Identifying cluster subtypes for the prevention of adolescent smoking acquisition

Addictive Behaviors 32 (2007) 228 – 247

Identifying cluster subtypes for the prevention of adolescent smoking acquisition Wayne F. Velicer *, Colleen A. Redding, Milena D. Anatchkova, Joseph L. Fava, James O. Prochaska Cancer Prevention Research Center, 2 Chafee Rd., University of Rhode Island, Kingston, RI 02881, United States

Abstract School-based smoking prevention programs are typically identical for all students. Tailoring prevention materials to focus on individual needs with an emphasis on students at highest risk is a promising alternative. Recent prevention programs have tailored materials based on the Stages of Acquisition, an extension of the Stages of Change used to tailor smoking cessation materials effectively for adults. Three stages of acquisition have been identified: Acquisition Precontemplation (aPC), Acquisition Contemplation (aC) and Acquisition Preparation (aPR). However, about 90% of nonsmoking adolescents classify themselves in the aPC stage. A cluster analysis was performed, using the Decisional Balance and Situational Temptations scales, for three random subsamples of adolescents within the aPC stage (N 1 = N 2 = N 3 = 514). Four distinct subtypes were identified in each subsample: High Risk, Protected, Ambivalent, and Risk Denial. External validity was established using family support for nonsmoking, peer variables, and stage classification at follow-up assessment (12, 24, and 36 months). Family support for nonsmoking was related to subtype much more strongly than peer interactions. Subjects in the Protected subgroup were the most likely to remain in the aPC stage at each follow-up assessment. Subtype membership, along with membership in the aC and aPR stages, provides important additional information for tailoring smoking prevention materials. Tailored interventions can focus on those adolescents at highest risk and limit or avoid expending resources on those at very low risk. D 2006 Elsevier Ltd. All rights reserved. Keywords: Stages of changes; Prevention; Cluster analysis; Smoking acquisition

* Corresponding author. Tel.: +1 401 8744254; fax: +1 401 8745562. E-mail address: [email protected] (W.F. Velicer). URL: http://www.uri.edu/research/cprc. 0306-4603/$ - see front matter D 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.addbeh.2006.03.041

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1. Identifying cluster subtypes for the prevention of smoking acquisition Tobacco use is the number one cause of preventable morbidity and mortality in the United States resulting in more than 430,000 deaths each year (McGinnis & Foege, 1993) and more than 5 million years of potential life lost (CDC, 1997). The costs to society include $75 billion of direct medical expenditures related to smoking alone each year and more than $82 billion in lost productivity (Fellows, Trosclair, Adams, & Rivera, 2002). School-based smoking prevention has emerged as a primary intervention focus because smoking acquisition usually occurs before age 18. Among adults who have ever smoked daily, 82% first tried cigarettes and 53% smoked daily before age 18, with most adult regular smokers already addicted by that age (USDHHS, 1994). School-based smoking prevention programs are typically identical for all students, often delivered to entire classes or groups of classes. Despite being the focus of an extensive nationwide effort, school-based smoking prevention programs have met with modest success (USDHHS, 1994; Lynch & Bonnie, 1994; Bruvold, 1993; Peterson, Kealey, Mann, Marek, & Sarason, 2000). While many interventions based on social-influence or life skills training show significant short-term results, the effects dissipate over time (Botvin, 2000; Flay, 2000). A long-term, comprehensive study by Peterson et al. (2000) showed no effects for a multi-year school-based prevention program and illustrates many of the limitations of current school-based programs. Comprehensive programs that include both school-based and community, media, or family components have shown more durable effects (Biglan, Ary, Smolkowski, Duncan, & Black, 2000; Flay, 2000; Pentz et al., 1990; Perry, Kelder, Murray, & Klepp, 1992). While the continued development and refinement of such comprehensive multicomponent programs is clearly important for the field of prevention, alternative approaches that tailor interventions to the needs of individual adolescents represent a promising alternative. Tailored programs have been successful with adult smoking cessation programs (Dijkstra & De Vries, 1999; Spencer, Pagell, Hallion, & Adams, 2002; Strecher, 1999; Velicer & Prochaska, 1999; Velicer, Prochaska, & Redding, 2006). These interventions have typically been computer-based programs that assess smokers on a series of critical variables and then tailor the materials based on the responses. They have also been labeled dcomputer-based interventionsT and dexpert system interventionsT. The development of effective tailored intervention involves several steps, starting with identifying the critical variables for tailoring, developing a set of empirical decision rules and developing alternative intervention materials for each unique intervention (Redding, Prochaska et al., 1999; Redding, Rossi et al., 1999; Velicer et al., 1993, 2006). Once the set of variables has been identified, the decision rules create discrete subgroups. The decision rules can focus on the variable dimension or on the subject dimension. In the variable dimension, the variables may be ordered with respect to importance, empirical cutoffs on variable values are developed and used to assign subjects to discrete groups. The materials are then tailored to discrete groups. For example, in one successful intervention (Velicer & Prochaska, 1999; Velicer et al., 1993), materials were first tailored to a person’s stage of change, then with respect to their scores on the Decisional Balance Inventory and Situational Temptations inventory, and then on scores on the Processes of Change. An alternative is to examine the subject dimension for homogenous subgroups using cluster analysis. The advantage of this approach is that the complete multivariate profile is employed in forming the subgroups rather than treating each variable individually with no regard to the other variables. In other words, configural information is preserved.

