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Consequently, we expected the program to have significant effect in particular in this specific at-risk age group hypothesis 2.

Finally, we explored whether gender and baseline education level of adolescents were potential moderators in the present study. Intervention effectiveness was studied by means of a cluster randomized controlled trial and encompassed the implementation of Smoke Alert in the experimental condition at school.

The intervention was being tested against a no-intervention control condition. Allocation ratio was and respondents from both conditions filled out a Web-based questionnaire at baseline and at 6-month follow-up, assessing smoking behavior, intention to start smoking, age, gender, and educational level.

The trial is registered in the ISRCTN Register ISRCTN Participants in the present study were students from secondary schools in the Netherlands. During the spring of , secondary schools throughout the country were approached by sending a letter to their principals.

The principals were asked to hand out the attached flyers to their teachers, inviting them to make use of a free computer-tailored smoking intervention in their classrooms, as part of an effectiveness trial.

Local health departments assisted in recruiting schools through announcements on websites and in newsletters. Teachers were invited to digitally sign up for participation to the Smoke Alert intervention.

After subscription, teachers received a letter with more extensive information about the purpose, design, and planning of the effectiveness study.

Teachers were requested to schedule 30 minutes, between 9 May and 10 June , for the students to complete the Web-based questionnaire in the classroom.

In contrast to the baseline assessment, which took place at school, the students were invited by email for the 6-month follow-up measurement.

Students who did not supply a valid email address at baseline were excluded from participation in the follow-up measurement. To stimulate response, students were told that they could win an iPod or cinema voucher by participating in the follow-up assessment.

Respondents in the control condition were given the opportunity to obtain computer-tailored advice after they filled out the follow-up questionnaire.

The Smoke Alert program was based on the I-Change Model, or the Integrated Model for exploring motivational and behavioral change [ 38 , 39 ].

According to the I-Change Model, behavior eg, smoking behavior is influenced by awareness factors knowledge, risk perceptions, and cues to action , motivational factors attitudes, social influence beliefs, and self-efficacy , and action factors action plans and goal actions Figure 1.

The I-Change Model [ 38 , 39 ]. The previous version of Smoke Alert [ 36 ] was revised on the basis of focus group discussions with adolescents suggesting improvements such as the use of avatars, different Web design, and less extensive feedback messages.

In order to increase recruitment and retention of adolescents, the revised version contained a combination of textual information with other content-related elements like graphics and animated videos [ 40 - 42 ].

The questionnaire and content of the feedback messages of Smoke Alert were updated versions of previously used questionnaires and feedback, derived from evidence-based interventions on smoking prevention and cessation [ 9 , 10 , 17 , 43 ].

Pilot tests revealed that the questionnaire should not be too long, resulting in a questionnaire that focused on assessing sociodemographics age, gender, and education level , intention to start smoking, and motivational determinants.

Three social cognitive concepts were measured according to the I-Change Model: namely, attitude towards smoking, perceived social influence, and self-efficacy not to smoke.

Perceived influences from the social environment were measured by 2 items that assessed social modeling. Self-efficacy was measured with 6 items via which students could indicate how sure they were that they could remain a non-smoker in certain situations.

These situations can be divided into 2 types: stressful situations eg, feeling nervous and social situations eg, at a party, when friends smoke [ 38 ].

The respondents used their unique log-in information, provided by their teachers, to access the intervention website at school.

Students in the experimental condition received their feedback on the computer screen immediately after filling out the questionnaire. The advice consisted of a home page, containing an introduction and a second animated video, as well as several subpages, each providing feedback on a specific determinant for a screenshot of the home page, see Multimedia Appendix 1.

Facts and figures were depicted on the right and left sides of the pages, also tailored to the answers of the students. The introduction consisted of a personal greeting that contained the name of the student, followed by a confirmation of their smoke-free status and intention to start smoking.

Students were praised for being a non-smoker. The video content was based on principles of social cognitive theory [ 44 ], also used in a previous booklet-based version in the Netherlands and Romania [ 8 , 43 ], and focused on social influences.

The subpages of the advice addressed the psychosocial determinants. The first subpage was dedicated to beliefs about smoking ie, attitude.

