Addictive Behaviors 34 (2009) 281–286
Contents lists available at ScienceDirect
Addictive Behaviors
Testing Social Cognitive Theory as a theoretical framework to predict smoking relapse
among daily smoking adolescents☆
Rinka M.P. Van Zundert ⁎, Linda M. Nijhof, Rutger C.M.E. Engels
Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands
a r t i c l e
i n f o
Keywords:
Smoking cessation
Relapse
Adolescents
Social Cognitive Theory
a b s t r a c t
Predictors of adolescent smoking relapse are largely unknown, since studies either focus on relapse among
adults, or address (long-term) smoking cessation but not relapse. In the present study, Social Cognitive
Theory (SCT) was used as a theoretical framework to examine the first and second lapses, as well as mild and
heavy relapse into smoking among 135 daily smoking adolescents who embarked on a serious quit attempt.
Baseline predictors were pros of smoking, pros of quitting, self-efficacy, and intensity of smoking. Using an
ecological momentary assessment (EMA) study design, participants were monitored three times a day during
4 weeks. A follow-up was administered 2 months after the monitoring period. Perceiving many pros of
smoking, reporting a low self-efficacy to quit, and high levels of baseline smoking significantly predicted
relapse within 3 weeks after quitting. The effects of pros of smoking and self-efficacy on relapse, however,
appeared to be accounted for by differences in intensity of smoking. Besides that pros of quitting showed a
marginal effect on abstinence at the 2-month follow-up, no long-term effects were detected.
© 2008 Elsevier Ltd. All rights reserved.
1. Introduction
Adolescents seem to undertake quit attempts frequently (Pallonen,
Murray, Schmid, Pirie, & Luepker, 1990; Presti, Ary, & Lichtenstein,
1992), but only few adolescents succeed in quitting (Stanton, 1995). It
is estimated that 95% to 99% of all unaided quit attempts among adults
end in relapses (Jarvis, 2003), most of which occur in the first few days
and weeks of quitting (Doherty, Kinnunen, Militello, & Garvey, 1995;
Jarvis, 2003). Adolescent smokers even seem to relapse as much as or
even more often than adults (Mermelstein, 2003; Pallonen, Murray,
Schmid et al., 1990; Presti, Ary, & Lichtenstein, 1992; Stanton,
McLelland, Elwood, Ferry, & Silva, 1996). Predictors of adolescent
smoking relapse are largely unknown, since studies on smoking
relapse are conducted almost invariably among adults. In addition,
most studies, both among adults and adolescents, examine predictors
of long-term smoking cessation, which only establishes distal relationships between predictors and outcomes over months and years. The
present study focuses on incidental lapses and relapse among daily
smoking adolescents who achieved at least 24 h abstinence.
A wide variety of factors, such as physiological and biological as
well as cognitive factors appear to determine whether individuals
☆ This research was supported by a grant from the Dutch Asthma Foundation and a
fellowship grant to Rutger Engels from the Netherlands Organization of Scientific
Research.
⁎ Corresponding author. Behavioural Science Institute, Radboud University, P.O. Box
9104, 6500 HE Nijmegen, the Netherlands. Tel.: +31 24 3612816; fax: +31 24 3612776.
E-mail address: [email protected] (R.M.P. Van Zundert).
0306-4603/$ – see front matter © 2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.addbeh.2008.11.004
successfully quit smoking or not. The present study concentrates on
the role of cognitive factors in adolescent smoking relapse. Social
Cognitive Theory (SCT) explains how people acquire and maintain
certain behavioural patterns, for example smoking. The cognitive
factors outcome expectations, self-efficacy, and intentions are important determinants of behaviour according to SCT (Bandura, 1986). In
the context of smoking, outcome expectations can be operationalized
as pros and cons of smoking, and intentions as motivation or readiness
to quit. Pros of smoking involve the perceptions of the advantages of
smoking, and cons refer to the disadvantages of smoking. Prior
research on the pros and cons of smoking also used measures of the
pros of quitting next to the pros of smoking (De Vries & Backbier, 1994;
Dijkstra, Bakker, & De Vries, 1997; Van Zundert, Van De Ven, Engels,
Otten, & Van Den Eijnden, 2007). Self-efficacy is often defined as the
ability to resist smoking in tempting situations, and intentions are
frequently framed in terms of motivation or readiness to quit
(Prochaska, Velicer, Guadagnoli, & Rossi, 1991; Van Zundert, Van De
Ven, Engels et al., 2007). As such, SCT offers a theoretical framework to
examine smoking behaviour.
