PREVENTIVE
MEDICINE
17, 135-154 (1988)
Affective and Social Influences Approaches to the Prevention
of Multiple Substance Abuse among Seventh Grade Students:
Results from Project SMART
WILLIAM B. HANSEN, PH.D.,* C. ANDERSON JOHNSON, PH.D.,*,~
BRIAN R. FLAY, D.PHIL., JOHN W. GRAHAM, PH.D., AND
JUDITH SOBEL, PH.D.
Division
of Health Behavior
Research,
Department
of Preventive
Health
Promotion
and Disease Prevention
Research,
University
Pasadena,
California
91101
Medicine,
and *Institute
of Southern
California,
for
TWO drug abuse prevention curricula were tested to determine their efftcacy in preventing
the onset of tobacco, alcohol, and marijuana use among adolescents. The first program
focused on prevention through social pressure resistance training. The second featured
affective education approaches to prevention. Curricula were tested on seventh grade students. Subjects were pretested just prior to the program and were post-tested at 12 and 24.
months. Post-test analyses indicated that the social program delivered to seventh grade
subjects was effective in delaying the onset of tobacco, alcohol, and marijuana use. No
preventive effect of the affective education program was observed. By the final post-test,
classrooms that had received the affective program had significantly more drug use than
controls. B 1988 Academic Press, Inc.
INTRODUCTION
Drug abuse, including cigarette and alcohol abuse, remains the major public
health problem in this country today. Between 450,000 and 600,000 premature
deaths are attributed to cigarettes, alcohol, and other drug abuse each year, nearly
one-third of all mortality in the United States (42-44). Although cigarette smoking
has declined from 43% among American adults in 1966 to the present rate of 32%,
the proportion of heavy smokers (~25 cigarettes per day) has increased from 26
to 37% among males, and 13 to 25% among females. Alcohol is widely understood
to have great addictive potential.
Among various drugs, tobacco and alcohol rank first and second in public
health significance, with the order depending upon where in the lifespan one
concentrates. Most deaths in youth are the result of accidents and/or acts of
violence (42) typically involving alcohol. Premature mortality and morbidity in
mid-life are most often the result of chronic disease, to which cigarette smoking is
the greatest contributor.
Marijuana abuse is also a factor in accidents and acute
respiratory disease. Marijuana’s contribution
to cancer and chronic respiratory
disease is unknown but there is reason to believe that long-term, heavy marijuana
smoking may be a serious lung cancer risk factor.
In addition to their contributions
to chronic disease, tobacco, alcohol, and
’ Supported by Grant l-R18-DA030496 from the National Institute on Drug Abuse.
2 To whom reprint requests should be addressed, at U.S.C., Institute for Health Promotion and
Disease Prevention Research, 35 North Lake Avenue, Suite 200, Pasadena, CA 91101.
135
0091-7435/88 $3.00
Copyright 0 1988 by Academic Press, Inc.
AlI rights of reproduction
in any form reserved.
136
HANSEN
ET AL.
marijuana are considered “gateway” drugs to other drug abuse because the likelihood of any substance abuse is greatly enhanced by the use of any of these three
(27). The use of other substances such as cocaine, amphetamines, barbiturates,
and opiates is extremely low for persons who have never experimented with any
one of the three drugs. Drug abuse also presents a serious obstacle to the intellectual and social development of young people.
In a 1982 national probability survey of high school seniors, the proportions
reporting use of various substances over the previous 30 days were as follows:
cigarettes, 30.0%; alcohol, 69.7%; marijuana/hashish, 28.5%; cocaine, 5.0%; stimulants, 10.7%; hallucinogens, 4.3%; and sedatives 3.4%. Reported daily use was
21.1% for cigarettes, 5.7% for alcohol, and 6.3% for marijuana (26). With the
exception of cocaine, and perhaps tobacco, there has been a slight decline in the
numbers of high school seniors reporting drug use in recent years. Of great concern, however, is the trend toward early onset. Risk for cancer, heart disease, and
violent death increase with longer exposure to drugs. A trend toward earlier and
longer lifetime exposure to drugs would suggest that unless effective, large scale,
prevention programs are implemented, drug abuse will take an increasing toll in
chronic disease, accidents, and acts of violence in years to come.
The value of drug abuse prevention has been understood for some time (8).
While notable progress is being made in effectively treating established substance
abuse behaviors (1, 28), even the best treatments are still not widely effective in
treating many substance abuse disorders (23). It may not be reasonable ever to
expect treatment programs to have a major impact on the health and social costs
associated with substance use. Preventing the initial onset of regular substance
use appears to be the most cost-effective approach to prevention of drug abuse
and its sequelae.
SOCIAL
INFLUENCES
APPROACHES
TO PREVENTION
With the recent development of effective social skills and resistance training
approaches for preventing (or at least delaying) the onset of cigarette smoking, the
possibility of preventing other substance use is enhanced. Based on social psychological theories and research, this research has led to remarkable success in
reducing the rates of onset of regular tobacco use (2, 4-6, 10, 12, 19, 22, 30-33,
38). The evaluations of these projects have been of improved quality over time,
although it is clear that no study has perfect validity (11). Taken together, the
weight of the evidence supports an effective technology for smoking prevention
(3, 11). These programs have enjoyed relative successat achieving the goals of
delaying onset and have been repeated in numerous settings (41).
