Multiple Tobacco Product Use and Perceived Cognitive Function Among Young Adults in Kuwait: A Cross-Sectional Study

Abstract

Background

Polytobacco use is a rapidly emerging global public health threat. Epidemiological data regarding the effect of polytobacco use on cognitive function is scarce. Thus, this study aimed to assess the association between polytobacco use and perceived cognitive functioning (PCF) difficulties among young adults.

Methods

A cross-sectional study enrolled university students (aged 18-30 years) in Kuwait. Participants self-reported current (past 30-day) use of e-cigarettes, conventional cigarettes, hookah, and heated tobacco products. PCF difficulties were assessed using a validated scale and categorized in 2 ways: (i) as a binary variable (‘within normal limits’ vs ‘mild-to-severe’ difficulties) and (ii) as an ordinal variable (‘within normal limits,’ ‘mild,’ ‘moderate,’ or ‘severe’ difficulties). Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using logistic regression models.

Results

Data from 1323 participants were analyzed (805 female participants). Current use of e-cigarettes, conventional cigarettes, hookah, heated tobacco, and ≥3 tobacco products were reported by 333 (25.2%), 294 (22.2%), 183 (13.8%), 158 (11.9%), and 189 (14.3%) participants, respectively. Mild, moderate, and severe PCF difficulties were reported by 208 (15.7%), 165 (12.5%), and 90 (6.8%) participants, respectively, with 463 (35.0%) participants collectively reporting ‘mild-to-severe’ PCF difficulties. Current use of e-cigarettes (aOR: 1.72, 95% CI: 1.22-2.43), conventional cigarettes (aOR: 1.56, 95% CI: 1.08-2.26), heated tobacco (aOR: 1.49, 95% CI: 1.00-2.21), and ≥3 tobacco products (aOR: 1.96, 95% CI: 1.30-2.97) were associated with increased odds of reporting ‘mild-to-severe’ PCF difficulties. Moreover, current use of ≥3 tobacco products was associated with increased odds of ‘moderate’ (aOR: 2.01, 95% CI: 1.14-3.54) and ‘severe’ (aOR: 3.29, 95% CI: 1.49-7.26) PCF difficulties.

Conclusion

Polytobacco use is common among young adults in Kuwait. This analysis demonstrated an association between current tobacco product use, particularly polytobacco use, and increased odds of experiencing perceived cognitive difficulties.

Keywords

tobacco polytobacco use multiple tobacco use heated tobacco e-cigarettes hookah cognitive function cognition youth young adults

Introduction

Tobacco use continues to be a major threat to global public health, with the rapid emergence of diverse nicotine and tobacco products further complicating preventive efforts. The Global Burden of Disease 2019 analysis estimated that tobacco smoking was responsible for 7.69 million deaths and 200 million disability-adjusted life-years.¹ Concurrently with the changing tobacco product landscape in recent years, tobacco product use patterns have also evolved.² Specifically, polytobacco use (also referred to as multiple tobacco product use; ie, currently using 2 or more tobacco products) has rapidly emerged, driven by easy accessibility to a variety of products and appealing marketing strategies.^3-5 Adolescents and young adults are the main demographic for polytobacco experimentation and use.^6,7 The National Youth Tobacco Survey–2024 estimated that 10.9% of US high school students use multiple tobacco products.⁸ Moreover, data from the Global Youth Tobacco Surveys indicated that the prevalence of dual use of e-cigarettes and conventional cigarettes exceeded 5% in 15 of the 67 countries analyzed.⁹ A systematic review reported a wide range of polytobacco product use among adults globally, with estimates of polytobacco use being as high as 11.9% in Denmark.¹⁰ Furthermore, the prevalence of polytobacco use was estimated to be 6.8% among adults in Japan.¹¹ Collectively, these findings emphasize the surge in polytobacco use irrespective of geographic borders.

Polytobacco use is associated with a wide range of health consequences, including increased nicotine dependence symptoms^12,13 and lower intentions to quit smoking,¹⁴ resulting in persistent tobacco use. Moreover, polytobacco users compared with tobacco nonusers have higher respiratory symptoms,^15,16 cardiovascular risk factors,¹⁷ and internalized mental health problems (anxiety and depression symptoms).^18,19 Few prior studies have assessed the association between patterns of tobacco product use and cognition. While animal models provide biological plausibility, for instance, mice exposed to e-cigarette vapor exhibit deficits in spatial learning and memory compared with unexposed control mice,²⁰ emerging epidemiological evidence suggests similar patterns in humans. A study analyzing the US National Youth Tobacco Survey found that exclusive e-cigarette use and dual use were significantly associated with serious difficulty concentrating, remembering, or making decisions in youth.²¹ Similarly, recent findings among Australian adolescents indicate that e-cigarette use is correlated with lower academic self-efficacy and increased mind-wandering.²² A study among adults found that subjective cognitive complaints in dual tobacco product users is almost double that of individuals who never used tobacco products.²³ Another study reported similar findings, with dual users of e-cigarettes and combustible cigarettes having increased rates of cognitive impairments compared with tobacco nonusers.²⁴

Exposure to nicotine (a psychoactive and addictive substance) and other chemicals in tobacco products during critical developmental periods like adolescence has been linked to impaired brain development and cognitive dysfunction.^25,26 Beyond cognitive risks, adolescence and young adulthood are critical periods of vulnerability for other adverse outcomes. Exposure to nicotine during this developmental window can disrupt brain maturation, heighten susceptibility to lifelong addiction, and is increasingly linked to mental health challenges such as anxiety and depression symptoms.^27-29 Hence, polytobacco users are vulnerable to being exposed to a wider range of harmful chemicals than nonusers and single tobacco product users.

