Understanding how a smoking cessation intervention changes beliefs, self-efficacy, and intention to quit: a secondary analysis of a pragmatic randomized controlled trial

Understanding how a smoking cessation intervention changes beliefs, self-efficacy, and intention... Abstract Although beliefs, self-efficacy, and intention to quit have been identified as proximal predictors of initiation or quitting in young adults, few studies have studied how these variables change after a smoking cessation intervention. To evaluate the changes in the beliefs, self-efficacy, and intention to avoid smoking and determine if these are potential mediators in quitting, following a smoking cessation intervention, aimed at tobacco-dependent college students. Single-blind, pragmatic randomized controlled trial with a 6-month follow-up. A total of 255 smoker students were recruited from September 2013 to February 2014. Participants were randomly assigned to intervention group (n = 133) or to control group (n = 122). The students in the intervention group received a multicomponent intervention based on the Theory of Triadic Influence (TTI). The strategies of this program consisted of a 50 min motivational interview conducted by a nurse and online self-help material. The follow-up included a reinforcing e-mail and group therapy. The smoking-related self-efficacy, belief, and intention scale was used to assess outcomes. Intention to quit smoking is partial moderator explaining 36.2% of the total effects in smoking cessation incidence. At 6 month follow-up, the differences in the mean scores of self-efficacy and intention related to stopping smoking were significantly higher in the intervention than in the control group. A multicomponent intervention based on the TTI, tailored to college students, positively increased the self-efficacy to avoid smoking and the intention to quit, suggesting intention as potential mediator of quitting. Implications Practice: A multicomponent smoking cessation program based on the Theory of Triadic Influences can be used to increase the self-efficacy and the intention to quit in college students. Policy: Policymakers who want to increase the intention to quit smoking among college students should consider the implementation of tailored multicomponent interventions. Research: Future research should be carried out to investigate the role of beliefs, self-efficacy, and intention to quit in smoking cessation outcomes of college students. INTRODUCTION The Organization for Economic Co-operation and Development (OECD) showed that 36% of young adults are expected to graduate from higher education at least once before reaching the age of 30 [1]. In Spain, approximately a million and a half students are enrolled in universities [2], representing 26% of the young adult population [3]. Cigarette smoking among college students is a matter of particular concern because their smoking prevalence (34.9%) [4] is higher than that of the noncollege young adults (27.7%) [5]. Several studies characterize college as a period of smoking transitions, including smoking initiation and smoking consolidation [6–8]. Although most college students believe that they are going to stop smoking before 30 years or upon becoming parents [9–10], 30% of them will have difficulty in quitting and will remain addicted for decades [11]. Assisting college students to quit tobacco is an important public health goal. In order to provide effective assistance for smoking cessation, programs must be adapted to student characteristics, such as their belief that tobacco use has positive effects on their health (e.g., stress relief) [12–13]; in addition, the different factors that influence successful quitting among this population must be determined [14–15]. Bully et al. [16] suggest that the intervention strategies must be based on behavioral change theories. Thereby, the researchers and practitioners can understand how and why the smoking cessation programs succeed or fail. In this study, our intervention was based on the Theory of Triadic Influence (TTI). This theory, based on more than 20 years of research, integrates variables and processes from sociological and psychological theories, giving rise to a metatheory that describes and explains the aspects that influence the acquisition of certain behaviors, thus, offering an ecological approach to change [17–18]. Furthermore, the theory has been validated in young smokers [18] and is considered as the most relevant theory to understand both onset and cessation of smoking [19]. The TTI [18–19] suggested that tobacco use is motivated by three sets of factors: personal aspects such as motivation, self-concept, and self-efficacy; an interpersonal–social aspect such as social normative beliefs; and a cultural–environmental aspect such as attitudes toward tobacco. These sets of factors exert an influence on the intentions and decisions related to tobacco [18]. Moreover, previous research has tested that the principal mediators in TTI are the self-efficacy (the ability to avoid smoking in tempting situations) and beliefs (such as the social and emotional expectation of smoking), and these directly influence the intentions to begin or quit smoking [18–20]. The main results of our study showed that a multicomponent intervention based on TTI, tailored to the college student, was effective in increasing smoking cessation among college students [21]. The result of the intervention was very promising; the biochemically validated smoking cessation (according to urine cotinine) incidence was 21.1% in the intervention group compared with 6.6% in the control group. To fully understand how the intervention worked, it is important to further explore its impact on the behavioral change components described above [22]. We hypothesize that the effect of a complex intervention such as tobacco cessation depends on self-efficacy, beliefs, and intention to quit. Firstly, in order to determine whether these factors exerted any influence on smoking cessation incidence, we explore the total effect in quitting explained by them. Secondly, this study focused on evaluating the changes in self-efficacy, beliefs, and intention to stop smoking, after a smoking cessation intervention aimed at college student smokers. RESEARCH DESIGN AND METHODS Research design This was a single-blind, pragmatic randomized controlled trial. In this study, participants were randomly assigned to intervention or control groups using a computer-generated allocation method. More details are described elsewhere [21]. Study population This study was conducted on two different campuses of the University of Navarra, located in two urban cities in Northern Spain. The study aimed to recruit students smoking at the time but was advertised among the whole undergraduate and masters’ students population (n = 8,050) for the academic year 2013–14, as a register of current smokers does not exist. Recruitment An informative session was held in each of the schools where the trial was to take place, informing the dean regarding the purpose and design of the study. The recruitment period was from September 2013 to February 2014. During this period, all undergraduate or masters’ students were sent an e-mail, inviting them to participate in the study. A member of the research team explained the study to students in all classes of each program. In addition, information booths manned by the investigators were set up on each university campus. Furthermore, the study was advertised on the university’s signboard, newspaper, and website. The strategy to increase success in recruiting candidates for the study is described in detail elsewhere [21]. Inclusion and exclusion criteria The inclusion criterion was being an undergraduate or masters’ students, aged 18–24 years, who smoked an average of at least one cigarette a week within the last 6 months [23]. The exclusion criterion was being a former smoker. Intervention and control protocols Intervention group Participants randomized to the intervention group received a multicomponent intervention based on previous recommendations made in the Surgeon General’s report [12] and the online support program for smoking prevention and cessation tailored for young adults established by the US Department of Health and Human Services [24]. In addition, the TTI informed the different strategies of this program. Table 1 shows a summary of the strategies of the multicomponent intervention focused on the three main mediator of TTI (self-efficacy, beliefs, and intention). Table 1 | Summary of the strategies of the multicomponent intervention focusing on the main three mediators of TTI (self-efficacy, beliefs, and intention Strategy Self-efficacy Beliefs Intention Motivational interviewa  Rationale ■  Decisional balance ■ ■ ■  Nicotine dependence ■ ■  Smoking perceived norms ■ ■  Financial cost of smoking ■ ■  Carbon monoxide feedback ■ ■  Personal plan for action and relapse prevention. ■ ■ Online self-help material  Decisions ■  Moods (stress management) ■  Social life ■ ■  Smoking health effects ■  Quitting (plan for action and relapse prevention) ■ ■  Self-monitoring ■ E-mail support  Encouraging decisions ■ ■  Improve motivation to quit ■  Seek help from their closest (social support) ■ Therapy group (12 students)  Social support ■ ■ ■  Motivation to quit ■  Social comparison ■ ■ ■ Strategy Self-efficacy Beliefs Intention Motivational interviewa  Rationale ■  Decisional balance ■ ■ ■  Nicotine dependence ■ ■  Smoking perceived norms ■ ■  Financial cost of smoking ■ ■  Carbon monoxide feedback ■ ■  Personal plan for action and relapse prevention. ■ ■ Online self-help material  Decisions ■  Moods (stress management) ■  Social life ■ ■  Smoking health effects ■  Quitting (plan for action and relapse prevention) ■ ■  Self-monitoring ■ E-mail support  Encouraging decisions ■ ■  Improve motivation to quit ■  Seek help from their closest (social support) ■ Therapy group (12 students)  Social support ■ ■ ■  Motivation to quit ■  Social comparison ■ ■ ■ aThe MI was based in the Herman and Fanhlander [25] method. View Large Table 1 | Summary of the strategies of the multicomponent intervention focusing on the main three mediators of TTI (self-efficacy, beliefs, and intention Strategy Self-efficacy Beliefs Intention Motivational interviewa  Rationale ■  Decisional balance ■ ■ ■  Nicotine dependence ■ ■  Smoking perceived norms ■ ■  Financial cost of smoking ■ ■  Carbon monoxide feedback ■ ■  Personal plan for action and relapse prevention. ■ ■ Online self-help material  Decisions ■  Moods (stress management) ■  Social life ■ ■  Smoking health effects ■  Quitting (plan for action and relapse prevention) ■ ■  Self-monitoring ■ E-mail support  Encouraging decisions ■ ■  Improve motivation to quit ■  Seek help from their closest (social support) ■ Therapy group (12 students)  Social support ■ ■ ■  Motivation to quit ■  Social comparison ■ ■ ■ Strategy Self-efficacy Beliefs Intention Motivational interviewa  Rationale ■  Decisional balance ■ ■ ■  Nicotine dependence ■ ■  Smoking perceived norms ■ ■  Financial cost of smoking ■ ■  Carbon monoxide feedback ■ ■  Personal plan for action and relapse prevention. ■ ■ Online self-help material  Decisions ■  Moods (stress management) ■  Social life ■ ■  Smoking health effects ■  Quitting (plan for action and relapse prevention) ■ ■  Self-monitoring ■ E-mail support  Encouraging decisions ■ ■  Improve motivation to quit ■  Seek help from their closest (social support) ■ Therapy group (12 students)  Social support ■ ■ ■  Motivation to quit ■  Social comparison ■ ■ ■ aThe MI was based in the Herman and Fanhlander [25] method. View Large The first session was a face-to-face 50-min meeting (motivational interviewing [MI]) consistent with the methods of Herman and Fanhlander [25]. During the interview, in order to improve student’s intention to quit, the nurse used reflective listening to stress positive change discussions. She remained nonconfrontational but continued to direct the conversation toward developing discrepancy and resolving ambivalence, influencing students’ beliefs. The nurse sought to reinforce the decision as well as increase students’ self-efficacy to avoid smoking, when elaborating a personal plan for changing smoking behavior for each participant who was prepared to stop smoking. In these sessions, the participants were invited to read the self-help material available on their college Moodle platform. This online self-help material focused on the following: (a) decisions, (b) moods, (c) social life, (d) smoking health effects, and (e) quitting. This material can be found in the online version of Pardavila et al. [21]. The follow-up program was scheduled according to the MI date. It consisted of four contacts: (a) e-mail 15 days before the MI, (b) group therapy 2 months after the MI (60 min), (c) a second follow-up visit 4 months after the MI (20 min), and (d) a final evaluation (15 min), conducted after 6 months [21]. Control group Participants randomized to the control group received brief advice (5–10 min) and a self-help pamphlet. All college students received the same input, which clearly advised quitting and highlighted the benefits of doing so. They also received a self-help pamphlet called “Stop smoking,” written by the Healthy University Project I research team. Before giving brief advice, the nurse assessed smokers’ habits and their willingness to quit. As is usually in this type of studies, there were no follow-up sessions for this group. At 6 months, the evaluation was carried out (15 min) [21]. Strategies to maintain fidelity In order to minimize interventionist effects, the same clinical nurse specialist (CNS) conducted all sessions with both groups. The CNS had 9 years of experience, and she was specially trained in smoking cessation strategies (MI, group therapy, and brief advice). Indeed, her training for the study was intensive, involving around 125 hrs. First, the CNS studied the theory related to the different techniques underlying the variety of strategies of both groups, and she discussed this with a two senior study staff. Second, the CNS was trained by viewing videotapes and seeing how an expert member of the study team carried out the different strategies. During this phase of the training, the CNS discussed these clinical examples with two