Differentiating the Determinants of Ever Having Used a Vape Versus Frequent Use: A Hurdle Modelling Approach

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Investigation into the psychological mechanisms underlying vaping among young people remains relatively nascent and has, to date, seldom differentiated between the initial decision to try vaping (i.e., ever-use) and the regulation of sustained patters of use (i.e., frequent use). Methods We surveyed 451 undergraduate students, collecting data on vaping use, attitudes toward vaping, subjective norms, perceived behavioural control, harm perceptions, and behavioural automaticity. A hurdle modelling approach was used to exam two distinct processes; (1) the predictors of ever-use versus no vaping use, and (2) the frequency of use among individuals with prior vaping experience. Results Findings supported the application of hurdle modelling, revealing distinct predictors for ever-use and frequent use. Specifically, the former was predicted by attitudes, subjective norms, and perceived behavioural control, whereas the latter was predicted solely by attitudes and behavioural automaticity. Notably, harm perceptions did not predict either ever-use or the frequency of use. Conclusions Findings indicate that both ever having used a vape and frequency of use are primarily driven by beliefs of the outcomes and experiential experiences of the behaviour, rather than by cognitive evaluations of harm. Although conclusions are limited by the cross-sectional design, current results point to the possibility that the psychological determinants of initial experimentation with vaping may differ from those that sustain frequent use. E-cigarettes Vape Theory of Planned Behaviour Risk Perceptions Nicotine Harm Introduction Following widespread acknowledgment of the adverse health consequences associated with smoked tobacco, public health agencies and governments globally have intensified efforts to reduce tobacco use prevalence [ 1 ]. A recent focus within these efforts has been the rapid proliferation of electronic nicotine delivery systems (ENDS), commonly known as e-cigarettes or vapes. These devices function by aerosolizing a propylene glycol solution—often containing nicotine and flavorants—via a battery-powered heating element. Due to their reduced harm relative to combustible tobacco products, vapes have been increasingly considered by some public health experts as a potential harm reduction tool or a transitional aid for individuals attempting to discontinue nicotine use altogether [ 2 ]. However, there is growing concern regarding the rapid uptake of vape use among young people, many of whom report little to no prior use of traditional tobacco products [ 3 – 5 ]. This trend is particularly concerning given the potential vulnerability of this age group to the effects of addictive substances [ 6 ]. While vapes may confer fewer toxicological risks than combustible cigarettes [ 7 ], they are nonetheless associated with a range of adverse physical and psychological health outcomes and retain the potent addictive properties of nicotine [ 8 – 10 ]. In response, the World Health Organization and national health authorities worldwide have identified the use of e-cigarettes or vapes, especially among young populations, as an urgent target for research and intervention [ 1 ]. The design of effective interventions to address vape uptake and use among young people requires a foundational understanding of the psychological mechanisms driving decision making for this behaviour. One theoretical model that has been extensively applied to health-related behaviours is the theory of planned behaviour (TPB) [ 11 ]. According to the model, behavioural intention—which proceed the enactment of behaviour—is shaped by three belief-based constructs: attitudes (beliefs of the likely outcomes and experiential experiences of the behaviour), subjective norms (beliefs of social pressure from important others to perform or not perform the behaviour), and perceived behavioural control (beliefs about how easy or difficult it is to perform the behaviour, as well as perceived capacity or resources available to execute that behaviour). A substantial body of meta-analytic evidence affirms the TPB as a robust model for predicting a wide range of health-related behaviours [ 12 ]. Within the context of e-cigarette use, several studies have demonstrated that the model is effective in predicting vaping behaviour among young people [ 4 , 13 – 15 ]. Notably, attitudes have consistently emerged as the strongest predictor of vaping behaviour, with affective or hedonistic attitudes—such as beliefs about enjoyment, fun, or pleasure—exerting a particularly strong influence. By contrast, perceptions about the health risks or harmfulness of vaping, while correlated with vape use [ 16 ], often show small or even negligible effects when considered alongside other social cognition constructs in predictive models [ 4 ]. These findings seem to indicate that, while normative influences and perceived behavioural control are relevant, beliefs about whether vaping is pleasurable or enjoyable or perceived as a positive experience appear to be the most potent drivers of behaviour. However, there is evidence that current research may conflate two distinct decisions: the decision to first ever use a vape and the subsequent decision regarding the sustained (or frequency of) use [ 4 ]. For instance, while norms typically exert only modest associations with vape use [ 4 , 14 , 15 ], other research has shown that initial experimentation with vaping is often encouraged, and in some cases actively facilitated, by peers or social groups [ 17 ]. This distinction is important, as different psychological processes may underlie the initiation of vaping compared to those that sustain continued use. Whereas social pressure and access may be central to first-time use, habitual patterns of frequent use are more likely driven by mechanisms such as dependency, automaticity, and cue activation—factors that may have limited relevance during initial experimentation [ 4 ]. Consequently, while existing evidence supports the applicability of the TPB in predicting vape use, primarily through attitudinal pathways, it remains an open empirical question whether the same psychological constructs account for both the initiation of vaping (i.e., ever-use) and sustained patterns of use (i.e., frequent use) to the same extent. A more nuanced understanding of these distinctions is critical for informing the development of targeted behavioural interventions and tailored public health