Locally, tobacco use remains a significant public health concern in Kuwait. Previous studies in a nationally representative sample estimated that 39.2% of adult men and 3.3% of adult women were current cigarette smokers.³⁰ E-cigarette use was even higher, reported by 47.6% of adult men and 14.4% of adult women.³¹ Among high school students in Kuwait, 46.8% of males and 9.3% of females reported current use of e-cigarettes, with 12.8% of the total student sample reporting concurrent use of e-cigarettes, conventional cigarettes, and hookah.³²

To this end, the scientific literature is limited in epidemiological studies that assess the association between polytobacco use and cognitive function among youth; a critical period for brain development.³³ Therefore, this study aimed to (i) measure the frequency of single, dual, and multiple use of tobacco products (e-cigarettes, conventional cigarettes, hookah, and heated tobacco products) and (ii) assess the association between polytobacco use and perceived cognitive functioning (PCF) difficulties among university students in Kuwait.

Methods

Study Setting, Design, and Participants

A cross-sectional study was conducted among university students (n = 1323) from the primary public university (Kuwait University, approximately enrolls 40 000 students) and 3 private universities across the State of Kuwait. Given the small geographical size and high urbanization of Kuwait, all included university campuses are located in the metropolitan area; thus, the study setting is exclusively urban. Participants were recruited using non-probability convenience sampling. The inclusion criteria were: (1) current enrollment as a university student in Kuwait, (2) age between 18 and 30 years, and (3) male or female sex (both sexes were invited). Exclusion criteria included individuals who were not university students and those outside the specified age range. Students in the target population were approached in various venues (lobbies, cafeterias, and lecture halls) across colleges and invited to participate. Recruitment was conducted by the study authors (medical students at Kuwait University), who provided prospective participants with a QR code to access the secure web-based questionnaire. Participants primarily used their personal mobile devices (eg, smartphones) to complete the survey; however, tablets were also provided by the research team for students who preferred to use them. Upon accessing the link, participants were presented with an electronic informed consent form. They were required to provide consent by selecting an agreement checkbox before they could proceed to the survey questions. Data collection occurred between October 9 and 16, 2025.

Questionnaire and Variable Definition

A self-administered questionnaire was compiled using validated instruments and standardized items to collect information on demographics, personal and parental smoking history, and to ascertain PCF difficulties and sleep disturbances (see Supplemental Material for the questionnaire). Prior to data collection, the questionnaire was pilot-tested among a small sample of university students to ensure question clarity, logical flow, and comprehensibility. No significant changes were required following the pilot phase. Questions were derived from the standardized National Youth Tobacco Survey³⁴ to assess the use of 4 specific tobacco products: e-cigarettes, conventional cigarettes, hookah (water pipe), and heated tobacco (heat-not-burn tobacco). For each tobacco product, participants reported lifetime use and the frequency of use (number of days) in the past 30 days. Based on these responses, the following variables were defined for each assessed tobacco product: ever use (any reported lifetime use), current use (any reported use in the past 30 days), former use (ever use but not current use [ie, no use in the past 30 days]), single product use (current use of only one of the 4 assessed tobacco products), dual product use (current use of any 2 of the 4 assessed tobacco products), and multiple product use (current use of ≥3 of the 4 assessed tobacco products).

PCF difficulties and sleep disturbance were ascertained using the validated Patient-Reported Outcomes Measurement Information System^® (PROMIS^®) Short Forms. Specifically, the PROMIS^® v2.0 Cognitive Function – Short Form 8a (8-item scale) assessed subjective cognitive difficulties in concentration, memory, language, mental acuity, and perceived changes in cognitive function.³⁵ Questions were rated on a 5-point scale ranging from ‘never’ (no difficulties) to ‘very often’ (frequent difficulties), yielding raw scores ranging from 8 to 40 (severe to no PCF difficulties). Sleep disturbance was ascertained using the PROMIS® v1.0 Sleep Disturbance – Short Form 6a (6-item scale).³⁶ These questions used a 5-point scale ranging from ‘very poor’ to ‘very good’ with raw scores ranging from 6 to 30 (no to severe sleep disturbance). Raw scores for both instruments were converted to T-scores using the web-based HealthMeasures Scoring Service provided by Assessment Center^SM.³⁷ Two categorizations were used for the PCF difficulties assessment: (i) as a binary variable: ‘within normal limits’ (T-Score ≥45.0) and ‘mild-to-severe difficulties’ (T-score <45.0) and (ii) as an ordinal variable: ‘within normal limits’ (T-Score ≥45.0), ‘mild difficulties’ (45.0> T-score ≥40.0), ‘moderate difficulties’ (40.0> T-Score ≥35.0), and ‘severe difficulties’ (T-Score <35.0).³⁸ Sleep disturbance was categorized as: ‘none-to-mild’ (T-Score <55.0) and ‘moderate-to-severe’ (T-Score ≥55.0) sleep disturbance.³⁸

Statistical Analysis

All statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA). The level for statistical significance (α) was set at 0.05 for all association tests. Descriptive statistics included frequencies and proportions for categorical variables, and the median and interquartile range (Q1: first quartile and, Q3: third quartile) for continuous variables. Univariable associations between categorical variables were assessed using the chi-squared (ꭓ²) test to evaluate differences in frequencies.

Multivariable analyses were conducted to assess the association between tobacco product use (e-cigarette, conventional cigarette, hookah, heated tobacco, and multiple tobacco product use) and PCF difficulties status. PCF difficulties status served as the outcome variable and was modeled using 2 approaches: (i) binary logistic regression: used when the PCF difficulties variable was dichotomized as ‘within normal limits’ (reference category) vs ‘mild-to-severe’ PCF difficulties and (ii) multinomial logistic regression: used when the PCF difficulties variable was categorized into 4 levels: ‘within normal limits’ (reference category), ‘mild difficulties’, ‘moderate difficulties’, and ‘severe difficulties’. Covariates were selected based on evidence of a potential univariable association (P < 0.2) with either the PCF difficulties status (outcome variable) or the any tobacco product use (exposure variable), and their effects were controlled for in all regression models. Consequently, the final multivariable logistic regression models were adjusted for the following potential confounders: sex, age, university of enrollment (public vs private), sleep disturbance, and parental smoking status. Adjusted odds ratios (aOR) and their 95% confidence intervals (CI) were estimated. The Hosmer–Lemeshow goodness-of-fit test was used to evaluate model adequacy.