Different programs feature alterations on the specific techniques used, but most
of the programs have featured one or more of the following components.
Peer Pressure
Resistance
Training
Students are instructed about the nature of peer pressure and are taught how to
resist pressures to use cigarettes. The format and content of training has varied
greatly but typically has included descriptions and demonstrations of resistance
techniques accompanied with some method of practicing these techniques.
PROJECT
Correction
of Normative
137
SMART
Expectations
Young people perceive tobacco use to be more prevalent and more acceptable
than it really is (39, 40). Prevention programs provide students with information
about true prevalence rates.
Inoculation
against
Mass
Media Messages
Some psychosocial prevention programs provide students with
about the advertising strategies used to promote tobacco and teach
ysis skills to counter the influence that advertisements may have.
extent, programs have also focused on countering the pro-tobacco
portrayed in popular music, movies, and television programming.
Information
about Parental
information
critical analTo a lesser
use images
and Other Adult Influences
Noting the correlation between parental cigarette smoking and student smoking, many programs include some information
about parental influences. The
message that parents need not be modeled is usually presented.
Peer Leadership
Most, but not all, of the programs referenced above include peer opinion leaders in the delivery of the program. The selection and training of peer leaders varies
greatly from program to program. Nonetheless, data generally support the efficacy of using students in the delivery of all or some of the program. At least two
studies have found peer leadership to contribute significantly to the robustness of
program effects (4, 19).
Other Components
Many smoking prevention programs have included various other components
that, although not strictly within the domain of the social influences model of
smoking onset, nonetheless fit within the framework. Many programs feature
information about the short-term and social consequences of smoking, e.g., the
ingestion of carbon monoxide, bad breath, etc. This type of information
was
stressed since it was suspected that it would be more salient and harder to deny
than long-term, severe consequences would be (16). In some programs (4, 12),
instruction on decision-making
was included. Making personal commitments
not
to smoke, either via contract or reading a commitment
that is videotaped, is also
included in many programs. This has been done to bolster nonsmoking attitudes
using strategies well known to attitude researchers (18, 20). The independent
effects of these additional components have not been established, however.
COPING
SKILLS
AND AFFECTIVE EDUCATION
ABUSE PREVENTION
APPROACHES
TO DRUG
During the same time period that the social influences approach was being
developed to prevent smoking, drug abuse prevention education, including programs for preventing alcohol abuse, were being developed that adopted a different
138
HANSEN
ET
AL.
strategy for intervention. The content of many of these programs focused on
potential influences to begin using drugs that were person-centered.
Much of the content of these programs was borrowed from in vogue educational
themes that featured affective components. Although the content of programs in
this tradition vary more from program to program, there are content themes that
emerge and are useful for characterizing this approach generally.
Enhancement
of SeEf-Esteem and Self-Image
Programs that focused on enhancing self-esteem or self-image have assumed
that one reason for the onset of substance use is that young people who begin
using drugs do so because of their poor self-perception. By providing self-concept
building activities, such as goal-setting and personal problem-solving, the motivation for use was expected to be reduced.
Stress Management
Some programs have included stress management and stress reduction techniques such as deep muscle relaxation training and deep breathing. The idea
behind this strategy is that drug use provides an escape from stress that can be
accomplished without substance use.
Values Clarification
The essential characteristic of value clarification activities is the examination of
an individual’s general values to discover the over-arching principles by which the
person wishes to live his or her life. Drug use is then considered in light of these
emergent values to demonstrate that drug use is inconsistent with the individual’s
existing value structure. This strategy assumesthat students who are committed
to a set of values will not use drugs as long as their values are kept salient.
Decision
Making
Janis and Mann (20) noted that many behavioral choices are made on the basis
of faulty decision-making algorithms. Since the consequences of substance use
provide such clear incentives for not using, many affective education programs
have included a rational decision-making component to minimize the opportunity
for emotional drug use decisions.
Goal-Setting
Several studies have suggested that drug use onset is negatively correlated with
an individual’s achievement motivation (21). This implies that for many who
become substance abusers, there is a deficit in their ability to set and achieve
positive goals. Programs have featured instruction in goal-setting as a means of
enhancing an individual’s achievement orientation.
Evaluations of affective education programs have been few and generally of
poor quality, providing no strong support for the effectiveness of these strategies
(13, 23, 37). One of the problems with the evaluated studies was a general lack of
emphasis on behavioral outcomes. Furthermore, the few behavioral studies con-
PROJECT
SMART
139
ducted do not suggest this general approach to be effective. Some recent approaches have blended elements of both the social skills and the affective education approach [e.g., Botvin’s Life Skills Training (4)] with some successin preventing smoking and other substance abuse. However, insofar as a comparison
between approaches is concerned, there has to date been no systematic investigation of one approach versus the other.
