The risk of smoking in relation to engagement with a school-based smoking intervention

The risk of smoking in relation to engagement with a school-based smoking intervention

Social Science & Medicine 56 (2003) 869–882 The risk of smoking in relation to engagement with a school-based smoking intervention Paul Aveyarda,*, W...

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Social Science & Medicine 56 (2003) 869–882

The risk of smoking in relation to engagement with a school-based smoking intervention Paul Aveyarda,*, Wolfgang A Markhamb, Joanne Almonda, Emma Lancashirea, K.K. Chenga a

Department of Public Health and Epidemiology, The University of Birmingham, Birmingham B15 2TT, UK b School of Education, The University of Birmingham, Birmingham B15 2TT, UK

Abstract Health promotion interventions cannot work if people do not engage with them. The aim of this study was to examine whether disengagement from an adolescent smoking prevention and cessation intervention was an independent risk factor for regular smoking 1 and 2 years later. The data were taken from a cluster randomised controlled trial, in the West Midlands, UK, based on the transtheoretical or stages of change model. In this trial, 8352 13–14-year old school pupils enrolled, and the data in this report were based on the 7413 and 6782 pupils present at 1 and 2 years follow-ups, respectively. The intervention group undertook three sessions using an interactive computer programme. At the end of the programme, pupils recorded their responses to it. Pupils were classed as engaged if they thought the intervention was both useful and interesting; all others were classed as disengaged. Random effects logistic regression related the number of times engaged to regular smoking at 1 and 2 years follow-up, adjusted for school absences and 11 potential confounders. The majority of pupils were engaged by the intervention. For participants using the intervention three times but not engaging once, the odds ratios (95% confidence intervals) for smoking at 1 and 2 years relative to the controls were 1.83 (1.41–2.39) and 1.70 (1.38–2.11). For those engaging three times, they were 0.79 (0.60–1.03) and 0.96 (0.75–1.21). There was no interaction with baseline intention to smoke, classified by stage of change, but there was a borderline significant interaction with baseline smoking status, with disengagement acting as a stronger risk factor among baseline never-smokers. We conclude that disengagement from interventions is a risk factor for smoking independently of experimentation with cigarettes. The best explanation is that disengagement from school, an established risk factor for smoking, generalises to disengagement from didactic school-based health promotion programmes. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Smoking; Adolescence; Schools; Smoking prevention; Randomised controlled trial; UK

Introduction A classic approach to smoking prevention is to use epidemiological methods to determine risk factors for smoking, such as social influences. Then, using this knowledge, the opposite and hopefully at least equal force is applied to adolescents to stop them becoming smokers, e.g. Peterson, Kealey, Mann, Marek, and *Corresponding author. Tel.: +44-121-414-4532; fax: +44121-414-6762. E-mail address: [email protected] (P. Aveyard).

Sarason (2000). Whether the social influences or alternative paradigms are chosen, the common element is the passive pupil in the middle of these opposing forces (Michell, 1994). In 1997 we started a cluster randomised trial using methods based on the transtheoretical model (TTM) (Prochaska & Diclemente, 1983; Pallonen, Prochaska, Velicer, Prokhorov, & Smith, 1998) to prevent smoking in non-smoking adolescents, and to assist stopping adolescent smokers (Sherratt & Almond, 1999; Aveyard et al., 2001, 1999). The TTM proposes that adolescents are in one of nine stages ranging from acquisition precontemplation (not

0277-9536/03/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 7 - 9 5 3 6 ( 0 2 ) 0 0 0 8 8 - 6

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intending to commence smoking in the next 6 months), to cessation maintenance (stopped smoking at least 6 months ago) (Pallonen et al., 1998). According to the model, the other concepts of the TTM—temptations, decisional balance, and processes are the driving forces that move individuals through the stages (Prochaska, Diclemente, & Norcross, 1992). This trial used the classic approach outlined above to reverse the seemingly inexorable transition to smoking that occurs throughout adolescence, by moving adolescents in the opposite direction through the stages. For example, participants in acquisition preparation could be told ‘‘To be more like others who were thinking about trying it [smoking], but have chosen to stay smoke free, think more about the cons of smoking’’, and some of these were then listed. Participants may well have asked why they should do this. The only reason to do so would be to avoid becoming a smoker, which, it is assumed, is as important to the adolescent as it is to the health educator. The assumption underpinning this approach, that individuals do not actively want to become smokers is common to many different theoretical perspectives. It is inherent in the social influences approach (Flay, 1985), for example, which suggests that, as West & Michell (1999) characterise it, some adolescents will be cajoled into smoking against their will. This assumption explains why the same young people who at the start of adolescence are fiercely anti-smoking have become smokers by the end of adolescence (Chassin, Presson, Sherman, & McGrew, 1987). Nevertheless, this remains an assumption, and, to our knowledge, an untested assumption. In this report, we derive a variable, engagement, which recorded whether individuals felt the TTM intervention in the trial was interesting and useful. Clearly, no intervention could ever be thought interesting and useful by every adolescent. However, non-smokers who thought the intervention was either uninteresting or not useful should be no more likely to become smokers in the future than young people who thought it was interesting and useful. Alternatively, if disengagement with the intervention reflected desire to smoke, then disengagement from the intervention would act as a risk factor for later smoking. However, controlling for smoking behaviour and intention to smoke, indicated by stage of change, should abolish the apparent risk of becoming a smoker in future arising from disengagement with the intervention. This is because in this paradigm, experimentation with smoking, intention to smoke, and disengagement have the same meaning: a wish to smoke in the future, but smoking behaviour and intention to smoke are more direct measures of this than is disengagement from the intervention. The aims of this study, therefore, were to examine whether disengagement from the intervention was associated with smoking at 1 and 2 years follow-up in the trial, and to show whether the effect of

disengagement was independent of smoking status or intention to smoke.