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Parallel to the stages of change for cessation, a staging algorithm has been developed for prevention, called the Acquisition Stages of Change. Three Stages of smoking Acquisition (Pallonen, Prochaska, Velicer, Prokhorov, & Smith, 1998) have been identified: Acquisition Precontemplation (aPC), Acquisition Contemplation (aC) and Acquisition Preparation (aPR). Other researchers have employed the same or similar stage of acquisition idea (Aveyard, Lancashire, Almond, & Cheng, 2002; Kremers, deVries, Mudde, & Candel, 2004; Kremers, Mudde, & deVries, 2001; Otake & Shimai, 2002), including the susceptibility concept of Unger, Johnson, Stoddard, Nezami, and Chou (1997) and Pierce, Choi, Gilpin, Farkas, and Merritt (1996). Prokhorov et al. (2002) suggested an algorithm that combines the stages of acquisition and susceptibility constructs. Recent smoking prevention programs (Hollis et al., 2005; Prochaska et al., submitted for publication; Redding et al., 2002) have tailored materials based on the Stages of Acquisition. However, about 90% of nonsmoking adolescents classify themselves in the aPC stage (Hollis et al., 2005; Plummer et al., 2001; Prochaska et al., submitted for publication; Redding, Evers et al., 1998; Redding, Rossi et al., 1998). Adolescents in this stage report no history of regular smoking and not planning to try smoking in the next 6 months. Such a large concentration of individuals within a single stage precludes differentiation. The goal of this study is to determine if a meaningful typology exists within the Acquisition Precontemplation subgroup of adolescent nonsmokers, and whether that typology is related to other smoking-related variables, including future smoking status. 1.1. Cluster analysis Cluster analysis (Everitt, Landau, & Leese, 2001; Milligan & Hirtle, 2003) is an empirical method of defining homogenous subgroups of individuals within a population. It can be viewed as a parsimony procedure that operates in the subject dimension comparable to a parsimony procedure that operates in the variable dimension like factor analysis. As such, it is particularly appropriate for identifying subgroups that could benefit from tailored interventions. Cluster analysis creates an empirical typology where the data determines the patterns that form the typology rather than a theoretical typology. Cluster analysis should be based on a data set that meets several requirements to achieve optimal results. First, the variables that the cluster analyses are based on should be carefully selected to represent an appropriate variable dimension. Irrelevant variables should be excluded and relevant variables included. A well-developed theoretical model can provide useful guidance for variable selection. For this study, the Criterion Measurement Model from the Transtheoretical Model was employed (Velicer et al., 2000; Velicer, Martin, & Collins, 1996; Velicer, Rossi, Prochaska, & DiClemente, 1996). Second, the variables should be relatively independent. Including variables that are highly redundant, i.e., highly correlated with each other, can produce an unintended weighting of one dimension of the variable dimension. The use of the Criterion Measurement Model, which involved three minimally correlated dimensions, facilitates achieving this second goal. 1.2. Criterion measurement model The Pros, Cons, and Self-efficacy are measures of the three basic constructs of the multivariate outcome space as defined by the Criterion Measurement Model (CMM; Velicer, Martin et al., 1996; Velicer, Rossi et al., 1996), which represents one dimension of the Transtheoretical Model (Prochaska & Velicer, 1997; Velicer et al., 2000). The multivariate model was proposed to represent the full spectrum

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of the change process as individuals move from Precontemplation to maintenance. The CMM includes three constructs: Habit Strength, Positive Evaluation Strength, and Negative Evaluation Strength. Positive Evaluation Strength reflects favorable beliefs about a behavior, while Negative Evaluation Strength represents the importance of the negative aspects of engaging in a behavior. The Pros and Cons scales from the Decisional Balance Inventory (Prochaska et al., 1994; Velicer, DiClemente, Prochaska, & Brandenburg, 1985) are appropriate measures of these constructs. The Pros and the Cons are similar to other expectancy-value concepts such as benefits, barriers, and costs (Cummings, Becker, & Maile, 1980). Habit Strength reflects the psychological or learned aspects of a behavior. Self-efficacy (Bandura, 1977), or the level of confidence that smoking cessation can be maintained across a variety of situations, can serve as an appropriate conceptualization of habit strength for smoking behavior. This can also be framed as the degree of temptation to smoke in a variety of situations (Velicer, DiClemente, Rossi, & Prochaska, 1990). 1.3. Clusters within adult smokers Cluster analysis based on the criterion measurement model has been employed in the area of smoking cessation and has produced robust replicable clusters. Two studies have examined subtypes within the stages of change for smoking cessation. Velicer, Hughes, Fava, Prochaska, and DiClemente (1995) conducted cluster analysis within each of the first four stages of change and found distinct subtypes that shared similar characteristics across the four groups defined by stage of change. In each stage, a profile was found that exemplified that stage, a profile similar to the next stage, and a profile that was similar to the previous stage. Within each stage, an unexpected profile was also found that reflected detachment or disinterest in the motivational aspects of smoking. The cluster subtypes were interpreted as strong support for conceptualizing the stages of change as discrete stages. Subtypes that looked more like the expected pattern of the next or previous stage may represent individuals misclassified by the stage of change algorithm or simply those in the process of progressing or regressing to an adjacent stage. Norman, Velicer, Fava, and Prochaska (2000) replicated the results of the Velicer et al. (1995) study in a representative sample of smokers recruited using random digit dial phone survey methodology. This sampling method produced a sample of smokers less likely to take the initiative to enroll in a smoking cessation study and thus, less motivated to quit smoking. However, Norman et al. (2000) found subtype patterns very similar to those found by Velicer et al. (1995) within each of the first three stages of change. A series of studies by Anatchkova, Velicer, and Prochaska (2005, 2006, in press) replicated the results of the two previous studies. 1.4. Overview of current study The goal of this study is to determine whether a meaningful typology exists with the Acquisition Precontemplation subgroup of adolescent nonsmokers. The study first focuses on internal validity and then on external validity. To establish internal validity, we conducted three cluster analyses on data from random subsamples of adolescents in the Acquisition Precontemplation stage. The cluster solutions were interpreted and labeled and then compared across samples. To establish external validity, we carried out a series of analyses on the typology to determine its validity. External validity was established using family support for nonsmoking, peer variables, and prospective smoking status 12, 24, and 36 months later. Demonstrating that an empirical typology contains clusters that are theoretically interpretable,