Ethical Consideration

Ethical approval for the study was granted by the Health Sciences Center Ethics Committee for Student Research at Kuwait University (Ref. 1015/2025). Permission to collect data was obtained from the relevant authorities prior to commencing data collection. Electronic written informed consent was obtained from all study participants before they completed the study questionnaire.

Results

Demographic Characteristics and Prevalence of PCF Difficulties

A total of 1323 university students participated in the current study; 518 (39.1%) were males and 805 (60.9%) were females (Table 1). The age of the participants ranged from 18 to 30 years old, with the majority being 18-21 years old (n = 862, 65.2%). Students were enrolled from both public and private universities, with the majority being from public universities (n = 1,127, 85.2%). Of the total study sample, 547 (41.3%) reported parental smoking. The PROMIS^® Cognitive Function 8-item scale was used to derive levels of PCF difficulties, with 860 (65.0%) of the participants falling into the ‘within normal limits’ of PCF difficulties category. Mild, moderate, and severe PCF difficulties were reported by 208 (15.7%), 165 (12.5%), and 90 (6.8%) of the study participants, respectively. Moderate-to-severe sleep disturbance was ascertained in 490 (37.1%) participants (Table 1).

Table 1.

Characteristics of the Total Study Participants (n = 1323)

Variable	n (%)
Sex
Male	518 (39.1)
Female	805 (60.9)
Age (completed years)
Median (Q1, Q3)	21.0 (19.0, 22.0)
18-21	862 (65.2)
22-25	377 (28.5)
26-30	84 (6.3)
Nationality
Kuwaiti	1170 (88.4)
Non-Kuwaiti	153 (11.6)
University of enrollment
Public University	1127 (85.2)
Private University	196 (14.8)
Parental smoking status
Father	498 (37.6)
Mother	14 (1.1)
Father and mother	35 (2.6)
Neither	776 (58.7)
Perceived cognitive functioning difficulties^a
Within normal limits	860 (65.0)
Mild difficulties	208 (15.7)
Moderate difficulties	165 (12.5)
Severe difficulties	90 (6.8)
Sleep disturbance status^b
None-to-mild disturbance	832 (62.9)
Moderate-to-severe disturbance	490 (37.1)
Missing, n	1

Q1: first quartile; Q3: third quartile.

^aThe PROMIS^® Cognitive Function 8-item scale was used to derive levels of perceived cognitive functioning difficulties according to established T-score cut points: “within normal limits” (T-score ≥45.0), “mild difficulties” (45.0 > T-score ≥40.0), “moderate difficulties” (40.0 > T-score ≥35.0), and “severe difficulties” (T-score <35.0).

^bSleep disturbance status was categorized according to the T-score on the PROMIS^® Sleep Disturbance 6-item scale as: none-to-mild (T-score <55.0) and moderate-to-severe (T-score ≥55.0) sleep disturbance.

Prevalence and Patterns of Tobacco Product Use

Table 2 presents the prevalence estimates of tobacco product use in the total study sample and by sex. Ever e-cigarette use was reported by 31.4% (n = 415) of the study population, while current (use in past 30 days) e-cigarette use prevalence was 25.2% (n = 333). Current conventional cigarette use was reported by 22.2% (n = 294), and current hookah use was estimated at 13.8% (n = 183). Current heated tobacco use was reported by 11.9% (n = 158). The prevalence of current single, dual, and ≥3 tobacco product use was estimated to be 7.6% (n = 101), 8.2% (n = 108), and 14.3% (n = 189), respectively. Overall, 30.1% (n = 398) of the participants reported current use of any tobacco products. Male participants reported significantly higher use of all assessed tobacco products compared to female participants (Table 2).

Table 2.

Prevalence of e-Cigarette, Conventional Cigarette, Hookah, and Heated Tobacco Use and Multiple Tobacco Product Use in the Total Analytical Sample and Stratified by Sex

	Total sample (n = 1323), % (n)	Males (n = 518), % (n)	Females (n = 805), % (n)	P-value^a
E-cigarette use
Ever	31.4 (415)	62.7 (325)	11.2 (90)	<0.001
Current^b	25.2 (333)	52.7 (273)	7.5 (60)	<0.001
Former^c	6.2 (82)	10.0 (52)	3.7 (30)	<0.001
Conventional cigarette use
Ever	29.2 (386)	62.0 (321)	8.1 (65)	<0.001
Current^b	22.2 (294)	49.6 (257)	4.6 (37)	<0.001
Former^c	7.0 (92)	12.4 (64)	3.5 (28)	<0.001
Hookah use
Ever	21.3 (282)	45.6 (236)	5.7 (46)	<0.001
Current^b	13.8 (183)	30.9 (160)	2.9 (23)	<0.001
Former^c	7.5 (99)	14.7 (76)	2.9 (23)	<0.001
Heated tobacco use
Ever	15.6 (206)	34.0 (176)	3.7 (30)	<0.001
Current^b	11.9 (158)	26.5 (137)	2.6 (21)	<0.001
Former^c	3.6 (48)	7.5 (39)	1.1 (9)	<0.001
Current multiple tobacco product use
Single tobacco product use	7.6 (101)	11.0 (57)	5.5 (44)	<0.001
Dual tobacco product use	8.2 (108)	17.6 (91)	2.1 (17)	<0.001
≥3 tobacco product use	14.3 (189)	32.8 (170)	2.4 (19)	<0.001
Any tobacco product use	30.1 (398)	61.4 (318)	9.9 (80)	<0.001

^aCalculated using chi-squared test to compare frequency distribution in males and females.

^bAny use in the past 30 days.

^cEver use, but no use in the past 30 days.

