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. 2023 Feb 28;32:102168. doi: 10.1016/j.pmedr.2023.102168

Factors associated with quitting among smoking cessation medication-assisted smokers and ex-smokers: A cross-sectional study in Australia

Amanual Getnet Mersha a,b,⁎, Parivash Eftekhari a,b, Michelle Kennedy a,b, Gillian Sandra Gould c
PMCID: PMC10009288  PMID: 36922959

Highlights

  • •

    One in three study participants who were assisted by smoking cessation medication quit smoking.

  • •

    Adherent use of smoking cessation medications was associated with a more than two-fold improvement in quitting.

  • •

    Implementing a smoke-free home policy during quitting journey was associated with improved quitting.

  • •

    Health programs are recommended to emphasis on improving adherence and creating smoke-free home.

Keywords: Adherence, Bupropion, Medication, Nicotine replacement therapy (NRT), Quitting, Smoking, Varenicline

Abstract

Effective smoking cessation medications (SCM) are available and are recommended for the treatment of tobacco smoking. In this study, we evaluated rate and factors associated with successful quitting among individuals who supported their quit attempt using SCMs in Australia. An observational online cross-sectional survey was conducted using a convenience sample of smokers and ex-smokers in Australia. A self-administered questionnaire was used to evaluate socio-demographic, psychological, smoking, and medication use characteristics. The Fagerstrom Test for Nicotine Dependence scale was used to assess the level of nicotine addiction. Logistic regression used to identify factors associated with smoking cessation. Of the 201 respondents, 33.3% had successfully quit smoking. Nicotine replacement therapy (NRT), varenicline, and bupropion were used by 71.6%, 19.9%, and 8.5% respectively. The rate of quitting was 30.6%, 47.5%, and 23.5% for participants who used NRT, varenicline, and bupropion, respectively. Six in ten (59.6%) of the participants who were adherent to SCMs reported continuous abstinence. Whereas 22.9% reported quitting among participants who were nonadherent to SCMs. Adherence to SCMs was significantly associated with increased rate of quitting (AOR = 2.67, 95% CI of 1.17–6.10). Additionally, having smoke-free home was associated with successful smoking cessation (AOR = 2.34, 95% CI of 1.13–4.90). In conclusion, one in three participants self-reported that they successfully quit smoking. Adherence to SCMs and smoke-free home were strongly associated with quitting. Smoking cessation programs and future studies are recommended to incorporate medication adherence as a core component. Home-targeted and family-inclusive interventions are recommended to manage smoke-free homes and enhance success of quitting attempts.

1. Introduction

1.1. Background

In Australia, smoking is attributed to one in every seven deaths (13.3%) and 9.3% of the total disease burden (Al-Yaman, 2017, Australian Institute of Health and Welfare. Australian Burden of Disease Study: impact and causes of illness and death in Australia, 2015). Smoking cessation is one of the most effective measures to improve health outcomes (GBD 2016 Risk Factors Collaborators, 2017, Collaborators, 2017). SCMs are safe and effective in improving the success of smoking cessation (Cahill et al., 2013). Higher rates of successful quitting were reported with SCMs such as transdermal nicotine patch and gum; as high as 50–60% (Hartmann-Boyce et al., 2018). The first-line medications (NRT, varenicline, or bupropion) are recommended by smoking cessation guidelines (Institute, 2021, U.S. Department of Health and Human Services. Smoking Cessation. A Report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2020) and professional societies around the world and in Australia such as the Royal Australian College of General Practitioners (College, 2021). In 2019, 16.8% and 6.3% of smokers used NRT and other SCMs (varenicline or bupropion) to support their quitting in Australia (Australian Institute of Health and Welfare, 2021).

Successful smoking cessation was found to be associated with various factors such as older age, marriage, higher income, alcohol abstinence, better mental health status, low level of nicotine dependence, and home rules against smoking (Lee and Kahende, 2007, Yong et al., 2014, Corsi et al., 2014). One of the main important factors that affect treatment outcomes is adherence to SCMs (Mersha et al., 2021). The effectiveness of SCMs is associated with proper and consistent medication use (Mersha et al., 2021). In a study conducted in the US, smoking cessation was found to be three times higher among participants who were adherent to NRT when compared to non-adherent participants (Shiffman et al., 2008). Similarly, studies conducted in Canada and China reported improved rates of smoking cessation among individuals who were adherent to SCMs compared to non-adherent counterparts (Voci et al., 2016, Lam et al., 2005). A meta-analysis of these studies indicated a consistent finding supporting the positive impact of adherence on the rate of successful smoking cessation (Mersha et al., 2021). The verified rate of smoking cessation was higher among individuals who were adherent to bupropion (Leischow et al., 2016) and varenicline (Catz et al., 2011) as compared to non-adherent individuals.

