The Impact of E-cigarette Usage on Cognitive Performance Level among University Students in Malaysia: A case-controlled study

doi:10.21203/rs.3.rs-2430416/v1

Download PDF

Research Article

The Impact of E-cigarette Usage on Cognitive Performance Level among University Students in Malaysia: A case-controlled study

https://doi.org/10.21203/rs.3.rs-2430416/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Introduction:

The electronic cigarette (e-cigarette) is an electronic device containing a cartridge filled with liquid nicotine and other chemicals, producing inhalable smoke. Malaysia is known to be one of the biggest markets for e-cigarettes due to the highest recorded number of young consumers. E-cigarette cause multiple pulmonary complications or direct injury secondary to the device explosion. Little is known about its effects on cognitive performance, and its potential hazard to the brain. Given a different sociodemographic background and e-cigarette profile compared to the other country, it is paramount to explore its potential effect on cognitive function among the young population in Malaysia.

Method:

This is a case-controlled study to explore the cognitive performance differences between e-cigarette users and non-users among university students in Malaysia. We performed a face-to-face cognitive evaluation using WHO Auditory Verbal Learning Tests (WHO-AVLT), Trail Making test (TMT), and Digit Span Test (DST). We assessed the knowledge and attitude towards e-cigarettes using a self-reported questionnaire and explored their association with the behaviour.

Results:

DST has shown a significant difference, with the e-cigarette users demonstrated a higher score compared to non-cigarette users with scores of 21.75 and 20.17, p < 0.05, respectively. There were no significant differences between the e-cigarette users and non-users for WHO-AVLT and TMT. E-cigarette users demonstrate a lower level of knowledge and attitude than users, showing a significant association with e-cigarette behavior.

Conclusion:

E-cigarette does not cause a cognitive performance decline among university students. This study provides the initial evidence of whether there is any potential for e-cigarette effect on cognitive function among our local young population. The finding may serve as a reference for a more robust related study in the future.

Cigarette smoking is the single most important cause of avoidable premature mortality worldwide, and quitting is known to rapidly reduce the risk of serious diseases such as lung cancer, cardiovascular disease, stroke, chronic lung disease, and other cancers (1). The electronic cigarette (e-cigarette) is an electronic device, also known as a vape, containing a cartridge filled with liquid nicotine and/or other chemicals, producing inhalable smoke (1). Since its introduction in the early 2000s, e-cigarette use has quickly gained popularity worldwide (1), especially among current and former smokers (2–4). E-cigarette manufacturers promote it as a safer, cheaper, and alternative product for smoking cessation (3, 4).

It has become a public health concern as its usage has garnered popularity (5). In 2009, Australia, Brazil, China, Uruguay (5), Brunei, Cambodia, Indonesia, Singapore, Thailand, and Vietnam (6) altogether banned the sale and marketing of e-cigarette. South East Asia has recorded the highest number of countries (6 out of 11) that banned the use of e-cigarette (6). Malaysia was found to be the country with the highest prevalence of users at 14%, followed by the Republic of Korea and Australia at 7% each, the United States at 6%, the United Kingdom at 4%, the Netherlands at 3%, Canada at 1%, and China at 0.05% according to the International Tobacco Control Policy Evaluation Project survey report conducted between 2009 and 2013(1). The prevalence of e-cigarette users among the young age population in Malaysia was also recorded to be among the highest in the South East Asia countries which was reported by 2015 Global Adult Tobacco Survey: Indonesia at 0.3%, Malaysia at 0.8%, Philippines at 1.7%, and Vietnam at 0.2% (5). About 39.9% of young students from higher intuitions, followed by 36% of young professionals from two high-density states in Malaysia (Kuala Lumpur and Selangor) were reported to consume e-cigarette (7).