messaging aimed at both preventing initiation and reducing sustained use. The Current Study The current study sought to extend prior research applying the TPB to the context of e-cigarette or vape use, by employing a hurdle modelling approach [ 18 , 19 ]. Hurdle models conceptualize the act of using a vape—even a single instance—as a distinct decision point or “hurdle”. Once this initial hurdle is crossed, the psychological processes that govern the frequency of use may diverge from those associated with the likelihood of any use (i.e., a zero or non-zero occurrence). In the current analysis, this modelling approach is utilised to examine how TPB constructs predict both (1) the likelihood of ever having used a vape device (Research Question 1, ever-use), and the frequency of use among individuals with prior vaping experience (Research Question 2, frequent use), within a unified analytic framework. It was hypothesised that attitudes toward vaping, subjective norms, perceived behavioural control, and harm perceptions would significantly predict both ever having used a vape (ever-use versus no use model) and the frequency of use (frequent use model). Additionally, behavioural automaticity—reflecting the habitual or non-conscious nature of repeated behaviour—was expected to contribute uniquely to frequent use beyond effects explained by the core TPB constructs. However, some degree of divergence in the relative contribution of TPB components across models was expected, reflecting the distinct psychological mechanisms that underlie initiation (ever-use) versus sustained (frequent) use. Methods Participants and Procedures Participants were recruited from the undergraduate population at a major U.S. university between September 2023 to April 2024 in exchange for course credit. A total of 486 students completed the baseline measures. However, 35 participants failed an attention check item (i.e., “Please select the “Disagree” option to show you are paying attention.”), resulting in a final sample of 451 ( M age = 20.29, SD age = 2.43; 88 male, 347 female, 16 non-binary). The sample was ethnically diverse: 271 Hispanic, 95 Asian American, 43 Caucasian, 19 African American, 4 Middle Eastern, 3 Native American, and 16 other. Consenting participants completed an online survey hosted on the Qualtrics platform. Informed consent was obtained from all participants included in the study, and all procedures were approved by the BLINDED FOR PEER REVIEW Institutional Review Board, in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. At the time of data collection, vape use was legal in California for individuals aged 21 and older. Additionally, the retail sale of flavoured tobacco products, excluding menthol, was banned. However, emerging evidence indicates that this ban may have had limited impact on underage vape access or flavoured vape purchasing [ 20 ], with many online and physical retail stores continuing to sell these products despite regulatory restrictions [ 21 ]. Measures Study measures were developed in accordance with TPB guidelines [ 22 ] and are provided in Appendix A (see online supplementary materials). Attitude. Attitude was measured using five items, each beginning with the common stem “For me, using an e-cigarette or vape in the next two weeks would be…”. Responses were recorded on a 7-point semantic differential scale (e.g., 1 = boring to 7 = fun ). Subjective Norm . Subjective norm was measured using three items (e.g., “It is mostly up to me whether I use an e-cigarette/vape”). Responses were recorded on a 7-point Likert scale ranging from 1 = Strongly Disagree to 7 = Strongly Agree . Perceived Behavioural Control . Perceived behavioural control was measured using three items (e.g., “Most people who are important to me would approve of me using an e-cigarette/vape”). Responses were recorded on a 7-point Likert scale ranging from 1 = Strongly Disagree to 7 = Strongly Agree . Harm Perceptions. Perceptions of the harmfulness of vape use was measured using a three-item scale (e.g., “How much do you think people harm themselves when they use e-cigarettes/vapes?”) [ 23 ]. Each item was rated on a 7-point scale ranging from 1 = Not at all to 7 = A great deal . Behavioural Automaticity. Among participants reporting any vape use, habitual usage was measured using the Self-Reported Behavioural Automaticity Index (SRBAI)[ 24 , 25 ]. The scale consists of four items (e.g., “Using an e-cigarette or vape is something I do automatically”), each scored on a 7-point Likert scale ranging from 1 = Strongly Disagree to 7 = Strongly Agree . Vape Use. Participants’ frequency of vape use was measured on a single item adapted from previous research [ 26 ]: “How often have you used an e-cigarette or vape device?”. Responses were recorded on an 11-point scale ranging from 0 Never to 10 Every day . Data Analysis Data were analysed using a hurdle model implemented via the pscl package in R [ 19 , 27 ]. Model fit was evaluated using Wald’s test, where a significant result supports the appropriateness of hurdle modelling over general linear modelling. Overall model performance was assessed using Cragg and Uhler’s pseudo R 2 , a common effect size measure for logistic models. The ever-use versus no use model included attitude, subjective norm, perceived behavioural control and harm perception as predictors. The frequent use model incorporated these same variables, with the addition of behavioural automaticity to capture habitual and potentially addictive processes. Both models controlled for age and gender. Complete model outputs are provided in Appendix B (see online supplementary materials). Results Descriptive statistics and zero-order correlations are presented in Table 1 . Of the total sample, 267 (59.2%) reported never having used a vape, while 184 (40.8%) reported at least one instance of use. Wald’s test for the hurdle model indicated a significant zero-hurdle effect, χ 2 (8) = 25.97, p = .001, supporting the appropriateness of hurdle modelling over a standard general linear modelling. The model accounted for a moderate proportion of variance in vape use, as indicated by Cragg and Uhler’s pseudo R 2 = .44. Full parameter estimates for both components of the model are reported in Table 2 . Table 1 Descriptive Statistics and Spearman’s Correlations Between All Study Variables 1 2 3 4 5 6 7 8 1. Attitudes - 2. Subjective Norms .31 *** - 3. Perceived Behavioural Control .37 *** .14 ** - 4. Harm Perceptions − .30 *** − .29 *** − .01 - 5. Behavioural Automaticity .42 *** .26 *** .15 ** − .14 ** - 6. Vape Use .62 *** .28 *** .40 *** − .12 * .47 *** - 7. Age .08 .07 .05 .07 .07 .17 *** - 8. Gender − .09 − .06 − .01 .00 − .03 − .09 − .11 * - M 1.69 1.74 5.17 4.27 1.43 2.21 20.29 0.20 SD 1.29 1.01 1.17 0.69 1.14 2.63 2.43 0.40 Table 2 Results from a Hurdle Modelling Approach Predicting Vape Use in U.S. Undergraduate Students Ever-Use versus No Use Frequent Use B SE p B SE p (Intercept) -9.57 *** 1.64 < .001 -1.61 1.34 .228 Attitudes 1.11 *** 0.15 < .001 0.30 ** 0.09 .001 Subjective Norms 0.28 * 0.13 .040 -0.03 0.11 .808 Perceived Behavioural Control 0.69 *** 0.14 < .001 -0.04 0.13 .779 Harm Perceptions 0.38 0.21 .071 0.07 0.17 .696 Age 0.07 0.05 .190 0.02 0.05 .622 Gender -0.39 0.33 .243 0.07 0.32 .832 Behavioural Automaticity 0.39 *** 0.08 < .001 Log(theta) -0.30 0.50 .548 In the binary component of the hurdle model—predicting the likelihood of any vape use (i.e., ever-use) versus no use—attitudes, subjective norms, and perceived behavioural control each significantly predicted an increased likelihood of having used a vape at least once. In contrast, perceptions of harm, age, and gender did not significantly predict the likelihood of ever having used a vape. In the count component, which predicted frequency of use among participants with prior vaping experience, only attitude and behavioural automaticity emerged as significant predictors. No significant effects were observed for subjective norms, perceived behavioural control, harm perceptions, age, or gender. Discussion The increasing prevalence of vaping among young people constitutes a pressing public health concern [ 1 , 3 – 5 ], underscoring the need to identify the psychological factors that contribute to both initiation and sustained engagement in this potentially harmful behaviour. The current study aimed to examine the applicability of the TPB, augmented with constructs of harm perception and behavioural automaticity, in predicting the use of ENDS, commonly known as e-cigarettes or vapes. Specifically, a hurdle modelling approach was employed to simultaneously assess predictors of any use (i.e., ever-use versus no use) and frequency of use among those with prior vaping experience. In the binary component of the model predicting vape initiation (i.e., ever-use versus no use), the TPB constructs of attitudes, subjective norms, and perceived behavioural control each emerged as significant predictors of ever having used a vape. These findings are broadly consistent with prior literature examining TPB constructs in the context of e-cigarette use among young adults [ 4 , 14 , 15 ]. Among these predictors, attitude emerged as the strongest correlate, with subjective norms and perceived behavioural control contributing more modest but non-zero effects. This pattern of findings suggests that perceiving vaping as a positive and pleasurable activity, one that is socially approved of and under personal control, may function as key psychological determinants of the likelihood of trying vaping. Notably, in the count component, only the TPB construct of attitudes predicted frequency of use among participants with prior vaping experience. This indicates that outcome expectancies and experiential beliefs, such as those emphasizing the positive and pleasurable aspects of vaping, may serve as primary drivers of both ever having used a vape and frequency of use. Accordingly, both the decision to try vaping and the sustained use over time may be shaped by individuals’ expected or previously experienced enjoyment associated with the behaviour. The findings may also indicate that while normative influences (such as social pressure and acceptability) and perceived behavioural control (including beliefs about ease and access) play a meaningful role in the decision to initiate vaping, supporting prior research in this context [ 17 ], their influence appears to diminish in deciding to frequently use. Moreover, perceptions of harms related to vape use did not significantly contribute to the predictive model when considered alongside the core TPB constructs. This finding mirrors previous research in undergraduate populations, such as that reported in Phipps et al. [ 4 ], where participants acknowledged the potential harms of vaping (as evidenced by high scores on the harm perception measure), yet such perceptions did not predict behaviour. Indeed, recent research has flagged the possibility that young people may even view vaping as comparably harmful to traditional combustible cigarette use [ 28 ]. These findings challenge prevailing assumptions that youth uptake is primarily driven by perceptions of reduced risk [ 1 ]. They also raise important implications for intervention strategies; namely, that public health messaging centred solely on risk communication may be insufficient. Instead, more effective behavioural interventions may need to directly target attitudinal beliefs, and possibly social influence mechanisms and perceptions of control to curb initiation among young people [ 29 , 30 ]. Finally, behavioural automaticity, a core feature of habitual action and a well-established correlate of nicotine dependence [ 4 , 31 ], emerged as a significant predictor of vape use frequency in the current study. The strong effect observed suggests that, for a subset of users, vaping behaviour may occur with minimal conscious deliberation, potentially as a routinised or automatic response to environmental cues or internal cravings. This finding aligns with previous research employing integrated behavioural models, in which behavioural automaticity has demonstrated predictive value for behavioural frequency, often independent of intentions or deliberative beliefs [ 32 – 34 ]. The current results also support prior findings specific to e-cigarette use [ 4 , 26 , 31 ], and may carry important implications for intervention design. Specifically, efforts to reduce vaping frequency among young users may need to address habitual and dependency-related processes, which are typically more resistant to change than intention-driven behaviours [ 35 ]. The current study offers several notable strengths, including its theory grounded design and its novel approach to differentiating between ever having used a vape and frequency of use within a single analytic framework. However, the findings should be interpreted in light of several notable limitations. Most critically, the cross-sectional nature of the data precludes the establishment of causal inferences or conclusions regarding temporal sequencing. Thus, while the observed results indicate potential differences in the psychological predictors of vape ever-use as compared to frequent use, such interpretations remain tentative