# Factors influencing perceptions of electronic cigarette and nicotine replacement therapy use among French smokers experiencing socioeconomic disadvantage: A latent class analysis

Marie Noel Al Zayat, Maria Melchior, Judith van der Waerden, Sarah

Mahdjoub, Gladys Ibanez, Fabienne El-Khoury

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Marie Noel Al Zayat, Maria Melchior, Judith van der Waerden, Sarah Mahdjoub, Gladys Ibanez, et al.. Factors influencing perceptions of electronic cigarette and nicotine replacement therapy use among French smokers experiencing socioeconomic disadvantage: A latent class analysis. Addictive Behaviors, 2025, 165, pp.108290. 10.1016/j.addbeh.2025.108290. hal-04998351

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# Factors influencing perceptions of electronic cigarette and nicotine replacement therapy use among French smokers experiencing socioeconomic disadvantage: A latent class analysis

Marie Noel Al Zayat<sup>a,*</sup>, Maria Melchior<sup>a</sup>, Judith van der Waerden<sup>a</sup>, Sarah Mahdjoub<sup>a</sup>, Gladys Ibanez<sup>a,b</sup>, Fabienne El-Khoury<sup>a ID</sup>

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

**Keywords:**

Smoking cessation

Nicotine substitutes

Nicotine replacement therapy

Socioeconomic inequality in smoking

Electronic cigarettes

Latent class analysis

## ABSTRACT

**Background:** Smokers experiencing socioeconomic disadvantage have higher smoking rates and lower quit success compared to the general population. While nicotine replacement therapy (NRT) and electronic cigarettes (e-cigarettes) are effective smoking cessation aids, their use in this population is limited. We examine perceptions of NRT and e-cigarettes among low-income smokers.

**Methods:** We analyzed baseline data from 167 low-income smokers interested in reducing or quitting smoking but not currently using e-cigarettes, enrolled in the French STOP trial, a randomized controlled trial evaluating a preference-based smoking cessation intervention. Baseline measures included demographics, smoking habits, mental health, and perceptions of NRT and e-cigarettes. Latent Class Analysis (LCA) identified subgroups based on perceptions, and multinomial logistic regression examined associations between subgroup membership and predictors.

**Results:** Three subgroups emerged: “adopters” (44 %) with positive views of both aids, “doubters of e-cigarettes” (35 %) who found the tools easy to use but questioned the effectiveness of e-cigarettes, and “resistors” (21 %) with negative perceptions of both aids. Older age, not receiving social benefits, and absence of depressive symptoms were linked to being a “doubter of e-cigarettes”, while heavy smoking was associated with being a “resistor”. Previous NRT use increased the probability of being a “doubter of e-cigarettes”, whereas prior e-cigarettes use decreased the likelihood of belonging to the “doubters of e-cigarettes” and “resistors” groups.

**Conclusion:** Low-income smokers hold diverse perceptions of NRT and e-cigarettes influenced by age, financial difficulties, mental health, and previous quit attempts. Tailored cessation interventions addressing these factors into account may improve quit rates in this population.

**Abbreviations:** SEP, Socioeconomic position; U.S., United States; e-cigarettes, Electronic cigarettes; NRT, Nicotine replacement therapy; STOP, Sevrage tabagique à l'aide des Outils dédiés selon la Préférence: Smoking cessation using preference-based tools; RCT, Randomized controlled trial; AME, Aide Médicale de l'État: Health Insurance for Undocumented Immigrants; AAH, Allocation aux Adultes Handicapés: Disabled Adults Allowance; ASS, Allocation de Solidarité Spécifique: Minimum Social Benefits; RSA, Revenu de Solidarité Active: Minimum Social Benefits; ASF, Allocation de Soutien Familial: Family Support Allowance; CF, Complément Familial: Family Supplement; APL, Aide Personnalisée au Logement: Housing Allowance; MSA, Mutualité Sociale Agricole: Agricultural Social Mutuality; Aspa, Allocation de Solidarité aux Personnes âgées: Solidarity Allowance for the Elderly; APA, Allocation Personnalisée d'Autonomie: Personalized Autonomy Allowance; PUMA, Protection Universelle MAladie: Universal Health Insurance; CSS, Complémentaire Santé Solidaire: Supplementary Solidarity Health Insurance; PHQ-2, Patient Health Questionnaire; mCEQ, modified Cigarette Evaluation Questionnaire; CPP, Comité de Protection de Personnes; SD, Standard deviation; LCA, Latent Class Analysis; AIC, Akaike's Information Criterion; BIC, Bayesian Information Criterion; aOR, Adjusted odds ratios; CI, Confidence intervals; ref, Reference category; INCA, Institut National de Cancer.