There are several possible explanations for the lack of successof coping and
affective approaches that deserve consideration. One of the features characterizing these studies is that the program focus is not specific to drug use prevention.
That is, when talking about decision-making, decision-making about drug use, per
se, was not always included. Contrary to what might be expected, values claritication activities rarely have tied the values that students specified to their potential drug use. In short, these programs typically featured diffuse strategies with
general goals, rather than being speci&alLy targeted to prevention of drug use.
PROJECT
SMART
The focus of Project SMART was to test a program based on the social influences model and a drug-specific program based on the affective education model
for their relative effectiveness in preventing the use of the gateway drugs (27),
tobacco, alcohol, and marijuana.
The distinction between social influence and affective program content reflects
a different set of assumptions about what are the most important determinants of
drug use onset. Affective education is based on the premise that many of the
reasons for drug use initiation have to do with personal shortcomings (e.g., low
self-esteem) and personal motivations (e.g., to feel better). Social influence programs, on the other hand, are quite clearly derived from a model that emphasizes
external factors, (e.g., role models and social pressures) as precipitators of drug
use. Whereas the social influences program has enjoyed successin preventing the
onset of tobacco use, there was no clear evidence that it could be effective in
reducing the onset of other substance use as well. However, at least one study (32)
has provided evidence suggesting that there may be a spill-over effect on alcohol
and marijuana behavior from tobacco prevention programs. A second study may
be interpreted similarly although the outcomes were less clear cut (24). Thus, a
primary feature of the test was to determine whether or not a program that specifically targeted multiple substances using the social influences model would be
successful.
A second major concern was the possibility that programs developed in the
affective area had not succeeded because of some feature that was lacking either
in the directness of the connection between the program and not using drugs or in
some other factor that may have enhanced the program sufficiently to make it
successful. For example, since the use of same-age peers or slightly older role
models had predominated in the social influences programs but not in the affective
programs, it is possible that the affective programs failed not due to content but
structure. Therefore, a second aim of Project SMART was to provide a test of
affective approaches in which these dissimilarities were corrected.
140
HANSEN
ET AL.
METHOD
Subjects and Experimental Design
Of 63 junior high school complexes in the Los Angeles Unified School District
available for assignment, 44 were randomly assigned to intervention and control
conditions using a multi-attribute
approach to enhance comparability
(14). The
results presented in this article will concern only the first cohort of seventh grade
students who served as subjects beginning the first year of the project. The experimental design of this component of Project SMART is depicted in Table 1.
In this cohort of the project, subjects were drawn from eight junior high
schools. A total of 2,863 seventh grade students was involved in the study. Sex
was equally represented with slightly fewer females (48.8%) than males (gender
information was missing on 95 subjects). The ethnic grouping of subjects was as
follows: 38.4% were hispanic; 30.5% were black; 21.7% were white; 5.8% were
Asian; and 3.5% were other, unknown, or not classifiable.
Curricula
Two programs were created that reflected the theoretical positions described
above. The content of each curriculum is summarized in Table 2. As can be seen,
each generally reflects the paradigmic orientation of either a social influences or
an affective education strategy. Each was balanced so as to maintain an equal
emphasis on aspects of the teaching strategies that were felt to be important in the
delivery of an effective substance abuse prevention program. For example, both
featured the use of peer opinion leaders as assistants in delivering the program (2).
Both strove to include activities that were about equal in terms of the balance
between didactic presentation and active student involvement. Both had activities
that were estimated by staff health educators and consulting classroom teachers to
be of approximately
equal enjoyability.
Prior to implementation
in treatment
schools, both curricula had been pilot-tested.
Procedures
Based on the assignment to conditions procedure (14), schools were approached and solicited to participate in the study and either receive treatment or
serve as no-treatment controls. Control schools subsequently received a program 1
for the following cohort of students. Schools were then scheduled to be pretested
and (for treatment schools) receive one of the two Project SMART curricula in
TABLE 1
EXPERIMENTAL DESIGN FOR PROJECT SMART COHORT 1”
Group
Schools
Classes
Affective 7th
Social 7th
Control 7th
2
2
4
24
25
35
Pretest
WL,
ws,
0,
0 X’s indicate the delivery of the Social/Affective program.
O’s indicate a measurement.