Although studies have been conducted to evaluate the factors associated with successful smoking cessation (Voci et al., 2016, Lam et al., 2005), the majority of these studies were among smokers in smoking cessation programs and may not provide an accurate representation of the general population in real world circumstances (Yong et al., 2014, Monsó et al., 2001). There is also a lack of knowledge about smoking cessation success among SCM-aided individuals within the Australian population. This study evaluated the rate and factors associated with quitting in Australia among smokers and ex-smokers who have used SCMs using a population-based survey. The findings from this study provide directions for future studies, smoking cessation health programs and policy.

2. Method

2.1. Study design and setting

An observational study design using a cross-sectional online survey was conducted among a convenience sample of adult smokers and recent ex-smokers in Australia. Eligible participants were adults aged 18 years and above; smokers and recent ex-smokers (who had quit smoking in the last 12 months); and had used SCMs (any form of NRT, varenicline, and/or bupropion) in their most recent quit attempt. Ethics approval was obtained from the University of Newcastle Human Research Ethics Committee, approval number H-2021–0073. The participants were informed that participation was entirely voluntary and informed consent was taken from each participant. The obtained information was kept anonymous and recorded in such a way that the respondent could never be identified.

2.2. Sampling and recruitment

The sample size was calculated by using a single population proportion formula with the following assumptions: a confidence level of 95%, margin of error of 5%, and the rate of smoking in Australia as 11.6% (Voci et al., 2016). A minimum sample of 158 participants was required to determine factors associated with smoking cessation. The survey was provided with an online survey provider, Pureprofile Australia Pty Ltd (https://www.pureprofile.com/) (Ltd, 2022). All participants who reported currently smoking or having quit smoking on the survey provider database were invited to undergo a screening process via email; those who meet the above-mentioned eligibility criteria were invited to complete the online survey. The online survey was designed and disseminated using Qualtrics software (Qualtrics, Provo, UT, USA). Participants were reimbursed with 15 Australian dollars (equal to about 10.35 U.S. dollars) for their time through the online survey provider, Pureprofile. Participants were recruited from January 2021 to July 2021.

2.3. Data collection tool

The data collection tool was prepared, tested, and distributed using Qualtrics software (Qualtrics XM, Provo, Utah, USA). The survey tool was prepared after a detailed literature review and evaluated by 3 experts in tobacco research and treatment. It consisted of four sections with a total of 61 items. The first section evaluates socio-demographic factors such as participants’ age; gender (male, female); remoteness (assessed using the Australian Statistical Geography Standard-Remoteness Area criteria); education level (never attended school, completed primary school, completed secondary school, completed college and above); region of participants. The frequency of drinking alcoholic beverages during the quit attempt was assessed as never, once per month, 2 to 4 times a month, 2 to 3 times a week, 4 or more times a week. The effect of social support on smoking cessation was evaluated by using the Oslo social support scale (OSSS-3) (Kocalevent et al., 2018). Participants with a score of 3 to 8, 9 to 11, and 12 to 14 indicate poor, moderate, and strong social support respectively.

The second and third sections evaluated participants’ smoking behaviours and SCM use characteristics. The level of nicotine dependence at the start of quitting attempt was assessed using the Fagerstrom Test for Nicotine Dependence (FTND) scale. The FTND scale contains six items that evaluate the number of cigarette consumption, the compulsion to use, and dependence. A cut point of 6 out of a maximum score of 10 on the FTND was used to categorise participants as low and moderate to high nicotine dependence (Heatherton et al., 1991). Previous quit attempts, frequency of anyone smoking at home, cravings for smoking, eating problems, and sleeping problems during quit attempts were also evaluated.

The type of SCM and adherence to these medications were evaluated. Adherence to SCM was assessed using a self-reported duration and weekly use pattern of the SCMs. Adherence was defined as the usage of SCMs for 4 weeks and an average of at least 5 days per week (Mersha et al., 2021, Hollands et al., 2013).