* Corresponding author at: Epidemiology Department, Sorbonne Université, INSERM UMR_S 1136, IPLESP, 27 rue Chaligny, 75012 Paris, France.

E-mail address: marie-noel.zayat@iplesp.upmc.fr (M.N. Al Zayat).

<footnote><sup>a</sup> Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Épidémiologie Sociale, Santé Mentale et Addictions, ESSMA, F75012, Paris, France</footnote>

<footnote><sup>b</sup> Sorbonne Université, Faculté de Médecine Pierre et Marie Curie, Département d'enseignement et de recherche en médecine générale, 4 place Jussieu, 75005 Paris, France</footnote>
# 1. Introduction

Despite significant progress in tobacco control, smoking remains a global health crisis, disproportionately affecting individuals experiencing socioeconomic disadvantage (Health at a Glance, 2023). In France, for instance, comprehensive anti-smoking measures have reduced the daily smoking rate among adults aged 18 to 75 years, including those experiencing socioeconomic disadvantage, from 29.5% in 2016 to 23.1% in 2023 (Pasquereau et al., 2023). However, despite this progress, disadvantaged populations continue to face disproportionately high smoking rates, with unemployed French adults reporting a significantly higher daily smoking rate of 35.8% (Pasquereau et al., 2023).

This socioeconomic disparity in smoking prevalence is also observed in many other high-income countries (Drope et al., 2018; Teshima et al., 2022). Individuals with lower levels of education, experiencing economic hardship, or unemployment often display higher smoking rates (Hiscock et al., 2012). Several factors contribute to this disparity. Individuals with socioeconomic disadvantage may experience early exposure to smoking environments (Cai et al., 2019), hold fewer negative perceptions of smoking risks (El-Khoury Lesueur et al., 2016), and belong to social networks with higher smoking prevalence (Hiscock et al., 2012). Further, these individuals often face greater life pressures and exposure to stress (Adler and Ostrove, 1999). In addition, research from several Western countries has linked lower socioeconomic position (SEP) with increased nicotine dependence, reduced self-efficacy, and decreased motivation to quit smoking (Siahpush et al., 2006).

Despite comparable quit attempt rates to smokers with higher SEPs, smokers experiencing socioeconomic disadvantage have significantly lower quit success rates (Kotz and West, 2009). Limited access to effective cessation services and financial constraints have been identified as major barriers to quitting among low-income smokers in the United States (U.S.) (Honjo et al., 2006; Browning et al., 2008), who are also more likely to attempt to quit without medical support (McCarthy et al., 2016) partly due to these barriers. Addressing these challenges is essential for improving quit rates among populations experiencing socioeconomic disadvantage.

Electronic cigarettes (e-cigarettes) and nicotine replacement therapy (NRT) have been shown to be effective smoking cessation aids among dependent smokers (Talwar et al., 2004; Hartmann-Boyce et al., 2022). However, their utilization is lower among individuals without insurance coverage in Germany and the U.S. (Kastaun et al., 2020; Zhang et al., 2019; Harlow et al., 2023). While numerous studies have examined attitudes toward e-cigarettes and NRT for smoking cessation (Larcombe et al., 2023; Michelle et al., 2019), most have not focused specifically on low-income individuals. Research addressing the perspectives of these populations remains limited and has relied primarily on qualitative methods (Spears et al., 2022). The limited evidence available suggests that perceptions of NRT and e-cigarettes among these smokers are diverse. Socioeconomic and psychological characteristics associated with disadvantaged groups are linked to inaccurate perceptions of harm of e-cigarettes (Charlie Albert Smith, 2021). A qualitative study found that some smokers experiencing socioeconomic disadvantage perceive NRT as ineffective, associated with adverse effects, and stigmatized, leading some smokers to prefer abrupt cessation (Duarte et al., 2020). Conversely, some disadvantaged smokers view e-cigarettes as a less harmful alternative to conventional cigarettes, with evidence of dual use and use in smoking-restricted environments (Spears et al., 2022; Lee et al., 2023). These cessation tools may be perceived differently depending on the user's ultimate goal, whether it is complete cessation of nicotine use or switching to a less harmful nicotine alternative: E-cigarettes may help smokers who want to quit tobacco but not nicotine, but they may not be appropriate for those seeking to quit both tobacco and nicotine altogether (Auer et al., 2024). Concerns about long-term health effects and regulatory ambiguity surrounding e-cigarettes have also been reported (Boland et al., 2017). Moreover, cultural and social

factors, such as social networks and community norms, also seem to influence these perceptions (Ramanadhan et al., 2017).

