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,h, 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.h The University of Queensland, Prince Charles Hospital Northside Clinical Unit, School of

Clinical Medicine, Herston, 4029, 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: 4,108

ABSTRACT

Introduction: Addiction is increasingly defined as a “brain disease” caused by changes to

neurochemistry. While nicotine addiction has historically been excluded in the brain disease

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

investigated whether Australian smokers endorse brain-based explanations of 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 a survey

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

regressions tested associations between 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 and those with more education were more

likely to endorse brain-based understandings of smoking. Participants who agreed smoking

changed brain chemistry were more likely 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 did not differ between those who agreed and disagreed

that smoking changed brain chemistry. However, those who agreed that smoking was a brain

disease had higher 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

reduced self-efficacy.

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.”

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].

4

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 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 [25].

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

[26-28]. 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 [29].

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

5

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 [30]. 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 [31]. 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 [32-34].

6

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

measured using the Heaviness of Smoking Index (HSI) [35]. Desire to quit was assessed by

the item “How much do you want to give up smoking?” with response options of: not at all, a

little bit, quite a bit, or very much. Those who responded “not at all” were classified as

having no to desire to quit, and compared to all others who expressed some level of desire to

quit. 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) [36-38] and other national surveys [39]. Response options were: “not at all

sure, slightly sure, moderately sure, very sure, or extremely sure.” There is no consensus in

the literature on how to analyse this item, and it has been treated as a continuous or

categorical variable in various studies. Because it is a single item five point item that is not

part of a larger scale, we decided against treating it as a continuous variable, and

dichotomized the responses for ease or interpretation. 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

7

“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

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

brain disorder.”

Data analysis

From the four brain beliefs items, two key items measuring the strength of endorsement of

brain-based explanations of smoking were selected for further 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.

8

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). 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.

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). SPSS v22 was used for data analysis. All

variables were entered into the model simultaneously, with categorical variables 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

9

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.

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)

[40]. 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 [36, 41], 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

10

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 the logistic regressions

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.

Endorsement of brain-based explanations of smoking

Figure 1 shows the percentage of the entire sample (N=1,538) who agreed or disagreed with

the items 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). There was a statistically significant

11

difference in age (p<0.001) between those who agreed that smoking changed the chemistry of

the brain (M=39.6, SD=14.6) and those who disagreed (M=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. Those who expressed a desire to quit smoking were more

likely than those who had no desire to quit to agree that smoking changed brain chemistry

(p<0.001).

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), were more likely

to express an intention to use medication (p<0.001), and were more likely to report a desire to

quit (p<0.001).

12

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 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

13

0.98, 95% CI 0.98-0.99), although this effect was small. Those who desired to quit had

greater odds of reporting higher self-efficacy (OR 2.70, CI 1.57-4.64).

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 (OR 0.80, 95% CI 0.55-1.16). 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 1.70, 95% CI 1.26-2.30).

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

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 [42]. They believed the

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

14

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 [43, 44]. 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 [45, 46]. 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.” [45] 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

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

efficacy, more years of education, a desire to quit smoking, 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, and

were more likely to intend to use medication 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

15

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 [47], 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 [48].

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

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

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

more research is required to confirm the findings, this suggests that neuroscience

explanations of addiction, at least in relation to smoking, are not associated with a “fixed”

mindset of behaviour that results in reduced self-efficacy [25]. In fact, the qualitative

evidence on this topic shows that addicted individuals often emphasise autonomy, choice and

responsibility, which more closely represents a “growth” mindset [31,42].

16

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 have a relationship with treatment preferences and self-

efficacy, but that the effect sizes are relatively small. However, 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. Overall, 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 [49]. 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 [50, 51]. 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 self-efficacy, or focus groups to allow greater discussion and analysis of the complexity

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

17

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 [31, 52, 53]. 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.

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Table 1 - Participant demographics

Total Sample n=1538

With opinionn=930

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

5446

5545

5248

Educational attainment % * No Bachelor degree Bachelor degree

68.731.3

62.337.7

78.621.4

Born in Australia % 75.3 75.9 74.3

* Statistically significant at the p<0.05 level.

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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 diseaseAgree Disagree p Agree Disagree p

Gender (%, N) Male Female

81.9 (484)81.7 (406)

18.1 (107)18.3 (91)

0.5050.8 (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)

0.0242.9 (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)

<0.00122.1 (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)

0.0938.2 (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)

<0.00151.5 (401)37.5 (128)

48.5 (377)62.5 (213)

<0.001

Chi-square tests of independence 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 interval around OR

P-value

Lower UpperMale sex (reference = females) 0.16 1.18 0.87 1.60 0.30Age -0.01 0.99 0.98 1.00 0.13EducationNo Bachelor degree (ref)Bachelor degree or higher 0.44 1.55 1.12 2.20 0.01Nicotine dependence (HSI) 0.12 1.13 1.02 1.25 0.02Self-efficacyLow (ref)High 0.01 1.01 0.73 1.39 0.96Desire to quitNo (ref)Yes 0.48 1.62 0.95 2.77 0.08Prior use of medicationNo (ref)Yes 0.99 2.68 1.94 3.70 <0.001Agreement that smoking changes brain chemistryDisagree (ref)Agree 0.41 1.51 1.03 2.21 0.03Agreement that smoking is a brain diseaseDisagree (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 interval around OR

P-value

Lower Upper

Male sex (reference = females) -0.19 0.83 0.62 1.10 0.19Age -0.02 0.98 0.98 0.99 0.001EducationNo Bachelor degree (ref)Bachelor degree or higher 0.26 1.29 0.96 1.74 0.09Nicotine dependence (HSI) -0.23 0.79 0.72 0.87 <0.001Desire to quitNo (ref)Yes 0.99 2.70 1.57 4.64 <0.001Prior use of medicationNo (ref)Yes -0.24 0.79 0.59 1.06 0.11Agreement that smoking changes brain chemistryDisagree (ref)Agree -0.22 0.80 0.55 1.16 0.24Agreement that smoking is a brain diseaseDisagree (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.

26

Figure 1. Endorsement of brain-based explanations of smoking

Smoking ch

anges the ch

emistry

of the brain

Smoking ca

uses b

rain damage

Smoking is

a brain disease

Smoking is

a brain disorder

0102030405060708090

100

12.9 16.3

38.4 40.6

57.9 54.6

34.4 32.629.3 29.2 27.2 26.7

Disagree Agree Don’t know

27