Maxime Mauduy
email: maxime.mauduy@u-paris.fr.

ORCID Nicolas Mauny

ORCID Hélène Beaunieux

ORCID Jessica Mange

Smoke Tobacco? French Validity of Brief Wisconsin Inventory of Smoking Dependence Motives Among (non-) Daily Smokers and Associations With Psychological Variables

Keywords: Brief Wisconsin Inventory of Smoking Dependence Motives, University student, Smoking motives, (Non-)Daily smokers, Psychological variables

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Given the consequences it has on health, tobacco use is considered a global health problem (World Health Organization, 2018). Tobacco dependence, generally defined as the experience of a strong need to smoke [START_REF] West | Tobacco smoking: Health impact, prevalence, correlates and interventions[END_REF], can lead to neuropsychological impairments even among young people [START_REF] Chamberlain | Association between Tobacco Smoking and Cognitive Functioning in Young Adults[END_REF]. Therefore, young people, especially university students, constitute a particularly interesting population group for researchers because, they are at risk of developing tobacco dependence in decades to come [START_REF] Kenford | Progression of college-age cigarette samplers: What influences outcome[END_REF][START_REF] Schulenberg | Monitoring the Future national survey results on drug use, 1975-2018: Volume II, college students and adults ages[END_REF]. In France, 28% of students are smokers [START_REF] Pasquereau | Tobacco and e-cigarette in France: levels of consumption according to the preliminary results from the 2016[END_REF], and among a sample of French student smokers, 15% are already heavily tobacco dependent [START_REF] Mauduy | Tobacco Dependence Among French University Students : A Cluster Analytic Approach to Identifying Distinct Psychological Pro fi les of Smokers[END_REF]. Knowing that a basic step for helping people quit smoking consists of identifying their underlying motivations for tobacco dependence, this study focuses on university students' smoking motives.

Drawing on a large body of work suggesting that dependence is a multidimensional and motivational phenomenon (e.g., [START_REF] Colby | Measuring nicotine dependence among youth: A review of available approaches and instruments[END_REF][START_REF] Moolchan | The Fagerstrom Test for Nicotine Dependence and the Diagnostic Interview Schedule Do they diagnose the same smokers ?[END_REF][START_REF] Radzius | A factor analysis of the Fagerstrom Tolerance Questionnaire[END_REF], [START_REF] Piper | A Multiple Motives Approach to Tobacco Dependence: The Wisconsin Inventory of Smoking Dependence Motives (WISDM-68)[END_REF] assumed that tobacco dependence could be measured based on the different motivations underlying tobacco use. As a result, the Wisconsin Inventory of Smoking Dependence Motives (WISDM, 68-item, Piper et al., 2004), and later on, its 37-item brief version (the B-WISDM, [START_REF] Smith | Development of the Brief Wisconsin Inventory of Smoking Dependence Motives[END_REF] were developed. Numerous studies have been conducted on heavy and daily tobacco users to test the psychometric qualities of the WISDM (e.g., Piasecki et al., 2010aPiasecki et al., , 2010b[START_REF] Piasecki | WISDM primary and secondary dependence motives: Associations with self-monitored motives for smoking in two college samples[END_REF][START_REF] Piper | Refining the tobacco dependence phenotype using the Wisconsin Inventory of Smoking Dependence Motives[END_REF][START_REF] Shenassa | Psychometric properties of the Wisconsin inventory of Smoking Dependence Motives (WISDM-68): A replication and extension[END_REF] and the B-WISDM (e.g., [START_REF] Ma | Evaluation of the brief Wisconsin Inventory of smoking dependence motives in African-American and European-American heavy smokers[END_REF][START_REF] Smith | Development of the Brief Wisconsin Inventory of Smoking Dependence Motives[END_REF][START_REF] Tombor | Denial of smoking-related risk among pregnant smokers[END_REF]. Researchers prefer this shorter version for practical purposes, but also because it shows good psychometric qualities. First, its dimensionality on 11 motivational factors was confirmed, namely, loss of control, automaticity, craving, tolerance, affiliative attachment, cognitive enhancement, cue exposure/associative processes, social/environmental goads, affective enhancement, taste, and weight control [START_REF] Pancani | Smoking behavior: A cross-sectional study to assess the dimensionality of the brief Wisconsin Inventory of Smoking Dependence Motives and identify different typologies among young daily smokers[END_REF]. In their study, [START_REF] Pancani | Smoking behavior: A cross-sectional study to assess the dimensionality of the brief Wisconsin Inventory of Smoking Dependence Motives and identify different typologies among young daily smokers[END_REF] compared a model with these 11 firstorder factors with error covariances to a model with 11 first-order and 2 second-order factors with error covariances. The rationale for testing these two second-order factors lies in the early work of [START_REF] Piper | Refining the tobacco dependence phenotype using the Wisconsin Inventory of Smoking Dependence Motives[END_REF] which highlights two subscales of the WISDM, namely primary dependence motives and secondary dependence motives. Primary dependence motives represent the core features of advanced tobacco dependence and consist of four scales (loss of control, automaticity, craving, and tolerance).

Secondary dependence motives represent instrumental motives for tobacco use and are composed of the seven remaining scales. Pancani et al.'s (2015) results support the 11-factor first-order model. Furthermore, these 11 motivational factors have satisfactory internal consistencies (e.g., [START_REF] Pancani | Smoking behavior: A cross-sectional study to assess the dimensionality of the brief Wisconsin Inventory of Smoking Dependence Motives and identify different typologies among young daily smokers[END_REF][START_REF] Smith | Development of the Brief Wisconsin Inventory of Smoking Dependence Motives[END_REF] and are conceptually similar for men and women (gender measurement invariance, [START_REF] Vajer | Psychometric properties and construct validity of the brief wisconsin inventory of smoking dependence motives in an internetbased sample of treatment-seeking Hungarian smokers[END_REF]. Finally, the B-WISDM has good predictive validity with measures of tobacco dependence, such as the Cigarette Dependence Scale (CDS; [START_REF] Pancani | Smoking behavior: A cross-sectional study to assess the dimensionality of the brief Wisconsin Inventory of Smoking Dependence Motives and identify different typologies among young daily smokers[END_REF], the Tobacco Dependence Screener [START_REF] Vajer | Psychometric properties and construct validity of the brief wisconsin inventory of smoking dependence motives in an internetbased sample of treatment-seeking Hungarian smokers[END_REF], the Fagerström Test for Nicotine Dependence [START_REF] Tombor | Denial of smoking-related risk among pregnant smokers[END_REF], and biochemical measures [START_REF] Smith | Development of the Brief Wisconsin Inventory of Smoking Dependence Motives[END_REF]. In sum, B-WISDM is an essential tool with good psychometric properties for understanding the different motivations underlying tobacco dependence.

Nevertheless, two elements limit the identification of these motivations among university students with the B-WISDM.