Understanding the perceptions of smokers experiencing socioeconomic disadvantage towards cessation aids is crucial, as their attitudes may influence the adoption and success of these tools among this group. This knowledge is essential to develop interventions that effectively address the unique needs of this population and promote smoking cessation. Therefore, by examining the perceptions of NRT and e-cigarettes among low-income smokers, this study aims to fill a critical gap in the literature and inform the development of more effective, tailored smoking cessation programs for these individuals.

# 2. Materials and Methods

## 2.1. Study design and participants

The current study uses data collected at enrollment in the STOP trial (El-Khoury et al., 2021), a pragmatic, randomized, multicenter, controlled trial (RCT) designed to evaluate the effectiveness of a smoking cessation intervention among smokers experiencing socioeconomic disadvantage. Participants were recruited between March 2021 and March 2024 by health care providers at 17 centers in five French regions (Ile de France (Parisian region), Provence-Alpes-Côte d'Azur, Pays de la Loire, Hauts de France, and Auvergne-Rhône-Alpes). The trial randomly assigned smokers experiencing socioeconomic disadvantage to either the intervention group, or the standard care control group. Participants in the intervention group could choose from several NRT options (such as patches, inhalers, gum, tablets, etc.) and/or receive a free e-cigarette to support their quit attempts.

Participants were adult smokers, consuming at least five cigarettes per day, and expressing a willingness to quit or reduce smoking. To qualify for the study, they had to be unemployed and/or eligible for at least one social benefit reserved for individuals living in France and experiencing socioeconomic disadvantage, including Social Security, Health Insurance for Undocumented Immigrants (AME), Disabled Adults Allowance (AAH), Minimum Social Benefits (ASS, RSA), Family Support Allowance (ASF), Family Supplement (CF), Housing Allowance (APL), Agricultural Social Mutuality (MSA), Solidarity Allowance for the Elderly (Aspa), Personalized Autonomy Allowance (APA), Universal Health Insurance (PUMA), or Supplementary Solidarity Health Insurance (CSS).

Exclusion criteria included pregnancy, breastfeeding, participation in another clinical trial, being under guardianship or legal curatorship, ongoing smoking cessation therapy at enrollment, current use of e-cigarettes, physical or mental incapacity to participate, inability to sign informed consent or communicate in French, and terminal or life-threatening illness. All participants provided written informed consent.

## 2.2. Measurements

All measures used in this analysis were assessed at enrollment, prior to delivery of the smoking cessation tools in the intervention group.

### 2.2.1. Demographic and socioeconomic variables

Several socio-demographic characteristics were collected. These variables included participants' sex (male, female), age (40 years or older, or under 40 years), current work situation (employed, unemployed looking for work, or unemployed not looking for work), and educational level (high school diploma, less than high school diploma, or university degree). Furthermore, they were asked about whether they received any social benefits reserved for people in financial difficulties (yes/no).

### 2.2.2. Cigarettes per day

We asked participants about the total number of cigarettes (manufactured and rolled) smoked per day. We cut this continuous variable
according to the median, which is 18 (less than 18, or equal to or greater than 18). This approach allowed for balanced group comparisons and simplified interpretation.

### 2.2.3. Patient health questionnaire (PHQ-2)

The French version of PHQ-2 was used to screen for likely depressive symptoms, with two short items: one assessing depressed mood and the other assessing loss of enjoyment or interest in activities (Kroenke et al., 2003; DellaBella et al., 2018). A cut-off score of 3 was used to identify individuals with potential depressive symptoms (score 3 or higher) (Kroenke et al., 2003), facilitating interpretation.

### 2.2.4. Quit attempts

Quit attempts were assessed by asking participants, "How many times have you quit smoking completely for at least 7 days?". One participant did not respond and was assumed to have never quit smoking for at least 7 days. Those with a response greater than zero were asked to indicate whether they used e-cigarettes or NRT during their last quit attempt. We then created a variable called "history of quit attempts and use of e-cigarettes or NRT". This variable had three modalities: Quit attempt(s) without using either NRT or e-cigarettes, quit attempt(s) with NRT, and quit attempt(s) with e-cigarettes (with or without NRT). Due to sample size limitations, further stratification of these groups was not feasible.