experts. Finally, the CNS delivered intervention and control strategies with a pilot cohort, receiving feedback from principal investigator, and a senior expert with a view to further standardizing these strategies. In order to increase the adherence to the protocol of both groups and to prevent the CNS from applying some techniques of the intervention group in the control group, an intervention and control manual was developed. The majority of the manual was given over to the data collected protocol and the procedures for the differences strategies. Study procedures One member of the research team performed the randomization. This researcher generated a blocked random number sequence, using EpiInfo version 7.0.9.7, and prepared the sealed opaque sequentially numbered envelopes (1–255) with the corresponding condition written inside. Once the student agreed to participate in the study, the envelope was opened to determine the group to which he/she would be assigned. Students were unaware of the randomization scheme and of the group assignment. On the first visit, the nurse completed a standardized written questionnaire, collecting demographic information and the student’s history of tobacco, including the Fageström Test of Nicotine Dependence (FTND) [26], and the stages of change according to Prochaska’s model [27]. Furthermore, the students completed the smoking-related self-efficacy, belief, and intention questionnaire with its assessment scale [28–29]. Prochaska’s stages of change [27] assess the desire and readiness to quit smoking. In this study, to determine the student’s stage of change, the CNS used two questions: (a) “Did you quit smoking?” The two possible answers were “yes” or “no”. (b) “If you make the decision to stop smoking definitely, when would you consider quitting?” There were four possible answers: “not in the next 6 months,” “yes, in the next 6 months,” “yes, in the next 30 days,” and “I cannot pinpoint the exact time.” According to their responses, students were classified into precontemplation (period in which smokers were not considering quitting smoking [at least not within the next 6 months]), contemplation (period in which smokers were seriously thinking about quitting smoking within the next 6 months), preparation (period when smokers were seriously thinking about quitting smoking within the next month and had also tried to quit smoking during the past year) [21]. The smoking-related self-efficacy, belief, and intention assessment scale was used before and after a smoking cessation intervention. This scale, as TTI hypothesizes, assumes that smoking-related self-efficacy and beliefs have direct effects on intention to continue smoking [30]. It consists of a 17-item questionnaire, classified into 3 dimensions: self-efficacy to avoid smoking with 4 items (e.g., if your friends offer you a cigarette, are you able not to smoke?), smoking-related beliefs (that included the TTI dimensions of beliefs and attitudes) with 10 items (e.g., Do you believe cigarette smoking can help people relax?), and intention to stop smoking with 3 items (e.g., Do you think you will smoke a cigarette at any time during the next year?). The score for each item varies from 1 (definitely yes) to 4 (definitely no) [27–28]. High scores indicate a higher self-efficacy to avoid smoking, a protective smoking-related belief and greater intention to quit. The scale had not been validated into Spanish. Consequently, a process of back translation with two bilingual people was undertaken. Lastly, to decide on the final Spanish version of the scale, both English versions (original and back translated), were compared item-by-item by a panel of experts to evaluate whether they measured the same [31]. The internal consistency for all items of the scale in the sample was 0.68. At 6 months after the initial interview, all students were interviewed again by the same nurse, and the smoking-related self-efficacy, belief, and intention questionnaire were once again completed. Outcome measures The outcome measures in the study were the difference in the mean of self-efficacy to avoid smoking, smoking-related beliefs, and intention to quit smoking between intervention and control subjects; the difference in the mean of those variables pre and postintervention or control program; and the proportion of the total effect of smoking cessation explained by self-efficacy to avoid smoking, smoking-related beliefs, and intention to stop smoking. Statistical analysis Baseline data were reported as mean (standard deviation [SD]) appropriate for continuous variables. Categorical variables were reported as percentages (n, %). The data were analyzed on an intention-to-treat basis, including in the analysis all participants who had an outcome measured regardless of whether they had completed all sessions [32]. The proportion of the total effect of smoking cessation explained by self-efficacy to avoid smoking, smoking-related beliefs, and intention to stop smoking was obtained by the ratio of the unadjusted to the adjusted by self-efficacy, beliefs, or intention relative risk [33]. We used bootstrap to calculate the 95% confidence interval (CI) for the proportion of total effect of each mediator. Changes within groups were analyses with paired t-tests; differences between groups were assessed with analysis of covariance (ANCOVA). To obtain unbiased results, these analyses were adjusted for all baseline variables and site. These analyses were also made with the students who did not quit smoking. The analyses were performed using Stata version 11.1 (StataCorp, College Station, TX, USA). Statistical significance was set at 5% (P-value < 0.05, based on two-tailed tests). Ethical considerations Ethical approval was sought and granted by the University of Navarra Research Ethics Committee (reference number = 055/2013). Informed consent was obtained from all the students participating in the trial. RESULTS Participants The flow chart for the evaluation of study participants is shown in Fig. 1. In total, 8,050 students, from 14 schools and 46 degree programs, were invited to take part in the research. Of those invited, 359 students agreed to participate; 255 of those who agreed to participate (age range = 18–24 years) met the inclusion criteria. Reasons for ineligibility were as follows: did not smoke (n = 2), refused to participate (n = 4), and unsuccessful attempts to contact (n = 98). The 255 participants who met the inclusion criteria were randomized to the intervention group (n = 133) or the control group (n = 122). In the intervention group, 83 (62.4%) completed all sessions; 24 (18.1%) attended o three sessions; and 26 (19.6%) only participated in the MI. Furthermore, 108 (81.2%) participants from the intervention group and 101 (82.9%) from the control group attended the evaluation sessions. Of the 255 subjects enrolled in the trial, 225 (88.2%) completed the 6 month follow-up. The remaining 30 were lost to follow-up, 19 in the intervention group and 11 in the control group. Fig 1 View largeDownload slide | Flow of participants throughout the trial. Fig 1 View largeDownload slide | Flow of participants throughout the trial. Baseline characteristics Table 2 shows the participants’ characteristics by group. Table 2 | Baseline characteristics of participants by groups Intervention (N = 133) Control (N = 122) Demographic characteristics Age, mean (SD) 20.1 (1.7) 20.5 (1.7) Sex, n (%)  Male 51 (38.4) 46 (37.7)  Female 82 (61.7) 76 (62.3) Area of degree, n (%)  Health sciences 50 (37.6) 61 (50.0)  Social sciences 64 (48.1) 44 (36.1)  Technological sciences 19 (14.3) 17 (13.9) Residence, n (%)  With parent 26 (19.6) 41 (33.6)  Residence hall 49 (38.8) 33 (27.1)  House/apartment 58 (43.6) 48 (39.3) Smoking-related characteristics Daily cigarettes, mean (SD) 9.5 (6.1) 8.7 (5.2) Years of smoking, mean (SD) 5.6 (2.3) 5.7 (2.4) FTND, mean (SD) 2.0 (1.7) 2.4 (2.0) Received previous advice, n (%)  No 121 (91.0) 101 (82.8)  Yes 12 (9.0) 21 (17.2) Quit attempts in past year, n (%)  No 33 (24.8) 34 (27.9)  Yes 100 (75.2) 88 (72.1) Stages of change (Prochaska’s model), n (%)  Precontemplation 41 (30.8) 47 (37.7)  Contemplation 53 (39.9) 36 (29.5)  Preparation 39 (29.3) 40 (32.8) Self-efficacy, mean (SD) 8.9 (3.1) 8.9 (3.7) Belief, mean (SD) 28.3 (4.5) 28.4 (4.3) Intention related to smoking, mean (SD) 6.1 (1.6) 6.4 (1.8) Intervention (N = 133) Control (N = 122) Demographic characteristics Age, mean (SD) 20.1 (1.7) 20.5 (1.7) Sex, n (%)  Male 51 (38.4) 46 (37.7)  Female 82 (61.7) 76 (62.3) Area of degree, n (%)  Health sciences 50 (37.6) 61 (50.0)  Social sciences 64 (48.1) 44 (36.1)  Technological sciences 19 (14.3) 17 (13.9) Residence, n (%)  With parent 26 (19.6) 41 (33.6)  Residence hall 49 (38.8) 33 (27.1)  House/apartment 58 (43.6) 48 (39.3) Smoking-related characteristics Daily cigarettes, mean (SD) 9.5 (6.1) 8.7 (5.2) Years of smoking, mean (SD) 5.6 (2.3) 5.7 (2.4) FTND, mean (SD) 2.0 (1.7) 2.4 (2.0) Received previous advice, n (%)  No 121 (91.0) 101 (82.8)  Yes 12 (9.0) 21 (17.2) Quit attempts in past year, n (%)  No 33 (24.8) 34 (27.9)  Yes 100 (75.2) 88 (72.1) Stages of change (Prochaska’s model), n (%)  Precontemplation 41 (30.8) 47 (37.7)  Contemplation 53 (39.9) 36 (29.5)  Preparation 39 (29.3) 40 (32.8) Self-efficacy, mean (SD) 8.9 (3.1) 8.9 (3.7) Belief, mean (SD) 28.3 (4.5) 28.4 (4.3) Intention related to smoking, mean (SD) 6.1 (1.6) 6.4 (1.8) FTND Fagerström Test for Nicotine Dependence; SD Standard Deviation. View Large Table 2 | Baseline characteristics of participants by groups Intervention (N = 133) Control (N = 122) Demographic characteristics Age, mean (SD) 20.1 (1.7) 20.5 (1.7) Sex, n (%)  Male 51 (38.4) 46 (37.7)  Female 82 (61.7) 76 (62.3) Area of degree, n (%)  Health sciences 50 (37.6) 61 (50.0)  Social sciences 64 (48.1) 44 (36.1)  Technological sciences 19 (14.3) 17 (13.9) Residence, n (%)  With parent 26 (19.6) 41 (33.6)  Residence hall 49 (38.8) 33 (27.1)  House/apartment 58 (43.6) 48 (39.3) Smoking-related characteristics Daily cigarettes, mean (SD) 9.5 (6.1) 8.7 (5.2) Years of smoking, mean (SD) 5.6 (2.3) 5.7 (2.4) FTND, mean (SD) 2.0 (1.7) 2.4 (2.0) Received previous advice, n (%)  No 121 (91.0) 101 (82.8)  Yes 12 (9.0) 21 (17.2) Quit attempts in past year, n (%)  No 33 (24.8) 34 (27.9)  Yes 100 (75.2) 88 (72.1) Stages of change (Prochaska’s model), n (%)  Precontemplation 41 (30.8) 47 (37.7)  Contemplation 53 (39.9) 36 (29.5)  Preparation 39 (29.3) 40 (32.8) Self-efficacy, mean (SD) 8.9 (3.1) 8.9 (3.7) Belief, mean (SD) 28.3 (4.5) 28.4 (4.3) Intention related to smoking, mean (SD) 6.1 (1.6) 6.4 (1.8) Intervention (N = 133) Control (N = 122) Demographic characteristics Age, mean (SD) 20.1 (1.7) 20.5 (1.7) Sex, n (%)  Male 51 (38.4) 46 (37.7)  Female 82 (61.7) 76 (62.3) Area of degree, n (%)  Health sciences 50 (37.6) 61 (50.0)  Social sciences 64 (48.1) 44 (36.1)  Technological sciences 19 (14.3) 17 (13.9) Residence, n (%)  With parent 26 (19.6) 41 (33.6)  Residence hall 49 (38.8) 33 (27.1)  House/apartment 58 (43.6) 48 (39.3) Smoking-related characteristics Daily cigarettes, mean (SD) 9.5 (6.1) 8.7 (5.2) Years of smoking, mean (SD) 5.6 (2.3) 5.7 (2.4) FTND, mean (SD) 2.0 (1.7) 2.4 (2.0) Received previous advice, n (%)  No 121 (91.0) 101 (82.8)  Yes 12 (9.0) 21 (17.2) Quit attempts in past year, n (%)  No 33 (24.8) 34 (27.9)  Yes 100 (75.2) 88 (72.1) Stages of change (Prochaska’s model), n (%)  Precontemplation 41 (30.8) 47 (37.7)  Contemplation 53 (39.9) 36 (29.5)  Preparation 39 (29.3) 40 (32.8) Self-efficacy, mean (SD) 8.9 (3.1) 8.9 (3.7) Belief, mean (SD) 28.3 (4.5) 28.4 (4.3) Intention related to smoking, mean (SD) 6.1 (1.6) 6.4 (1.8) FTND Fagerström Test for Nicotine Dependence; SD Standard Deviation. View Large Self-efficacy to avoid smoking, smoking-related beliefs, and intention to stop smoking outcomes The total effect of the intervention on smoking cessation versus the control group was equal to a relative risk of 3.21% (95% CI = 1.52 to 6.78; p = .002). After adjusting for the difference of self-efficacy to avoid smoking and intention to stop smoking, the relative risk decreased to 2.69% (95% CI = 1.33 to 5.44; p = .006) and 2.41% (95% CI = 1.22 to 4.75; p = .011), respectively. Nevertheless, the difference in smoking-related beliefs increased to 3.33% (95% CI = 1.58 to 6.98; p = 0.001). Thus, the percentage of excess risk of the intervention group on smoking cessation explained by the difference of intention to stop smoking scores was 36.1% (95% CI = 4.4 to 72.08). However, the percentage of excess risk of the intervention group on smoking cessation explained by the differences of smoking-related belief and the difference of self-efficacy to avoid smoking scores was −5.30% (95% CI = −9.10 to 21.00), and 23.5% (95% CI = −4.7 to 57.6), respectively. These percentages of excess risk were not statistically significant. Table 3 shows the differences in the mean of self-efficacy, belief, and intention related to smoking scores before and after the smoking cessation program. Furthermore, Table 4 shows the comparisons between self-efficacy, belief, and intention related to smoking at 6 month follow-up. Table 3 | Differences in the mean of self-efficacy, belief, and intention related to smoking scores before and after smoking cessation program Intervention p value* Control p value* (N = 114) (N = 111) Self-efficacy, mean (95% CI) 1.85 (1.22 to 2.49) <.001 0.83 (0.16 to 1.49) .015 Belief, mean (95% CI) 1.89 (0.96 to 2.81) <.001 1.82 (1.07 to 2.57) <.001 Intentions, mean (95% CI) 1.32 (0.90 to 1.75) <.001 0.46 (0.08 to 0.84) .018 Intervention p value* Control p value* (N = 114) (N = 111) Self-efficacy, mean (95% CI) 1.85 (1.22 to 2.49) <.001 0.83 (0.16 to 1.49) .015 Belief, mean (95% CI) 1.89 (0.96 to 2.81) <.001 1.82 (1.07 to 2.57) <.001 Intentions, mean (95% CI) 1.32 (0.90 to 1.75) <.001 0.46 (0.08 to 0.84) .018 *p-value from paired Student’s t-test. View Large Table 3 | Differences in the mean of self-efficacy, belief, and intention related to smoking scores before and after smoking cessation program Intervention p value* Control p value* (N = 114) (N = 111) Self-efficacy, mean (95% CI) 1.85 (1.22 to 2.49) <.001 0.83 (0.16 to 1.49) .015 Belief, mean (95% CI) 1.89 (0.96 to 2.81) <.001 1.82 (1.07 to 2.57) <.001 Intentions, mean (95% CI) 1.32 (0.90 to 1.75) <.001 0.46 (0.08 to 0.84) .018 Intervention p