The fourth section assessed self-reported psychological symptoms during the most recent quit attempt. Previous studies illustrated a high sensitivity and specificity of the Depression, Anxiety, and Stress Scale (DASS-21) in assessing mental illnesses in individuals with substance use disorder (Beaufort et al., 2017). Depression, anxiety, and stress levels were scored and classified into no symptoms, mild, moderate, severe, and extremely severe symptoms.

2.4. Outcome variable

Successful smoking cessation was defined as a self-reported continuous abstinence from smoking cigarettes or other tobacco products since their last quit attempt. It was assessed by asking whether the participant had smoked and the frequency of smoking per month. This method of assessment was effectively used in previous population-based studies (Lam et al., 2005, Schneider et al., 2003).

2.5. Data analyses

Statistical analyses were conducted using Stata software (V16, Stata Corp LP, College Station, TX). Data were analysed using descriptive and analytic statistics. Frequency and percentages were used to describe the characteristics of the participants. Possible associations between variables were assessed by using cross-tabulation and Pearson’s Chi-square test.

Univariate logistic regression was conducted to evaluate the crude association between factors and the success of smoking cessation, defined as ‘ a self-reported continuous abstinence from smoking since the participant’s quit date’. Factors that demonstrated a significant association in the univariate analyses and factors that illustrated strong association in previous studies such as level of nicotine dependence were further adjusted using multivariable logistic regression. A statistically significant association was declared when the P-value was<0.05. Results were presented using odds ratio (OR) and 95% confidence interval (CI).

3. Results

3.1. Socio-demographic characteristics of participants

The survey was completed by 201 participants who had used SCMs in the last 12 months to assist their recent quit attempt. The median age of participants was 47 years old. The majority of the study participants were female (N = 142, 70.6%) and resided in major cities of Australia (N = 151, 75.1%). The Chi-square test suggested an association between successful quit attempts with marital status of participants (Table 1).

Table 1.

Sociodemographic characteristics of smokers and ex-smokers in Australia (n = 201).

Variables Quit smoking (n=201) P-value
YesFrequency
(%)		 NoFrequency
(%)
Age Median – 47 (IQR = 34–62) years old 0.091
Gender 0.827
Female 48 (71.6%) 94 (70.2%)
Male 19 (28.4%) 40 (29.8%)
Education level 0.369
Completed primary or secondary school 30 (44.8%) 69 (51.5%)
Completed college and above 37 (55.2%) 65 (48.5%)
Employment 0.920
Employed 38 (56.7%) 77 (57.5%)
Unemployed 29 (43.3%) 57 (42.5%)
Marital status 0.025*
Married 30 (44.8%) 52 (38.8%)
Never married 12 (17.9%) 48 (35.8%)
Divorced/ Widowed/ Separated 25 (37.3%) 34 (25.4%)
Remoteness 0.419
Major cities of Australia 48 (71.6%) 103 (76.9%)
Regional and remote Australia 19 (28.4%) 31 (23.1%)
States 0.918
New South Wales 21 (31.3%) 48 (35.8%)
Victoria 18 (26.9%) 34 (25.8%)
Queensland 13 (19.4%) 21 (15.7%)
South Australia 7 (10.4%) 15 (11.2%)
Western Australia 8 (11.9%) 16 (12%)
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P-value - chi square test; * - Significant p-value, IQR - Interquartile range.

3.2. Psychosocial characteristics of participants

Of the respondents, respectively 43.8%(N = 88), 40.5%(N = 81) and 36.3%(N = 73) reported the experience of anxiety, stress, and depression symptoms during their most recent quit attempts. The majority of the participants who reported psychological symptoms experienced a milder degree of the conditions. Six out of ten participants (N = 127, 63.2%) reported drinking alcohol-containing beverages during their quit attempt. The Chi-square test indicated an association between the psychological characteristics and successful quitting. Further analysis was conducted to evaluate the true association between these factors and the success of quitting by categorising psychological symptoms, social support, and alcohol use into two categories for each predictor variable as illustrated in Table 2.

Table 2.

Psychosocial characteristics of smokers and ex-smokers during smoking quit attempt in Australia (n = 201).