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internally consistent, and have expected relationships with other relevant variables provides important evidence for a typology’s utility and acceptance for practical applications (Aldenderfer & Blashfield, 1984; Everitt et al., 2001; Milligan & Hirtle, 2003).

2. Method 2.1. Participants During the 1995–1996 school year, 6955 9th grade students from 22 Rhode Island high schools were recruited into a 4-year study examining smoking behavior, sun protection habits, and dietary fat reduction. Participating schools were matched on relevant variables using the multi-attribute utility measurement approach (MAUM; Graham, Flay, Johnson, Hansen, & Collins, 1984) and were assigned to either an intervention condition or to a non-intervention condition (Plummer et al., 2001). Of the 6955 eligible 9th graders, 509 (7.3%) were dropped because of parental refusal and 79 (1.1%) were dropped because of student refusal. Of the remaining 6367 students, 5011 (78.7%) completed an in-class baseline paper-and-pencil questionnaire. The remaining 1356 (21.3%) students did not complete the baseline survey mainly due to absenteeism and scheduling difficulties. Of the 5011 students, 2976 were in schools assigned to the intervention condition. The measures used for this analysis were only available for students in the intervention condition. Of the students assigned to intervention, 2836 (95.3%) completed a computer-based expert system smoking questionnaire and received an intervention. 28 of these participants did not provide information on their smoking status and were excluded from further analyses. The remaining 2808 formed the sample that contained the appropriate measures required for the analyses in this paper. Of the students who completed these measures, 798 (28.4%) were current or former smokers and received the smoking cessation program. The remaining 2010 was classified as not currently smoking and received the acquisition measures and intervention (Redding, Prochaska et al., 1999; Redding, Rossi et al., 1999). Of these nonsmokers, 1753 (87.2%) were in the Acquisition Precontemplation stage. This data was cleaned to remove an additional 211 inconsistent responders (i.e., individuals who indicated they were both nonsmokers and that they had tried smoking), resulting in a final sample of 1542. This sample was divided randomly into three subsamples of 514 participants each and the cluster analysis was conducted within each subsample. Table 1 presents the demographic description of the total sample for the study and the 1542 subjects who were in the Acquisition PC stage. The majority of the aPC sample was white (77.8%), reflecting the state’s demographics. The sample was approximately evenly split with regards to gender (male = 788, female = 750) with an average age of 14.6 (S.D. = 0.5). 2.2. Procedure The data employed in the initial cluster analysis is taken from the first intervention session administered through the interactive expert system program. The program assessed all constructs of the Transtheoretical Model and followed with normative feedback based upon each individual’s responses. The computer sessions were completed using laptop computers in classrooms. More detail on the procedures used in this study can be found in Plummer et al. (2001). A detailed description of the expert system technology is provided elsewhere (Pallonen et al., 1998; Redding, Prochaska et al., 1999;

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Table 1 Demographic variables for total sample and proportion of sample in the Acquisition Precontemplation (aPC) stage Variable

Total sample (N = 5011)

Acquisition PC sample (N = 1542)

Gender Males Females Race White Latino/Hispanic Black/African American Asian American Indian/Eskimo Other Smoking status Smokers Nonsmokers Status not identified Cessation stage of change (smokers) Precontemplation Contemplation Preparation Action Maintenance Acquisition stage of change (nonsmokers) Precontemplation Contemplation Preparation

Frequency (%) 2494 (49.8%) 2484 (49.6%)

Frequency (%) 788 (51.1%) 750 (48.6%)

3737 (76.2%) 510 (10.4%) 250 (5.1%) 201 (4.1%) 61 (1.2%) 142 (2.9%)

1200 (78.8%) 131 (8.6%) 86 (5.7%) 40 (2.6%) 20 (1.3%) 45 (3.0%)

Continuous variables

Mean (S.D.)

Mean (S.D.)

Age Body-mass index Height (in.) Weight (lb)

14.7 21.0 65.4 129.9

14.6 21.0 65.3 129.7

925 (18.5%) 3851 (76.9%) 235 (4.7%) 418 329 190 221 374

0 1542 (100%) 0

(27.3%) (21.5%) (12.4%) (14.4%) (24.4%)

0 0 0 0 0

2829 (88.4%) 159 (5.0%) 213 (6.7%)

0 0

(0.6) (3.7) (3.8) (27.8)

1542 (100%)

(.5) (3.9) (3.8) (28.3)

Redding, Rossi et al., 1999; Velicer et al., 2006). Follow-up assessments occurred at 12, 24, and 36 months. 2.3. Measures 2.3.1. Stages of change The stage of change assessment was administered to both smokers and nonsmokers. First, current smoking status was assessed with two questions that distinguished smokers, nonsmokers and exsmokers. This first question was bHave you ever smoked a cigaretteQ? The possible choices were (1) No, I have never smoked even one cigarette, (2) Yes, a few times but never weekly, (3) Yes, I used to smoke weekly or more but I quit, or (4) Yes, I smoke right now. The second question was bWhich of these statements best describes your smoking nowQ? Possible choices were (1) I have never smoked cigarettes,