Factors Associated with PCF Difficulties and Tobacco Use

Table 3 details the univariable associations between personal characteristics and both PCF difficulties status and current use of any tobacco products. In the total study sample, 463 (35.0%) participants were classified to have ‘mild-to-severe’ PCF difficulties. Female participants were more likely than male participants to report mild-to-severe PCF difficulties (37.1% vs 31.7%, P = 0.041). Participants aged 26-30 years were also more likely to report mild-to-severe PCF difficulties than younger participants. Furthermore, participants who reported parental smoking were significantly more likely to report mild-to-severe PCF difficulties compared to those without parental smoking (39.1% vs 32.1%, P = 0.008). Sleep disturbance was also associated with PCF difficulties status: having a ‘moderate-to-severe’ sleep disturbance increased the likelihood of reporting mild-to-severe PCF difficulties compared to ‘none-to-mild’ sleep disturbance (52.0% vs 25.0%, P < 0.001; Table 3).

Table 3.

Univariable Associations Between Study Participant Characteristics and Perceived Cognitive Functioning Difficulties and Any Tobacco Product Use

Variable	Perceived cognitive functioning (PCF) difficulties: Mild-to-severe^a (T-score <45)		Any current tobacco product use
Variable	% (n/total)	P-value^b	% (n/total)	P-value^b
Overall	35.0 (463/1323)	–	30.1 (398/1323)	–
Sex
Male	31.7 (164/518)	0.041	61.4 (318/518)	<0.001
Female	37.1 (299/805)		9.9 (80/805)
Age (completed years)
18-21	34.0 (293/862)	0.023	23.6 (203/862)	<0.001
22-25	34.2 (129/377)		38.7 (146/377)
26-30	48.8 (41/84)		58.3 (49/84)
University of enrollment
Public University	35.9 (404/1127)	0.120	27.2 (306/1127)	<0.001
Private University	30.1 (59/196)		46.9 (92/196)
Parental smoking status
Yes	39.1 (214/547)	0.008	36.0 (197/547)	<0.001
No	32.1 (249/776)		25.9 (201/776)
Sleep disturbance^c
None-to-mild	25.0 (208/832)	<0.001	27.0 (225/832)	0.002
Moderate-to-severe	52.0 (255/490)		35.1 (172/490)

^aPerceived cognitive functioning ‘mild-to-severe’ difficulties was defined as a T-score on the PROMIS^® Cognitive Function 8-item scale <45.0.

^bCalculated using chi-squared test.

^cSleep disturbance status was categorized according to the T-score on the PROMIS^® Sleep Disturbance 6-item scale as: none-to-mild (T-score <55.0) and moderate-to-severe (T-score ≥55.0) sleep disturbance.

Regarding current use of any tobacco products, its prevalence showed an increasing pattern with age (P < 0.001). Any tobacco product use was also significantly higher among participants who attended private compared to public universities (46.9% vs 27.2%, P < 0.001), among those with a parental history of smoking compared to none (36.0% vs 25.9%, P < 0.001), and in participants who reported ‘moderate-to-severe’ sleep disturbance compared to ‘none-to-mild’ sleep disturbance (35.1% vs 27.0%, P = 0.002; Table 3).

Adjusted Associations Between Tobacco Use and PCF Difficulties

Table 4 presents the adjusted associations between tobacco product use and PCF difficulties status, controlling for sex, age, university of enrollment, sleep disturbance, and parental smoking status. Compared to never-users of the respective tobacco products, current e-cigarette use was associated with increased odds of mild-to-severe PCF difficulties (aOR: 1.72, 95% CI: 1.22-2.43). Moreover, current use of conventional cigarettes (aOR: 1.56, 95% CI: 1.08-2.26) and current use of heated tobacco products (aOR: 1.49, 95% CI: 1.00-2.21) showed similar associations with increased odds of mild-to-severe PCF difficulties. However, current hookah use was not statistically significantly associated with PCF difficulties status (aOR: 1.33, 95% CI: 0.90-1.96). The odds of mild-to-severe PCF difficulties increased linearly with the number of tobacco products used. Compared to non-users, the odds were 1.30 (95% CI: 0.81-2.08) for single-product users, 1.45 (95% CI: 0.89-2.36) for dual users, and 1.96 (95% CI: 1.30-2.97) for users of ≥3 tobacco products (Table 4).

Table 4.

Adjusted Associations Between Use of Individual and Multiple Tobacco Products and Perceived Cognitive Functioning Difficulties (Outcome Variable)

	n	Perceived cognitive functioning (PCF) difficulties: Mild-to-severe^a (T-score <45.0)		P-value
	n	% (n)	aOR^b (95% CI)	P-value
E-cigarette use
Never	908	33.4 (303)	1.00 (reference)	–
Former	82	31.7 (26)	1.15 (0.67, 1.95)	0.618
Current	333	40.2 (134)	1.72 (1.22, 2.43)	0.002
Conventional cigarette use
Never	937	34.3 (321)	1.00 (reference)	–
Former	92	28.3 (26)	0.84 (0.50, 1.43)	0.521
Current	294	39.5 (116)	1.56 (1.08, 2.26)	0.018
Hookah use
Never	1041	34.5 (359)	1.00 (reference)	–
Former	99	34.3 (34)	1.06 (0.65, 1.74)	0.808
Current	183	38.3 (70)	1.33 (0.90, 1.96)	0.154
Heated tobacco use
Never	1117	34.7 (387)	1.00 (reference)	–
Former	48	18.8 (9)	0.45 (0.21, 1.13)	0.202
Current	158	42.4 (67)	1.49 (1.00, 2.21)	0.050
Current multiple tobacco product use
None	925	33.2 (307)	1.00 (reference)	–
Single tobacco product use	101	37.6 (38)	1.30 (0.81, 2.08)	0.282
Dual tobacco product use	108	36.1 (39)	1.45 (0.89, 2.36)	0.133
≥3 tobacco product use	189	41.8 (79)	1.96 (1.30, 2.97)	0.001

aOR: adjusted odds ratio; CI: confidence interval.

^aPerceived cognitive functioning ‘mild-to-severe’ difficulties was defined as a T-score on the PROMIS^® Cognitive Function 8-item scale <45.0.