Variables Quit smoking (n=201) P-value
Yes Frequency (%) No Frequency (%)
Social support 0.001*
Good social support (OSSS-3 score ≥ 9) 45 (67.2%) 53 (39.5%)
Poor social support (OSSS-3 score ≤ 8) 22 (32.8%) 81 (60.5%)
Stress 0.001*
No stress (DASS score ≤ 18) 50 (75.8%) 69 (51.5%)
Had symptoms of stress(DASS score ≥ 19) 16 (24.2%) 65 (48.5%)
Anxiety 0.037*
No anxiety (DASS score ≤ 9) 49 (73.1%) 64 (47.8%)
Had symptoms of anxiety (DASS score ≥ 10) 18 (26.9%) 70 (52.2%)
Depression 0.001*
No depression (DASS score ≤ 12) 53 (79.1%) 75 (55.9%)
Had symptoms of depression (DASS score ≥ 13) 14 (20.9%) 59 (44.1%)
Alcohol use 0.019*
No 34 (50.7%) 40 (29.9%)
Yes 33 (49.3%) 94 (70.1%)
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DASS-Depression, Anxiety, and Stress Scale; OSSS-Oslo Social Support Scale; P-value – Chi-square test; * - Significant p-value.

3.3. Smoking characteristics and self-reported smoking cessation

One-third of the participants (N = 67, 33.3%) had successfully quit smoking. Among the participants, 39% (N = 78) were assessed to have had a moderate to high nicotine dependence at the start of their quit attempt according to the FTND, and 85.6% (N = 172) reported more than one previous quit attempt. Craving for smoking was reported by 82% (N = 165) of the participants. One in four participants (N = 57, 28.4%) were found to be adherent to SCMs. Among participants who were adherent to the medications, 59.6% (N = 34/57) had successfully quit smoking. Whereas 22.9% (N = 33/144) reported quitting among participants who were nonadherent to the SCM.

The most commonly utilised SCM was NRT (N = 144, 71.6%) followed by varenicline (N = 40, 19.9%) and bupropion (N = 17, 8.5%). The rate of quitting was 30.6%, 47.5%, and 23.5% for participants who used NRT, varenicline, and bupropion respectively (p-value = 0.089). Of the participants, 41.3% (N = 83) reported the presence of family members or friends who were smoking at home during their quit attempt. Pearson’s Chi-square test showed the following associations with quitting: adherence to SCMs; and presence of family members or friends who were smoking at home during their quit attempt (Table 3).

Table 3.

Smoking, quitting and smoking cessation medication use patterns of smokers and ex-smokers in Australia (n = 201).

Variables Quitting smoking (n=201) P-value
Yes
Frequency (%)		 No
Frequency (%)
Adherence to SCMs 0.001*
Yes 34 (50.7%) 23 (17.2%)
No 33 (49.3%) 111 (82.8%)
Level of nicotine dependence 0.219
Low dependence (FTND score ≤ 4) 37 (55.2%) 86 (64.2%)
Moderate to high dependence (FTND score ≥ 5) 30 (44.8%) 48 (35.8%)
Ever had quit attempt previously 0.887
Yes 57 (85.1%) 115 (85.2%)
No 10 (14.9%) 19 (14.2%)
Type of SCM 0.089
NRT 44 (65.6%) 100 (74.6%)
Varenicline 19 (28.4%) 21 (15.7%)
Bupropion 4 (6%) 13 (9.7%)
Experienced sleeping problems during quit attempt 0.839
Yes 27 (40.3%) 56 (41.8%)
No 40 (59.7%) 78 (58.2%)
Experienced eating problems during quit attempt 0.842
Yes 33 (49.3%) 64 (47.8%)
No 34 (50.7%) 70 (52.2%)
Used physical exercise to cope with withdrawal symptoms 0.763
Yes 30 (44.8%) 57 (42.5%)
No 37 (55.2%) 77 (57.5%)
Experienced craving during quit attempt 0.086
Yes 55 (82.1%) 110 (82.1%)
No 12 (17.9%) 24 (17.9%)
Others smoking inside living home 0.020*
Yes 20 (29.8%) 63 (47%)
No 47 (70.2%) 71 (53%)
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FTND-Fagerstrom Test for Nicotine Dependence; SCMs-Smoking cessation medications; NRT-Nicotine replacement therapy; P-value - chi-square test; * - Significant p-value.

