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The effects of mindfulness on adolescent cigarette smoking: Measurement, mechanisms, and theory
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The effects of mindfulness on adolescent cigarette smoking: Measurement, mechanisms, and theory
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Content
THE EFFECTS OF MINDFULNESS ON ADOLESCENT CIGARETTE SMOKING:
MEASUREMENT, MECHANISMS, AND THEORY
by
David S. Black
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PREVENTIVE MEDICINE)
May 2011
Copyright 2011 David S. Black
ii
Acknowledgements
I thank Dr. Steve Sussman for imparting his wisdom in order to guide me
through the perils and promises of academia, Dr. Luanne Rohrbach for careful
critiques and promoting theory in my work, Dr. Joel Milam for validating my
research interests, Dr. Jerry Grenard and Dr. Ping Sun for statistical advice during
my doctoral training, Marny Barovich for helping me address administration tasks,
Dr. Nathaniel Riggs and Dr. Devon Brooks both for support and willingness to help a
student in need, and thanks to both David Frasers for making the hard years fun.
And a very special thank you to Neda Roosta for your amazing love and
companionship over our wonderful years together.
iii
Table of Contents
Acknowledgements ii
List of Tables v
List of Figures vi
Abstract vii
Introduction 1
Specific Aims 1
Background and Significance 5
Overview of the Dissertation 25
Chapter 1: Psychometric assessment of the Mindful Attention
Awareness Scale (MAAS) among Chinese adolescents 27
1.1 Abstract 27
1.2 Introduction 28
1.3 Methods 32
1.4 Results 40
1.5 Discussion 44
Chapter 2: Mindfulness moderates the effect of cigarette smoking
intentions and refusal self-efficacy on adolescent smoking
frequency in the theory of planned behavior 57
2.1 Abstract 57
2.2 Introduction 58
2.3 Methods 62
2.4 Results 66
2.5 Discussion 68
Chapter 3: Testing the indirect effect of trait mindfulness on
adolescent cigarette smoking through negative affect and
perceived stress mediators
77
3.1 Abstract 77
3.2 Introduction 78
3.3 Methods 83
3.4 Results 88
3.5 Discussion 89
Conclusion 102
iv
Bibliography 107
v
List of Tables
Table 1: Demographic characteristics of Chinese adolescents at
time 1 48
Table 2: CFA results to assess unidimensionality of the MAAS at
three points in time 49
Table 3: MAAS reliability estimates at three time points 51
Table 4: EFA results for convergent/ discriminant validity of the
MAAS at time 3 52
Table 5: CFA goodness-of-fit indices to assess gender invariance
of the 15-item MAAS at time 1 53
Table 6: CFA goodness-of-fit indices to assess gender invariance
of the 6-item MAAS at time 3 54
Table 7: Intercorrelations between the full and short MAAS and
other factors to asses nomological validity 55
Table 8: Incremental validity
a
of MAAS in relation to mental illness
measures 56
Table 9: Demographic characteristics of sample 73
Table 10: Descriptive statistics and correlations for study variables 74
Table 11: Demographic characteristics of Chinese adolescents at T1 94
Table 12: Bivariate correlations across multiple time points 95
Table 13: Cross-sectional mediation analysis for past 30-day smoking
frequency 96
Table 14: Longitudinal mediation path analysis for past 30-day
smoking frequency 97
Table 15: Linear growth estimates of mean MAAS scores across
13 month period 99
vi
List of Figures
Figure 1: Theory of planned behavior (TPB) conceptual model 23
Figure 2: Moderating effect of the MAAS on the association between
ITS and smoking 75
Figure 3: Moderating effect of the MAAS on the association between
SRSE and smoking 76
Figure 4: Randomly selected individual trajectories for mean MAAS
Scores 98
Figure 5: MAAS linear growth model adjusted for covariates 100
Figure 6: Meditation analysis conceptual path model 101
vii
Abstract
The pioneering field of mindfulness science, which fits within the broader
field of complementary and integrative medicine, has grown substantially over the
past three decades. Empirical findings obtained from the study of mindfulness are
promising and suggest the construct is associated with improved mental and
physical health and more optimal wellbeing. The following dissertation, which is
comprised of three studies, continues work in this pioneering field by investigating
mindfulness with special emphasis on adolescence. This dissertation is the second of
only two studies to examine dispositional mindfulness and health indices among
youth. Study I comprises the measurement component of the dissertation as it
evaluates the psychometric properties of the Mindful Attention Awareness Scale
(MAAS), which is an operational measure of dispositional mindfulness. Study II
examines cognitive antecedents to adolescent smoking as it explores the moderating
effect of mindfulness on important decision-making processes in the theory of
planned behavior. Study III examines affective antecedents to adolescent smoking as
it tests the indirect effect of mindfulness on smoking frequency through its impact
on depressive affect, anger affect, and perceived stress mediators. Taken together,
these studies work in unison to advance our understanding of dispositional
mindfulness and how it functions in relation to adolescent health
1
Introduction
Specific Aims
Although a substantial amount of research has examined the etiology of
adolescent smoking in Western countries, research is lacking in many developing
countries. China is one such developing country where one-third of the world’s
smokers inhabit, and smoking in this country has been identified as a leading public
health problem. Moreover, recent reports indicate that smoking initiation among
youth in China appears to be increasing. These trends indicate a note of caution to
the public health of China and warrant an increased research effort aimed at
preventing smoking initiation among Chinese adolescents. To address this public
health problem, this dissertation project will contribute to the literature by further
examining adolescent smoking etiology among Chinese adolescents and
contributing further knowledge regarding risk and protective factors of adolescent
smoking behavior.
The three studies comprising this dissertation project will investigate how
the burgeoning construct of mindfulness may contribute to adolescent smoking
prevention research and etiology. Overall, the three studies are interrelated as they
all aim to determine the utility of mindfulness in explaining adolescent smoking
behavior through previously established cognitive and affective mechanisms of
action. Study I will be the first of its kind to test if the Mindful Attention and
Awareness Scale (MAAS) is as a valid psychometric measure among a sample of
Chinese adolescents. This study will provide valuable information to inform the use
2
of the MAAS measure in dissertation study II and study III. Findings from study I are
expected to add to the current literature by validating the MAAS scale for use in the
Chinese adolescent population and to suggest further study of the measure among
other groups of adolescents. In addition, study I will inform future research by
determining if mindfulness maintains its expected direction of relationship with
related psychosocial constructs in a young Chinese population.
Study II will examine how mindfulness operates on two main cognitive
processes, smoking refusal self-efficacy and intention to smoke, that are integral to
the theory of planned behavior (TPB) framework. TPB has shown utility to explain
and predict variance in adolescent smoking behavior in previous research. Findings
from study II are expected to contribute to the adolescent smoking prevention
research literature by determining if mindfulness has a modifying effect on the
relationship between (a) smoking intentions and smoking frequency and (b)
smoking refusal self-efficacy and smoking frequency. TPB constructs are often core
program components of adolescent smoking prevention programming, thus findings
from this study may further inform interventions aiming to prevent adolescent
smoking by determining subgroup contexts that influence smoking-related
cognitive processes.
Study III will be the first of its kind to examine the affective processes that
mediate the association between mindfulness and adolescent smoking. Considering
the current empirical evidence suggesting that mindfulness is inversely associated
with both negative affect and substance use, mediation models are needed to
3
determine the mechanisms underlying the association between mindfulness and
smoking behavior. Findings from study III are expected to contribute to the current
smoking prevention research literature by elucidating one possible affective
mechanism linking mindfulness to smoking behavior, that is through depressive
affect, anger affect and perceived stress. Moreover, support for our proposed
mechanism would indicate the utility of integrating mindfulness-based
interventions within adolescent smoking prevention programs that contain affect
regulation content. These studies pioneer a new direction for the use of mindfulness
in smoking prevention research. The specific aims for this dissertation project are as
follows:
Study I
1. Assess the construct validity of the Mindful Attention Awareness Scale (MAAS) in
a sample of Chinese adolescents using confirmatory factor analysis (CFA)
procedures.
a) Determine if the 15-item measurement model of the MAAS shows
evidence for unidimensionality, reliability, and convergent/ discriminant
validity criteria.
b) Determine if a 6-item short scale of the MAAS retains evidence for
unidimensionality, reliability, and convergent/ discriminant validity criteria.
2. Examine if the MAAS maintains factorial invariance across male and female
adolescents.
4
3. Examine if the MAAS correlates in the expected direction with related
psychosocial constructs indicated in previous literature.
Study II
4. Test the moderating effect of dispositional mindfulness on the theory of planned
behavior (TPB) constructs--smoking refusal self-efficacy and intentions to smoke--
in the prediction of adolescent smoking behavior.
a) Determine if the association between intention to smoke and smoking
behavior is moderated by mindfulness level while adjusting for potential
covariates in a student-nested within school hierarchical model.
b) Determine if the association between smoking refusal self-efficacy and
smoking behavior is moderated by mindfulness level while adjusting for
potential covariates in a student-nested within school hierarchical model.
