This is the peer reviewed version of the following article: [Morphett K, Carter A, Hall W,

Lucke J, Partridge B, Gartner C. Do neurobiological understandings of smoking influence

quitting self-efficacy or treatment intentions? Nicotine & Tobacco Research 2017 Jun 22.

doi: 10.1093/ntr/ntx144. [Epub ahead of print]], which has been published in final form at

[DOI 10.1093/ntr/ntx144].

Embargo period 12 months from first publication online (22 June 2017)

Do neurobiological understandings of smoking influence quitting self-

efficacy or treatment intentions?

Kylie Morphett PhD a,e*, Adrian Carter PhD b,c, Wayne Hall PhDb,d, Jayne Lucke PhDa,f,

Brad Partridge PhDg, and Coral Gartner PhD a,b

a University of Queensland School of Public Health, Public Health Building, Corner of

Wyndham Street and Herston Road, Herston, 4029, Queensland, Australia.

b University of Queensland Centre for Clinical Research, Royal Brisbane and Women’s

Hospital Site, Herston, 4029, Queensland, Australia.

c School of Psychological Sciences and Monash Institute of Cognitive and Clinical

Neurosciences, Monash University, Clayton, 3800, Victoria, Australia.

d University of Queensland Centre for Youth Substance Abuse, Floor K, Mental Health

Centre, Royal Brisbane and Women’s Hospital, Herston, 4029, Queensland, Australia.

e University of Queensland School of Medicine, Royal Brisbane and Women’s Hospital Site,

Herston, 4029, Queensland, Australia.

f LaTrobe University, Australian Research Centre in Sex, Health and Society, Melbourne,

3000, Australia.

g Research Development Unit, Caboolture Hospital, Metro North Hospital and Health Service

(MNHHS), Caboolture, 4510, Queensland, Australia.

* Author to whom correspondence should be addressed; Kylie Morphett, University of

Queensland School of Public Health, Public Health Building, Corner of Wyndham Street

and Herston Road, Herston, 4029, Queensland, Australia. E-Mail: k.morphett@uq.edu.au;

Tel: +61-(0)7-3346-5475.

Competing interests: None

Keywords: health communication, neuroscience, nicotine addiction, lay beliefs, smoking

cessation.

Word Count: 3,687

ABSTRACT

Introduction: Addiction is increasingly defined as a “brain disease” that is caused by long-

lasting changes to neurochemistry. While nicotine addiction has historically been excluded in

the brain disease model of addiction (BDMA), it is beginning to be labelled as a chronic brain

disease. We investigated whether Australian smokers endorse brain-based explanations of their

smoking, and whether these beliefs are associated with quitting self-efficacy or treatment

intentions.

Method: Cross-sectional study of Australian smokers (N=1,538) who completed an online

survey measuring their agreement with statements on the brain's role in smoking. Binary

logistic regressions tested associations between agreement with these items and

sociodemographic variables, quitting self-efficacy and intention to use cessation medications.

Results: The majority (57.9%) agreed that smoking changed brain chemistry and 34.4% agreed

that smoking was a brain disease. Younger participants and those with higher levels of

education were more likely to endorse brain-based understandings of smoking. Participants

who agreed that smoking changed the chemistry of the brain were moderately more likely than

those who disagreed to report an intention to use cessation medicines (OR 1.5, 95% CI 1.0-2.2)

as were those who agreed that smoking was a brain disease (OR 1.5, 95% CI 1.1-2.1). Self-

efficacy levels did not differ between those who agreed and disagreed that smoking changed

brain chemistry. However, those who agreed that smoking was a brain disease were more

likely to have high self-efficacy than those who disagreed (OR 1.7, 95% CI 1.3-2.3).

Conclusion: A neurobiological view of smoking does not dominate public understandings of

smoking in Australia. Endorsement of neurobiological explanations of smoking were

associated with increased intention to use cessation aids, but were not associated with fatalism

in relation to quitting.