Method The data are taken from a previously reported trial, and the full report is freely available at www.bmj.com/ cgi/reprint/319/7215/948 (Aveyard et al., 1999), and updated results are also published (Aveyard et al., 2001). Briefly, 89 West Midlands schools were selected randomly with probability proportional to size, and were approached to participate in a randomised trial of smoking prevention and cessation. Fifty-two (58.4%) schools agreed and these schools were randomised into intervention and control status. Letters were sent to parents asking them to ask their children to opt out if they objected, and pupils were also explicitly offered the chance to decline participation and were made aware of the option of withdrawing at any stage without reason. Pupils in both the intervention and control schools completed a baseline paper questionnaire. All Year 9 pupils (aged 13–14) were eligible to participate and 4125 pupils in the 26 intervention schools participated (93.1% of registered pupils), and 4227 pupils in the 26 control schools participated (91.1% of registered pupils). This questionnaire was administered under examination conditions at baseline in the first term of Year 9. The same questionnaire administered identically was completed in the first term of Year 10 (pupils aged 14–15) to give the 1-year follow-up data in this report, and in the first term of Year 11 (pupils aged 15–16) to give the 2year follow-up data in this report. At 1-year (Year 10) follow-up, 7444 (89.1%) pupils were present and smoking status could be allocated to 7413 (99.6%) of those followed up, and these 7413 pupils provided the data analysed at the 1-year follow-up in this report. At baseline in this cohort, 426 (11.6%) in the intervention schools and 427 (11.4%) in the control schools were regular smokers, similar to the national average for that school year in 1996 (Jarvis, 1997). At 2-year (Year 11) follow-up, two control schools refused permission to follow-up pupils because of concern about the time taken. Year 11 is the main examination year. Not counting these in the percentage, 6819 (84.6%) original participants were present at 2-year follow-up. Smoking status was allocated to 6782 participants (99.5% of those followed up), and these 6782 participants provided the data analysed at 2-year follow-up in this report. At baseline in this cohort, 379 (10.7%) in the intervention schools and 332 (10.2%) in the control schools were regular smokers, similar to the national average for that school year in 1996 (Jarvis, 1997). The paper questionnaire was based on a questionnaire used in a parallel trial in the United States by Prochaska. It contained questions used to code smoking status as

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never, occasional, current regular smoker, and exregular smoker on each of the three occasions. In the UK, adolescent regular smokers are defined as those who smoke at least one cigarette per week (Bewley, Day, & Ide, 1973; Jarvis, 1997); a definition also used in the US (Leventhal & Cleary, 1980). We used regular smoking defined in this way as our outcome variable. Smoking status was provisionally defined by reference to responses to two questions. One question was very similar to the standard question used in the UK to define smoking status (Bewley et al., 1973), where respondents described the amount of cigarettes consumed in six categories. The second question was derived from an algorithm published by Pallonen et al. (1998) that is used to allocate stage of change, where respondents selfidentified as never, occasional, regular, and ex-regular smokers. For smokers, responses were checked with responses to four other questions on cigarette consumption in the past 24 h, 7 and 30 days, and a question on when smoking commenced. For ex-smokers, responses to the two main questions were checked with responses to those questions for regular smokers, plus two additional questions on intention to give up smoking in the future, where the response ‘I have already given up’ was expected. For never and occasional smokers, responses to the two main questions were checked against each other only. The degree of inconsistency between provisional smoking status and these checking questions was rated on a scale of seriousness. Serious inconsistency resulted in unknown smoking status being allocated. At the 1-year follow-p, 7147 (96.4%) of those with known smoking status gave totally logically consistent answers. At the 2-year follow-up, 6579 (97.0%) of those with known smoking status gave totally consistent answers. In a submitted manuscript, we showed that in test–retest and parallel form assessments, the kappa (k) (95% CI) for smoking status, categorised as never, occasional, regular, and ex-smoking, were 0.77 (0.66–0.87), and 0.77 (0.75–0.78), respectively, indicating good agreement (Altman, 1991). For regular smoking and not regular smoking, the outcome variable in this report, the k (95% CIs) were 0.87 (0.68–1.00), and 0.85 (0.82–0.87), respectively, indicating very good agreement (Altman, 1991). Anglicised versions of the questions necessary to allocate stage of change were included in the questionnaire. An algorithm, derived from Pallonen et al. (1998), was used to allocate stage of change. Stage of change could not be allocated to 745 (10.0%) participants with known smoking status at the 1-year followup. Stage of change could not be allocated to 511 (7.5%) of participants with known smoking status at the 2-year follow-up. Unknown stage was used as a category in the analyses described below. In the test–retest and parallel form assessments described above, the k (95% CI) for stage of change were 0.46 (0.28–0.63), and

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0.52 (0.50–0.54), respectively, indicating only moderate agreement. Agreement for stage of change was, however, significantly higher for those in acquisition precontemplation—pupils with no intention to try smoking in the next 6 months. In the trial, our intention for control group schools was that they would receive no intervention. However, smoking-related education is part of the English National Curriculum and most if not all pupils in control schools would have received this education. In addition, as a reward for their co-operation, we supplied teachers in control group schools with three lesson plans on smoking, two on facts about smoking with quizzes, and one lesson on different methods of persuading people to stop smoking. We have no data on how many lessons control group pupils received on smoking, nor whether these materials were used. Participants in the intervention schools received three components of the intervention, one component in each term of Year 9. The intervention consisted of three whole class lessons, which will not be considered further in this report, and three sessions using an interactive computer programme based on the TTM, which were intended to prevent smoking in baseline non-smokers or assist cessation in baseline smokers. The computer programme was developed by Prochaska and colleagues (Pallonen et al., 1998; Redding et al., 1999), and commercially anglicised and piloted to ensure it was acceptable to the target audience. Pupils entered a unique identity number into the computer programme. The computer programme consisted of questionnaires to define smoking status and the constructs of the TTM. After each computer questionnaire, pupils were given feedback on what stage they were in, for example cessation precontemplation, and how their use of the processes of change, for example, compared to others in cessation precontemplation. On second or third use, participants received feedback on changes from previous sessions. These questionnaires were interspersed with video clips of young people talking about smoking. The questionnaire was presented on screen and simultaneously through headphones. To deliver this intervention, we set up a classroom with portable computers with headphones, and whole classes came in turn to use the computers throughout the day (Sherratt & Almond, 1999). The programme was not available on the school computers outside the trial. At the end of each of the three computer sessions, participants completed five questions with Likert-type responses on their reactions to the computer programme, previously reported (Aveyard et al., 1999). This questionnaire was part of the computer programme, and it was necessary to complete it to exit the programme. Responses to this questionnaire were therefore 100% complete. We used two items from this questionnaire to define engagement. We classified pupils