value* Control p value* (N = 114) (N = 111) Self-efficacy, mean (95% CI) 1.85 (1.22 to 2.49) <.001 0.83 (0.16 to 1.49) .015 Belief, mean (95% CI) 1.89 (0.96 to 2.81) <.001 1.82 (1.07 to 2.57) <.001 Intentions, mean (95% CI) 1.32 (0.90 to 1.75) <.001 0.46 (0.08 to 0.84) .018 *p-value from paired Student’s t-test. View Large Table 4 | Comparisons self-efficacy, belief, and intention related to smoking at 6 month follow-up. Unadjusted difference p value* Adjusted difference p value† Self-efficacy, mean (95% CI) 1.02 (0.11 to 1.93) .029 1.09 (0.13 to 2.06) .027 Belief, mean (95% CI) 0.07 (−1.12 to 1.26) .913 0.25 (−1.02 to 1.51) .702 Intentions, mean (95% CI) 0.87 (0.30 to 1.43) .003 0.78 (0.20 to 1.37) .009 Unadjusted difference p value* Adjusted difference p value† Self-efficacy, mean (95% CI) 1.02 (0.11 to 1.93) .029 1.09 (0.13 to 2.06) .027 Belief, mean (95% CI) 0.07 (−1.12 to 1.26) .913 0.25 (−1.02 to 1.51) .702 Intentions, mean (95% CI) 0.87 (0.30 to 1.43) .003 0.78 (0.20 to 1.37) .009 Note: Estimates and associated statistic refer to differences of mean. *ANCOVA test. †ANCOVA test adjusted for all baselines variables and site (Pamplona or San Sebastian). View Large Table 4 | Comparisons self-efficacy, belief, and intention related to smoking at 6 month follow-up. Unadjusted difference p value* Adjusted difference p value† Self-efficacy, mean (95% CI) 1.02 (0.11 to 1.93) .029 1.09 (0.13 to 2.06) .027 Belief, mean (95% CI) 0.07 (−1.12 to 1.26) .913 0.25 (−1.02 to 1.51) .702 Intentions, mean (95% CI) 0.87 (0.30 to 1.43) .003 0.78 (0.20 to 1.37) .009 Unadjusted difference p value* Adjusted difference p value† Self-efficacy, mean (95% CI) 1.02 (0.11 to 1.93) .029 1.09 (0.13 to 2.06) .027 Belief, mean (95% CI) 0.07 (−1.12 to 1.26) .913 0.25 (−1.02 to 1.51) .702 Intentions, mean (95% CI) 0.87 (0.30 to 1.43) .003 0.78 (0.20 to 1.37) .009 Note: Estimates and associated statistic refer to differences of mean. *ANCOVA test. †ANCOVA test adjusted for all baselines variables and site (Pamplona or San Sebastian). View Large Table 5 shows the differences in the mean of self-efficacy, belief, and intention related to smoking scores before and after the smoking cessation program among the 189 students who were not identified as quitters. Analysis of the differences in the mean scores between groups revealed a significant main effect only for intention to stop smoking in favor of the intervention group (mean [95% CI], p score: unadjusted difference 0.67 [0.14 to 1.20], 0.014; adjusted difference: 0.60 [0.04 to 1.15], 0.037). Table 5 | Differences in the mean of self-efficacy, belief, and intention related to smoking scores before and after smoking cessation program in no quitter. Intervention p value* Control p value* (N = 86) (N = 103) Self-efficacy, mean (95% CI) 1.10 (0.50 to 1.70) .004 0.40 (−0.22 to 1.02) .207 Belief, mean (95% CI) 1.30 (0.24 to 2.36) .017 1.78 (0.98 to 2.57) <.001 Intentions, mean (95% CI) 0.81 (0.38 to 1.24) <.001 0.15 (−0.18 to 0.47) .380 Intervention p value* Control p value* (N = 86) (N = 103) Self-efficacy, mean (95% CI) 1.10 (0.50 to 1.70) .004 0.40 (−0.22 to 1.02) .207 Belief, mean (95% CI) 1.30 (0.24 to 2.36) .017 1.78 (0.98 to 2.57) <.001 Intentions, mean (95% CI) 0.81 (0.38 to 1.24) <.001 0.15 (−0.18 to 0.47) .380 *p-value from paired Student’s t-test. View Large Table 5 | Differences in the mean of self-efficacy, belief, and intention related to smoking scores before and after smoking cessation program in no quitter. Intervention p value* Control p value* (N = 86) (N = 103) Self-efficacy, mean (95% CI) 1.10 (0.50 to 1.70) .004 0.40 (−0.22 to 1.02) .207 Belief, mean (95% CI) 1.30 (0.24 to 2.36) .017 1.78 (0.98 to 2.57) <.001 Intentions, mean (95% CI) 0.81 (0.38 to 1.24) <.001 0.15 (−0.18 to 0.47) .380 Intervention p value* Control p value* (N = 86) (N = 103) Self-efficacy, mean (95% CI) 1.10 (0.50 to 1.70) .004 0.40 (−0.22 to 1.02) .207 Belief, mean (95% CI) 1.30 (0.24 to 2.36) .017 1.78 (0.98 to 2.57) <.001 Intentions, mean (95% CI) 0.81 (0.38 to 1.24) <.001 0.15 (−0.18 to 0.47) .380 *p-value from paired Student’s t-test. View Large DISCUSSION This study suggests that intention to quit smoking is partial moderator explaining 36.2% of the total effects in smoking cessation incidence. Furthermore, the result of this research shows that a structured multicomponent program based on TTI and conducted at a university can achieve a significant change in self-efficacy to avoid smoking and intentions related to stop smoking of the college students compared with control ones. Studies into multicomponent interventions associate an increased self-efficacy to avoid smoking with greater success in quitting [34–35]. However, previous research has not identified which component of the intervention has been able to influence the increase in self-efficacy in college student smokers. We suggest that the principal component that has an impact on the increase in self-efficacy is MI. More specifically, MI strategies as feedback emphasize personal responsibility, and an empathetic counseling style increases self-efficacy [36]. This statement is consistent with the findings from our research and the results of previous studies that used MI for smoking cessation in this target population [37], in diabetes patients [38], and in smoker adolescents with psychiatric disorders [39]. Another component of the intervention that could increase the perceived self-efficacy to avoid smoking is group therapy. This component encouraged participants to initiate discussion of their motivations, concerns, and difficulties during the smoking process. These discussions helped the students to realize that their experience during the smoking cessation was similar to that of other colleagues. In addition, these discussions help them identify some strategies that other students performed (e.g., if they had an urge to smoking, calling a friend or playing a phone game). This could increase their perceived self-efficacy as Washington [40] shows in study conducted with chemically dependent women. The control group also increases their self-efficacy to avoid smoking. These results are similar to the Brown et al. [39] research in adolescent with psychiatric disorders, but contrary to the Bolger et al. [37] study with college students. The principal differences of Brown et al. [39] and our study with Bolger et al. [37] research were that the participants in the control group received 5–10 min of advice to quit smoking and a self-help pamphlet, whereas Bolger et al. [37] provided participants with their expired CO level (score) as a motivational strategy, and information and pamphlet about smoking-related risks. Although self-efficacy to avoid smoking increased significantly, the percentage of excess risk was not statistically significant, suggesting that the change of self-efficacy might reflect rather than mediate in a successful quitting. These results are similar to the meta-analysis on self-efficacy and smoking cessation that found that controlling for smoking status substantially reduced the relationship between self- efficacy and smoking [41]. On the other hand, both groups experienced an increase in beliefs, but this did not seem to explain the difference in the cessation outcomes. In contrast, the association between belief and increase in the effect of smoking cessation is not significant. This could be explained by the fact that in the control group, both the brief advice and the self-help material were tailored to smoking-related beliefs, while in the intervention group, only the online self-help program focused on this dimension. Our clinical experience in developing the research suggests to us that many of the college students have a low risk perception of smoking. The students did not perceive tobacco as harmful for their health and their life expectancy. Moreover, they saw the youthfulness as a time for smoking without risk because they will stop smoking before their graduation or before becoming parents. They perceived a positive benefit in consuming tobacco such as gaining more friends or becoming more relaxed. These considerations are similar to those reported in previous studies [9–10,12,42–43]. Another reason that could explain the effectiveness of this intervention is that the intention to stop smoking also increased. Based on this result, and on our clinical experience, the MI appeared to be more effective for changing the intentions to stop smoking than brief advice. This could be explained because MI is a client-centered, directive method for enhancing intrinsic motivation to change by exploring and resolving ambivalence [36,44–45]. In addition, the basis of MI is that people are autonomous; therefore, the healthcare clinicians adopt a collaborator rather than a paternalistic role [44]. In our previous study, the relationship between both (participants and nurse) was based on confidence, freedom, and not on judgment. Furthermore, the nurse was trained in MI procedures and followed a specific protocol tailored to the youth characteristics [21]. In addition, MI is based on the theoretical stages of change, which help the health professional to determine how ready a student is prepared to quit [46] and to improve the progress through these stages [47]. As a result, the effect of MI on intention to quit is in the progress through the stages of the change model. The main findings of our randomized controlled trial revealed that individuals who received the intervention moved further along the active stages of change [21]. These results are consistent with previous studies with college students who used MI as a strategy [25,48]. Even though the use of the stages of change model in smoking cessation is questioned [49–50], the results of this study suggest that there is a relationship between the intention to quit and the progression in the stages of change. As this study results show the increases on the intention to stop smoking may not necessarily correspond with behavior change, however, some studies showed that the students who moved to more active stages of change obtained more positive results in terms of smoking cessation [21,27,35,51]. Furthermore, the mean in all mediators’ outcomes was increased in the control group at 6 month follow-up; these increases were statistically significant. These results indicated the potential benefit of brief advice given by a health professional. The challenge as to whether or not this brief advice is beneficial depends on the possibility of being able to offer the said advice to college students on a routine basis [52]. Five main limitations in this study need to be acknowledged. First, the finding provided by this research is only applicable in college students. Second, the study was conducted on a single university and there may have been some contamination between the groups despite the fact that they made a written commitment to refrain from speaking about the study until it was finished. The difference in self-efficacy to avoid smoking and intention to stop smoking between groups suggests that if this bias had occurred, the rate was low. Third, nearly, 11.8% of the participants in the study were lost during the follow-up, and 37.6% of the subjects randomized to the intervention group did not receive the complete protocol. However, a common drawback of studies, and in educational programs conducted in community-based clinical settings, is the low compliance of subjects with the intervention protocol. In our study, this was related to time issues and loss of interest in participating in the research trial. Nevertheless, the rate of lost subjects is lower than in other studies with this population. Our loss during the follow-up was less than 20%, which means there was no significant risk of bias [53]. This could be explained because one member of the research team contacted participants by phone and e-mail for the final evaluation. Fourth, we did not record the different sessions of the intervention group, and thus, it is impossible to verify the fidelity. In contrast, we applied preplanned strategies that were used to maintain the fidelity of the intervention. Specifically, in the design, CNS training, and the intervention delivery. Fifth, the scale for assessing smoking-related self-efficacy, belief, and intention was not validated in Spanish at the time of the study. However, a rigorous process of back translation and comparison of both English versions by experts was developed in order to obtain a final Spanish version [54]. Internal consistency for a Spanish version of the Scale was questionable (α = 0.68). Nevertheless, alpha Cronbach of 0.60 is considered acceptable to exploratory studies [54]. Furthermore, the English version of the scale had been validated to assess young smoker’s ability to avoid smoking in emotional or social situations, to measure their associated belief toward smoking, and to evaluate their intention to stop smoking [28]. Based on this, we considered this scale to be adequate for this study. This study has theoretical, methodological, and practical strengths. One of the primary strengths of this study was the evaluation of the self-efficacy, beliefs, and intention related to quitting smoking as potentials mediators. These results explained how and why college students stop smoking. Furthermore, the conceptual model of the scale for assessing smoking-related self-efficacy, belief, and intention is based on the fact that a certain type of behavior is more likely to occur if three conditions are met: one has the necessary skills to perform said behavior, there are no environmental constraints against performing said behavior, and if a strong intention to perform the behavior exists [28]. These assumptions are similar to TTI principles [18]. More specifically, this theory takes into account the fact that the main pathways of behavioral change are self-efficacy, belief, attitudes toward behavior and decision or intention. Finally, this study could help clinicians understand the process of change and, based on this design, be able to improve intervention or replicate this intervention in other contexts. In conclusion, a multicomponent intervention based on the TTI, tailored to college students, positively increased the self-efficacy to avoid smoking and the intention to quit, suggesting intention as potential mediator of quitting. Furthermore, the finding of this study provides strategies for improving the smoking cessation process in college students. In addition, the results of this research suggest that MI should be included in smoking cessation interventions. Nevertheless, further research is recommended to test the changes in self-efficacy to avoid smoking, belief related with tobacco use, and intention to quit with the addiction across a variety of population groups. This study was funded by the Chair of María Egea, University of Navarra (Spain). Compliance with Ethical Standards Conflict of Interest: All authors declare that they have no conflict of interest Authors’ Contributions: MPB and NCA contributed in the conception and design of this study. MPB and SPG organizated the database. MPB performed the statistical analysis. MPB wrote the manuscript with the support from ACA, MD, MPG, SPG, and NCA. All authors discussed the results, contributed to the manuscript revision, and approved the final version of the manuscript. Primary Data: We ensure that the content of this article is original and has not previously been published or has not been sent simultaneously to another journal. The authors of the paper have full control of all primary data and that they agree to allow the journal to review their data if requested. Moreover, all authors are in agreement with the content of this paper and its publication, and with ceding copyright in accordance with legal requirements if it is published. Ethical Approval: The Research Ethics Committee of the University of Navarra, Pamplona (Spain) approved the research. [Reference number 055/2013]. Informed Consent: Informed consent was obtained from all the participants of the trial. References 1. OECD . Education at a Glance: OECD Indicators . Paris : OECD Publishing ; 2016 . 