IMPLICATIONS

Explaining tobacco dependence in neurobiological terms is unlikely to induce feelings of

fatalism in relation to smoking cessation. Those who endorse biomedical explanations of

smoking may be more open to using cessation pharmacotherapies. Describing smoking in terms

of alterations in brain chemistry may be more acceptable to smokers than labelling smoking a

“brain disease” or “brain disorder.”

4

INTRODUCTION

Drug addiction is increasingly portrayed as a biological phenomenon in which the brain plays

the central role. The National Institute of Drug Abuse (NIDA) have long argued that addiction

is a “chronic and relapsing brain disease” [1]. In 2016, the US Surgeon general released a report

claiming that addiction is a brain disease, and treating it as such would reduce the stigma and

blame associated with addiction, overcoming many of the barriers to addiction treatment [2].

Similarly the American Society for Addiction Medicine have defined addiction as a “primary,

chronic disease of brain reward, motivation, memory and related circuitry” [3]. While nicotine

has historically been treated differently to other psychoactive drugs [4], the “brain disease

model of addiction” (BDMA) also encompasses nicotine addiction because, like other drugs,

nicotine produces long-term changes to neurochemical pathways in the brain [5-7]. Hence,

smoking is increasingly medicalised by being labelled a “chronic brain disorder” [8] and a

“chronic disease.” [9-11]. A recent report on e-cigarettes and young people by the US Surgeon

General has emphasised the potentially damaging effects of nicotine on the adolescent brain

[12] and an associated video warns the public of the dangers of “brain risks” to young people

from use of e-cigarettes [13].

Smokers are exposed to biomedical explanations of tobacco dependence via the media, where

the neurobiological aspects of smoking are reported in articles with titles such as “Smokers

who quit may have brains hard-wired for success” [14] and “Quitting is a brain game.” [15]

Some clinicians have recommended discussing neurobiological aspects of nicotine addiction

with smokers in clinical consultations to help them understand why quitting is difficult and to

reduce self-blame [16]. Also, novel treatments for smoking cessation based on neuroscience,

such as transcranial magnetic stimulation and various new pharmacotherapies, are being

investigated [17, 18].

5

Proponents of the BDMA believe it will reduce the stigma of drug dependence and lead to the

development of more efficacious treatments [19-21]. Those critical of biomedical models have

expressed concern that the belief that addiction is “hard-wired” in the brain may lead to fatalism

and a diminished sense of self-efficacy [22-24]. Neuroscience based explanations could

contribute to what Carol Dweck has labelled a “fixed mindset” where individuals believe that

nature determines their behaviour, rather than a “growth” mindset that encourages attempts to

change problematic behaviours (add Dweck, 2012).

Alternatively, understanding addiction as a neurobiological disorder may increase positive

perceptions of targeted cessation pharmacotherapies. Knowing that medications are available,

and understanding how they work, could make quitting smoking seem easier and encourage

more quit attempts. Research from the genetics field has looked at the impact of genetic

understandings of tobacco addiction on smokers’ sense of control and treatment preferences

[25-27]. Mixed findings and variations in study design limit the conclusions that can be drawn

from these studies. Moreover, it remains to be seen if people respond to genetic and

neuroscience information in similar ways, given important differences between the two [28].

This paper examines the extent to which Australian smokers endorse neurobiological

explanations of smoking, and whether endorsement of neuroscientific explanations of smoking

are associated with quitting self-efficacy or preferences for using particular smoking cessation

methods.

METHOD

6

Design and Sampling

An online survey was completed by 1,538 Australian smokers. All participants were recruited

from a commercial online research panel in 2015. Panel members were recruited from online

and offline sources. Survey completers received points for participation that could be converted

into gift vouchers. The invitation strategy was adjusted daily with quotas to obtain a sample

representative of the demographic profile of the population of Australian smokers in terms of

age and gender [29]. In order to be eligible, participants had to be 18 years old or older, an

Australian citizen or resident, to smoke daily, and to have smoked more than 100 cigarettes in

their lifetime. The Human Research Ethics Committee of the University of Queensland granted

ethics approval for this study (Approval number: 2009001022).