3.4. Factors associated with smoking cessation

The following factors were found to be significantly associated with quitting smoking in the univariate logistic analysis: being alcohol free (COR = 2.04, 95% CI of 1.12–3.71); having good social support (COR = 3.13, 95% CI of 1.69–5.79); not having symptoms of anxiety (COR = 2.98, 95% CI of 1.57–5.63); not having symptoms of stress (COR = 2.94, 95% CI of 1.53–5.67); not having symptoms of depression (COR = 2.98, 95% CI of 1.51–5.88); using varenicline for cessation support (COR = 2.06, 95% CI of 1.01–4.20); being adherent to SCMs (COR = 4.97, 95% CI of 2.57–9.58); low level of nicotine dependence (COR = 1.45, 95% CI of 1.09–2.63); and having an environment where people did not smoke at home (COR = 2.08, 95% CI of 1.12–3.89). Among these factors, adherence to SCMs and not having someone smoking at home were associated with successful smoking cessation attempts in the multivariable logistic analysis.

Being adherent to SCMs was found to be positively associated with successful quitting as compared to non-adherent participants (AOR = 2.67, 95% CI of 1.17–6.10, p-value 0.019). Having a home where friends or family members did not smoke during the participant’s quit attempt improved the rate of successful smoking cessation by at least two-fold (AOR = 2.34, 95% CI of 1.13–4.90, p-value 0.023). (Table 4).

Table 4.

Factors associated with smoking cessation in univariate and multivariable analyses among smokers and ex-smokers in Australia (n = 201).

Variables (12) Quit smoking (n=201) COR (95% CI) AOR (95% CI)
Yes
Frequency (%)		 No
Frequency (%)
Alcohol drinking
No 34 (50.7%) 40 (29.9%) 2.04 (1.12–3.71)* 1.69 (0.83–3.47)
Yes 33 (49.3%) 94 (70.1%) 1 1
Level of social support
Good social support 45 (67.2%) 53 (39.5%) 3.13 (1.69–5.79)** 2.06 (0.95–4.43)
Poor social support 22 (32.8%) 81 (60.5%) 1 1
Level of stress
No symptom of stress 50 (75.8%) 69 (51.5%) 2.94 (1.53–5.67)** 1.99 (0.79–4.99)
Symptom of stress 16 (24.2%) 65 (48.5%) 1 1
Level of anxiety
No symptom of anxiety 49 (73.1%) 64 (47.8%) 2.98 (1.57–5.63)* 0.90 (0.35–2.38)
Symptom of anxiety 18 (26.9%) 70 (52.2%) 1 1
Level of depression
No symptom of depression 53 (79.1%) 75 (55.9%) 2.98 (1.51–5.88)* 0.97 (0.34–2.75)
Symptom of depression 14 (20.9%) 59 (44.1%) 1 1
Type of SCM used
NRT 44 (65.6%) 100 (74.6%) 1 1
Varenicline 19 (28.4%) 21 (15.7%) 2.06 (1.01–4.20)* 1.54 (0.64–3.68)
Bupropion 4 (6%) 13 (9.7%) 0.69 (0.22–2.26) 0.71 (0.19–2.65)
Adherence
Yes 34 (50.7%) 23 (17.2%) 4.97 (2.57–9.58)** 2.67 (1.17–6.10)*
No 33 (49.3%) 111 (82.8%) 1 1
Level of nicotine dependence
Low dependence 37 (55.2%) 86 (64.2%) 1.45 (1.09–2.63) 1.33 (0.63–2.81)
Moderate to high dependence 30 (44.8%) 48 (35.8%) 1 1
Others smoking inside living home
Yes 20 (29.8%) 63 (47%) 1 1
No 47 (70.2%) 71 (53%) 2.08 (1.12–3.89)* 2.34 (1.13–4.90)*
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*p-value < 0.05; ** p-value < 0.01; SCM-Smoking cessation medications; NRT-Nicotine replacement therapy; COR-Crude odds ratio; AOR-Adjusted odds ratio.

4. Discussion

In an Australian cross-sectional study of 201 participants who had used SCMs to aid a quit attempt, one in three participants successfully quit smoking. The rate of self-reported successful quitting was found to be associated with adherence to SCMs and having a smoke-free home, i.e., where friends and family members did not smoke at home during the quit attempt.