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(2) I have tried smoking a few times but I don’t smoke now, (3) I used to smoke regularly but I quit, or (4) I am a smoker. The second question was asked in order to verify the students’ responses to the first. Never smokers and experimental smokers answered 1 or 2 to both items and were classified as nonsmokers and asked subsequent questions to determine stages of smoking acquisition, whereas current and former smokers answered 3 or 4 to both items and were then asked questions relevant for stages of smoking cessation. With current smoking status measured, smokers and nonsmokers answered separate questions to assess stage of cessation or acquisition. For nonsmokers, two questions were used to measure acquisition stage. Nonsmokers were asked if they were thinking about trying or planning to try smoking within the next 30 days (Acquisition-Preparation Stage) or six months (AcquisitionContemplation Stage). Participants who reported they were not thinking of trying smoking in the next 6 months were classified into the Acquisition-Precontemplation Stage (Pallonen et al., 1998; Plummer et al., 2001). 2.3.2. Decisional Balance measure The Decisional Balance Inventory was administered to all participants. The inventory assessed six items reflecting the Pros of Smoking and 6 items reflecting the Cons of Smoking, for a total of 12 items. The measure was originally adopted from the Decisional Balance Inventory developed with adult smokers (Velicer et al., 1985; Ward, Velicer, Rossi, Fava, & Prochaska, 2004). The measure was verified in an adolescent sample (Pallonen et al., 1998). The items employed a 5-point Likert scale ranging from 1 (not important) to 5 (extremely important). The two scales demonstrated good internal validity (Pros, a = 0.75; Cons, a = 0.84). 2.3.3. Situational Temptations to Smoke measure The original Situational Temptations to Smoke measure was developed and verified with adult smokers (Velicer et al., 1990). This is a measure was based on the self-efficacy construct of Bandura (1977, 1982) and the relapse prevention literature. The temptation and self-efficacy measures use the same items but with two different response formats (see Velicer et al., 1990). The adolescent Situational Temptations measure was modified for nonsmokers to assess temptations to try smoking (Ding, Pallonen, Migneault, & Velicer, 1994, Ding, Pallonen, & Velicerm, 1995; Pallonen et al., 1998). The items also employed a 5-point Likert scale ranging from 1 (not at all tempted) to 5 (extremely tempted). The best fitting measurement model was a hierarchical highly correlated five factors model (Plummer et al., 2001). The factors (Positive Social Situations, Negative Affect Situations, Social Pressure, Weight Concerns and Curiosity about Cigarette Smoking) are each defined by two items. All the items are presented in Plummer et al. (2001). The total scale (a = 0.86) was employed in this study. 2.3.4. Family support for nonsmoking scale A brief 4-item scale was administered to all students to rate how often their family discussed and provided active support for avoiding smoking in the past month on a 5-point Likert scale. This scale demonstrated unidimensionality, good internal consistency (a = 0.90) and a strong relationship to current smoking status and stages of readiness for cessation (Redding, Evers et al., 1998; Redding, Rossi et al., 1998) in both high school students and their parents (Redding, Prochaska et al., 1999; Redding, Rossi et al., 1999). In this sample, Cronbach’s internal consistency coefficient was calculated as well (a = 0.90). To verify the measurement model of this measure in this sample of nonsmokers, a structural equation

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model was fit to the four items and is illustrated in Fig. 1. The Comparative Fit Index (CFI; Bentler, 1990) was 0.98 and all items had loadings of 0.78 or higher. 2.3.5. Peer influences measures There were three items that assessed the smoking patterns among peers and siblings. The three items were fairly independent and could not be meaningfully combined into a single scale. Each item was treated as a separate variable. The items asked students: (a) How many of their friends smoke cigarettes (1—none to 6—almost all); (b) Whether their best friend smoked cigarettes (Yes/No); and (c) Whether any of their brothers or sisters smoked cigarettes (Yes/No). 2.4. Procedure 2.4.1. Sample The sample of 1542 students in the Acquisition Precontemplation stage was randomly divided into three subsamples of 514 students each and a cluster analysis was performed with each subsample independently. 2.4.2. Variables The three variables employed in the cluster analysis were the Pros of Smoking and Cons of Smoking from the Decisional Balance Inventory and the total score from the Situational Temptations Scale. 2.4.3. Standardization of variables The three variables used in each cluster analysis were standardized to T-scores (mean (M) = 50, standard deviation (S.D.) = 10). Standardization was computed across the entire sample of 1542 students. Standardization served both to equalize the contribution of each variable and to put the variables on a comparable metric. 2.4.4. Distance metric and clustering algorithm The squared Euclidean distance metric was employed because it is particularly sensitive to the cluster profile characteristics of shape, level, and scatter among the variables (Cronbach & Gleser, 1953; Encourage each other to stay away from cigarettes.

.81 .86 Family Support

Discuss how smoking is unhealthy.

.89 Remind each other to avoid cigarette smoking

.78

Share ideas on how to stay a nonsmoker or quit cigarettes.

Fig. 1. Structural equation model for family influences scale.

