INTRODUCTION

The age of smoking onset is decreasing in both developed and developing countries[1] and most of the developed countries have implemented several policies in order to reduce smoking rates, more specifically among adolescents. This is because the age at initiation of cigarette smoking is one of the important determinants of tobacco dependence possibility, likelihood of smoking cessation, and the risk of unwilling health consequences.[2, 3, 4, 5] Many researchers have considered smoking behavior in adolescents as a transition or movement in some stages. Although, smoking onset and continuation are connected processes per se, in several studies it has been attempted to make this process into several stages so that primary and secondary prevention could be included. In the study conducted by Leventhal and Cleary in 1980s, it was suggested that smoking includes a complex continuum and in order to a person to be a smoker, different stages should be passed.[6] Subsequently, Flay et al. (1983)[7] and Stern et al. (1987)[8] accepted the multistage nature of smoking and proposed stages in a more sophisticated way. The initial stages then were shortened and described by Flay in 1993.[9] The following six stages were illustrated by Myhew et al. in 2000[10] using previous studies: percontemplation, contemplation or preparatory stage, tried or initiation, experimenter, regular, and established or daily smoker. Kremers et al. (2001 and 2004)[11, 12] showed that nonsmokers in the percontemplation stage are not homogeneous and they can be included in three groups of committers, immotives, and progressives. The first group is sure that they will not smoke in the future. The second group does not plan to start smoking in the future (they do not have any definite program to do so), and the third group want to smoke, but not in the next six months. In addition, for the measurement of smoking stages and cessation, an algorithm has been proposed by Pallonen et al. (1998),[13] which is commonly used in the studies related to smoking stages in the adolescents. However, it is not a comprehensive method for the measurement of the smoking stages.

Understanding the process of adolescent smoking and identifying the predictors of passing through smoking stages are of great importance for policy makers in the development of effective strategies that prevent cigarette smoking. Notwithstanding the importance of smoking stages evaluation in adolescents, there is not an appropriate instrument for its measurement. This study aimed to design a suitable tool for this purpose and to assess its validity by latent class analysis (LCA) model, which is an appropriate technique for the evaluation of staging algorithm validity.[14, 15, 16] The LCA tests this hypothesis whether individual responses to the algorithm questions can be identified by smoking stage (latent variable) by examining the response patterns. Moreover, the validity and reliability of this algorithm was studied in this paper.

METHODS

RESULTS

Algorithm

Final algorithm is presented in the Figure 1. First, the adolescents chose one of the main six options in the left. Then, they completed the second part of that option. In fact, this algorithm was constructed according to the algorithm proposed by Pallonen et al.,[13] Myhew et al.[10] and Kremers et al.[12] Smoking stages of the three above studies can be easily obtained by this algorithm.

[Figure ID: F1] Figure 1. Algorithm for the acquisition and cessation stages

Content validity

In the examining of content validity of this algorithm, the percentage of questionnaire relevancy for both groups was 100%. The clarity percent of the experts and lay experts were obtained 75% and 100%, respectively. Through assessment of smoking stages reliability [9 stages, Table 1], which was performed in 154 students with a two-week interval, the mean of intra-class correlation coefficient (ICC) of the questions was obtained 0.929 (CI 95%: 0.903-0.948).

Table 1. Probability of endorsing particular responses to the aqusision staging algorithm conditional upon stage membership

Construct validity of algorithm

For identifying the smoking stages, by using LCA and considering two other questions (smoking in the last month and last week), validity of the algorithm was studied in 4834 students (69 subjects were excluded due to the lack of data on any of 5 observable variables), so that result of LCA model for the whole sample demonstrated nine interpretable classes (G² = 0.051, df = 1, P = 0.821). Moreover, the obtained results of LCA with nine interpretable classes in females (n = 2796) (G² = 0.026, df = 1, P = 0.803) and males (n = 2038) were valid (G² = 0.003, df = 1, P = 0.956). These findings indicate that the model is excellent fit to the data. In other words, as it was shown in Table 1, both male and female students could be categorized in 9 classes (9 smoking stages) comprehensively based on the observed response patterns.