Study III
5. Test the influence of dispositional mindfulness on adolescent smoking behavior
through its impact on a self-regulation model of negative affect.
a) Test the direct effect mindfulness on adolescent smoking behavior after
adjusting for potential covariates in a student-nested within school
hierarchical model.
b) Analyze the indirect effect of mindfulness on adolescent smoking behavior
through its influence on perceived stress, depressive symptoms, and anger
after adjusting for potential covariates in a student-nested within school
hierarchical model.
5
Background and Significance
Public Health Concern of Adolescent Smoking in China
Adolescence is a critical developmental stage for the initiation of cigarette
smoking. In the United States, 40 percent of adolescents initiate smoking by age 14,
and progression to regular or daily cigarette smoking peaks at ages 15 to 16 years
(USDHHS, 1994). The vast majority of smokers begin using cigarettes by the time
they are 18 years old (Johnston, O'Malley & Bachman, 1998), and by age 19 most
people who will become regular smokers have already begun the addictive process
(Choi, Gilpin, Farkas & Pierce, 2001). In developed countries such as the United
States, cigarette smoking among adolescents has decreased in general over the past
few decades. In 2007, ever trying a cigarette dropped to 50.3% from its highest
prevalence of 71.3% in 1995, and current cigarette use (i.e., smoked at least one
cigarette in the past 30 days) dropped to 20% from its highest prevalence of 36.4%
in 1997 (CDC, 2008). Based on national statistics, current smoking rates in the
United States appear to be the lowest on record to date. However, developing
countries such as China have not encountered such success in decreasing smoking
among adolescents.
China produces 42% of global tobacco products and consumes 31% of global
tobacco products making it the largest tobacco producer and one of the largest
tobacco consumers in the world (Pan & Hu, 2008). A national survey conducted in
30 provinces of China in 1996 estimated that 63% of Chinese males and 3.8% of
Chinese females were current smokers, a total accounting for one third of smokers
6
worldwide (Yang et al., 1999). More recent reports indicate that the total number of
smokers in China increased from 320 million in 1996 to 350 million in 2002 (Yang,
Parkin, Ferlay, Li & Chen, 2005). Of these smokers, it was estimated that 9 million
were adolescents from ages 15 to 19, accounting for 18% of all male and 0.28% of
all female adolescents in China (Zhang & Cai, 2003). These findings are
disconcerting considering data from national surveys in China indicate that smoking
prevalence among adolescents is increasing and young smokers appear to hold
positive attitudes toward smoking (Hesketh, Ding & Tomkins, 2001; Li, Fang &
Stanton, 1999; Yang et al., 1999; Yang, Ma, Liu & Zhou, 2005). Moreover, as the
average age of smoking initiation in China is declining (Yang et al., 1999), the
frequency of cigarette use among smokers increased to an average of 14.8 cigarettes
per day in 2002 (Yang et al., 2005) from 10 cigarettes per day in 1990 (Zhang & Cai,
2003). These trends are a harbinger of a sizeable smoking-related disease burden in
China.
Health Risks of Smoking Emphasized in China
Much of our current knowledge about smoking behavior and its health
consequences comes from Western countries because smoking-related morbidity,
mortality, and etiology data have been well documented in this region. In the year
2000, global mortality associated with smoking was estimated at 4.83 million deaths
(Ezzati & Lopez, 2004), and smoking-related morbidities remain vast and include
chronic obstructive pulmonary disease, chronic bronchitis, emphysema, lung cancer,
esophageal cancer, stomach cancer, liver cancer, tuberculosis, stroke, and ischemic
7
heart disease (Liu et al., 1998). Although smoking poses a health threat to both
developed and developing countries, smoking-related morbidity and mortality
appears to have its greatest health-related impact on developing countries. For
example, most major causes of morbidity and mortality in China are related to
passive or active exposure to tobacco (Chelala, 1998; Lam, He, Li, He & Liang, 1997;
Lam, Ho, Hedley, Mak & Peto, 2001). Currently, smoking contributes to four of the
five leading causes of death in China, and over one million Chinese people die each
year from various smoking-related disorders (Zhang & Cai, 2003). It is estimated
that given the current trends in smoking deaths attributable to smoking in China
will increase to more than 2 million in 2025 (Niu et al., 1998) and 3 million in 2050
(Weiss, Palmer, Chou, Mouttapa & Johnson, 2008; Zhang & Cai, 2003). Although the
health of populations in both developed and developing countries is adversely
affected by passive and active tobacco smoke, the burden of smoking-related
morbidity and mortality appears greatest in developing countries such as China.
This can be attributed to the fact that smoking related deaths and disease
progression occur at younger ages in developing countries mainly due to lack of
access to adequate medical and curative treatment, thus accounting for a greater
loss of life and quality of life years in developing countries relative to developed
countries. It is important to note that smoking also has a specific impact on the
Chinese population. For example, there is evidence that Chinese smokers are at an
increased risk of smoking-related communicable disease such as tuberculosis
relative to other populations (Zhang & Cai, 2003). To combat smoking-related
8
morbidity and mortality, the public health problem of smoking needs to be
addressed in the Chinese adolescent population by developing and testing
innovative research that can strengthen adolescent smoking prevention efforts.
Rationale for the Prevention of Adolescent Smoking in China
The prevention of smoking among Chinese adolescents has been described as
the single greatest public health objective for preventing non-communicable disease
in developing countries (Yang et al., 1999). A highly plausible strategy for reducing
future smoking-related morbidity and mortality in China would be preventing
adolescent smoking because most smoking initiation worldwide is initiated during
adolescence or young adulthood (Kumra & Markoff, 2000), and because as indicated
previously trends indicate younger ages of smoking initiation coupled with a greater
prevalence of adolescent smokers in China. In addition, a general rationale for
preventing the initiation and ceasing the continuation of adolescent smoking has
been well articulated in the literature (see Lamkin, Davis & Kamen, 1998). The
rationale for smoking prevention efforts targeted at adolescents is founded on the
consideration that: 1) if smoking does not start during adolescence or young
adulthood, it is unlikely ever to occur (USDHHS, 1994); 2) the probability of
cessation among adults is inversely related to age at initiation (Breslau & Peterson,
1996; Coambs, Li & Kozlowski, 1992); 3) even infrequent experimental smoking in
adolescence significantly increases the risk of adult smoking (Chassin, Presson,
Sherman & Edwards, 1990); and 4) once smoking has begun, cessation is difficult
and smoking is likely to become a long-term addiction (Tyas & Pederson, 1998).
9
Considering the aforementioned rationale justifying adolescent smoking prevention
endeavors, and also considering the problematic smoking trends in China, further
studies are needed to identify and advance means to prevent smoking among
Chinese youth. Further research is warranted to gain a clearer understanding of the
factors that lead to smoking behavior among this population. This understanding is
most likely to be acquired by identifying and examining antecedents of adolescent
cigarette smoking.
Antecedents to Adolescent Cigarette Smoking
Antecedents are defined as those events or variables that precede other
events or variables in time. In the disease prevention and health promotion
literature, antecedents have been used synonymously with the terms risk and
protective factors. Our clearest examples of antecedents perhaps stem from disease
etiology. For example, a well-recognized antecedent to small cell lung carcinoma is
exposure to passive or active tobacco smoke. Examples of psychosocial antecedents
to adolescent cigarette smoking initiation might include exposure to peer and
parental smoking and exposure to pro-smoking media advertisement. Based on
findings from cross-sectional and prospective studies, a multitude of antecedents to
adolescent cigarette smoking have been identified in previous research. According
to an extensive review of the literature on the topic (Tyas & Pederson, 1998),
antecedents to adolescent cigarette smoking are extremely vast, and thus, are best
grouped within a typology. Considering that the vast multitude of antecedents to
adolescent smoking are beyond the scope of this project, the following dissertation
10
will focus solely on specific affective and cognitive antecedents that have been
shown utility in previous literature to explain variance in adolescent smoking
behavior.
Affect is a term used interchangeably with mood, temperament, and emotion
(Pressman & Cohen, 2005). It has been described as a transient positive or negative
emotional state that varies in frequency and severity across time, and is rooted in
environmental conditions and individual differences (Lamkin & Houston, 1998). The
most widely studied conceptualization of affect, negative affect, refers to a variety of
unpleasant mood states such as anger, irritability, fear, anxiety, stress, hostility, and
depression (APA, 1994). Negative affect is indicated to play an important role in
health and health behavior in that it is thought to represent a principal antecedent
to behavior and subsequent disease and poor health outcomes such as the role of
depression in alcohol abuse and subsequent cirrhosis of the liver (Cohen &
Pressman, 2006; Steptoe, Wardle & Marmot, 2005).
Negative affect, classified in this dissertation as depressive affect, perceived
stress, and anger, is as an important construct in adolescent smoking etiology.