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as engaged if they reported the programme was very interesting or interesting (as opposed to boring, or very boring), and also reported the programme was useful (strongly agree or agree as opposed to neither agree nor disagree, disagree, or strongly disagree). Pupils who thought the intervention was boring, or who did not think it was useful were classified as disengaged. We tabulated the proportion of pupils that found the programme interesting, useful, and were engaged by smoking status and number of computer sessions attended. The effect of engagement on the risk of smoking at outcome We analysed the risk of regular smoking at 1 (Year 10) and 2 years (Year 11) follow-up in relation to the number of computer sessions attended and engagement with each of those sessions. Logistic regression was used to calculate ORs and 95% CIs, and Wald statistics. In the intervention schools, the computer sessions were integrated into the health education curriculum of that year, so the only way pupils could have avoided these sessions was if they or their parents objected. Having originally consented to enter the trial, no pupils withdrew. Therefore non-attendance at the computer session was a function of not being at school that day. However, absence from school is associated with smoking and also with risk factors for the uptake of smoking (Charlton & Blair, 1989). Individuals who were engaged only once might therefore have a high risk of smoking simply because they were absentees on the two occasions, rather than because of the effect of disengagement per se. We therefore entered the number of computer sessions received along with the number of times engaged as dummy terms into a logistic regression equation with regular smoking as the outcome. We subsequently replaced these dummy terms with linear terms, because the effect of both these variables appeared linear, calculating Wald statistics for these terms. We then adjusted for eleven potential risk factors for smoking. These risk factors are fully tabulated elsewhere (Aveyard et al., 1999), but were ethnic group (white/non-white), gender, age, deprivation (five categories based on the area of residence), maternal smoking, paternal smoking, sibling smoking, best friend’s smoking, contact with mother, contact with father, and baseline smoking status (defined as never, occasional, current regular smoker, ex-regular smoker). All these, bar age, were entered as dummy terms. We used multilevel modelling for Windows with school as a random effect to account for the clustering of pupils within schools. The interaction between engagement and baseline smoking status We then examined whether the effect of engagement differed by smoking status. To do this, we fitted

interaction terms for each smoking status (never, regular, and ex, with occasional smokers as the reference category)  a linear term for the number of times the programme was used, and  a linear term for the number of times engaged, calculating the Wald statistics for these terms. For this we used the fully adjusted model with linear terms for number of times used and number of times engaged. ORs, however, can be hard to assimilate and are potentially misleading with outcomes as common as smoking in adolescence (Davies, Crombie, & Tavakoli, 1998). To overcome this problem, we calculated the modelled percentage of smokers in the group that used the intervention three times but were disengaged on each occasion, and the group who used it three times and engaged on every occasion. To avoid these risks applying only to a specific sub-group of the population, we out of necessity removed the ten other potential confounders, leaving in the model the number of times used, number of times engaged, smoking status, and the interactions between these factors. We plotted the percentage regular smoking at 1 and 2 years followup against number of times engaged for each baseline smoking status. The interaction between engagement and baseline stage of change One possibility is that engagement simply reflects intention to smoke. Stage of change was measured as part of the trial (Aveyard et al., 1999), which categorises intention to smoke in the future (Pallonen et al., 1998). In the introduction, we suggested that individuals that wanted to become smokers, for example, might be disengaged from an intervention to keep them nonsmoking. We therefore used data on baseline stage to examine whether controlling for stage removed the effect of engagement in the fully adjusted model above. However, as stage is also categorised by reference to smoking status (Pallonen et al., 1998), there was strong collinearity between stage and smoking status. We therefore removed baseline smoking status from the fully adjusted models. In addition, we created interaction terms for the linear term of number of times engaged  baseline stage and added these to the fully adjusted models for smoking at 1 and 2 years outcomes. We calculated Wald statistics for the main and interaction effects. Sensitivity analysis The aim of this paper was to examine whether disengagement predicted smoking, and not vice versa. However, individual’s baseline smoking status in the above analyses was recorded in the first term of Year 9, and pupils used the computer programme once on the day of completing the baseline paper questionnaire, once

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in the second term, and once in the third term of Year 9. There was evidence (see Results) that smokers were more likely to be disengaged at baseline. Therefore evidence that disengagement predicts smoking could simply reflect the effect of pupils becoming smokers and hence becoming disengaged from interventions later in the year. Smoking could precede disengagement in the above analyses. We addressed this in sensitivity analysis, by excluding all those baseline non-regular smokers (never and occasional smokers) who became regular smokers during the three terms of the intervention. We therefore examined the influence of disengagement on risk of becoming a regular smoker between the end of the intervention (third term of Year 9) and the 1-year follow-up (first term of Year 10), and 2-year follow-up (first term of Year 11). This analysis is biased however. Firstly, we excluded from the intervention group all non-regular smokers who became regular smokers during the intervention phase, constituting the first 7–8 months of follow-up. The equivalent group could not be excluded from the control group, because we had no similar data on smoking status during the year. This would reduce the apparent risk of smoking in the intervention group relative to the control group, but should not influence the apparent risk of smoking with respect to number of times engaged within the intervention group. The second bias, however, would influence the effect of engagement on smoking status. If disengagement truly predicted smoking uptake, then more smokers would be excluded from the disengaged groups than engaged groups. Thus, in this analysis, if disengagement emerged as a risk factor for smoking, it was overcoming a bias in the opposite direction. For this analysis therefore, we excluded all baseline (Year 9) occasional smokers and never-smokers who at any point on the three interventions during Year 9 admitted to regular smoking. For convenience, we also excluded those few (0.8%) whose smoking status was unclear on the baseline questionnaire (Year 9), which were included in the cohorts described above. However, when we looked at baseline (Year 9) ex-smokers, over 90% of them were regular smokers at either the second or third intervention during Year 9. To avoid excluding all of these, we categorised them as regular smokers. This accords with evidence that in young teenagers, smoking pauses are a common behaviour pattern in smokers (Goddard, 1990), and they are not truly exsmokers as we normally understand the term. We used the fully adjusted model described above and fitted linear terms for number of times engaged and number of times the programme was used, with regular smoking as the outcome at 1 and 2 years follow-up. We then fitted interaction terms for number of times used  baseline regular smoking, number of times used  baseline never smoked, number of times engaged  baseline regular smoking, and number of times engaged  baseline never

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smoked, leaving baseline occasional smokers as the reference group.