### 2.2.5. Perceptions of NRT and e-cigarettes

To assess perceptions of NRT and e-cigarettes, we drew initial inspiration for question design from the modified Cigarette Evaluation Questionnaire (mCEQ) (Cappelleri et al., 2007). However, the questions were extensively adapted during the pilot phase (Héron et al., 2021), incorporating significant modifications to align them with the specific objectives and population of our research. These questions assessed whether participants believed that NRT and e-cigarettes separately help them avoid smoking, are an adequate replacement for cigarettes, and are easy to use. Responses were collected using a five-point Likert scale. For descriptive statistics, we grouped responses "not at all" and "rather not" as "no" and "absolutely" and "rather" as "yes," with "neither yes nor no" as a neutral option. This approach maintained the ordinal nature of the data while reducing the risk of model instability and improving interpretability. One answer to the question "Are e-cigarettes easy to use?" was missing, so we replaced the missing value with the median of the numerical score for this item, which is 3.

## 2.3. Ethical approval

The STOP trial was approved by the "Ile de France II" Institutional review board on September 8, 2020 (CPP Ile de France II; Ref no.: 20.01.31.65528 RIPH2 HPS).

## 2.4. Statistical analysis

Analyses were performed using SAS version 9.4. Descriptive statistics were employed to summarize sample characteristics: continuous variables were described using means and standard deviations (SD), while categorical variables were presented as relative frequencies. To identify unobserved subgroups within the population based on their perceptions of both NRT and e-cigarettes, we used Latent Class Analysis (LCA), as the indicators and latent variables we aimed to measure were categorical. LCA reveals discrete latent classes by analyzing response patterns to various factors, thus identifying distinct response classes (Sinha et al., 2021).

The 6-item questionnaire (3 per tool) on perceptions of NRT and e-cigarettes was used to categorize smokers experiencing socioeconomic disadvantage into classes or subgroups based on their responses. We first evaluated models with 2 to 5 classes using PROC LCA (Lanza et al., 2015). We fitted each LCA model with 888 random seeds and 50

iterations to facilitate model identification. Model fit criteria included Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), entropy, and G-squared, while model identification was based on the log-likelihood value. Conditional probabilities (item response probabilities) were calculated and each individual was assigned to the class with the highest posterior probability. Model selection was primarily based on minimizing the BIC, which is more appropriate for simple latent class models (Linzer and Lewis, 2011), and we have taken into account the interpretability and parsimony of the model.

Next, multinomial logistic regression was performed to examine the association between selected class memberships and predictors, with the first class serving as the reference group. Predictors with a p-value of less than 0.2 in the bivariate analysis were retained in the multivariable model (Supplementary Material, Tables 3 and 4). Adjusted odds ratios (aOR) and 95 % confidence intervals (95 % CI) were calculated.

## 3. Results

The sociodemographic and health characteristics of the 167 participants are presented in Table 1. The sample was balanced in terms of sex (52 % male, 48 % female). Most participants were aged 40 or older (75 %), with a mean age of 49 years (SD = 13.2), and were unemployed (74 %). The majority of participants (69 %) had completed high school or held a university degree. A substantial proportion (38 %) received social benefits.

Smoking intensity was high, with half (n = 84; 50 %) reporting smoking 18 or more cigarettes per day (mean = 18.40 (SD = 10.40)). Regarding mental health, about 2 in 5 participants (n = 61; 37 %) reported possible depressive symptoms. In addition, participants reported an average of two previous attempts to quit smoking. Approximately two-thirds of participants (n = 105; 63 %) did not quit smoking using NRT or e-cigarettes, while a smaller proportion (n = 27; 16 %) had used

<caption>Table 1</caption>

Sociodemographic characteristics, cigarettes per day, mental health and quit attempts of participants in the French STOP trial. N = 167 (1n (%); Mean (SD)).