Several studies have demonstrated that adult smokers' perceptions
of the pros and cons of smoking and quitting affect their quitting
behaviour (De Vries & Backbier, 1994; Dijkstra, De Vries, & Bakker,
1996; Greening, 1997; Hansen, Collins, Johnson, & Graham, 1985;
Prochaska, DiClemente, & Norcross, 1992; Rose, Chassin, Presson, &
Sherman, 1996). Individuals who report to attribute relatively few
advantages to smoking and many benefits to quitting are more likely
to achieve smoking cessation. However, relatively little is known
about the influence of the pros and cons on smoking relapse
282
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specifically. It is conceivable that adolescents who perceive smoking to
be highly advantageous and quitting to have relatively few advantages
are the ones who will relapse and revert to smoking after embarking
on a quit attempt. Another predictor which seems to play an
important role in smoking relapse is self-efficacy to resist smoking.
Research has consistently shown that low self-efficacy is related to
smoking relapse among adults (for an overview, see Gwaltney, Metrik,
Kahler, & Shiffman, in press). Among adolescents, self-efficacy has not
yet been tested in association with relapse. Lastly, motivation to quit
has been found to be a precursor of smoking cessation among
adolescents (Engels, Knibbe, De Vries, & Drop, 1998; Lichtenstein,
Lando, & Notwehr, 1994; Osler & Prescott, 1998). Conclusively, there
are some indications from both the literature on smoking cessation
and from adult studies that components of the SCT can predict relapse.
However, most studies that used the SCT to explain smoking cessation
and relapse have focused on aspects of the SCT rather than capturing
the model including outcome expectations, self-efficacy, and intentions as a whole. Moreover, to our knowledge, SCT has not yet been
used to predict relapse among adolescents. In the present study, we
examined whether SCT derived smoking-specific cognitions predicted
adolescents' lapses and relapse after a serious quit attempt.1
Besides the influence of adolescents' smoking-specific cognitions,
the effect of intensity of smoking on relapse was taken into account.
Intensity of smoking refers to the number of cigarettes smoked per
day. Previous research has shown mixed results regarding the relation
between intensity of smoking and smoking relapse. Some adult
studies have found that heavy smokers are at greater risk for relapse
during a quit attempt compared to light smokers (Curry, Thompson,
Sexton, & Omenn, 1989; Senore, Battista, Shapiro, Segnan, Ponti et al.,
1998). In contrast, other studies showed that the number of cigarettes
smoked per day did not predict whether persons would succeed or fail
during their attempts to quit smoking (Fiore, Novotny, Pierce, Giovino,
Hatziandreu, et al., 1990; Kenford, Fiore, Jorenby, Smith, Wetter, et al.,
1994). Nicotine dependence is in part determined by intensity of
smoking (Pierce & Gilpin, 1996), and there have been several studies
that explored nicotine dependence in relation to adolescent smoking
cessation (Engels, Knibbe, De Vries et al., 1998; Horn, Fernandes, Dino,
Massey, & Kalsekar, 2003), but not to relapse. Conclusively, the impact
of baseline smoking on adolescent relapse has not yet been examined.
Despite scarce evidence from previous research among adults and
the contrasting findings above, we expected that a high intensity
of smoking would predict the first lapses, as well as relapse into
smoking within 3 weeks, and that high baseline levels of smoking
would lower the odds that quitters would be abstinent at the 2-month
follow-up.
The purpose of the present study was to provide prospective
information on the effects of SCT-derived smoking-specific cognitions
and intensity of smoking on relapse among adolescent daily smokers.
A number of 135 daily smoking adolescents in the ages of 15 to 20
participated in an Ecological Momentary Assessment (EMA) study in
which they embarked on a serious attempt to quit smoking.
Participants answered daily questions about their quitting experiences three times a day over a period of 4 weeks (1 week precessation, and 3 weeks post-cessation). We hypothesized that high
scores on pros of smoking, low scores on pros of quitting, low selfefficacy to resist smoking, and intensive tobacco use at baseline would
predict the following five outcome variables: A first lapse, a second
lapse, mild and heavy relapse into smoking as observed during the
3 weeks after the quit attempt, and current smoking at the 2-month
follow-up.
