Applicability of the Theory of Planned Behavior for Predicting Alcohol Use in Spanish Early Adolescents

International Journal of Environmental Research and Public Health Article Applicability of the Theory of Planned Behavior for Predicting Alcohol Use in Spanish Early Adolescents Olalla Cutrín1,2,* , Isotta Mac Fadden 3, Stephanie L. Ayers 2, Stephen S. Kulis 2, Jose Antonio Gómez-Fraguela 1and Flavio F. Marsiglia 2 1Department of Psychobiology and Clinical Psychology, Universidade de Santiago de Compostela, 15705 A Coruña, Spain; [email protected] 2Global Center for Applied Health Research, Arizona State University, Arizona, AZ 85281, USA; Stephanie.L.A[email protected] (S.L.A.); [email protected] (S.S.K.); [email protected] (F.F.M.) 3Department of Nursing, Universidad de Sevilla, 41004 Sevilla, Spain; [email protected] *Correspondence: [email protected] Received: 8 October 2020; Accepted: 15 November 2020; Published: 18 November 2020   Abstract: According to the theory of planned behavior (TPB), intentions to perform a specific behavior are the result of attitudes, norms, and perceived control, and in turn, intentions and perceived control are the main predictors of the behavior. This study aimed to test the applicability of TPB in predicting alcohol use in normative pre-adolescents. The sample was composed of 755 Spanish adolescents aged 11 to 15 (M=12.24; SD =0.56), 47.1% females, from 12 state secondary schools in Spain. The results of path analysis indicate that positive attitudes towards alcohol, favorable norms towards alcohol, and offer vulnerability (perceived control) are significantly positively related to intentions to use alcohol as well as negatively related to actual behavioral control (i.e., actual strategies to avoid alcohol use). In turn, intentions to use and actual control predict higher alcohol frequency and heavy drinking. Significant indirect effects of these antecedents were found on alcohol outcomes through the mediation of intentions and actual control. The findings suggest that the validity and applicability of the TPB in normative pre-adolescents depend on the severity of alcohol use and point to a need to consider negative social influence in decision making processes in early adolescence. Keywords: alcohol use; adolescence; planned behavior; attitudes; resistance strategies 1. Introduction Alcohol consumption among adolescents is a critical area requiring effective prevention and health promotion approaches. Alcohol is the most consumed psychoactive substance among Spanish adolescents (14–18 years), with epidemiological data reporting that 77.9% have consumed alcohol at least once in their lives and more than half of them (58.5%) in the last month [ 1 ]. On average, alcohol use in Spain begins at 14 years of age, and weekly use begins at 15.2 years [ 1 ]. Decision making is one of the cognitive functions most studied in relation to alcohol consumption in adolescence [ 2 ]. Decision making is defined as ability to select a course of action from a set of possible behavioral alternatives [ 3 ]. In this regard, if unhealthy habits are acquired in adolescence, the probability that the same behavioral patterns will be maintained in adulthood is increased, and the likelihood of adopting new healthy habits is reduced [ 4 , 5 ]. Considering these alarming facts, prevention of early initiation and substance abuse in adolescence is an important strategy to address increasing rates of alcohol and other drug dependency [ 6 ]. The National Strategy on Addictions 2017–2024 points out that prevention in this field should be carried out mainly within the education system and emphasizes the need for evidence-based prevention programs aimed at adolescents [7]. Int. J. Environ. Res. Public Health 2020,17, 8539; doi:10.3390/ijerph17228539 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020,17, 8539 2 of 15 1.1. Theory of Planned Behavior The theory of planned behavior (TPB) [ 8 – 10 ] (see Figure 1) has been the dominant theoretical framework to investigate health-related behavior in the past three decades [ 11 ]. The TPB is an extension of the theory of reasoned action [ 12 , 13 ]. According to both theories, the most important predictor of a specific behavior is the individual’s intention to perform the behavior, which encompasses the motivational factors that influence a behavior. The TPB [ 10 ] proposes that three antecedents are related to intentions: attitudes (favorable or unfavorable attitude toward the behavior; named behavioral beliefs), subjective norms (perceived social pressure; named normative beliefs), and perceived behavioral control (beliefs about the resources and opportunities available that may facilitate or impede the behavior; named control beliefs). The TPB differs from the theory of reasoned action in its addition of perceived behavioral control as an antecedent of intentions to