2. M. de Educación, Cultura y Deporte . Datos y Cifras del Sistema Universitario Español. Curso 2015–2016 . 2016 ; Available at https://sede.educacion.gob.es/publiventa/d/21461/19/1. Accessibility verified August 3, 2017 . 3. M. de Educación, Cultura y Deporte . Panorama de la educación. Indicadores de la OCDE 2016 . 2016 ; Available at http://www.mecd.gob.es/dctm/inee/eag/panorama2016okkk.pdf?documentId=0901e72b82236f2b. Accessibility verified August 3, 2017 . 4. Varela-Mato V , Cancela JM , Ayan C , Martín V , Molina A . Lifestyle and health among Spanish university students: Differences by gender and academic discipline . Int j Environ Res Public Health . 2012 ; 9 ( 8 ): 2728 – 2741 . Google Scholar CrossRef Search ADS PubMed 5. Observatorio Español sobre Drogas y Toxicomanías (OEDT) . Encuesta domiciliaria sobre el consumo de alcohol y drogas en España . 2015 ; Available at http://www.pnsd.msssi.gob.es/profesionales/sistemasInformacion/sistemaInformacion/encuestas_EDADES.htm. Accessibility verified August 3, 2017 . 6. Bernat DH , Klein EG , Forster JL . Smoking initiation during young adulthood: A longitudinal study of a population-based cohort . j Adolesc Health . 2012 ; 51 ( 5 ): 497 – 502 . Google Scholar CrossRef Search ADS PubMed 7. Freedman KS , Nelson NM , Feldman LL . Smoking initiation among young adults in the United States and Canada, 1998–2010: A systematic review . Prev Chronic Dis . 2012 ; 9 : E05 . Google Scholar PubMed 8. Brown J . A review of the evidence on technology-based interventions for the treatment of tobacco dependence in college health . Worldviews Evid Based Nurs . 2013 ; 10 ( 3 ): 150 – 162 . Google Scholar CrossRef Search ADS PubMed 9. Aryal UR , Bhatta DN . Perceived benefits and health risks of cigarette smoking among young adults: Insights from a cross-sectional study . Tob Induc Dis . 2015 ; 13 ( 1 ): 22 . Google Scholar CrossRef Search ADS PubMed 10. Hoeppner BB , Bidwell LC , Colby SM , Barnett NP . Smoking patterns and their relationship to drinking among first-year college students . Nicotine Tob Res . 2014 ; 16 ( 6 ): 743 – 752 . Google Scholar CrossRef Search ADS PubMed 11. Centers for Disease Control and Prevention . Best Practices for Comprehensive Tobacco Control Programs-2014 . Atlanta, GA : U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health ; 2014 12. US Department of Health and Human Services . Preventing Tobacco Use among Youth and Young Adults: A Report of the Surgeon General . Atlanta, GA : US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health ; 2012 . 13. Ordás B , Fernández D , Ordóñez C et al. Changes in use, knowledge, beliefs and attitudes relating to tobacco among nursing and physiotherapy students: A 10-year analysis . j Adv Nurs . 2015 ; 71 ( 10 ): 2326 – 2337 . Google Scholar CrossRef Search ADS PubMed 14. Lee HS , Catley D , Harris KJ . Improving understanding of the quitting process: Psychological predictors of quit attempts versus smoking cessation maintenance among college students . Subst Use Misuse . 2014 ; 49 ( 10 ): 1332 – 1339 . Google Scholar CrossRef Search ADS PubMed 15. Michie S , Brown J , Geraghty AW et al. Development of stopadvisor: A theory-based interactive internet-based smoking cessation intervention . Transl Behav Med . 2012 ; 2 ( 3 ): 263 – 275 . Google Scholar CrossRef Search ADS PubMed 16. Bully P , Sánchez Á , Zabaleta-del-Olmo E , Pombo H , Grandes G . Evidence from interventions based on theoretical models for lifestyle modification (physical activity, diet, alcohol and tobacco use) in primary care settings: A systematic review . Prev Med . 2015 ; 76 ( Suppl ): S76 – S93 . Google Scholar CrossRef Search ADS PubMed 17. Flay BR . Understanding environmental, situational and intrapersonal risk and protective factors for youth tobacco use: The Theory of Triadic Influence . Nicotine Tob Res . 1999 ; 1 ( Suppl 2 ): S111 – S114 . Google Scholar CrossRef Search ADS PubMed 18. Flay BR , Snyder FJ , Petraitis J . The theory of triadic influence . In: DiClemente RJ , Crosby RA , Kegler MC , eds. Emerging Theories in Health Promotion Practice and Research , 2nd ed . San Francisco, CA : Jossey-Bass ; 2009 : 451 – 510 . Google Scholar CrossRef Search ADS 19. Holman LR , Bricker JB , Comstock BA . Psychological predictors of male smokeless tobacco use initiation and cessation: A 16-year longitudinal study . Addiction . 2013 ; 108 ( 7 ): 1327 – 1335 . Google Scholar CrossRef Search ADS PubMed 20. Sterling K , Ford K , Park H , Diamond P , McAlister A . Assessing the invariance of smoking-related self-efficacy, beliefs, and intention among high school current smokers . Subst Use Misuse . 2013 ; 48 ( 7 ): 477 – 483 . Google Scholar CrossRef Search ADS PubMed 21. Pardavila-Belio MI , García-Vivar C , Pimenta AM , Canga-Armayor A , Pueyo-Garrigues S , Canga-Armayor N . Intervention study for smoking cessation in Spanish college students: Pragmatic randomized controlled trial . Addiction . 2015 ; 110 ( 10 ): 1676 – 1683 . Google Scholar CrossRef Search ADS PubMed 22. Martinez C , Fernandez E . Commentary on Pardavila-Belio et al. (2015): Reporting on randomized control trials or giving details on how the cake was made–the list of ingredients and the implementation process . Addiction . 2015 ; 110 ( 10 ): 1684 – 1685 . Google Scholar CrossRef Search ADS PubMed 23. Stanton A , Grimshaw GM . Tobacco cessation interventions for young people . Cochrane Database Syst Rev . 2013 ;( 8 ): CD003289 . 24. US Department of Health and Human Services . Smokefree Teen , 2012 ; Available at https://teen.smokefree.gov/. Accessibility verified August 4, 2017 . 25. Herman KC , Fahnlander BA . Motivational intervention to reduce cigarette smoking among college students: Overview and exploratory investigation . J College Counselling . 2003 ; 6 : 46 – 55 . Google Scholar CrossRef Search ADS 26. Heatherton TF , Kozlowski LT , Frecker RC , Fagerström KO . The Fagerström Test for Nicotine Dependence: A revision of the Fagerström Tolerance Questionnaire . Br j Addict . 1991 ; 86 ( 9 ): 1119 – 1127 . Google Scholar CrossRef Search ADS PubMed 27. Prochaska JO , DiClemente CC . Stages and processes of self-change of smoking: Toward an integrative model of change . j Consult Clin Psychol . 1983 ; 51 ( 3 ): 390 – 395 . Google Scholar CrossRef Search ADS PubMed 28. Ford KH , Diamond PM , Kelder SH , Sterling KL , McAlister AL . Validation of scales measuring attitudes, self-efficacy, and intention related to smoking among middle school students . Psychol Addict Behav . 2009 ; 23 ( 2 ): 271 – 278 . Google Scholar CrossRef Search ADS PubMed 29. Sterling KL , Ford KH , Park H , McAlister AL . Scales of smoking-related self-efficacy, beliefs, and intention: Assessing measurement invariance among intermittent and daily high school smokers . Am j Health Promot . 2014 ; 28 ( 5 ): 310 – 315 . Google Scholar CrossRef Search ADS PubMed 30. Landrum Sterling K , Diamond PM , Dolan Mullen P , Pallonen U , Ford KH , McAlister AL . Smoking-related self-efficacy, beliefs, and intention: Assessing factorial validity and structural relationships in 9th-12th grade current smokers . Addict Behav . 2007 ; 32 ( 9 ): 1863 – 1876 . Google Scholar CrossRef Search ADS PubMed 31. Pegalajar J , Acosta A , Castillo M , Higueras L , Padilla JL . Spanish adaptation of the memory characteristics questionnaire (MCQ) . Span j Psychol . 2015 ; 18 : e101 . Google Scholar CrossRef Search ADS PubMed 32. Elkins MR , Moseley AM . Intention-to-treat analysis . J Physiother . 2015 ; 61 ( 3 ): 165 – 167 . Google Scholar CrossRef Search ADS PubMed 33. Kaufman JS , Maclehose RF , Kaufman S . A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation . Epidemiol Perspect Innov . 2004 ; 1 ( 1 ): 4 . Google Scholar CrossRef Search ADS PubMed 34. Cupertino AP , Berg C , Gajewski B et al. Change in self-efficacy, autonomous and controlled motivation predicting smoking . j Health Psychol . 2012 ; 17 ( 5 ): 640 – 652 . Google Scholar CrossRef Search ADS PubMed 35. Mujika A , Forbes A , Canga N et al. Motivational interviewing as a smoking cessation strategy with nurses: An exploratory randomised controlled trial . Int j Nurs Stud . 2014 ; 51 ( 8 ): 1074 – 1082 . Google Scholar CrossRef Search ADS PubMed 36. Miller WR , Rollnick S. La entrevista Motivacional [Motivational Interviewing] . Barcelona : Ediciones Paidós Ibérica ; 2008 . 37. Bolger K , Carter K , Curtin L , Martz DM , Gagnon SG , Michael KD . Motivational interviewing for smoking cessation among college students . J College Stud Psychother . 2010 ; 24 ( 2 ): 116 – 129 . Google Scholar CrossRef Search ADS 38. Chen SM , Creedy D , Lin HS , Wollin J . Effects of motivational interviewing intervention on self-management, psychological and glycemic outcomes in type 2 diabetes: A randomized controlled trial . Int j Nurs Stud . 2012 ; 49 ( 6 ): 637 – 644 . Google Scholar CrossRef Search ADS PubMed 39. Brown RA , Ramsey SE , Strong DR et al. Effects of motivational interviewing on smoking cessation in adolescents with psychiatric disorders . Tob Control . 2003 ; 12 ( Suppl 4 ): IV3 – 10 . Google Scholar CrossRef Search ADS PubMed 40. Washington OG . Effects of group therapy on chemically dependent women’s self-efficacy . j Nurs Scholarsh . 2000 ; 32 ( 4 ): 347 – 352 . Google Scholar CrossRef Search ADS PubMed 41. Gwaltney CJ , Metrik J , Kahler CW , Shiffman S . Self-efficacy and smoking cessation: A meta-analysis . Psychol Addict Behav . 2009 ; 23 ( 1 ): 56 – 66 . Google Scholar CrossRef Search ADS PubMed 42. Murphy-Hoefer R , Griffith R , Pederson LL , Crossett L , Iyer SR , Hiller MD . A review of interventions to reduce tobacco use in colleges and universities . Am j Prev Med . 2005 ; 28 ( 2 ): 188 – 200 . Google Scholar CrossRef Search ADS PubMed 43. Tavolacci MP , Ladner J , Grigioni S , Richard L , Villet H , Dechelotte P . Prevalence and association of perceived stress, substance use and behavioral addictions: A cross-sectional study among university students in France, 2009-2011 . bmc Public Health . 2013 ; 13 : 724 . Google Scholar CrossRef Search ADS PubMed 44. Richardson L . Motivational interviewing: Helping patients move toward change . j Christ Nurs . 2012 ; 29 ( 1 ): 18 – 24 . Google Scholar CrossRef Search ADS PubMed 45. Apodaca TR , Longabaugh R . Mechanisms of change in motivational interviewing: A review and preliminary evaluation of the evidence . Addiction . 2009 ; 104 ( 5 ): 705 – 715 . Google Scholar CrossRef Search ADS PubMed 46. Miller NH . Motivational interviewing as a prelude to coaching in healthcare settings . j Cardiovasc Nurs . 2010 ; 25 ( 3 ): 247 – 251 . Google Scholar CrossRef Search ADS PubMed 47. Borland R , Balmford J , Benda P . Population-level effects of automated smoking cessation help programs: A randomized controlled trial . Addiction . 2013 ; 108 ( 3 ): 618 – 628 . Google Scholar CrossRef Search ADS PubMed 48. Prokhorov AV , Yost T , Mullin-Jones M et al. “Look at your health”: Outcomes associated with a computer-assisted smoking cessation counseling intervention for community college students . Addict Behav . 2008 ; 33 ( 6 ): 757 – 771 . Google Scholar CrossRef Search ADS PubMed 49. Riemsma RP , Pattenden J , Bridle C et al. Systematic review of the effectiveness of stage based interventions to promote smoking cessation . BMJ . 2003 ; 326 ( 7400 ): 1175 – 1177 . Google Scholar CrossRef Search ADS PubMed 50. Cahill K , Lancaster T , Green N . Stage-based interventions for smoking cessation . Cochrane Database Syst Rev . 2010 ;( 11 ): CD004492 . 51. Canga N , De Irala J , Vara E , Duaso MJ , Ferrer A , Martínez-González MA . Intervention study for smoking cessation in diabetic patients: A randomized controlled trial in both clinical and primary care settings . Diabetes Care . 2000 ; 23 ( 10 ): 1455 – 1460 . Google Scholar CrossRef Search ADS PubMed 52. Stead LF , Buitrago D , Preciado N , Sanchez G , Hartmann-Boyce J , Lancaster T . Physician advice for smoking cessation . Cochrane Database Syst Rev . 2013 ;( 5 ): CD000165 . 53. Thomas R , Perera R . School-based programmes for preventing smoking . Cochrane Database Syst Rev . 2006 ;( 3 ): CD001293 54. Navarta-Sánchez MV , Senosiain García JM , Riverol M et al. Factors influencing psychosocial adjustment and quality of life in Parkinson patients and informal caregivers . Qual Life Res . 2016 ; 25 ( 8 ): 1959 – 1968 . Google Scholar CrossRef Search ADS PubMed © Society of Behavioral Medicine 2018. All rights reserved. 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Understanding how a smoking cessation intervention changes beliefs, self-efficacy, and intention to quit: a secondary analysis of a pragmatic randomized controlled trial