Post-test 1
Post-test 2
Q3
0,
OS
09
09
09
141
PROJECT SMART
TABLE 2
SUMMARY OF CURRICULUM COMPONENTS FOR Two SUBSTANCE ABUSE PREVENTION CURRICULA
Session
1
2
3
4
10
11
12
Social curriculum
Affective curriculum
Introduction
Format of the program
Promoting group identification
Motivations
Motivators for using and not
using drugs
The nature of peer pressure
Normative expectations
Clarifying misperceived prevalence
of drug use
The nature of influence of friends
Consequences
Consequences of using and not
using drugs
Using consequences to resist
pressure
Resisting peer pressure
Techniques to say “no”
Scripting resistance to offers
to use drugs
Role playing
Social situations that encourage
drug use
Role playing techniques
Role playing resisting peer
pressure to use drugs
Adult influences
Role playing resisting peer
pressure to use drugs
The nature of modeling
Positive and negative
parental influences
Talking to adults about
drugs
Media influences
Media awareness
Advertising techniques
Evaluating advertising influences
Resisting media influences
Entertainment influences
Influences to use and not use
drugs in entertainment
Social activism in behalf of
non-drug use in entertainment
Alternatives
The concept of alternatives
Alternatives to drug use
Saying “no” (practice)
Positive friendships
Qualities of good friends
Techniques to develop and
keep good friends
Saying “no” (practice)
Public commitment
Videotaping of students
commitment to say “no” to
pressure to use drugs
Introduction
Format of the program
Self-esteem/worth enhancement
Motivations
Motivators for using and not
using drugs
Sources of stress
Alternatives
Alternative solutions to problems
Deep breathing
Self monitoring
Goal setting-Part
I
Goal setting essentials
Drug use interference in personal
goals
Deep breathing (practice)
Goal setting-Part
II
Setting a personal goal
Deep breathing (practice)
Consequences
Monitoring personal goals
Consequences of using and not
using drugs
Deep breathing (practice)
Decision making
Monitoring personal goals
The decision making process
Deciding about drug use
Self-esteem
The value of self-esteem
Building self-esteem
Complimenting others
Assertiveness-Part
I
Defining assertive behavior
Making assertive statements
Muscle tension-relaxation
Assertiveness-Part
II
Introduction to role playing
Role playing assertiveness
Muscle tension-relaxation
Summary
Achieving goals
Role playing assertiveness
Alternatives
Public commitment
Videotaping of students
commitment to engage in
alternatives instead of
using drugs
142
HANSEN
ET AL.
either the fall or spring semester. Based on this schedule, entire schools were
pretested during their fall or spring semester. Post-tests were collected from students during the subsequent school year, approximately
12 months after students
completed their pretests.
Pre- and post-test data were collected from students by means of paperand-pencil questionnaires and by the collection of saliva specimens administered
by trained data collectors. The primary purpose for the collection of the biological
samples was to enhance self-reports of cigarette and marijuana smoking behavior
following the methods described by Evans et al. (9) and Luepker et al. (29).
Questionnaire items assessed tobacco, alcohol, and marijuana use, demographics,
and a number of other psychosocial constructs. The questionnaire is fully described in Graham et al. (15). Tobacco, alcohol, and marijuana use questions
assessed lifetime use, 30-day use, 7-day use, and customary use. The same questionnaire was administered at all three points of data collection.
Subsequent to pretesting, students in schools assigned to the Social and Affect
conditions received the appropriate curriculum over a semester period, the program was delivered 1 day each week. Staff health educators alternated with regular classroom teachers in delivering the 12 sessions of each program. Prior to
implementation,
classroom teachers were given a l-day training workshop to
provide them with essential information and skills to deliver the aspects of the
program they were assigned to deliver.
RESULTS
Index Construction
and Analysis Plan
Data were aggregated into classroom scores so that the data from individuals
who were in the same classroom at pretest were collapsed to create classroom
scores that formed the basis of all analyses. The analyzed sample thus consisted
of 36 Control classrooms, 25 Social classrooms, and 24 Affective classrooms. Two
separate data sets were constructed. The first included subjects who were present
at the pretest and initial post-test (Data Set l-2); the second included subjects who
were present at pretest and the final post-test (Data Set l-3).
Tobacco, alcohol, and marijuana items were each combined into “use” indices
similar to the Minnesota Smoking Index (35) with pretest classrooms as the aggregating unit. The index essentially reduces a number of variables into a score
that represents average number of cigarettes, alcoholic drinks, or marijuana joints
used per student per week. These indices were analyzed separately to assess
impact of programming on each substance use with two planned comparisons that
compared the Social program vs Controls and the Affective program vs Controls.
Index data were analyzed using analysis of variance and analysis of covariance
using pretest scores as the covariates. Additionally,
dichotomous 30-day use data
were analyzed using Fisher’s Exact test.
Two sets of analyses were conducted on index data. The first involved the
analysis of data for only those subjects who did not use a given substance in the
30 days prior to the pretest. The second used pretest scores as covariates and
included data from all subjects in the sample irrespective of their pretest use. Both
PROJECT
SMART
143
approaches were adopted to limit the bias that pretest difference might have on
interpreting outcome analyses.
Pretest
Comparability
Each index was examined to determine the equivalence of planned comparison
groups at pretest. There were significant pretest differences between Social curriculum and Control subjects’ alcohol use in both Data Set l-2 [F(1,329) = 12.55,
P < 0.0011 and Data Set l-3 [F(1,329) = 3.28, P < 0.081 with Social subjects
reporting less drinking than Control subjects in both cases. Control and Social
subjects differed on tobacco in Data Set l-2 [F(1,329) = 3.76, P < 0.06] but not
in Data Set l-3 [F(1,329) = 1.21, not significant]. There were no pretest differences for either data set for marijuana use. There were no differences between the
Affective curriculum and Control subjects’ alcohol use, tobacco use, or marijuana
use.
Thirty-day use scores were also examined to determine pretest comparability.