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Edelbrock, 1979). Ward’s minimum variance clustering algorithm, a hierarchical agglomerative procedure, was used for all analyses (Ward, 1963). It has been shown to be one of the better clustering methods in several simulation studies (Blashfield, 1976; Milligan, 1980; Milligan & Cooper, 1987; Milligan & Hirtle, 2003). 2.4.5. Determining the number of clusters Since there is no definitive procedure for determining the number of clusters, several methods were used (Milligan & Cooper, 1985). The inverse scree test (Lathrop & Williams, 1987, 1989) along with the pseudo F test (Calinski & Harabasz, 1974) was used initially to narrow the range of cluster solutions that would be interpreted. The second level of testing was visual inspection of the cluster profiles. Attention was paid to the shape, level, and scatter of the profiles. 2.4.6. External validation The cluster analysis interpretation and replication served to establish the internal validity of the cluster. External validity was established by relating cluster membership to variables not included in the development of the clusters. For this study, two different baseline variables sets were employed: (1) Relation to the Family Support for Nonsmoking Scale, and (2) Relation to the Peer Influences Scales. These two sets of variables were appropriate for establishing external validation of the cluster solution because they were not used in the cluster analysis, but they have theoretical relevance to the clustering variables. In addition, all subjects were classified by stage of Acquisition of smoking or stage of cessation at follow-up assessments (12 months, 24 months, and 36 months) and the proportion of each cluster remaining in the Acquisition PC stage was calculated for all available participants at each assessment occasion.

3. Results 3.1. Cluster subtypes in sample 1 Using the set of guidelines for determining the number of clusters described above, it was determined that between three and five clusters best represented the data. Comparison of the three, four, and five cluster solutions revealed that the four-cluster solution was the most interpretable. The four-cluster solution replicated nicely across the three samples. Fig. 2 displays the cluster profiles from Sample 1. Table 2 provides both the raw score means and standard deviations and the standardized (T-score) means and standard deviation for the four clusters in each of the three samples. The four clusters were interpreted with respect to Level, Scatter, and Shape. Level refers to the overall mean of the profile. Scatter refers to the variability of the profiles. Shape is the pattern of high and low scores for the profile. Cluster 1 (N = 383) was named Protected, and is characterized by an inverted V shape, with average level and average scatter. This pattern, with low Pros of Smoking and low Temptation to Smoke, and high Cons of Smoking, indicates a non-smoker who is indicating little interest in smoking. Members of this group should be at relatively low risk of starting to smoke. Cluster 2 (N = 49) was named Risk Denial, and is characterized by a V shape similar to the High Risk cluster. However, all three scores are below average, indicating very limited engagement with smoking.

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Protected (n=383)

80.00

Ambivalent (n=44) Risk Denial (n=49) High Risk (n=38)

70.00

72.54

68.81

T Scores (M = 50; SD = 10)

66.19

60.00

53.87

50.00

50.10 48.43 46.10

45.09

47.91 47.61 46.23

40.00

30.00

29.50

20.00 Pros

Cons

Temptations

Variables

Fig. 2. Four cluster profiles for sample 1.

For the Pros of Smoking and the Temptation to Smoke, this is a positive characteristic since their level is about equal to the Protected group. The most striking characteristic for this group is their extremely low score on the Cons of Smoking. This group has an average score that is two standard deviations below the mean (T-score = 29.50) and about two standard deviations lower than any other group. This group should be at moderate to high risk for smoking. Cluster 3 (N = 44) was named Ambivalent, and the shape of this cluster showed the largest variability across samples. In Sample 1, the level was average, but with a high mean for Pros. Due to this high value, the scatter was an average of approximately one and a-half standard deviation. This pattern appears to indicate a lack of strong convictions regarding smoking and members of this group should be at moderate risk to start smoking. Cluster 4 (N = 38) was labeled High Risk, and is characterized by a V shape, average level and high scatter. The pattern is the opposite of the Protected cluster. This pattern, with very high Pros of Smoking and very high Temptation to Smoke, and low Cons of Smoking seems to contradict the classification as an Acquisition Precontemplator. Given the high Temptation to Smoke and high Pros associated with smoking, a member of this cluster would appear to be very vulnerable to smoking. 3.2. Cluster subtypes in sample 2 The four cluster solution was replicated in Sample 2 following the same procedure. The four-cluster solution was independently determined to be the optimal solution. The four-cluster profiles were

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Table 2 Raw score and standard score means and standard deviations for the four clusters on the pros, cons, and temptations Cluster 1, protected

Cluster 2, ambivalent

Cluster 3, risk denial

Cluster 4, high risk

Part I. Random sample 1 (N = 514) N 383 Raw T Pros M 6.34 46.09 S.D. 0.81 2.87 Cons M 28.03 53.87 S.D. 2.73 5.15 Temptations M 11.23 47.91 S.D. 2.38 5.85

44 Raw 12.00 2.93 26.03 2.96 11.11 1.55

T 66.19 10.39 50.09 5.60 47.61 3.82

49 Raw 7.00 1.63 15.10 4.5 10.55 1.04

T 48.43 15.8 29.50 8.58 46.22 2.56

38 Raw 12.74 3.57 23.37 5.22 21.26 5.42

T 68.81 12.67 47.90 5.85 72.54 13.3

Part 2. Random sample 2 (N = 514) N 204 Raw T Pros M 6.06 45.09 S.D. 0.32 1.15 Cons M 29.83 57.28 S.D. 0.42 0.78 Temptations M 10.72 46.93 S.D. 1.41 3.46

247 Raw 7.30 1.71 24.90 3.39 11.84 2.84

T 49.50 6.05 47.98 6.38 49.39 6.98

31 Raw 7.13 2.40 11.13 3.90 11.71 3.34

T 48.89 8.49 22.01 7.34 49.07 8.20

32 Raw 14.66 3.38 24.84 4.83 18.91 8.31

T 75.62 12.02 47.87 9.11 66.75 20.41

Part 3. Random sample 3 (N = 514) N 334 Raw T Pros M 6.11 45.26 S.D. 0.38 1.35 Cons M 28.01 53.84 S.D. 2.68 5.06 Temptations M 10.56 46.26 S.D. 1.04 2.56