perform the behavior and as a direct predictor of such behavior. Thus, the TPB posits that the influence of attitudes and subjective norms is completely mediated by intentions, while perceived behavioral control, along with intentions, can influence the behavior directly. Moreover, according to both theories, to accurately predict a specific behavior, antecedents and intentions should be specifically related to that particular behavior. The more favorable the attitude and subjective norm and the lower the perceived control, the stronger the intentions are to engage in a behavior. Int. J. Environ. Res. Public Health 2020, 17, x 2 of 15 1.1. Theory of Planned Behavior The theory of planned behavior (TPB) [8–10] (see Figure 1) has been the dominant theoretical framework to investigate health-related behavior in the past three decades [11]. The TPB is an extension of the theory of reasoned action [12,13]. According to both theories, the most important predictor of a specific behavior is the individual’s intention to perform the behavior, which encompasses the motivational factors that influence a behavior. The TPB [10] proposes that three antecedents are related to intentions: attitudes (favorable or unfavorable attitude toward the behavior; named behavioral beliefs), subjective norms (perceived social pressure; named normative beliefs), and perceived behavioral control (beliefs about the resources and opportunities available that may facilitate or impede the behavior; named control beliefs). The TPB differs from the theory of reasoned action in its addition of perceived behavioral control as an antecedent of intentions to perform the behavior and as a direct predictor of such behavior. Thus, the TPB posits that the influence of attitudes and subjective norms is completely mediated by intentions, while perceived behavioral control, along with intentions, can influence the behavior directly. Moreover, according to both theories, to accurately predict a specific behavior, antecedents and intentions should be specifically related to that particular behavior. The more favorable the attitude and subjective norm and the lower the perceived control, the stronger the intentions are to engage in a behavior. Figure 1. Theory of Planned Behavior [10]. In the same way that beliefs, motivation, and ability can differently influence a specific behavior [10], they could also differ depending on the specific developmental stage. Previous research has tested the applicability of the TPB to predict alcohol use in pre-adolescents [14], adolescents [15], and mostly in young adults [16]. Although these studies found significant relationships between the components of the TPB and drinking behavior, a recent meta-analytic review concluded that positive attitudes towards alcohol as well as self-efficacy (perceived behavioral control) have a larger association with intentions to use alcohol in adults compared to adolescents [17]. Furthermore, although the TPB has been the reference approach during the last decades, it is not exempt of criticisms that argue for the use of more comprehensive theories [11]. Recent research has proposed that other cognitive, affective, and behavioral components should be taken into account to better explain adolescent health risk behavior [18,19]. These findings suggest the need to review theories and empirically test their applicability in different development stages for improving the efficacy of preventive interventions on problematic behaviors in adolescents. Therefore, this study aims to test the applicability of the TPB on Spanish early adolescents’ alcohol use. Figure 1. Theory of Planned Behavior [10]. In the same way that beliefs, motivation, and ability can differently influence a specific behavior [ 10 ], they could also differ depending on the specific developmental stage. Previous research has tested the applicability of the TPB to predict alcohol use in pre-adolescents [14], adolescents [15], and mostly in young adults [ 16 ]. Although these studies found significant relationships between the components of the TPB and drinking behavior, a recent meta-analytic review concluded that positive attitudes towards alcohol as well as self-efficacy (perceived behavioral control) have a larger association with intentions to use alcohol in adults compared to adolescents [ 17 ]. Furthermore, although the TPB has been the reference approach during the last decades, it is not exempt of criticisms that argue for the use of more comprehensive theories [ 11 ]. Recent research has proposed that other cognitive, affective, and behavioral components should be taken into account to better explain adolescent health risk behavior [ 18 , 19 ]. These findings suggest the need to review theories and empirically test their applicability in different development stages for improving the efficacy of preventive interventions on problematic