1
Since a strong intrinsic intention or motivation to quit smoking had been one of
the primary criteria for participation, we expected extremely little variation on this
variable and did not include this variable in the baseline questionnaire.
2. Methods
2.1. Participants
Participants were 135 daily smoking adolescents in the ages of 15
to 20 years, who were highly motivated to quit. Participants were
recruited through advertisements and articles about the study that
were published and displayed in newspapers, on websites, and in
community centers. Being between 15 and 19 years of age, having a
strong motivation to quit, and smoking at least one cigarette per day
were the main selection criteria. Exclusion criteria were participation
in a smoking cessation program, and use of anti-depressants. Two
participants who had turned 20 in the month prior to the study were
allowed to participate. The sample originally comprised 176 adolescents who were enrolled in the study. For the present analyses, we
excluded 17 individuals who withdrew prior to the target quit day, 9
who had too many missing values to establish whether they had
achieved 24 h abstinence or not, 1 participant who failed to reach 24 h
abstinence at least once during the study, and 14 participants who had
not successfully returned their baseline questionnaire. The final
sample thus consisted of 135 adolescents. Of those 135 participants,
120 (88.%) completed the 2-month follow-up.
The final sample of 135 adolescents consisted of 86 girls (63.7%),
and 49 boys. Ages were distributed as follows: 15 (2.2%), 16 (31.1%), 17
(29.6%), 18 (16.3%), 19 (17.8%), and two persons had just turned 20
(1.5%) (M = 17.2, SD = 1.2). Participants resided across all four regions of
the Netherlands. All adolescents received regular education, and all
levels of educational attainment were represented: Lower vocational
training (53.9%), higher vocational training (14.6%), pre-university
education (13.8%), and college (17.7%). Most respondents lived at
home with their parents (89.5%), whereas 7.6% lived in student
housing, with his or her grandparents (0.7%), or with a romantic
partner (2.2%). The average number of years that participants had
been smoking daily was 2.9 (SD = 1.6). At the time of enrollment in the
study, smoking rate was distributed as follows: 1–5 cigarettes per day
(11.9%), 6–10 cigarettes per day (34.3%), 11–20 cigarettes per day
(47.0%), 21–30 cigarettes per day (3.7%), and 31 or more cigarettes per
day (3.0%). Although use of nicotine replacement was allowed, only 1
participant reported to have used nicotine patches.
2.2. Procedure
Participants were asked to complete a baseline questionnaire 1 week
prior to starting the ‘diary period’ during which they were monitored
daily for a total of 4 weeks. During the first week of monitoring,
participants were instructed to smoke ad lib. The eighth day was the
assigned quit day for each participant. A quit attempt was considered as
such when participants were abstinent for at least 24 consecutive hours,
as was evidenced by 3 consecutive reports of non-smoking. Following
the assigned quit day, subjects were monitored for an additional
3 weeks. On each day of monitoring, participants were asked to
complete the same Internet-based questionnaire three times a day — in
the morning (to be completed between 10 am and 12 pm), the afternoon
(3 pm–5 pm), and evening (8 pm–10 pm). Each questionnaire was
identical and contained questions on smoking behaviour since the
previous questionnaire. The questionnaires took approximately 3 min to
complete. Participants who failed to complete a questionnaire within
the designated interval were sent a text message to remind them. If
participants found the Internet to be inaccessible during a sampling
interval, they were asked to complete a paper version of the
questionnaire during the interval and to submit their report online as
soon as they had access to Internet again. A follow-up was administered
2 months after the end of the diary period. Participants received 40 euros
if they completed the full 4 weeks of the diary period, and 10 additional
euros upon completion of the 2-month follow-up. All data were
collected between October 2006 and March 2007.
R.M.P. Van Zundert et al. / Addictive Behaviors 34 (2009) 281–286
283
2.3. Measures
2.4. Strategy for analyses
2.3.1. Pros of smoking and pros of quitting
Pros of smoking involved the perceptions of the advantages of
smoking, and pros of quitting involved the perceptions of the
advantages of smoking cessation as constructed by De Vries and
Backbier (1994). These measures have been validated in other studies
(cf. Dijkstra, Bakker, De Vries, 1997). Response categories of both scales
ranged from 1 (totally disagree) to 4 (totally agree). Example items of
the 10 pros of smoking were: “Smoking helps to cope with stress”, and
“Smoking helps to concentrate”. Cronbach's alpha was .72. The scale
for pros of quitting consisted of 13 items, with items such as “To quit
smoking decreases the risk for lung cancer”, and “To quit smoking
increases my health”. Cronbach's alpha was .82.