86 Raw 11.91 3.55 26.21 3.53 11.63 2.41

T 65.86 12.62 50.44 6.67 48.87 5.93

76 Raw 7.63 2.11 18.22 6.00 14.42 3.62

T 50.67 7.49 35.46 11.31 55.73 8.89

18 Raw 11.17 3.66 25.11 3.66 25.17 4.68

T 63.23 13.02 48.38 6.92 55.73 11.49

interpreted and the results replicated the four-cluster solution described above for Sample 1. Fig. 3 presents the cluster profiles for Samples 2. The interpretation of the profiles (Cluster 1 (N = 204) Protected; Cluster 2 (N = 247) Ambivalent; Cluster 3 (N = 31) Risk Denial; and Cluster 4 (N = 32) High Risk) is the same as for the corresponding profiles with the same names in Sample 1. 3.3. Cluster subtypes in sample 3 The four-cluster solution was also replicated in Sample 3 following the same procedure. The fourcluster solution was independently determined to be the optimal solution. The four-cluster profiles were interpreted and the results replicated the four-cluster solution described above for Sample 1. Fig. 4 presents the cluster profiles for Samples 3. The interpretation of the profiles (Cluster 1 (N = 334) Protected; Cluster 2 (N = 86) Ambivalent; Cluster 3 (N = 76) Risk Denial; and Cluster 4 (N = 18) High Risk) is the same as for the corresponding profiles with the same names in Sample 1.

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80.00 75.63

Protected (n=204) Ambivalent (n=247) Risk Denial (n=31) High Risk (n=32)

66.75

60.00 57.28

50.00

49.50 48.89

47.98 47.87

49.40 49.07 46.63

45.09

40.00

30.00

22.01

20.00 Pros

Cons

Temptations

Variables

Fig. 3. Four cluster profiles for sample 2. 82.13

80.00 Protected (n=334) Ambivalent (n=86) Risk Denial (n=76) High Risk (n=18)

70.00 65.86

T Scores (M = 50; SD = 10)

T Scores (M = 50; SD = 10)

70.00

63.23

60.00 55.73 53.84

50.00

50.68

50.45 48.38

45.26

48.87 46.26

40.00

35.46

30.00

20.00 Pros

Cons Variables

Fig. 4. Four cluster profiles for sample 3.

Temptations

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3.4. External validity For the external validity analyses, the data was combined from the three samples. The data from all subjects in the three samples who received the same cluster label were combined, producing four combined clusters (Protected, N = 921; Ambivalent, N = 377; High Risk, N = 88; Risk Denial, N = 156) and cluster membership was used as the grouping variables for subsequent analyses. 3.5. Cluster membership and family support for nonsmoking A one-way analysis of variance was performed with four-cluster groups serving as the grouping variable and the standardized total score from the Family Support for Nonsmoking scale as the dependent measure. There was a significant difference between the groups, F(3, 1493) = 23.02, p b 0.001, g 2 = 0.0544. A follow-up Tukey test found that people in the Protected Group (M = 51.6, S.D. = 9.8) had significantly higher scores than participants in the other three clusters. The Ambivalent Group (M = 49.3, S.D. = 9.5) scored significantly higher than the Risk Denial Group (M = 45.03, S.D. = 9.8). The High Risk Group (M = 47.5, S.D. 8.9) did not differ significantly from the Risk Denial Group. 3.6. Cluster membership and peer influences Three separate independent items with different response formats measured peer pressure. A oneway analysis of variance was performed with the responses from the item (How many of your friends smoke?) as the dependent measure. There was a significant difference between the groups, F(3, 1532) = 4.64, g 2 = 0.09. The Protected Group (M = 2.4, S.D. = 1.4) reported a significantly lower number of smoking friends than people in the High Risk (M = 2.9, S.D. = 1.6) Group. The number of smoking friends for people in the Risk Denial (M = 2.6, S.D. = 1.4) and the Ambivalent (M = 2.6, S.D. = 1.4). The other two items involved a binary response format so two chi-square tests were performed. There were no significant differences between groups for the smoking status of siblings item, v 2(3, N = 1535) = 2.51, p N 0.05. There were significant differences between the groups for the smoking habits of the best friend item, v 2(6, N = 1534) = 15.18, p b 0.05, Cramer’s V = 0.070. The High Risk Group had the highest percent of best friends who smoked (15.9%), followed by the Ambivalent Group (13.1%), and the two lowest groups, the Risk Denial Group (9.0%) and the Protected Group (9.0%). 3.7. Initial longitudinal validity An important test of the validity of the clusters is the ability of cluster membership to predict future smoking behavior at the three follow-up assessments (12-month post baseline, 24 months, and 36 months). A complete analysis of the longitudinal data is beyond the scope of this paper. In this section, we will provide a preliminary analysis employing initial cluster membership and using available data from the follow-up assessments. Each subject was classification as still in aPC, having advanced to one of the other acquisition stages (aC or aPR), or having started smoking. Table 3 presents each member of the four original clusters with their classification at the 12, 24, and 36-month assessments. The proportion of the sample retained at

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Table 3 Prospective smoking status 12, 24, and 36 months later by baseline cluster Baseline cluster (N)

% Classified as aPC (N)

% in aC or aPR (N)

% Started smoking (N)

Part 1. 12-month assessment Protected (N = 706) Ambivalent (N = 275) Risk denial (N = 116) High risk (N = 66)

88.7 81.5 77.6 60.6

(626) (224) (90) (40)

