Nicotine Dependence and Mental Health Among Syrian University Students: The Role of E‐Cigarette Use: A Cross‐Sectional Study

ABSTRACT

Background and Aims

Nicotine dependence is a growing concern, particularly among young adults. While e‐cigarettes are marketed as smoking cessation tools, evidence suggests they may contribute to increased dependence and dual use. Additionally, nicotine use has been linked to mental health disorders, yet research exploring these associations among Syrians is limited. This study examines e‐smoking patterns among Syrian university students, assesses nicotine dependence, and investigates correlations with anxiety and depression.

Methods

A cross‐sectional web‐based survey was distributed via social media, targeting university students. A 53‐item questionnaire collected demographic and smoking data, while validated scales, namely Hooked on Nicotine Checklist (HONC), E‐cigarette Dependence Scale (EDS), Patient Health Questionnaire‐9 (PHQ‐9), and Generalized Anxiety Disorder‐7 (GAD‐7), were used to assess nicotine dependency, depression, and anxiety.

Results

Among 1092 participants, 661 (60.5%) were female. A total of 376 (34.4%) were current or past smokers. High nicotine dependence was observed, with 61% of e‐cigarette users scoring above 21 on the EDS. Mean dependence scores among vapers were 28.85 ± 20.1 (EDS) and 5.8 ± 3.07 (HONC). Daily e‐cigarette use correlated positively with dependence (p < 0.0001). Weak but significant associations were found between dependence and anxiety (p = 0.032) and depression (p = 0.002).

Conclusion

E‐cigarette use among Syrian university students is linked to nicotine dependence and mental health concerns. Our findings challenge their role in smoking cessation and highlight the need for regulatory policies and public health interventions.

1. Introduction

Nicotine is a potent addictive substance with psychoactive properties [1]. Additionally, it ranks third among common psychoactive substances after caffeine and alcohol [2]. Moreover, nicotine‐related disorders, like nicotine dependence and nicotine withdrawal, have been widely recognized as mental disorders. Many studies have discussed the relationship between nicotine and other psychiatric disorders, such as depression and anxiety, and identified subunits of nicotinic acetylcholine receptors as the main factor in this association [3].

Sources of nicotine have evolved in recent years, ranging from traditional tobacco to patches and electronic smoking devices, commonly known as e‐cigarettes, vapes, or vape pens. These devices' popularity has surged recently, especially among young adults, raising concerns about their impact on health [4], particularly mental health, during a critical developmental stage when the brain is still maturing [5]. On top of that, there is ongoing debate regarding whether e‐cigarettes serve as an effective smoking cessation tool, with some studies indicating that users may be less likely to quit traditional cigarettes altogether [6]. Understanding these connections is crucial for developing effective interventions and policies to address the mental health needs of this demographic. Additionally, while the association between nicotine use and mental health disorders has been studied globally, limited research explores these dynamics among Syrian young adults, particularly in the context of emerging nicotine sources like e‐cigarettes.

This study aims to assess e‐smoking prevalence and patterns among Syrian young adults represented by university students, evaluate nicotine dependence, and explore its associations with depression, anxiety, and demographic factors. It seeks to provide insights for targeted interventions and public health policies.

2. Methods

This is an observational cross‐sectional study. A web‐based survey consisting of a 53‐item self‐reported questionnaire was distributed on social media platforms. The platforms included student groups on Facebook, Telegram, and WhatsApp, where the link was sent for them to fill out. The survey was launched in January 2023; we published brief information about the survey and a direct link to the survey on social media. The brief information included an invitation to contribute, the name of the principal investigator, title, aims of the study, and the sponsoring university. After accessing the link, a more detailed explanation of the study was displayed, including the aim of the study, the construction of the survey questionnaire, and the expected time required to answer.

The survey targeted university students in Syria and was written in Arabic. The survey was anonymous and could be reached via Google Forms. The survey was open for 50 days; no payment was provided to participants. The questionnaire took approximately 5–10 min to complete and was reviewed to ensure that its content was readable by two authors.

