Research design and participants

We conducted a cross-sectional analysis that utilized a self-report anonymous questionnaire completed between November 2019 and March 2020. The survey aimed to recruit a minimum of 527 participants. This estimate was based on a sample of 4.6% of the university students in Shanghai that had used e-cigarettes at least once in their lifetime, and margin of error of 3%. We also considered a survey rejection rate of 20%. A stratified random sample of 589 registered undergraduates at Hangzhou Normal University received an online survey through a platform (https://www.wjx.cn/) with their consent. There were 75 majors in the university. Every major was coded and five majors (Science, Engineering, Medicine, Management Science and Arts) were selected randomly through the Statistical Package for the Social Sciences (SPSS) version 22.0. All the classes were then coded according the year in the university among the selected majors. A class as the unit was randomly selected for each grade and each major by the same software. Finally, 563 completely filled in questionnaires were available with a response rate of 95.6%.

Survey measures

The questionnaire was divided into five parts: basic information, e-cigarette use of family and friends, knowledge of e-cigarettes, attitudes about e-cigarettes, and e-cigarette use.

Basic information included demographic characteristics and ever conventional cigarette use. Demographic characteristics included gender, age, ethnic group (Han, ethnic minority), year in school (first, second, third and fourth), major, urban/rural residence, parents’ educational level (primary or less, secondary, tertiary), and per capita household monthly income: 0–5000, 5001–10000, >10000 RMB (1000 Chinese Renminbi about US$160).

Ever conventional cigarette use was ascertained by: ‘Have you ever smoked cigarettes at least once in your lifetime?’, with response options of ‘Yes’ and ‘No’.

The second part of the questionnaire included ever use of e-cigarettes by father, mother, siblings, friends, and roommates, which were ascertained by dichotomous (yes/no) questions: ‘Has your father ever smoked cigarettes at least once in his lifetime?’, ‘Has your mother ever smoked cigarettes at least once in her lifetime?’, ‘Have your siblings ever smoked cigarettes at least once in their lifetime?’, ‘Have your friends ever smoked cigarettes at least once in their lifetime?’, and ‘Have your roommates ever smoked cigarettes at least once in their lifetime?’.

Knowledge of e-cigarettes was assessed with four questions (yes /no/unclear): ‘Do e-cigarettes contain nicotine?’, ‘Are e-cigarettes tobacco products?’, ‘Are e-cigarettes carcinogenic?’, and ‘Are e-cigarettes addictive?’.

We developed 19 items rated on a five-point Likert scale (1=strongly disagree to 5=strongly agree) to evaluate the students’ attitudes about e-cigarettes based on literature review and expert consultation. We used principal component analysis to group these items into five domains: Accessibility (2 items about how accessible e-cigarettes are to students, mainly regarding price and store), Acceptability (2 items about whether the use of e-cigarettes is socially accepted), Safety (4 items about how safe e-cigarettes are regarding health), Supervision (5 items about whether minors should have restricted access to e-cigarettes), and Restriction (6 items about whether the advertisement online for e-cigarettes and sale online of e-cigarettes should be prohibited). Confirmatory factor analysis confirmed good model fit for the scale. (Supplementary file). A total score for each domain was calculated from the average of all items for individuals. The higher the score, the more positive and supportive the attitude toward e-cigarettes. The Cronbach’s alpha for the scale was 0.815 and the test-retest reliability of the scale was highly significant (intraclass correlation coefficient was 0.85). The score of the total scale and five domains were used to explore the associations between students’ attitudes about e-cigarettes and e-cigarette use.

The last part of the questionnaire included four dichotomous (yes/no) questions to assess the prevalence of e-cigarette use among university students. Participants were asked: ‘Have you ever used an e-cigarette in your lifetime?’, ‘Have you used an e-cigarette in the past year?’, ‘Have you used an e-cigarette between 1 and 29 of the past 30 days?’, ‘Have you used an e-cigarette on all of the past 30 days?’. If they answered ‘yes’ to the first, third and fourth question, they were classified as ‘ever users’, ‘current non-daily users’ and ‘current daily users’, respectively.

We conducted a pilot test with 40 students – 20 males and 20 females – at the same university by convenience sampling in October 2019, including 10 freshmen, 10 sophomores, 10 junior and 10 senior students. We made adjustments to the unclear items that students identified during the pilot test.