Several studies based on data from samples residing in various countries have found
significant cross-sectional and longitudinal associations between adolescent
smoking and indicators of negative affect. For example, cross-sectional and
longitudinal studies have identified a positive relationship between smoking and
depressive affect (Covey & Tam, 1990; Ferdinand, Blüm & Verhulst, 2001; Glied &
Pine, 2002; Haarasilta, Marttunen, Kaprio & Aro, 2004; Hu, Davies & Kandel, 2006;
11
Kandel & Davies, 1986; Killen et al., 1997; Prinstein & La Greca, 2009; Weiss et al.,
2005; Windle & Windle, 2001), perceived stress (Byrne & Mazanov, 2003; Castro,
Maddahian, Newcomb & Bentler, 1987; Dugan, Lloyd & Lucas, 1999; Finkelstein,
Kubzansky & Goodman, 2006; Siqueira, Diab, Bodian & Rolnitzky, 2000; Skara,
Sussman & Dent, 2001), and anger (Forgays, Forgays, Wrzesniewski & Bonaiuto,
1993; Johnson & Gilbert, 1991; Musante & Treiber, 2000; Seltzer & Oechsli, 1985;
Siqueira et al., 2000). Empirical reviews and meta-analyses have also supported a
significant association between these negative affect constructs and adolescent
smoking, and suggest that, overall, negative affect is an important predictor of
adolescent smoking (Chaiton, Cohen, O'Loughlin & Rehm, 2009; Kassel, Stroud &
Paronis, 2003; Munafò, Zetteler & Clark, 2007; Schepis & Rao, 2005). These findings
highlight the importance of incorporating negative affect in adolescent smoking
etiology research and in developing treatment applications for smoking prevention.
Cognitive antecedents also contribute much to our understanding of
adolescent smoking. Cognitions are those mental processes related to knowing and
perceiving. It is well established in the scientific literature that cognitions about a
specific behavior affect the probability of future performance of that behavior.
Cognitive antecedents in the context of health behavior can be conceptualized as
social-cognitive processes that determine how thoughts, perceptions, and decision-
making processes precede the onset and continuation of a behavior. Cognitive
antecedents commonly examined in the adolescent smoking prevention literature
include, but are not limited to, smoking-related attitudes and beliefs, perceived
12
social and peer smoking norms, perceived susceptibility to smoking, perceived
social pressures, perceived smoking behaviors, intentions to smoke, and self-
efficacy expectations (Jackson, 1998; Kremers, De Vries, Mudde & Candel, 2004;
McAlister, Krosnick & Milburn, 1984). These variables, examined either
independently or within theoretical models, have proven useful in understanding,
explaining, and predicting variance in adolescent smoking behavior. More
specifically, a large literature has supported the utility of the theory of planned
behavior (TPB) in explaining and predicting variance in adolescent smoking
behavior.
Research examining TPB constructs indicates that pro-smoking attitudes
(Epstein, Botvin & Spoth, 2003; Hanson, 1999; Kremers et al., 2004; Maher &
Rickwood, 1997; Oei & Burton, 1990; Virgili, Owen & Sverson, 1991; Wang, 1996)
and perceived smoking norms (Botvin et al., 1993; Epstein et al., 2003; Pederson &
Lefcoe, 1986; Primack, Switzer & Dalton, 2007; Wilkinson & Abraham, 2004) are
associated with smoking initiation and continued smoking among adolescents.
Perceived behavioral control and related measures of self-efficacy are also shown to
be associated with adolescent smoking behavior. For example, higher smoking
refusal self-efficacy is shown to have an independent and inverse association with
smoking initiation among adolescents (Choi et al., 2001; Kremers et al., 2004; Ma et
al., 2008; Maassen, Kremers, Mudde & Joof, 2004; Sussman, Dent, Flay, Hansen &
Johnson, 1987; de Vries, Dijkstra & Kuhlman, 1988). Further, studies have found
that intention to smoke in the future is positively associated with smoking behavior
13
among adolescents and is one the most potent predictors of adolescents’ future
smoking behavior (Ary & Biglan, 1988; Chassin, Presson, Sherman, Corty &
Olshavsky, 1984; Eckhardt, Woodruff & Elder, 1994; de Vries, Backbier, Kok &
Dijkstra, 1995; Wilkinson & Abraham, 2004), and that lack of intentions to smoke is
inversely associated with smoking behavior (McNeill et al., 1989).
The use of structural equation modeling techniques has allowed all TPB
constructs to be examined simultaneously in the prediction of smoking. Several
studies using SEM procedures have shown that TPB model constructs function to
predict smoking in the hypothesized directions (O'callaghan, Callan & Baglioni,
1999; Van De Ven, Engels, Otten & Van Den Eijnden, 2007; Wilkinson & Abraham,
2004). For example, one study found that TPB constructs predicted both current
and future adolescent smoking in all theorized directions (Harakeh, Scholte,
Vermulst, de Vries & Engels, 2004). A second study attempted to integrate four
theories related to smoking initiation and found that intention to smoke stood out
as an important longitudinal predictor of smoking across all theories considered
(Collins & Ellickson, 2004). Further, when considering the integration of constructs
from these four theories, those that remained significant were constructs contained
within TPB--smoking norms and intentions to smoke.
As articulated in the previous section, affective and cognitive antecedents
play an important role in our understanding of adolescent smoking etiology.
However, both affective and cognitive antecedents and their corresponding models
have explained only a limited amount of variance in adolescent smoking behavior.
14
Research is needed that continues to identify constructs that have additional utility
in explaining and predicting adolescent smoking behavior. This research has the
ultimate aim of advancing our knowledge of the antecedents to adolescent smoking
and improving adolescent smoking prevention programming. The next section
transitions into a discussion of a relatively new empirical construct, mindfulness,
which has only been recently studied and applied in the field of adolescent health
behavior research. The construct of mindfulness will be considered in the context of
health and discussed in terms of its integration into recognized frameworks of
adolescent smoking.
The Construct of Mindfulness and its Application to Health
Western empiricism has examined the construct of mindfulness for almost
40 years, and a definition of mindfulness has been continuously revised and clarified
over this period. The current use of the term mindfulness stems from Eastern
psychological practices, specifically Buddhist psychology, which referred to the
concept of mindfulness over 2,500 years ago. Mindfulness is a term stemming from
the Pali language, whereby Sati is combined with Sampajana, and is translated
directly as awareness, circumspection, discernment, and retention (Shapiro, 2009).
These Pali renderings have been considered by scholars to suggest that mindfulness
means to pay attention to what is occurring in one’s immediate experience with care
and discernment (Shapiro & Carlson, 2009). As the concept of mindfulness was
gradually introduced to Western science, many contended that mindfulness and its
associated methods of cultivation (e.g., meditation techniques) were esoteric, bound
15
to religious beliefs, and a capacity attainable only by certain people. However,
decades of research beginning in the 1970’s have defrayed these myths, and
mindfulness is now recognized as an inherent characteristic of human
consciousness (i.e., it is a capacity of human attention and awareness) that varies in
degree within and between individuals, can be measured empirically, and is
inherently independent of religious, spiritual, or cultural beliefs.
One of the most popular Western definitions of mindfulness comes from Jon
Kabat-Zinn, the original founder of the mindfulness research movement and the
creator of the mindfulness based stress reduction (MBSR) program. He defined
mindfulness as, “paying attention in a particular way: on purpose, in the present
moment, and nonjudgementally” (Kabat-Zinn, 1994, p. 4). Similar definitions are
cited in the literature (e.g., Brown & Ryan, 2004; Jha, Krompinger & Baime, 2007;
Siegel, 2007a) and all suggest that mindfulness has to do with an attention to and
awareness oriented to what is occurring in the present moment. To further grasp
the definition of mindfulness, the term can be contrasted with the experience of
mindlessness that occurs when attention is scattered by past memories or future
plans and worries, in turn, leading to a limited field of awareness and attention.
Although conceptual definitions of mindfulness were initially useful in
advancing initial mindfulness research and practice, the development of an
operational definition of mindfulness was most useful for advancing empirical
research on the topic. Research into the development of an operational definition of
mindfulness has accumulated over the last decade. For example, a series of
16
measurement studies developing the Mindful Attention Awareness Scale (MAAS)
revealed a first validated and reliable psychometric measure of dispositional
mindfulness (Brown & Ryan, 2003). In this pioneering work that attempted to
operationalize mindfulness, dispositional mindfulness was defined to be an innate
human capacity of attention and awareness oriented to the present moment.
Moreover, the MAAS provided a measure that did not directly equate mindfulness
with any form of meditation practice. Additional measures of mindfulness have also
been validated since that pioneering study (e.g., Kentucky Inventory of Mindfulness
Skills--Baer, Smith & Allen, 2004; Toronto Mindfulness Scale--Lau et al., 2006;
Cognitive and Affective Mindfulness Scale--Feldman, Hayes, Kumar, Greeson &
Laurenceau, 2007; Five Facet Mindfulness Questionnaire--Baer, Smith, Hopkins,
Krietemeyer & Toney, 2006; Freiburg Mindfulness Inventory--Buchheld, Grossman
& Walach, 2001; Philadelphia Mindfulness Scale--Cardaciotto, Herbert, Forman,
Moitra & Farrow, 2008). Measurement development in this respect has spurred the
use of mindfulness as an empirical construct in psychosocial and behavioral health
research and applied behavioral therapy.