The participant information sheet and the consent were collected on the first page of the survey. Upon providing consent, participants were moved to the next screen containing the full study questionnaire. Data regarding gender, age, specialty, smoking status, and smoking frequency were collected, followed by the following study tools.

3. Study Tools

• 1. Hooked on nicotine checklist HONC: This tool was used to assess the loss of autonomy over nicotine use. It consists of 10 questions answered with yes or no. The total score is answered by summing the positive responses. Higher scores indicate higher dependence and loss of control over nicotine [7]. The Arabic version was tested for reliability and internal consistency in this study post hoc, where Cronbach's Alpha was equal to 0.84, which means good reliability (0.8 > α ≥ 0.9 is considered “good”). Corrected Item‐Total Correlations: Ranged from 0.38 to 0.62 (all > 0.3, acceptable). Alpha if Item Deleted: Remained stable (~0.81), suggesting no problematic items.
• 2. E‐cigarette Dependence Scale (EDS): The EDS is a validated tool designed to measure nicotine dependence among e‐cigarette users. The 22‐question version was used in this study. Each question is given a score of 0, 1, 2, 3, 4 for never, rarely, sometimes, often, or almost always, respectively. Higher scores indicate higher dependence [8]. The Arabic version was tested for reliability and internal consistency in this study post hoc, where Cronbach's Alpha was equal to 0.965, which means excellent reliability (α ≥ 0.9 is considered “excellent”). Corrected Item‐Total Correlations: Ranged from 0.42 to 0.83 (all > 0.3, confirming strong item consistency). Alpha if Item Deleted: Remained stable (~0.94), suggesting no problematic items.
• 3. Depressive symptoms: Depressive symptoms among participants were assessed using the 9‐item Patient Health Questionnaire (PHQ‐9). Participants rated the frequency of depression‐related symptoms experienced over the preceding 2 weeks on a 4‐point Likert scale (0 = “not at all” to 4 = “nearly every day”). A score of ≥ 10, aligning with established clinical thresholds, was used to define clinically significant depressive symptoms. This cutoff is consistent with validated recommendations for PHQ‐9 interpretation. The Arabic version was validated in a previous study and showed good psychometric properties [9]. The present study obtained a Cronbach's alpha of 0.89 for the PHQ‐9, suggesting strong internal reliability.
• 4. Anxiety symptoms: Anxiety symptoms were assessed using the 7‐item Generalized Anxiety Disorder (GAD‐7) scale, a well‐validated instrument with established reliability and validity in general population studies. This tool has been used in previous research to measure generalized anxiety disorder (GAD) in individuals. The GAD‐7 items are scored on a 4‐point Likert scale (0 = “not at all” to 3 = “nearly every day”), yielding a total score range of 0–21. To optimize diagnostic accuracy, a cutoff score of ≥ 10 was applied to identify clinically significant anxiety symptoms, as recommended in prior validation studies. The Arabic version was validated in a previous study and showed good psychometric properties [9]. The present study obtained a Cronbach's alpha of 0.93 for the GAD‐7 scale, suggesting strong internal reliability.
• 5.All smokers completed the PHQ‐9 and GAD‐7 scales, while only smokers completed the EDS and HONC scales to later compare the results.

3.1. Ethical Considerations

This study complies with the Declaration of Helsinki for research involving human subjects. Ethical approval to conduct this study was obtained from the Ethical Committee at the International University for Science and Technology (IUST) (ID: PH‐24‐3). The objective of the study was explained at the beginning of the questionnaire. Participants gave their informed consent before filling out the survey. The participation was completely voluntary. No names were recorded to ensure secrecy in data collection.

3.2. Data Analysis

Descriptive statistics summarized demographic variables, smoking patterns, and dependence levels, presenting frequencies, percentages, and mean scores. Inferential analyses included one‐way ANOVA tests to examine correlations between demographic factors (age, gender, and specialization) and nicotine dependence scores (ECDS and HONC). T‐tests were used to assess relationships between gender and specialization with dependence scores. Linear regression was applied to assess relationships between daily e‐cigarette usage and dependence levels, as well as the impact of dependence on mental health outcomes such as anxiety (GAD‐7) and depression (PHQ‐9).