It is widely believed that e-cigarette have much lesser health implications than tobacco smoking. Hence, e-cigarette have become a favorable alternative option to tobacco smoking or to wean off the withdrawal symptoms upon quitting tobacco smoking (8). There is an increasing trend of health hazards secondary to e-cigarette that are frequently reported. These health hazards are more related to respiratory system complications such as lipoid pneumonia, acute eosinophilic pneumonia, hypersensitivity pneumonia, organizing pneumonia, diffuse alveolar hemorrhage, multiple reactive pulmonary nodules, and subacute bronchiolitis(9). There are also direct injuries from the device explosion that cause mouth, tongue injuries and dental injuries, complex facial fractures, thermal injuries, nickel contact allergy, C1 and C2 fractures, and fatal intoxication after ingesting liquids(9).

Young and middle-aged adults who consume tobacco smoking have long been shown to have a higher risk for cognitive decline upon neurocognitive assessment, including memory, attention, and executive function deficits (10, 11). Morphologically, the development of the prefrontal cortex and its activities can be stunted by early nicotine exposure during adolescence and early adulthood, which leads to long-lasting cognitive impairment (12). Recently, in addition to the respiratory complications that have been frequently reported, e-cigarette have also been shown to impact the cognitive performance level (13–15). This phenomena can be explained by considering that the amount of nicotine delivered by e-cigarette is comparable to the amount of nicotine delivered by the traditional combustible cigarettes (16).

Several aspects of e-cigarette use in Malaysia were reported in various studies, but limited studies were on cognitive performance among Malaysian university students (7, 17, and 18). Cognitive deficits are described as impairments in information processing, which can lead to difficulty paying attention, processing, and responding to information quickly, recalling information, initiating speech, reasoning, and making judgments (19). This neurological process is crucial in university students in order for them to excel in their tertiary education. It is essential to conduct a study on the effect of e-cigarette smoking among young adults in Malaysia, which this study can give rise to a different outcome compared to other published studies, considering the different background populations, the type of e-cigarette consumed which depends on what is available in the local market and the consumption pattern. The findings of this study will provide the initial baseline evidence of the effect of e-cigarette consumption, particularly on cognitive performance, which is currently still under study in Malaysia.

Ethical consideration:

The study was approved by the institution’s Medical Research Ethics Committee. Each participant received and read the patient’s information sheet, and written consent was obtained prior to the intervention.

Study design and setting

A case-control design study was conducted from September 2021 to January 2022. University students from higher institutions in Malaysia who are interested in this study were recruited. All these students were studying the same course but from different universities. In this study, cases were defined as university students who actively use e-cigarette and possess good general health. The control group was university students who were non-smokers or non-users of e-cigarette and had no previous history of such use and possessed good general health. Students who were either on regular psychotropic medication, had a current or past history of alcohol or drug abuse, or used smokeless tobacco or nicotine replacement therapy were excluded from this study. Participants were recruited by snowballing non-probability sampling. They were approached via phone calls before they were enrolled in this study. The information about the study was explained to the participants, and inform consent were obtained upon their agreement to participate in this study.

Data collections procedure

Data was collected using a structured questionnaire on sociodemographic characteristics (e.g., age, gender, level of education) and e-cigarette characteristic profile (duration of usage, type of e-cigarette used, and frequency of usage). For cognitive performance evaluation, trained occupational therapists evaluated cognitive performance via face-to-face assessment with the participants using WHO Auditory Verbal List Learning Tests, Digit Span, and Trail Making Test. Knowledge and attitude on e-cigarette usage are obtained using a self-reported questionnaire from the participants.

Instruments:

b) WHO Auditory Verbal Learning Tests (WHO-AVLT)

WHO-AVLT is used to assess the memory performance of the participants aged 16 years and older. This test evaluates different aspects of learning and memory. It is sensitive to detect cognitive decline in general populations and individuals with neurological conditions (20). The immediate recall trial provides information on acquisition and learning rate, and the delayed and recognition trials explore aspects of information retention. The test is designed as a list-learning paradigm in which the individual hears a list of 15 nouns and is asked to recall as many words from the list as possible. The higher the score, the greater the memory functioning of the individual.

c) Trail Making Test (TMT)