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Abstract

Abstract Although beliefs, self-efficacy, and intention to quit have been identified as proximal predictors of initiation or quitting in young adults, few studies have studied how these variables change after a smoking cessation intervention. To evaluate the changes in the beliefs, self-efficacy, and intention to avoid smoking and determine if these are potential mediators in quitting, following a smoking cessation intervention, aimed at tobacco-dependent college students. Single-blind, pragmatic randomized controlled trial with a 6-month follow-up. A total of 255 smoker students were recruited from September 2013 to February 2014. Participants were randomly assigned to intervention group (n = 133) or to control group (n = 122). The students in the intervention group received a multicomponent intervention based on the Theory of Triadic Influence (TTI). The strategies of this program consisted of a 50 min motivational interview conducted by a nurse and online self-help material. The follow-up included a reinforcing e-mail and group therapy. The smoking-related self-efficacy, belief, and intention scale was used to assess outcomes. Intention to quit smoking is partial moderator explaining 36.2% of the total effects in smoking cessation incidence. At 6 month follow-up, the differences in the mean scores of self-efficacy and intention related to stopping smoking were significantly higher in the intervention than in the control group. A multicomponent intervention based on the TTI, tailored to college students, positively increased the self-efficacy to avoid smoking and the intention to quit, suggesting intention as potential mediator of quitting. Implications Practice: A multicomponent smoking cessation program based on the Theory of Triadic Influences can be used to increase the self-efficacy and the intention to quit in college students. Policy: Policymakers who want to increase the intention to quit smoking among college students should consider the implementation of tailored multicomponent interventions. Research: Future research should be carried out to investigate the role of beliefs, self-efficacy, and intention to quit in smoking cessation outcomes of college students. INTRODUCTION The Organization for Economic Co-operation and Development (OECD) showed that 36% of young adults are expected to graduate from higher education at least once before reaching the age of 30 [1]. In Spain, approximately a million and a half students are enrolled in universities [2], representing 26% of the young adult population [3]. Cigarette smoking among college students is a matter of particular concern because their smoking prevalence (34.9%) [4] is higher than that of the noncollege young adults (27.7%) [5]. Several studies characterize college as a period of smoking transitions, including smoking initiation and smoking consolidation [6–8]. Although most college students believe that they are going to stop smoking before 30 years or upon becoming parents [9–10], 30% of them will have difficulty in quitting and will remain addicted for decades [11]. Assisting college students to quit tobacco is an important public health goal. In order to provide effective assistance for smoking cessation, programs must be adapted to student characteristics, such as their belief that tobacco use has positive effects on their health (e.g., stress relief) [12–13]; in addition, the different factors that influence successful quitting among this population must be determined [14–15]. Bully et al. [16] suggest that the intervention strategies must be based on behavioral change theories. Thereby, the researchers and practitioners can understand how and why the smoking cessation programs succeed or fail. In this study, our intervention was based on the Theory of Triadic Influence (TTI). This theory, based on more than 20 years of research, integrates variables and processes from sociological and psychological theories, giving rise to a metatheory that describes and explains the aspects that influence the acquisition of certain behaviors, thus, offering an ecological approach to change [17–18]. Furthermore, the theory has been validated in young smokers [18] and is considered as the most relevant theory to understand both onset and cessation of smoking [19]. The TTI [18–19] suggested that tobacco use is motivated by three sets of factors: personal aspects such as motivation, self-concept, and self-efficacy; an interpersonal–social aspect such as social normative beliefs; and a cultural–environmental aspect such as attitudes toward tobacco. These sets of factors exert an influence on the intentions and decisions related to tobacco [18]. Moreover, previous research has tested that the principal mediators in TTI are the self-efficacy (the ability to avoid smoking in tempting situations) and beliefs (such as the social and emotional expectation of smoking), and these directly influence the intentions to begin or quit smoking [18–20]. The main results of our study showed that a multicomponent intervention based on TTI, tailored to the college student, was effective in increasing smoking cessation among college students [21]. The result of the intervention was very promising; the biochemically validated smoking cessation (according to urine cotinine) incidence was 21.1% in the intervention group compared with 6.6% in the control group. To fully understand how the intervention worked, it is important to further explore its impact on the behavioral change components described above [22]. We hypothesize that the effect of a complex intervention such as tobacco cessation depends on self-efficacy, beliefs, and intention to quit. Firstly, in order to determine whether these factors exerted any influence on smoking cessation incidence, we explore the total effect in quitting explained by them. Secondly, this study focused on evaluating the changes in self-efficacy, beliefs, and intention to stop smoking, after a smoking cessation intervention aimed at college student smokers. RESEARCH DESIGN AND METHODS Research design This was a single-blind, pragmatic randomized controlled trial. In this study, participants were randomly assigned to intervention or control groups using a computer-generated allocation method. More details are described elsewhere [21]. Study population This study was conducted on two different campuses of the University of Navarra, located in two urban cities in Northern Spain. The study aimed to recruit students smoking at the time but was advertised among the whole undergraduate and masters’ students population (n = 8,050) for the academic year 2013–14, as a register of current smokers does not exist. Recruitment An informative session was held in each of the schools where the trial was to take place, informing the dean regarding the purpose and design of the study. The recruitment period was from September 2013 to February 2014. During this period, all undergraduate or masters’ students were sent an e-mail, inviting them to participate in the study. A member of the research team explained the study to students in all classes of each program. In addition, information booths manned by the investigators were set up on each university campus. Furthermore, the study was advertised on the university’s signboard, newspaper, and website. The strategy to increase success in recruiting candidates for the study is described in detail elsewhere [21]. Inclusion and exclusion criteria The inclusion criterion was being an undergraduate or masters’ students, aged 18–24 years, who smoked an average of at least one cigarette a week within the last 6 months [23]. The exclusion criterion was being a former smoker. Intervention and control protocols Intervention group Participants randomized to the intervention group received a multicomponent intervention based on previous recommendations made in the Surgeon General’s report [12] and the online support program for smoking prevention and cessation tailored for young adults established by the US Department of Health and Human Services [24]. In addition, the TTI informed the different strategies of this program. Table 1 shows a summary of the strategies of the multicomponent intervention focused on the three main mediator of TTI (self-efficacy, beliefs, and intention). Table 1 | Summary of the strategies of the multicomponent intervention focusing on the main three mediators of TTI (self-efficacy, beliefs, and intention Strategy Self-efficacy Beliefs Intention Motivational interviewa  Rationale ■  Decisional balance ■ ■ ■  Nicotine dependence ■ ■  Smoking perceived norms ■ ■  Financial cost of smoking ■ ■  Carbon monoxide feedback ■ ■  Personal plan for action and relapse prevention. ■ ■ Online self-help material  Decisions ■  Moods (stress management) ■  Social life ■ ■  Smoking health effects ■  Quitting (plan for action and relapse prevention) ■ ■  Self-monitoring ■ E-mail support  Encouraging decisions ■ ■  Improve motivation to quit ■  Seek help from their closest (social support) ■ Therapy group (12 students)  Social support ■ ■ ■  Motivation to quit ■  Social comparison ■ ■ ■ Strategy Self-efficacy Beliefs Intention Motivational interviewa  Rationale ■  Decisional balance ■ ■ ■  Nicotine dependence ■ ■  Smoking perceived norms ■ ■  Financial cost of smoking ■ ■  Carbon monoxide feedback ■ ■  Personal plan for action and relapse prevention. ■ ■ Online self-help material  Decisions ■  Moods (stress management) ■  Social life ■ ■  Smoking health effects ■  Quitting (plan for action and relapse prevention) ■ ■  Self-monitoring ■ E-mail support  Encouraging decisions ■ ■  Improve motivation to quit ■  Seek help from their closest (social support) ■ Therapy group (12 students)  Social support ■ ■ ■  Motivation to quit ■  Social comparison ■ ■ ■ aThe MI was based in the Herman and Fanhlander [25] method. View Large Table 1 | Summary of the strategies of the multicomponent intervention focusing on the main three mediators of TTI (self-efficacy, beliefs, and intention Strategy Self-efficacy Beliefs Intention Motivational interviewa  Rationale ■  Decisional balance ■ ■ ■  Nicotine dependence ■ ■  Smoking perceived norms ■ ■  Financial cost of smoking ■ ■  Carbon monoxide feedback ■ ■  Personal plan for action and relapse prevention. ■ ■ Online self-help material  Decisions ■  Moods (stress management) ■  Social life ■ ■  Smoking health effects ■  Quitting (plan for action and relapse prevention) ■ ■  Self-monitoring ■ E-mail support  Encouraging decisions ■ ■  Improve motivation to quit ■  Seek help from their closest (social support) ■ Therapy group (12 students)  Social support ■ ■ ■  Motivation to quit ■  Social comparison ■ ■ ■ Strategy Self-efficacy Beliefs Intention Motivational interviewa  Rationale ■  Decisional balance ■ ■ ■  Nicotine dependence ■ ■  Smoking perceived norms ■ ■  Financial cost of smoking ■ ■  Carbon monoxide feedback ■ ■  Personal plan for action and relapse prevention. ■ ■ Online self-help material  Decisions ■  Moods (stress management) ■  Social life ■ ■  Smoking health effects ■  Quitting (plan for action and relapse prevention) ■ ■  Self-monitoring ■ E-mail support  Encouraging decisions ■ ■  Improve motivation to quit ■  Seek help from their closest (social support) ■ Therapy group (12 students)  Social support ■ ■ ■  Motivation to quit ■  Social comparison ■ ■ ■ aThe MI was based in the Herman and Fanhlander [25] method. View Large The first session was a face-to-face 50-min meeting (motivational interviewing [MI]) consistent with the methods of Herman and Fanhlander [25]. During the interview, in order to improve student’s intention to quit, the nurse used reflective listening to stress positive change discussions. She remained nonconfrontational but continued to direct the conversation toward developing discrepancy and resolving ambivalence, influencing students’ beliefs. The nurse sought to reinforce the decision as well as increase students’ self-efficacy to avoid smoking, when elaborating a personal plan for changing smoking behavior for each participant who was prepared to stop smoking. In these sessions, the participants were invited to read the self-help material available on their college Moodle platform. This online self-help material focused on the following: (a) decisions, (b) moods, (c) social life, (d) smoking health effects, and (e) quitting. This material can be found in the online version of Pardavila et al. [21]. The follow-up program was scheduled according to the MI date. It consisted of four contacts: (a) e-mail 15 days before the MI, (b) group therapy 2 months after the MI (60 min), (c) a second follow-up visit 4 months after the MI (20 min), and (d) a final evaluation (15 min), conducted after 6 months [21]. Control group Participants randomized to the control group received brief advice (5–10 min) and a self-help pamphlet. All college students received the same input, which clearly advised quitting and highlighted the benefits of doing so. They also received a self-help pamphlet called “Stop smoking,” written by the Healthy University Project I research team. Before giving brief advice, the nurse assessed smokers’ habits and their willingness to quit. As is usually in this type of studies, there were no follow-up sessions for this group. At 6 months, the evaluation was carried out (15 min) [21]. Strategies to maintain fidelity In order to minimize interventionist effects, the same clinical nurse specialist (CNS) conducted all sessions with both groups. The CNS had 9 years of experience, and she was specially trained in smoking cessation strategies (MI, group therapy, and brief advice). Indeed, her training for the study was intensive, involving around 125 hrs. First, the CNS studied the theory related to the different techniques underlying the variety of strategies of both groups, and she discussed this with a two senior study staff. Second, the CNS was trained by viewing videotapes and seeing how an expert member of the study team carried out the different strategies. During this phase of the training, the CNS discussed these clinical examples with two experts. Finally, the CNS delivered intervention and control strategies with a pilot cohort, receiving feedback