In Data Set 1-2, there were significantly more users among Control subjects than
among Social subjects (x2 = 12.3, P < 0.005). There were also more subjects in
the Control condition than in the Social condition who reported drinking alcohol
(x2 = 19.0, P < 0.0001). In Data Set 1-3, the use of tobacco among all subjects (x2
= 5.8, P < 0.02) and alcohol use among all subjects (x2 = 5.1, P < 0.05) was also
greater among Control than Social subjects. The prevalence of alcohol use among
Affective subjects was higher than among Control subjects (x2 = 6.0, P < 0.02).
No other pretest comparisons were significant.
Effects
of Programming
The results of comparison analyses examining the effects of programming
on
index values are presented in Table 3.
As can be seen, a number of significant differences emerged. Analyses of means
indicate that for both tobacco and alcohol, onset among non-users at pretest, and
use by all available subjects, was less among those who received the Social
curriculum. For example, index smoking means at eighth grade for the seventh
grade non-smokers in the Social condition was 0.397 whereas the comparable
Control subjects’ smoking index score was 0.569. Index smoking means for the
analysis which included all subjects yielded scores of 0.544 and 0.888, respectively, for Social and Control subjects. Similarly, onset of alcohol use by eighth grade
was 0.267 for Social subjects compared with 0.390 for Controls. The prevalence of
alcohol use was also lower among subjects in the Social condition than among
those in the Control condition, with alcohol index means of 0.351 and 0.631,
respectively.
Marijuana index scores for pretest non-users were only slightly
lower among Social program students (0.202) compared with Controls (0.260).
Finally, marijuana indices for analyses including all Social and Control subjects,
respectively, were 0.259 and 0.421, again indicating lower use among those who
received the Social curriculum. Except for marginal results for tobacco and alcohol, those who received the Social program were not observed to have reduced
use at the second year post-test.
The Affective program appeared to have a negative impact, with all analyses at
144
HANSEN
ET AL.
TABLE
3
RESULTS OF ANALYW OF COVAR~ANCE AND PLANNED COMPARISONS EXAMINING THE IMPACT OF
PROJECT SMART ONTHE INCIDENCEANDPREVALENCEOFTOBACCO, ALCOHOL,AND
MARIJUANA USE
Post-test 1
Post-test 2
Behavior
Analysis
Comparison
F
P
d”
F
Tobacco
Incidence
Affect/control
Social/control
Affect/control
Social/control
Affect/control
Social/control
Affect/control
Social/control
Affect/control
Social/control
Affect/control
Social/control
6.83
2.36
0.009
-
0.1
+
4.00
4.99
1.95
0.05
0.03
+
30.52
2.54
21.44l
2.13
0.2
-
2.34
7.56
0.1
0.006
+
-
3.71
0.06
+
8.39
0.55
4.22
2.88
0.004
0.5
0.04
0.09
-
Tobacco
Prevalence
Alcohol
Incidence
Alcohol
Prevalence
Marijuana
Incidence
Marijuana
Prevalence
+
2.14
0.93
13.18
3.57
13.19
0.00
15.37
0.11
P
d
0.0001
-
0.1
0.0001
0.2
+
+
0.2
-
0.3
0.0003
0.06
0.0003
0.9
0.0001
0.7
+
-
n Direction of difference for significant and marginal findings. A plus (+) indicates a positive
program vs control outcome. A minus (-) indicates a negative program vs control outcome.
the first and second year follow-up measures indicating negative programmatic
effects. By the final post-test, these effects were quite pronounced. For example,
the index mean for tobacco at final post-test for Affective students was 1.508
while the mean for Controls was 0.878. The index means for alcohol at the final
post-test were 1.171 (Affective) and 0.724 (Control). Likewise, means of marijuana use for these two groups were 0.846 and 0.480, respectively.
We also considered the proportions of non-users who began use at various
levels, perhaps a more meaningful indicator of onset. Figures 1, 2 and 3 present
rates of onset among those who were non-users at pretest. As can be seen, these
findings generally corroborate those from index analyses. Figure 1 reveals that the
onset of tobacco use was significantly lower among students who received the
Social program at post-tests 1 and 2; 13.0% compared with 18.2% for controls at
the first post-test, and 11.8% compared with 17.8% at the second post-test. This
translates into a 37.5% reduction in smoking onset 1 year after the program and a
32.4% reduction in onset 2 years after the program that can be atttributed to the
Social program. It appears, furthermore, that the effect of the Social program was
greatest in reducing onset of heavier smoking. For example, at the level of five or
more cigarettes in the preceding 30 days, the reduction was about two-thirds at the
first post-test (5.3% vs 1.7% for Control and Social conditions, respectively), and
three-fourths by the final post-test (6.0% vs 1.4%).
There was a tendency for drinking onset to be less among subjects in the Social
condition than for Control subjects (see Fig. 2). This difference was significant at
the level of two or more drinks in the preceding 30 days (6.0% vs 9.9%, or a 39%
reduction), but not at lower levels of drinking. The same pattern of effect continued at the final post-test with treatment effects becoming clearly significant only
at the level of two or more drinks (6.8% vs 10.3%, a 34% reduction).