The Trail Making Test (TMT) is used to evaluate the attention and executive function of the individual. TMT is one of the most widely used instruments in neuropsychological assessment as an indicator of the speed of cognitive processing and executive functioning (21). Several other cognitive domains, such as visual search, perceptual/motor speed, speed of processing, working memory, and general intelligence, are among the most frequently described to contribute to TMT performance. The test consists of two parts (A and B). The direct score of each part is represented by the time of completion of the tasks. It consists of two parts in which the subject is instructed to connect a set of 25 dots as quickly as possible while still maintaining accuracy. Time to complete to connect the dots will be taken. The shorter the time taken to complete the dots, the better attention and executive function of the individual. This test is valid and reliable to be administered to the general population and individuals with cognitive deficits.

d) Digit Span Test (DST)

The Digit Sequence test is an instrument to measure one’s verbal working memory, overall intelligence, attention, and cognitive functioning. It consists of digits forward and digits backward. The higher the score, the better the executive function and learning potential of the individual. The test is valid and reliable to be used with the general population and individuals with cognitive deficits.

e) Knowledge & Attitude towards E-Cigarette

This is a self-reported questionnaire that measures the knowledge and attitude toward e-cigarette users. It consists of 10 items that explore their perceptions of e-cigarette' benefits, safety, and consumption risks. It is a 4-point Likert scale with 1 for strongly disagree to 4 for strongly agree. Higher the scores, the poorer the knowledge and attitude toward e-cigarette.

Analysis:

All data were analyzed using SPSS version 22. Normality testing was conducted to determine the distribution of the data. Descriptive analyses were conducted to analyze the socio-demographic characteristics. In order to compare the level of cognitive performance, knowledge, and attitude between case and control, the T-test was deployed to determine the difference between the two groups if the data is normally distributed or the Mann-Whitney U test if the data is not normally distributed. In order to identify the predictive ability of knowledge and attitude towards e-cigarette on the use of the e-cigarette, binary logistic regression was deployed.

A total no of 140 participants was successfully recruited in this study. Table 1 depicts the demographic characteristics of the participants. Both controls and cases have an equal no of participants. 49 out of 70 participants of the e-cigarette users reported the mean duration of e-cigarette consumption was 29.7 months. More than half (52%) of e-cigarette users consume e-cigarette more than once daily.

Table 1: Demographic characteristic and e-cigarette profile features of e-cigarette users

Frequency (%)

Age

18-25

26-35

135 (96.4)

5 (3.6)

Gender

Male

Female

105 (75.0)

35 (25.0)

Level of Education

Diploma

Degree

Master

21 (15.0)

118 (84.3)

1 (.7)

Characteristics

Vape users

Never users

70 (50.0)

Duration of Usage (months), mean ( SD)

Vape users

29.66 ( 24.015)

Type of E-cigarette juice

Without nicotine (with flavours)

≤ 6mg nicotine d. ≤ 9mg nicotine

≤ 12mg nicotine

≥ 16mg nicotine

Unsure

None

11 (7.9)

14 (10.0)

10 (7.1)

27 (19.3)

8 (5.7)

70 (50.0)

Frequency of usage

More than once daily

Once daily

More than once weekly

Once weekly

More than once monthly

Non user

52 (37.1)

7 (5.0)

2 (1.4)

3 (2.1)

6 (4.3)

70 (50.0)

Table 2: Comparing Digit Span score between e-cigarette users and non—e-cigarette users

Variables

E-cigarette users

(n=70)

Mean (SD)

Non-e-cigarette users

(n=70)

Mean (SD)

Mean diff.

(95% CI)

t-stats

(df)

value

Digit span score

21.75 (4.56)

20.17 (4.83)

0.01, 3.16

1.99 (138)

0.048

Table 2 demonstrates the mean difference between the digit span score of e-cigarette users and non-e-cigarette users is statistically significant (p = 0.048; 95%CI 0.01, 3.16), with the e-cigarette users having a higher mean score (Mean = 21.75, SD = 4.56) than non-e-cigarette users (Mean = 20.17, SD = 4.83). The higher the mean score shows, the higher level of cognitive performance.