behaviors in adolescents. Therefore, this study aims to test the applicability of the TPB on Spanish early adolescents’ alcohol use. Int. J. Environ. Res. Public Health 2020,17, 8539 3 of 15 1.2. Social Pressure and Negative Peer Influence on Adolescent Alcohol Use It is well established that the state of health and well-being of adolescents is related to the different environments and relational and ecological contexts to which adolescents belong (families, neighbors, friends, peers, school climate, social, and environmental aspects, etc.) [ 20 ]. According to the TPB, the action of the individual is guided, in part, by subjective norms, which are based on normative beliefs about the social pressure to perform the behavior [ 10 ]. Social pressure to use substances has been consistently linked to higher levels of substance use in adolescence [ 21 , 22 ] and such pressure comes mainly from peers, who have been posited as a strong influence on drinking behavior from early through late adolescence [ 23 ]. Evidence has found that peer’s substance use is specifically associated with adolescent substance use rather than with other types of problematic behavior [ 21 , 24 , 25 ]. In this regard, offers of alcohol and other substances from intimate friends, relatives, or romantic partners can be particularly influential. This is because those offering are familiar with persuasion triggers, have the opportunity for repeated offers, and can exploit the expectation for maintaining a long-term, reciprocal relationship with the adolescent [ 26 – 28 ]. These influential processes make the effective management of refusal strategies more challenging in adolescence [ 29 , 30 ]. Therefore, the offers received can potentially influence the decision-making process of the adolescent to use or not to use alcohol. Taking the offers into account within the TPB model ensures that both active (explicit drug offers) and passive (overestimation of friends’ use; i.e., favorable subjective norms) social pressure is addressed [ 22 ]. This more holistic approach is closer to dual-processing approaches that consider a heuristic social-influenced path to risky behavior in addition to the rational reasoning posited in the TPB [19]. 1.3. Resistance Strategies as a Measure of an Actual Behavioral Control The incorporation of resistance strategies into the behavioral repertoire of adolescents is implicated in how they resist using substances and avoid risky behaviors successfully [ 27 , 31 ]. Previous research has pointed to the following strategies as the most common behavioral resources used by adolescents [ 32 , 33 ]: “Refuse” offers by saying no verbally or non-verbally; “Explain” why the adolescent does not want to use substances; “Avoid” situations where substances are used; and “Leave” the situations where substances are used. These four strategies are taught in “Mantente REAL” (originally “keepin’ it REAL” [ 34 ]), a school-based universal prevention program for substance use and other risk behaviors designed for Secondary Education (middle schools). In line with the TPB [ 10 ], the REAL strategies are the resources available to an adolescent that determine, to some extent, the likelihood of alcohol use; that is, actual behavioral control. Thus, the current study further extends the TBP by including actual behavioral control as part of the model to be tested to predict alcohol use in early adolescence. Therefore, this study aims to test the TPB while applying it to the Drug Resistance Strategies (DRS) prevention model used in Mantente REAL. The REAL strategies can be considered as a reflection of actual behavioral control and the offers received by adolescents as a reflection of social pressure to use alcohol. 1.4. The Current Study This study aims to test the applicability of the TPB in predicting alcohol use in a general pre-adolescent Spanish population and goes beyond the previous research by considering the potential application of the TPB to a DRS prevention model (see Figure 2); that is, considering the role of resistance strategies as a measure of an actual behavioral control. According to the TPB, to accurately predict a behavior, antecedents should be specifically related to such behavior. Therefore, our measures testing TPB are specifically related to alcohol use, except the measure of peer offers that comprises alcohol as well as other drug offers. Furthermore, empirical evidence has demonstrated that negative social influence is a robust risk factor for alcohol use at this specific developmental stage. Hence, in line with a dual-processing approach [ 19 ], the current study examines an additional model including offers Int. J. Environ. Res. Public Health 2020,17, 8539 4 of 15 received as a measure of active social pressure to use alcohol [ 22 ]. Considering resistance strategies together with negative social influence in the model