in the absence of longitudinal or experimental evidence. Secondly, all constructs were assessed via self-report measures, which despite being formulated based on TPB guidelines, are inherently subject to biases such as social desirability, recall inaccuracies, and other uncontrolled extraneous influences. Future research would benefit from incorporating objective measures or more intensive observational methodologies to corroborate self-report data. Finally, the sample, although a demographic shown to be at elevated risk for vaping [ 36 ], comprised exclusively of undergraduate university students at a major university in California. As such, the generalisability of findings to other populations, cultural contexts, or age groups may be limited. Future investigations should seek to examine the drivers of vape use in other at-risk populations, such as adolescents in secondary education or non-university affiliated young adults, to enhance the ecological validity and broader applicability of the results. This study found that vape initiation (i.e., ever-use) was predicted by attitudes, subjective norms, and perceived behavioural control, while frequency of use was linked only to attitudes and behavioural automaticity. Notably, harm perceptions were unrelated to either. Though the cross-sectional design limits causal interpretations, the findings suggest different factors may underlie trying versus frequently using vapes. Declarations Author Contribution CRediT: Conceptualization: DJP, ZG, MH, KH; Data curation: DJP, ZG; Formal Analysis: DJP; Funding acquisition: ; Investigation: ZG; Methodology: DJP, ZG, MH, KH; Project administration: DJP, MH; Resources: DJP, ZG, MH, KH; Software: ; Supervision: MH, KH; Validation: ; Visualization: ; Writing – original draft: DJP, ZG, MH, KH; Writing – review & editing: DJP, ZG, MH, KH Data Availability All materials, data, analysis scripts, and model outputs are available on the Open Science Framework, https://osf.io/4h2px/ References World Health Organization. Tobacco Fact Sheet. Geneva: World Health Organization; 2022. Ashour A. Use of Vaping as a Smoking Cessation Aid: A Review of Clinical Trials. J Multidisciplinary Healthc. 2023;16:2137–44. Hammond D, Wackowski OA, Reid JL, O’Connor RJ. Use of JUUL e-cigarettes among youth in the United States. Nicotine Tob Res. 2020;22:827–32. Phipps DJ, Nott N, Hamilton K. 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Appetite. 2021;162:105183. Struik LL, Dow-Fleisner S, Belliveau M, Thompson D, Janke R. Tactics for drawing youth to vaping: content analysis of electronic cigarette advertisements. J Med Internet Res. 2020;22:e18943. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2026 Read the published version in International Journal of Behavioral Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6836345","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":471773231,"identity":"f4a2092e-157f-49ab-a492-91fa9e904e85","order_by":0,"name":"Daniel J Phipps","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDCCAyBUwMDMwMADYjPIMTYAyQoDQloMEFqMwVrOENDCwABWwAPmJ4J0MJzBo4Pv+NmDBz4YMLAzSPcePFy4oy69eUaOAcOBAtxaJM/kJRycAXKYzLmEwzPPHM5tBGvB4zCDAzkGh3lAWiSADN62A2AtzB/waTn/xuDwH4SWunRGgrbcAKpkQGhhTiCoRfLGG4ODPQYSzGwyZwxAfjFs7HlWcACfFr7zOcYfflTYJPNL9xh/BoaYvGF78sYHB/7g1gIFEslsEgwMzKBoNJyQAIksQsCOAaZFnp8oDaNgFIyCUTCCAABNrVb325BQ1AAAAABJRU5ErkJggg==","orcid":"","institution":"University of Jyväskylä","correspondingAuthor":true,"prefix":"","firstName":"Daniel","middleName":"J","lastName":"Phipps","suffix":""},{"id":471773232,"identity":"1af484c0-f3b6-49f5-bb1e-68cac5cd4e9d","order_by":1,"name":"Zoe Griffith","email":"","orcid":"","institution":"University of California, Merced","correspondingAuthor":false,"prefix":"","firstName":"Zoe","middleName":"","lastName":"Griffith","suffix":""},{"id":471773233,"identity":"a808d619-ddda-4232-b0b9-c3faee8d5087","order_by":2,"name":"Martin S Hagger","email":"","orcid":"","institution":"University of California, Merced","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"S","lastName":"Hagger","suffix":""},{"id":471773234,"identity":"a630054b-07d1-4451-8f84-35fd66232732","order_by":3,"name":"Kyra Hamilton","email":"","orcid":"","institution":"Griffith University","correspondingAuthor":false,"prefix":"","firstName":"Kyra","middleName":"","lastName":"Hamilton","suffix":""}],"badges":[],"createdAt":"2025-06-06 10:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6836345/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6836345/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12529-026-10448-4","type":"published","date":"2026-03-04T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":104250613,"identity":"92fc68e4-21a5-4ea0-bfb3-3f8e56929664","added_by":"auto","created_at":"2026-03-09 16:01:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":546436,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6836345/v1/868f9627-52d7-4c18-a364-1b012415ae37.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differentiating the Determinants of Ever Having Used a Vape Versus Frequent Use: A Hurdle Modelling Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFollowing widespread acknowledgment of the adverse health consequences associated with smoked tobacco, public health agencies and governments globally have intensified efforts to reduce tobacco use prevalence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A recent focus within these efforts has been the rapid proliferation of electronic nicotine delivery systems (ENDS), commonly known as e-cigarettes or vapes. These devices function by aerosolizing a propylene glycol solution—often containing nicotine and flavorants—via a battery-powered heating element. Due to their reduced harm relative to combustible tobacco products, vapes have been increasingly considered by some public health experts as a potential harm reduction tool or a transitional aid for individuals attempting to discontinue nicotine use altogether [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, there is growing concern regarding the rapid uptake of vape use among young people, many of whom report little to no prior use of traditional tobacco products [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This trend is particularly concerning given the potential vulnerability of this age group to the effects of addictive substances [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. While vapes may confer fewer toxicological risks than combustible cigarettes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], they are nonetheless associated with a range of adverse physical