Table 3: Comparing WHOAVLT score between e-cigarette users and non-e-cigarette users group

WHOAVLT score

Mean Rank

Sum Rank

Median

Z score

value

E-cigarette users

68.60

4802

29.00

-.57

.57

Non- cigarettes users

72.40

5068

29.00

Table 3 shows the mean rank and sum of the rank number of the two groups, the e-cigarette, and the non-e-cigarette users group. The group with a higher rank has the highest WHOAVLT total score. In this case, the non-e-cigarette users group has the highest WHOAVLT score. Mann-Whitney U test in Table 3 was conducted to determine whether there is a difference in WHOAVLT score between the e-cigarette and non-e-cigarette users group. The test revealed that WHOAVLT scores were similar for both e-cigarette users (Mdn = 29.00, n = 70) and non-e-cigarette users (Mdn = 29.00, n = 70), U = 2317.00, z = -.57, p = .57. The results indicate that there is a non-significant difference between the two groups.

Table 4. Comparing Trail Making Test A and B score between e-cigarette users and non-e-cigarette users group

Trail Making Test A
	Mean rank	Sum rank	Median	Z score	P value
E-cigarette users (n=70)	73.67	5157.00	36.05	-.93	.36
Non e-cigarette users (n=70)	67.33	4713.00	34.50	-.93	.36
Trail Making Test B
E-cigarette users (n=70)	71.86	5030.50	59.50	-.39	.69
Non e-cigarette Users (n=70)	69.14	4839.50	57.30	-.39	.69

Table 4 shows the mean rank and sum of ranks for the two groups, the non-e-cigarette users and the e-cigarette users. The group with a higher rank has poorer performance in the Trail Making Test. In this case, the non-e-cigarette users group showed higher performance in Trail Making Test (A) and (B) compared to the e-cigarette users group.

Mann-Whitney U test was conducted to determine whether there is a difference in Trail Making Test (A) score between the e-cigarette and the non-e-cigarette users group. The test revealed that the time taken to complete Trail Making Test A was higher for the e-cigarette users group (Mdn = 36.05) than the non-e-cigarette users group (Mdn = 34.50) (Table 4). However, there is no significant difference between the two groups. The Mann-Whitney U test (Table 4) also showed a similar result for Trail Making Test (B), where the time taken was higher for the e-cigarette users group (Mdn = 59.50) than non-e-cigarette users group (Mdn = 57.30). However, there was a non-significant difference between the two groups.

Table 5: Students’ knowledge and attitude related to e-cigarette among university students (n=101)

	Item Statement	Median (IQR)/ Mean (SD)*		p-value	Effect size, r / d**
		Vape users	Non-users
Item 1	Smoke from e-cigarette is just water	2.00 (2.00)	1.00 (1.00)	.023^a	0.23
Item 2	E-cigarette don't contain tar	3.00 (1.00)	2.00 (2.00)	<.001^a	0.53
Item 3	E-cigarette aren't addictive	2.00 (1.00)	1.00 (1.00)	.011^a	0.25
Item 4	E-cigarette aren't a tobacco product	2.86 (0.94)*	2.23 (0.87)*	.001^b	0.69**
Item 5	E-cigarette don't produce smoke	2.00 (2.00)	1.00 (1.00)	<.001^a	0.35
Item 6	Using e-cigarette feels cleaner than smoking	4.00 (1.00)	2.00 (2.00)	<.001^a	0.57
Item 7	E-cigarette are safer than smoking	3.00 (1.00)	2.00 (2.00)	<.001^a	0.42
Item 8	Teens use e-cigarette to get the same buzz they get from tobacco cigarettes	2.78 (0.82)*	2.58 (0.98)*	.281^b	0.22**
Item 9	E-cigarette help people quit using cigarettes	3.00 (1.00)	2.00 (1.00)	<.001^a	0.52
Item 10	E-cigarette vapor is dangerous to babies and kids	3.00 (1.00)	4.00 (1.00)	.523^a	0.06
Total KA		27.60 (3.30)	21.42 (5.48)	<.001^b	1.42**
^aMann-Whitney U Test, ^bIndependent t-test