Results Table 1 shows the intervention was deemed interesting and useful by most participants on each of the three computer sessions, which accords with our sense that it was popular when we delivered it (Sherratt & Almond, 1999). (Feedback from the teachers about the whole class lessons suggested they were also popular (Aveyard et al., 1999).) This, however, did not make the intervention work. The percentage smoking in those followed up at 1 year was 18.8% in the TTM arm and 17.5% in the control arm: a difference (95% CI) of 1.3% (1.9%–4.9%) (Aveyard et al., 1999). At 2 years, the corresponding figures were 23.5% and 22.4%: a difference (95% CI) of 1.1% (1.8%–4.2%) (Aveyard et al., 2001). There were no differences in the percentage making positive movements in stage (Aveyard et al., 2001). The effect of engagement on the risk of smoking at outcome Table 2 shows the effect of number of computer sessions attended and number of times young people were engaged by the computer programme, when mutually adjusted, relative to the risk of smoking in the control group. Reading down the columns shows the effects of number of computer sessions attended when engagement is constant, and reading along the lines shows the effects of engagement when number of computer sessions attended is constant. Those who used the computer few times had a higher risk of smoking than those who used it more times, and those who were engaged by it had a lower risk of smoking than those who were not. Generally, adjustment for confounders attenuated slightly but did not eliminate these risks. The effect was apparent when assessed at 1 (Year 10) and 2 years (Year 11) follow-up. Replacing the dummy terms for number of interventions and number of times engaged with linear terms showed that there was evidence for a linear relation for both in the adjusted models. At 1 year the Wald statistic for the linear term for number of interventions was w2 ¼ 14:0; d.f.=1, po0:001 and for engagement it was w2 ¼ 36:0; d.f.=1, po0:001: The ORs (95% CIs) were 1.19 (1.09–1.31) for total interventions, and 0.74 (0.68–0.82) for number of times engaged. At 2 years the Wald statistic for the linear term for number of interventions was w2 ¼ 9:6; d.f.=1, po0:001; and for engagement it was w2 ¼ 23:3; d.f.=1, po0:001: The ORs (95% CIs) at 2 years were 1.14 (1.05–1.25) for total interventions, and 0.81 (0.74–0.88) for number of times engaged. This

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Table 1 Use of computer program and reaction to it by those in the intervention schools 1st use

2nd use

3rd use

Smokers

Nonsmokers

Smokers

Nonsmokers

Smokers

Nonsmokers

n (%)

n (%)

n (%)

n (%)

n (%)

n (%)

426 (100.0)

3239 (99.9)

414 (97.2)

3180 (98.1)

322 (75.6)

2618 (80.7)

261 (61.3)

2377 (73.4)

209 (50.5)

2078 (65.3)

144 (44.7)

1513 (57.8)

326 (76.5)

2931 (90.5)

252 (60.9)

2503 (78.7)

162 (50.3)

1659 (63.4)

237 (55.6)

2274 (70.2)

186 (44.9)

1895 (59.6)

117 (36.3)

1294 (49.4)

379 (100.0)

3148 (99.9)

366 (96.6)

3099 (98.3)

290 (76.5)

2559 (81.2)

229 (60.4)

2315 (73.5)

185 (50.5)

2036 (65.7)

133 (45.9)

1478 (57.8)

288 (76.0)

2857 (90.8)

226 (61.7)

2450 (79.1)

149 (51.4)

1631 (63.7)

202 (53.3)

2216 (70.4)

164 (44.8)

1864 (60.1)

109 (37.6)

1265 (49.4)

a

All those followed up at 1 year Participating in the intervention Number taking interventionb Reaction to intervention Session useful (% agree or strongly agree)c Session interesting (% very interesting or interesting)c Engagedc All those followed up at 2 yearsd Participating in the intervention Number taking interventionb Reaction to intervention Session useful (% agree or strongly agree)c Session interesting (% very interesting or interesting)c Engagedc a

426 baseline regular smokers and 3243 baseline non-regular smokers (3669 total). % of those present at baseline. c % of those present at session. d 379 baseline regular smokers and 3151 baseline non-regular smokers (3530 total). b

confirms the impression of Table 2 that engagement had a larger influence on smoking risk than number of times the computer was used. (The OR for number of times used suggests increased risk of smoking because it expresses the risk for pupils who did not engage at all. The equivalent of Table 2 from the model with linear terms was similar to Table 2. This is available on request.)

The interaction between engagement and baseline smoking status In the fully adjusted models, we included interaction terms for engagement by smoking status. At 1 year, the main effects of engagement and number of uses remained significant: w2 ¼ 14:5; d.f.=1, po0:001 and w2 ¼ 6:5; d.f.=1, p ¼ 0:011; respectively. There was weak evidence that the effect of disengagement depended upon baseline smoking status: w2 ¼ 6:8; d.f.=3, p ¼ 0:078 for the interaction term. The ORs (95% CIs) for smoking at 1 year for the linear engagement terms for never, occasional, ex, and regular smokers were 0.64 (0.50–0.80), 0.74 (0.63–0.86), 0.79 (0.59–1.06), and 0.91 (0.70–1.18), respectively. This indicates that the effect of

engagement was strongest for never-smokers, if it varied at all by baseline smoking status. At 2 years, the main effects of engagement was still significant (w2 ¼ 4:1; d.f.=1, p ¼ 0:042), but the main effect of number of uses was not (w2 ¼ 0:5; d.f.=1, p ¼ 0:49). There was weak evidence for the interaction of number of times engaged  baseline smoking status: w2 ¼ 7:2; d.f.=3, p ¼ 0:065: The ORs (95% CIs) for smoking at 2 years for the linear engagement terms for never, occasional, ex, and regular smokers were 0.71 (0.58–0.87), 0.86 (0.75–0.99), 0.96 (0.73–1.26), and 0.78 (0.60–1.01), respectively. This indicates the effect of engagement was strongest for never-smokers, as it was with the outcome assessed at 1-year follow-up. On removal of the ten other potential confounders from these equations, the point estimates of these main effects and interaction terms differed only slightly from those in the fully adjusted model described above. Figs. 1 and 2 show the modelled percentage of smokers derived from these models in Years 10 and 11, respectively. These figures confirm the impression that engagement generally had a larger effect than did number of uses, and that the implications of engagement or disengagement on the risk of smoking was substantial for nearly all baseline smoking statuses.