145
PROJECT SMART
YEAR 3
IMPARISONS
CvsA
ens
SHEIKS EXACT
ROBABlLlTlES
h)
.
mm
I..
I..
. . . .
. .
. . . .
. . . .
.
.
1
ONSET AT YEAR 2
m
ONSET AT YEAR 3
... I .....
....
pco.15
p c 0.10
pco.05
p-co.01
p c 0.001
FIG . 1. Onset of tobacco use among non-users at Year 1. Comparing control (C), affective (A), and
social (S) conditions.
Figure 3 reveals a significant treatment effect at the first post-test for marijuana
use onset at all levels combined (10.9% vs 6.8% for the Control and Social conditions, respectively). This amounts to a 33% reduction in marijuana use onset. By
the final post-test, however, this difference was no longer evident.
In contrast to these findings for the Social program, proportionally more subjects who received the Affective program had used all three substances at both
waves. Compared to Controls, those receiving the Affective program had 20.1 and
86.4% increases in tobacco use, 30.9 and 42.4% increases in onset of alcohol, and
47.3 and 74.2% increases in marijuana use at post-tests 1 and 2, respectively. By
the final post-test, onset of tobacco use was greater in the Affective than the
Control condition across the entire range of usage levels (see Fig. 1). For alcohol
the effect was significant up through two to four drinks in the preceding 30-day
HANSEN ET AL.
I
I-1
ONSET AT YEAR 2
m
ONSET AT YEAR 3
-
I
I
I
S-10 DRINKS
OR MOHE
I
11 OR MORE
ORINKS
YEAA 2
COMPARISONS
CnA
ens
. . .
rV
. .
N
.
.
rnD
. .
YEAR 3
COMPARISONS
C YI A
c 1s s
.
l mm
FlsftEfrs EXACT
PROBAllLlTlES
m
p.zB.16
pc 0.10
p < 0.05
l
. .
. . .
P<O-10
l mmm
p-z O.DOl
FIG. 2. Onset of alcohol use among non-users at Year 1. Comparing control (C), affective (A), and
social (S) conditions.
level (see Fig. 2). For marijuana the effect was significant up through 5-10 times
in the preceding 30 days (see Fig. 3).
These data were also examined for the effects of programming on those who
were already using substances at pretest. There appeared to be small programmatic effects on reducing subjects’ tendencies to use more, with Social subjects
who were using alcohol at pretest less likely than similar Controls to increase their
use by the final post-test (see Table 4). Although the rate of marijuana use increase
was 60.1% lower among Social subjects when compared with Controls, numbers
were too small for this to result in a significant difference. On the other hand,
subjects who used tobacco, alcohol, or marijuana at baseline and received the
Affective curriculum were more likely than similar Controls to increase their use
of these substances by both post-test occasions (see Table 4).
A similar analysis examining reductions in use among pretest users was also
conducted (see Table 5). In this analysis, those who received the Affective pro-
147
PROJECT SMART
I----lOftSET
AT YEAR
2
ONSET AT YEAR 3
C
A
S
I TIME OR MORE
YEAR 2
DMPAIIISOWS
CnA
ens
. . .
. .
VEAR 3
OMPAAISOWS
CnA
ens
. . . .
ISHER’S EXAC
ROSA8IllflE!
N
, < 0.15
p-z 0.10
. . p < 0.05
mm. ,<o.ot
. . . , < 0.001
.
FIG. 3. Onset of marijuana use among non-users at Year 1. Comparing control (C), affective (A),
and social (S) conditions.
gram appeared to be more likely to reduce their tobacco and alcohol use at the
initial post-test. This trend was not observed at the final post-test. Interestingly,
subjects who received the Social program reduced their alcohol use less than
Controls at the final post-test; this was in contrast to the positive effects of this
program on preventing onset.
Attrition
Analyses
It has been noted (17) that attrition is a serious threat to the validity of prevention research. Attrition can limit the degree to which findings can be generalized
to all participants, particularly if one type of subject is more likely to drop out of
the study. Further, if there is differential attrition by condition or if there is a
behavior status by condition interaction in attrition such that different kinds of
148
HANSEN
ET AL.
TABLE
4
INCREASED USE AMONG USERS IN YEAR 1 COMPARING
AFFECTIVE
AND CONTROL
AND SOCIAL
AND CONTROL
WITH SIGNIFICANCE
BASED ON FISHER’S EXACT PROBABILITIES
Proportion who increased use
Substance
Tobacco
Alcohol
Marijuana
*
**
***
****
P
P
P
P
Group
Post-test 1
Post-test 2
Affective
Control
Social
Affective
Control
Social
Affective
Control
Social
so.s**
38.2
30.8
37.3***
22.3
28.2
42.3
34.5
15.4*
49.3*
39.2
38.9
48.0***
37.7
22.6**
55.3
41.9
16.7
< 0.15.
< 0.10.
< 0.05.
< 0.01.
people are dropping out of different conditions, the internal validity of a study is
brought into question.