A continuing line of empirical research suggests mindfulness is a promising
construct in the context of human health and behavior. Several experimental, quasi-
experimental, case studies, research reviews, and meta-analyses have suggested
that various conceptualizations of mindfulness have a positive impact on several
biological, psychosocial, and behavioral indicators of health across child, adolescent
and adult populations. A recent meta-analysis showed that interventions with
17
content aiming to enhance mindfulness showed mean effect sizes of approximately
0.50 for physical and mental wellbeing (Grossman, Niemann, Schmidt & Walach,
2004). These findings corroborate additional reviews that suggest mindfulness-
based interventions may improve health outcomes with at least median effect sizes
(range .15-1.65) for patients and children facing a variety of health issues and
psychosocial comorbidities (see Baer, 2003; Black, Milam & Sussman, 2009).
Mindfulness disposition and interventions targeted at increasing
mindfulness have also been associated with positive changes in cognition and affect.
For example, mindfulness enhances cognitive processes such as attentional
processing, reduced ruminative thinking, and self-regulation (Cahn & Polich, 2006;
Chambers, Lo & Allen, 2008; Creswell, Way, Eisenberger & Lieberman, 2007; Farb et
al., 2010; Goldin, Ramel & Gross, 2009; Ivanovski & Malhi, 2007). Similarly,
mindfulness is associated with improved affect such that it is associated with
reduced stress (Carlson, Speca, Faris & Patel, 2007; Marcus et al., 2009; Robert,
Tacon, Randolph & Caldera, 2004; Tang et al., 2007) and depressive symptoms and
anger (Black et al., 2009; Broderick, 2005; Dobkin, 2008; Schroevers & Brandsma,
2010; Zvolensky et al., 2006). Moreover, mindfulness is associated with improved
behavioral outcomes such as reduced rates of smoking (Altner, 2002; Davis,
Fleming, Bonus & Baker, 2007) and reductions in other substance use (Bowen,
Witkiewitz, Dillworth & Marlatt, 2007; Bowen et al., 2006; Witkiewitz, Marlatt &
Walker, 2005; Zgierska et al., 2008). However, no known study to date has
specifically examined the influence of mindfulness on adolescent smoking behavior,
18
which indicates a need for pioneering research in this area. A consideration of
theory in the next section elucidates how mindfulness may function to influence
smoking-related affective and cognitive processes that precede adolescent smoking
behavior.
Theory: Integrating Mindfulness with Affective and Cognitive Models of Smoking
Although still in early years of conception, theories that best describe the
underlying mechanism of mindfulness on affect, cognition, and behavior are
information-processing models of mindfulness (see Breslin, Zack & McMain, 2002;
Brown, Ryan & Creswell, 2007; Coffey & Hartman, 2008; Lutz, Slagter, Dunne &
Davidson, 2008; Shapiro, Carlson, Astin & Freedman, 2006; Wells, 2002). These
theories suggest that mindfulness is a quality and efficiency of information
processing guided by the use of attention and awareness, which are the two
underlying functions of human consciousness (Westen, 1999). Awareness refers to
the general monitoring of internal and external experience and attention refers to a
process of focusing awareness on a limited range of stimuli (Nyanaponika, 1972).
Because attention and awareness are an inherent mental capacity among humans,
so too is mindfulness.
With a greater level of attention and awareness via mindfulness, greater
deliberate processing of salient information is allowed as cognitive networks are
made available in the present moment as opposed to mindless and habitual patterns
of information processing that are guided by previously established emotional and
cognitive response sets based in memory. For example, new experiences often have
19
previously categorized emotional and cognitive sets established in the brain and
these sets are involuntarily applied to these new situations even if these sets lack
appropriateness to the current situation (Fernandez-Duque, Baird & Posner, 2000).
However, being mindful increases available attention and awareness; therefore, it
expands possibilities for cognitive and emotional responses to stimuli by increased
monitoring and regulating of emotional stimuli (Siegel, 2007b). Thus, a greater
awareness and self-regulated processing of attention to the present moment allows
cognitions and emotions to be more readily transparent and voluntarily processed
(Breslin et al., 2002). Because these characteristics of mindfulness are considered to
operate upon, rather than within, cognitions and emotions, mindfulness is
considered to be a metacognitive skill within the system referred to as the executive
control.
This theoretical conceptualization of mindfulness has important implications
for affective and cognitive processing and associated behavioral outcomes such as
smoking. For example, because mindfulness is associated with an objective rather
than subjective stance toward thoughts and emotions it may promote self-regulated
affect. This objective stance towards thoughts and emotions has been termed
reperceiving (Shapiro et al., 2006) in the mindfulness literature and is a term akin to
western psychological concepts of decentering (Safran & Segal, 1990) and
deautomatization (Deikman, 1982). When a person reperceives the effects of
thoughts and emotions, that person is more aware of the underlying causes and
contexts of these sensations and how they impel involuntary behavioral reactions.
20
Thus, greater attention to and awareness of affect and cognitions experienced in the
present moment may lead to the initiation of self-regulated information processing
and voluntary behavioral responses. Consequently, rather than thoughts and
emotions directing the flow of energy and information in the brain, mindfulness
enhances the ability to voluntarily direct this flow of information and energy (Siegel,
2007a).
Research in the field of mental health and psychotherapy has supported the
notion that mindfulness improves affective and cognitive functioning. For example,
maladaptive cognitive patterns such as rumination and avoidance (Teasdale et al.,
2000) are coping mechanisms often used to handle unwanted thoughts and
emotions. Rumination is a clinging to negative cognitions and emotions and
avoidance is pushing them out of awareness (e.g., distraction, repression). Both are
ineffective means for handling distress over time (Linehan, 1993) and may actually
worsen cognitive and emotional processing (Ingram, 1984; Lyubomirsky & Nolen-
Hoeksema, 1995; Nolen-Hoeksema, 1991; Wegner, 1994). Because mindfulness
involves a more direct experience of events in the mind and body, it prevents
ruminative and other negatively biased information processing approaches
(Fennell, 2004; Teasdale, Segal & Williams, 1995). Moreover, as additional attention
is released from maladaptive thinking when mindful, more mental resources are
made available to process information related to current experience, which results
in improving cognitive inhibition (Shiffrin & Schneider, 1977). Additional
attentional resources also increases access to information that might otherwise
21
remain outside awareness, resulting in a wider perspective on and acceptance of
experience. For example, studies have shown that mindfulness is associated with
indicators of improved affect (Baer et al., 2008; Feldman et al., 2007; Lutz,
Brefczynski-Lewis, Johnstone & Davidson, 2008; Sears & Kraus, 2009) including
reduced depression (Coffey & Hartman, 2008) and increased cognitive awareness of
rumination (Rapgay & Bystrisky, 2009).
Pertaining to affective processes, smoking is often considered to result from
an attempt to self-manage the frequency and intensity of negative affect states
(Khantzian, 1997). This mechanism has been referred to as the self-medication
hypothesis, which suggests that adolescents smoke cigarettes to derive the
psychopharmacological effects of nicotine in an attempt to regulate affective states
(Glass, 1990; Penny & Robinson, 1986; Whalen, Jamner, Henker & Delfino, 2001).
Research studies support this notion and suggests that smoking has ameliorating
effects on negative affect and promotes feelings of calm and relaxation (Gilbert,
1979; Glassman, Covey, Stetner & Rivelli, 2001; Jamner, Shapiro & Jarvik, 1999;
Parrott, 1998). It follows that mindfulness may possibly interrupt the link between
negative affective states and adolescent smoking.
Mindfulness is postulated to increased awareness of and attention to
perceptions of negative affect from a decentered perspective. Therefore,
mindfulness functions to reduce negative affect therefore nullifying the need to self-
regulate negative affect by means of deriving the psychopharmacological effects of
smoking nicotine. Further, because mindfulness brings increased attention to and
22
awareness of emotions in the present moment and promotes voluntary responses, it
may allow for the awareness and selection of healthy behavioral coping responses
such as exercise, which promote relaxation and calm, to regulate the experience of
an emotion, obviating the need for smoking as a behavioral response.
Mindfulness is also expected to influence the cognitive processes related to
adolescent smoking behavior. With this being the first study to determine the
impact of mindfulness on a recognized cognitive model of health behavior change
among youth, it is best to examine its influence on a well-tested theory of health
behavior. Thus, we examine TPB, which has been studied extensively in the health
behavior literature. TPB (Ajzen, 1991) an extension of Theory of Reasoned Action;
Fishbein & Ajzen, 1975) addresses the influences of socio-cognitive antecedents on
health behavior through attitudes, beliefs, social norms, behavioral control and
behavioral intentions (see Figure 1). In TPB, Attitude refers to the degree to which a
person has a favorable or unfavorable evaluation or appraisal of a specific behavior
and its consequences. Subjective norms refers to the perceived social pressures to
perform or not to perform a specific behavior based on perceptions of the views and
behaviors of family or friends. Perceived behavioral control, which is most
compatible with the concept of perceived self-efficacy (Bandura, 1977), refers to the
perceived ease or difficulty of performing a specific behavior, or the perception of
control over the successful completion of a particular behavior. Two forms of self-
efficacy have mainly been studied in the context of adolescent smoking: use self-
efficacy, which refers to a person’s belief in their ability to obtain and successfully
23
use cigarettes and refusal self-efficacy, which refers to a person’s beliefs in their
abilities to resist social pressure to begin smoking. The most proximal predictor of
behavior in TPB is behavioral intention or the motivational factors that influence a
behavior, which indicate how much effort a person is planning to exert in order to
perform a specific behavior.