First we calculated the relative occurrence of the relapse variables,
and computed correlations among independent variables. Next, we
examined the predictive power of the SCT-derived cognitions and
intensity of smoking on the various outcome variables by means of
survival analyses, using a Cox proportional hazards regression model.
Survival analysis is concerned with studying the time between the
entry to a study and a subsequent event (such as death, or in the
present case ‘relapse’). A Cox regression model provides an estimate of
the hazard (or risk) of the event for individuals on the basis of their
individual characteristics (such as cognitions) thereby taking into
account when the event occurred. Since the follow-up was measured
at a fixed time point (2 months after the end of the diary period), we
used logistic regressions to test effects on abstinence at follow-up.
Since the pros of smoking and self-efficacy were highly correlated and
thus at risk to cause multicollinearity, we present both the univariate
and the multivariate analyses. Lastly, sex and age did not significantly
predict any of the outcome variables and were therefore not included
in the model as possible confounding variables.
2.3.2. Self-efficacy to resist smoking
Self-efficacy represented adolescents' perceived ability to resist
smoking in tempting situations and was measured using a scale that
had been developed for adolescents specifically (Kremers, Mudde, &
De Vries, 2001). To the question “When you have quit smoking, how
easy or difficult would it be for you not to smoke in the following
situations?”, respondents could answer on a 5-point Likert scale
ranging from 1 (very easy) to 5 (very difficult). Exemplary situations of
the 18 situations given were: “When you are watching television”, and
“When you feel angry”. Alpha was .83.
2.3.3. Intensity of smoking
Intensity of smoking refers to the number of cigarettes adolescents
smoked per day at baseline. Response choices were: 1 (less than one
cigarette per day), 2 (1–5 cigarettes per day), 3 (6–10 cigarettes per
day), 4 (11–20 cigarettes per day), 5 (21–30 cigarettes per day), and 6
(31 or more cigarettes per day).
2.3.4. Outcome variables
The following variables were the five outcomes of interest: First
lapse, second lapse, ‘mild’ and ‘heavy’ relapse within 3 weeks after the
quit attempt, and smoking status at the 2-month follow-up. Whether
a first lapse had occurred was established by any report of smoking
(even if only a puff) after having accomplished 24 h of abstinence.
Similarly, the event of a second lapse was defined as any report of
smoking after the first lapse. Relapse was defined in two ways: 1)
smoking at least 1 cigarette per day for three consecutive days (‘mild
relapse’), and 2) smoking at least 5 cigarettes per day for three
consecutive days (‘heavy relapse’). Smoking status at the 2-month
follow-up was measured through the question: “Have you maintained
abstinence since the end of the diary period?” Response choices were:
‘1’ “Yes, I am still a non-smoker”, ‘2’ “No, I am smoking again, but I
currently smoke less than when I entered the study”, and ‘3’ “No, I am
smoking again at the same level as when I entered the study”. Scores
were recoded and dichotomised into ‘abstinent’ (response choice 1),
and ‘smoking’ (response choices 2–3). All outcome variables were
dichotomous with score ‘1’ representing non-smoking, and ‘2’
indicating the occurrence of smoking (occurrence of first and second
lapses, and relapse). All independent variables were measured
through the baseline questionnaire.
3. Results
3.1. Descriptive analyses
The majority of the participants experienced at least one lapse
during monitoring (70.4%, n = 95 of 135), and 58.5% (n = 79 of 135) also
reported a second lapse. ‘Mild’ relapse defined as ‘smoking at least 1
cigarette on three consecutive days’ occurred for 46 participants
(34.1%, n = 46 of 135). ‘Heavy’ relapse defined as ‘smoking at least 5
cigarettes on three consecutive days’ occurred for 27 participants
(20.0%, n = 27 of 135). At follow-up, 29.6% of the initial sample of 135
adolescents were still abstinent, and 59.3% were smoking again (11.1%
of the subjects did not participate in the follow-up).