can further understanding of the underlying mechanisms of decision making to use alcohol in early adolescence. The overall hypothesis guiding the study from a TPB perspective is that having higher levels of antecedent variables will lead to higher intentions to use alcohol as well as lower behavioral control, both of which leading to higher alcohol use. The current findings might also contribute to the development of prevention guidelines and augment the effectiveness of interventions aimed at preventing adolescent alcohol use in Spain. Int. J. Environ. Res. Public Health 2020, 17, x 4 of 15 of the underlying mechanisms of decision making to use alcohol in early adolescence. The overall hypothesis guiding the study from a TPB perspective is that having higher levels of antecedent variables will lead to higher intentions to use alcohol as well as lower behavioral control, both of which leading to higher alcohol use. The current findings might also contribute to the development of prevention guidelines and augment the effectiveness of interventions aimed at preventing adolescent alcohol use in Spain. Figure 2. Theory of Planned Behavior Applied to a Drug Resistance Strategies Prevention Model (based on [10]). 2. Materials and Methods 2.1. Participants and Procedure The final sample of this study was composed of 755 Spanish adolescents aged 11 to 15 (M = 12.24; SD = 0.56), 47.1% females, from Santiago de Compostela (Galicia, NW Spain; 46% of the sample) and Sevilla (Andalucia, S Spain; 54% of the sample). In all, 40.1% of adolescents were born in Galicia, 50.3% in Andalucia, 3.4% in another part of Spain and 6.1% in another country. Almost half the students lived with up to 3 other persons at home (45.5%) and the other half of students reported to live with 4 or more persons (54.5%). Most students reported their parents have a level of education equal to or higher than high school (61.2%) and always have enough money at home for food, transportation, utilities, school fees, and clothes (60.9%). Adolescents were enrolled in the 1st grade of compulsory secondary education in the 2018/2019 school year in 12 state secondary schools, 6 in the region of Santiago and 6 in the city of Sevilla. The schools participated in a test of the “Mantente REAL” drug prevention program [35] (originally “keepin’ it REAL” [34]). Schools were selected by convenience sampling, but randomly assigned to the experimental and control conditions. A total of 934 students were enrolled in the participating schools but 179 of them did not have parental consent, did not assent or were not in school at the first survey. Full information about school recruitment and randomization are reported elsewhere in a test of the intervention’s effects [35]). Because this study is not aimed at analyzing intervention effects, rather controlling for their potential influence on alcohol use, details about the rationale for the intervention are not specified here. Data were collected from student respondents in the Winter of 2018/2019 (T1) and the Spring 2019 (T2), approximately four months after the pre-test. Self-reported questionnaires were answered by adolescents before and after the program implementation to evaluate variables of interest (the rate Figure 2. Theory of Planned Behavior Applied to a Drug Resistance Strategies Prevention Model (based on [10]). 2. Materials and Methods 2.1. Participants and Procedure The final sample of this study was composed of 755 Spanish adolescents aged 11 to 15 (M=12.24; SD =0.56), 47.1% females, from Santiago de Compostela (Galicia, NW Spain; 46% of the sample) and Sevilla (Andalucia, S Spain; 54% of the sample). In all, 40.1% of adolescents were born in Galicia, 50.3% in Andalucia, 3.4% in another part of Spain and 6.1% in another country. Almost half the students lived with up to 3 other persons at home (45.5%) and the other half of students reported to live with 4 or more persons (54.5%). Most students reported their parents have a level of education equal to or higher than high school (61.2%) and always have enough money at home for food, transportation, utilities, school fees, and clothes (60.9%). Adolescents were enrolled in the 1st grade of compulsory secondary education in the 2018/2019 school year in 12 state secondary schools, 6 in the region of Santiago and 6 in the city of Sevilla. The schools participated in a test of the “Mantente REAL” drug prevention program [ 35 ] (originally “keepin’ it REAL” [ 34 ]). Schools were selected by convenience sampling, but randomly assigned to the experimental and control conditions. A total of 934 students were enrolled in the participating schools but 179 of them did not have parental consent, did not assent or were not in school at the first survey. Full information about school recruitment and randomization are reported elsewhere in a test of the intervention’s effects [ 35 ]). Because