First, although the psychometric properties of B-WISDM have always been tested on heavy or daily smokers (e.g., [START_REF] Pancani | Smoking behavior: A cross-sectional study to assess the dimensionality of the brief Wisconsin Inventory of Smoking Dependence Motives and identify different typologies among young daily smokers[END_REF][START_REF] Vajer | Psychometric properties and construct validity of the brief wisconsin inventory of smoking dependence motives in an internetbased sample of treatment-seeking Hungarian smokers[END_REF], it has never been tested on university students. This limits the B-WISDM external validity to this specific population [START_REF] Vajer | Psychometric properties and construct validity of the brief wisconsin inventory of smoking dependence motives in an internetbased sample of treatment-seeking Hungarian smokers[END_REF]. Indeed, student smokers differ from the general population in two respects. Firstly, the majority of them are not daily smokers, but occasional smokers and are thus considered "light" smokers [START_REF] Levinson | Smoking, but not smokers: Identity among college students who smoke cigarettes[END_REF][START_REF] Moran | Social smoking among US college students[END_REF][START_REF] Thompson | Prevalence and characteristics of smokers at 30 Pacific Northwest colleges and universities[END_REF]. Second, student smokers also seem to be distinguished according to the reasons that lead them to smoke [START_REF] Mauduy | Tobacco Dependence Among French University Students : A Cluster Analytic Approach to Identifying Distinct Psychological Pro fi les of Smokers[END_REF]. For example, we can distinguish between smokers who smoke for coping, for social reasons (e.g., conforming to the social norm, partying), and for pro-smoking attitudes (e.g., [START_REF] Mauduy | Tobacco Dependence Among French University Students : A Cluster Analytic Approach to Identifying Distinct Psychological Pro fi les of Smokers[END_REF][START_REF] Moran | Social smoking among US college students[END_REF][START_REF] Shiffman | Patterns of intermittent smoking: An analysis using Ecological Momentary Assessment[END_REF][START_REF] Sutfin | Tobacco Use by College Students: A Comparison of Daily and Nondaily Smokers[END_REF]. This indicates a diversity of reasons, motivations for smoking among students, a diversity that the B-WISDM scale would make it possible to study if it were validated among daily and non-daily university student smokers. However, while the original version of the WISDM was used with college student smokers (e.g., [START_REF] Piasecki | WISDM primary and secondary dependence motives: Associations with self-monitored motives for smoking in two college samples[END_REF], and the B-WISDM has already been used to study the characteristics of non-daily smokers (e.g., [START_REF] Scheuermann | Correlates of converted and native nondaily smoking[END_REF][START_REF] Scheuermann | Smoking dependence across the levels of cigarette smoking in a multiethnic sample[END_REF] or tested in a nonclinical sample [START_REF] Adkison | Psychometric Characteristics of the Brief Wisconsin Inventory of Smoking Dependence Motives Among a Nonclinical Sample of Smokers[END_REF], only the primary dependence motives and secondary dependence motives scores were used, and neither the dimensionality nor the measurement invariance of B-WISDM according to the type of smoker (daily vs. non-daily) were pretested. Testing the psychometric qualities of B-WISDM with occasional and daily university students' smokers is thus necessary to justify its relevance to this particular smoker population.

Second, although B-WISDM has good psychometric properties in terms of dimensionality, internal consistency, and predictive validity with tobacco dependence, researchers have shown little interest in its convergent validity. Several psychological variables associated with tobacco dependence have been identified among university students: attitude towards smoking (i.e., students' evaluation of smoking, e.g., [START_REF] Mao | Psychosocial correlates of cigarette smoking among college students in China[END_REF], perceived smoking social norms (i.e., students' perceptions about how significant others approve of smoking, e.g., Riou [START_REF] Riou França | Are social norms associated with smoking in French university students? A survey report on smoking correlates. Substance Abuse Treatment[END_REF], perceived behavioral control to resist smoking (i.e., students' perception of their ability not to smoke, e.g., [START_REF] Jalilian | Cognitive factors related to cigarettes smoking among college students: An application of theory of planned[END_REF], smoker identity (i.e., the extent to which students view themselves as smokers, e.g., [START_REF] Levinson | Smoking, but not smokers: Identity among college students who smoke cigarettes[END_REF][START_REF] Moran | Social smoking among US college students[END_REF], eating disorders [START_REF] Eisenberg | Eating disorder symptoms among college students: Prevalence, persistence, correlates, and treatment-seeking[END_REF], anxiety (e.g., [START_REF] Bierhoff | Psychological Risk Factors for Alcohol, Cannabis, and Various Tobacco Use among Young Adults: A Longitudinal Analysis[END_REF], and depression (e.g., [START_REF] Schleicher | The role of depression and negative affect regulation expectancies in tobacco smoking among college students[END_REF]. Hence, on the one hand, the B-WISDM highlights several motivations for tobacco dependence, and, on the other hand, several psychological variables predict tobacco dependence, but little is known about the associations between these smoking dependence motives and psychological variables. We believe these smoking motives to be associated with specific psychological variables, making these latter the psychological matter of the motivations for tobacco dependence. First, we expect both smoking identity and perceived behavioral control to be two core psychological components of the primary dependence motives (e.g., [START_REF] Falomir-Pichastor | Tobacco dependence and smoking cessation: The mediating role of smoker and ex-smoker selfconcepts[END_REF][START_REF] Lee | Improving understanding of the quitting process: Psychological predictors of quit attempts versus smoking cessation maintenance among college students[END_REF][START_REF] Mauduy | Tobacco Dependence Among French University Students : A Cluster Analytic Approach to Identifying Distinct Psychological Pro fi les of Smokers[END_REF]. Second, we expect the other psychological variables to be more related to instrumental motivations to smoke, namely secondary dependence motives. Specifically, attitudes would be primarily associated with taste motives, social norms with social goads motives, eating disorders with weight control motives, and anxiety and depression with coping motives, that is affective and cognitive enhancements, and affiliative attachment. Thus, investigating the associations between the smoking motives and the psychological variables would be of great interest because it would allow us to (1) provide additional evidence of the good psychometric properties of the B-WISDM, (2) better understand the processes underlying tobacco dependence among university students, and (3) better assist university students to quit smoking by focusing prevention efforts on the psychological processes underlying their motivations for smoking.

Thus, the present study aims to test the validity of the B-WISDM among a French university student population. For this purpose, we test the B-WISDM dimensionality in 11 first-order motivational factors, its measurement invariance for types of smokers, namely daily and non-daily smokers, as well as its concurrent validity with tobacco dependence and convergent validity with several psychosocial variables already identified in the literature as being associated with tobacco dependence.

Methods

Procedure and Participants

This study was carried out on a convenience sample of 687 student smokers from the University of Caen Normandy. The participants were recruited by mail at their institutional address and were asked if they wished to participate in an online survey on tobacco smoking (via the Limesurvey® application, November 2021). No compensation was provided to the participants.

Ethics

All participants were volunteers and gave their consent before starting the survey.

Measures

Details of all measures and items described below are available on the supplemental online material at https://osf.io/2zh6c/?view_only=76269d9b547642d6849a291e3ee24212.

Demographics and Smoking History

We measured gender, age, academic level, age of smoking onset, smoking parents, cigarette consumption per day, recent attempts to quit smoking [START_REF] Perski | Does consistent motivation to stop smoking improve the explanation of recent quit attempts beyond current motivation? A cross-sectional study[END_REF], and motivation to quit smoking (MTSS, [START_REF] Kotz | Predictive validity of the Motivation To Stop Scale (MTSS): A single-item measure of motivation to stop smoking[END_REF].