<table><thead><tr><th>Participant's characteristics</th><th>n = 167<sup>1</sup></th></tr></thead><tbody><tr><td><strong>Socio-demographic</strong></td><td></td></tr><tr><td><strong>Age</strong></td><td></td></tr><tr><td>40 years or older</td><td>125 (75 %)</td></tr><tr><td>Under 40 years</td><td>42 (25 %)</td></tr><tr><td><strong>Sex</strong></td><td></td></tr><tr><td>Female</td><td>80 (48 %)</td></tr><tr><td>Male</td><td>87 (52 %)</td></tr><tr><td><strong>Employment status</strong></td><td></td></tr><tr><td>Employed</td><td>43 (26 %)</td></tr><tr><td>Unemployed looking for work</td><td>56 (33 %)</td></tr><tr><td>Unemployed not looking for work</td><td>68 (41 %)</td></tr><tr><td><strong>Educational level</strong></td><td></td></tr><tr><td>Less than high school diploma</td><td>50 (30 %)</td></tr><tr><td>High school diploma</td><td>72 (43 %)</td></tr><tr><td>University degree</td><td>43 (26 %)</td></tr><tr><td>Missing</td><td>2</td></tr><tr><td><strong>Recipient of social benefits</strong></td><td></td></tr><tr><td>No</td><td>104 (62 %)</td></tr><tr><td>Yes</td><td>63 (38 %)</td></tr><tr><td><strong>Cigarettes per day</strong></td><td></td></tr><tr><td><strong>Total number of cigarettes smoked per day</strong></td><td></td></tr><tr><td>&lt;18</td><td>83 (50 %)</td></tr><tr><td>≥18</td><td>84 (50 %)</td></tr><tr><td><strong>Total number of cigarettes smoked per day</strong></td><td>18.40 (10.40)</td></tr><tr><td><strong>Mental Health</strong></td><td></td></tr><tr><td><strong>Depressive symptoms (PHQ-2 Score)</strong></td><td></td></tr><tr><td>Not likely</td><td>106 (63 %)</td></tr><tr><td>Likely (PHQ-2 Score ≥ 3)</td><td>61 (37 %)</td></tr><tr><td><strong>Quit Attempts</strong></td><td></td></tr><tr><td><strong>Number of quit attempts (at least 7 days)</strong></td><td>1.90 (2.80)</td></tr><tr><td><strong>History of quit attempts and use of e-cigarettes or NRT</strong></td><td></td></tr><tr><td>Quit attempt(s) without using either NRT or e-cigarettes</td><td>105 (63 %)</td></tr><tr><td>Quit attempt(s) with NRT</td><td>35 (21 %)</td></tr><tr><td>Quit attempt(s) with e-cigarettes (with or without NRT)</td><td>27 (16 %)</td></tr></tbody></table>
e-cigarettes, with or without NRT, in their quit attempt. Furthermore, 21 % of the individuals had attempted to quit smoking using NRT.

## 3.1. Perceptions of NRT and e-cigarettes

Participants had mixed views about the effectiveness and convenience of NRT and e-cigarettes. A substantial majority of people (59 %) believed that NRT helped them avoid smoking, compared to a smaller proportion (42 %) for e-cigarettes. While perceptions of both tools as cigarette replacements were comparable; with roughly one-third of participants agreeing that each was an adequate substitute (32 % for NRT, and 36 % for e-cigarettes), both NRT and e-cigarettes were perceived as relatively easy to use by 74 % and 72 % of participants, respectively. (Fig. 1).

## 3.2. Latent class model selection

After analyzing fit statistics for 2–5 class solutions, the three-class solution was selected as the best match. Although the five-class model had the lowest AIC and G-squared, the three-class model yielded the lowest BIC, which is considered one of the most reliable criteria for model selection (Magidson et al., 2004) (Supplementary Material, Table 1). In addition, the three-class model provided better interpretability due to a clear separation of the identified classes. In summary, the three classes were as follows:

**“Adopters”** (44 % of the sample): Individuals with a positive view of both NRT and e-cigarettes as smoking aids, believing that they are easy to use and effective.

**“Doubters of e-cigarettes”** (35 % of the sample): Individuals with mixed perceptions who found the tools easy to use but questioned the effectiveness of e-cigarettes.

**“Resistors”** (21 % of the sample): Individuals with negative attitudes toward both NRT and e-cigarettes, concerning both user-friendliness and effectiveness.

Fig. 2 shows the conditional probability distribution (standard errors) for perceptions of NRT and e-cigarettes across the three latent classes, with “yes” as the response category. (Supplementary Material, Table 2).

## 3.3. Factors predicting class membership

Multivariable multinomial regression analysis identified several factors associated with membership in different classes regarding perceptions of e-cigarettes and NRT. (Table 2).

Considering “adopters” as the reference category, older individuals were more likely to be classified as “doubters of e-cigarettes” (aOR = 3.01, 95 % CI: 1.10–8.73) compared to those under 40 years of age.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Response distribution (%) on perceptions of electronic cigarettes (e-cigarettes) and nicotine replacement therapy (NRT) among smokers experiencing socioeconomic disadvantage. N = 167.</figcaption></figure>

Additionally, individuals who did not receive social benefits (aOR = 3.93, 95 % CI: 1.65–9.36) and who did not report depressive symptoms (aOR = 3.33, 95 % CI: 1.38–8.04) were more likely to be classified as “doubters of e-cigarettes”. Neither of these predictors was significantly associated with “resistors”. Sex and employment status were not significant predictors for either class.