this study is not aimed at analyzing intervention effects, rather controlling for their potential influence on alcohol use, details about the rationale for the intervention are not specified here. Data were collected from student respondents in the Winter of 2018/2019 (T1) and the Spring 2019 (T2), approximately four months after the pre-test. Self-reported questionnaires were answered by adolescents before and after the program implementation to evaluate variables of interest (the rate of sample attrition between pre- and post-tests was 10.1%). Most of students who answered to both Int. J. Environ. Res. Public Health 2020,17, 8539 5 of 15 surveys completed more than 80% of the items (97.2% of students in T1 and 85.7% in T2). For the data collection, qualified research team members visited the schools, explained the objectives of the research, and provided proper instructions to the adolescents. Parental consent was requested and, subsequently, adolescent assent was obtained before questionnaires were administered. Adolescent participation was voluntary, and confidentiality of information was completely guaranteed. Compliance with ethical standards was assured throughout the investigation and the project was approved by the Bioethics Committee of the University of Santiago de Compostela and the University Pablo de Olavide. 2.2. Instruments Positive attitudes towards alcohol use (T1): The degree to which adolescents have a favorable or unfavorable evaluation of alcohol use was measured with the item “Is it OK for someone your age to consume alcohol?” Responses were scored from 0 (definitely not)to3(definitely yes). Favorable subjective norms towards alcohol (T1): The degree to which students perceived social pressure to use alcohol was assessed by asking “Now think about the friends you hang out with. How many do you think have consumed alcohol at least once?” Adolescents responded a scale ranged from 0 (none of them)to5(all of them). Alcohol offer vulnerability (Perceived behavioral control) (T1): The concept of perceived ease or difficulty to use alcohol was measured as vulnerability to alcohol offers. Adolescents answered to “What would you say if a family member offered you alcohol?” in a response scale scored from 0 (surely I could say no)to3(I couldn’t say no). Intentions to use alcohol (T1): Adolescents indicated their motivation to use alcohol responding to the item “If you had the chance this weekend, would you consume alcohol?” in a scale ranging from 0 (definitely not)to3(definitely yes). Actual behavioral control (T1): Adolescents reported the frequency with which they would use specific REAL behavioral strategies to resist alcohol offers (Refuse, saying no directly; Explain, giving reasons to decline; Avoid, not going to places or situations with potential offers; Leave, exiting situations where offers are made; [ 33 ]) in the following situation: “If a friend offers you a beer at a party, would you . . . ”. Responses were scored from 0 (never) to 4 (almost always). The mean score of the four items was used (α=0.63). Offers received (T1): The frequency of two types of substance offers were measured: alcohol offers, regardless of who was the offeror (“In the last 30 days, how many times did someone offer you an alcoholic drink?”), and offers received by peers, regardless of the type of substance (“In your life, how many times did a friend or other youth offer you a drug?”). Responses ranged from 0 (never) to 4 (10 times or more). Recent alcohol use (T2): Two different measures of alcohol use in the past 30 days were assessed [22,36] . Adolescents reported their frequency of alcohol use (“In the last 30 days, how many times have you had an alcoholic drink?”) and the frequency of heavy drinking episodes (“In the last 30 days, how many times did you drink five or more alcoholic drinks in a row (on the same occasion)?”). Responses were scored from 0 to 6 (none—only once—2–3 times—4–9 times—10–19 times—20–39 times—40 times or more). Covariates: The following variables were assessed at T1 to be included as covariates: “outcome at pre-test” (alcohol use frequency and heavy drinking frequency in T1), “intervention site” (Santiago de Compostela—1 versus Sevilla—0), “intervention condition” (Experimental group—1 versus Control group—0) and “gender” (Male—1 versus Female—0). 