and psychological health outcomes and retain the potent addictive properties of nicotine [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In response, the World Health Organization and national health authorities worldwide have identified the use of e-cigarettes or vapes, especially among young populations, as an urgent target for research and intervention [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe design of effective interventions to address vape uptake and use among young people requires a foundational understanding of the psychological mechanisms driving decision making for this behaviour. One theoretical model that has been extensively applied to health-related behaviours is the theory of planned behaviour (TPB) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. According to the model, behavioural intention—which proceed the enactment of behaviour—is shaped by three belief-based constructs: attitudes (beliefs of the likely outcomes and experiential experiences of the behaviour), subjective norms (beliefs of social pressure from important others to perform or not perform the behaviour), and perceived behavioural control (beliefs about how easy or difficult it is to perform the behaviour, as well as perceived capacity or resources available to execute that behaviour). A substantial body of meta-analytic evidence affirms the TPB as a robust model for predicting a wide range of health-related behaviours [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Within the context of e-cigarette use, several studies have demonstrated that the model is effective in predicting vaping behaviour among young people [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Notably, attitudes have consistently emerged as the strongest predictor of vaping behaviour, with affective or hedonistic attitudes—such as beliefs about enjoyment, fun, or pleasure—exerting a particularly strong influence. By contrast, perceptions about the health risks or harmfulness of vaping, while correlated with vape use [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], often show small or even negligible effects when considered alongside other social cognition constructs in predictive models [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese findings seem to indicate that, while normative influences and perceived behavioural control are relevant, beliefs about whether vaping is pleasurable or enjoyable or perceived as a positive experience appear to be the most potent drivers of behaviour. However, there is evidence that current research may conflate two distinct decisions: the decision to first ever use a vape and the subsequent decision regarding the sustained (or frequency of) use [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For instance, while norms typically exert only modest associations with vape use [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], other research has shown that initial experimentation with vaping is often encouraged, and in some cases actively facilitated, by peers or social groups [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This distinction is important, as different psychological processes may underlie the initiation of vaping compared to those that sustain continued use. Whereas social pressure and access may be central to first-time use, habitual patterns of frequent use are more likely driven by mechanisms such as dependency, automaticity, and cue activation—factors that may have limited relevance during initial experimentation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Consequently, while existing evidence supports the applicability of the TPB in predicting vape use, primarily through attitudinal pathways, it remains an open empirical question whether the same psychological constructs account for both the initiation of vaping (i.e., ever-use) and sustained patterns of use (i.e., frequent use) to the same extent. A more nuanced understanding of these distinctions is critical for informing the development of targeted behavioural interventions and tailored public health messaging aimed at both preventing initiation and reducing sustained use.\u003c/p\u003e\n\u003ch3\u003eThe Current Study\u003c/h3\u003e\n\u003cp\u003eThe current study sought to extend prior research applying the TPB to the context of e-cigarette or vape use, by employing a hurdle modelling approach [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Hurdle models conceptualize the act of using a vape—even a single instance—as a distinct decision point or “hurdle”. Once this initial hurdle is crossed, the psychological processes that govern the frequency of use may diverge from those associated with the likelihood of any use (i.e., a zero or non-zero occurrence). In the current analysis, this modelling approach is utilised to examine how TPB constructs predict both (1) the likelihood of ever having used a vape device (Research Question 1, ever-use), and the frequency of use among individuals with prior vaping experience (Research Question 2, frequent use), within a unified analytic framework. It was hypothesised that attitudes toward vaping, subjective norms, perceived behavioural control, and harm perceptions would significantly predict both ever having used a vape (ever-use versus no use model) and the frequency of use (frequent use model). Additionally, behavioural automaticity—reflecting the habitual or non-conscious nature of repeated behaviour—was expected to contribute uniquely to frequent use beyond effects explained by the core TPB constructs. However, some degree of divergence in the relative contribution of TPB components across models was expected, reflecting the distinct psychological mechanisms that underlie initiation (ever-use) versus sustained (frequent) use.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\n "},{"header":"Methods","content":"\u003ch2\u003eParticipants and Procedures\u003c/h2\u003e\u003cp\u003eParticipants were recruited from the undergraduate population at a major U.S. university between September 2023 to April 2024 in exchange for course credit. A total of 486 students completed the baseline measures. However, 35 participants failed an attention check item (i.e., “Please select the “Disagree” option to show you are paying attention.”), resulting in a final sample of 451 (\u003cem\u003eM\u003c/em\u003e age = 20.29, \u003cem\u003eSD\u003c/em\u003e age = 2.43; 88 male, 347 female, 16 non-binary). The sample was ethnically diverse: 271 Hispanic, 95 Asian American, 43 Caucasian, 19 African American, 4 Middle Eastern, 3 Native American, and 16 other.