Of the 6,520 invited participants who clicked on the link to the survey, 4,273 did not smoke

daily, 49 had not smoked at least 100 cigarettes and 16 exited the survey before completing the

eligibility questions and were excluded. Of those who met the eligibility criteria (N=2,182),

625 dropped out before completing the survey. Seven identified as duplicate cases caused by a

computer error were removed from the dataset. Despite reporting daily smoking on the

screening questions, 12 participants stated that they smoked zero cigarettes per day on a

subsequent question and were excluded from the dataset. For further details of the recruitment

process see Supplementary File 1.

Measures

The survey was informed by a literature review and the results of a qualitative study that has

been published elsewhere [30]. Given the paucity of research in this area, the survey design

was also informed by research in related areas, such as studies of the attitudes of persons

experiencing other drug addictions towards the role of the brain in their addictions [31-33].

7

Sociodemographic variables are included in Table 1. Level of nicotine dependence was

measured using the Heaviness of Smoking Index (HSI) [34]. Quitting self-efficacy was

measured using a single item: “If you decided to give up smoking completely in the next six

months, how sure are you that you would succeed?” This item has been used extensively in

the International Tobacco Control Policy Evaluation Project (ITC) [35-37] and other national

surveys [38]. Response options were: “not at all sure, slightly sure, moderately sure, very sure,

or extremely sure.” For the purposes of analysis, responses were dichotomized so that those

who responded “not at all” or “slightly sure” were labelled as having low self-efficacy, and the

remaining responses were combined to represent “moderate/high” self-efficacy.

Participants were provided with a list of smoking cessation strategies and asked to check all

that they had previously used. Those who reported having used nicotine replacement therapy

(NRT) or prescription medicines (Champix or Zyban) were coded as having used a cessation

medication. Intention to use pharmacotherapy in future quit attempts was assessed with the

question “If you decided to make a quit attempt, how likely is it that would use the following

method.” Response options were: “definitely wouldn’t use, probably wouldn’t use, probably

would use, definitely would use, and don’t know". Those who selected “probably” or

“definitely would use” for NRT or prescription medicines were categorised as intending to use

medications. All other responses were classed as not intending to use medication.

Four items were developed to assess strength of endorsement of beliefs about the role of

neurobiology in smoking. Participants were asked to rate the extent to which they agreed with

the following statements on a four-point scale (strongly disagree, disagree, agree, strongly

agree) with the option of “don’t know” response: “Smoking is a brain disease”; “Smoking

8

changes the chemistry of the brain”; “Smoking damages the brain”; and “Smoking is a brain

disorder.”

Data analysis

Two key items measuring the strength of endorsement of brain-based explanations of smoking

were selected for analysis: 1)“Smoking changes the chemistry of the brain”; and 2) “Smoking

is a brain disease.” These two items were chosen for further investigation because they

represented two ways that the role of the brain in smoking has been portrayed. The first item

selected for further analysis, that “Smoking changes the chemistry of the brain” represents the

scientific view that smoking influences neurobiological mechanisms that then make it difficult

to quit. The second item chosen, “Smoking is a brain disease” represents the controversial

NIDA labelling of addiction as a chronic “brain disease.” Empirically, the brain disease and

brain chemistry items elicited different response patterns amongst participants, while there was

significant covariance between other items (for example between “Smoking is a brain disease”

and “Smoking is a brain disorder”), which contributed to the decision to retain only these two

items for further analysis.

For each of these items, the five point Likert scale was converted into a dichotomous variable

comprised of disagree (disagree combined with strongly disagree = 0) and agree (agree

combined with strongly agree = 1). We conducted chi-squared and t-tests to determine

differences between those who selected “don’t know” compared to those who expressed an

opinion about brain-based explanations of smoking (Table 1). However, because our primary

research question was whether opinions on brain-based explanations of smoking are related to

outcome variables, we excluded from the primary analyses those who selected “don’t know”

responses on these items.