^bAdjusted for sex, age, university of enrollment, PROMIS^® sleep disturbance score, and parental smoking status.

A subsequent analysis further categorized PCF status into 4 levels of difficulties (within normal limits [n = 860], mild difficulties [n = 208], moderate difficulties [n = 165], and severe difficulties [n = 90]) to evaluate its association with tobacco product use (Figure 1). Single tobacco product use was not associated with any level of PCF difficulties. In contrast, dual tobacco product use, compared to non-use, was only associated with increased odds of severe PCF difficulties (aOR: 2.82, 95% CI: 1.17-6.80). Furthermore, current use of ≥3 tobacco products was associated with increased odds of moderate (aOR: 2.01, 95% CI: 1.14-3.54) and severe PCF difficulties (aOR: 3.29, 95% CI: 1.49-7.26). The number of tobacco products used demonstrated a dose-response relationship with severe PCF difficulties (Figure 1).

Figure 1.

Associations between multiple tobacco product use and perceived cognitive functioning (PCF) difficulties.

Discussion

The aim of this cross-sectional study was to estimate the prevalence of polytobacco use and assess its association with PCF difficulties among young adults in Kuwait. E-cigarettes were the most currently used tobacco product (25.2%), followed by conventional cigarettes (22.2%). Around one third of the enrolled participants reported any tobacco product use in the past 30 days, with 14.3% reporting current use of ≥3 tobacco products. Overall, male participants reported higher use of tobacco products than female participants. Female gender, increased age, parental smoking status, and ‘moderate-to-severe’ sleep disturbances were associated with increased likelihood of reporting ‘mild-to-severe’ PCF difficulties. Current use of e-cigarettes, conventional cigarettes, and heated tobacco products were associated with increased odds of ‘mild-to-severe’ PCF difficulties. Additionally, current use of ≥3 tobacco products was also associated with higher odds of ‘mild-to-severe’ PCF difficulties. Moreover, increased odds of ‘severe’ PCF difficulties were associated with current use of dual and ≥3 tobacco products. These findings show that polytobacco use is associated with increased negative impacts on PCF compared to single or no use of tobacco products.

Our findings confirm the high burden of tobacco use among youth in Kuwait. The observed prevalence of polytobacco use (14.3% using ≥3 products) is consistent with the 12.8% previously reported among high school students in Kuwait.³² Similarly, multiple tobacco product use was reported by 10.9% of high school students in the US.⁸ On the other hand, current use of heated tobacco products (an emerging smoking modality) was reported by 11.9% of our study participants, which is consistent with a prevalence estimate (10.9%) among adults in Japan.³⁹ Regionally, our findings mirror recent trends observed in neighboring Gulf Cooperation Council (GCC) countries. For instance, a recent meta-analysis among university students in Saudi Arabia reported a pooled smoking prevalence of 24.5%, which is similar to our estimate for conventional cigarette use.⁴⁰ Furthermore, a study across multiple Arab countries identified e-cigarettes as the leading form of tobacco use among university students (21.2%).⁴¹ Collectively, these results highlight substantial tobacco use and poly-use among study participants, underscoring the need for public health interventions.

The association between tobacco product use and cognitive function among youth remains relatively unexplored in the scientific literature. The current study showed a statistically significant association between current e-cigarette use and ‘mild-to-severe’ PCF difficulties (aOR = 1.72). This finding was consistent with previous research by Novak et al (2024), where they found that among adolescents, exclusive e-cigarette users have a higher risk of cognitive impairment compared to exclusive conventional cigarette users.²² Additionally, in this study, current conventional cigarette and heated tobacco use were associated with increased odds of ‘mild-to-severe’ PCF difficulties. Nonetheless, current hookah use did not show a statistically significant association with PCF difficulties. The observed association between current use of heated tobacco products and PCF difficulties in the current report (aOR = 1.49) is considered novel. To our knowledge, no prior studies have reported such an association. An in vivo experiment using rats showed that exposure to heated tobacco aerosol extract injected intravenously caused an increase in regional cerebral blood flow to a magnitude similar to that of exposure to conventional combustible cigarette smoke extract.⁴² Such a finding further highlights the potential negative impact of heated tobacco on brain development and cognition.

Moreover, results of this report showed that as the number of tobacco products used increased, the odds of reporting ‘mild-to-severe’ PCF difficulties increased, with highest odds observed among those who reported currently using ≥3 tobacco products. For example, the odds of single tobacco users reporting ‘mild-to-severe’ PCF difficulties was 1.30 compared to non-users. Whereas the odds of those using ≥3 tobacco products were 1.96 times the odds of non-users. Furthermore, when assessing different levels of PCF difficulties, dual tobacco product use (aOR: 2.82) and ≥3 tobacco product use (aOR: 3.29) were associated with increased odds of ‘severe’ PCF difficulties. These findings reinforce existing evidence found in a study conducted by Cai et al, where they found that dual tobacco product users were more likely to have subjective cognitive complaints.²⁴ Our findings of polytobacco product use being associated with increased PCF difficulties add to the limited existing knowledge and highlight an important and emerging public health issue, specifically among youth. Future studies using objective assessment of cognitive functioning are needed to corroborate the findings of this analysis.

Nicotine, the major psychoactive component of tobacco, produces a biphasic effect on cognition. This effect is highly dependent on the duration and pattern of tobacco use. Looking at short-term use, nicotine is known for momentarily sharpening focus and alertness.⁴³ This is due to its stimulation of nicotinic acetylcholine receptors (nAChR), which in turn release dopamine and other neurotransmitters that induce cognitive improvement.^43,44 On the other hand, long-term use of nicotine causes chronic stimulation of nAChR, leading to upregulation and desensitization.⁴⁵ Once the receptors are desensitized, attention and memory deficits are experienced.^43,45 Similar studies have found that exposure to other compounds found in e-cigarettes, like heavy metals, are associated with impaired cognitive function through their neurotoxic effects, which include oxidative stress, inflammation, and DNA damage.^22,46 Such biological mechanisms may explain the observed stronger association between polytobacco product use (stacking effect) and ‘severe’ PCF difficulties in our study sample of young adults.