from principal investigator, and a senior expert with a view to further standardizing these strategies. In order to increase the adherence to the protocol of both groups and to prevent the CNS from applying some techniques of the intervention group in the control group, an intervention and control manual was developed. The majority of the manual was given over to the data collected protocol and the procedures for the differences strategies. Study procedures One member of the research team performed the randomization. This researcher generated a blocked random number sequence, using EpiInfo version 7.0.9.7, and prepared the sealed opaque sequentially numbered envelopes (1–255) with the corresponding condition written inside. Once the student agreed to participate in the study, the envelope was opened to determine the group to which he/she would be assigned. Students were unaware of the randomization scheme and of the group assignment. On the first visit, the nurse completed a standardized written questionnaire, collecting demographic information and the student’s history of tobacco, including the Fageström Test of Nicotine Dependence (FTND) [26], and the stages of change according to Prochaska’s model [27]. Furthermore, the students completed the smoking-related self-efficacy, belief, and intention questionnaire with its assessment scale [28–29]. Prochaska’s stages of change [27] assess the desire and readiness to quit smoking. In this study, to determine the student’s stage of change, the CNS used two questions: (a) “Did you quit smoking?” The two possible answers were “yes” or “no”. (b) “If you make the decision to stop smoking definitely, when would you consider quitting?” There were four possible answers: “not in the next 6 months,” “yes, in the next 6 months,” “yes, in the next 30 days,” and “I cannot pinpoint the exact time.” According to their responses, students were classified into precontemplation (period in which smokers were not considering quitting smoking [at least not within the next 6 months]), contemplation (period in which smokers were seriously thinking about quitting smoking within the next 6 months), preparation (period when smokers were seriously thinking about quitting smoking within the next month and had also tried to quit smoking during the past year) [21]. The smoking-related self-efficacy, belief, and intention assessment scale was used before and after a smoking cessation intervention. This scale, as TTI hypothesizes, assumes that smoking-related self-efficacy and beliefs have direct effects on intention to continue smoking [30]. It consists of a 17-item questionnaire, classified into 3 dimensions: self-efficacy to avoid smoking with 4 items (e.g., if your friends offer you a cigarette, are you able not to smoke?), smoking-related beliefs (that included the TTI dimensions of beliefs and attitudes) with 10 items (e.g., Do you believe cigarette smoking can help people relax?), and intention to stop smoking with 3 items (e.g., Do you think you will smoke a cigarette at any time during the next year?). The score for each item varies from 1 (definitely yes) to 4 (definitely no) [27–28]. High scores indicate a higher self-efficacy to avoid smoking, a protective smoking-related belief and greater intention to quit. The scale had not been validated into Spanish. Consequently, a process of back translation with two bilingual people was undertaken. Lastly, to decide on the final Spanish version of the scale, both English versions (original and back translated), were compared item-by-item by a panel of experts to evaluate whether they measured the same [31]. The internal consistency for all items of the scale in the sample was 0.68. At 6 months after the initial interview, all students were interviewed again by the same nurse, and the smoking-related self-efficacy, belief, and intention questionnaire were once again completed. Outcome measures The outcome measures in the study were the difference in the mean of self-efficacy to avoid smoking, smoking-related beliefs, and intention to quit smoking between intervention and control subjects; the difference in the mean of those variables pre and postintervention or control program; and the proportion of the total effect of smoking cessation explained by self-efficacy to avoid smoking, smoking-related beliefs, and intention to stop smoking. Statistical analysis Baseline data were reported as mean (standard deviation [SD]) appropriate for continuous variables. Categorical variables were reported as percentages (n, %). The data were analyzed on an intention-to-treat basis, including in the analysis all participants who had an outcome measured regardless of whether they had completed all sessions [32]. The proportion of the total effect of smoking cessation explained by self-efficacy to avoid smoking, smoking-related beliefs, and intention to stop smoking was obtained by the ratio of the unadjusted to the adjusted by self-efficacy, beliefs, or intention relative risk [33]. We used bootstrap to calculate the 95% confidence interval (CI) for the proportion of total effect of each mediator. Changes within groups were analyses with paired t-tests; differences between groups were assessed with analysis of covariance (ANCOVA). To obtain unbiased results, these analyses were adjusted for all baseline variables and site. These analyses were also made with the students who did not quit smoking. The analyses were performed using Stata version 11.1 (StataCorp, College Station, TX, USA). Statistical significance was set at 5% (P-value < 0.05, based on two-tailed tests). Ethical considerations Ethical approval was sought and granted by the University of Navarra Research Ethics Committee (reference number = 055/2013). Informed consent was obtained from all the students participating in the trial. RESULTS Participants The flow chart for the evaluation of study participants is shown in Fig. 1. In total, 8,050 students, from 14 schools and 46 degree programs, were invited to take part in the research. Of those invited, 359 students agreed to participate; 255 of those who agreed to participate (age range = 18–24 years) met the inclusion criteria. Reasons for ineligibility were as follows: did not smoke (n = 2), refused to participate (n = 4), and unsuccessful attempts to contact (n = 98). The 255 participants who met the inclusion criteria were randomized to the intervention group (n = 133) or the control group (n = 122). In the intervention group, 83 (62.4%) completed all sessions; 24 (18.1%) attended o three sessions; and 26 (19.6%) only participated in the MI. Furthermore, 108 (81.2%) participants from the intervention group and 101 (82.9%) from the control group attended the evaluation sessions. Of the 255 subjects enrolled in the trial, 225 (88.2%) completed the 6 month follow-up. The remaining 30 were lost to follow-up, 19 in the intervention group and 11 in the control group. Fig 1 View largeDownload slide | Flow of participants throughout the trial. Fig 1 View largeDownload slide | Flow of participants throughout the trial. Baseline characteristics Table 2 shows the participants’ characteristics by group. Table 2 | Baseline characteristics of participants by groups Intervention (N = 133) Control (N = 122) Demographic characteristics Age, mean (SD) 20.1 (1.7) 20.5 (1.7) Sex, n (%)  Male 51 (38.4) 46 (37.7)  Female 82 (61.7) 76 (62.3) Area of degree, n (%)  Health sciences 50 (37.6) 61 (50.0)  Social sciences 64 (48.1) 44 (36.1)  Technological sciences 19 (14.3) 17 (13.9) Residence, n (%)  With parent 26 (19.6) 41 (33.6)  Residence hall 49 (38.8) 33 (27.1)  House/apartment 58 (43.6) 48 (39.3) Smoking-related characteristics Daily cigarettes, mean (SD) 9.5 (6.1) 8.7 (5.2) Years of smoking, mean (SD) 5.6 (2.3) 5.7 (2.4) FTND, mean (SD) 2.0 (1.7) 2.4 (2.0) Received previous advice, n (%)  No 121 (91.0) 101 (82.8)  Yes 12 (9.0) 21 (17.2) Quit attempts in past year, n (%)  No 33 (24.8) 34 (27.9)  Yes 100 (75.2) 88 (72.1) Stages of change (Prochaska’s model), n (%)  Precontemplation 41 (30.8) 47 (37.7)  Contemplation 53 (39.9) 36 (29.5)  Preparation 39 (29.3) 40 (32.8) Self-efficacy, mean (SD) 8.9 (3.1) 8.9 (3.7) Belief, mean (SD) 28.3 (4.5) 28.4 (4.3) Intention related to smoking, mean (SD) 6.1 (1.6) 6.4 (1.8) Intervention (N = 133) Control (N = 122) Demographic characteristics Age, mean (SD) 20.1 (1.7) 20.5 (1.7) Sex, n (%)  Male 51 (38.4) 46 (37.7)  Female 82 (61.7) 76 (62.3) Area of degree, n (%)  Health sciences 50 (37.6) 61 (50.0)  Social sciences 64 (48.1) 44 (36.1)  Technological sciences 19 (14.3) 17 (13.9) Residence, n (%)  With parent 26 (19.6) 41 (33.6)  Residence hall 49 (38.8) 33 (27.1)  House/apartment 58 (43.6) 48 (39.3) Smoking-related characteristics Daily cigarettes, mean (SD) 9.5 (6.1) 8.7 (5.2) Years of smoking, mean (SD) 5.6 (2.3) 5.7 (2.4) FTND, mean (SD) 2.0 (1.7) 2.4 (2.0) Received previous advice, n (%)  No 121 (91.0) 101 (82.8)  Yes 12 (9.0) 21 (17.2) Quit attempts in past year, n (%)  No 33 (24.8) 34 (27.9)  Yes 100 (75.2) 88 (72.1) Stages of change (Prochaska’s model), n (%)  Precontemplation 41 (30.8) 47 (37.7)  Contemplation 53 (39.9) 36 (29.5)  Preparation 39 (29.3) 40 (32.8) Self-efficacy, mean (SD) 8.9 (3.1) 8.9 (3.7) Belief, mean (SD) 28.3 (4.5) 28.4 (4.3) Intention related to smoking, mean (SD) 6.1 (1.6) 6.4 (1.8) FTND Fagerström Test for Nicotine Dependence; SD Standard Deviation. View Large Table 2 | Baseline characteristics of participants by groups Intervention (N = 133) Control (N = 122) Demographic characteristics Age, mean (SD) 20.1 (1.7) 20.5 (1.7) Sex, n (%)  Male 51 (38.4) 46 (37.7)  Female 82 (61.7) 76 (62.3) Area of degree, n (%)  Health sciences 50 (37.6) 61 (50.0)  Social sciences 64 (48.1) 44 (36.1)  Technological sciences 19 (14.3) 17 (13.9) Residence, n (%)  With parent 26 (19.6) 41 (33.6)  Residence hall 49 (38.8) 33 (27.1)  House/apartment 58 (43.6) 48 (39.3) Smoking-related characteristics Daily cigarettes, mean (SD) 9.5 (6.1) 8.7 (5.2) Years of smoking, mean (SD) 5.6 (2.3) 5.7 (2.4) FTND, mean (SD) 2.0 (1.7) 2.4 (2.0) Received previous advice, n (%)  No 121 (91.0) 101 (82.8)  Yes 12 (9.0) 21 (17.2) Quit attempts in past year, n (%)  No 33 (24.8) 34 (27.9)  Yes 100 (75.2) 88 (72.1) Stages of change (Prochaska’s model), n (%)  Precontemplation 41 (30.8) 47 (37.7)  Contemplation 53 (39.9) 36 (29.5)  Preparation 39 (29.3) 40 (32.8) Self-efficacy, mean (SD) 8.9 (3.1) 8.9 (3.7) Belief, mean (SD) 28.3 (4.5) 28.4 (4.3) Intention related to smoking, mean (SD) 6.1 (1.6) 6.4 (1.8) Intervention (N = 133) Control (N = 122) Demographic characteristics Age, mean (SD) 20.1 (1.7) 20.5 (1.7) Sex, n (%)  Male 51 (38.4) 46 (37.7)  Female 82 (61.7) 76 (62.3) Area of degree, n (%)  Health sciences 50 (37.6) 61 (50.0)  Social sciences 64 (48.1) 44 (36.1)  Technological sciences 19 (14.3) 17 (13.9) Residence, n (%)  With parent 26 (19.6) 41 (33.6)  Residence hall 49 (38.8) 33 (27.1)  House/apartment 58 (43.6) 48 (39.3) Smoking-related characteristics Daily cigarettes, mean (SD) 9.5 (6.1) 8.7 (5.2) Years of smoking, mean (SD) 5.6 (2.3) 5.7 (2.4) FTND, mean (SD) 2.0 (1.7) 2.4 (2.0) Received previous advice, n (%)  No 121 (91.0) 101 (82.8)  Yes 12 (9.0) 21 (17.2) Quit attempts in past year, n (%)  No 33 (24.8) 34 (27.9)  Yes 100 (75.2) 88 (72.1) Stages of change (Prochaska’s model), n (%)  Precontemplation 41 (30.8) 47 (37.7)  Contemplation 53 (39.9) 36 (29.5)  Preparation 39 (29.3) 40 (32.8) Self-efficacy, mean (SD) 8.9 (3.1) 8.9 (3.7) Belief, mean (SD) 28.3 (4.5) 28.4 (4.3) Intention related to smoking, mean (SD) 6.1 (1.6) 6.4 (1.8) FTND Fagerström Test for Nicotine Dependence; SD Standard Deviation. View Large Self-efficacy to avoid smoking, smoking-related beliefs, and intention to stop smoking outcomes The total effect of the intervention on smoking cessation versus the control group was equal to a relative risk of 3.21% (95% CI = 1.52 to 6.78; p = .002). After adjusting for the difference of self-efficacy to avoid smoking and intention to stop smoking, the relative risk decreased to 2.69% (95% CI = 1.33 to 5.44; p = .006) and 2.41% (95% CI = 1.22 to 4.75; p = .011), respectively. Nevertheless, the difference in smoking-related beliefs increased to 3.33% (95% CI = 1.58 to 6.98; p = 0.001). Thus, the percentage of excess risk of the intervention group on smoking cessation explained by the difference of intention to stop smoking scores was 36.1% (95% CI = 4.4 to 72.08). However, the percentage of excess risk of the intervention group on smoking cessation explained by the differences of smoking-related belief and the difference of self-efficacy to avoid smoking scores was −5.30% (95% CI = −9.10 to 21.00), and 23.5% (95% CI = −4.7 to 57.6), respectively. These percentages of excess risk were not statistically significant. Table 3 shows the differences in the mean of self-efficacy, belief, and intention related to smoking scores before and after the smoking cessation program. Furthermore, Table 4 shows the comparisons between self-efficacy, belief, and intention related to smoking at 6 month follow-up. Table 3 | Differences in the mean of self-efficacy, belief, and intention related to smoking scores before and after smoking cessation program Intervention p value* Control p value* (N = 114) (N = 111) Self-efficacy, mean (95% CI) 1.85 (1.22 to 2.49) <.001 0.83 (0.16 to 1.49) .015 Belief, mean (95% CI) 1.89 (0.96 to 2.81) <.001 1.82 (1.07 to 2.57) <.001 Intentions, mean (95% CI) 1.32 (0.90 to 1.75) <.001 0.46 (0.08 to 0.84) .018 Intervention p value* Control p value* (N = 114) (N = 111) Self-efficacy, mean (95% CI) 1.85 (1.22 to 2.49) <.001 0.83 (0.16 to 1.49) .015 Belief, mean (95% CI) 1.89 (0.96 to 2.81) <.001 1.82 (1.07 to 2.57) <.001 Intentions, mean (95% CI) 1.32 (0.90 to 1.75) <.001 0.46 (0.08 to 0.84) .018 *p-value from paired Student’s t-test. View Large Table 3 | Differences in the mean of self-efficacy, belief, and intention related to smoking scores before and after smoking cessation program Intervention p value* Control p value* (N = 114) (N = 111) Self-efficacy, mean (95% CI) 1.85 (1.22 to 2.49) <.001 0.83 (0.16 to 1.49) .015 Belief, mean (95% CI) 1.89 (0.96 to 2.81) <.001 1.82 (1.07 to 2.57) <.001 Intentions, mean (95% CI) 1.32 (0.90 to 1.75) <.001 0.46 (0.08 to 0.84) .018 Intervention p value* Control p value* (N = 114) (N = 111) Self-efficacy, mean (95% CI) 1.85 (1.22 to 2.49) <.001 0.83 (0.16 to 1.49) .015 Belief, mean (95% CI) 1.89 (0.96 to 2.81) <.001 1.82 (1.07 to 2.57) <.001 Intentions, mean (95% CI) 1.32 (0.90 to 1.75) <.001 0.46 (0.08 to 0.84) .018 *p-value from paired Student’s t-test. View Large Table 4 | Comparisons self-efficacy, belief, and intention related to smoking at 6 month follow-up. Unadjusted difference p value* Adjusted difference p value† Self-efficacy, mean (95% CI) 1.02 (0.11 to 1.93) .029 1.09 (0.13 to 2.06) .027 Belief, mean (95% CI) 0.07 (−1.12 to 1.26) .913 0.25 (−1.02 to 1.51) .702 Intentions, mean (95% CI) 0.87 (0.30 to 1.43) .003 0.78 (0.20 to 1.37) .009 Unadjusted difference p value* Adjusted difference p value† Self-efficacy, mean (95% CI) 1.02 (0.11 to 1.93) .029 1.09 (0.13 to 2.06) .027 Belief, mean (95% CI) 0.07 (−1.12 to 1.26) .913 0.25 (−1.02 to 1.51) .702 Intentions, mean (95% CI) 0.87 (0.30 to 1.43) .003 0.78 (0.20 to 1.37) .009 Note: Estimates and associated statistic refer to differences of mean. *ANCOVA test. †ANCOVA test adjusted for all baselines variables and site (Pamplona or San Sebastian). View Large Table 4 | Comparisons self-efficacy, belief, and intention related to smoking at 6 month follow-up. Unadjusted difference p value* Adjusted difference p value† Self-efficacy, mean (95% CI) 1.02 (0.11 to 1.93) .029 1.09 (0.13 to 2.06) .027 Belief, mean (95% CI) 0.07 (−1.12 to 1.26) .913 0.25 (−1.02 to 1.51) .702 Intentions, mean (95% CI) 0.87 (0.30 to 1.43) .003 0.78 (0.20 to 1.37) .009 Unadjusted difference p value* Adjusted difference p value† Self-efficacy, mean (95% CI) 1.02 (0.11 to 1.93) .029 1.09 (0.13 to 2.06) .027 Belief, mean (95% CI) 0.07 (−1.12 to 1.26) .913 0.25 (−1.02 to 1.51) .702 Intentions, mean (95% CI) 0.87 (0.30 to 1.43) .003 0.78 (0.20 to 1.37) .009 Note: Estimates and associated statistic refer to differences of mean. *ANCOVA test. †ANCOVA test adjusted for all baselines variables and site (Pamplona or San Sebastian). View Large Table 5 shows the differences in the mean of self-efficacy, belief, and intention related to smoking scores before and after the smoking cessation program among the 189 students who were not identified as quitters. Analysis of the differences in the mean scores between groups revealed a significant main effect only for intention to stop smoking in favor of the intervention group (mean [95% CI], p score: unadjusted difference 0.67 [0.14 to 1.20], 0.014; adjusted difference: 0.60 [0.04 to 1.15], 0.037). Table 5 | Differences in the mean of self-efficacy, belief, and intention related to smoking scores before and after smoking cessation program in no quitter. Intervention p value* Control p value* (N = 86) (N = 103) Self-efficacy, mean (95% CI) 1.10 (0.50 to 1.70) .004 0.40 (−0.22 to 1.02) .207 Belief, mean (95% CI) 1.30 (0.24 to 2.36) .017 1.78 (0.98 to 2.57) <.001 Intentions, mean (95% CI) 0.81 (0.38 to 1.24) <.001 0.15 (−0.18 to 0.47) .380 Intervention p value* Control p value* (N = 86) (N = 103) Self-efficacy, mean (95% CI) 1.10 (0.50 to 1.70) .004 0.40 (−0.22 to 1.02) .207 Belief, mean (95% CI) 1.30 (0.24 to 2.36) .017 1.78 (0.98 to 2.57) <.001 Intentions, mean (95% CI) 0.81 (0.38 to 1.24) <.001 0.15 (−0.18 to 0.47) .380 *p-value from paired Student’s t-test. View Large Table 5 | Differences in the mean of self-efficacy, belief, and intention related to smoking scores before and after smoking cessation program in no quitter. Intervention p value* Control p value* (N = 86) (N = 103) Self-efficacy, mean (95% CI) 1.10 (0.50 to 1.70) .004 0.40 (−0.22 to 1.02) .207 Belief, mean (95% CI) 1.30 (0.24 to 2.36) .017 1.78 (0.98 to 2.57) <.001 Intentions, mean (95% CI) 0.81 (0.38 to 1.24) <.001 0.15 (−0.18 to 0.47) .380 Intervention p value* Control p value* (N = 86) (N = 103) Self-efficacy, mean (95% CI) 1.10 (0.50 to 1.70) .004 0.40 (−0.22 to 1.02) .207 Belief, mean (95% CI) 1.30 (0.24 to 2.36) .017 1.78 (0.98 to 2.57) <.001 Intentions, mean (95% CI) 0.81 (0.38 to 1.24) <.001 0.15 (−0.18 to 0.47) .380 *p-value from paired Student’s t-test. View Large DISCUSSION This study suggests that intention to quit smoking is partial moderator explaining 36.2% of the total effects in smoking cessation incidence. Furthermore, the result of this research shows that a structured multicomponent program based on TTI and conducted at a university can achieve a significant change in self-efficacy to avoid smoking and intentions related to stop smoking of the college students compared with control ones. Studies into multicomponent interventions associate an increased self-efficacy to avoid smoking with greater success in quitting [34–35]. However, previous research has not identified which component of the intervention has been able to influence the increase in self-efficacy in college student smokers. We suggest that the principal component that has an impact on the increase in self-efficacy is MI. More specifically, MI strategies as feedback emphasize personal responsibility, and an empathetic counseling style increases self-efficacy [36]. This statement is consistent with the findings from our research and the results of previous studies that used MI for smoking cessation in this target population [37], in diabetes patients [38], and in smoker adolescents with psychiatric disorders [39]. Another component of the intervention that could increase the perceived self-efficacy to avoid smoking is group therapy. This component encouraged participants to initiate discussion of their motivations, concerns, and difficulties during the smoking process. These discussions helped the students to realize that their experience during the smoking cessation was similar to that of other colleagues. In addition, these discussions help them identify some strategies that other students performed (e.g., if they had an urge to smoking, calling a friend or playing a phone game). This could increase their perceived self-efficacy as Washington [40] shows in study conducted with chemically dependent women. The control group also increases their self-efficacy to avoid smoking. These results are similar to the Brown et al. [39] research in adolescent with psychiatric disorders, but contrary to the Bolger et al. [37] study with college students. The principal differences of Brown et al. [39] and our study with Bolger et al. [37] research were that the participants in the control group received 5–10 min of advice to quit smoking and a self-help pamphlet, whereas Bolger et al. [37] provided participants with their expired CO level (score) as a motivational strategy, and information and pamphlet about smoking-related risks. Although self-efficacy to avoid smoking increased significantly, the percentage of excess risk was not statistically significant, suggesting that the change of self-efficacy might reflect rather than mediate in a successful quitting. These results are similar to the meta-analysis on self-efficacy and smoking cessation that found that controlling for smoking status substantially reduced the relationship between self- efficacy and smoking [41]. On the other hand, both groups experienced an increase in beliefs, but this did not seem to explain the difference in the cessation outcomes. In contrast, the association between belief and increase in the effect of smoking cessation is not significant. This could be explained by the fact that in the control group, both the brief advice and the self-help material were tailored to smoking-related beliefs, while in the intervention group, only the online self-help program focused on this dimension. Our clinical experience in developing the research suggests to us that many of the college students have a low risk perception of smoking. The students did not perceive tobacco as harmful for their health and their life expectancy. Moreover, they saw the youthfulness as a time for smoking without risk because they will stop smoking before their graduation or before becoming parents. They perceived a positive benefit in consuming tobacco such as gaining more friends or becoming more relaxed. These considerations are similar to those reported in previous studies [9–10,12,42–43]. Another reason that could explain the effectiveness of this intervention is that the intention to stop smoking also increased. Based on this result, and on our clinical experience, the MI appeared to be more effective for changing the intentions to stop smoking than brief advice. This could be explained because MI is a client-centered, directive method for enhancing intrinsic motivation to change by exploring and resolving ambivalence [36,44–45]. In addition, the basis of MI is that people are autonomous; therefore, the healthcare clinicians adopt a collaborator rather than a paternalistic role [44]. In our previous study, the relationship between both (participants and nurse) was based on confidence, freedom, and not on judgment. Furthermore, the nurse was trained in MI procedures and followed a specific protocol tailored to the youth characteristics [21]. In addition, MI is based on the theoretical stages of change, which help the health professional to determine how ready a student is prepared to quit [46] and to improve the progress through these stages [47]. As a result, the effect of MI on intention to quit is in the progress through the stages of the change model. The main findings of our randomized controlled trial revealed that individuals who received the intervention moved further along the active stages of change [21]. These results are consistent with previous studies with college students who used MI as a strategy [25,48]. Even though the use of the stages of change model in smoking cessation is questioned [49–50], the results of this study suggest that there is a relationship between the intention to quit and the progression in the stages of change. As this study results show the increases on the intention to stop smoking may not necessarily correspond with behavior change, however, some studies showed that the students who moved to more active stages of change obtained more positive results in terms of smoking cessation [21,27,35,51]. Furthermore, the mean in all mediators’ outcomes was increased in the control group at 6 month follow-up; these increases were statistically significant. These results indicated the potential benefit of brief advice given by a health professional. The challenge as to whether or not this brief advice is beneficial depends on the possibility of being able to offer the said advice to college students on a routine basis [52]. Five main limitations in this study need to be acknowledged. First, the finding provided by this research is only applicable in college students. Second, the study was conducted on a single university and there may have been some contamination between the groups despite the fact that they made a written commitment to refrain from speaking about the study until it was finished. The difference in self-efficacy to avoid smoking and intention to stop smoking between groups suggests that if this bias had occurred, the rate was low. Third, nearly, 11.8% of the participants in the study were lost during the follow-up, and 37.6% of the subjects randomized to the intervention group did not receive the complete protocol. However, a common drawback of studies, and in educational programs conducted in community-based clinical settings, is the low compliance of subjects with the intervention protocol. In our study, this was related to time issues and loss of interest in participating in the research trial. Nevertheless, the rate of lost subjects is lower than in other studies with this population. Our loss during the follow-up was less than 20%, which means there was no significant risk of bias [53]. This could be explained because one member of the research team contacted participants by phone and e-mail for the final evaluation. Fourth, we did not record the different sessions of the intervention group, and thus, it is impossible to verify the fidelity. In contrast, we applied preplanned strategies that were used to maintain the fidelity of the intervention. Specifically, in the design, CNS training, and the intervention delivery. Fifth, the scale for assessing smoking-related self-efficacy, belief, and intention was not validated in Spanish at the time of the study. However, a rigorous process of back translation and comparison of both English versions by experts was developed in order to obtain a final Spanish version [54]. Internal consistency for a Spanish version of the Scale was questionable (α = 0.68). Nevertheless, alpha Cronbach of 0.60 is considered acceptable to exploratory studies [54]. Furthermore, the English version of the scale had been validated to assess young smoker’s ability to avoid smoking in emotional or social situations, to measure their associated belief toward smoking, and to evaluate their intention to stop smoking [28]. Based on this, we considered this scale to be adequate for this study. This study has theoretical, methodological, and practical strengths. One of the primary strengths of this study was the evaluation of the self-efficacy, beliefs, and intention related to quitting smoking as potentials mediators. These results explained how and why college students stop smoking. Furthermore, the conceptual model of the scale for assessing smoking-related self-efficacy, belief, and intention is based on the fact that a certain type of behavior is more likely to occur if three conditions are met: one has the necessary skills to perform said behavior, there are no environmental constraints against performing said behavior, and if a strong intention to perform the behavior exists [28]. These assumptions are similar to TTI principles [18]. More specifically, this theory takes into account the fact that the main pathways of behavioral change are self-efficacy, belief, attitudes toward behavior and decision or intention. Finally, this study could help clinicians understand the process of change and, based on this design, be able to improve intervention or replicate this intervention in other contexts. In conclusion, a multicomponent intervention based on the TTI, tailored to college students, positively increased the self-efficacy to avoid smoking and the intention to quit, suggesting intention as potential mediator of quitting. Furthermore, the finding of this study provides strategies for improving the smoking cessation process in college students. In addition, the results of this research suggest that MI should be included in smoking cessation interventions. Nevertheless, further research is recommended to test the changes in self-efficacy to avoid smoking, belief related with tobacco use, and intention to quit with the addiction across a variety of population groups. This study was funded by the Chair of María Egea, University of Navarra (Spain). Compliance with Ethical Standards Conflict of Interest: All authors declare that they have no conflict of interest Authors’ Contributions: MPB and NCA contributed in the conception and design of this study. MPB and SPG organizated the database. MPB performed the statistical analysis. MPB wrote the manuscript with the support from ACA, MD, MPG, SPG, and NCA. All authors discussed the results, contributed to the manuscript revision, and approved the final version of the manuscript. Primary Data: We ensure that the content of this article is original and has not previously been published or has not been sent simultaneously to another journal. The authors of the paper have full control of all primary data and that they agree to allow the journal to review their data if requested. Moreover, all authors are in agreement with the content of this paper and its publication, and with ceding copyright in accordance with legal requirements if it is published. Ethical Approval: The Research Ethics Committee of the University of Navarra, Pamplona (Spain) approved the research. [Reference number 055/2013]. Informed Consent: Informed consent was obtained from all the participants of the trial. References 1. OECD . Education at a Glance: OECD Indicators . Paris : OECD Publishing ; 2016 . 