2.3. Statistical Analyses IBM SPSS Statistics 24 (IBM, Armonk, NY, USA) and MPLUS 7 (Muth é n & Muth é n, Los Angeles, CA, USA) were used to conduct the statistical analyses. First, descriptive analyses (means, standard deviations, and range) and bivariate correlations among the study variables were conducted. Next, path analysis models were estimated to examine the relationships between antecedents Int. J. Environ. Res. Public Health 2020,17, 8539 6 of 15 (i.e., attitudes, norms, and offer vulnerability/perceived control), intentions, actual control, and recent alcohol use (see Figure 3). Mediation effects of antecedents on alcohol use through intentions as well as actual behavioral control were analyzed. Independent models were conducted for each measure of alcohol use (i.e., alcohol frequency and heavy drinking). Path models controlled for outcome at pre-test, city/intervention site, intervention condition, and gender. In addition, to test the potential influence of social pressure in decisions to use alcohol, another two models were estimated controlling also for alcohol offers (regardless of offeror) and peer offers (regardless of substance); these two variables were set to correlate (see Figure 4). All the models were adjusted for the school-level clustering of data and employed full-information maximum likelihood (FIML) estimation to account for attrition to the post-test and item missing data. The robust maximum likelihood method (MLR), which is robust to a non-normal distribution, was used to compute the models and the goodness-of-fit indexes χ2 /DF, CFI, RMSEA, and SRMR were used to evaluate the model fit. The following criteria were considered for an optimum fit: χ2 /DF <2–3, CFI >0.95, RMSEA and SRMR <0.05; and for an acceptable or reasonable fit: χ2/DF <4, CFI >0.90, and RMSEA and SRMR <0.08 [37]. Int. J. Environ. Res. Public Health 2020, 17, x 6 of 15 attitudes, norms, and offer vulnerability/perceived control), intentions, actual control, and recent alcohol use (see Figure 3). Mediation effects of antecedents on alcohol use through intentions as well as actual behavioral control were analyzed. Independent models were conducted for each measure of alcohol use (i.e., alcohol frequency and heavy drinking). Path models controlled for outcome at pre-test, city/intervention site, intervention condition, and gender. In addition, to test the potential influence of social pressure in decisions to use alcohol, another two models were estimated controlling also for alcohol offers (regardless of offeror) and peer offers (regardless of substance); these two variables were set to correlate (see Figure 4). All the models were adjusted for the schoollevel clustering of data and employed full-information maximum likelihood (FIML) estimation to account for attrition to the post-test and item missing data. The robust maximum likelihood method (MLR), which is robust to a non-normal distribution, was used to compute the models and the goodness-of-fit indexes χ2/DF, CFI, RMSEA, and SRMR were used to evaluate the model fit. The following criteria were considered for an optimum fit: χ2/DF < 2–3, CFI > 0.95, RMSEA and SRMR < 0.05; and for an acceptable or reasonable fit: χ2/DF < 4, CFI > 0.90, and RMSEA and SRMR < 0.08 [37]. Figure 3. General Path Model Predicting Recent Alcohol Use (Alcohol Frequency and Heavy Drinking Separately). Figure 3. General Path Model Predicting Recent Alcohol Use (Alcohol Frequency and Heavy Drinking Separately). Int. J. Environ. Res. Public Health 2020, 17, x 7 of 15 Figure 4. Path Model Predicting Recent Alcohol Use (Alcohol Frequency and Heavy Drinking Separately) Controlling for Negative Social Influence. 3. Results Table 1 shows descriptive results (means, standard deviations, and ranges) of the main continuous variables of the study for the total sample and both site subsamples. Students reported low means on intentions to use alcohol and its antecedents (positive attitudes towards alcohol, favorable subjective norms towards alcohol, and vulnerability to alcohol offers), as well as low frequencies of alcohol use and, especially, of heavy drinking at both t1 and t2. Students reported having used alcohol for the first time at a mean age of 10.36 (SD = 2.16). Higher means were reported for actual behavioral control; that is, the frequency with which students would use behavioral strategies (Refuse, Explain, Avoid, and Leave) to refuse alcohol offers. Over two-thirds of the total sample (67.8%) reported they would use REAL strategies “often” or “almost always”. Regarding offers, 14% of total students reported having received one or more alcohol offers in the last 30 days and 30.9% received offers of alcohol, tobacco, or other drugs from peers during their lifetime. Table 1. Descriptive Statistics of the Main Variables of the Study for the Total Sample and for the Both Cities. Variables Total Sample Santiago Sample Sevilla Sample M (SD) M (SD) M (SD) Range Positive attitudes 0.14 (0.38) 0.12 (0.32) 0.15 (0.42) 3 Favorable norms 0.56 (1.04) 0.40 (0.89) 0.70 (1.13) 5 Offer vulnerability 0.27 (0.61) 0.23 (0.58) 0.30 (0.63) 3 Intentions 0.14 (0.49) 0.10 (0.39) 0.18 (0.56) 3 Actual control 1.55 (0.84) 1.62 (0.82) 1.49 (0.87) 3 Alcohol frequency T1 0.15 (0.59) 0.12 (0.49) 0.24 (0.66) 6 Figure 4. Path Model Predicting Recent Alcohol Use (Alcohol Frequency and Heavy Drinking Separately) Controlling for Negative Social Influence. Int. J. Environ. Res. Public