\u003c/p\u003e\u003cp\u003eConsenting participants completed an online survey hosted on the Qualtrics platform. Informed consent was obtained from all participants included in the study, and all procedures were approved by the BLINDED FOR PEER REVIEW Institutional Review Board, in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. At the time of data collection, vape use was legal in California for individuals aged 21 and older. Additionally, the retail sale of flavoured tobacco products, excluding menthol, was banned. However, emerging evidence indicates that this ban may have had limited impact on underage vape access or flavoured vape purchasing [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], with many online and physical retail stores continuing to sell these products despite regulatory restrictions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003ch3\u003eMeasures\u003c/h3\u003e\u003cp\u003eStudy measures were developed in accordance with TPB guidelines [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and are provided in Appendix A (see online supplementary materials).\u003c/p\u003e\u003cp\u003e \u003cb\u003eAttitude.\u003c/b\u003e Attitude was measured using five items, each beginning with the common stem “For me, using an e-cigarette or vape in the next two weeks would be…”. Responses were recorded on a 7-point semantic differential scale (e.g., 1 = \u003cem\u003eboring\u003c/em\u003e to 7 = \u003cem\u003efun\u003c/em\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003eSubjective Norm\u003c/b\u003e. Subjective norm was measured using three items (e.g., “It is mostly up to me whether I use an e-cigarette/vape”). Responses were recorded on a 7-point Likert scale ranging from 1 = \u003cem\u003eStrongly Disagree\u003c/em\u003e to 7 = \u003cem\u003eStrongly Agree\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e \u003cb\u003ePerceived Behavioural Control\u003c/b\u003e. Perceived behavioural control was measured using three items (e.g., “Most people who are important to me would approve of me using an e-cigarette/vape”). Responses were recorded on a 7-point Likert scale ranging from 1 = \u003cem\u003eStrongly Disagree\u003c/em\u003e to 7 = \u003cem\u003eStrongly Agree\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e \u003cb\u003eHarm Perceptions.\u003c/b\u003e Perceptions of the harmfulness of vape use was measured using a three-item scale (e.g., “How much do you think people harm themselves when they use e-cigarettes/vapes?”) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Each item was rated on a 7-point scale ranging from 1 = \u003cem\u003eNot at all\u003c/em\u003e to 7 = \u003cem\u003eA great deal\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e \u003cb\u003eBehavioural Automaticity.\u003c/b\u003e Among participants reporting any vape use, habitual usage was measured using the Self-Reported Behavioural Automaticity Index (SRBAI)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The scale consists of four items (e.g., “Using an e-cigarette or vape is something I do automatically”), each scored on a 7-point Likert scale ranging from 1 = \u003cem\u003eStrongly Disagree\u003c/em\u003e to 7 = \u003cem\u003eStrongly Agree\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e \u003cb\u003eVape Use.\u003c/b\u003e Participants’ frequency of vape use was measured on a single item adapted from previous research [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]: \u003cem\u003e“How often have you used an e-cigarette or vape device?”.\u003c/em\u003e Responses were recorded on an 11-point scale ranging from 0 \u003cem\u003eNever\u003c/em\u003e to 10 \u003cem\u003eEvery day\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eData were analysed using a hurdle model implemented via the pscl package in R [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Model fit was evaluated using Wald’s test, where a significant result supports the appropriateness of hurdle modelling over general linear modelling. Overall model performance was assessed using Cragg and Uhler’s pseudo \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, a common effect size measure for logistic models. The ever-use versus no use model included attitude, subjective norm, perceived behavioural control and harm perception as predictors. The frequent use model incorporated these same variables, with the addition of behavioural automaticity to capture habitual and potentially addictive processes. Both models controlled for age and gender. Complete model outputs are provided in Appendix B (see online supplementary materials).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics and zero-order correlations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the total sample, 267 (59.2%) reported never having used a vape, while 184 (40.8%) reported at least one instance of use. Wald\u0026rsquo;s test for the hurdle model indicated a significant zero-hurdle effect, χ\u003csup\u003e2\u003c/sup\u003e(8)\u0026thinsp;=\u0026thinsp;25.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001, supporting the appropriateness of hurdle modelling over a standard general linear modelling. The model accounted for a moderate proportion of variance in vape use, as indicated by Cragg and Uhler\u0026rsquo;s pseudo \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.44. Full parameter estimates for both components of the model are reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDescriptive Statistics and Spearman\u0026rsquo;s Correlations Between All Study Variables\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Attitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Subjective Norms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.31\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Perceived Behavioural Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.37\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Harm Perceptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.30\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Behavioural Automaticity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.26\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.15\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Vape Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.62\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.40\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.12\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.17\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.11\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eResults from a Hurdle Modelling Approach Predicting Vape Use in U.S. Undergraduate