4. Results

In total, 1092 participants have filled out the online survey. The majority were aged between 21 and 23 years, representing 38% of the sample. Women formed 60.5% of the sample, while men were 39.5%. Fifty‐six percent of the participants are related to the health field (doctors, dentists, or pharmacists), while the others were not related. Table 1 provides detailed frequencies and percentages of the demographic characteristics.

Table 1.

Demographic characteristics.

Variables	Frequencies	Percentage
Gender
Female	661	60.5
Male	431	39.5
Age
18–20	191	17.5
21–23	415	38.0
24–27	289	26.5
28–30	197	18.0
Specialty
Health field‐related	612	56.0
Other	480	44.0

Results showed 34.4% of the participants were either current or previous smokers. In all, 19.23% of all participants smoke traditional cigarettes (only or with electronic cigarettes), while 21.53% use electronic cigarettes (only or with traditional cigarettes or used to smoke), and 15.9% use both. The frequencies and percentages of the smoking habits are shown in Table 2.

Table 2.

Smoking status details.

Variables	Frequencies	Percentage
Smoking
Non‐smoker	714	65.38
Smoker
Electronic only (currently)	147	13.4
Previous smoker (electronic only)	21	1.9
Traditional only	142	13
Both	68	6.23
Number of cigarettes/days (groups are also used in the figures)
0–4 (Group 1)	25	6.6
5–9 (2)	104	27.6
10–14 (3)	44	11.7
15–19 (4)	19	5
20–29 (5)	7	1.86
30+ (6)	49	13

The mean score of the E‐cigarette Dependence Scale is 28.85 with a standard deviation of 20.1, representing high dependence (scores higher than 21). The percentage of participants with scores higher than 21 was 61% of electronic cigarette smokers and 13.2% of all participants. One‐way ANOVA test showed that age was not correlated with E‐CDS score (F (3) = 0.68, p = 0.56), which means that no specific age group was correlated with higher scores. Additionally, there was no relationship between gender and E‐CDS score (T (236) = 1.56, p = 0.12). Moreover, the specialization (health‐related and not health‐related) had no impact on E‐CDS score, T (236) = 0.62, p = 0.98.

The mean of the HONC scale among electronic cigarette smokers was 5.8 with a standard deviation of 3.07, indicating that e‐cigarette smokers in this research have lost some degree of autonomy over their vaping. Ninety‐three percent of them had scores greater than 0. Age, gender, and specialization were not significantly correlated with HONC scores, F (3) = 1.68, p = 0.17, T (236) = 0.38, p = 0.70, T (236) = 0.89, p = 0.09 in order.

Linear regression analysis revealed a significant positive relationship between daily e‐cigarette use and EDS scores (B = 6.4, t  = 9.2, p < 0.0001, CI [5–7.7]) and HONC scores as well (B = 0.55, t = 4.6, p < 0.0001, CI [0.31–0.78]). This indicates that with each increase in daily usage of e‐cigarettes, there will be an increase in the level of dependence on e‐cigarettes based on the ECDS scale and HONC scale. Figures 1 and 2 represent the increase in the means of these scales with the increase in daily usage.

Figure 1.

Illustrates the increase in mean HONC scale scores as the number of times an e‐cigarette is used daily increases.

Figure 2.

Illustrates the increase in mean ECDS scale scores as the number of times an e‐cigarette is used daily increases.

4.1. Anxiety, Depression, and Smoking

A one‐way ANOVA test showed a significant correlation between smoking status and GAD‐7 score and PHQ‐9 score (F (4) = 4.1, p = 0.003, and F (4) = 5.8, p < 0.001 in order). Smoking both types of cigarettes had the highest mean of scores in both GAD and PHQ. All means are shown in Table 3.

Table 3.

Means represent the level of anxiety and depression among groups with different smoking statuses.