9

For each of these two key brain-related items, contingency tables and the Pearson’s chi-squared

statistic were used to examine which categorical variables were associated with scores on these

two items. Age and level of nicotine addiction (HSI) were analysed as continuous variables

using t-tests. Categorical independent variables were gender (male=0, female=1), education

(did not complete Bachelor degree = 0, completed Bachelor degree =1), intention to use

cessation medication (no intention = 0, intention=1), and self-efficacy (low=0, moderate or

high =1).

A binary logistic regression analysis then explored the relationship between endorsement of

each of the two key neurobiological explanations of smoking and intention to use medication

in a quit attempt. Intention to use medication was entered as the outcome variable (0=no

intention and don’t know, 1=intend to use). Predictor variables were entered using the Enter

method in SPSS v22, and categorical variables were dummy coded. Demographic variables

and smoking characteristics were gender (male=0, female=1), age, level of education (did not

complete Bachelor degree=0, completed Bachelor degree=1), HSI score, self-efficacy (low=0,

moderate or high=1), desire to quit (0=no desire, 1=some desire) and past use of cessation

medications (0=no, 1 = yes). The included brain-based items were “Smoking is a brain disease”

(0=disagree, 1=agree) and “Smoking changes the chemistry of the brain” (0=disagree,

1=agree).

Another binary logistic regression analysis investigated the relationship between endorsements

of neurobiological explanations of smoking and self-efficacy. The outcome variable was a

dichotomised version of self-efficacy (0=low, 1= moderate or high). Predictor variables were

the same smoking characteristics and brain-related items used in the logistic regression

described above.

10

RESULTS

Participant demographics

Participant demographics are presented in Table 1. Of the entire sample, participant age ranged

from 18-88 years old with a mean of 43 years (SD 16.1). The proportion of the sample born in

Australia aligned closely with national population data (72.8% born in Australia) [39]. In

relation to education, 26.4% had no post-secondary qualification, 32.2% had completed some

post-secondary education at less than bachelor degree level, and 31.3% had completed a

bachelor degree or higher.

The mean number of cigarettes smoked per day was 15 (SD 9.6). Based on previous studies,

where the HSI has been categorized as low dependence for scores 0 or 1, moderate dependence

for scores 2-4, and high dependence for scores 5-6 [35, 40], 74.2% of participants reported

moderate or high nicotine dependence. Only 7.9% of participants expressed no interest in

quitting, with 38% stating that they wanted to give up “a little bit” and 54.2% wanting to quit

“quite a bit” or “very much.” Almost half (47%) reported low levels of quitting self-efficacy,

and 49.6% had used cessation medication for a past quit attempt (either NRT or prescription

medication). Approximately two thirds (67.2%) of participants said that they would use

cessation medications if they were to make a quit attempt.

Table 1 shows differences between those who were excluded from further analysis because

they responded “don’t know” to either of the two brain-related items, and those who provided

an opinion for both. Those who gave a “don’t know” response were older and had lower levels

of education.

11

Endorsement of brain-based explanations of smoking

Figure 1 shows the percentage of participants who agreed or disagreed with a number of

specific statements about the role of the brain in smoking. The majority (57.9%) agreed or

strongly agreed that smoking changes brain chemistry. The findings were similar for the

statement that smoking damages the brain (54.6% agree or strongly agree). Fewer participants

agreed that smoking was a brain disease (34.4% agree or strongly agree) or a brain disorder

(32.6% agree or strongly agree). There were high proportions of “don’t know” responses for

each item, suggesting that many were unfamiliar with the role of the brain in smoking, or did

not feel confident enough to make a judgment. As a neutral option was not included in the

scale, it is also possible that those who had a neutral position selected the “don’t know” option.

Who endorses brain-based explanations of smoking?