The public health implications of tobacco use among youth have been widely discussed in the scientific literature. Nonetheless, rates remain high, with recent data showing increasing trends in the dual use of e-cigarettes and conventional cigarettes.^47,48 Global data indicate that most countries lack sufficient anti-tobacco campaigns and appropriate cessation services for this demographic.⁴⁹ In Kuwait, despite the existence of tobacco control legislation,³² the high prevalence of polytobacco use observed in this study suggests that current regulations, particularly regarding the marketing and accessibility of novel products, may be insufficient. Therefore, there is an urgent need to develop and implement policies to protect children and young adults from the ramifications of tobacco product use. The American Academy of Pediatrics has made fifteen public policy recommendations to combat tobacco use in youth, including: (i) the need for prevention, screening, and treatment programs; (ii) higher tobacco prices; (iii) raising the minimum sales age to 21 years; (iv) banning flavored tobacco products; and (v) comprehensive smoke-free laws.⁵⁰ In the context of our findings, local public health authorities in Kuwait should prioritize these strategies. Furthermore, educational campaigns could specifically highlight the potential adverse effects of polytobacco use on cognitive function, a message likely to resonate with university students concerned with academic performance.

A major strength of this study is the large sample, which enabled the assessment of the prevalence of polytobacco use and its effect on perceived cognitive function status in young adults in Kuwait. The PROMIS^® v2.0 Cognitive Function scale short form 8a, used in the report, assessed ‘perceived’ cognitive functioning difficulties, which may not represent ‘actual’ cognitive function. However, the PROMIS^® Cognitive Function Short Form uses an item-response theory (IRT) approach, making it a precise, reliable, and psychometrically rigorous instrument for measuring perceived cognitive function in large population-based studies.^35,51 In a sample of patients aged ≥65 years, the PROMIS^® Cognitive Function scale showed moderate correlation with results of the Montreal Cognitive Assessment scores (MoCA; an objective assessment tool for cognitive impairment), as these tools assess related but distinct aspects of cognition.⁵² The PROMIS^® Cognitive Function scale is subjective and relies on self-perception while the MoCA is objective and relies on tested performance. This study is subject to some limitations. A formal power analysis and sample size calculation were not performed prior to data collection. Although the identification of statistically significant associations suggests the sample size was sufficient to detect these effects, the lack of an a priori calculation remains a limitation. Moreover, a temporal relationship cannot be established as both the exposure (tobacco use) and outcome (PCF difficulties) were measured simultaneously. The study relies on self-reporting, which makes it subject to recall and social desirability bias. However, both were accounted for, to some extent, by formulating a well-structured, neutral, and anonymous questionnaire. The study provides a ‘snapshot’ of smoking behavior which may not represent long-term patterns or variability over time.

Conclusion

The prevalence of single, dual, and polytobacco use among young adults in this study was high. Current use of e-cigarettes, conventional cigarettes, and heated tobacco products was independently associated with an increased likelihood of ‘mild-to-severe’ PCF difficulties. Moreover, we observed a dose-response relationship between the number of tobacco products used (single, dual, ≥3 vs non-use) and ‘severe’ PCF difficulties. These findings add to the evidence linking polytobacco use to adverse health outcomes. Given the rising trends in uptake and polytobacco use among young adults, future research should prioritize developing targeted public health interventions to reduce initiation and promote cessation.

Supplemental Material

Supplemental Material - Multiple Tobacco Product Use and Perceived Cognitive Function Among Young Adults in Kuwait: A Cross-Sectional Study

Supplemental Material for Multiple Tobacco Product Use and Perceived Cognitive Function Among Young Adults in Kuwait: A Cross-Sectional Study by Alhaton Al Ansari, Dalal Akbar, Daniah Al Saleh, Deema Al Qehs, Hanai Al Kandari, Rayan Al Duwailah, Sarah Al Rashdan, Yara Al Mutairi, Ali H. Ziyab in Tobacco Use Insights.

Footnotes

Acknowledgements

We would like to convey our thanks to all the participants in our study.

ORCID iD

Ali H. Ziyab

Ethical Considerations

The study was approved by the Health Sciences Center Ethics Committee for Students Research at Kuwait University (Ref. 1015/2025) on October 12, 2025.

Consent to Participate

Written informed consent was obtained from all subjects before enrollment in the study.

Author Contributions

AAl-A, DA, DAl-S, DAl-Q, HAl-K, RAl-D, S-Al-R, and YAl-M contributed to conceiving, designing, planning and conducting the study, contributed to analyzing and interpreting the data, and drafted the manuscript. AHZ contributed to conceiving, designing, and planning the study, supervised study implementation, contributed to data analysis and interpretation, revised the manuscript. All authors critically revised the manuscript for important intellectual content. The manuscript has been read and approved by all authors.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets generated and/or analyzed during the current study are not publicly available to protect participants’ privacy and comply with the Ethics Committee requirements, but de-identified participant level data pertaining to the current study are available from the corresponding author on reasonable request.*

Supplemental Material

Supplemental material for this article is available online.