Students\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eEver-Use versus No Use\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eFrequent Use\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.57\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective Norms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Behavioural Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarm Perceptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioural Automaticity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog(theta)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the binary component of the hurdle model\u0026mdash;predicting the likelihood of any vape use (i.e., ever-use) versus no use\u0026mdash;attitudes, subjective norms, and perceived behavioural control each significantly predicted an increased likelihood of having used a vape at least once. In contrast, perceptions of harm, age, and gender did not significantly predict the likelihood of ever having used a vape. In the count component, which predicted frequency of use among participants with prior vaping experience, only attitude and behavioural automaticity emerged as significant predictors. No significant effects were observed for subjective norms, perceived behavioural control, harm perceptions, age, or gender.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe increasing prevalence of vaping among young people constitutes a pressing public health concern [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], underscoring the need to identify the psychological factors that contribute to both initiation and sustained engagement in this potentially harmful behaviour. The current study aimed to examine the applicability of the TPB, augmented with constructs of harm perception and behavioural automaticity, in predicting the use of ENDS, commonly known as e-cigarettes or vapes. Specifically, a hurdle modelling approach was employed to simultaneously assess predictors of any use (i.e., ever-use versus no use) and frequency of use among those with prior vaping experience.\u003c/p\u003e \u003cp\u003eIn the binary component of the model predicting vape initiation (i.e., ever-use versus no use), the TPB constructs of attitudes, subjective norms, and perceived behavioural control each emerged as significant predictors of ever having used a vape. These findings are broadly consistent with prior literature examining TPB constructs in the context of e-cigarette use among young adults [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Among these predictors, attitude emerged as the strongest correlate, with subjective norms and perceived behavioural control contributing more modest but non-zero effects. This pattern of findings suggests that perceiving vaping as a positive and pleasurable activity, one that is socially approved of and under personal control, may function as key psychological determinants of the likelihood of trying vaping.\u003c/p\u003e \u003cp\u003eNotably, in the count component, only the TPB construct of attitudes predicted frequency of use among participants with prior vaping experience. This indicates that outcome expectancies and experiential beliefs, such as those emphasizing the positive and pleasurable aspects of vaping, may serve as primary drivers of both ever having used a vape and frequency of use. Accordingly, both the decision to try vaping and the sustained use over time may be shaped by individuals\u0026rsquo; expected or previously experienced enjoyment associated with the behaviour. The findings may also indicate that while normative influences (such as social pressure and acceptability) and perceived behavioural control (including beliefs about ease and access) play a meaningful role in the decision to initiate vaping, supporting prior research in this context [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], their influence appears to diminish in deciding to frequently use.\u003c/p\u003e \u003cp\u003eMoreover, perceptions of harms related to vape use did not significantly contribute to the predictive model when considered alongside the core TPB constructs. This finding mirrors previous research in undergraduate populations, such as that reported in Phipps et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], where participants acknowledged the potential harms of vaping (as evidenced by high scores on the harm perception measure), yet such perceptions did not predict behaviour. Indeed, recent research has flagged the possibility that young people may even view vaping as comparably harmful to traditional combustible cigarette use [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These findings challenge prevailing assumptions that youth uptake is primarily driven by perceptions of reduced risk [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. They also raise important implications for intervention strategies; namely, that public health messaging centred solely on risk communication may be insufficient. Instead, more effective behavioural interventions may need to directly target attitudinal beliefs, and possibly social influence mechanisms and perceptions of control to curb initiation among young people [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, behavioural automaticity, a core feature of habitual action and a well-established correlate of nicotine dependence [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], emerged as a significant predictor of vape use frequency in the current study. The strong effect observed suggests that, for a subset of users, vaping behaviour may occur with minimal conscious deliberation, potentially as a routinised or automatic response to environmental cues or internal cravings. This finding aligns with previous research employing integrated behavioural models, in which behavioural automaticity has demonstrated predictive value for behavioural frequency, often independent of intentions or deliberative beliefs [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The current results also support prior findings specific to e-cigarette use [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and may carry important implications for intervention design. Specifically, efforts to reduce vaping frequency among young users may need to address habitual and dependency-related processes, which are typically more resistant to change than intention-driven behaviours [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current study offers several notable strengths, including its theory grounded design and its novel approach to differentiating between ever having