Statistical analysis

SPSS (version 22.0) was used to perform analyses. Descriptive statistics including mean and standard deviation (SD) for quantitative variables, and frequency and percentage (%) for qualitative data. Individuals who had ever used an e-cigarette were regarded as ever users, and those who had never used e-cigarettes as non-users. Independent sample t-tests were performed to compare attitudes about e-cigarette use between ever users and non-users. We used χ² tests to compare the prevalence of e-cigarette use by demographics and other characteristics. As e-cigarette use was a dichotomous measure, a binary logistic regression model was selected to explore the motivational factors. In logistic regression analyses, ever e-cigarette use was the dependent variable. Covariates in the logistic model included the significant variables except for the variables with sparse data, which may lead to large odds ratio and wide confidence interval. Two-sided p<0.05 and p<0.01 were considered statistically significant.

Participant characteristics

Of the participants, 59.9% were female and most were ethnic Han (95.7%). The average age of the 563 university students was 20.38 years (SD=1.32). More than half of the fathers and mothers of the students had received secondary education, 55.3% and 58.4, respectively. There were 11.2% of the respondents reporting ever conventional cigarette use. Among fathers, mothers, siblings, friends, and roommates, those who had the highest rate of ever vaping were roommates, accounting for 14.9%. Table 1 shows participant demographics and other characteristics.

Knowledge about e-cigarettes

As shown in Table 2, approximately two-thirds of students were aware of the addictiveness of e-cigarettes, and 51.0% participants considered that e-cigarettes are carcinogenic. However, fewer than half of participants (42.6%) were sure that e-cigarettes contain nicotine. In addition, fewer than one-third of students (31.1%) identified e-cigarettes as tobacco products.

Attitudes towards e-cigarettes

Self-reported attitudes about e-cigarettes are shown in Table 3. The mean value of the total scale was 2.60 (SD=0.49). Of the five dimensions, the Safety dimension had the highest mean value (Mean=3.34), which was followed by Restriction dimension and Accessibility dimension (Mean=2.66, Mean=2.64, respectively). The lowest mean value occurred for Supervision dimension (Mean=2.00). Similarly, the mean value for Acceptability was only 2.40.

The participants who were ever users of e-cigarettes had more favorable attitudes towards e-cigarettes than the students who were non-users in the Supervision dimension and Restriction dimension (p<0.05).

E-cigarette use and its correlates

Ever users (who used e-cigarettes at least once in their lifetime) accounted for 8.2% of the students. Of these, ever users who used e-cigarettes at least once in the past year accounted for 7.3%, current non-daily users accounted for 2.0%, current daily users accounted for 1.1%. The other 89.6% of ever users were all former users.

As shown in Table 4, Pearson’s chi-squared tests showed that ever e-cigarette users and non ever users differed by year in school (df=3, p=0.001), major (df=4, p=0.000), urban/rural region (p=0.002), per capita household monthly income (df=2, p=0.000), conventional cigarette use (p=0.000), friends’ ever e-cigarette use (p=0.000), roommates’ ever e-cigarette use (p=0.000), perception of whether e-cigarettes contain nicotine (df=2, p=0.001), perception of whether e-cigarettes are a tobacco product (df=2, p=0.005), and perception of whether e-cigarettes are addictive (df=2, p=0.000).

Multiple logistic regression analyses of ever e-cigarette use indicated that students who come from urban areas were more likely to use e-cigarettes (adjusted odds ratio, AOR=0.40; p=0.016). Besides, the students whose friends and roommates ever vaped were more likely to report e-cigarette use (AOR=8.29; p=0.000 and AOR=4.75; p=0.000, respectively). Finally, ever e-cigarette use was strongly associated with the self-reported attitudes toward e-cigarettes in Supervision dimension. The more favorable the students were towards e-cigarettes, the more likely they were to use them (AOR=1.67; p=0.010). All results are presented in Table 5.

Limitations

There are several limitations in this study. First, participants consisted exclusively of Chinese students from a university, so the generalizability is limited. In the future, surveys with larger sample sizes and conducted in more universities are needed to get a more comprehensive understanding of the latest e-cigarette use status. Second, another main limitation of this study is information bias. For example, some students used e-cigarettes in their lifetime but did not report it in the survey. Compared with face-to-face interviews, online surveys may be more difficult to ensure data quality. Finally, given the research design of our study, we were unable to analyze causal relationships.