French Brief Wisconsin Inventory of Smoking Dependence Motives

The B-WISDM, developed by [START_REF] Smith | Development of the Brief Wisconsin Inventory of Smoking Dependence Motives[END_REF], was translated into French and then backtranslated into English by a professional translation service for verification purposes. The B-WISDM is a questionnaire composed of 37 items rated on a 7-point scale ranging from 1 = Not true for me at all to 7 = Extremely true for me. It comprised 11 subscales that represent different smoking dependence motivations [START_REF] Smith | Development of the Brief Wisconsin Inventory of Smoking Dependence Motives[END_REF][START_REF] Pancani | Smoking behavior: A cross-sectional study to assess the dimensionality of the brief Wisconsin Inventory of Smoking Dependence Motives and identify different typologies among young daily smokers[END_REF]: loss of control, automaticity, craving, tolerance, affiliative attachment, cognitive enhancement, cue exposure/associative processes, social/environmental goads, affective enhancement, taste, and weight control (see the supplemental material for the items translated).

Concurrent Validity Measures

Tobacco dependence was measured using the validated French-language Cigarette Dependence Scale (CDS-5;[START_REF] Etter | A comparison of the content-, construct-and predictive validity of the cigarette dependence scale and the Fagerström test for nicotine dependence[END_REF]. This 5-item (i.e., Item 1 -"Please rate your addiction to cigarettes on a scale of 0-100: I am NOT addicted to cigarettes at all = 0; I am extremely addicted to cigarettes = 100"; Item 2 -"On average, how many cigarettes do you smoke per day?"; Item 3 -"Usually, how soon after waking up do you smoke your first cigarette?"; Item 4 -"For you, quitting smoking for good would be"; Item 5 -"After a few hours without smoking, I feel an irresistible urge to smoke") selfreport questionnaire provides a continuous score (between 5 and 25) for cigarette addiction. The CDS has shown high internal consistency, good predictive validity, as well as high test-retest reliability (e.g., [START_REF] Etter | A comparison of the content-, construct-and predictive validity of the cigarette dependence scale and the Fagerström test for nicotine dependence[END_REF][START_REF] Etter | Comparing the validity of the Cigarette Dependence Scale and the Fagerström Test for Nicotine Dependence[END_REF][START_REF] Etter | A self-administered questionnaire to measure dependence on cigarettes: The cigarette dependence scale[END_REF], thus overcoming the psychometric limitations of the Fagerström Test for Nicotine Dependence.

Convergent Validity Measures

Attitudes, perceived social norms, and perceived behavioral control to resist smoking were assessed based on the Theory of Planned Behavior (TPB, [START_REF] Ajzen | The theory of planned behavior[END_REF][START_REF] Jalilian | Cognitive factors related to cigarettes smoking among college students: An application of theory of planned[END_REF][START_REF] Mauduy | Tobacco Dependence Among French University Students : A Cluster Analytic Approach to Identifying Distinct Psychological Pro fi les of Smokers[END_REF]. The assessment included attitude towards smoking (4-item, Cronbach α = .92), perceived social norms for smoking (3-item, Cronbach α = .81), and perceived behavioral control to resist smoking (3-item, Cronbach α = .75) rated on Likert-type scale scored from 1 = do not agree to 5 = strongly agree.

This scale measuring three factors of the TPB has acceptable fits in French [START_REF] Mauduy | Tobacco Dependence Among French University Students : A Cluster Analytic Approach to Identifying Distinct Psychological Pro fi les of Smokers[END_REF].

Smoker identity was assessed using the Smoker Self-Concept Scale (SSCS; 5-item Likert-type scale scored from 1 = do not agree to 5 = strongly agree; Cronbach α =.85, [START_REF] Shadel | Individual differences in self-concept among smokers attempting to quit: Validation and predictive utility of measures of the smoker self-concept and abstainer self-concept[END_REF]. The SSCS assesses the importance of being a smoker for one's self-concept and has shown great predictive and discriminant validities, good internal consistency [START_REF] Shadel | Individual differences in self-concept among smokers attempting to quit: Validation and predictive utility of measures of the smoker self-concept and abstainer self-concept[END_REF], and excellent fits in French [START_REF] Mauduy | Tobacco Dependence Among French University Students : A Cluster Analytic Approach to Identifying Distinct Psychological Pro fi les of Smokers[END_REF].

Eating disorders were assessed with the French version of the SCOFF questionnaire [START_REF] Parker | Eating Disorders in Graduate Students: Exploring the SCOFF Questionnaire as a Simple Screening Tool[END_REF]. It consists of responding to five questions related to food in a yes/no format. Scores can range from 0 to 5 (i.e., answer yes to all 5 questions), with a score of 2 or higher indicating a likely case of an eating disorder.

Anxiety was measured with the French version of the State-Trait Anxiety Inventory (STAI, [START_REF] Spielberger | Manual for the State-Trait Anxiety Inventory[END_REF] which is composed of 20 items (Cronbach α = .91) on a 4-point scale ranging from no (1) to yes (4). It has shown good validity and acceptable reliability [START_REF] Barnes | Reliability generalization of scores on the Spielberger state-trait anxiety inventory[END_REF]. Depression was measured with the short French version of the Beck Depression Inventory (BDI, [START_REF] Beck | Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation[END_REF]. It consists of 13 items, each composed of four statements reflecting various severity degrees of depression-related symptoms. The BDI has shown high internal consistency, good predictive validity, and high test-retest reliability [START_REF] Beck | Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation[END_REF][START_REF] Beck | Internal consistencies of the original and revised Beck Depression Inventory[END_REF].

Statistical Analyses

First, the dimensionality and reliability of the B-WISDM have been tested. Three models derived from the literature were tested using a robust confirmatory factor analysis (CFA). Model 1 contained two first-order correlated factors, namely the primary dependence motives and secondary dependence motives. Model 2 contained 11 first-order correlated factors. Model 3 included 11 firstorder correlated factors (as model 2) and 2 second-order factors, namely primary dependence motives and secondary dependence motives. Robust CFAs were used to manage the multivariate non-normality of the data (i.e., Kurtosis test > 20, [START_REF] Mardia | Applications of Some Measures of Multivariate Skewness and Kurtosis in Testing Normality and Robustness Studies[END_REF]. As standard maximum likelihood estimation assumes multivariate normality, we used the maximum likelihood estimation with robust (Huber-White) standard errors. Five fit indices were used to evaluate each model. The Satorra-Bentler Chi-square (χ²) test indicates the fit goodness of the model when the value is statistically significant. The robust comparative fit index (CFI) and robust Tucker-Lewis index must be greater than 0.95 to demonstrate a good model fit, but values above 0.90 are still considered acceptable ( [START_REF] Boateng | Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer[END_REF][START_REF] Brown | Confirmatory Factor Analysis for Applied Research[END_REF]. The robust root mean squared error of approximation (RMSEA) indicates a good model fit when its values are lower than 0.05 and an adequate fit when they are lower than 0.08. The standardized root mean square residual (SRMR) had to be lower than 0.08 [START_REF] Boateng | Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer[END_REF]. To identify whether one more restricted model has a better fit than another, three criteria have to be met. First, we compared the models using the specialized computation for the Satotta-Bentler χ² difference [START_REF] Satorra | A scaled difference chi-square test statistic for moment structure analysis[END_REF]. The value of the χ² difference must be significant (i.e., p <.05). However, since the χ² test may be sensitive to the sample size, we also used CFI and RMSEA values. The CFI must show an increase higher than .01 (CFIdiff > .01, [START_REF] Cheung | Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance[END_REF], and the RMSEA must show a decrease of over 0.015 (RMSEAdiff < .015). The internal consistencies for the optimal model identified were then calculated using Cronbach's alpha. Values above 0.90 are considered excellent, and values above 0.70 are considered satisfactory [START_REF] Boateng | Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer[END_REF][START_REF] Nunnally | Psychometric theory[END_REF].