0.96 (0.75–1.21) 0.71 (0.58–0.87) Model with dummy terms for number of times used and number of times engaged and no interaction terms. a

0.91 (0.75–1.09) 0.77 (0.64–0.93) 0.62 (0.51–0.77) 1.09 (0.87–1.35) 1.47 (1.16–1.87) 1.02 (0.86–1.22) 1.40 (1.17–1.67) 1.19 (1.00–1.43) 0.74 (0.36–1.53) 1.65 (1.19–2.28) 1.49 (1.15–1.94) 1.45 (0.80–2.66) 2.00 (1.53–2.61) 1.70 (1.38–2.11) At 2 years Used once Used twice Used three times

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The interaction between engagement and baseline stage of change

0.72 (0.58–0.89) 0.97 (0.78–1.21)

0.79 (0.60–1.03) 0.63 (0.50–0.78) 0.93 (0.76–1.13) 0.79 (0.65–0.96) 2.62 (1.50–4.61) 2.17 (1.59–2.96) 1.83 (1.41–2.39) At 1 year Used once Used twice Used three times

1.99 (1.00–3.94) 1.86 (1.31–2.63) 1.76 (1.32–2.34)

1.81 (1.51–2.17) 1.50 (1.25–1.80) 1.26 (1.05–1.52)

1.63 (1.30–2.04) 1.13 (0.88–1.44) 1.51 (1.16–1.98)

0.68 (0.53–0.87) 0.92 (0.72–1.17)

Adjusted Unadjusted Adjusted Unadjusted Adjusted Unadjusted Adjusted Unadjusted

Engaged twice Engaged once Engaged no times

Table 2 OR (95% CI) for smoking by number of times computer program used and number of times engaged relative to those in the control arma

Engaged three times

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The linear term for number of times engaged was the same when adjusted for baseline stage (and the other variables) as when adjusted for baseline smoking status (and the other variables). The OR (95% CI) at 1 year was 0.74 (0.67–0.81), (w2 ¼ 41:3; d.f.=1, p ¼ o0:001). At 2 years, the OR (95% CI) for engagement was 0.79 (0.73–0.86), (w2 ¼ 30:4; d.f.=1, po0:001). There was little evidence to support the hypothesis that the effect of engagement varied by baseline stage of change at 1 and 2 years (w2 ¼ 6:5; d.f.=8, p ¼ 0:59; and w2 ¼ 10:3; d.f.=8, p ¼ 0:24; respectively). The main effects of engagement remained significant however (w2 ¼ 33:1; d.f.=1, po0:001; and w2 ¼ 22:6; d.f.=1, po0:001 at 1 and 2 years). There was no support for the hypothesis that engagement was reflecting intention to become a smoker as described by stage. The ORs (95% CIs) for smoking at Years 10 and 11 follow-up for participants in acquisition precontemplation, with no thoughts about commencing smoking in the next 6 months, were the same as those reported above for all participants. At 1 year the OR (95% CI) was 0.73 (0.65– 0.81), and at 2 years it was 0.80 (0.73–0.88).

Sensitivity analysis Excluding non-smokers who became smokers during the course of the intervention did not change the predictive ability of engagement greatly from the models above (Table 3). The influence of engagement on risk of smoking remained significant in the models with no interaction terms (w2 ¼ 14:9; d.f.=1, po0:001 for Year 10 follow-up, and w2 ¼ 12:4; d.f.=1, po0:001 for Year 11 follow-up). At 1-year follow-up in Year 10, the OR (95% CI) was 0.80 (0.71–0.89), and at 2-year follow-up in Year 11, it was 0.83 (0.75–0.92). However, the risks from number of interventions altered slightly. At 1 year the OR (95% CI) was 1.05 (0.94–1.17), and at 2 years it was 1.06 (0.96–1.17). (This is compatible with the reduction in risk of smoking in the TTM group as a whole arising from exclusion of those who became smokers in the first part of the year.) There was statistically significant evidence that the influence of engagement varied by baseline smoking status. The interaction terms for smoking status  number of uses, and smoking status  number of times engaged contributed significantly to the model: Year 10 follow-up w2 ¼ 10:3; d.f.=4, p ¼ 0:035; and Year 11 follow-up w2 ¼ 15:4; d.f.=4, p ¼ 0:0040 for the inclusion of these interaction terms. Table 3 shows that the influence of engagement was smallest on occasional smokers, larger on current/ex-smokers, and largest of all on never-smokers.

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876

Occasional smokers % regular smokers at 1 year

% regular smokers at 1 year

Never smokers 7% 6% 5% 4% 3% 2% 0

1

2

25%

20%

15%

10%

3

0

Number of times engaged

3

Current smokers % regular smokers at 1 year

% regular smokers at 1 year

Ex smokers

50% 45% 40% 35% 30% 25% 1

2

Number of times engaged

55%

0

1

2

85% 80% 75% 70% 65% 60%

3

0

Number of times engaged

1

2

3

Number of times engaged

Used once

Used twice

Used three times

Fig. 1. The modelled percentage smoking at 1-year follow-up by baseline smoking status.