References

Tobacco Collaborators

GBD

. Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990-2019: a systematic analysis from the global burden of disease study 2019. Lancet. 2021;397(10292):2337-2360. doi:10.1016/S0140-6736(21)01169-7

O’Connor

Schneller

Felicione

Talhout

Goniewicz

Ashley

. Evolution of tobacco products: recent history and future directions. Tob Control. 2022;31(2):175-182. doi:10.1136/tobaccocontrol-2021-056544

Rubenstein

Pacek

McClernon

. Multiple tobacco product use conceptual framework: a 2021 update on evidence. Nicotine Tob Res. 2022;24(8):1208-1217. doi:10.1093/ntr/ntac032

Chen

. Dual and poly-use of novel and conventional nicotine and tobacco product use in Europe: challenges for population health, regulatory policies, and the ways ahead. Front Public Health. 2023;11:1093771. doi:10.3389/fpubh.2023.1093771

Zhang

Bannon

Tzu-Hsuan Chen

Filippidis

. Dual and poly-nicotine and tobacco use among adolescents in the United States from 2011 to 2022. Addict Behav. 2024;152:107970. doi:10.1016/j.addbeh.2024.107970

Tashakkori

Park-Lee

Roh

Christensen

. Multiple tobacco product use among youth E-Cigarette users: national youth tobacco survey, 2020. J Adolesc Health. 2023;73(4):769-775. doi:10.1016/j.jadohealth.2023.05.025

Harrell

Naqvi

SMH

Plunk

Martins

. Patterns of youth tobacco and polytobacco usage: the shift to alternative tobacco products. Am J Drug Alcohol Abuse. 2017;43(6):694-702. doi:10.1080/00952990.2016.1225072

Jamal

Park-Lee

Birdsey

, et al. Tobacco product use among middle and high school students - national youth tobacco survey, United States, 2024. MMWR Morb Mortal Wkly Rep. 2024;73(41):917-924. doi:10.15585/mmwr.mm7341a2

Sreeramareddy

Acharya

Manoharan

. Electronic cigarettes use and ‘dual use’ among the youth in 75 countries: estimates from global youth tobacco surveys (2014-2019). Sci Rep. 2022;12(1):20967. doi:10.1038/s41598-022-25594-4

10.

Chen

Girvalaki

Mechili

Millett

Filippidis

. Global patterns and prevalence of dual and poly-tobacco use: a systematic review. Nicotine Tob Res. 2021;23(11):1816-1820. doi:10.1093/ntr/ntab084

11.

Yamamoto

Abbas

Cooray

Yokoyama

Tabuchi

. Estimating the prevalence of and clarifying factors associated with multiple tobacco product use in Japan: a cross-sectional study in 2022. J Epidemiol. 2025;35(5):222-229. doi:10.2188/jea.JE20240153

12.

Sung

Wang

Yao

Lightwood

Max

. Polytobacco use and nicotine dependence symptoms among US adults, 2012-2014. Nicotine Tob Res. 2018;20(suppl_1):S88-S98. doi:10.1093/ntr/nty050

13.

Huh

Min Lee

Cho

. Comparison of nicotine dependence between single and multiple tobacco product users among South Korean adults. Tob Induc Dis. 2022;20:22. doi:10.18332/tid/145899

14.

Azagba

Shan

Latham

. Adolescent dual use classification and its association with nicotine dependence and quit intentions. J Adolesc Health. 2019;65(2):195-201. doi:10.1016/j.jadohealth.2019.04.009

15.

Zavala-Arciniega

Cook

Hirschtick

, et al. Longitudinal associations between exclusive, dual and polytobacco use and respiratory illness among youth. BMC Public Health. 2024;24(1):2159. doi:10.1186/s12889-024-19582-8

16.

Patel

Buszkiewicz

Cook

Arenberg

Fleischer

. Longitudinal association of exclusive and dual use of cigarettes and cigars with asthma exacerbation among US adults: a cohort study. Respir Res. 2024;25(1):305. doi:10.1186/s12931-024-02930-y

17.

Chen

Huo

Mattey-Mora

Bidulescu

Parker

. Assessing the association between e-cigarette use and cardiovascular disease: a meta-analysis of exclusive and dual use with combustible cigarettes. Addict Behav. 2024;157:108086. doi:10.1016/j.addbeh.2024.108086

18.

Cho

Goldenson

Stone

, et al. Characterizing polytobacco use trajectories and their associations with substance use and mental health across mid-adolescence. Nicotine Tob Res. 2018;20(suppl_1):S31-S38. doi:10.1093/ntr/ntx270

19.

Kwon

Nam

Lee

. Poly-tobacco use and mental health in South Korean adolescents. Tob Induc Dis. 2024;22:83. doi:10.18332/tid/187077

20.

Prasedya

Ambana

Martyasari

NWR

Aprizal

Nurrijawati

Sunarpi . Short-term E-cigarette toxicity effects on brain cognitive memory functions and inflammatory responses in mice. Toxicol Res. 2020;36(3):267-273. doi:10.1007/s43188-019-00031-3

21.

Xie

. Association of electronic cigarette use with self-reported difficulty concentrating, remembering, or making decisions in US youth. Tob Induc Dis. 2020;18:106. doi:10.18332/tid/130925

22.

Novak

Gyawali

Wang

. Association between E-Cigarettes, cognition and mood in adolescents. Subst Use Misuse. 2024;59(12):1820-1827. doi:10.1080/10826084.2024.2383597

23.

Xie

Ossip

Rahman

O’Connor

. Electronic cigarette use and subjective cognitive complaints in adults. PLoS One. 2020;15(11):e0241599. doi:10.1371/journal.pone.0241599

24.

Cai

Bidulescu

. E-cigarette use or dual use of E-cigarette and combustible cigarette and mental health and cognitive impairment: findings from the national health interview survey, 2020-2021. J Affect Disord. 2024;351:878-887. doi:10.1016/j.jad.2024.01.056

25.

Counotte

Goriounova

, et al. Lasting synaptic changes underlie attention deficits caused by nicotine exposure during adolescence. Nat Neurosci. 2011;14(4):417-419. doi:10.1038/nn.2770

26.

Castro

Lotfipour

Leslie

. Nicotine on the developing brain. Pharmacol Res. 2023;190:106716. doi:10.1016/j.phrs.2023.106716

27.

Reynolds

Faure

Barik

. Adolescent nicotine exposure and persistent neurocircuitry changes: unveiling lifelong psychiatric risks. Mol Psychiatr. 2025;30(11):5534-5545. doi:10.1038/s41380-025-03110-0

28.

Fluharty

Taylor

Grabski

Munafo

. The association of cigarette smoking with depression and anxiety: a systematic review. Nicotine Tob Res. 2017;19(1):3-13. doi:10.1093/ntr/ntw140

29.