Second, to test the measurement invariance of types of smokers (daily and non-daily), we conducted a series of multigroup CFAs on the previously identified model. Three nested models with increasing constraints were estimated. First, the configural invariance, which corresponds to a model without constraints, tests whether the dimensionality of the FB-WISDM and the item composition of the factors are identical between groups. A configural invariance between groups indicates that the groups (non-daily or daily smokers) have a similar representation of the underlying construct. Second, the metric invariance, which constrained the factor loadings to be identical in all groups, tests whether the contribution of each item to a factor (as measured by the factor loadings or standardized regression coefficient) is identical in all groups. Metric invariance indicates that the understanding of the content of each item is not influenced by the type of smokers. Third, scalar invariance, which constrained both factor loadings and intercepts to be identical in all groups, tests whether the intercept for each item is not influenced by the groups. Scalar invariance indicates that the differences between groups on the means of measure variables (i.e., items) are due to group differences on the mean of the common latent variable (i.e., factor). Thus, scalar invariance is required to compare groups in terms of latent means. To test measurement invariance, we compared configural to metric invariance, and then, if metric invariance is retained, we compared metric to scalar invariance. To show measurement invariance, at least one of the three following criteria had to be met. First, the specialized computation for the Satotta-Bentler χ² difference between the two models has to be not significant, second, the CFIdiff between the two models should be less than or equal to .01, and third, the RMSEAdiff between the two models should be less than or equal to .015. It should be noted that, since the Satotta-Bentler χ² difference test may be sensitive to the sample size, more attention has been given to the CFIdiff and RMSEAdiff indicators to evaluate the measurement invariance. Furthermore, we also tested the measurement invariance for gender (men and women) to verify that the gender invariance previously shown among adult daily smokers [START_REF] Vajer | Psychometric properties and construct validity of the brief wisconsin inventory of smoking dependence motives in an internetbased sample of treatment-seeking Hungarian smokers[END_REF] is replicated among university students.

Third, the concurrent and convergent validity were tested using a multivariate regression model that includes all the FB-WISDM dimensions of the identified model as predictors, and tobacco dependence, attitudes, social norms, perceived behavioral control to resist smoking, smoker identity, eating disorders, anxiety, and depression as outcome variables. Estimation of the standardized coefficients of the multivariate model enables us to conclude which FB-WISDM dimensions are significantly or non-significantly associated with which outcomes. The Bootstrap method (N = 1000) was used to assess the stability of the results.

All statistical analyses were conducted using the R software (version 4.05; R Core Team). The package mvn [START_REF] Korkmaz | MVN: An R Package for Assessing Multivariate Normality[END_REF] was used to assess the multivariate nonnormality, the package lavaan [START_REF] Rosseel | lavaan: An R Package for Structural Equation[END_REF] to test the dimensionality, the measurement invariance, the reliability and concurrent and convergent validities of the FB-WISDM.

Results

Characteristics of the Sample

Table 1 presents the characteristics of the sample. Students were considered current smokers when they indicated that they had already smoked 100 cigarettes in their lifetime and had smoked at least one time in the past 30 days [START_REF] Adkison | Psychometric Characteristics of the Brief Wisconsin Inventory of Smoking Dependence Motives Among a Nonclinical Sample of Smokers[END_REF][START_REF] Riou França | Are social norms associated with smoking in French university students? A survey report on smoking correlates. Substance Abuse Treatment[END_REF]. Among the 687 smokers (Mage = 20.06, SD = 2.39), 63.5% were female (2.2% did not choose an answer). On average, they started smoking at age 16.1 years (SD = 2.03) and currently smoke 6.34 cigarettes per day (SD = 16.3). A majority of participants are non-daily smokers (63.5%). Finally, almost half of the participants reported having made one attempt to quit during the past year (41.4%) and are motivated to quit smoking (42.6%).

Tests of FB-WISDM Dimensionality

Among the 687 participants, 29 had at least one missing data on the FB-WISDM. To address the problem of missing data, we used the full-information maximum likelihood estimation (T. [START_REF] Lee | A Comparison of Full Information Maximum Likelihood and Multiple Imputation in Structural Equation Modeling With Missing Data[END_REF].

Robust Confirmatory Factor Analyses

The fit indices of the three tested models are depicted in Table 2. Robust CFAs of Model 1 indicated an inadequate fit with the data, while Model 2 and Model 3 displayed acceptable fit indices.

The specialized computation for the Satorra-Bentler χ² difference, as well as fit indices, indicated that Model 2 fitted significantly better the data than Model 1 (χ²diff = 1721.60, dfdiff = 11, p <.001; CFIdiff = 0.271; RMSEAdiff = -0.061), indicating that a two-dimension scale, namely, the composite of four subscales related to the primary dependence motives and the composite of nine subscales related to the secondary dependence motives, is not adequate to account for the data. Model 2 showed a significantly better fit than Model 3 on Satorra-Bentler χ² difference (χ²diff = 277.91, dfdiff = 43, p <.001) but not on CFI and RMSEA indices (CFIdiff = 0.015; RMSEAdiff = 0.004). These results suggest that the 11 firstorder dimensions of the FB-WISDM (Model 2) represent the optimal solution, although we cannot reject Model 3. We further compared models 2 and 3 by examining the factor loadings and correlations between factors. No differences were found between the two models for the first-order factors: they displayed standardized factor loadings that are all above 0.60 as well as positive and significant correlations between all factors. However, two elements weaken the relevance of Model 3. First, the loadings of the second-order factors are different, with primary dependence motives values ranging from 0.77 to 0.98 and secondary dependence motives ranging from 0.27 to 0.86. The dimensionality of the secondary dependence motives is challenged by the three following sub-scales: social/environmental goads, taste, and weight control. Second, discriminant validity is also challenged by the high correlations between the primary dependence motives and secondary dependence motives factors (r =0.86). Thus, we consider Model 2 as the optimal solution to fit the data. The results of the robust CFA for Model 2 are displayed in Table 3.

Cronbach's Alpha

All Cronbach's alpha values of the 11 first-order factors were higher than 0.70 (see Table 3), indicating an excellent internal consistency of the FB-WISDM.

Tests of Measurement Invariances through Multiple Group Robust CFA

Multiple group CFAs were used to test the measurement invariance (configural, metric, scalar) of the FB-WISDM for types of smokers (daily and non-daily smokers) and gender (men and women).