Discussion These results show that both non-attendance at school and non-engagement with an anti-smoking intervention were risk factors for smoking, independently of other established risk factors. The strongest risk factor was non-engagement. Sensitivity analysis showed that these results could not be explained by reverse causation: smoking causing disengagement. Disengagement preceded smoking uptake. The influence of disengagement was largest for never-smokers, though there was limited evidence to support this. There was strong evidence that the effect of disengagement was independent of stage of change, and little evidence that the effect of engagement varied by stage of change. There are five possible explanations for these findings. Five possible explanations for the findings The first explanation is that these findings are artefactual, resulting from loss to follow-up or social desirability biases. Of those lost to follow-up, the

percentage engaged on first, second, and third use were 61.1%, 53.8%, and 50.6% in Year 10, and 62.5%, 50.2%, and 47.7% in Year 11. This shows lower levels of engagement than among those followed up (Table 1), suggesting that the population followed up and the population lost to follow-up were different. Had followup been complete, then perhaps there would have been no association between engagement and smoking. However, loss to follow-up in the intervention arm was 11% and 17% at 1 and 2 years, respectively. With such a low proportion being lost, the effect of engagement on smoking in those lost to follow-up would have to be an extreme reversal of the gradient observed in those followed up to produce the results in Table 2. This is implausible. It is possible that some pupils felt that smoking was undesirable so played down the extent of their smoking, and also overstated the interest and usefulness of the intervention. Others may have done exactly the reverse for the same underlying reason: their view of what was socially desirable. Consequently, social desirability bias could potentially explain the observed gradient in risk of

P. Aveyard et al. / Social Science & Medicine 56 (2003) 869–882 Occasional smokers % regular smokers at 2 years

% regular smokers at 2 years

Never smokers 9%

8%

7%

6% 0

1

2

30% 28% 26% 24% 22% 20%

3

0

Number of times engaged

% regular smokers at 2 years

% regular smokers at 2 years

2

3

Current smokers

53%

52%

51%

50% 1

1

Number of times engaged

Ex smokers

0

877

2

80%

75%

70%

65% 0

3

Number of times engaged

1

2

3

Number of times engaged Used once Used twice Used three times

Fig. 2. The modelled percentage smoking at 2-year follow-up by baseline smoking status.

smoking stemming from disengagement. However, we think this is unlikely. All pupils completed this questionnaire, responding only to a computer. There was no prospect obvious to pupils that their responses would be shared with anyone at school, and pupils were assured of this. Consequently, there was no reason for pupils to report socially desirable responses rather than how they truly felt. We related information on engagement stemming from up to 2 years previously to smoking status, so it seems unlikely also that pupils could create artificial congruence between their views of the intervention and their smoking status at follow-up. The feedback questions were clearly demarcated from the intervention, and probably appeared less salient than the many questions comprising the intervention itself. However, there was evidence of congruence within each feedback questionnaire. A second question in the feedback questionnaire asked pupils to score the session as very valuable, valuable, not very valuable, and worthless. Assuming that this question tapped the same underlying concept as the question on usefulness, the Cronbach’s alphas for these two items were 0.56, 0.72, and 0.76 for the first, second, and third use, respectively, suggesting at least moderate reliability. The final reason

why social acceptability bias is an unlikely explanation of the findings stems from the size of risk emanating from disengagement. Among baseline never-smokers engaging with the computer programme three times, half of those who became smokers would have had to declare that they were not smoking to have the same true smoking prevalence as those who did not engage with the programme at all but used it three times (Fig. 1). We did not confirm smoking status biochemically, so we cannot exclude this. However, national surveys with and without biochemical validation suggest that nearly all pupils report smoking accurately whether confirmation is used or not (e.g. Goddard & Higgins (2000); Jarvis, 1997). It seems implausible, then, that social acceptability bias could explain these results, though it may account for a small part of the gradient we observed. A second possible explanation proposes that disengagement itself is unimportant. Instead, disengagement marks out pupils at intrinsically high risk of smoking. Implicit in this explanation is that disengaged pupils must not only be intrinsically more at risk of smoking, but that higher risk is not mediated by, or strongly correlated with, the confounding variables we adjusted for (gender, ethnicity, age, parental, sibling, best friend

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Table 3 Sensitivity analysis: the influence of engagement and number of uses of the computer intervention on the risk of smoking at one and two year follow upa

Outcome at 1 year All smoking statusesb Used once Used twice Used three times Occasional smokersc Used once Used twice Used three times Regular/ex-smokersc Used once Used twice Used three times Never-smokersc Used once Used twice Used three times Outcome at 2 years All smoking statusesb Used once Used twice Used three times Occasional smokersc Used once Used twice Used three times Regular/ex-smokersc Used once Used twice Used three times Never-smokersc Used once Used twice Used three times

Engaged no times

Engaged once

Engaged twice

Engaged three times

1.05 (0.94–1.17) 1.10 (0.89–1.36) 1.15 (0.84–1.58)

0.83 (0.74–0.94) 0.87 (0.78–0.98) 0.92 (0.81–1.03)

0.69 (0.55–0.88) 0.73 (0.58–0.92)

0.58 (0.41–0.82)

0.86 (0.69–1.07) 0.74 (0.48–1.14) 0.64 (0.33–1.21)

0.75 (0.56–0.99) 0.64 (0.48–0.85) 0.55 (0.42–0.73)

0.56 (0.32–0.98) 0.48 (0.27–0.84)

0.41 (0.18–0.97)

1.08 (0.86–1.37) 1.17 (0.73–1.88) 1.27 (0.63–2.57)

0.90 (0.66–1.24) 0.98 (0.71–1.35) 1.06 (0.77–1.46)

0.81 (0.59–1.24) 0.88 (0.64–1.21)

0.73 (0.53–1.01)

1.19 (0.91–1.56) 1.42 (0.82–2.43) 1.69 (0.75–3.79)

0.80 (0.55–1.17) 0.95 (0.65–1.39) 1.13 (0.78–1.66)

0.64 (0.44–0.84) 0.76 (0.52–1.00)

0.51 (0.35–0.75)

1.06 (0.96–1.17) 1.12 (0.92–1.37) 1.19 (0.88–1.61)

0.88 (0.79–0.98) 0.93 (0.84–1.03) 0.99 (0.89–1.10)

0.77 (0.63–0.95) 0.82 (0.67–1.01)

0.68 (0.50–0.93)

0.83 (0.69–0.99) 0.68 (0.48–0.98) 0.56 (0.33–0.97)