The mean scores and standard deviations for the independent
variables were as follows: Pros of smoking (M = 2.71, SD = .47), pros of
quitting (M = 3.45, SD = .44), self-efficacy (M = 2.48, SD = .61), and
intensity of smoking (M = 3.53, SD = .87). Pearson correlations among
the independent variables showed that the pros of quitting were not
correlated with any of the other predictors. The pros of smoking were
strongly negatively correlated with self-efficacy (r = −.54, p b .001), and
also with intensity of smoking (r = −.20, p b .01), which indicates that
those who perceived smoking to be advantageous were more likely to
be heavy smokers and to have low self-efficacy to remain abstinent. Selfefficacy and intensity of smoking were positively correlated (r = −.21,
p b .01), indicating that participants with higher levels of baseline
smoking reported lower self-efficacy.
3.2. Survival analyses
The univariate analyses showed that neither one of the independent variables predicted time to first and second lapses, mild relapse,
nor smoking at follow-up (Table 1). However, perceiving smoking as
highly advantageous predicted time to heavy relapse (HR = 2.87,
CI = 1.15–7.16, p b .01) — each one-point increase in the pros of smoking
Table 1
Univariate survival analyses
1st lapse
Pros of smoking
Pros of quitting
Self-efficacy
Intensity of smoking
2nd lapse
Mild relapse
Heavy relapse
Smoking at follow-up
HR
95% CI
HR
95% CI
HR
95% CI
HR
95% CI
OR
95% CI
1.07
.77
1.03
.96
.67–1.63
.48–1.22
.74–1.43
.76–1.22
1.22
.99
.94
1.03
.76–1.95
.60–1.64
.65–1.35
.79–1.33
1.22
1.54
.95
1.31
.66–2.28
.77–3.06
.59–1.52
.96–1.80
2.87⁎
1.81
.46⁎
1.84⁎⁎
1.15–7.16
.70–4.68
.24–.89
1.25–2.71
1.24
.39
1.34
1.05
.51–3.02
.14–1.04
.71–2.55
.68–1.64
Note. *p b 0.05, **p b 0.01. HR = hazard ratio; OR = odds ratio.
284
R.M.P. Van Zundert et al. / Addictive Behaviors 34 (2009) 281–286
Table 2
Multivariate survival analyses
1st lapse
Pros of smoking
Pros of quitting
Self-efficacy
Intensity of smoking
2nd lapse
Mild relapse
Heavy relapse
Smoking at follow-up
HR
95% CI
HR
95% CI
HR
95% CI
HR
95% CI
OR
95% CI
1.14
.77
1.49
.93
.70–1.63
.48–1.22
.74–1.43
.76–1.22
1.22
1.00
1.00
1.00
.69–2.15
.60–1.66
.65–1.54
.76–1.30
1.16
1.68
1.11
1.33
.56–2.35
.84–3.35
.62–1.98
.94–1.86
1.36
1.87
.66
1.69⁎
.50–3.66
.72–4.84
.30–1.44
1.13–2.51
2.26
.36†
1.82
.97
.72–7.17
.13–1.00
.81–4.10
.60–1.57
Note. †p = 0.051, * p b 0.05, **p b 0.01. HR = hazard ratio; OR = odds ratio.
increased the risk of suffering heavy relapse by almost 3 times. Lower
levels of self-efficacy (HR = .46, CI = .24–.89, p b .01) as well as high
levels of baseline smoking (HR = 1.84, CI = 1.25–2.71, p b .01) were also
predictive of heavy relapse. The multivariate analyses (Table 2)
showed that the initially significant effects of pros of smoking and
self-efficacy on heavy relapse diminished when intensity of smoking
was accounted for.2 Of the cognitions, only the pros of quitting showed
a trendwise effect on abstinence at follow-up, indicating that
endorsing many pros of quitting provided a protective effect on
prolonged abstinence.
4. Discussion
In the present study, smoking-specific cognitions derived from the
Social Cognitive Theory (Bandura, 1986) were hypothesized to predict
the outcomes of a serious quit attempt of daily smoking adolescents.
The main findings show that a strong endorsement of the pros of
smoking, low self-efficacy to quit, and baseline smoking status
significantly predicted relapse within 3 weeks after quitting. The
first and second lapses as well as abstinence 2 months later appeared
to be largely unaffected by baseline smoking status and smokingspecific cognitions.