Measurement Invariance for Types of Smokers

For all three invariance levels of types of smokers, the models have good fit indices (see Table 4). The Satorra-Bentler χ² difference between the first model (configural invariance) and the second one (metric invariance) is significant (χ²diff = 137.80, dfdiff = 37, p <.001) but CFI and RMSEA values do not differ (CFIdiff = .005; RMSEAdiff =.001). Then, the Satorra-Bentler χ² difference between the second and the third model (scalar invariance) is significant (χ ²diff = 92.62, dfdiff = 26, p <.001), but there was no significant change in fit indices (CFIdiff =.005; RMSEAdiff =.001). Thus, scalar invariance is not rejected, which means that the factor loadings and intercepts of the 11 first-order factors are invariant between types of smokers. Concretely, daily and non-daily smokers have a similar understanding of the different smoking motivations measured by the FB-WISDM.

Measurement Invariance for Gender

For all three gender invariance levels, the models have good fit indices (see Table 4). The Satorra-Bentler χ² difference between the first model (configural invariance) and the second one (metric invariance) is significant (χ²diff = 55.11, dfdiff = 37, p =.028) but the CFI and RMSEA values do not differ.

Then, fit indices did not change between the second and the third model (scalar invariance, χ²diff = 29.96, dfdiff = 26, p =.27; CFIdiff = 0.0; RMSEAdiff = 0.001). Thus, scalar invariance is not rejected, which means that the factor loadings and intercepts of the 11 first-order factors are gender invariant. Men and women have a similar understanding of the different motivations for smoking.

Tests of FB-WISDM Concurrent and Convergent Validity

The results, displayed in Table 5, support both concurrent and convergent validity of the FB-WISDM. First, tobacco dependence is mainly and positively predicted by primary dependence motives, namely, lack of control, craving, and tolerance, and is also predicted by associative processes. Second, attitudes are mainly and positively associated with taste and affective enhancement, and, to a lesser extent, negatively associated with lack of control. Social norms are only positively associated with social goads. Perceived behavioral control is mainly and negatively associated with craving and associative processes. Smoker identity is mainly associated with lack of control and affiliative attachment, and, to a lesser extent, associative processes and tolerance. Eating disorders are only associated with weight control. Anxiety is mainly associated with affective enhancement, and, to a lesser extent, with affiliative attachment and weight control, and is negatively associated with tolerance. Finally, depression is mainly associated with affective enhancement, weight control and affiliative attachment, and, to a lesser extent, negatively associated with taste.

Discussion

This study aimed to test the psychometric qualities of B-WISDM on a sample of daily and nondaily university student smokers. The questionnaire had already been validated but only on heavy or daily adult smokers. Overall, the results highlight the good psychometric qualities of the FB-WISDM among university students. In line with previous studies, our results confirm the dimensionality in 11 first-order motivational factors, their satisfactory internal consistencies [START_REF] Ma | Evaluation of the brief Wisconsin Inventory of smoking dependence motives in African-American and European-American heavy smokers[END_REF][START_REF] Pancani | Smoking behavior: A cross-sectional study to assess the dimensionality of the brief Wisconsin Inventory of Smoking Dependence Motives and identify different typologies among young daily smokers[END_REF][START_REF] Smith | Development of the Brief Wisconsin Inventory of Smoking Dependence Motives[END_REF][START_REF] Vajer | Psychometric properties and construct validity of the brief wisconsin inventory of smoking dependence motives in an internetbased sample of treatment-seeking Hungarian smokers[END_REF], and gender invariance [START_REF] Vajer | Psychometric properties and construct validity of the brief wisconsin inventory of smoking dependence motives in an internetbased sample of treatment-seeking Hungarian smokers[END_REF]. On the other hand, and beyond having carried out the first study to test the validity of the B-WISDM on university students, we have also contributed to the literature by highlighting two new findings that we will discuss.

First, this study highlights the applicability of the B-WISDM to a non-daily, occasional smoking population. Tested only among heavy or daily smokers (e.g., [START_REF] Pancani | Smoking behavior: A cross-sectional study to assess the dimensionality of the brief Wisconsin Inventory of Smoking Dependence Motives and identify different typologies among young daily smokers[END_REF][START_REF] Smith | Development of the Brief Wisconsin Inventory of Smoking Dependence Motives[END_REF][START_REF] Vajer | Psychometric properties and construct validity of the brief wisconsin inventory of smoking dependence motives in an internetbased sample of treatment-seeking Hungarian smokers[END_REF], the validity of the B-WISDM among occasional smokers was lacking. Based on measurement invariance by types of smokers, this study is the first one to show that motivations underlying tobacco dependence are conceptually identical for daily and non-daily university student smokers. This result, beyond extending the applicability of the B-WISDM to different types of smokers, is of great interest because it would allow us to investigate motivational changes explaining the development of daily smoking in occasional smokers [START_REF] Kenford | Progression of college-age cigarette samplers: What influences outcome[END_REF][START_REF] Schulenberg | Monitoring the Future national survey results on drug use, 1975-2018: Volume II, college students and adults ages[END_REF], using the B-WISDM. Measurement invariance by type of smokers is a necessary condition for using the B-WISDM to compare daily and non-daily student smokers according to their motivations to smoke.

Since it is thus possible to study and compare the smoking motivations of daily and non-daily student smokers using the B-WISDM, it would be possible in future longitudinal research to use it to track the trajectories of student smokers and student smoking motivations. Existing data on the trajectories of cigarette smoking suggest that 25-50% of non-daily youth smokers quit smoking, 35-50% maintain nondaily smoking, and 10-25% switch to daily smoking [START_REF] Dutra | Beyond experimentation: Five trajectories of cigarette smoking in a longitudinal sample of youth[END_REF][START_REF] Kenford | Progression of college-age cigarette samplers: What influences outcome[END_REF][START_REF] Kvaavik | Nondaily smoking: A population-based, longitudinal study of stability and predictors[END_REF][START_REF] Robertson | Nondaily, Low-Rate Daily, and High-Rate Daily Smoking in Young Adults: A 17-Year Follow-Up[END_REF][START_REF] Wetter | Prevalence and Predictors of Transitions in Smoking Behavior among College Students[END_REF][START_REF] White | Transitions into and out of light and intermittent smoking during emerging adulthood[END_REF]. Thus, by using the B-WISDM in future longitudinal studies, it would be possible to identify whether certain motivations to smoke are likely to cause a student who initially smokes non-daily to switch to daily smoking. In the same way, it would be possible to identify smoking motivations that are less likely to cause students who are nondaily smokers to develop smoking habits in the future.