	PHQ‐9	GAD‐7
Electronic only (currently)	10.0748	7.8231
Traditional only	10.2183	7.6479
Both	12.1912	8.3676
Previous smoker (electronic only)	5.1429	3.7619

The mean of GAD‐7 scores of e‐cigarette smokers was 7.6 with a standard deviation of 5.33, indicating mild anxiety. Additionally, the mean PHQ‐9 score among e‐cigarette smokers was 10.2 (SD = 7.07), indicating moderate depression. A linear regression analysis was conducted to examine the relationship between EDS and HONC and anxiety levels measured by GAD‐7. The results revealed a significant positive relationship, but it is weak. This indicates that higher dependence on e‐cigarettes is associated with increased anxiety levels (Table 4). Similarly, linear regression between EDS and HONC with depression levels measured by PHQ‐9 revealed a significant positive relationship (Table 4). The results indicate that when anxiety level or depression level increases, the level of dependence on e‐cigarette (EDCS) and nicotine dependence (HONC) increases as well.

Table 4.

Association between nicotine dependence and depression and anxiety.

	B	T	p value	Confidence interval 95%
	GAD‐7 scale
EDS	0.039	0.46	0.032	0.003–0.074
HONC	2.1	3.9	0.001	0.22–0.69
	PHQ‐9 scale
EDS	0.071	3.088	0.002	0.026–0.117
HONC	0.654	4.338	0.000	0.357–0.952

5. Discussion

The use of e‐cigarettes has been increasingly regulated in the United States, with one of the most significant legislative measures prohibiting the sale and use of these products for individuals under the age of 21 [10], Despite these restrictions, adolescents often find alternative ways to obtain e‐cigarettes, including purchasing them online, acquiring them through older peers, or having adults buy them on their behalf [11]. Unfortunately, these kinds of restrictions are not implemented in Syria, making acquiring them even easier for the youth. Many studies have established a link between e‐cigarette use and mental health issues. Studies have reported suicide attempts to be heightened among individuals who use e‐cigarettes [12], further highlighting the importance of understanding the mental health consequences of vaping, which was the aim of our study.

Our study's participants had a mean age of 22.1 years. Young adults aged 18–24 are the age group most likely to vape [13, 14], with factors like social influence playing the biggest part in its popularity among them, along with other factors like the perception that vapes are less harmful than traditional smoking and the good taste of vapes, as reported in previous studies [15, 16, 17].

Over a third of participants were smokers or ex‐smokers in our sample. This prevalence is slightly higher than the global smoking prevalence reported in 2020 (32.6%) [18], and less than that reported in Egyptian and Australian studies [19, 20], but aligns with findings from a study conducted in England, which identified 34.0% of their sample as social smokers [21]. In contrast, nearly 20% of our sample were traditional smokers or vapers, while nearly 15% used both. Nicotine addiction can explain this dual use, as smokers are usually supporters of vaping [22], and some of them might vape as a way of getting more nicotine into their system [23].

Nicotine dependence among smokers in our study was assessed using EDS and HONC to ensure an acceptable understanding of the sample's nicotine dependence. Our study's participants demonstrated high nicotine dependency using the EDS scale, as over 60% of vapers and nearly 13% of all participants were dependent on nicotine. Moreover, vapers in our sample started to lose autonomy over their vaping, as demonstrated by their HONC scores. These results contradict the prevalent perception that e‐cigarettes are a good smoking cessation tool [6], which can be attributed to factors like the high nicotine content of some vapes, symptoms of habitual use, social acceptability, dual use of electronic and traditional cigarettes, and the frequent use pattern of e‐cigarettes [24, 25], which was also evident in our sample, as EDS and HONC scales were correlated with daily vape use. A previous study concluded that vapes should not be used as a viable smoking cessation tool, since they have proven ineffective and could potentially lead to serious lung injuries [6]. It is known that stressors, such as economic instability, displacement, and psychological distress, can motivate populations to smoke, which is an important aspect in our sample, since Syria has been going through an over‐decade‐long armed conflict, which would explain this high nicotine dependency [26].

