characteristics at Baseline by Intervention
Group (N?=?227)




Age, mean (SD), y 46.2 (12.0) 46.1 (12.0) 46.4 (12.0)
Gender (female), No. (%) 126 (55.5) 67 (59.3) 59 (51.8)
Race (white), No. (%) 195 (85.9) 102 (90.3) 93 (81.6)
Hispanic/Latino, No. (%) 8 (3.5) 4 (3.5) 4 (3.5)
Education level, No. (%)
High school graduate

or less
63 (27.8) 31 (27.4) 32 (28.1)

At least some college 138 (60.8) 66 (58.4) 72 (63.2)
At least some graduate

26 (11.5) 16 (14.2) 10 (8.8)

Income ($), No. (%)
<11 500 32 (14.1) 12 (10.6) 20 (17.5)
11 501?50 000 91 (40.1) 45 (39.8) 46 (40.4)
50 001?100 000 71 (31.3) 40 (35.4) 31 (27.2)
>100 000 19 (8.4) 11 (9.7) 8 (7.0)
Don?t know/refuse 14 (6.2) 5 (4.4) 9 (7.9)20
Marital status, No. (%)
Single 88 (38.8) 50 (44.2) 38 (33.3)
Single, live with partner 34 (15.0) 11 (9.7) 23 (20.2)
Married 62 (27.3) 33 (29.2) 29 (25.4)
Separated, divorced,

43 (18.9) 19 (16.8) 24 (21.1)

Employment status, No. (%)
Employed full time 127 (55.9) 66 (58.4) 61 (53.5)
Employed part-time 22 (9.7) 15 (13.3) 7 (6.1)
Unemployed 47 (20.7) 21 (18.6) 26 (22.8)
Other 31 (13.7) 11 (9.7) 20 (17.5)
BMI (baseline), mean (SD),

27.3 (5.0) 27.1 (4.7) 27.6 (5.3)

Cigarettes per day
(baseline), mean (SD)

17.0 (7.8) 16.9 (7.5) 17.1 (8.1)

Readiness to quit, mean

7.9 (1.8) 8.0 (1.8) 7.8 (1.8)

Confidence to quit, mean

7.8 (1.9) 7.8 (1.9) 7.8 (1.9)

Motivated to quit, mean

8.7 (1.4) 8.7 (1.5) 8.7 (1.5)

Fagerstr?m score, mean

4.9 (2.1) 4.9 (2.1) 4.9 (2.0)

BMI? =? body mass index; SD? =? standard deviation. No significant between-
group differences.

Nicotine & Tobacco Research, 2019, Vol. 21, No. 111520

baseline. Relapsers had highest mean scores on readiness to quit
(9.18, SD?=?1.07), and Slow Quitters had the lowest mean readiness
scores (7.50, SD? =? 2.15) at baseline. However, Quit Date Quitters
had significantly higher confidence to quit compared to other classes,

and Slow Quitters had the lowest mean confidence scores: Quit Date
Quitters?=?8.56 (SD?=?1.46), Non-quitters?=?7.62 (SD?=?1.92), Slow
Quitters? =? 1.50 (SD? =? 1.98), Relapsers? =? 8.47 (SD? =? 1.97). Slow
quitters and Relapsers trended toward higher motivation scores at

Figure?2. Latent class?Model (LCM) resulting patterns of quitting over time among all participants.