Second, this study highlights the convergent validity of the B-WISDM with psychological variables, well-known to be associated with tobacco use and dependence. This result is particularly interesting for prevention purposes, as it potentially makes the B-WISDM a very useful diagnostic tool to identify the underlying psychological reasons for an individual's smoking, in the first stage of prevention. Then, in the second stage, the B-WISDM would be able to guide prevention actions targeting the psychological variables that are specifically associated with people's reasons for smoking through the use of adapted strategies. In our results, two categories of psychological variables seem to emerge, depending on whether they are more associated with primary dependence motives or with secondary dependence motives. On the one hand, smoker identity and perceived behavioral control to resist smoking are mainly associated with motivations related to lack of control, tolerance, and craving, thus accounting for three out of four primary dependence motives. These findings are not surprising considering that perceived behavioral control [START_REF] Lee | Improving understanding of the quitting process: Psychological predictors of quit attempts versus smoking cessation maintenance among college students[END_REF][START_REF] Mauduy | Tobacco Dependence Among French University Students : A Cluster Analytic Approach to Identifying Distinct Psychological Pro fi les of Smokers[END_REF] and smoker identity [START_REF] Falomir-Pichastor | Tobacco dependence and smoking cessation: The mediating role of smoker and ex-smoker selfconcepts[END_REF][START_REF] Mauduy | Tobacco Dependence Among French University Students : A Cluster Analytic Approach to Identifying Distinct Psychological Pro fi les of Smokers[END_REF] are key determinants of tobacco dependence and cessation success. As primary dependence motives are the core components of tobacco dependence [START_REF] Piper | Refining the tobacco dependence phenotype using the Wisconsin Inventory of Smoking Dependence Motives[END_REF], it seems essential for any tobacco dependence prevention program to target these two psychological variables. The development of new prevention strategies, such as the social modeling technique [START_REF] Burn | Social Psychology and the Stimulation of Recycling Behaviors: The Block Leader Approach[END_REF]; see [START_REF] Webb | Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence[END_REF] for a meta-analysis) for increasing people's confidence to resist smoking, or the multi-categorization technique [START_REF] Kang | Multiple Identities in Social Perception and Interaction: Challenges and Opportunities[END_REF] for encouraging people who smoke to define themselves not only as smokers but also as members of other social groups and then reduce the potential role of the problematic identity in driving behaviors, could be considered for this purpose. On the other hand, attitudes towards smoking, perceived smoking social norms, eating disorders, anxiety, and depression are uniquely associated with specific secondary dependence motives. Thus, our results would suggest targeting positive smoking attitudes among student smokers because of taste and affective enhancement, with for instance the framing technique [START_REF] Gallagher | Health Message Framing Effects on Attitudes, Intentions, and Behavior: A Meta-analytic Review[END_REF], the perceived smoking social norms because of social goads, with the personalized normative feedback technique [START_REF] Vallentin-Holbech | Effects of the social norms intervention The GOOD Life on norm perceptions, binge drinking and alcohol-related harms: A clusterrandomised controlled trial[END_REF], eating disorders because of weigh control, with dissonance-based prevention programs (see [START_REF] Stice | Meta-analytic review of dissonance-based eating disorder prevention programs : Intervention , participant , and facilitator features that predict larger e ff ects[END_REF] for a meta-analytic review), and anxiety and depression because of affiliative attachment and affective enhancement, with mindfulness-based interventions [START_REF] Hofmann | Mindfulness-Based Interventions for Anxiety and Depression[END_REF].

This study, however, has limitations that we must highlight. First, a biochemical measure of tobacco dependence [START_REF] Bize | Biomedical risk assessment as an aid for smoking cessation[END_REF] or a combination of measures [START_REF] Hughes | Concordance of different measures of nicotine dependence: Two pilot studies[END_REF] would have allowed us to ensure that the participants were smokers. Second, we have not collected information on the use of other nicotine products. People who use nicotine products other than cigarettes likely have higher levels of primary dependence motives (e.g., loss of control, craving) than those who do not use other nicotine products. This could have influenced our results if differences in the use or non-use of other nicotine products were observed between daily and non-daily smokers. Third, given that our study was conducted with French students and that it is a correlational study, further research could attempt to replicate our results. For this reason, caution must be taken about the prevention recommendations we have made. The sample does not allow us for generalizability across age groups and non-student populations.

In conclusion, after having been validated on heavy smokers or daily smokers in English, Italian, and Hungarian participants, this study shows that the B-WISDM has also good psychometric qualities among French daily and non-daily university student smokers. Examining its convergent validity with different psychological variables (attitude, social norms, perceived behavioral control, smoker identity, and eating disorders) has provided a better understanding of the underlying motivations for tobacco dependence and a guide for smoking reduction efforts among university students. Future research must now extend the external validity of the B-WISDM to other populations of non-daily smokers, and investigate the involvement of other psychological variables in understanding motivations underlying tobacco dependence. Tobacco dependence was assessed with the French version of the CDS-5 [START_REF] Etter | A self-administered questionnaire to measure dependence on cigarettes: The cigarette dependence scale[END_REF]. Attitudes, social norms, and PBC were assessed based on the theory of planned behavior [START_REF] Mauduy | Tobacco Dependence Among French University Students : A Cluster Analytic Approach to Identifying Distinct Psychological Pro fi les of Smokers[END_REF]. Smoker identity was assessed using the Smoker Self-Concept Scale [START_REF] Shadel | Individual differences in self-concept among smokers attempting to quit: Validation and predictive utility of measures of the smoker self-concept and abstainer self-concept[END_REF][START_REF] Mauduy | Tobacco Dependence Among French University Students : A Cluster Analytic Approach to Identifying Distinct Psychological Pro fi les of Smokers[END_REF]. Eating disorders were assessed with the French version of the SCOFF questionnaire [START_REF] Parker | Eating Disorders in Graduate Students: Exploring the SCOFF Questionnaire as a Simple Screening Tool[END_REF]. Anxiety was measured with the French version of the State-Trait Anxiety Inventory [START_REF] Spielberger | Manual for the State-Trait Anxiety Inventory[END_REF]. Depression was measured with the short French version of the Beck Depression Inventory [START_REF] Beck | Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation[END_REF]. Statistically significant at *** p < .001; ** p < .01; * p < .05

Tables

  The study was notified to and authorized by the "Commission Nationale de l'Informatique et des Libertés" with the registration number [u24-20171109-01R1]. Besides, the participants' anonymity was guaranteed by the University Information System Direction (DSI). This survey was conducted in full agreement with the Declaration of Helsinki (World Medican Association, 2008) and the ethical standards set by the Psychology Department, which follows the American Psychological Association Ethical Principles of Psychologists and the Code of Conduct (American Psuychological Association, 2017) for the ethical treatment of human participants.

Table 1

 1 Sample Characteristics

	Overall sample (N = 669)		Non-daily smokers' sample (n = 436; 63.5%)	Daily smokers' sample (n = 251; 36.5%)
	M (SD)	Range	M (SD)	M (SD)

Table 2

 2 The Fit Indices for the Three Confirmatory Factor Analysis Models of FB-WISDM

	90% Confidence
	Interval

Note. CFI = comparative fit index; RMSEA = root mean squared error of approximation; SRMR = standardized root mean square residual; TLI = Lewis index.

Table 3

 3 Factor Loadings and Internal Consistencies of the French B-WISDMNote. N = 687. The table shows the results of the CFA and internal consistency for Model 2.