Figure 1. Theory of planned behavior (TPB) conceptual model
According to the general mechanistic framework of TPB in the context of
smoking behavior, the greater the smoking perceived behavioral control (i.e., either
high smoking use self-efficacy or low smoking refusal self-efficacy) and the stronger
the intention to smoke, the more likely smoking behavior will be performed. When a
behavior poses no serious problem of volitional control, it can be predicted from
behavioral intentions with considerable accuracy. However, when a person has
Attitude
toward
behavior
Perceived
behavioral
control
Subjective
norm
Intention
Behavior
24
limited volitional control over a behavior, models accounting for perceived
behavioral control appear to be more accurate. Thus, performance of a given
behavior should increase with increasing behavioral control. Meta-analysis show
that, in general, TPB accounts for 21-31% of the variance in behavior depending on
the measurement methods used (Armitage & Conner, 2001). For accurate prediction
of behavior in TPB, all model constructs should be behavior-specific. For example, if
the predicted outcome is smoking behavior, all model constructs should be
measured in the context of smoking behavior (e.g., smoking attitudes, perceived
smoking norms, smoking refusal self-efficacy, intentions to smoke) and not in other
more general contexts. After providing an overview of TPB we now suggest how
mindfulness will function on this model.
Considering that mindfulness is associated with increased awareness of and
attention to present moment cognitions, mindful individuals may be more aware of
and pay closer attention to their underlying smoking cognitions than those less
mindful. This heightened awareness of and attention to smoking-related cognitions
is important considering that forgetting to initiate intended action is one of the most
common reasons for the intention-behavior discrepancy (Sheeran, Abraham &
Orbell, 1999). Therefore, mindfulness appears to be an important moderating
construct of the relationships between smoking-related cognitions that are proximal
to smoking behavior such as intentions to smoke and smoking refusal self-efficacy.
Although only one study has been conducted in this area, the initial findings from
this study are suggestive of such an effect. This single study, which was carried out
25
by Chatzisarantis & Hagger (2007), sampled adults attending college and found that
those higher in mindfulness were more likely to enact their behavioral intentions as
compared to those lower in mindfulness. Moreover, those with higher mindfulness
were also less likely to be influenced by counter-intentional binge drinking habits
relative to those less mindful. Considering these initial empirical findings and the
theoretical premise of mindfulness, it can be hypothesized that mindful individuals
are expected to be more attuned to the underlying processes that lead to their
decisions to smoke relative to those less mindful, and thus may be more inclined to
perform their intended behaviors. Research is needed among adolescents to
determine if mindfulness moderates the relationships between decision-making
cognitive processes and behaviors in the context of smoking behavior. This
dissertation project sets out to establish initial findings in this pioneering area.
Overview of the Dissertation
The introduction of this dissertation project identified adolescent smoking as
an important public health problem and emphasized concern for the current
adolescent smoking trends in China. Important affective and cognitive antecedents
to adolescent smoking were discussed in an attempt to address the need for future
research to investigate additional constructs that may show promise to explain
adolescent smoking behavior. The relatively new and burgeoning construct of
mindfulness was offered as one pioneering area of investigation to explain
additional processes and contexts preceding adolescent smoking behavior. Finally, a
theoretical basis of mindfulness and the initial empirical research on this construct
26
were offered to explain how mindfulness may function on affective and cognitive
processes preceding adolescent smoking behavior.
This dissertation proposal now transitions to a report of three studies that
explore three unique areas of investigation in mindfulness science. The aim of these
studies is to determine if mindfulness proves to be an important construct to
models of smoking etiology among Chinese adolescents. Study I tests the
psychometric properties of the Mindful Attention Awareness Scale (MAAS) in a
sample of Chinese adolescents to determine the validity of its use in this population
and to inform the measures application in Study II and Study III. Study II tests the
moderating influence of mindfulness on smoking intentions and smoking refusal
self-efficacy constructs within the theory of planned behavior. Study III examines
the indirect predictive influence of mindfulness on smoking through depressive
affect, perceived stress, and anger affect mediators. These studies will be the first to
determine the utility of the mindfulness construct in adolescent smoking etiology,
and will elucidate the mechanisms by which mindfulness may further inform
smoking prevention programming in China more specifically and for adolescents
more broadly.
27
Chapter 1
Psychometric Assessment of the Mindful Attention Awareness Scale (MAAS)
Among Chinese Adolescents
1.1 Abstract
The Mindful Attention Awareness Scale (MAAS) has the longest empirical
track record as a valid measure of trait mindfulness. Most of what is understood
about trait mindfulness comes from administering the MAAS to relatively
homogenous samples of Caucasian adults. This study rigorously evaluates the
psychometric properties of the MAAS among Chinese adolescents attending high
school in Chengdu, China. Classrooms from 24 schools were randomly selected to
participate in the study. Three waves of longitudinal data (N=5,287 students) were
analyzed. MAAS construct, nomological, and incremental validity were evaluated as
well as its measurement invariance across gender using latent factor analyses.
Participants mean age was 16.2 years (SD=0.7) and 51% were male. The 15-item
MAAS had adequate fit to the one-dimensional factor structure at Wave 1 and this
factor structure was replicated at Wave 2. A MAAS 6-item short-scale was well-fit to
the data at Wave 3. The MAAS maintained reliability (Cronbach’s α=.89-.93; test-
retest r=.35-.52), convergent/discriminant validity and explained additional
variance in mental health measures beyond other psychosocial constructs. Both the
15 and 6 item scales displayed at least partial factorial invariance across gender.
Evidence from this study suggests the MAAS is a sound measure of trait mindfulness
among Chinese adolescents. To reduce respondent burden, the MAAS 6-item short-
28
scale of the MAAS identified in this study provides an option to measure trait
mindfulness among adolescents.
1.2 Introduction
From what the human mind can perceive and articulate with language,
consciousness appears to be comprised of awareness and attention. Awareness
being a continual background monitoring process, and attention being a function of
focusing awareness on a limited range of experience to heighten sensitivity to that
experience (Westen, 1999). All humans, except those with certain types of brain
damage, have an inherent capacity to attend to and be aware of ongoing experience.
However, there is substantial variability in these faculties of consciousness both
within and between individuals. Because some degree of consciousness is carried
with us wherever we go, it is a process that has often been taken for granted and
understudied in Western science. However, consciousness and its relationship to
the human condition has recently blossomed as a new frontier in Western science.
Interest has developed regarding the human capacity for enhanced attention to and
awareness of life’s experiences, which has been termed trait mindfulness. Trait
mindfulness, also referred to in some literature as day-to-day mindfulness or
dispositional mindfulness, is defined by Brown and Ryan (2003) as an inherent state
of consciousness varying between and within humans that is characterized by the
presence or absence of attention to or awareness of what is occurring in present
experience.
29
Using a series of psychometric development studies, Brown and Ryan (2003)
operationalized trait mindfulness by the 15-item unidimensional Mindful Attention
Awareness Scale (MAAS). In the Brown and Ryan study, the MAAS had good internal
consistency (α ≥ .82) and 4-week test-retest reliability (interclass r = .81), and was
positively correlated with number of years of meditation practice (r = .36, p < .05),
which is a specific technique aiming to increase mindfulness. MAAS scores were also
significantly higher among meditation practitioners relative to non-practitioners
(Cohen’s d = .50; Brown and Ryan, 2003), and a different study reported MAAS
scores to be significantly correlated with other psychometrically sound measures of
mindfulness (r with Freiburg Mindfulness Inventory; FMI = .31, p < .01; r with
Kentucky Inventory of Mindfulness Skills; KIMS = .51, p < .01; r with Cognitive
Affective Mindfulness Scale; CAMS = .51, p < .01; r with Mindfulness Questionnaire;
MQ = .38; p < .01; Baer et al., 2006).
By operationalizing the MAAS as a valid measure of trait mindfulness, new
research has begun to uncover the relationship between trait mindfulness and
human health. Initial findings in the field of mindfulness research suggests that trait
mindfulness has important implications for human health and functioning. For
example, studies have found the MAAS to be significantly and inversely associated,
in medium-to-large magnitude, with a variety of mental health indicators (e.g.,
anxiety, hostility, depression, impulsiveness, somatization, disturbed mood,
neuroticism, and negative affect) and positively associated with mental and physical
health (e.g., self-esteem, optimism, positive affect, autonomy, self-control, perceived
30
general health, physical functioning, and life satisfaction) (Brown and Ryan, 2003;
Fetterman, Robinson, Ode & Gordon, 2010; Thompson & Waltz, 2007; Zvolensky et
al., 2006). Moreover, the MAAS has maintained a significant relationship with
wellbeing even after adjusting for other important psychosocial measures (Brown
and Ryan, 2003), indicating its incremental validity as a unique mental health
construct.