0.83 (0.67–1.04) 0.69 (0.55–0.86) 0.57 (0.45–0.71)

0.69 (0.44–1.08) 0.57 (0.37–0.90)

0.58 (0.30–1.13)

1.15 (0.94–1.42) 1.33 (0.88–2.02) 1.54 (0.83–2.87)

0.96 (0.73–1.27) 1.11 (0.85–1.46) 1.29 (0.98–1.69)

0.93 (0.71–1.00) 1.07 (0.82–1.41)

0.89 (0.68–1.18)

1.16 (0.93–1.44) 1.34 (0.87–2.07) 1.56 (0.82–2.98)

0.86 (0.64–1.14) 0.99 (0.75–1.32) 1.15 (0.87–1.53)

0.73 (0.55–0.71) 0.85 (0.64–0.83)

0.63 (0.47–0.84)

a

Excluding baseline never and occasional smokers who became smokers during the course of the intervention. Model with linear terms for number of computer sessions attended and number of times engaged. b Model without interaction terms. c Model with interaction terms.

smoking, socio-economic deprivation, contact with parents, baseline smoking status). Furthermore, the true risk factor, for which disengagement is simply a surrogate, must not only cause smoking, but cause disengagement as an unimportant by-product and explain the possible interactions. There are several established risk factors not included in our multivariable analysis, such as depression, low self-esteem, parental attitudes to tobacco, and parenting style (Tyas & Pederson, 1998). However, it is hard to imagine how parenting style, for example, causes smoking and disengagement, but disengagement is not an integral part of the mechanism by which adolescent smoking

occurs. This possibility, that disengagement is an important manifestation of the processes that lead young people to smoke is considered in our fourth and fifth explanations. However, on our second explanation, we conclude that confounding is an unlikely explanation of the relation between disengagement and smoking. The third explanation of the results is that the intervention really did work for those that it engaged. This explanation, however, is unlikely. There was no evidence that the intervention worked at all (Aveyard et al., 1999, 2001), yet the majority of pupils who experienced it were engaged. Furthermore, if we allow that the intervention caused pupils engaging with it to

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resist becoming smokers or stop smoking, we must conclude that the intervention caused pupils who experienced it and did not engage with it to start or persist with smoking. Because individuals were not randomised into engagement or non-engagement, we cannot know for certain whether the intervention caused this effect, or whether engagement was a characteristic of those at low risk of smoking. However, we think that the latter explanation is more likely. Our fourth explanation is perhaps the simplest and was described in the introduction. The explanation for the findings could be that individuals who wanted to become or stay smokers did not engage with an intervention to prevent them doing so. This is the most parsimonious explanation. We were unable to find parallel results in smoking prevention, but an antiracist educational intervention only hardened racist attitudes among already racist students (Miller, 1969), which supports this explanation. However, in our study, the effect of disengagement in individuals with no intention of becoming smokers or no previous experimentation with smoking was at least as large and possibly larger than the effect of disengagement on those who had. If disengagement simply reflected a wish to smoke, then disengaged individuals were either lying, or their intentions were not captured by the smoking status and intention questions. Similarly, for those that had started to experiment with thoughts of smoking or smoking itself, disengagement continued to act as risk factor, despite controlling for intention to smoke, indexed by stage of change. Both these findings are hard to fit with this proposed explanation, and we think it is unlikely to be true. Our fifth explanation is, in our view, the most likely. We propose that disengagement functions as a risk factor for smoking because disengaged individuals were disengaged from the school generally, and disengagement from school is an important risk factor for smoking. There is a growing literature that school disengagement is associated with an increased risk of smoking and other harmful health behaviours (McLellan, Rissel, Donnelly, & Bauman, 1999), which explains why disengagement was a risk factor independently of stage or smoking status. This explanation could explain also why disengagement was a stronger risk factor for those who had not experimented with smoking compared to those who had. We propose that disengagement from the intervention reflected both individual’s views about the intervention itself, which had no implication for future smoking, and individual’s views about school generally, which are important in future smoking. The latter assertion is supported by evidence from other quantitative (Conrad, Flay, & Hill, 1992) and qualitative (Michell, 1997a, b) studies that disengagement from school is associated with smoking, and in this study, by the finding that baseline regular smokers were less

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likely to be engaged than baseline non-regular smokers (Table 1). The reason that disengagement was not a strong risk factor for baseline smokers is that most baseline regular smokers were already disengaged from school (Michell, 1997a, b; Glendinning, Shucksmith, & Hendry, 1994), and disengagement therefore primarily reflected their view of the intervention itself. Most never smoking pupils would be engaged with school generally, however (Michell, 1997a, b; Glendinning et al., 1994). For never-smokers who were disengaged, therefore, disengagement reflected disengagement from school as well as their views of the intervention, and to the extent that it reflected disengagement from school generally, it was a risk factor for smoking uptake. We are not proposing that disengagement from school represents a new isolated risk factor for smoking. The evidence suggests that disengagement from school is associated with a repertoire of risky behaviours and attitudes signifying rebellion against school’s and society’s expectations (Nutbeam, Smith, Moore, & Bauman, 1993). This rebellion is not simply a consequence of individual and familial risk factors, for we adjusted for these. Rather, it represents a particular reaction of individual pupils to their experiences of their schooling. It seems likely to us, and at least plausible, that school characteristics, as well as individual characteristics, influence how many pupils and which pupils are predisposed to reject schools and go on to rebel against school authority. Implications for the understanding of the process of smoking acquisition If our fifth explanation is accepted, these findings show that for pupils disengaged from didactic interventions and thus from school, even externally provided and generally popular health education programmes, such as the one provided in this trial, are tainted by the poor image of the school. Thus disengagement generalises from schooling and academic activities to didactic health education. Ironically, in this case, the disengaged minority was actually correct in their assessment when they viewed this programme as useless. However, it is unlikely that even if the didactic school programme had been effective, their reaction would have been different. There is a second implication for the literature on smoking and school disengagement. Many previous studies have been cross-sectional (e.g. McLellan et al., 1999; Simons-Morton et al., 1999b; Simons-Morton, Crump, Haynie, & Saylor, 1999; Samdal, Nutbeam, Wold, & Kannas, 1998; Resnick et al., 1997; Nutbeam, Aar, & Catford, 1989), so it has been impossible to know which came first, smoking or disengagement. Smoking uptake might lead individuals into youth cultures where disrespect for school is the norm. However, other studies