				Standard
	Factor	Estimate	SE	Estimate	Z	p	α
	Affiliative Attachment
	Item 11	1.821	0.268	0.821	6.794	< .001	.86
	Item 22	1.336	0.191	0.830	6.982	< .001
	Item 26	1.498	0.221	0.844	6.766	< .001
	Automaticity
	Item 1	1.628	0.109	0.776	14.975	< .001	.89
	Item 10	1.612	0.103	0.774	15.648	< .001
	Item 14	1.732	0.118	0.900	14.698	< .001
	Item 25	1.519	0.118	0.827	12.845	< .001
	Loss of Control
	Item 2	1.513	0.285	0.872	5.318	< .001	.91
	Item 16	1.338	0.258	0.783	5.185	< .001
	Item 21	1.883	0.346	0.911	5.439	< .001
	Item 35	1.547	0.290	0.835	5.342	< .001
	Cognitive Enhancement
	Item 6	1.786	0.066	0.896	27.128	< .001	.92
	Item 13	1.818	0.069	0.917	26.205	< .001
	Item 32	1.566	0.067	0.855	23.237	< .001
	Craving
	Item 4	1.678	0.242	0.855	6.928	< .001	.91
	Item 17	1.665	0.243	0.851	6.862	< .001
	Item 23	1.723	0.253	0.840	6.823	< .001
	Item 29	1.490	0.217	0.841	6.855	< .001
	Cue Exposure / Associative Processes
	Item 8	1.555	0.173	0.612	8.973	< .001	.73
	Item 12	2.022	0.155	0.692	13.022	< .001
	Item 24	2.079	0.167	0.793	12.435	< .001
	Taste
	Item 5	1.505	0.116	0.790	12.956	< .001	.84
	Item 15	1.706	0.136	0.857	12.575	< .001
	Item 20	1.519	0.110	0.747	13.771	< .001
	Tolerance
	Item 3	1.433	0.141	0.718	10.176	< .001	.87
	Item 28	1.732	0.146	0.863	11.821	< .001
	Item 31	1.555	0.158	0.676	9.841	< .001
	Item 36	1.803	0.148	0.894	12.182	< .001
	Weight Control
	Item 7	1.665	0.095	0.867	17.566	< .001	.88
	Item 19	1.073	0.092	0.752	11.615	< .001
	Item 34	1.755	0.100	0.912	17.534	< .001

Table 4

 4 The Fit Indices for the Three Multigroups Confirmatory Factor Analysis Models of FB-WISDM for Type of Smokers and GenderNote. CFI = comparative fit index; RMSEA = root mean squared error of approximation; SRMR = standardized root mean square residual; TLI = Tucker-Lewis index.

		Model description	Satorra-Bentler χ²	df	CFI	TLI	RMSEA	90% Confidence Interval Lower Upper	SRMR
	Type of smokers (daily vs. non-daily)
	Model 1	Configural Invariance	2644.08	1126	0.919	0.905	0.059	0.056	0.063	0.052
	Model 2	Metric Invariance	2766.83	1163	0.914	0.901	0.060	0.057	0.064	0.063
	Model 3	Scalar Invariance	2875.13	1189	0.914	0.901	0.060	0.057	0.064	0.063
	Gender (men vs. women)
	Model 1	Configural Invariance	2916.475	1126	0.917	0.902	0.065	0.062	0.069	0.049
	Model 2	Metric Invariance	2972.161	1163	0.916	0.904	0.065	0.061	0.068	0.054
	Model 3	Scalar Invariance	3002.432	1189	0.916	0.906	0.064	0.061	0.067	0.054

Table 5

 5 Standardized Coefficients from a Multivariate Linear Regression Model Describing the Association between the French B-WISDM and Tobacco Dependence and Psychological Variables

		Tobacco dependence	Attitudes	Social norms	PBC	Smoker identity	Eating disorders	Anxiety	Depression
	Adjusted R²	.761	.319	.317	.520	.447	.211	.247	.282
	Loss of Control	.252 ***	-.199 *	.046	-.050	.252 ***	-.081	-.108	-.113
	Craving	.342 ***	.023	-.004	-.319 ***	-.013	-.005	.215	.169
	Automaticity	.044	-.002	.025	-.048	-.010	.056	-.002	.006
	Tolerance	.376 ***	.045	-.047	.015	.156 *	-.030	-.212 *	-.123
	Affiliative Attachment	-.058 *	.047	.025	-.008	.192 ***	.071	.182 *	.234 **
	Associative Processes	.017	-.003	-.051	-.387 ***	.123 *	.038	.043	-.044
	Social Goads	-.004	.001	.538 ***	.046	-.009	-.019	.028	.057
	Taste	.024	.481 ***	.053	.009	.082	-.049	-.119	-.143 *
	Weight Control	.035	-.015	.064	.025	-.024	.332 ***	.129 *	.226 ***
	Affective Enhancement	-.082	.181 ***	.008	-.067	.009	.047	.305 ***	.269 ***
	Cognitive Enhancement	-.012	.049	.026	.003	.059	-.002	.024	.029
	Note. PBC: Perceived Behavioral Control.

The work reported was supported by RIN Tremplin Grant 19E00906 of Normandie Région, France.

Data Availability Statement

Data from this study will be provided by the first author upon request.

Declaration of Interest Statement

The authors report no conflict of interest.

Author Bigraphies

Maxime Mauduy, PhD, is an associate professor of social psychology at Paris Cité University (France) in the Laboratoire de Psychologie Sociale (Research Unit 4471). His research interests are cognitive dissonance, social norms, school bullying, and students' substance use (alcohol and tobacco), where he studies their underlying psychological processes to improve and/or develop new prevention strategies.

Nicolas


Léa Pillette

Elise Grevet

Claire Dussard

Franck Amadieu

David Gasq

Emeline Pierrieau

Jacques Py

Hakim Si-Mohammed

Nathalie George

Camille Jeunet-Kelway

An online tool to facilitate the assessment of BCI acceptability

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

An online tool to facilitate the assessment of BCI acceptability L. Pillette

Introduction:

The acceptability of a technology corresponds to the explicit willingness of potential users to use said technology before having tried it [START_REF] Alexandre | Acceptance and acceptability criteria: a literature review[END_REF]. Assessing the acceptability of new technologies provides valuable insights regarding people's expectations and how to favour technology adoption. Our systematic review of the literature reveals that only four articles report assessments of BCI or neurofeedback (NF) acceptability that are based on validated questionnaire or model [START_REF] Pillette | Are Brain-Computer Interfaces and Neurofeedback acceptable?[END_REF]. We hypothesise that the lack of studies on this subject is, at least in part, due to the absence of acceptability models and questionnaires that are specific to BCIs/NF. Thus, we propose an online tool -https://bciacceptability-tool.cnrs.fr/ -(see Fig. 1) that aims at providing BCI and NF researchers with an adapted questionnaire to evaluate the acceptability of their device.

Material, Methods and Results:

We based our tool on the 3 rd version of the technology acceptance model [START_REF] Venkatesh | Technology acceptance model 3 and a research agenda on interventions[END_REF] and the 2 nd version of the unified theory of acceptance and use of technology [START_REF] Venkatesh | Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology[END_REF] as they are the most widespread and result from many studies on different technologies. As those models are generic, they do not enable accounting for all the determinants of BCI acceptability. Therefore, we designed a new model including new determinants that should be considered to assess BCI acceptability, e.g., temporal behavioral intention takes into account the duration of the BCI use. This model is very detailed in order to be applicable to the majority of contexts of BCI use. Nevertheless, it is flexible and adapted to different use cases. Indeed, based on the answers provided by the users on the online form, the tool enables them to download a questionnaire that is context adapted.

Discussion: The model developed has been validated and specified for post-stroke motor rehabilitation through a large-scale study involving 753 persons without motor disabilities [START_REF] Grevet | Modelling the acceptability of BCIs for motor rehabilitation after stroke: a large scale study on the general public[END_REF]. We plan to improve the tool by adapting the factors to assess depending on each context of use with the results of future acceptability studies, which it will foster and allow carrying out in a reproducible and reliable manner.