Table?2. Participant Demographics by Pattern

Quit Date Quitters,

n?=?36 Non-quitters, n?=?162 Slow Quitters, n?=?12 Relapsers, n?=?17

Age, mean (SD), y 49.47 (12.50) 45.25 (11.41) 48.33 (11.84) 47.12 (15.48)
Gender (female), No. (%) 18 (50.0) 94 (58.0) 6 (50.0) 8 (47.1)
Race (white), No. (%) 34 (94.4) 147 (90.7) 10 (83.3) 15 (88.2)
Hispanic/Latino, No. (%) 1 (2.8) 7 (4.3) 0 (0) 0 (0)
Education level, No. (%)
High school graduate or less 10 (27.8) 48 (29.6) 2 (16.7) 3 (17.6)
At least some college 18 (50.0) 99 (61.1) 9 (75.0) 12 (70.6)
At least some graduate school 8 (22.2) 15 (9.3) 1 (8.3) 2 (11.8)
Income ($), No. (%)
<11 500 2 (5.6) 25 (15.4) 3 (25.0) 2 (11.8)
11 501?50 000 16 (44.4) 62 (38.3) 6 (50.0) 7 (41.2)
50 001?100 000 12 (33.3) 50 (30.9) 2 (16.7) 7 (41.2)
>100 000 5 (13.9) 12 (7.4) 1 (8.3) 1 (5.9)
Don?t know/refuse 1 (2.8) 13 (8.0) 0 (0) 0 (0)
Marital status, No. (%)
Single 14 (38.9) 65 (40.1) 4 (33.3) 5 (29.4)
Single, live with partner 3 (8.3) 16 (16.0) 2 (16.7) 3 (17.6)
Married 11 (30.6) 43 (26.5) 2 (16.7) 6 (35.3)
Separated, divorced, widowed 8 (22.2) 28 (17.3) 4 (33.3) 3 (17.6)
Employment status, No. (%)
Employed full time 23 (63.9) 87 (53.7) 7 (58.3) 10 (58.8)
Employed part-time 3 (8.3) 14 (8.6) 2 (16.7) 3 (17.6)
Unemployed 4 (11.1) 39 (24.1) 2 (16.7) 2 (11.8)
Other 6 (16.7) 22 (13.6) 1 (8.3) 2 (11.8)
BMI (baseline), mean (SD), kg/m2 27.54 (4.08) 27.40 (5.11) 28.88 (5.34) 25.46 (4.98)
Cigarettes per day (baseline), mean (SD) 15.75 (7.99) 16.99 (7.81) 14.75 (7.03) 18.12 (8.28)
Fagerstr?m score, mean (SD) 4.69 (2.27) 4.94 (2.02) 5.17 (2.76) 5.18 (1.67)

BMI?=?body mass index; SD?=?standard deviation.

Nicotine & Tobacco Research, 2019, Vol. 21, No. 11 1521

baseline (9.25, SD? =? 1.06 and 9.29, SD? =? 1.05, respectively) com-
pared to Quit Date Quitters (8.92, SD? =? 1.27) and Non-quitters
(8.54, SD?=?1.52). There were no other differences in baseline vari-
ables between classes (ps > .05).

Treatment Dose Effects
There was no significant difference in the average number of
classes attended (of possible 24)? during treatment between Yoga
(mean?=?16.49, SD?=?5.92) and Wellness (mean?=?16.61, SD?=?7.42)
participants (p > .05). Among Yoga participants, there was a signifi-
cant association between total dose of treatment received and 7PPA
(OR?=?1.12, 95% CI?=?1.09% to 1.26%), such that each additional
session attended was associated with a 12% increase in the odds
of being quit at EOT. There was no significant dose effect among
Wellness participants (p > .05).

Home Practice
Home practice of Yoga was tracked weekly through 6-month
follow-up. At EOT, 47% of Yoga participants reported practic-
ing Yoga at home (an average of 2.56?days/week). At 3?months,
Yoga participants reported an average of 90.00? min/week of
Yoga (SD? =? 50.90) and 75.36? min/week of Yoga (SD? =? 74.25)
at 6?months.


This is the first large-scale randomized controlled trial to suc-
cessfully recruit and retain adult smokers in the United States
that examined Yoga as a complementary treatment for smok-
ing cessation. This study compared Yoga to a Wellness control
to complement standard smoking cessation counseling based on
cognitive-behavioral therapy, a well-tested, effective intervention
for tobacco cessation.31 The study design was stringent in that
both groups were proved with a high-quality, intensive therapy for
smoking cessation. Thus, we could test the relative efficacy of Yoga
to a comparison arm that effectively controlled for contact time,
attention, and subject burden.

Results indicated that in this sample, Iyengar Yoga was highly
feasible and acceptable based on strong attendance and retention
rates. Participation was enthusiastic as demonstrated by our 94.7%
retention rate across both groups.

Longitudinally adjusted models showed significant results favor-
ing Yoga. Participants in the Yoga group were 37% more likely to
quit than those in Wellness group. Yoga also had differential effects
between heavier and lighter smokers in that lighter smokers were
over twice as likely to quit if given?Yoga.