The finding that pros of smoking and self-efficacy significantly
predicted heavy relapse is in line with our own hypotheses as well as
findings from prior research on adolescent smoking cessation
(Hansen, Collins, Johnson et al., 1985) and adult relapse (Gwaltney,
Metrik, Kahler et al., in press). More specifically, this means that
individual differences in smoking-specific cognitions that adolescents
set out on their quit attempts with in part determine successful
cessation. It therefore might be fruitful to target smoking-specific
cognitions before adolescents begin their quit attempt. However, it is
possible that the effects of pros of smoking and self-efficacy on heavy
relapse can be attributed to smoking status. We found that inclusion of
baseline smoking status attenuated the effects of the cognitions to
non-significance, which is in line with recent findings suggesting that
smoking status and nicotine dependence are potentially dominant
over cognitive strategies in predicting smoking cessation (Kleinjan,
Van Den Eijnden, Van Leeuwe, Brug, Van de Ven, et al., 2007).
Furthermore, in contrast with our hypothesis, pros of quitting were
not significantly related to any of the relapse variables, except for a
marginal effect on abstinence at follow-up. It is possible that the
perceived pros of quitting are more relevant in an early stage of
cessation – such as the trajectory that precedes smoking cessation and
in forming intentions to quit (e.g., Van Zundert, Van De Ven, Engels et
al., 2007) – than in preventing relapse once abstinence is achieved.
Perception of the pros of smoking, on the contrary, was associated
with relapse, but not with abstinence at follow-up. This is in line with
previous studies showing that pros of smoking are particularly related
to the action stage of the stages of change and to relapse, and that cons
of smoking are related to long-term abstinence (Dijkstra, Tromp, &
Conijn, 2003; Hansen, Collins, Johnson et al., 1985; Pallonen, 1998).
2
Since pros of smoking and self-efficacy were highly correlated, we also ran the
multivariate model with either one of these two variables omitted. Findings showed
that this did not change the findings; the effect of pros of smoking was still overruled
by intensity of smoking, as was the effect of self-efficacy.
Compared to the pre-action phases, the pros of quitting have been
found to be endorsed to a lesser extent among adults during the actual
action phase (Dijkstra, De Vries, & Bakker, 1996; Pallonen, 1998). Such
a decline in acknowledging the benefits of cessation may be plausible
in the light of the temptations and smoking cues that smokers who are
attempting to quit encounter. It is possible that when quitters
experience the negative effects of quitting (e.g., withdrawal symptoms), they are inclined to downplay the advantages of quitting. If
such a change in perception indeed occurs, this would render baseline
individual differences in the pros of quitting unable to explain the
outcome of the attempt. Whether this postulate is valid needs to be
tested by measuring the pros of smoking and quitting during the quit
attempt.
Contrary to our hypotheses, pros of smoking, self-efficacy, and
baseline smoking status did not predict abstinence at follow-up. In the
case of self-efficacy this contradicts most studies among adults (e.g.,
Shiffman, Balabanis, Paty, Engberg, Gwaltney et al., 2000; Stuart,
Borland, & McMurray, 1994). An explanation for this may be that selfefficacy changes in response to the experiences during the quit
attempt. During a quit attempt, self-efficacy has been found to
decrease in reaction to the event of a lapse, to a higher urge to smoke,
and by negative affect (Gwaltney, Shiffman, & Sayette, 2005; Shiffman,
Balabanis, Paty et al., 2000; Shiffman, Hickcox, Paty, Gnys, Kassel et al.,
1997;). It is possible that self-efficacy changes in response to quitting
experiences to such an extent that baseline self-efficacy becomes
insignificant in predicting prolonged cessation. Studies among adults
have demonstrated that self-efficacy is indeed a dynamic construct
and that the day-to-day variations in self-efficacy play an important
role in lapses and relapse (Shiffman, Balabanis, Paty et al., 2000;
Stuart, Borland, & McMurray, 1994), but these results have hitherto not
been replicated for adolescents. Future research is recommended to
examine the dynamic effects of self-efficacy among adolescents to
gain a more in-depth understanding of the interplay between
smoking-specific cognitions and adolescent smoking cessation and
relapse.