To date, the key limitation to the scientific literature on the MAAS is its use in
relatively homogenous samples, which limits the measures generalizability to more
diverse populations. For example, the inception of the MAAS by Brown and Ryan
(2003) was based on data from mainly White college students and adults residing in
the U.S. Furthermore, psychometric replication studies assessing the MAAS have
again focused on college students and adult community residents of mainly White
ethnicity (Cordon & Finney, 2008; Hansen, Lundh, Homman & Wangby-Lundh,
2009; MacKillop & Anderson, 2007; Thompson & Waltz, 2007; Van Dam, Earleywine
& Borders, 2010; Zvolensky et al., 2006).
Only three studies identified to date have assessed the MAAS among other
populations using relatively stringent psychometric assessment methods. Carlson &
Brown (2005) assessed the validity of the MAAS using latent factor analysis with
factorial invariance procedures, and found the MAAS functioned comparably among
Canadian adult cancer patients in a clinical setting relative to demographically-
matched Canadians in the local community. In the Carlson study, higher MAAS
scores among cancer cases were associated with lower mood disturbance and
31
stress. Hansen et al. (2009) surveyed Swedish youth aged 19 to 20 who had just
begun military service as well as a second sample of adolescents (mean age = 16.2,
SD = 1.4) from five Swedish high schools. Internal consistency reliability was
adequate in the military sample (α = .77) and good in the school sample (α = .85).
MAAS scores were inversely correlated with trait anxiety (r = -.35, p < .05) in the
military sample, and inversely correlated with self-harming behavior in the
adolescent sample attending school (r = -.31, p<.01). Finally, Christopher,
Charoensuk, Gilbert, Neary & Pearce (2009) assessed measurement invariance of
the MAAS between students attending a private university in Thailand and
American students attending a Pacific Northwestern university in the United States.
Data supported the MAAS to have configural, metric, and latent mean invariance, but
not scalar invariance across these populations; indicating at least partial support for
the stability of the MAAS among Thai college students.
Scientific evidence to date suggests that the MAAS is a sound measure of trait
mindfulness among a relatively homogenous population, with initial evidence for
stability across some heterogeneous populations. Psychometric replication studies
in new populations are needed prior to examining theoretical relationships between
trait mindfulness and other constructs in these new populations. The current study
assesses the psychometric validity of the MAAS in a Chinese adolescent population
attending high school in Chengdu, China. This study applies rigorous statistical
methods to assess multiple dimensions of construct validity of the MAAS in this
32
population. This study adds to the current literature by being the first to assess the
psychometric validity of the MAAS among Chinese adolescents.
1.3 Methods
Participants and Procedures
Data were collected as part of a longitudinal study conducted by
collaborating researchers from the Pacific Rim Transdisciplinary Tobacco and
Alcohol Use Research Center (TTAURC). The objective of the TTAURC project was to
investigate the determinants of health behavior among adolescents in Chengdu,
China. All consent procedures and survey instruments for this study were approved
by the Institutional Review Boards of the University of Southern California and
Chengdu, China Centers for Disease Control and Prevention. A total of twenty-four
schools (N=24) in Chengdu, China enrolled in the study. Within the 24 schools that
participated, there were a total number of 1,060 classes. A total of 338 classes were
randomly selected to participate in this study.
Parental consent forms were distributed to students within the selected
classrooms, and those students acquiring written or verbal parental consent and
giving personal assent completed a self-reported paper-and-pencil questionnaire in
their classroom during school hours. The students whose parent did not sign the
parental participation permission form and/or who did not assent were excluded
from the study. Participants voluntarily took part in the study and were informed
that they could discontinue their participation at any time. Classroom teachers were
not present during the survey period so that participating students would feel
33
confident about the confidentiality of their responses. The same participants
completed surveys in their respective classroom from 10
th
to 12
th
grade for a total of
five waves of data collection. This study examines the data specifically from wave 2,
wave 3, and wave 5 because the measures of interest were collected during these
waves. For clarity purposes, these waves of data collection are referred to as Time 1,
Time 2 and Time 3, respectively, in the current study.
Measures
Demographic data included respondent self-reported age, gender, and parent
education (see Table 1).
Mindful Attention Awareness Scale (MAAS; Brown & Ryan, 2003). The MAAS
is a 15-item single-dimension measure of trait mindfulness. The MAAS measures the
frequency of open and receptive attention to and awareness of ongoing events and
experience. Response options range from 1 (almost never) to 6 (almost always).
Items are reverse coded and higher scores indicate a greater degree of mindfulness.
To control for social desirability, respondents are instructed to respond to the MAAS
in a way that reflects their actual experience rather than in a way they think their
experience should be. At Time 1 and Time 2, the full 15-item MAAS measure was
used. At Time 3, a 6-item short-form of the MAAS was used to reduce respondent
burden.
Diagnostic Interview Schedule for Children (DIS-C; Costello, Edelbrock &
Costello, 1985). The DIS-C is a comprehensive measure of childhood
psychopathology, which is inclusive of Attention Deficit Hyperactivity Disorder
34
(ADHD). Inattention is as a lack of attention or care to tasks at hand, and
hyperactivity is defined as being abnormally or easily excitable. A total of six items
from the DIS-C were used to measure ADHD. An example inattention item is “I have
difficulty keeping my attention on tasks or activities”, and an example hyperactivity
item is “I have feelings of restlessness”. Response options range from 1 (never) to 5
(very often). Higher scores reflect a higher degree of ADHD symptoms. The DIS-C
was measured at Time 3. The DIS-C has been reported as psychometrically sound in
previous research (e.g., (Shaffer, Fisher, Lucas, Dulcan & Schwab-Stone, 2000)), and
internal consistency reliability was good in the current study (α = .86).
Perceived Social Self-Efficacy (PSSE; Smith & Betz, 2000). The PSSE scale
measures confidence in the respondent’s ability to engage in the social interaction
skills necessary to initiate and maintain interpersonal relationships. The directions
which introduce the item set are “Please tell us how much confidence you have that
you could perform each of these activities successfully. How much confidence do
you have that you could…”. Examples of the 6-items measured include, “Share with a
group of people an interesting experience you once had” and “Find someone to
spend a weekend afternoon with”. Response options ranged from 1 (no confidence
at all) to 5 (complete confidence). Higher scores indicate higher social self-efficacy.
The PSSE scale has shown good reliability in previous research (Cronbach’s α = .94;
3-week test-retest r = .82; (Smith & Betz, 2000)) and a comparable estimate of
internal consistency was found in our sample (α = .96). PSSE was measured at Time
1 and Time 2.
35
Self-Control Scale (SCS; Tangney, Baumeister & Boone, 2004). The SCS is a
measure of self-control, defined as the respondent’s ability to override or change
their inner responses as well as to interrupt undesired behavioral tendencies and
refrain from acting on them. The directions introducing the item set are, “Please
indicate how much each of the following statements reflects how you typically are”.
Examples of the 8-items measured include, “Sometimes I can’t stop myself from
doing something, even if I know it is wrong” and “I often act without thinking
through all the alternatives”. Response options range from 1 (not at all like me) to 5
(completely like me). Higher scores are coded to indicate higher self-control. The SCS
has shown good reliability in previous research (Cronbach’s α = .89; 3-week test-
retest r = .89; (Tangney et al., 2004)), and a comparable estimate of internal
consistency was found in our sample (α = .82). SCS was measured at Time 1 and
Time 2.
UPPS Impulsive Behavior Scale (UPPS-IBS; Whiteside, Lynam, Miller &
Reynolds, 2005). The UPPS-IBS measures impulsivity, defined as the tendency to
engage in impulsive behavior under conditions of negative affect in order to
alleviate negative emotions despite the potentially harmful longer-term
consequences. It signifies a difficulty in controlling or coping with urges to act in
response to unpleasant emotions and is associated with giving into cravings and
temptations. The directions introducing the 6-item set include, “Mark the answer
which best describes how you generally feel or react”. Example items include, “I
have trouble controlling my impulses” and “When I get upset I often act without
36
thinking”. Response options range from 1 (strongly agree) to 4 (strongly disagree).
Higher scores indicate higher impulsivity. The UPPS-IBS has shown good internal
consistency reliability in previous research(α = .89; Whiteside et al., 2005) and
comparable internal consistency was found in our sample (α = .93). The UPPS-IBS
was measured at Time 3.
Mental Ailment Measures. Three measures of mental ailment were assessed
to determine the nomological and incremental validity of the MAAS. These measures
are well-recognized and validated and include the Center for Epidemiologic Studies
Depression Scale (CESD; Radloff, 1977); Perceived Stress Scale (PSS; Cohen,
Kamarck & Mermelstein, 1983); and Aggression Questionnaire (AQ; Buss & Perry,
1992). Three-items from the CESD, 6-items from the PSS, and 3-items from the AQ
were measured. All three mental ailment measures were assessed at Time 1, Time
2, and Time 3.
Analyses
Data cleaning and descriptive statistics were conducted using SAS 9.1
software. Data were imported into Mplus version 5 and frequencies were cross-
examined between Mplus and SAS to assure correctness of transferred data.
Construct validity was assessed following the sequential procedures outlined by
(O'Leary-Kelly & Vokurka, 1998). First, unidemensionality, which refers to the
existence of a single factor underlying a set of measures, was tested using
Confirmatory Factor Analysis (CFA) to assure that the measured indicators of the
MAAS represented a single latent factor. Unidimensionality is supported when all
37
factor loadings are relatively large and statistically significant in a model having
good fit to the data.