Moreover, there was a significant association between quit attempts, past e-cigarettes or NRT use, and membership in the “doubters of e-cigarettes” and “resistors” latent classes. Compared to those who did not use NRT or e-cigarettes to quit smoking, individuals who had ever quit using NRT were more likely to be in the “doubters of e-cigarettes” class (aOR = 3.12, 95 % CI: 1.17–8.30). Conversely, individuals who had previously attempted to quit using e-cigarettes (with or without NRT), were significantly less likely to be “doubters of e-cigarettes” or “resistors” compared to those who did not use NRT or e-cigarettes to quit (aOR = 0.27, 95 % CI: 0.08–0.92, aOR = 0.20, 95 % CI: 0.05–0.84).

Further, heavy smokers (smoking 18 or more cigarettes per day) had higher odds of being classified as “resistors” (aOR = 2.51, 95 % CI: 1.02–6.16) compared to the reference group.

# 4. Discussion

## 4.1. Main findings

This study identified three distinct latent classes based on how smokers experiencing socioeconomic disadvantage perceived the effectiveness and usability of NRT and e-cigarettes for smoking cessation. It further examines the associations of these classes with smoking status, mental health and quit attempts. Three distinct latent classes were identified: “adopters”, “doubters of e-cigarettes”, and “resistors”. The most prevalent group, “adopters”, exhibited a generally positive attitude toward both NRT and e-cigarettes. Multivariable multinomial logistic regression analysis revealed that older age, not receiving social benefits, better mental health, and previous use of NRT (but not e-cigarettes) increased the likelihood of belonging to the “doubters of e-cigarettes” group rather than the “adopters” group. These “doubters of e-cigarettes” found NRT and e-cigarettes generally easy to use but perceived e-cigarettes as ineffective. Additionally, e-cigarettes use (regardless of past NRT use) was linked with being an “adopter”, i.e., having positive perceptions of these tools.

### 4.1.1. “Adopters” class: key factors and Motivations

The largest class (44 %), “adopters”, indicates a willingness among smokers experiencing socioeconomic disadvantage to use these aids in their efforts to quit smoking. This group perceived both NRT and e-cigarettes as effective cessation tools, highlighting their potential utility in smoking cessation programs targeting smokers from socioeconomic disadvantaged backgrounds. These findings underscore the importance of offering this group a range of cessation options. While the comparative efficacy of e-cigarettes and NRT in high-risk populations remains an area of active research, both modalities appear to hold promise in aiding smoking cessation among low-income smokers (Gentry, 2019; Levett et al., 2023).

Key characteristics of “adopters” include a younger age, worse mental health, and a history of e-cigarette use. Younger smokers, with generally shorter smoking histories, may find their habits less ingrained and thus easier to break (Messer et al., 2008). Additionally, their openness to novel products and variety, such as different e-cigarette flavors (Tsai et al., 2018), may contribute to their adoption of these aids. Our results are consistent with existing research highlighting the predominance of younger adults among e-cigarettes and NRT users (Sapru et al., 2020; Villanti et al., 2016; Chido-Amajuoyi et al., 2020). Conversely, a qualitative study found that uneducated young adults had negative perceptions of cessation aids, possibly due to misconceptions about different types of NRT (Lee et al., 2022).

While some research suggests a positive association between mental
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Conditional Probability distribution with “yes” as the response category for perceptions of nicotine replacement therapy (NRT) and electronic cigarettes (e-cigarettes) for each latent class.</figcaption></figure>

**Table 2**

Results of multivariable multinomial logistic regression analysis for perception classes of e-cigarettes and nicotine replacement therapy (NRT). aOR = adjusted Odds Ratio, CI = Confidence Interval; \* p-value < 0.05; \*\* p-value < 0.01 (Note: Quit attempt(s) without using either NRT or e-cigarettes 105 (63 %), quit attempt(s) with NRT 35 (21 %), and quit attempt(s) with e-cigarettes (with or without NRT) 27 (16 %)).