After excluding the participants who selected “don’t know” for either of the key brain-related

items, agreement with the statement that “smoking changed brain chemistry” was not

significantly associated with gender, level of self-efficacy, or level of nicotine dependence but

it was strongly associated with age (see Table 2). Those who agreed that smoking changed the

chemistry of the brain were approximately 4 years younger than those who disagreed (44.1,

SD = 17.6).

Endorsement was also associated with education. Those who had a Bachelor degree were more

likely than those without a degree to agree that smoking changed brain chemistry (p=0.02)

Those who agreed that smoking changed brain chemistry were more likely than those who

disagreed to intend to use medication on their next quit attempt (p<0.001). There was no

statistically significant association between agreement that smoking changed brain chemistry,

or level of self-efficacy.

12

The findings were similar for endorsement of the statement that “smoking is a brain

disease”. Level of nicotine dependence was not related to endorsement while age was strongly

related. Participants who agreed that smoking was a brain disease were younger (M=38.2 years,

SD=16.6) than those who disagreed (M=43.7 years, SD=14.3) (p<0.001). Females were

significantly more likely to disagree that smoking was a brain disease than males (56.9% versus

49.2%). Again, those with a university degree were more likely to agree that smoking was a

brain disease (55.3%) than those without a degree (42.9%). Those who agreed that smoking

was a brain disease had higher self-efficacy (p<0.001) and were more likely to express an

intention to use medication (p<0.001).

Is intention to use medication associated with beliefs about the neurobiological basis of

smoking?

Table 3 shows that intention to use cessation medication was not related to age (OR 0.99, 95%

CI 0.98-1.00) or gender (OR 1.18, 95% CI 0.87 -1.60), once other sociodemographic factors

were controlled for. Education was significantly related to intention to use cessation

medications: those who had a university degree were more likely than those with no high school

education to intend to use medications (OR 1.55, 95% CI 1.12-2.20). Those with higher levels

of nicotine dependence were more likely (OR 1.13, 95% CI 1.02-1.25) to intend to use

medications than those with lower levels of nicotine dependence. There were no statistically

significant difference in intention to use medication between those with low and higher levels

of self-efficacy, or between those with high or low desire to quit. Participants who agreed that

smoking changed the chemistry of the brain were more likely to report an intention to use

medication (OR 1.51, 95% CI 1.03-2.21) as were those who agreed that smoking was a brain

disease (OR 1.54, 95% CI 1.11-2.13). These effect sizes were statistically significant but only

13

of moderate size. The biggest predictor of intention to use medications was past use: those who

had used cessation medications in the past were more than twice as likely to intend to do so in

the future (OR 2.68, 95% CI 1.94-3.70).

Is smoking cessation self-efficacy associated with beliefs about the neurobiological basis

of smoking?

The results of the binary logistic regression analysis testing the relationship between self-

efficacy and beliefs about the role of the brain in smoking are shown in Table 4. Gender,

education, and past use of medication did not make a statistically significant contribution to

the final model. Those who were younger were less likely to report low self-efficacy (OR 0.98,

95% CI 0.98-0.99), although this effect was small.

As expected, participants with high dependence had significantly lower levels of self-efficacy

than those with low dependence (OR = 0.79, 95% CI 0.0.72-0.87). There was no difference in

self-efficacy between those who agreed and those who disagreed that smoking changed the

chemistry of the brain but those who said that they “don’t know” were more likely to have low

self-efficacy (OR 0.62, 95% CI 0.42-0.91). For the brain disease item, there was a statistically

significant difference between those who agreed and disagreed: those who agreed that smoking

was a brain disease were more likely to have high self-efficacy than those who disagreed (OR

1.73, 95% CI 1.32-2.26).