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have clarified the time sequence, showing that school disengagement among never-smokers precedes smoking uptake, e.g. Krohn, Massey, Skinner, and Lauer, 1983; Skinner, Massey, Krohn, and Lauer, (1985) and others summarised in Conrad et al. (1992). What no previous studies have done, however, is examine the influence of disengagement in young people who are occasional smokers, regular smokers, ex-smokers, as well as neversmokers, nor have they examined the interaction with intention to smoke as we have done. A dominant paradigm in youth smoking is that non-smokers are influenced by smokers to smoke and, paralleling this, non-smokers are influenced into anti-school attitudes (Conrad et al., 1992; Flay, 1985). What our investigation shows is that school disengagement occurs before thoughts of smoking, so that separate processes are needed to explain school disengagement first, and smoking uptake second. One explanation may be that smokers influence non-smokers first to undervalue schooling. In a Machiavellian way, smokers know that detachment from school is necessary to recruit a new smoker. However, this explanation does not fit with evidence that for many young people, smoking is simply not an important issue relative to other concerns (Michell, 1997a, b). A more plausible explanation, in our view, though with little direct evidence to support it, is that school disengagement leaves young people bereft of a value system and identity, because they no longer cleave to the values of the school. Such young people seek alternatives, and many alternative identities are more pro-smoking than the identities adopted by young people still attached to school. The caveat to these implications, of course, is that we have no direct evidence that the disengaged were disengaged from school more generally, rather than simply disengaged from this programme, but this hypothesis fits the observations best. Implications for health promotion research and practice In this paper, we have used the term didactic to describe the smoking intervention. By didactic we mean that adults (in this case public health advisors but usually teachers) chose the subject to be addressed in the health education lesson, selected the activities to be undertaken, the pacing, and the sequencing of these activities. An implicit assumption of this approach is that these adults are the only source of relevant knowledge and that the pupils have no knowledge, unless it agrees with the knowledge imparted by the adults. Our findings support other authors’ calls for changes in health education curriculum development and pedagogic practice (Bullock, Haywood and Mac an Ghaill (1996); Combes, 1989). The health education curriculum should, we propose, recognise that young people are actively involved making sense of the socio-

ecological influences of their lives, and in making choices about how best to function in the world in which they live. It should also acknowledge that young people are interested in issues related to health and well being, and many recognise that, as disadvantaged and disenfranchised members of society, collective action is the key to facilitating improvements in health and well being. Our assertions are supported by the recent increased involvement of young people in vegetarianism and ecological issues such as reclaiming the streets for pedestrians. Currently in England, the content and teaching methods of personal, social, and health education is subject to Government advice, and not mandatory instruction, as it is for most other subjects. It is possible to capitalise on this flexibility by increasing young people’s involvement in the selection of the health education topics to be addressed and thereby actively encouraging them to define their own health agendas. Health education should, we propose, also be used to develop the skills that are required for collective action, rather than focussing mainly on the acquisition of life skills for the individual, such as assertiveness skills. A possible drawback of this approach is that smoking for example may not be selected as an issue to be addressed in health education. Michell (1997a, b) proposed that researchers have overemphasised the importance of smoking in young people’s lives. Even if smoking was not raised as an issue, would this be such a bad thing? Instead young people might become more aware of the influence of social, economic, and political conditions on health, which are after all the most fundamental determinants of health, including smoking (Jarvis & Wardle, 1999). Our study findings also have implications for pedagogic practice. We propose that in health education classes, greater acknowledgement should be made of the contribution of pupils to the creation of knowledge. Pupil-centred techniques, such as role-plays and mind maps, should be encouraged at the expense of teacher or adult-centred techniques. Employing these ‘learning how to learn techniques’ facilitates learning about oneself, one’s own values, and the values of others. These techniques also facilitate understanding of how individual’s health related choices are located within the social, economic, and political circumstances of individual’s lives. Support for this approach was provided by Lynch, (1995). He used personal construct theory to demonstrate that adolescent smokers are not homogenous with respect to the role that smoking played in their lives. A pupil-centred pedagogic approach may not prevent the uptake of smoking, but will, we believe, facilitate greater personal insights into why a person chooses to smoke. This, we suspect, is more likely to lead to successful attempts to give up when a person is ready. However, caution is urged when using these techniques, as pupils need to become familiar and relaxed with their

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use to benefit from this approach. Lack of familiarity could explain Michell and West’s (1996) experience of using role-plays during smoking education lessons. They reported that pupils enacted situations in which actors experienced coercive peer pressure to smoke, even though two other research methods showed no pupils had ever experienced any such pressure. Michell and West suggested that rather than using the role-plays appropriately to recreate lived experience, the young people role-played situations that they believed would satisfy the watching adults. Although our suggestions for school health education may appear radical, there are similarities between our proposed approach and the approaches for promoting health within the community outlined by Labonte and Robertson (1996) and Loeb, Markham, Naidoo, and Wills (1998). A third implication of these results relates to research in health education. If school disengagement is a key variable that influences health and health behaviour, and if the effect of school disengagement cannot be overcome by externally provided and popular health education, then school disengagement deserves to be the focus of a sustained research programme. We also need evidence of whether the pupil-centred approach to the curriculum and pedagogic practice influences young people’s health behaviour. Even if our suggested approach to health education is not effective in preventing smoking, it has the potential to promote engagement with the school (Bernstein, 1977), which may have other beneficial consequences for health (Power, Manor, & Fox, 1991). In the light of these findings, we could ask if prevention could ever work. These results imply that where school-based smoking prevention programmes persist with didactic approaches and take no action to engage those already disengaged from school, then for these disengaged young people, prevention is unlikely to work.

Acknowledgements This trial was funded by the health authorities of the West Midlands. The computerised intervention described is copyright of Pro-Change. Professor Prochaska and his colleagues and Public Management Associates, UK, designed the intervention and we are grateful to them for their help.

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