Table 5 illustrates the knowledge and attitude toward the use of e-cigarette among the study participants. Only 101 out of 140 participants completed the self-reported questionnaire, with 58 participants from the users group and 43 from the non-users group. It is important to note a higher score indicates a lower level of knowledge and attitude. There is a significant difference (p < 0.05) in knowledge and attitude level related to e-cigarette usage between the users and non-users, with a total score of 27.60 and 21.42, respectively. The non-users demonstrate a higher level of knowledge compared to the non-users.

Table 6: Predicting the use of e-cigarette using knowledge and attitude towards e-cigarette using the binary logistic regression

Predictors	B	S.E.	Wald	df	p-value	Exp(B)	95% CI
							Lower	Upper
KA	.353	.075	21.955	1	<.001	1.423	1.228	1.649

Table 6 shows the result of binary logistic regression to identify the predictive ability of knowledge and attitude towards e-cigarette on the use of the e-cigarette. Based on the model summary, 45.1% of e-cigarette use among university students can be explained by their knowledge and attitude toward e-cigarette. This model showed a good fit with a significance value of .747 using the Hosmer-Lemeshow Test. Furthermore, the analysis found that the higher the score of knowledge and attitude towards e-cigarette, the more likely the university student, specifically 1.423 times more likely, to use the e-cigarette.

There is a significant difference in knowledge and attitude levels between the users and the non-users, which the non-users demonstrating a lower level of knowledge and attitude. This lack of knowledge and attitude increases the likelihood of the students consuming e-cigarette. They believe e-cigarette serve as an alternative and safer to tobacco smoking.

This study attempted to explore the effect of e-cigarette on cognitive performance among university students. In this study, e-cigarette demonstrated that there is no significant effect in reducing cognitive performance among university students in Malaysia. There are several factors that might contribute to this finding. Firstly, the duration of e-cigarette consumption may need to be longer to produce such complications, which in this study, the mean duration of e-cigarette exposure is only 29 months. In addition, the population of this study is young, healthy individuals which no comorbidities that may enhance the risk of having potential complications. This is supported by a study looking into the association between smokers and subjective cognitive complaints, which showed a higher association in the population aged between 35 to 49 years old (15). Another reason that might contribute to this finding is the type of e-cigarette they consumed might be different compared to the e-cigarette that is marketed in other countries in terms of the type of liquid chemical contents or the amount of nicotine, which may be bound to the different production processes or regulatory policies(5).

Interestingly, contrary to what we have hypothesized, we found that e-cigarette users have a higher WHO AVLT score. This study's finding is similar to a randomized crossed-over control trial, which the researchers explored the effect of different e-cigarette containing 24 mg to 0 mg nicotine. This study demonstrated that there was no significant cognitive performance difference between these two different groups (13). It has also been shown that while low doses of nicotine exposure might improve cognitive functions, higher doses could impair cognitive functions. This scenario might be dependent on the interaction of nicotinic receptor systems with neural systems underlying cognitive functions that is dependent on the concentration of nicotine which acts as a stimulant in a much lower dose (22). A randomized controlled trial also has shown that the e-cigarette users group of 18 mg nicotine has shown an improvement in working memory performance compared to the placebo group (14).

Compared to other similar studies (14, 15), our study has eliminated the bias of pre-existing differences in education and intellectual level that may influence the outcome. This factor can be controlled by ensuring the population comes from the same background of education level and studying a similar course, which in this case is the bachelor of engineering.