5.4 (38) 8.0 (22) 11.2 (13) 16.7 (11)

5.9 (42) 10.5 (29) 11.2 (13) 22.7 (15)

Part 2. 24-month assessment Protected (n = 622) Ambivalent (N = 247) Risk denial (N = 93) High risk (N = 60)

86.0 82.2 74.2 75.0

(535) (203) (69) (45)

3.1 4.4 7.6 8.3

(19) (11) (7) (5)

10.9 13.2 18.3 16.8

(68) (33) (17) (10)

Part 3. 36-month assessment Protected (N = 602) Ambivalent (N = 249) Risk denial (N = 87) High risk (N = 53)

82.1 77.5 63.2 66.0

(494) (193) (55) (35)

4.9 4.8 8.0 7.6

(29) (12) (7) (4)

13.2 17.6 28.7 26.5

(79) (44) (25) (14)

aPC = Acquisition Precontemplator; aC = Acquisition Contemplator; aPR = Acquisition Preparation.

each assessment was: Month 12, 75.4% (N = 1163), Month 24, 66.3% (N = 1022); and Month 36, 64.3% (N = 991). There was no differential attrition by original cluster group by assessment, v 2(6) = 1.64, p N 0.05. The proportion remaining in Acquisition PC for each cluster was compared for each assessment by forming a 4 (Cluster Membership) by 2 (aPC or Other) contingency table. For 12 months a significant difference was found, v 2(3) = 43.73, p b 0.01, Cramer’s V = 0.194. The proportion remaining in aPC was the highest for the Protected cluster (88.7%) and the lowest for the High Risk cluster (60.6%), with the Ambivalent and Risk Denial intermediate (81.5% and 77.6%). For 24 months a significant difference was found, v 2(3) = 12.06, p b 0.01, Cramer’s V = 0.109. The proportion remaining in aPC was again the highest for the Protected cluster (86.0%) and the lowest for the High Risk and Risk Denial Clusters (75.0% and 74.2%), with the Ambivalent cluster intermediate (82.2%). For 36 months a significant difference was found, v 2(3) = 21.51, p b 0.01, Cramer’s V = 0.147. The proportion remaining in aPC was again the highest for the Protected cluster (82.1%) and the lowest for the High Risk cluster and Risk Denial Clusters (66.0% and 63.2%), with the Ambivalent cluster intermediate (77.5%). The effect size was the largest at 12 months and still remained large at both 24 and 36 months.

4. Discussion The results of this study have the potential to guide the development of tailored interventions for the prevention of smoking acquisition. The initial sample of aPC participants was divided into three subsamples and an independent cluster analysis replicated the same four clusters in each subsample.

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4.1. Description of the clusters The four clusters were labeled on the basis of their shape, level, and scatter. Three of the clusters (Protected, Ambivalent, and High Risk) could be easily interpreted. They had high face validity and the interpretations were supported by the external validity information. The largest cluster in each of the samples was the Protected cluster (59.7% of aPC’s), which was characterized by an inverted V shape, with average level and high scatter. This pattern, with very low pros of smoking and very low temptation to smoke, and very high cons of smoking, describes a nonsmoker who is indicating little interest in smoking. Members of this group should be at relatively low risk of starting to smoke. This group had the highest scores on the Family Support for Nonsmoking Scale, indicating a high level of parental involvement in preventing smoking. This group also had the lowest average number of smoking friends and the lowest percentage of best friends who smoked. Members of this group were the most likely to remain in the Acquisition Precontemplation stage at each follow-up assessment (12, 24, and 36 months). This was also the largest cluster overall, with 59.7% of the sample. This result is consistent with the typical finding that approximately 50% of an adolescent sample is unlikely to even experiment with smoking. (Other adolescent groups, including Acquisition Contemplation, Acquisition Preparation, and current/former Smokers were not included in this study.) The High Risk cluster (5.7% of aPC’s) had almost the opposite profile, characterized by a V shape, average level and high scatter. This pattern, with very high pros of smoking and very high temptation to smoke, and low cons of smoking seems to contradict their classification as an Acquisition Precontemplators, given the high temptation to smoke and high pros associated with smoking. Members of this cluster were judged to be very vulnerable to smoking. This was the smallest group overall (5.7%). This group had a very low score on the Family Support for Nonsmoking Scale, indicating a low level of parental involvement in preventing smoking initiation. This group also had the highest average number of smoking friends and the highest percentage of best friends who smoked. Members of this group were among the least likely to remain in the Acquisition Precontemplation stage at each follow-up assessment (12, 24, and 36 months). The Ambivalent cluster (24.4% of aPC’s) demonstrated the highest level of variability across the subsamples. In all three subsamples, the means for the Cons and Temptations were approximately at the same level, but the levels of the Pros were high in two of the samples. Members of this group were judged to be at moderate risk to start smoking. This was the second largest group overall (24.4%). This group scored in between the Protected and High Risk groups on the Family Support for Nonsmoking Scale, indicating a moderate level of parental involvement in preventing smoking acquisition. This group also scored in between the Protected and High Risk groups with respect to the number of smoking friends and the percentage of best friends who smoked. Members of this group were intermediate between the Protected and High Risk clusters with respect to the proportion remaining in the Acquisition Precontemplation stage at each follow-up assessment (12, 24, and 36 months). The most difficult cluster to interpret was the Risk Denial group (10.1% of aPC’s). It was characterized by a V shape similar to the High Risk cluster. However, all three scores were below average, indicating very minimal engagement with smoking. For the pros of smoking and the temptation to smoke, their level is about equal to the Protected group. On the cons of smoking, however, they had an average score that was almost two standard deviations below the mean (Tscore = 30.91). This very low score on the cons of smoking formed the basis for this group’s name, since it suggested some denial or minimization of the negative aspects of smoking. This group had the