In the above, we have mainly discussed the results for heavy
relapse and the 2-month follow-up. However, the null findings for the
first and second lapse and mild relapse raise questions and are
important to discuss as well. It is possible that the first few slips are
primarily caused by momentary states such as negative affect, or the
experience of withdrawal symptoms, rather than that they are a
consequence of with which attitude towards smoking and quitting
adolescents set out on their cessation trials. Research among adults
has shown that dynamic effects of smoking-specific cognitions such as
self-efficacy and withdrawal symptoms can account for the first few
lapses (Gwaltney, Shiffman, Balabanis, & Paty, 2005; Piasecki, Jorenby,
Smith, Fiore, & Baker, 2003; Shiffman, Balabanis, Paty, et al., 2000).
Most studies, however, do not distinguish the first lapse from a second
lapse or full relapse, while the interval between a lapse and full relapse
is distinctly different from the interval between quitting and the first
lapse (Shiffman, Balabanis, Paty et al., 2000). Although experiencing a
first lapse is very likely to instigate a second lapse, and to eventually
lead to relapse (Piasecki, 2006), our results show that independent
variables that predict relapse do not necessarily predict the first
lapses. Especially since the first lapse is a powerful indicator for later
relapse, it is important to gain a deeper understanding of which
R.M.P. Van Zundert et al. / Addictive Behaviors 34 (2009) 281–286
factors affect the first lapses among adolescents. In addition, it is
interesting to notice that the results differ depending on which
definition of relapse is used: ‘mild’ versus ‘heavy’ relapse. Since the
literature on smoking relapse among adolescents is still relatively
underdeveloped, it is important to acknowledge that percentages of
relapse rates as well as effects of possible predictors greatly differ as a
function of definition.
4.1. Limitations
Important strengths of the present study are that it is the first
prospective study testing Social Cognitive Theory in relation to
adolescent smoking relapse in a sample of daily smoking adolescents.
In addition, daily reports of smoking have allowed for the first and
second lapses and relapse to be distinguished from one another, and
to take into account the time to the event. On the other hand, some
aspects of this study may be considered as limitations, such as the
relatively small sample size, the relative homogeneity of the sample,
and the lack of biochemical verification of abstinence. To start with,
one might posit that the sample size might cause lack of statistical
power. Nonetheless, we did find some of the associations to be
significant which would not have been possible if there had been a
serious power problem. Second, to be able to have adolescents
undertake a serious quit attempt, motivation to quit must be high.
Accordingly, this was a selection criterion for participation. This high
motivation to quit may have restricted the range of scores on the
smoking-specific cognitions, as was visible in the relatively small
standard deviations, and may have tempered the associations
between the cognitions and the outcome variables. Lastly, we did
not use biochemical verification to ensure that participants had
achieved 24 h abstinence. However, the fact that 27.5% of the
participants did not show 24 h abstinence on the target quit day
suggests that participants felt free enough to honestly report whether
or not they had smoked. Moreover, several studies among adolescents
have indicated that self-reports of smoking and quitting behaviour are
valid and reliable (Dolcini, Adler, & Ginsberg, 1996; Stanton, McLelland, Elwood et al., 1996).
4.2. Recommendations
Both the perception of the pros of smoking and self-efficacy appear
to be affected and overruled by the intensity of smoking when it
comes to heavy relapse. However, with the lapse of time after the quit
attempt, the pros of quitting become more relevant again. This
suggests that cessation interventions for adolescent smokers should
have a dynamic character, and should intervene on different aspects at
different stages of the cessation process. With the effect of baseline
smoking status being strong and overruling the effects of cognitions
on relapse, one might advocate to primarily target nicotine dependence when adolescents are in the action phase of the quit attempt.
Prior research shows that withdrawal symptoms seem to be
successfully reduced by using nicotine patches in adolescents
(Smith, House Jr, Croghan, Gauvin, Colligan et al., 1996), but the few
studies on the effects of nicotine patch treatment on adolescent
smoking cessation have revealed inconsistent findings. Hurt et al.
(2000) found that use of nicotine patches did not improve cessation
rates. In addition, Moolchan et al. (2005) showed that use of nicotine
patches increased cessation rates, but the nicotine patch intervention
was accompanied by cognitive-behavioural therapy. Our results
suggest that an approach similar to the latter study would be fruitful,
and that both nicotine dependence and cognitions should be targeted
in cessation interventions and relapse prevention among adolescents.
Lastly, given that our findings do not support the notion that Social
Cognitive Theory nor baseline smoking status explain the first few
lapses, future research is recommended to explore other possible
predictors, such as withdrawal symptoms.
285
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