A low level of knowledge and attitude towards e-cigarette has shown to be one of the influencing factors for students to involve in e-cigarette smoking. They perceived that e-cigarette provide an alternative option to conventional smoking that is believed to be more toxic. This study is similar to a survey in which the most popular reason for e-cigarette consumption is their belief in the less toxicity effect of e-cigarette and to prepare them to quit conventional smoking (23). This study demonstrated that even in individuals with a higher tertiary level of education, interestingly, they still have a misperception of e-cigarette.

Despite no significant demonstrable reduction of cognitive performance level shown in this study, this study has provided population-based evidence of a young age group on the effect of cognitive performance among e-cigarette users. This evidence can provide a baseline comparison if similar research is conducted in other age groups. In addition, with this evidence, we can prohibit the claims that e-cigarette can enhance or cause a decline in cognitive performance upon consumption or its cessation, irrespectively (24).

Study limitations

Prolonged exposure to e-cigarette may demonstrate a higher risk of affecting cognitive performance. A similar study should be carried out on different age group populations and also taking into account of prolong usage. The younger adolescent age group, given their developing pre-frontal cortex or advanced age group with long-standing exposure and multiple comorbidities, might give a different outcome. A more challenging cognitive assessment can be administered to give more robust results, such as Wechsler Adult Intelligence Scale (WAIS-IV), which can match the population accordingly and future recommendation.

Ethical Approval and consent to participate

The study was approved by the institution’s Medical Research Ethics Committee of Universiti Teknologi MARA, Universiti Malaysia Sabah and International Islamic University of Malaysia. Written and informed consent was obtained prior to the intervention. All methods are performed in accordance with the Declaration of Helsinki

Consent for publication- Not applicable

Availability of Data and Materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Conflict of interest

All authors have no conflict of interest to declare

Funding

This study is funded by Universiti Teknologi MARA ,Faculty of Medicine Research Grant (600-RMC/GPK 5/3 (218/2020)

Acknowledgment

We would like to acknowledge Mohamad Qayyum Sabri for his contribution to this study

Author contributions

Kamaruddin and Che Daus were responsible for the literature review and wrote parts of the manuscript

Mohamad Hashim was responsible for the data analysis and wrote parts of the manuscript

Ahmedy and SF Salim were responsible for refining the table and improved the clarity of the manuscript