These messages had the general intention of countering beliefs about the positive effects of smoking eg, smoking will make me feel relaxed, smoking will make me popular and to strengthen beliefs about the negative effects eg, smoking will cost me a lot of money.

The second subpage addressed the perceived social influence. When most people in the environment of the student were non-smokers, the feedback confirmed that smoking is not the norm in the Netherlands.

The third subpage was dedicated to self-efficacy. For situations where the student expected difficulties in remaining a non-smoker, strategies were offered to help the student to get through these situations without initiating smoking eg, thinking about the reasons for being a non-smoker.

The final subpage focused on action plans. The feedback reflected on every action plan the student had indicated he or she would use in situations where someone would offer a cigarette.

Examples of action plans were provided when a student was not planning to use a certain action plan. The main message regarding action plans was: by preparing yourself for the situation when someone offers you a cigarette, you will be more confident and it will be easier to refuse the cigarette.

Examples of the computer-tailored feedback messages are provided in Table 1. A copy of the advice a PDF file was sent when an email address was voluntarily provided.

This way, the students could re-read or print their advice at home. Examples of feedback messages [ 36 ].

The primary outcome measure was smoking behavior defined as smoking at least occasionally. Respondents were asked to pick a statement that best described them out of 9 smoking-related statements [ 9 , 17 , 36 , 45 ].

To quantify the intervention effects on smoking initiation, we assessed the percentage of baseline non-smokers that indicated to smoke at follow-up.

To be able to detect this difference with a power of. To include at least non-smokers in the study, a total of students was required. The schools were randomly assigned to the experimental or control condition.

Randomization was performed automatically by computer software that was developed specifically for the execution of Web-based computer-tailored programs [ 47 ].

The teachers were informed by email about their allocation to either the experimental or control condition, with unique log-in information for each student attached.

The teachers who were allocated to the control condition were told that their students could take part in the intervention 6 months later, after filling out the baseline and follow-up questionnaire.

This way, all teachers who signed up could be offered the Smoke Alert program. All analyses were done using MLwiN multilevel modelling for Windows , since adolescents were nested within schools.

Ignoring this nesting structure may inflate type I errors and lead to too narrow confidence intervals for treatment effects [ 48 ]. Previous Dutch studies on smoking prevention at primary schools [ 17 ] and smoking cessation at secondary schools [ 36 ] also used this type of analysis.

To check whether the randomization was successful, both conditions were compared on age, gender, educational level, and intention to start smoking.

Dropout was checked using multilevel logistic regression analysis with attrition at post-test as outcome, and baseline demographic variables and intention to start smoking as predictors.

Interaction terms of predictors with treatment condition were included in the model to analyze whether predictors for dropout differed by condition.

Differences between the conditions on smoking initiation were analyzed by multilevel logistic regression analysis.

Demographic variables and significant baseline differences were entered as covariates. Interactions of these covariates with treatment condition were also included to examine inequalities in the effects of the intervention on smoking initiation.

Interactions with a P value higher than. To accommodate missing values in the effect analyses, the multiple imputation procedure in MLwiN was employed, the results being based on 50 imputed datasets.

This procedure saves cases for the analysis and can be considered an intention-to-treat analysis. Analysis under multiple imputation is valid when the data are missing at random [ 49 ], that is, when the missingness only depends on variables included in the analysis.

In this case, it is considered to be the best method available for imputing missing values [ 49 ]. This study was part of a larger study on the effectiveness of the Smoke Alert study for which ethical clearance was obtained [ 36 ].

In total, 89 schools signed up for participation, resulting in a total of 10, students. At baseline, 83 out of 89 schools responded to the questionnaire, 4 schools indicated that they no longer had time to participate, and 2 schools did not explain their non-response to the baseline questionnaire.

A total of students completed the baseline questionnaire, did not meet the inclusion criteria, resulting in a total of non-smokers that remained for participation in the follow-up measurement.

Mean age of the respondents at baseline was approximately 14 years SD 1. Of the participants, The CONSORT flowchart Figure 2 shows the flow of respondents from enrollment in the study to allocation to the experimental E and control C condition, and whether they were included in the analysis.

After 2 email reminders, participants completed the follow-up questionnaires. Non-responding students received an invitation by email to briefly indicate their current smoking status by selecting a statement that best described their behavior.

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