Our present study identified a significant association between nicotine dependence and depression, as smokers exhibited mild depressive symptoms, which progressively increased alongside higher EDS and HONC scores. Research from Canada reported similar findings, demonstrating that e‐cigarette‐induced depression was particularly pronounced among individuals with high nicotine dependence, whereas those who used e‐cigarettes infrequently exhibited fewer depressive symptoms [27]. Additionally, other studies have identified a unique relationship between e‐cigarette use and depression, independent of other forms of smoking [28].

Similarly, nicotine dependence was associated with anxiety in our sample. Previous research has explored the connection between nicotine dependence and anxiety, with findings indicating a complex interaction between smoking behaviors and anxiety disorders. Data from a US study revealed a significant difference in the prevalence of GAD among smokers who had used e‐cigarettes in the past 12 months (23.6%) compared to non‐smokers (16.4%). Although this study did not conclusively establish that e‐cigarette users were more susceptible to developing GAD, it emphasized the role of anxiety in preventing successful smoking cessation, particularly due to increased cravings during periods of heightened anxiety [29]. This suggests that nicotine dependence may not only contribute to anxiety symptoms but also serve as a barrier to smoking cessation, reinforcing the cycle of addiction. Similar results regarding the association between depression and anxiety, and nicotine were reported in previous studies [19, 20, 30].

This correlation can be explained by several interconnected mechanisms, including the release of dopamine triggered by nicotine, which temporarily improves mood but disrupts the brain's natural dopamine production over time. Furthermore, many individuals use nicotine as a self‐prescribed remedy for depression and anxiety, creating a bidirectional relationship between these mental health disorders and nicotine dependence. Finally, nicotine can mimic symptoms of anxiety and depression, and its chronic use alters the body's stress response by increasing cortisol levels, thereby heightening susceptibility to these conditions [30, 31, 32, 33, 34].

We studied age, gender, and university specialties' association with nicotine dependence and found that the association was not statistically significant. Previous studies reported different results regarding the influence of demographic factors on nicotine dependence. A study from the United States reported that how different demographic factors affected nicotine dependence varied across different populations [35], while a Malaysian study reported that male gender and low income were important predictors of nicotine dependence [36]. Lastly, a Turkish study reported that adolescents and young adults were particularly vulnerable to peer pressure and other psychosocial variables [37].

5.1. Conclusions

The findings of this study highlight the increasing prevalence of e‐cigarette use among young adults and emphasize its association with mental health issues, including depression and anxiety. Despite regulatory efforts in some countries, e‐cigarettes remain accessible, particularly in regions like Syria, where restrictions are minimal or non‐existent. The study challenges the perception of e‐cigarettes as effective smoking cessation tools, revealing that high nicotine content and frequent use contribute to dependency and dual usage of traditional cigarettes and e‐cigarettes. These insights underscore the importance of public health interventions to address vaping behaviors and their broader impact on mental well‐being.

5.2. Limitations

The scales were tested for reliability after data collection. Despite the fact it showed an excellent reliability, it does not guarantee construct validity. Therefore, results should be interpreted with caution. Moreover, due to a lack of similar studies in countries culturally similar to Syria, a more comprehensive comparison of results could not be done. Additionally, our results cannot be generalized on other populations and age groups. Lastly, a big proportion of our sample were students of health‐related facilities, which could be a potential source of bias.

Author Contributions

M.B.A., O.A.E., L.Y.‐A., and K.J. wrote the manuscript. L.Y.‐A. conducted the statistical analysis. K.J. supervised the whole process. S.A., N.H., R.H., and K.J. collected the data. All authors have read and approved the final version of the manuscript. M.B.A. had full access to all of the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis.

Funding

The authors received no specific funding for this work.

Ethics Statement

This study complies with the Declaration of Helsinki for research involving human subjects. Ethical approval to conduct this study was obtained from the Ethical Committee at the International University for Science and Technology (IUST) (ID: PH‐24‐3). The objective of the study was explained at the beginning of the questionnaire. Participants gave their informed consent before filling out the survey. The participation was completely voluntary. No names were recorded to ensure secrecy in data collection.

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Transparency Statement

The lead author Mohammad Basheer Alameer affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.