Abu Hassan refined the overall manuscript and proofread the content

Gravely S, Fong GT, Cummings KM, Yan M, Quah AC, Borland R, et al. Awareness, trial, and current use of electronic cigarettes in 10 countries: Findings from the ITC project. International journal of environmental research and public health. 2014;11(11):11691-704.
Farsalinos KE, Spyrou A, Stefopoulos C, Tsimopoulou K, Kourkoveli P, Tsiapras D, et al. Nicotine absorption from electronic cigarette use: comparison between experienced consumers (vapers) and naïve users (smokers). Scientific reports. 2015;5(1):1-9.
Weaver SR, Huang J, Pechacek TF, Heath JW, Ashley DL, Eriksen MP. Are electronic nicotine delivery systems helping cigarette smokers quit? Evidence from a prospective cohort study of US adult smokers, 2015–2016. PloS one. 2018;13(7):e0198047.
Eastwood B, Dockrell M, Arnott D, Britton J, Cheeseman H, Jarvis MJ, et al. Electronic cigarette use in young people in Great Britain 2013–2014. Public Health. 2015;129(9):1150-6.
Organization WH. WHO Study Group on Tobacco Product Regulation: Report on the Scientific Basis of Tobacco Product Regulation: Third Report of a WHO Study Group: World Health Organization; 2009.
Schmid T. Asia’s E-Cig Regulations: Confusion & Uncertainty. Tobacco Asia. http://www. tobaccoasia.com/ features/asia-e-cig-regulations-confusion-uncertainty/. Published May , 2016. Accessed August 28, 2022.
Wong LP, Mohamad Shakir SM, Alias H, Aghamohammadi N, Hoe VC. Reasons for using electronic cigarettes and intentions to quit among electronic cigarette users in Malaysia. Journal of community health. 2016;41(6):1101-9.
Bhatnagar A, Whitsel LP, Ribisl KM, Bullen C, Chaloupka F, Piano MR, et al. Electronic cigarettes: a policy statement from the American Heart Association. Circulation. 2014;130(16):1418-36.
Gülşen A, Uslu B. Health hazards and complications associated with electronic cigarettes: a review. Turkish Thoracic Journal. 2020;21(3):201.
Chamberlain SR, Odlaug BL, Schreiber LR, Grant JE. Association between tobacco smoking and cognitive functioning in young adults. The American Journal on Addictions. 2012;21:S14-S9.
Sabia S, Elbaz A, Dugravot A, Head J, Shipley M, Hagger-Johnson G, et al. Impact of smoking on cognitive decline in early old age: the Whitehall II cohort study. Archives of general psychiatry. 2012;69(6):627-35.
Counotte DS, Goriounova NA, Li KW, Loos M, Van Der Schors RC, Schetters D, et al. Lasting synaptic changes underlie attention deficits caused by nicotine exposure during adolescence. Nature neuroscience. 2011;14(4):417-9.
Caponnetto P, Maglia M, Cannella MC, Inguscio L, Buonocore M, Scoglio C, et al. Impact of different e-cigarette generation and models on cognitive performances, craving and gesture: a randomized cross-over trial (CogEcig). Frontiers in Psychology. 2017;8:127.
Dawkins L, Turner J, Hasna S, Soar K. The electronic-cigarette: effects on desire to smoke, withdrawal symptoms and cognition. Addictive behaviors. 2012;37(8):970-3.
Xie Z, Ossip DJ, Rahman I, O’Connor RJ, Li D. Electronic cigarette use and subjective cognitive complaints in adults. PloS one. 2020;15(11):e0241599.
Jackler RK, Ramamurthi D. Nicotine arms race: JUUL and the high-nicotine product market. Tobacco control. 2019;28(6):623-8.
Goh YH, Dujaili JA, Blebil AQ, Ahmed SI. Awareness and use of electronic cigarettes: Perceptions of health science programme students in Malaysia. Health Education Journal. 2017;76(8):1000-8.
Robert Lourdes TG, Abd Hamid HA, Mohd Yusoff MF, Rodzlan Hasani WS, Mat Rifin H, Saminathan TA, et al. Factors associated with e-cigarette usage and the reasons for initiation among Malaysian adolescents. Asia Pacific Journal of Public Health. 2019;31(7_suppl):44S-52S.
Trivedi JK. Cognitive deficits in psychiatric disorders: Current status. Indian journal of psychiatry. 2006;48(1):10.
Fernaeus SE, Östberg P, Wahlund LO, Hellström Å. Memory factors in Rey AVLT: Implications for early staging of cognitive decline. Scandinavian Journal of Psychology. 2014;55(6):546-53.
Strauss E, Sherman EM, Spreen O. A compendium of neuropsychological tests: Administration, norms, and commentary: American chemical society; 2006.
Kwan LY, Eaton DL, Andersen SL, Dow-Edwards D, Levin ED, Talpos J, et al. This is your teen brain on drugs: In search of biological factors unique to dependence toxicity in adolescence. Neurotoxicology and teratology. 2020;81:106916.
Puteh SEW, Manap RA, Hassan TM, Ahmad IS, Idris IB, Sham FM, et al. The use of e-cigarette among university students in Malaysia. Tobacco induced diseases. 2018;16.
Grana R, Benowitz N, Glantz SA. E-cigarette: a scientific review. Circulation. 2014;129(19):1972-86.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

The Impact of E-cigarette Usage on Cognitive Performance Level among University Students in Malaysia: A case-controlled study

Status:

Version 1

Abstract

Introduction

Method

Ethical consideration:

Study design and setting

Data collections procedure

Instruments:

b) WHO Auditory Verbal Learning Tests (WHO-AVLT)

c) Trail Making Test (TMT)

d) Digit Span Test (DST)

Analysis:

Results

Discussion

Study limitations

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1