Overall, attrition rate of this study was 37% from pretest to initial post-test and
52% from pretest to final post-test. This includes inter- and intrasystem transfer as
well as school dropouts. The analyses that follow were appropriate to this study.
Differences between those missing and those present at follow-up. There was
no differential attrition by sex at initial post-test (x2 = 0.76, n.s.). There were
slightly more males (53.8%) than females (49.7%) who dropped out by final posttest (x2 = 4.66, P < 0.03). Differential attrition occurred by ethnicity at both
REDUCED
USE AMONG
CONTROL
TABLE
5
USERS AT YEAR 1 COMPARING
AFFECTIVE
AND CONTROL AND SOCIAL
WITH SIGNIFICANCE
BASED ON FISHER’S EXACT PROBABILITIES
Proportion of quitters
Substance
Tobacco
Alcohol
Marijuana
*Pco.ls.
** P s 0.10.
*** P s 0.05.
**** P c 0.01.
Group
Post-test 1
Post-test 2
Affective
Control
Social
Affective
Control
Social
Affective
Control
Social
66.3***
54.5
42.3
58.8***
so.4
54.3
56.6
61.8
38.4*
57.3
53.7
61.1
69.8
63.3
43.8***
61.5
51.6
28.6
AND
PROJECT
SMART
149
post-tests (x2 = 97.11, P < 0.0001 and x2 = 20.15, P < 0.0001, respectively) with
black students most likely not to be found at follow-up (49 and 56%), and hispanits (37 and 55%), Asians (29 and 42%), and whites (28 and 48%) a little less likely
to drop out.
The baseline smoking index score of those present at follow-up was significantly lower than for those who were not present at the initial [F(1,2753) = 21.83, P
< O.OOOl] and final post-tests [F(1,2750) = 26.44, P < O.OOOl]. Baseline smoking
index scores for those present and not present at the initial post-test were 0.684
and 0.439, respectively, and for the final post-test were 0.655 and 0.395. The same
pattern held for the marijuana index scores comparing droppers and stayers at
initial [F(1,2482) = 11.28, P < 0.00081 and final post-test [F(1,2480) = 29.95, P <
O.OOOl]. Marijuana use was greater among droppers at each post-test (0.313 and
0.326) than among stayers (0.186 and 0.131). There were no significant differences
between followed and nonfollowed subjects on the pretest alcohol index scores at
the initial post-test [F(1,2658) = 1.15, P > 0.251. At the final post-test, however,
nonfollowed subjects had significantly more pretest alcohol use [F(1,2655) =
7.76, P < 0.0061. Baseline index scores for alcohol use were 0.423 and 0.318 for
absentees and those present at the final post-test, respectively. Thus, as might be
expected from previously reported research (14), those who used substances were
more likely to drop out of the study.
Differences in rates ofattrition.
There was differential attrition by condition. At
the initial post-test, there was greater attrition among those in the Social condition
(45%) than among those in the Control (39%; x2 = 7.12, P < 0.008). There was
less attrition among Affective subjects (30%) than among Controls (x2 = 18.60, P
< 0.0001). At the final post-test, there was equal attrition among Control (60%)
and Social (60%) subjects and less attrition among those who received the Affective program (37%) than among Controls (x2 = 116.58, P < 0.0001).
Condition by attrition status interactions. The differences in rates of attrition by
condition have important implications
for the internal validity of the study primarily if these differences are disproportionately
high among those at greatest risk
for onset. Interactions between condition and attrition status on tobacco, alcohol,
and marijuana index scores were used to test for differences in the baseline substance use of dropouts among conditions. These analyses followed those of outcome analyses for each of the planned programmatic comparisons. There were no
significant condition and attrition status interaction effects for any behavioral
outcome, suggesting no tendency for higher risk subjects to be lost to follow-up in
one or another experimental
condition. Thus, while there was greater attrition
among Social subjects compared with Controls at the initial post-test, the threat to
internal validity posed by this differential attrition among conditions appears to be
mitigated by the fact that this attrition was not related to substance use.
The rate of attrition by condition and the character of those dropping out
yielded inconsistent results. To determine if there was an overall threat of differential attrition, mean pretest use scores of dropouts were multiplied by rates of
attrition for each of the dependent variables which were then analyzed using
analysis of variance. At the first post-test, only two analyses approached significance. For alcohol use, Social subjects’ attrition/use scores (0.109) were lower
150
HANSEN
ET AL.
than the scores for Controls (0.152; F(1,959) = 1.97, P = 0.16). A similar pattern
held for marijuana use with Social subjects’ attrition/use scores (0.070) less than
those for Control subjects (0.122; F(1,893) = 2.57, P = 0.11).
At the final post-test, two attrition/use analyses reached significance while two
were marginally significant. Attrition/use
scores were less for Social dropouts
(0.274) than for Control dropouts (0.324; F(1,1431) = 4.88, P = 0.03). For alcohol, attrition/use scores for dropouts from the Social condition were less (0.121)
than those for Controls (0.267; F(1,1362) = 14.96, P = 0.0001). The marijuana
analysis yielded similar findings with Social subjects’ attrition/use index again
lower (0.121) than that of Controls’ (0.178; F(1,1271) = 2.32, P = 0.13). Finally,
results for alcohol comparing Affective subjects’ attrition/use
scores (0.214)
yielded marginally significant results compared with Controls (0.267; F( 1,1362) =
2.01, P = 0.16).