In order to determine the smoking cessation stages, validity of the algorithm was examined in 218 students. The LCA model demonstrated five interpretable valid classes (G² = 0.001, df = 1, P = 0.975). The results could be observed in Table 2. Similarly, these results indicate the excellent fitness of the model. That is, with respect to the observed response patterns for 4 observable variables related to smoking cessation, students could be categorized in 5 classes (5 stages of smoking cessation). Table 3 shows labels and descriptions of smoking stages, smoking cessation, and their measurements regarding to the algorithm.

Table 2. Probability of endorsing particular responses to the cessation staging algorithm conditional upon stage membership (n=218)

Table 3. Names, definations and measurements of smoking acquisition stages and cessation stages

DISCUSSION

This study designed an algorithm with high content validity and clarity. The reliability of the algorithm was also proved high within a two-week interval. Aveyard et al.[18] revealed that the algorithm proposed by Pallonen et al.[13] for smoking stages has a moderate reliability by analyzing two samples. It should be noted that most of their studied subjects, as well as previous studies conducted on smoking stages in adolescents who were the in the precontemplation stage.[16, 19] In addition, in contrast to our study, they have not considered students in precontemplation stage in three groups which proposed by Kremers et al.[12] Therefore, the inclusion of majority of sample in one group and integrating three groups in one group cause the reliability to be increased. In our study, although we considered individuals in precontemplation stage in three separated groups, 65 percent of the sample was in the committer stage, which could increase the reliability.

The large sample size, the test-retest method for assessment of reliability and possibility of a change in smoking stage of adolescents in a two-week interval were strengths of this study in identifying the algorithm reliability.

We used the LCA to examine if the adolescents in the smoking and cessation stages could be classified based on their responses to the stage algorithm. The results of the LCA suggested that response patterns to this algorithm highly corresponded with 9 smoking and 5 smoking cessation stages. As reported in Table 1, all of the smoking stages (9 stages) could be interpreted with respect to item-response probabilities. For example, in the first stage (committer) those who have never smoked with probability of 0.983, they were confident (0.997%) that they will never start smoking in the future, and those who have not smoked in the last month (0.997%) and they have not smoked in the last week (0.999). Another interesting example is the individuals in stage 5 (preparator) who have never smoked (0.963%). They plan to start smoking in the next month with probability of 0.985 and they have smoked neither in the last month nor in the last week (100%).

It can be seen in Table 2 that according to item-response probabilities, smoking cessation stages can be interpreted. For example, individuals in the first stage (precontemplation) do not think about quitting in the next 6 months (0.834%) and they do not intend to quit smoking with 100% probability in the next month. They have never tried to quit smoking (0.756%) and with. 991% probability have never stopped smoking completely. Another example in this table is the individuals in the second stage (contemplation) who 100% intend to quit smoking in the next 6 months and with 0.906% probability do not think about quitting in the next month and with 0.927% probability have never gave up smoking completely.

The validity of questionnaires for stage measuring and stage definition itself has been questioned.[20, 21] It has been shown that the algorithm suggested by Pallonen et al.[13] to measure the smoking stages and cessation have theoretical and methodological problems.[20] Although the questions of this algorithm assess current behavior, quitting attempts, intention to change, and time since quitting, none of them are measured completely. Recently, in a study conducted by Guo et al.[16] other limitations have been raised. More specifically, they demonstrated that the transition across the stages is not sequential. In terms of staging assessment, even though our designed algorithm is comprehensive than the previous one, it does not measure behaviors and intentions comprehensively. Moreover, due to the cross-sectional nature of the present study, it cannot be claimed about the sequential nature of transition across the stages. Also, our method was confirmatory, not exploratory. It means that we did not want to study how individuals could be classified based on their responses to this algorithm, rather we aimed to study whether response patterns to this algorithm corresponds to our stages (9 stages for smoking and 5 stages for smoking cessation). The insignificant result of the LCA model is positive answer to this question. It should be mentioned that although all of the stages could be interpreted according to item-response probabilities, large number of the stages cause the non-significant result of the test.

CONCLUSIONS

Our findings showed that this algorithm is transparent, valid, and reliable. So this algorithm can be used for assessment of smoking stages in adolescents. However, the reliability was studied only in smoking stages and not for cessation stages. Thus, further studies are required to evaluate reliability of cessation stages. And a longitudinal study to assess the transition across the stages and their sequential nature are needed.