<table><thead><tr><th rowspan="2">Variables</th><th>Latent Classes</th><th>(Ref: "Adopters")<br>Resistors</th></tr><tr><th>Doubters of e-<br>cigarettes<br>aOR<sup>1</sup> (95 % CI<sup>1</sup>)</th><th>aOR<sup>1</sup> (95 %<br>CI<sup>1</sup>)</th></tr></thead><tbody><tr><td><strong>Age</strong></td><td></td><td></td></tr><tr><td>Under 40 years</td><td>ref</td><td></td></tr><tr><td>40 years or older</td><td>3.01 (1.10, 8.73) \*</td><td>0.60 (0.22, 1.70)</td></tr><tr><td><strong>Sex</strong></td><td></td><td></td></tr><tr><td>Female</td><td>ref</td><td></td></tr><tr><td>Male</td><td>1.11 (0.50, 2.50)</td><td>1.45 (0.60, 3.56)</td></tr><tr><td><strong>Employment status</strong></td><td></td><td></td></tr><tr><td>Employed</td><td>ref</td><td></td></tr><tr><td>Unemployed looking for work</td><td>1.46 (0.54, 3.96)</td><td>2.10 (0.60, 7.46)</td></tr><tr><td>Unemployed not looking for work</td><td>0.65 (0.24, 1.77)</td><td>2.61 (0.76, 8.95)</td></tr><tr><td><strong>Recipient of social benefits</strong></td><td></td><td></td></tr><tr><td>Yes</td><td>ref</td><td></td></tr><tr><td>No</td><td>3.93 (1.65, 9.36) \**</td><td>1.13 (0.46, 2.80)</td></tr><tr><td><strong>Total number of cigarettes smoked per day</strong></td><td></td><td></td></tr><tr><td>&lt;18</td><td>ref</td><td></td></tr><tr><td>≥18</td><td>2.04 (0.91, 4.57)</td><td>2.51 (1.02,<br>6.16) \*</td></tr><tr><td><strong>Depressive symptoms (PHQ-2)</strong></td><td></td><td></td></tr><tr><td>Likely (PHQ-2 Score ≥ 3)</td><td>Ref</td><td></td></tr><tr><td>Not likely</td><td>3.33 (1.38, 8.04) \**</td><td>1.28 (0.52, 3.14)</td></tr><tr><td><strong>History of quit attempts and use of e-cigarettes or NRT</strong></td><td></td><td></td></tr><tr><td>Quit attempt(s) without using either NRT<br>or e-cigarettes</td><td>ref</td><td></td></tr><tr><td>Quit attempt(s) with e-cigarettes (with or<br>without NRT)</td><td>0.27 (0.08, 0.92) \**</td><td>0.20 (0.05,<br>0.84) \*</td></tr><tr><td>Quit attempt(s) with NRT</td><td>3.12 (1.17, 8.3) \**</td><td>0.81 (0.21, 3.06)</td></tr></tbody></table>

well-being and attitudes toward cessation aids (Brunette et al., 2017), other research indicates no significant relationship between mental health status and perceptions of e-cigarette harm (Charlie Albert Smith, 2021) highlighting the inconsistent nature of the existing evidence. Our findings complete these preliminary understandings, revealing that “adopters” are more likely to report poorer mental health. This suggests a potential self-medication hypothesis, where individuals with

depressive symptoms may view smoking cessation as a means to improve overall well-being and perceive e-cigarettes as a helpful tool for mood regulation (Tran and Morrell, 2023; Oyapero et al., 2023). In addition, increased interactions with healthcare professionals, who often recommend cessation tools, particularly NRT, along with mental health care (Hitsman et al., 2009; García-Gómez et al., 2019), may contribute to this trend.

### 4.1.2. “Doubters of e-cigarettes” and “Resistors”: characteristics and influencing factors

The “doubters of e-cigarettes” and “resistors” classes exhibited skepticism or outright resistance to NRT and e-cigarettes as smoking cessation aids. While “doubters of e-cigarettes” acknowledged the ease of use of these aids, they questioned their effectiveness, especially regarding e-cigarettes, and expressed a slight preference for NRT. Our results are in agreement with the Population Assessment of Tobacco and Health (PATH) study, where low-income smokers often viewed e-cigarettes as a supplement rather than a substitute for cigarettes (Alyssa et al., 2019). Other studies have shown that while NRT is considered essential for successful quitting, e-cigarettes are less favored (Boland et al., 2017).

Factors associated with being a “doubter of e-cigarettes” include older age, better mental health, lack of social benefits, and a history of quitting with NRT. Older smokers tend to have a stronger dependence on cigarettes, making quitting more difficult (Baumeister, 2017). They may also be less informed about novel cessation methods (Nyman et al., 2019) or referred by health professionals to use NRT (Hung et al., 2011). Moreover, those without mental health issues may feel less need to use smoking cessation aids or fear the post-cessation stress associated with failed quit attempts (Kim et al., 2019), leading them to distance themselves from these products. Further research is needed to understand the impact of depressive symptoms on the use of cessation aids among smokers experiencing socioeconomic disadvantage. Additionally, disadvantaged, marginalized people who are not entitled to social benefits may face financial barriers that limit their access to or willingness to try cessation aids, which are often seen as an additional expense (Young-Wolff, 2018). This financial burden may contribute to their skepticism. Therefore, free access to NRT and e-cigarettes could encourage low-income smokers to use and adhere to these treatments.