The rate of self-reported smoking cessation in this study is higher than medication unaided population-based studies that reported successful rates of quitting between 5 and 10% at 6 to 12 months of a quit attempt (Hughes et al., 2004, Andritsou et al., 2016). Our finding is in line with studies that evaluated rates of quitting among smokers who were assisted with NRT (31.5%), and varenicline (27.6%) (Cahill et al., 2013(5):Cd009329.). This disparity in the rate of successful quit attempts among population-based studies can be explained by the reduction in the occurrence and strength of urges to smoke or cravings by reducing the extent of withdrawal symptoms among SCM aided participants (Benowitz, 2009).

Adherence to SCMs improved the likelihood of reporting success in smoking cessation by more than two-fold. Similarly, a meta-analysis of randomised controlled trials conducted in the UK, China, and the US indicated two-fold higher rates of quitting among participants who were adherent to SCMs as compared to non-adherent participants (Mersha et al., 2021). Adherence to SCM reduces the craving for smoking and relapsing by providing adequate nicotine and/or reducing the rewarding effects of smoking on the nicotine receptor sites (Benowitz, 2009). Participants who are adherent to the SCMs are more likely to have a higher motivation to quit smoking and thus, improve the success of their quit attempt (Mersha et al., 2020). Interventions directed at improving adherence to medications were found to increase the rates of successful smoking cessation (Hollands et al., 2013). Moreover, the success of smoking cessation was improved by medication adherence interventions such as providing counseling that focused on the issue of medication adherence, monitoring of medication use, automated medication adherence phone calls, daily reminders, and other adherence-focused interventions (Hollands et al., 2019).

A smoke-free home where friends or family members did not smoke during the participant’s quit attempt improved the rate of successful smoking cessation. There is a well-defined link between smoke-free homes and smoking behaviours (Haardörfer et al., 2018). In a systematic review of cross-sectional and longitudinal studies conducted in 2009, creating and managing smoke-free homes are associated with improved quit attempts, higher rates of smoking cessation, and reduced relapse (Mills et al., 2009). A longitudinal study indicated a 50% to 70% higher chance of improving the quit rate among participants whose friends or family members did not smoke at home (Hyland et al., 2009, Messer et al., 2008). Likewise, another study found that smokers who lived in a home where smoking was not allowed were almost five times more successful at quitting smoking when compared to those who lived in a home where there were no smoking restrictions (Vijayaraghavan et al., 2013). A systematic review of randomised controlled trials conducted in 2015 showed that home-targeted interventions such as counseling, self-help materials; tobacco smoke air pollution feedback were found to be effective in creating a smoke-free home and improved quitting (Rosen et al., 2015).

4.1. Strength and limitations of the study

This study used a cross-sectional study design to evaluate the effects of various socio-demographic, psychological and medication related factors on the rate of successful smoking cessation in the Australian population. Online surveys have advantages such as the ability to recruit geographically diverse participants especially with the COVID-19 pandemic where lockdown restrictions limit face-to-face data collection. However, this method has several limitations such as difficulties in determining the exact denominator to calculate the response rate (Menon and Muraleedharan, 2020;33(5):e100264-e., Hays et al., 2015).

To assess the potential representativeness of the participants in this study, a comparison is made here between the participants’ socio-demographic characteristics and national data. Although the participant characteristics of this data is comparable with the national data of Australia in the majority of socio-demographic characteristics, a few sociodemographic figures show discrepancies. For instance, in our study most of the participants are in the age group of over 40 years old and the national data similarly reports the highest daily smoking proportions among people in their 40 s and 50 s (xxxx, Australian Institute of Health and Welfare, 2019). Likewise, most participants utilised NRT, which is comparable with the national smoking medication use data (Australian Institute of Health and Welfare, 2019).

Conversely, the gender proportion and geographical remoteness varied from the report of the Australian Institute of Health and Welfare and the Australian Bureau of Statistics report (Australian Institute of Health and Welfare, 2021). The majority of the participants in our study were female (70.6%) as compared to 51% in the 2018 Australian Bureau of Statistics report (Australian Institute of Health and Welfare, 2021) and from major cites of Australia (75.1%) as compared to 72% in the 2021 Australian Institute of Health and Welfare report (Lindson et al., 2021). A possible explanation for the latter is that it may have been easier for urban residents to access the internet compared to those in remote and very remote areas of Australia. An Australian Bureau of Statistics report show higher internet access among people living in major cities as compared to people living in remote parts of Australia (88% versus 79%) (Menon and Muraleedharan, 2020).