Model fit was assessed via the comparative fit index (CFI), Tucker-Lewis
Index (TLI), root mean square error of approximation (RMSEA) with its 90%
confidence interval (CI), and standardized root mean square residual (SRMR). Good-
fit criteria for this study is similar to previous work (Cordon & Finney, 2008) and
includes CFI and TLI values of .90-94, RMSEA estimates of .08-.10, and SRMR
estimates of .06-.08. Well-fit criteria included CFI and TLI values of .95 and above,
RMSEA estimates of .01-.07, and SRMR estimates of .01-.05. Because the MAAS
measured indicators were normally distributed (all MAAS indicators had skewness
< 1.3 and kurtosis < 1.1), the maximum likelihood estimation default in Mplus was
used to produce fit indices and model parameters. To provide a metric for latent
factors, the path from the first factor loading was set at a value of 1.0, which is the
default in Mplus.
Second, reliability, which pertains to the consistency and stability of a
measure, was assessed with test-retest, internal consistency (i.e., Cronbach’s α,
item-total r, and inter-item r) and parallel-forms estimates. Parallel-forms reliability
was assessed by examining the 6-item MAAS at Time 3 in relation to the remaining
8-items of the MAAS at time 1 and 2 that were not measured at Time 3. Third,
convergent/ discriminant validity, the degree to which a measure is attributable to
variations in the specified latent factor and not some other factor, was assessed
using Exploratory Factor Analysis (EFA) procedures outlined by (Farrell & Rudd,
38
2009). EFA allows for the determination of the number of latent factors underlying
measured indicators and elucidates cross-loadings of measured indicators on two or
more latent factors. A measured indicator that reflects a latent factor should load
highest on its respective latent factor and relatively lower on latent factors that
represent a different trait. Factor cross-loadings ≥.30 indicated lack of discriminant
validity.
Factorial invariance of the MAAS was assessed across gender; this
assessment indicates the MAAS has the same meaning for both males and females.
The factorial invariance of the MAAS across gender was tested with the sequential
constraint imposition procedures outlined by (Dimitrov, 2010). Measurement
invariance was assessed by testing (1) configural invariance -- invariance across the
pattern of free and fixed model parameters, (2) measurement invariance -- consists
of [a] metric invariance (i.e., equal factor loadings across gender), [b] scalar
invariance (i.e., equal item intercepts across gender), and [c] uniqueness invariance
(equal item error variances/covariances across gender), and (3) structural
invariance -- invariance of factor variances/ covariances across gender.
Invariance assessment begins with Model 0, which is the least constrained
solution, indicating a total lack of invariance. Subsequent restrictions for equality of
specific parameters across groups are imposed producing nested models that are
compared using the χ
2
difference test. Model 1 constrains factor loadings (indicates
weak measurement invariance). M2 constrains factor loadings and item intercepts
(indicates strong measurement invariance). Model 3 constrains factor loadings, item
39
intercepts, and residual item variances/ covariances (indicates strict measurement
invariance). Model 4 constrains factor loadings, item intercepts, and factor
variances/ covariances (indicates structural invariance). Each model has more
constraints than the previous model, thus each model is nested within its previous
model (e.g., M1 is nested within M0). If the fit of the nested model is not worse than
that of the previous model according to a χ
2
differences test, then statistical
invariance is supported for the relevant parameters. Because the χ
2
difference test
may be overly restrictive, especially when sample size is large (Quintana & Maxwell,
1999), practical differences in CFI were also compared between models. Previous
research has suggested that CFI reductions of ≤ .01 indicate a change in fit that is not
practically significant (Cheung & Rensvold, 2002).
Nomological validity, the degree to which the MAAS behaves as it should
within a system of related constructs, was assessed by examining the
interrelationships between the MAAS and other latent factors. According to its
behavior in previous research, we expected the MAAS to have a strong positive
correlation with self-control and a strong inverse correlation with ADHD symptoms
and impulsivity. We expected the MAAS not to be strongly correlated with social
self-efficacy. Incremental validity was assessed by examining the correlation
between the MAAS and mental ailment constructs after adjusting for other
psychosocial covariates.
40
1.4 Results
Demographics. At Time 1 participant ages ranged from 14 to 20 years-old (M
= 16.2, SD =0.7; see Table 1) and proportions of males and females were equivalent.
Unidimensionality Assessment. Table 2 provides the CFA results for the 15-
item MAAS at Time 1 and Time 2. Results for the 6-item MAAS are reported at Time
3. Measurement errors were allowed to correlate between 6-items at Time 1 and
Time 2 for the 15-item MAAS (i.e., item 1 with 2; item 4 with 5; and item 12 with 14)
to improve model fit. The 15-item MAAS at Time 1 and Time 2 had large and
significant standardized factor loadings and good model fit after correlating the
specified errors. The 6-item MAAS at Time 3 was well-fit without correlated
measurement errors. The 6-item MAAS at Time 3 had large and significant
standardized factor loadings and was well-fit to the data. The 6-item MAAS had a
larger mean of factor loadings relative to the 15-item MAAS measured at either
Time 1 or Time 2. The amount of variance explained in a single measured indicator
by the latent factor is estimated by r
2
. R
2
is an estimate of the total amount of
variance explained in the measured indicators by the latent factor. The MAAS latent
factor explained more total variance in the 6-items measured at Time 3 (R
2
= .58)
relative to the 15-items measured at either Time 1 (R
2
= .42) or Time 2 (R
2
= .47).
Reliability Assessment. Table 3 provides measure reliability estimates for the
MAAS at Time 1, Time 2, and Time 3. The interval between Time 1 and Time 2 was 5
months, 9 months between Time 2 and 3, and 16 months between Time 1 and 3.
Test-retest reliability correlations between the repeated MAAS measures were all of
41
medium-to-large magnitude and statistically significant. As expected, these
correlations were stronger for shorter time intervals and relatively weaker for
longer time intervals. Parallel-form reliability was assessed by estimating the
correlation between the Time 3 6-item MAAS and the Time 1 and Time 2 remaining
8 items not measured at Time 3. Parallel-forms correlations were all of medium-to-
large magnitude and statistically significant. As with the test-retest reliability
results, parallel-forms correlations were stronger for shorter time intervals and
relatively weaker for longer time intervals.
MAAS internal consistency reliability estimates were of good quality (α range
across time = .89-.93). Mean item-total correlation (ITC) estimates of internal
consistency were all of large magnitude and statistically significant (i.e., r range
across time = .61 - .71, all p’s < .01). Mean inter-item correlation (ITC) estimates of
internal consistency were of medium-to-large magnitude and statistically significant
(i.e., r range across time = .42 - .58, all p’s < .01). Item 15 consistently had the
weakest correlation with the remaining items, and was the only item to have
correlations within the .21 - .30 range with other items.
Convergent/ Discriminant Validity Assessment. The EFA model for Time 1
included the 15-item MAAS, 8-item SCS, and 6-item PSSE (EFA Tables for Time 1 and
Time 2 not shown due to space limitations; contact corresponding author for
details). Rotated loadings from the 3-factor model indicated that latent factors
loaded appropriately on their respective indicators, and there were no MAAS
measure indicator cross-loadings ≥ .30, suggesting discriminant validity. The 15-
42
item convergent factor loadings of the MAAS ranged from .49 - .75 at Time 1. The
EFA model for Time 2 included the 15-item MAAS, 8-item SCS, and 6-item PSSE.
Rotated loadings from the 3-factor model indicated that latent factors loaded
appropriately on their respective indicators, and there were no MAAS factor cross-
loadings ≥ .30, suggesting discriminant validity. The 15-item convergent factor
loadings of the MAAS ranged from .56 - .76 at Time 2. The EFA model for Time 3
included the 6-item MAAS, 6-item DIS-C, and 6-item UPPS-IBS. Rotated loadings
from the 3-factor model indicated that latent factors loaded appropriately on their
respective indicators, and there were no MAAS factor cross-loadings ≥ .30,
suggesting discriminant validity (see Table 4). The 6-item convergent factor
loadings of the MAAS ranged from .53 - .85 at Time 3.
Gender Invariance Assessment. Table 5 provides gender invariance results for
the MAAS at Time 1. The good fit for the gender stratified models indicated that the
15-item MAAS displayed configural invariance at Time 1. M0 was the baseline model
comprised of the total sample. Measurement invariance results indicated the 15-
item MAAS showed partial metric invariance (i.e., most, but not all, factor loadings
were equal across gender), partial scalar invariance (i.e., most, but not all intercepts
were equal across gender), and partial uniqueness invariance (i.e., most, but not all,
error variance/covariances were equal across gender). The 15-item MAAS lacked
evidence for structural invariance (i.e., non-invariant factor variances and
covariances). Although the Δχ
2
test indicated that M1, M2, and M3 had only partial
43
invariance, ΔCFI indicated that the quantifiable differences between these models
were not of practical significance.
Table 6 provides gender invariance results for the 6-item MAAS at Time 3.