DISCUSSION

One aim of this study was to investigate the proportion of Australian smokers who endorse

brain-based explanations of smoking. Such research has not been conducted in Australia

previously, but it can indicate the extent to which smoking has been medicalised and whether

14

smokers have adopted neuroscientific explanations of addiction. The results demonstrate that

around one third of our sample of Australian smokers agreed that smoking was a brain disease,

and a similar proportion did not know whether this was true. A higher proportion of participants

agreed that smoking changes the chemistry of the brain, but a substantial proportion of

participants were uncertain. This is consistent with recently published qualitative work, which

found that most of the participants acknowledged that smoking influenced their brain while

disagreeing that it was a brain disease (add Morphett, 2017). They believed the brain disease

terminology was inaccurate and likely to lead to an increase in stigma.

We also found that those who were less educated were more likely to select the “don't know”

response. This is consistent with other research on health and public understandings of science

[41, 42]. It was unclear whether this is because the less educated have less knowledge of the

topic of smoking and the brain, or they are less likely to express opinions on unfamiliar topics.

The complexities of the “don’t know” response have been outlined by social science

researchers [43, 44]. A “don’t’ know” response does not always signify ignorance, but can

reflect “the absence of representation, to a sense that the question is irrelevant to the respondent

and/or it may relate to the defensive needs of the individual.” [43] Other items in the survey

did not have such high proportions of participants selecting the “don’t know” response. For

example, on the items asking about intention to use various quitting options, rates of “don’t

know” responses were mostly between 10-14%, suggesting that the high levels of “don’t know”

options for the brain-related questions was not due to a general disengagement with the survey.

Another aim was to examine whether socio-demographic variables predicted endorsement of

brain-based understandings of smoking. Endorsements of brain-based beliefs about smoking

were not uniform across social groups. Those who endorsed the stronger form of the “brain

15

disease” explanation of smoking were more likely to be: male, younger, have greater self-

efficacy, more years of education, and intended to use medication on their next quit attempt.

Those who agreed with the less controversial language that smoking changes the chemistry of

the brain were also younger, more highly educated, more likely to intend to use medication,

and had higher self-efficacy than those with disagreed. These findings are discordant with

predictions that the biomedical models of addiction will reduce self-efficacy in addicted

individuals [23] but supports predictions that it could be related to the use of medications for

cessation. As these are cross-sectional data, it is not possible to determine the direction of the

relationship.

A third aim was to assess whether endorsement of brain-based explanations of smoking were

associated with intention to use medication. While there was a positive relationship between

intention to use cessation medication and endorsement of brain-based explanations of smoking,

the strength of the association was modest. Based on these results, promoting brain-based

explanations of smoking is unlikely to substantially increase the use of cessation medications.

But it is unlikely to discourage uptake of cessation aids. A greater effect was seen for past use

of medication, with those who had used medication in the past around three times more likely

to do so in the future. This is consistent with research that those who have used cessation

pharmacotherapies often report finding them helpful [45], and that smokers who had used NRT

or bupropion in the past were more likely to perceive them as helpful than those who had not

tried them [46].

The last aim was to investigate whether acceptance of smoking as a brain disease was

associated with self-efficacy. We found that agreement that smoking was a brain disease was

16

associated with higher self-efficacy. This conflicts with predictions that promotion of

biomedical understandings of addiction will increase fatalism about smoking [23].

The size of many of these statistically significant associations were small, reflecting the large

sample size used in this study. Statistical significance does not necessarily mean that the

predictor variable will have practical significance or that changing the variable will have a

substantial population level impact on smoking. Our findings suggest that endorsement of

brain-based explanations of smoking may have a small effect on treatment preferences and

self-efficacy. While these effects may be small, the belief that smoking changes brain chemistry

or is a brain disease in some cases had a larger effect than age, gender and level of nicotine

dependence. However, the promotion of neurobiological models of smoking appear unlikely

to negatively impact quitting at the population level.

A number of limitations of our study must be acknowledged. Firstly, participants were not

recruited via random sampling. Participants were members of an online market research panel

so may differ from smokers who were not members of this panel. While resourcing

requirements ruled out the possibility of other sampling methods, such as random digit dialling,

the changing nature of survey recruitment means that online panels are increasingly being used

as a cost-effective and valid means of collecting survey data [47]. In addition, the validity of

older methods of random sampling is being undermined by the increasing use of mobile phones

and reductions in survey response rates [48, 49]. Also, our sample closely matched the

Australian population of smokers in relation to age, gender, and being born overseas.