The “resistors” group displayed negative attitudes toward both NRT and e-cigarettes, aligning with commonly cited barriers to adopting either tool. Predominantly composed of heavy smokers with high nicotine dependence, this group often expressed doubts about the
efficacy of these tools in overcoming their addiction. Long-term smoking cultivates a complex interplay of physical dependence and psychological attachment, often leading to an overestimation of the challenges associated with quitting (Baumeister, 2017). This can foster negative perceptions of NRT and e-cigarettes, particularly among heavy smokers who may find the reduced nicotine levels of these alternatives less satisfying (Hajek et al., 2017). While e-cigarettes have gained popularity among heavy smokers, often as a dual-use option or smoke-free alternatives (Hedman et al., 2018), “resistors” were less likely to have tried quitting with e-cigarettes, either alone or in combination with NRT, potentially reinforcing their negative views. This contrasts with findings suggesting that individuals who attempted to quit using e-cigarettes often encounter challenges but maintain optimism about their potential effectiveness (Garey et al., 2019).

## 4.2. Strengths

This study is the first to use LCA to explore classes of smokers experiencing socioeconomic disadvantage who are seeking to quit or reduce their consumption, regarding their perceptions on the effectiveness and usability of NRT and e-cigarettes for smoking cessation. A key advantage of this study was the use of multivariable analysis to examine these perceptions. Furthermore, the involvement of healthcare professionals in data collection enhanced the accuracy and reliability of the data.

## 4.3. Limitations

The generalizability of our findings is limited by the study design and sample characteristics, particularly the small sample size, as well as the limited number of sociodemographic variables included in the analysis. First, the inclusion of smokers seeking to quit or reduce consumption may not accurately represent the broader population of smokers, as 41 % of daily smokers in France in 2021 expressed no desire to quit (Guignard et al., 2021). Second, the focus on individuals experiencing socioeconomic disadvantage restricts the applicability of results to higher SEP populations. Third, excluding current e-cigarette users may bias the sample toward individuals with specific attitudes toward e-cigarettes. Additionally, self-reported data on smoking behavior and perceptions may be subject to recall bias and social desirability bias. Finally, the bootstrap likelihood ratio test, typically used to determine the optimal number of latent classes, could not be applied using SAS software due to the polytomous nature of the items (Dziak and Lanza, 2016). Therefore, alternative model fit criteria, such as the BIC, were used to assess model fit. These limitations should be considered when interpreting the study findings.

## 5. Conclusion

In summary, our study identified distinct subgroups among smokers experiencing socioeconomic disadvantage seeking to quit or reduce their consumptions, based on their perceptions of e-cigarettes and NRT. Factors such as older age, absence of social benefits, mental health issues, and previous quit attempts predisposed individuals to view e-cigarettes as ineffective. Conversely, those who used e-cigarettes, alone or with NRT, were more likely to perceive these aids positively and to adopt them. These findings highlight the nuanced influences of demographic and behavioral factors on smokers' attitudes toward cessation aids. Identifying groups like “adopters”, “doubters of e-cigarettes”, and “resistors”, can inform tailored cessation strategies. Policymakers should integrate smokers' preferences into programs, such as offering counseling on preferred methods. Targeted support is essential for heavy smokers, individuals with mental health issues, and those from disadvantaged backgrounds.

### Declaration of generative AI and AI-assisted technologies

The authors confirm that no generative AI or AI-assisted technologies

were used in the writing or creation of this manuscript, including data analysis, interpretation, or the development of figures and tables.

### Funding sources

This work was supported by a grant from the French National Cancer Institute (INCA). Award/Grant number: IRESP- AAP TABAC 2018. The funders of the study had no role in study design, data collection, data analysis, data interpretation, writing of the report, or in the decision to submit the article for publication.

### CRediT authorship contribution statement

**Marie Noel Al Zayat:** Writing – original draft, Visualization, Software, Formal analysis, Data curation. **Maria Melchior:** Writing – review & editing, Resources, Methodology, Funding acquisition. **Judith van der Waerden:** Writing – review & editing, Methodology, Formal analysis. **Sarah Mahdjoub:** Writing – review & editing, Project administration. **Gladys Ibanez:** Writing – review & editing, Resources, Methodology, Funding acquisition. **Fabienne El-Khoury:** Writing – review & editing, Validation, Supervision, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.

### 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

Our warmest thanks to all the participants who generously shared their time with us. A special thank you as well to the STOP's RCT project manager and to all study investigators.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.addbeh.2025.108290.

### Data availability

Data will be made available on request.

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