Oliver

Wang

Yarak

, et al. Memory under siege: the cognitive costs of smoking and vaping. Brain Behav Immun Health. 2025;49:101102. doi:10.1016/j.bbih.2025.101102

30.

Alali

Longenecker

Alwotyan

AlKandari

Al-Mulla

Al Duwairi

. Prevalence of smoking in the Kuwaiti adult population in 2014: a cross-sectional study. Environ Sci Pollut Res Int. 2021;28(8):10053-10067. doi:10.1007/s11356-020-11464-x

31.

Alshaibani

Alajmi

Alabduljalil

, et al. Prevalence of use, perceptions of harm and addictiveness, and dependence of electronic cigarettes among adults in Kuwait: a cross-sectional study. Tob Induc Dis. 2023;21:90. doi:10.18332/tid/163300

32.

Esmaeil

Alshammasi

Almutairi

, et al. Patterns of electronic cigarette, conventional cigarette, and hookah use and related passive exposure among adolescents in Kuwait: a cross-sectional study. Tob Induc Dis. 2020;18:59. doi:10.18332/tid/123499

33.

Goriounova

Mansvelder

. Nicotine exposure during adolescence alters the rules for prefrontal cortical synaptic plasticity during adulthood. Front Synaptic Neurosci. 2012;4:3. doi:10.3389/fnsyn.2012.00003

34.

Birdsey

Cornelius

Jamal

, et al. Tobacco product use among U.S. middle and high school students - national youth tobacco survey, 2023. MMWR Morb Mortal Wkly Rep. 2023;72(44):1173-1182. doi:10.15585/mmwr.mm7244a1

35.

Iverson

Marsh

Connors

Terry

. Normative reference values, reliability, and item-level symptom endorsement for the PROMIS(R) v2.0 cognitive function-short forms 4a, 6a and 8a. Arch Clin Neuropsychol. 2021;36(7):1341-1349. doi:10.1093/arclin/acaa128

36.

Buysse

Moul

, et al. Development and validation of patient-reported outcome measures for sleep disturbance and sleep-related impairments. Sleep. 2010;33(6):781-792. doi:10.1093/sleep/33.6.781

37.

HealthMeasures scoring service powered by assessment Center^SM. https://www.assessmentcenter.net/ac_scoringservice. Accessed 11/17/2025.

38.

Rothrock

Cook

O’Connor

Cella

Smith

Yount

. Establishing clinically-relevant terms and severity thresholds for Patient-Reported Outcomes Measurement Information System((R)) (PROMIS((R))) measures of physical function, cognitive function, and sleep disturbance in people with cancer using standard setting. Qual Life Res. 2019;28(12):3355-3362. doi:10.1007/s11136-019-02261-2

39.

Odani

Tabuchi

. Prevalence of heated tobacco product use in Japan: the 2020 JASTIS study. Tob Control. 2022;31(e1):e64-e65. doi:10.1136/tobaccocontrol-2020-056257

40.

Alanazi

. Prevalence of smoking among undergraduate students in the Kingdom of Saudi Arabia: a meta-analysis. Tob Induc Dis. 2025;23:23. doi:10.18332/tid/190797

41.

Sallam

Alnazly

Sajwani

, et al. Vaping leads tobacco consumption among university students in Arab countries: a study of behavioral and psychosocial factors associated with smoking. Front Public Health. 2025;13:1636757. doi:10.3389/fpubh.2025.1636757

42.

Uchida

Moriya

Morihara

Shimura

Kagitani

. Cerebral cortical vasodilation via nicotinic receptors by heated tobacco product aerosol extract in rats. J Vasc Res. 2024;61(6):318-326. doi:10.1159/000541726

43.

Yuan

Cross

Loughlin

Leslie

. Nicotine and the adolescent brain. J Physiol. 2015;593(16):3397-3412. doi:10.1113/JP270492

44.

Wang

, et al.

Nicotine's effect on cognition, a friend or foe?

Prog Neuropsychopharmacol Biol Psychiatry. 2023;124:110723. doi:10.1016/j.pnpbp.2023.110723

45.

Mansvelder

McGehee

. Cellular and synaptic mechanisms of nicotine addiction. J Neurobiol. 2002;53(4):606-617. doi:10.1002/neu.10148

46.

Novak

Wang

. The effect of e-cigarettes on cognitive function: a scoping review. Psychopharmacology (Berl). 2024;241(7):1287-1297. doi:10.1007/s00213-024-06607-8

47.

Njie

Kirksey Jones

Jacques

, et al. Changes in tobacco product use among students aged 13 to 15 years in 34 countries, global youth tobacco survey, 2012-2020. Prev Chronic Dis. 2023;20:E68. doi:10.5888/pcd20.220410

48.

Sreeramareddy

Acharya

Manoharan

. Changes in E-cigarette use, cigarette smoking, and dual-use among the youth (13-15 years) in 10 countries (2013-2019)-Analyses of global youth tobacco surveys. Nicotine Tob Res. 2024;26(2):142-150. doi:10.1093/ntr/ntad124

49.

Rajvong

Tarasenko

Ciobanu

. Tobacco cessation, anti-tobacco education, and smoke-free schools: Findings from the global youth tobacco survey. Tob Prev Cessat. 2024;10(November):57. doi:10.18332/tpc/193569

50.

Jenssen

Walley

Boykan

, et al. Protecting children and adolescents from tobacco and nicotine. Pediatrics. 2023;151(5):e2023061804. doi:10.1542/peds.2023-061804

51.

Saffer

Lanting

Koehle

Klonsky

Iverson

. Assessing cognitive impairment using PROMIS((R)) applied cognition-abilities scales in a medical outpatient sample. Psychiatry Res. 2015;226(1):169-172. doi:10.1016/j.psychres.2014.12.043

52.

Edelen

Harrison

Rodriguez

, et al. Evaluation of PROMIS cognitive function scores and correlates in a clinical sample of older adults. Gerontol Geriatr Med. 2022;8:23337214221119057. doi:10.1177/23337214221119057

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.26 MB