2. M. de Educación, Cultura y Deporte . Datos y Cifras del Sistema Universitario Español. Curso 2015–2016 . 2016 ; Available at https://sede.educacion.gob.es/publiventa/d/21461/19/1. Accessibility verified August 3, 2017 . 3. M. de Educación, Cultura y Deporte . Panorama de la educación. Indicadores de la OCDE 2016 . 2016 ; Available at http://www.mecd.gob.es/dctm/inee/eag/panorama2016okkk.pdf?documentId=0901e72b82236f2b. Accessibility verified August 3, 2017 . 4. Varela-Mato V , Cancela JM , Ayan C , Martín V , Molina A . Lifestyle and health among Spanish university students: Differences by gender and academic discipline . Int j Environ Res Public Health . 2012 ; 9 ( 8 ): 2728 – 2741 . Google Scholar CrossRef Search ADS PubMed 5. Observatorio Español sobre Drogas y Toxicomanías (OEDT) . Encuesta domiciliaria sobre el consumo de alcohol y drogas en España . 2015 ; Available at http://www.pnsd.msssi.gob.es/profesionales/sistemasInformacion/sistemaInformacion/encuestas_EDADES.htm. Accessibility verified August 3, 2017 . 6. Bernat DH , Klein EG , Forster JL . Smoking initiation during young adulthood: A longitudinal study of a population-based cohort . j Adolesc Health . 2012 ; 51 ( 5 ): 497 – 502 . Google Scholar CrossRef Search ADS PubMed 7. Freedman KS , Nelson NM , Feldman LL . Smoking initiation among young adults in the United States and Canada, 1998–2010: A systematic review . Prev Chronic Dis . 2012 ; 9 : E05 . Google Scholar PubMed 8. Brown J . A review of the evidence on technology-based interventions for the treatment of tobacco dependence in college health . Worldviews Evid Based Nurs . 2013 ; 10 ( 3 ): 150 – 162 . Google Scholar CrossRef Search ADS PubMed 9. Aryal UR , Bhatta DN . Perceived benefits and health risks of cigarette smoking among young adults: Insights from a cross-sectional study . Tob Induc Dis . 2015 ; 13 ( 1 ): 22 . Google Scholar CrossRef Search ADS PubMed 10. Hoeppner BB , Bidwell LC , Colby SM , Barnett NP . Smoking patterns and their relationship to drinking among first-year college students . Nicotine Tob Res . 2014 ; 16 ( 6 ): 743 – 752 . Google Scholar CrossRef Search ADS PubMed 11. Centers for Disease Control and Prevention . Best Practices for Comprehensive Tobacco Control Programs-2014 . Atlanta, GA : U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health ; 2014 12. US Department of Health and Human Services . Preventing Tobacco Use among Youth and Young Adults: A Report of the Surgeon General . Atlanta, GA : US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health ; 2012 . 13. Ordás B , Fernández D , Ordóñez C et al. Changes in use, knowledge, beliefs and attitudes relating to tobacco among nursing and physiotherapy students: A 10-year analysis . j Adv Nurs . 2015 ; 71 ( 10 ): 2326 – 2337 . Google Scholar CrossRef Search ADS PubMed 14. Lee HS , Catley D , Harris KJ . Improving understanding of the quitting process: Psychological predictors of quit attempts versus smoking cessation maintenance among college students . Subst Use Misuse . 2014 ; 49 ( 10 ): 1332 – 1339 . Google Scholar CrossRef Search ADS PubMed 15. Michie S , Brown J , Geraghty AW et al. Development of stopadvisor: A theory-based interactive internet-based smoking cessation intervention . Transl Behav Med . 2012 ; 2 ( 3 ): 263 – 275 . Google Scholar CrossRef Search ADS PubMed 16. Bully P , Sánchez Á , Zabaleta-del-Olmo E , Pombo H , Grandes G . Evidence from interventions based on theoretical models for lifestyle modification (physical activity, diet, alcohol and tobacco use) in primary care settings: A systematic review . Prev Med . 2015 ; 76 ( Suppl ): S76 – S93 . Google Scholar CrossRef Search ADS PubMed 17. Flay BR . Understanding environmental, situational and intrapersonal risk and protective factors for youth tobacco use: The Theory of Triadic Influence . Nicotine Tob Res . 1999 ; 1 ( Suppl 2 ): S111 – S114 . Google Scholar CrossRef Search ADS PubMed 18. Flay BR , Snyder FJ , Petraitis J . The theory of triadic influence . In: DiClemente RJ , Crosby RA , Kegler MC , eds. Emerging Theories in Health Promotion Practice and Research , 2nd ed . San Francisco, CA : Jossey-Bass ; 2009 : 451 – 510 . Google Scholar CrossRef Search ADS 19. Holman LR , Bricker JB , Comstock BA . Psychological predictors of male smokeless tobacco use initiation and cessation: A 16-year longitudinal study . Addiction . 2013 ; 108 ( 7 ): 1327 – 1335 . Google Scholar CrossRef Search ADS PubMed 20. Sterling K , Ford K , Park H , Diamond P , McAlister A . Assessing the invariance of smoking-related self-efficacy, beliefs, and intention among high school current smokers . Subst Use Misuse . 2013 ; 48 ( 7 ): 477 – 483 . Google Scholar CrossRef Search ADS PubMed 21. Pardavila-Belio MI , García-Vivar C , Pimenta AM , Canga-Armayor A , Pueyo-Garrigues S , Canga-Armayor N . Intervention study for smoking cessation in Spanish college students: Pragmatic randomized controlled trial . Addiction . 2015 ; 110 ( 10 ): 1676 – 1683 . Google Scholar CrossRef Search ADS PubMed 22. Martinez C , Fernandez E . Commentary on Pardavila-Belio et al. (2015): Reporting on randomized control trials or giving details on how the cake was made–the list of ingredients and the implementation process . Addiction . 2015 ; 110 ( 10 ): 1684 – 1685 . Google Scholar CrossRef Search ADS PubMed 23. Stanton A , Grimshaw GM . Tobacco cessation interventions for young people . Cochrane Database Syst Rev . 2013 ;( 8 ): CD003289 . 24. US Department of Health and Human Services . Smokefree Teen , 2012 ; Available at https://teen.smokefree.gov/. Accessibility verified August 4, 2017 . 25. Herman KC , Fahnlander BA . Motivational intervention to reduce cigarette smoking among college students: Overview and exploratory investigation . J College Counselling . 2003 ; 6 : 46 – 55 . Google Scholar CrossRef Search ADS 26. Heatherton TF , Kozlowski LT , Frecker RC , Fagerström KO . The Fagerström Test for Nicotine Dependence: A revision of the Fagerström Tolerance Questionnaire . Br j Addict . 1991 ; 86 ( 9 ): 1119 – 1127 . Google Scholar CrossRef Search ADS PubMed 27. Prochaska JO , DiClemente CC . Stages and processes of self-change of smoking: Toward an integrative model of change . j Consult Clin Psychol . 1983 ; 51 ( 3 ): 390 – 395 . Google Scholar CrossRef Search ADS PubMed 28. Ford KH , Diamond PM , Kelder SH , Sterling KL , McAlister AL . Validation of scales measuring attitudes, self-efficacy, and intention related to smoking among middle school students . Psychol Addict Behav . 2009 ; 23 ( 2 ): 271 – 278 . Google Scholar CrossRef Search ADS PubMed 29. Sterling KL , Ford KH , Park H , McAlister AL . Scales of smoking-related self-efficacy, beliefs, and intention: Assessing measurement invariance among intermittent and daily high school smokers . Am j Health Promot . 2014 ; 28 ( 5 ): 310 – 315 . Google Scholar CrossRef Search ADS PubMed 30. Landrum Sterling K , Diamond PM , Dolan Mullen P , Pallonen U , Ford KH , McAlister AL . Smoking-related self-efficacy, beliefs, and intention: Assessing factorial validity and structural relationships in 9th-12th grade current smokers . Addict Behav . 2007 ; 32 ( 9 ): 1863 – 1876 . Google Scholar CrossRef Search ADS PubMed 31. Pegalajar J , Acosta A , Castillo M , Higueras L , Padilla JL . Spanish adaptation of the memory characteristics questionnaire (MCQ) . Span j Psychol . 2015 ; 18 : e101 . Google Scholar CrossRef Search ADS PubMed 32. Elkins MR , Moseley AM . Intention-to-treat analysis . J Physiother . 2015 ; 61 ( 3 ): 165 – 167 . Google Scholar CrossRef Search ADS PubMed 33. Kaufman JS , Maclehose RF , Kaufman S . A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation . Epidemiol Perspect Innov . 2004 ; 1 ( 1 ): 4 . Google Scholar CrossRef Search ADS PubMed 34. Cupertino AP , Berg C , Gajewski B et al. Change in self-efficacy, autonomous and controlled motivation predicting smoking . j Health Psychol . 2012 ; 17 ( 5 ): 640 – 652 . Google Scholar CrossRef Search ADS PubMed 35. Mujika A , Forbes A , Canga N et al. Motivational interviewing as a smoking cessation strategy with nurses: An exploratory randomised controlled trial . Int j Nurs Stud . 2014 ; 51 ( 8 ): 1074 – 1082 . Google Scholar CrossRef Search ADS PubMed 36. Miller WR , Rollnick S. La entrevista Motivacional [Motivational Interviewing] . Barcelona : Ediciones Paidós Ibérica ; 2008 . 37. Bolger K , Carter K , Curtin L , Martz DM , Gagnon SG , Michael KD . Motivational interviewing for smoking cessation among college students . J College Stud Psychother . 2010 ; 24 ( 2 ): 116 – 129 . Google Scholar CrossRef Search ADS 38. Chen SM , Creedy D , Lin HS , Wollin J . Effects of motivational interviewing intervention on self-management, psychological and glycemic outcomes in type 2 diabetes: A randomized controlled trial . Int j Nurs Stud . 2012 ; 49 ( 6 ): 637 – 644 . Google Scholar CrossRef Search ADS PubMed 39. Brown RA , Ramsey SE , Strong DR et al. Effects of motivational interviewing on smoking cessation in adolescents with psychiatric disorders . Tob Control . 2003 ; 12 ( Suppl 4 ): IV3 – 10 . Google Scholar CrossRef Search ADS PubMed 40. Washington OG . Effects of group therapy on chemically dependent women’s self-efficacy . j Nurs Scholarsh . 2000 ; 32 ( 4 ): 347 – 352 . Google Scholar CrossRef Search ADS PubMed 41. Gwaltney CJ , Metrik J , Kahler CW , Shiffman S . Self-efficacy and smoking cessation: A meta-analysis . Psychol Addict Behav . 2009 ; 23 ( 1 ): 56 – 66 . Google Scholar CrossRef Search ADS PubMed 42. Murphy-Hoefer R , Griffith R , Pederson LL , Crossett L , Iyer SR , Hiller MD . A review of interventions to reduce tobacco use in colleges and universities . Am j Prev Med . 2005 ; 28 ( 2 ): 188 – 200 . Google Scholar CrossRef Search ADS PubMed 43. Tavolacci MP , Ladner J , Grigioni S , Richard L , Villet H , Dechelotte P . Prevalence and association of perceived stress, substance use and behavioral addictions: A cross-sectional study among university students in France, 2009-2011 . bmc Public Health . 2013 ; 13 : 724 . Google Scholar CrossRef Search ADS PubMed 44. Richardson L . Motivational interviewing: Helping patients move toward change . j Christ Nurs . 2012 ; 29 ( 1 ): 18 – 24 . Google Scholar CrossRef Search ADS PubMed 45. Apodaca TR , Longabaugh R . Mechanisms of change in motivational interviewing: A review and preliminary evaluation of the evidence . Addiction . 2009 ; 104 ( 5 ): 705 – 715 . Google Scholar CrossRef Search ADS PubMed 46. Miller NH . Motivational interviewing as a prelude to coaching in healthcare settings . j Cardiovasc Nurs . 2010 ; 25 ( 3 ): 247 – 251 . Google Scholar CrossRef Search ADS PubMed 47. Borland R , Balmford J , Benda P . Population-level effects of automated smoking cessation help programs: A randomized controlled trial . Addiction . 2013 ; 108 ( 3 ): 618 – 628 . Google Scholar CrossRef Search ADS PubMed 48. Prokhorov AV , Yost T , Mullin-Jones M et al. “Look at your health”: Outcomes associated with a computer-assisted smoking cessation counseling intervention for community college students . Addict Behav . 2008 ; 33 ( 6 ): 757 – 771 . Google Scholar CrossRef Search ADS PubMed 49. Riemsma RP , Pattenden J , Bridle C et al. Systematic review of the effectiveness of stage based interventions to promote smoking cessation . BMJ . 2003 ; 326 ( 7400 ): 1175 – 1177 . Google Scholar CrossRef Search ADS PubMed 50. Cahill K , Lancaster T , Green N . Stage-based interventions for smoking cessation . Cochrane Database Syst Rev . 2010 ;( 11 ): CD004492 . 51. Canga N , De Irala J , Vara E , Duaso MJ , Ferrer A , Martínez-González MA . Intervention study for smoking cessation in diabetic patients: A randomized controlled trial in both clinical and primary care settings . Diabetes Care . 2000 ; 23 ( 10 ): 1455 – 1460 . Google Scholar CrossRef Search ADS PubMed 52. Stead LF , Buitrago D , Preciado N , Sanchez G , Hartmann-Boyce J , Lancaster T . Physician advice for smoking cessation . Cochrane Database Syst Rev . 2013 ;( 5 ): CD000165 . 53. Thomas R , Perera R . School-based programmes for preventing smoking . Cochrane Database Syst Rev . 2006 ;( 3 ): CD001293 54. Navarta-Sánchez MV , Senosiain García JM , Riverol M et al. Factors influencing psychosocial adjustment and quality of life in Parkinson patients and informal caregivers . Qual Life Res . 2016 ; 25 ( 8 ): 1959 – 1968 . Google Scholar CrossRef Search ADS PubMed © Society of Behavioral Medicine 2018. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

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Translational Behavioral MedicineOxford University Press

Published: Feb 23, 2018

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