Future studies could employ more sophisticated educational materials to better inform

participants about the neurobiology of smoking and assess its impact on cessation attempts and

17

self-efficacy, or focus groups to allow greater discussion and analysis of the complex

neuroscientific research. Moreover, only self-reported attitudes have been investigated thus far,

and it is unknown whether intentions and attitudes will impact on behaviour. Behavioural

choice experiments examining the impact of neurobiological explanations on participants’

actual behaviour (e.g., smoking, treatment choices, and quit attempts) are recommended.

On these results, it is possible that emphasising the role of the brain could increase intention to

use cessation pharmacotherapies but any such effect is likely to be small. Many factors

influence a smoker's preference for cessation methods and their sense of self-efficacy, and the

complexity of smokers’ conceptions of addiction, the brain, and agency have been

demonstrated in qualitative studies [30, 50, 51]. Overall, our results suggest that a

neurobiological view of smoking does not dominate public understandings of nicotine

addiction among smokers in Australia. When smokers do endorse brain-based explanations of

smoking, this does not appear to reduce cessation self-efficacy, as has been suggested by some

critics of medicalisation.

ACKNOWLEDGEMENTS

The authors would like to thank the participants who took the time to complete the survey.

Thank you to Taverner Research and the Online Research Unit (ORU) for administering the

survey. This work was funded by an Australian Research Council Discovery Grant (Grant ID:

DP120100732) awarded to WH. KM was supported by an Australian Postgraduate Award and

a UQ Advantage top up scholarship. WH was supported by a National Health and Medical

Research Council Australia Fellowship (Grant ID: 569738). CG and AC were supported by

National Health and Medical Research Council Fellowships.

18

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Table 1 - Participant demographics

* Statistically significant at the p<0.05 level.

Total Sample

n=1538

With opinion

n=

Excluded due to

don’t know

response

Age, mean (SD)* 43.0 (16.1) 39.7 (15.2) 48.1 (16.4)

Gender %

Male

Female

54

46

55

45

52

48

Educational attainment % *

No Bachelor degree

Bachelor degree

68.7

31.3

62.3

37.7

78.6

21.4

Born in Australia % 75.3 75.9 74.3

23

Table 2 - Proportion of smokers who endorsed brain-based explanations of smoking stratified by demographics and smoking characteristics

Strata Smoking changes the chemistry of the brain Smoking is a brain disease

Agree Disagree p Agree Disagree p

Gender (%, N)

Male

Female

81.9 (484)

81.7 (406)

18.1 (107)

18.3 (91)

0.50

50.8 (308)

43.1 (221)

49.2 (298)

56.9 (292)

0.01

Age (M, SD) 39.55

(14.55)

44.12

(17.64)

<0.001 38.18

(14.26)

43.66

(16.63)

<0.001

Highest education

No Bachelor degree

Bachelor degree or higher

79.9(560)

85.3(330)

20.1 (141)

14.7 (57)

0.02

42.9 (309)

55.3 (220)

57.1 (412)

44.7 (178)

<0.001

Desire to quit (%, N)

Not at all

To some extent

62.5 (50)

83.3 (840)

37.5 (30)

16.7 (168)

<0.001

22.1 (19)

49.4 (510)

77.9 (67)

50.6 (523)

<0.001

Level of dependence (HSI) (M, SD)

2.53 (1.54) 2.64 (1.60) 0.39 2.54 (1.56)

2.53 (1.56) 0.92

Self-efficacy (%, N)

Low

Moderate/High

79.9 (365)

83.2 (525)

20.1 (92)

16.8 (106)

0.09

38.2 (186)

54.3 (343)

61.8 (301)

45.7 (289)

<0.001

Intention to use medication

Intend to use medication

No intention to use

medication

85 (661)

73.9 (229)

15 (117)

26.1 (81)

<0.001

51.5 (401)

37.5 (128)

48.5 (377)

62.5 (213)

<0.001

X2 used to test for statistical significance for categorical variables. T-test used for continuous variables. Agree = agree plus strongly agree, disagree = disagree

plus strongly disagree. Medication = prescription medication or NRT.