Health 2020,17, 8539 7 of 15 3. Results Table 1shows descriptive results (means, standard deviations, and ranges) of the main continuous variables of the study for the total sample and both site subsamples. Students reported low means on intentions to use alcohol and its antecedents (positive attitudes towards alcohol, favorable subjective norms towards alcohol, and vulnerability to alcohol offers), as well as low frequencies of alcohol use and, especially, of heavy drinking at both t1 and t2. Students reported having used alcohol for the first time at a mean age of 10.36 (SD =2.16). Higher means were reported for actual behavioral control; that is, the frequency with which students would use behavioral strategies (Refuse, Explain, Avoid, and Leave) to refuse alcohol offers. Over two-thirds of the total sample (67.8%) reported they would use REAL strategies “often” or “almost always”. Regarding offers, 14% of total students reported having received one or more alcohol offers in the last 30 days and 30.9% received offers of alcohol, tobacco, or other drugs from peers during their lifetime. Table 1. Descriptive Statistics of the Main Variables of the Study for the Total Sample and for the Both Cities. Variables Total Sample Santiago Sample Sevilla Sample M (SD) M (SD) M (SD) Range Positive attitudes 0.14 (0.38) 0.12 (0.32) 0.15 (0.42) 3 Favorable norms 0.56 (1.04) 0.40 (0.89) 0.70 (1.13) 5 Offer vulnerability 0.27 (0.61) 0.23 (0.58) 0.30 (0.63) 3 Intentions 0.14 (0.49) 0.10 (0.39) 0.18 (0.56) 3 Actual control 1.55 (0.84) 1.62 (0.82) 1.49 (0.87) 3 Alcohol frequency T1 0.15 (0.59) 0.12 (0.49) 0.24 (0.66) 6 Heavy drinking T1 0.04 (0.26) 0.02 (0.16) 0.05 (0.32) 6 Alcohol frequency T2 0.30 (0.76) 0.17 (0.63) 0.42 (0.85) 6 Heavy drinking T2 0.06 (0.29) 0.03 (0.23) 0.08 (0.33) 6 Alcohol offers 0.24 (0.70) 0.15 (0.55) 0.31 (0.80) 4 Peer drug offers 0.31 (0.64) 0.21 (0.49) 0.39 (0.74) 4 Results of bivariate correlations for the total sample (see Table 2) indicated significant and positive intercorrelations among antecedents, intentions, alcohol use, and offers, as well as significant and inverse intercorrelations among all of them with actual behavioral control (use of strategies). After applying the Bonferroni correction, the coefficient between heavy drinking at T1 and actual behavioral control was not significant. Path analysis was conducted to test the TPB applied to a DRS model (see Figure 3). Several models were analyzed to examine the following relationships: positive attitudes and favorable subjective norms towards alcohol and offer alcohol vulnerability (perceived behavioral control) are directly related to intentions to use alcohol as well as actual behavioral control. Offer vulnerability, intentions and actual control are directly related to alcohol use. Therefore, attitudes, norms, and offer vulnerability can be indirectly related to alcohol use through the mediation of intentions and actual control. Models were tested separately for alcohol frequency and heavy drinking, and after testing a TPB general model, a second model was estimated including controls for negative social influences, as measures by exposure to offers of alcohol and other substances. The general path model (controlled for intervention site, intervention condition, and gender) did not show adequate overall fit indexes for alcohol frequency ( χ2 (5) =71.84, p=0.000; CFI =0.86 ; RMSEA =0.13 ; SRMR =0.04), whereas for heavy drinking, CFI and SRMR presented adequate values ( χ2 (5) =34.91, p=0.000; CFI =0.91; RMSEA =0.09; SRMR =0.04). Table 3displays the standardized results of path models. The first three columns of coefficients present the general model for the alcohol frequency outcome. The next column shows the predictors for the heavy drinking outcome, where results predicting intentions and actual control were the same as for alcohol frequency. The remaining columns are for an adjusted model adding negative social influence measures Int. J. Environ. Res. Public Health 2020,17, 8539 8 of 15 (alcohol offers and peer drug offers), where results predicting intentions and actual control were the same for alcohol frequency and heavy drinking outcomes. Results for the general model indicated, as regards covariates, that gender was significantly related to actual behavioral control and alcohol frequency was predicted by outcome at pre-test, intervention site and intervention condition. Positive attitudes, favorable norms, and offer vulnerability (perceived behavioral control) were significantly and positively related to intentions to use alcohol, as well as negatively related to actual behavioral control (use of REAL strategies). In turn, intentions to use alcohol, positively, and actual behavioral control, negatively, significantly predicted both alcohol frequency and heavy drinking. Offer vulnerability significantly and positively predicted alcohol