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lowest score on the Family Support for Nonsmoking Scale, indicating a low level of parental involvement in preventing smoking acquisition. However, this group and the Protected group had the lowest average number of smoking friends and a low percentage of best friends who smoked. At the first follow-up assessment (12 months), members of this group were intermediate between the Protected and High Risk clusters with respect to the proportion remaining in the aPC stage. At the second and third follow-up assessment (24, and 36 months), members of this group were among the least likely to remain in the aPC stage. There are several possible interpretations for this profile. One is that this cluster involves a group of adolescents who are relatively naive about smoking. The low score on the Family Support for Nonsmoking scale and the low number of peers who smoke supports this. Another is that this cluster reflects a more general denial of the dangers of smoking. The very low score, approximately two standard deviations below the mean, suggests that a more active process may be involved. 4.2. Tailoring material with cluster profiles Most previous tailored interventions have focused on the variable dimension. A typical approach is to tailor materials first on the variable that has the greatest importance empirically, i.e., the variable with the largest effect size, or the variable that the theory views as the most important variable, i.e., stage of change for the Transtheoretical Model. Materials are then tailored on the next variables or variable set, again based on either empirical or theoretical importance. This process is repeated for each set of variables to be included in the tailoring. The development of materials based on the subject space is a bit more complex but can be easily implemented by computer-based interventions. The clusters represent four distinct subgroups. The goal of a tailored intervention would be to develop unique materials appropriate for each subgroup. In future applications, it would be necessary to assign subjects to the appropriate subgroup. One way to accomplish this would be to calculate the squared Euclidian distance between the new subject and each of the four clusters. The subject would then be assigned to the subgroup that produced the smallest distance and the material appropriate for the subgroup would be selected. For example, it is clear that the High Risk Group requires interventions that will focus on increasing their awareness of the potential problems associated with smoking and decreasing their view of smoking as a relatively attractive behavior choice. The high Situational Temptations score indicates that their current non-smoking status may be more the result of current environmental constraints, i.e., a lack of opportunities, and less a reflection of any personal conviction. The relatively low level of involvement by the parents suggests another potential avenue for intervention. The Ambivalent Group has a Cons score that is about the same as the Protected Group but they are much higher on the other two scales. This suggests that interventions with this group need to focus on decreasing the Pros of smoking and decreasing their current relatively high level of temptation to experiment with smoking. The Protected Group appears to require reinforcement of their current attitudes and regular monitoring to detect any change in attitudes or status. For the Risk Denial Group, the most distinctive characteristic is their extremely low score on the Cons of Smoking. More than any other group, this group seems to be either unaware of or actively denying the negative aspects of smoking. If this is simply the result of ignorance, exposure to information should be an effective intervention. However, if active denial is involved, as the extremely low score suggests, the problem will involve first disarming this defense mechanism.

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4.3. Family support for nonsmoking scale An important peripheral aspect of this project was the development of the Family Support for Nonsmoking Scale. One recent review of the literature (Avenevoli & Merikangas, 2003) described one of the important gaps in the field as the lack of high quality measures for assessing family influences on smoking behaviors. This brief 4-item scale demonstrated unidimensionality, good internal consistency (Alpha = 0.90) and a well-defined structure (CFI = 0.98 with all items loading 0.78 or higher on the latent variable.). 4.4. Longitudinal validation of clusters This study involved only a preliminary longitudinal data analysis. A critical next step is to perform a more extensive exploration of the implications of the cluster subtypes longitudinally. One critical longitudinal study would involve investigating the stability of the clusters reported here. What proportion of the members of the identified subgroups would still be members of the same subgroup 12 or 24 months in the future? One approach to studying this problem would be the use of latent transition analysis (Collins & Wugalter, 1992; Graham, Collins, Wugalter, Chung, & Hansen, 1991; Martin, Velicer, & Fava, 1996; Velicer et al., 1995; Velicer, Martin et al., 1996; Velicer, Rossi et al., 1996). Another important longitudinal study would involve extending the external validity studies described here to other variables at 12, 24, or more months in the future. Of particular interest would be smoking behaviors for the members of the four groups. The existence of the cluster profilers should not be viewed as adding additional stages to the stages of change construct or having any direct implications for the stage of change construct. The cluster analysis took place within a single stage (aPC). The variables involved are from a different dimension of the model and do not involve the constructs (behavior intention and behavior duration) that define the temporal evolutionary dimension represented by stage. 4.5. Conclusions The results of this study serve to both provide a potential explanation about why smoking prevention programs have had only a limited level of success and to provide a potential alternative approach to developing future programs that will be more effective. The explanation for the failure involves the extremely high proportion of non-smokers who classify themselves in the Acquisition Precontemplation stage (~ 90%). This suggests that most non-smokers will not view interventions about smoking as personally relevant. Since they are currently not smoking and report no plans to try smoking in the future, they are likely to ignore information that is aimed at preventing smoking. The results of this study add to our understanding of the vast majority of adolescent nonsmokers, those who are not thinking about trying smoking in the next 6 months. Their baseline attitudes towards smoking and their temptation to try it are related to other variables such as family support for nonsmoking or peer influences, consistent with increased risk for smoking. Four clusters were reliably identified among these adolescents and their cluster membership was also predictive of future smoking intentions and behavior up to three years in the future. The cluster profiles can provide a basis for the design of tailored interventions for smoking prevention.

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