Although most participants did not achieve smoking abstinence,
participants in both groups significantly decreased their daily cigar-
ette consumption by the EOT. Yoga produced a greater reduction
in smoking rate by EOT compared to Wellness, particularly among
those who were heavier smokers at baseline. Thus, Yoga had a posi-
tive effect on smoking among all participants compared to controls.
Those who were smoking fewer cigarettes at baseline were more
likely to quit if they received Yoga, and heavy smokers were more
likely to cut down, a positive step on the path to improved health
and eventual cessation.

LCM revealed 4 distinct patterns of quitting behavior that
differed significantly between treatment arms. Yoga partici-
pants were more likely to be represented in the two ?successful?

patterns of quitting: either those able to quit on the designated
quit day (week 4)? and remain quit, or those who experienced a
gradual reduction in smoking rate leading to full abstinence by
EOT. In contrast, Wellness participants were more likely to never
achieve full abstinence or to quit early (before the designated
quit day), and relapse to smoking before EOT. Future work is
needed to identify whether these patterns can be predicted and
treatment adjustments made for individuals likely to relapse
early or never?quit.

Results of this study also provide an intriguing hint that some
variables or combination of variables may predict these patterns.
Readiness, confidence, and motivation for quitting have been pre-
dictive of successful smoking cessation in multiple studies.32,33
However, in this study we saw noteworthy differences in the dis-
tribution of these constructs based on pattern of quitting. For
example, Relapsers scored high on all three measures (readiness,
confidence, and motivation), whereas Slow Quitters scored high
on motivation, but lower on readiness and far lower in confi-
dence. It is possible that other factors may predict which pattern
an individual falls into and thus suggest options for enhanced
treatment approaches. For example, it would be unsurpris-
ing if Relapsers were relatively low on mindfulness and/or high
on impulsivity at program entry. If supported, a Yoga program
emphasizing mindfulness training might address these deficits
more intensely at the beginning and perhaps reduce the likelihood
of early quitting that leads to quick relapse. Similarly, there is a
substantial literature showing that comorbid affective symptoms
such as anxiety and depression are associated with smoking and
predictive of postcessation relapse.34,35 It may be fruitful to iden-
tify whether and how the patterns of quitting and non-quitting
found in the present study may map onto these specific affective
states as well as transdiagnostic markers of emotional vulnerabil-
ity, such as anxiety sensitivity, low distress tolerance, and similar
psycho-affective characteristics.

Both treatment arms were labor-intensive for participants, requir-
ing 3 hours weekly for 8 weeks. To enhance potential dissem-
ination, it would be valuable to test whether a home-delivered
Yoga program would be effective. Both arms were provided with
a strong well-validated cognitive-behavioral therapy smoking
cessation program, leading to higher abstinence rates than might
be the seen in less intensive programs. The Wellness program
was not a real-world comparison, in that smoking cessation pro-
grams are not typically paired with Wellness programs. However,
this comparison was chosen to avoid any bias that might result
from unequal subject burden between conditions. We did not use
medications to aid smoking cessation although medications are
the current standard of care.31 We were concerned that if Yoga
produced a small positive effect on cessation, that effect might
be effectively washed out by the addition of medications, which
typically produce a doubling of effect size.31 To detect a poten-
tially small intervention effect above and beyond the impact of
medications would require a much larger sample. Individuals
seeking a program like Yoga to aid them in quitting smoking
may be disinclined to use medications, although there are cur-
rently no data to support or refute this. Similarly, many individu-
als cannot use or choose not to use quit-smoking medications
because of cost, interactions with their other medications, or

Nicotine & Tobacco Research, 2019, Vol. 21, No. 111522

medical contraindications;36 therefore, we studied Yoga as a po-
tential option for those who cannot or will not use medications
for quitting.


Yoga appears to aid some smokers during quit attempts. Yoga may
be particularly effective for lighter smokers, and higher doses of
Yoga (eg, more frequent classes or programs of longer duration) may
be most effective. Additional work is needed to determine whether
we can identify predictors of quitting patterns and use this informa-
tion to make adjustments to therapy in order to maximize treatment
efficacy for each smoker.

Funding for this study was provided by a grant from the National Institutes
of Health, National Center for Complementary and Integrative Health (R01
AT006948 to BB).

Declaration of Interests
The authors have no competing financial interests to declare.

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