frequency but not heavy drinking. Significant indirect effects of antecedents on alcohol use were found for the general model. The results indicated significant mediation effects on alcohol frequency for: attitudes through intentions ( β =0.07, p=0.008) and actual control ( β =0.01, p=0.032); norms through intentions ( β =0.06, p<0.001 ); and offer vulnerability through intentions ( β =0.11, p=0.015) and actual control ( β =0.02, p=0.032). Significant mediation effects were also found on heavy drinking for attitudes through intentions ( β =0.07, p=0.008) and actual control ( β =0.01, p=0.032); norms through intentions ( β=0.06 ,p<0.001); and offer vulnerability through intentions ( β =0.11, p=0.015) and actual control (β=0.02, p=0.032). On the other hand, the social pressure path model (additionally controlled for alcohol offers and peer drug offers; see Figure 4) fits better than the general model, showing adequate CFI and SRMR indexes for alcohol frequency ( χ2 (5) =64.69, p=0.000; CFI =0.93; RMSEA =0.13; SRMR =0.02) and overall adequate fit indexes for heavy drinking ( χ2 (5) =30.07, p=0.000; CFI =0.96; RMSEA =0.08 ; SRMR =0.02). After controlling for negative social influence, the path model showed similar results for covariates, but different significant relationships among the main variables of the model (see Table 3). The results indicated that positive attitudes were only significantly and positively related to intentions to use alcohol and offer vulnerability was significantly related to intentions (positively), actual behavioral control (negatively), and alcohol frequency (positively). In turn, intentions to use alcohol significantly and positively predicted heavy drinking at T2 and actual behavioral control significantly and negatively predicted alcohol frequency at T2. Having received alcohol offers was significantly and positively related to intentions to use alcohol and alcohol frequency, whereas having received drug offers by peers was significantly related to intentions to use alcohol (positively), actual behavioral control (negatively), and both outcomes (positively). In this model, the direct effects of intentions on alcohol frequency and heavy drinking were reduced, while the inclusion of offers received contributed to increase the variance explained by the model. The results showed fewer significant indirect effects in the social pressure model. Mediation effects on alcohol frequency were found only for offer vulnerability through actual behavioral control ( β=0.01 , p=0.005). Mediation effects on heavy drinking were found for attitudes ( β =0.04, p=0.005) and offer vulnerability (β=0.07, p=0.017) through intentions to use alcohol. Int. J. Environ. Res. Public Health 2020,17, 8539 9 of 15 Table 2. Bivariate Correlations Between the Main Variables of the Study for the Total Sample. Variables Positive Attitudes Favorable Norms Offer Vulnerability Intentions Actual Control Alcohol Freq T1 Heavy Drink T1 Alcohol Freq T2 Heavy Drink T2 Alcohol Offers Peer Drug Offers Positive attitudes 1 Favorable norms 0.40 *** 1 Offer vulnerability 0.37 *** 0.48 *** 1 Intentions 0.46 *** 0.49 *** 0.57 *** 1 Actual control −0.22 *** −0.27 *** −0.33 *** −0.27 *** 1 Alcohol freq. t1 0.35 *** 0.46 *** 0.38 *** 0.59 *** −0.20 *** 1 Heavy drinking t1 0.21 *** 0.31 *** 0.27 *** 0.44 *** −0.11 **,a 0.50 *** 1 Alcohol freq. t2 0.31 *** 0.38 *** 0.39 *** 0.45 *** −0.21 *** 0.53 *** 0.17 *** 1 Heavy drinking t2 0.21 *** 0.29 *** 0.30 *** 0.37 *** −0.17 *** 0.41 *** 0.15 *** 0.60 *** 1 Alcohol offers 0.30 *** 0.47 *** 0.34 *** 0.44 *** −0.19 *** 0.49 *** 0.37 *** 0.42 *** 0.32 *** 1 Peer drug offers 0.39 *** 0.52 *** 0.39 *** 0.49 *** −0.26 *** 0.50 *** 0.32 *** 0.46 *** 0.33 *** 0.53 *** 1 aNon-significant after applying the Bonferroni correction to account for the multiple comparisons. ** p<0.01. *** p<0.001. Table 3. Standardized Results of Path Analysis Models Predicting Alcohol Frequency and Heavy Drinking at T2. Variables General Model Social Pressure Model Intentions Actual Control Alcohol Frequency T2 Heavy Drinking T2 Intentions Actual Control Alcohol Frequency T2 Heavy Drinking T2 Intercept −0.08 1.96 *** 0.45 *** 0.32 *** −0.12 ** 1.98 *** 0.32 *** 0.17 Outcome pretest 0.41 *** 0.08 0.31 *** 0.01 Intervention site −0.02 0.04 −0.11 *** -0.06 0.00 0.03 −0.09 *** −0.04 Intervention condition 0.00 −0.01 −0.05 ** −0.06 −0.00 −0.01 −0.06 *** −0.06 Gender 0.01 0.05 ** −0.01 −0.01 0.01 0.05 ** 0.00 −0.00 Positive attitudes 0.24 *** −0.08 * 0.19 *** −0.06 Favorable norms 0.21 *** −0.11 * 0.09 −0.07 Offer vulnerability 0.38 *** −0.24 *** 0.17 ** 0.13 0.34 *** −0.23 *** 0.14 * 0.09 Intentions 0.15 * 0.30 ** 0.09 0.21 ** Actual control −0.06 *** −0.07 * −0.04 *** −0.05 Alcohol offers 0.14 * 0.02 0.11 * 0.13 Peer drug offers 0.18 * −0.11 * 0.18 *** 0.17 ** R2 0.42 0.13 0.38 0.20 0.47 0.14 0.44 0.24 *p<0.05; ** p<0.01; *** p<0.001.