Significance: Using the free, online tool that we propose, researchers can download a BCI-oriented acceptability questionnaire adapted to their context of use.

Figure 1 .

 1 Figure 1. Screenshot of our online tool.

1* , E. Grevet 2 , C. Dussard 3 , F. Amadieu 4 , D. Gasq 5 , E. Pierrieau 2 , J. Py 6 , H. Si-Mohammed 7 , N. George 3 , C. Jeunet 2

	1 Univ Rennes, Inria, CNRS, IRISA, Rennes, France; 2 Univ. Bordeaux, CNRS, EPHE, INCIA, Bordeaux, France;
	3 Institut du Cerveau et de la Moelle Epinière, ICM, CNRS / Inserm / Sorbonne Université, Paris, France ; 4 CNRS,
	Univ. Toulouse 2, France; 5 INSERM, Univ. Toulouse 3, France;
	6 Univ. Toulouse Jean Jaurès, France; 7 Univ. Lille, CNRS, Central Lille, CRiStAL, France
	* IRISA, Rennes, France. E-mail: lea.pillette@irisa.fr


Enhancing the Virtual Reality experience using Brain-Computer Interfaces

Gaël Van der Lee François Cabestaing, Hakim Si-Mohammed

Virtual Reality

Virtual Reality or VR is a "Computer-generated simulation of a three-dimensional environment the user is able to view and manipulate or interact with" [START_REF] Kardong-Edgren | A Call to Unify Definitions of Virtual Reality[END_REF].

Virtual reality is transitioning from a phase of initial hype to a period of increased adoption and practical applications. One area where VR is predicted to see significant growth is in simulation, with a survey conducted by the National League for Nursing and publisher Wolters Kluwer suggesting that adoption of VR for simulation could increase from 10% to 45% within the next five years. [START_REF] Kardong-Edgren | A Call to Unify Definitions of Virtual Reality[END_REF].

The ever-increasing use of Virtual Reality (VR) has brought forth the need to address the challenges that impede its adoption and undermine its potential to enhance the user experience. The necessity to resolve such issues stems the prospect of its continued growth, which calls for comprehensive measures to be taken to ensure its effective utilization. Failure to address the challenges of VR can impede its development, rendering it less effective and potentially hindering its ability to fully realize its potential as a technology.

The problem of Cybersickness

Cybersickness (CS) is a term used to refer to symptoms, such as nausea, headache, and dizziness that users experience during or after virtual reality immersion [START_REF] Caserman | Cybersickness in current-generation virtual reality head-mounted displays: Systematic review and outlook[END_REF].

Studies have indicated that this phenomenon affects a majority of virtual reality users, ranging between 60% and 95% of users [START_REF] Caserman | Cybersickness in current-generation virtual reality head-mounted displays: Systematic review and outlook[END_REF] [5], with up to 13% prematurely terminating their exposure due to the severity of the symptoms [START_REF] Caserman | Cybersickness in current-generation virtual reality head-mounted displays: Systematic review and outlook[END_REF]. Despite being extensively researched, the underlying causes of cybersickness remain uncertain and several theories are actively being investigated [START_REF] Davis | A Systematic Review of Cybersickness[END_REF].

The most widely accepted theory is the sensory conflict theory. It states that the the discrepancy between the visual, vestibular and proprioceptive senses cause CS, as well as expectation and experiences [START_REF] Zhang | Motion Sickness: Current Knowledge and Recent Advance[END_REF].

Currently, cybersickness is mostly measured using questionnaires presented after VR exposure. However, they have been criticized due to their subjective and delayed nature:

"Questions remain with regard to what questionnaires of presence and CS are truly measuring […] according to a meta-analysis, almost 50% of questionnaire studies documented effects of social desirability on their results" [START_REF] Weech | Presence and Cybersickness in Virtual Reality Are Negatively Related: A Review[END_REF] Measuring CS using EEGs

Utilizing Passive Brain-Computer Interfaces (BCIs), specifically Electroencephalograms (EEGs), offers a promising approach to address Cybersickness and enhance the user experience in virtual reality. BCIs provide objective measurements of neural activity to overcome the limitations of subjective assessments, allowing for real-time feedback and mitigation. Moreover, BCIs hold potential to reduce interindividual variance and enable individualized and adaptive solutions.

As such, research in this field has been growing rapidly, as shown in Figure 1. Numerous strategies have been reported in the literature and have exhibited promising outcomes. Considerable investigation has been carried out to identify electroencephalogram (EEG) neuromarkers of cybersickness [START_REF] Chang | Brain activity during cybersickness: A scoping review[END_REF]. Notably, deep learning methods have been effectively employed by several research teams for cybersickness detection, with a mean precision of 93% [START_REF] Yildirim | A Review of Deep Learning Approaches to EEG-Based Classification of Cybersickness in Virtual Reality[END_REF]. Furthermore, some researchers are extending beyond the scope of detection and exploring multi-subject classification or replicating state-of-the-art performance while introducing constraints.

Future works

The expanding field of research concerning VR and BCIs has generated a multitude of intriguing questions and challenges yet to be addressed. The following list outlines some of the avenues that warrant further investigation in this field, which will be explored in the course of this thesis:

1. Delving into the neuromarkers that correlate with cybersickness, 2. Unraveling the underlying neural patterns that give rise to cybersickness.

3. Establishing a standardized research methodology and vocabulary that spans both VR and BCI domains. 4. Devising novel methods to probe the subjective experience and cognitive state of VR users. 5. Identifying the links between different assessment techniques, such as subjective ratings, BCI measures, and physiological symptoms, to facilitate the detection and monitoring of specific mental states. 6. Utilizing the brain activity data in real-time to dynamically modify the VR experience.

Figure 1 .

 1 Figure 1. Number of papers studying BCIs and Cybersickness each year

  The project aims to develop passive BCI systems that can detect users' mental states in real-time and adapt the virtual content to improve immersion and reduce cybersickness. By leveraging neurophysiological markers, the GENESIS project aims to pave the way for the next generation of virtual reality systems.The GENESIS project is part of the CHIST-ERA consortium, which is a collaborative initiative between research funding organizations in Europe and beyond. CHIST-ERA aims to support novel and multidisciplinary research projects that have the potential to lead to significant breakthroughs in information and communication technologies. Funded by Horizon Europe within the European Innovation Council's Pathfinder programme, CHIST-ERA supports high-risk and high-impact research projects, including

	Brain Computer interfaces Lab, University of Lille
	Genesis project
	GENESIS (LeveraGing nEuromarkers for Next-gEn immerSIve Systems) is a Euro-
	pean research project focused on improving virtual reality experiences using brain-
	computer interface methods, particularly EEG. The project is led by the Centre de
	Recherche en Informatique, Signal et Automatique de Lille in France, and includes
	three other partners:
	Centre de Recherche Inria Rennes -Bretagne Atlantique (France)
	ETH Zurich (Switzerland)
	KOÇ UNIVERSITY's Intelligent User Interfaces Laboratory (Turkey)
	University of Essex

GENESIS, to strengthen European capabilities in promising new or emerging ICT research topics.