24

Table 3 - Binary logistic regression model regressing intention to use cessation

medications on socio-demographic and smoking characteristics

Factor B Odds

Ratio

95% (CI) confidence

internal around OR

P-value

Lower Upper

Male sex (reference = females) 0.16 1.18 0.87 1.60 0.30

Age -0.01 0.99 0.98 1.00 0.13

Education

No Bachelor degree (ref)

Bachelor degree or higher

0.44

1.55

1.12

2.20

0.01

Nicotine dependence (HSI) 0.12 1.13 1.02 1.25 0.02

Self-efficacy

Low (ref)

High

0.01

1.01

0.73

1.39

0.96

Desire to quit

No (ref)

Yes

0.48

1.62

0.95

2.77

0.08

Prior use of medication

No (ref)

Yes

0.99

2.68

1.94

3.70

<0.001

Agreement that smoking

changes brain chemistry

Disagree (ref)

Agree

0.41

1.51

1.03

2.21

0.03

Agreement that smoking is a

brain disease

Disagree (ref)

Agree

0.43

1.54

1.11

2.13

0.01 N=930. Outcome variable: no intention to use medications=0, intend to use medications =1. Sex: Female = 0,

Male =1. Nicotine dependence (HSI):0-6. Desire to quit: 0 = No desire, 1=Yes. Self-efficacy: 0=Low,

1=Moderate/High. Prior use of medication = ever use of NRT or prescription medications for smoking cessation.

Agreement that smoking changes the chemistry of the brain = agree and strongly agree. Disagree that smoking

changes the chemistry of the brain = disagree and strongly disagree. Agreement that smoking is a brain disease =

agree and strongly agree. Disagree that smoking is a brain disease = disagree and strongly disagree.

25

Table 4 - Binary logistic regression model regressing level of self-efficacy on socio-

demographic and smoking characteristics

Factor B Odds

Ratio

95% (CI) confidence

internal around OR

P-

value

Lower Upper

Male sex (reference = females) -0.19 0.83 0.62 1.10 0.19

Age -0.02 0.98 0.98 0.99 0.001

Education

No Bachelor degree (ref)

Bachelor degree or higher

0.26

1.29

0.96

1.74

0.09

Nicotine dependence (HSI) -0.23 0.79 0.72 0.87 <0.001

Desire to quit

No (ref)

Yes

0.99

2.70

1.57

4.64

<0.001

Prior use of medication

No (ref)

Yes

-0.24

0.79

0.59

1.06

0.11

Agreement that smoking changes

brain chemistry

Disagree (ref)

Agree

-0.22

0.80

0.55

1.16

0.24

Agreement that smoking is a

brain disease

Disagree (ref)

Agree

0.53

1.70

1.26

2.30

<0.001 N=930. Self-efficacy: low = 0, moderate and high = 1. Sex: Female=0, Male = 1. Desire to quit: 0=No desire, 1=

some desire, Prior use of medication = ever used NRT or prescription medications for smoking cessation.

Agreement that smoking changes the chemistry of the brain = agree and strongly agree. Disagree that smoking

changes the chemistry of the brain = disagree and strongly disagree. Agreement that smoking is a brain disease =

agree and strongly agree. Disagree that smoking is a brain disease = disagree and strongly disagree.

Figure 1. Endorsement of brain-based explanations of smoking

26

12.916.3

38.4 40.6

57.954.6

34.4 32.629.3 29.2 27.2 26.7

0

10

20

30

40

50

60

70

80

90

100

Smoking changes thechemistry of the brain

Smoking causes braindamage

Smoking is a brain disease Smoking is a brain disorder

Disagree Agree Don’t know