used a vape and frequency of use within a single analytic framework. However, the findings should be interpreted in light of several notable limitations. Most critically, the cross-sectional nature of the data precludes the establishment of causal inferences or conclusions regarding temporal sequencing. Thus, while the observed results indicate potential differences in the psychological predictors of vape ever-use as compared to frequent use, such interpretations remain tentative in the absence of longitudinal or experimental evidence. Secondly, all constructs were assessed via self-report measures, which despite being formulated based on TPB guidelines, are inherently subject to biases such as social desirability, recall inaccuracies, and other uncontrolled extraneous influences. Future research would benefit from incorporating objective measures or more intensive observational methodologies to corroborate self-report data. Finally, the sample, although a demographic shown to be at elevated risk for vaping [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], comprised exclusively of undergraduate university students at a major university in California. As such, the generalisability of findings to other populations, cultural contexts, or age groups may be limited. Future investigations should seek to examine the drivers of vape use in other at-risk populations, such as adolescents in secondary education or non-university affiliated young adults, to enhance the ecological validity and broader applicability of the results.\u003c/p\u003e \u003cp\u003eThis study found that vape initiation (i.e., ever-use) was predicted by attitudes, subjective norms, and perceived behavioural control, while frequency of use was linked only to attitudes and behavioural automaticity. Notably, harm perceptions were unrelated to either. Though the cross-sectional design limits causal interpretations, the findings suggest different factors may underlie trying versus frequently using vapes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCRediT: Conceptualization: DJP, ZG, MH, KH; Data curation: DJP, ZG; Formal Analysis: DJP; Funding acquisition: ; Investigation: ZG; Methodology: DJP, ZG, MH, KH; Project administration: DJP, MH; Resources: DJP, ZG, MH, KH; Software: ; Supervision: MH, KH; Validation: ; Visualization: ; Writing \u0026ndash; original draft: DJP, ZG, MH, KH; Writing \u0026ndash; review \u0026amp; editing: DJP, ZG, MH, KH\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll materials, data, analysis scripts, and model outputs are available on the Open Science Framework, https://osf.io/4h2px/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. 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(2023) Predicting sugar intake using an extended theory of planned behavior in a sample of adolescents: The role of habit and self-control. Brain Behav e3200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGardner B, Richards R, Lally P, Rebar A, Thwaite T, Beeken RJ. Breaking habits or breaking habitual behaviours? Old habits as a neglected factor in weight loss maintenance. Appetite. 2021;162:105183.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStruik LL, Dow-Fleisner S, Belliveau M, Thompson D, Janke R. Tactics for drawing youth to vaping: content analysis of electronic cigarette advertisements. J Med Internet Res. 2020;22:e18943.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"E-cigarettes, Vape, Theory of Planned Behaviour, Risk Perceptions, Nicotine, Harm","lastPublishedDoi":"10.21203/rs.3.rs-6836345/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6836345/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe rising popularity of vaping among young people constitutes a significant public health concern, underscoring the need to identify factors that contribute to youth initiation and continued engagement in this potentially harmful behaviour. Investigation into the psychological mechanisms underlying vaping among young people remains relatively nascent and has, to date, seldom differentiated between the initial decision to try vaping (i.e., ever-use) and the regulation of sustained patters of use (i.e., frequent use).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe surveyed 451 undergraduate students, collecting data on vaping use, attitudes toward vaping, subjective norms, perceived behavioural control, harm perceptions, and behavioural automaticity. A hurdle modelling approach was used to exam two distinct processes; (1) the predictors of ever-use versus no vaping use, and (2) the frequency of use among individuals with prior vaping experience.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFindings supported the application of hurdle modelling, revealing distinct predictors for ever-use and frequent use. Specifically, the former was predicted by attitudes, subjective norms, and perceived behavioural control, whereas the latter was predicted solely by attitudes and behavioural automaticity. Notably, harm perceptions did not predict either ever-use or the frequency of use.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFindings indicate that both ever having used a vape and frequency of use are primarily driven by beliefs of the outcomes and experiential experiences of the behaviour, rather than by cognitive evaluations of harm. Although conclusions are limited by the cross-sectional design, current results point to the possibility that the psychological determinants of initial experimentation with vaping may differ from those that sustain frequent use.\u003c/p\u003e","manuscriptTitle":"Differentiating the Determinants of Ever Having Used a Vape Versus Frequent Use: A Hurdle Modelling Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 09:48:11","doi":"10.21203/rs.3.rs-6836345/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1dd98066-2e42-4b8f-91e9-645b9cb46fee","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:00:51+00:00","versionOfRecord":{"articleIdentity":"rs-6836345","link":"https://doi.org/10.1007/s12529-026-10448-4","journal":{"identity":"international-journal-of-behavioral-medicine","isVorOnly":false,"title":"International Journal of Behavioral Medicine"},"publishedOn":"2026-03-04 15:57:37","publishedOnDateReadable":"March 4th, 2026"},"versionCreatedAt":"2025-06-17 09:48:11","video":"","vorDoi":"10.1007/s12529-026-10448-4","vorDoiUrl":"https://doi.org/10.1007/s12529-026-10448-4","workflowStages":[]},"version":"v1","identity":"rs-6836345","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6836345","identity":"rs-6836345","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}