Another major limitation of the study is the possibility of reverse causation, such that relapse would determine adherence to the medications rather than vice versa. The study was dependent in a retrospective self-report that may have resulted in recall bias and influenced the findings. Lastly, smoking cessation was assessed as a self-reported outcome and biochemical verification was not possible to verify smoking status. Even with these limitations, this study indicated the importance of adherence to the success of smoking cessation in Australia.

4.2. Implications for practice, policy, and research

Strategies targeted at improving adherence are recommended to improve smoking cessation success. The following interventions targeted at improving medication adherence are recommended and should be a core component of interventional strategies targeting smoking cessation: providing detailed instructions on how to use SCMs; detailed information about effectiveness, safety, and need of medications; tracking medication use and provide feedback; and avoiding triggers that result in medication non-adherence (Hollands et al., 2019). Arranging and provide medications for a subsidised cost at health care and policy levels may improve adherence and successful smoking cessation (Stevenson et al., 2017).

The Royal Australian College of General Practitioners (RACGP), the largest professional general practice organisation in Australia, guideline mentioned the importance of medication adherence for successful smoking treatment outcome, but recommendations on adherence counselling and other strategies of improving adherence is not well covered in the guideline. Thus, further consideration is required to improve adherence such as recommendations about discussing adherence at every follow up visit and personalising treatment regimens i.e., helping patients titrate the dose or form of SCMs to be most adherent and effective (NICE, 2021).

More inclusive and home-targeted interventions that involves family members are recommended to help create and manage smoke-free homes and enhance the success of quitting attempts. Supporting individuals to have clear communications about smoke-free homes (Persoskie et al., 2015) and implementing smoke-free homes has demonstrated improvements in the rates of successful quitting of up to four times [32; 46]. Polices restricting indoor smoking and community-based education regarding smoke-free homes should be advocated to maintain a supportive environment during quit attempts. Future implementation studies evaluating interventions incorporating strategies that can both improve medication adherence and create a smoke-free home are worthy of exploration. As far as we are aware, this combination has not been a specific focus of programs in Australia.

5. Conclusions

In a cross-sectional on-line survey of smokers and ex-smokers in Australia who had attempted to quit smoking using SCMs, the rate of successful smoking cessation was 33.3%. Being adherent to SCMs was found to increase the rate of successful quitting by more than two times. Moreover, having a smoke-free home where no family members and friends smoked doubled the rate of smoking cessation. Comprehensive interventions targeted at improving adherence to SCMs and creating smoke-free homes are worthy of trial to improve successful smoking cessation.

Ethics approval and consent to participate

The study was conducted approved by the Ethics Committee of the University of Newcastle (approval Number H-2021–0073) and informed consent was obtained from all participants involved in the study. This study was conducted in accordance with the Guidelines of the ethical review process of the University of Newcastle and the National Statement on Ethical Conduct in Human Research.

Funding

No funding has been received to conduct this study.

CRediT authorship contribution statement

Amanual Getnet Mersha: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Parivash Eftekhari: Methodology, Investigation, Writing – review & editing, Supervision. Michelle Kennedy: Methodology, Investigation, Writing – review & editing, Supervision. Gillian Sandra Gould: Methodology, Investigation, Writing – review & editing, Supervision.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

A.G.M is supported by the University of Newcastle Vice-Chancellor’s Higher Degree by Research Training Scholarship. G.S.G is supported by a National Health and Medical Research Council Translating Research into Practice Fellowship and the Australian Federal Government, Department of Health. MK is supported by the National Health and Medical Research Council Early Career Research Fellowship. PE is supported by funding from the Australian Federal Government, Department of Health.

Contributor Information

Amanual Getnet Mersha, Email: AmanualGetnet.Mersha@uon.edu.au.

Parivash Eftekhari, Email: parivash.eftekhari@newcastle.edu.au.

Michelle Kennedy, Email: michelle.kennedy11@newcastle.edu.au.

Gillian Sandra Gould, Email: gillian.gould@scu.edu.au.

Data availability

Data will be made available on request.

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