Thus, despite an increased rate of attrition among Social subjects, the overall
picture suggests that there was either no effect of attrition on end results, or that
if there was an effect, it was to mask slightly the effectiveness of the Social
program. In the end, there is no strong reason to suspect that the results obtained
were attributable to attrition artifacts.
DISCUSSION
The results of this study to date suggest that the most efficacious program for
reducing drug abuse onset is a program that features social infhrences resistance
training as a major focus. The Social curriculum, which included content that
addressed peer, parental, and mass media influences to use drugs, reduced the
onset and prevalence of tobacco, alcohol, and marijuana use at the l-year followup. There was marginal evidence that tobacco use among baseline smokers was
reduced by the time of the final post-test for pretest non-users. The Affective
program on the other hand apparently increased use compared with Controls,
with increases becoming more pronounced over time.
There are several limitations to this study that need to be considered in interpreting these findings. There was less drinking at baseline among subjects who
received the Social curriculum. It is possible that the social milieu in these schools
was less conducive to drinking and possibly to other forms of drug use as well, and
that there was a difference among conditions at pretest. Our approach to analysis
was implemented specifically to reduce such threats. While analysis of covariance
cannot correct for pretest differences perfectly, there is enough convergence
among the various analyses to suggest that a real effect was observed. Further,
since baseline tobacco and marijuana use levels were equivalent, this lends additional support to the interpretation
of a real treatment effect.
Attrition analyses resulted in partly predictable findings. Except for their alcohol use, those not present at follow-up were more likely to use substances than
those tested again, a finding that has been observed previously (17). The Social
condition group experienced the greatest attrition. However, there was not any
differential attrition by level of risk for drug use onset. Thus, the most potentially
damaging explanation for the differential attrition observed was nullified. While
there may always be some suspicion about differential attrition that went unde-
PROJECT
SMART
151
tected, based on the tests conducted, there does not appear to be a significant
threat of attrition that can be directed at the outcome data.
The observed marginal effectiveness of the Social program for alcohol as well
as tobacco reaffirms the social pressures resistance training paradigm that has
been advocated now for over a decade as a promising means of preventing the
onset of substance abuse. While no single study can provide definitive proof that
social pressure resistance training works for preventing the onset of all gateway
substances, these results are supportive of the approach and call for further experimental verification.
However tentative these results, there has already been widespread adoption of
the Project SMART approach to drug abuse prevention. For example, Project
DARE, a program derived in large part from Project SMART but which is delivered by police officers, is currently being implemented
in many cities (7, 34).
Additional experimental programs, (e.g., the RAND drug abuse prevention curriculum, Project ALERT) also use the same approach as Project SMART (36). In
light of the popularity of this approach, continued research on the effectiveness of
the program is important (25). Research on refinements to the program continues
to be conducted.
While the Social approach received some support, the Affective approach as a
stand-alone program for drug abuse prevention received none. In part, this may
have been the result of the character of the program. It is possible for instance that
subjects received mixed messages in this program and may have actually come to
see drugs as a means of coping with stress and enhancing self-esteem. It was also
apparent from reports of the staff health educators delivering the program that the
Affective program was more difficult to implement, at least in comparison to the
Social program.
It is also possible that the apparently harmful effect of the Affective program
may be an artifact of a control group that performed abnormally. In evaluations of
prevention programs, it is expected that control groups will have a considerable
onset, reflecting the normal pattern in our society. In this case, the control group’s
onset was actually less than expected. The subsequent cohort of students in the
control schools received versions of the Social and the Affect programs. It may be
that there were “trickle up” effects of these programs. Alternately, it may have
just been that the particular controls selected were not equivalent to either of the
treatment condition schools. The result of either of these alternatives is that the
Affective program would appear to be simply ineffective at prevention. The Social
program on the other hand would appear to be relatively more effective.
Whatever the explanation, the approaches used in that program, including goal
setting, behavioral alternatives, stress management, and self-image enhancement
did not appear to contribute to prevention and may have enhanced experimentation. Research examining this approach in the past also has failed to show positive
results. Together these findings suggest that when delivered alone, there is little
indication of affective education approaches preventing drug use onset. In conjunction with other approaches, there may be incremental benefits and those
should be explored. For example, a combined program in which social pressure
resistance training is combined with particular affective educational strategies
152
HANSEN
ET AL.
may show even greater impact. As a greater understanding of the factors that
cause drug abuse emerges, and as imaginative new programmatic approaches are
proposed, we may discover that multi-component
approaches work best to prevent drug abuse onset.
ACKNOWLEDGMENTS
The authors acknowledge the assistance of Kim McGuigan, Jill Pearson, Luanne Rohrbach, Beth
Lundy, Debbie Sobol, Johanna Goldberg, Nata Preis, Monica Moreno, Ruth Rich, and the administration and teachers of the Los Angeles Unified School District in the completion of this project.
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