The well- fit male and female models indicated that the 6-item MAAS displayed
configural invariance at Time 3. Measurement invariance results indicate that the 6-
item MAAS showed full metric invariance (all factor loadings were equal across
gender), partial scalar invariance (most, but not all, intercepts were equal across
gender), and full uniqueness invariance (all error variance/covariances were equal
across gender). Results indicated that the 6-item MAAS had structural invariance at
Time 3 (invariant factor variances and covariances). Although the Δχ
2
test indicated
that M2 had only partial invariance, ΔCFI indicated that this lack of model fit was not
of practical significance. CFA analyses for the 6-item MAAS short scale were
repeated with the Time 2 data (table not shown) to replicate the gender invariance
findings at Time 3. Factorial invariance methods indicated that same pattern of
invariance results as Time 3 as indicated in Table 6.
Nomological Validity Assessment. Table 7 provides the intercorrelations
between the MAAS factor and other psychosocial factors to asses nomological
validity. Both the 15-item and 6-item version of the MAAS were assessed to verify
that each version of the measure correlated comparably with other factors. Results
indicated that the two versions of the MAAS had equivalent or highly comparable
correlations with other factors. The MAAS factor was positively and significantly
correlated with the SCS factor at Time 1 and Time 2. The MAAS factor was inversely
44
and significantly correlated with the CESD, PSS, AQ, UPPS-IBS, and DIS-C factors. The
MAAS factor correlation with the PSSE factor was either non-significant or
significant but of small magnitude across time, and likely and artifact of large
sample size.
Incremental Validity Assessment. Table 8 provides the cross-sectional
structural regression estimates for two mental ailment measures, CESD and PSS,
regressed on the MAAS and related factors. Results indicate that the MAAS
explained additional variance in both mental ailments above and beyond the
variance explained by SCS and PSSE at Time 1 and Time 2. Similarly, the MAAS
explained additional variance in these mental health factors beyond DIS-C and
UPPS-IBS factors at Time 3.
1.5 Discussion
The purpose of the current investigation was to assess the psychometric
properties of the MAAS among Chinese adolescents to determine the
generalizability of a trait mindfulness measure to this population. The 15-item
single-dimension structure of the MAAS as reported by Brown & Ryan (2003) had
adequate fit, and all measured indicators loaded significantly on the MAAS latent
factor. The 15-item MAAS upheld good internal consistency, test-retest, and parallel
forms reliability, and the MAAS factor loadings were all high and cross-loadings of
indicators across factors were all low, indicating convergent/ discriminant validity.
The MAAS was inversely related to mental health ailments, which replicates
previous research (Brown & Ryan, 2003; Fetterman et al., 2010; Thompson & Waltz,
45
2007; Zvolensky et al., 2006); it had medium-to-large inverse correlations with
depressive symptoms, perceived stress, and aggression and maintained a significant
inverse relationship with mental ailments even after controlling for other
psychosocial, attentional, and self-regulation constructs. Support for these
psychometric findings are relatively strong considering that they were replicated at
a second wave.
The current study also indicated that a single-dimension 6-item short scale
(i.e., consisting of items 7, 8, 9, 10, 13, 14 from Brown & Ryan, 2003) was well-fit to
the data. The six measured indicators significantly loaded on the MAAS latent factor,
and the average factor loading was higher for the 6-item short scale compared to the
15-item MAAS scale. Moreover, based on model fit indices, the 6-item MAAS
outperformed the 15-item MAAS. Previous research, based on an American college
sample, showed that 5 out of the 6 items used in our 6-item short scale provided the
majority of information collected by the MAAS (Van Dam et al., 2010). Thus, the
current study replicates previous findings indicating the utility of a short-scale
MAAS. It could be suggested that reducing the number of items of the MAAS restricts
the nomological net of measures for which the latent factor represents. However,
evidence is lacking for this substantive argument considering that the nomological
and incremental associations between the MAAS and other theoretical constructs in
the current study were of exact or almost exact magnitude and significance.
This study continues an important line of scientific inquiry that attempts to
establish the generalizability of scales that operationalize trait mindfulness. To date,
46
this appears to be the fourth study in a recent line of studies that aim to test the
psychometric validity of the MAAS among a demographically diverse population.
This is the second known study, besides (Christopher et al., 2009), to rigorously
assess the psychometrics of the MAAS among a population residing in East Asia. The
current study adds to this literature by determining that the MAAS is
psychometrically sound measure of trait mindfulness among the typical adolescent
residing in China who attends high school. Moreover, nomological and incremental
validity results from the current study indicate that the MAAS may have an
important role in etiological and intervention studies that aim to address adolescent
mental health and its behavioral sequelae in this region. Examination of mental
illness constructs among this population is important given the high rates of
harmful behaviors (e.g., cigarette smoking) and associated diseases among Chinese
adolescents (Cheng, 1999).
This study is limited in that it lacked multiple methods to assess the MAAS
which did not allow for a formal multitrait-multimethod analysis of
convergent/discriminant validity. The lack of a comparable sample of American or
European adolescents also limited our ability to test measurement invariance across
these populations to gain further evidence for the generalizability of the MAAS.
However, previous work has found the MAAS to be invariant across Thai and
American college students (Christopher et al., 2009), perhaps allowing initial
assumptions that the MAAS would remain invariant across Chinese and American
47
adolescents. However, this conjecture requires statistical assessment in future
studies.
Conclusions
Mindfulness continues to gain empirical support as an important construct in
the field of mental and behavioral health. Because this study used rigorous methods
to support the validity of the MAAS among the Chinese adolescents, research can
now progress to examine the empirical relationships between mindfulness and
mental and behavioral health among this population. The current study provided
initial evidence that the MAAS was inversely correlated with mental health ailments,
and suggests that trait mindfulness has a promising future for etiological studies
among this population. The current study also provided support for a 6-item short
scale of the MAAS which can be used to reduce respondent burden on adolescents.
Future research should use methods to replicate these findings to determine if the
15-item and 6-item MAAS remain sound measures among other adolescent
populations.
48
Table 1. Demographic characteristics of Chinese adolescents at time 1 (N=5,287)
Variable M SD N % Range
Gender
Female 2,583 48.9
Male 2,704 51.1
Age 16.2 0.7 14-20
Parent Education 4.1 1.6 1-7
MAAS 4.4 0.9 1-6
SCS 3.4 0.8 1-5
PSSE 3.2 0.8 1-5
DIS-C 2.4 0.8 1-5
UPPS-IBS 2.4 0.7 1-4
Notes. MAAS = Mindful Attention Awareness Scale, SCS = Self-Control Scale, PSSE =
Perceived Social Self-Efficacy; DIS-C = Diagnostic Interview Schedule for Children;
UPPS-IBS = UPPS Impulsive Behavior Scale
49
Table 2. CFA results to assess unidimensionality of the MAAS at three points in time
Measured
Indicator
Time 1 Time 2 Time 3
MAAS
a
M SD FL r
2
M SD FL r
2
M SD FL r
2
1 4.36 1.32 .55 .28 4.40 1.32 .61 .35
2 4.20 1.39 .56 .30 4.26 1.37 .64 .38
3 4.26 1.36 .60 .41 4.25 1.37 .69 .47
4 4.21 1.46 .59 .33 4.24 1.43 .64 .39
5 4.38 1.41 .62 .37 4.34 1.39 .68 .44
6 4.13 1.47 .59 .34 4.13 1.43 .60 .36
7 4.51 1.37 .73 .54 4.56 1.31 .75 .58 4.52 1.34 .71 .50
8 4.55 1.41 .73 .55 4.62 1.38 .75 .57 4.46 1.40 .78 .61
9 4.32 1.31 .74 .56 4.33 1.32 .76 .58 4.29 1.32 .81 .65
10 4.59 1.28 .74 .55 4.58 1.26 .76 .58 4.54 1.26 .79 .63
11 4.11 1.43 .62 .39 4.13 1.42 .65 .43
12 4.64 1.31 .65 .40 4.61 1.31 .70 .47
13 4.07 1.55 .67 .44 4.13 1.51 .70 .48 4.10 1.47 .73 .53
14 4.43 1.33 .73 .52 4.45 1.32 .76 .57 4.33 1.31 .76 .57
15 5.02 1.25 .52 .26 4.98 1.23 .58 .34
Average FL .64 .68 .76
Factor R
2
.42 .47 .58
Notes.
a
Items 1-15 are presented in the same order as Brown and Ryan (2003); FL = Factor Loading; r
2
= 1 - residual variance for
the measured indicator; Factor R
2
= the sum of the standardized factor loadings / number of measured indicators
49
50
Table 2: Continued
Notes. Fit statistics:
Time 1 CFA Model Fit: N=5,272, χ
2
=2,166.40, df=87, CFI=.94, TLI=.93, RMSEA=.07 (CI=.06-.07), SRMR=.04
Time 2 CFA Model Fit: N=4,885, χ
2
=2,944.28, df=87, CFI=.93, TLI=.91, RMSEA=.08 (CI=.07-.08), SRMR=.04
Time 3 CFA Model Fit: N=3,500, χ
2
=211.11, df=9, CFI=.98, TLI=.97, RMSEA=.08 (CI=.07-.09), SRMR=.02
50
51
Table 3. MAAS reliability estimates at three time points
MAAS Time 1 MAAS Time 2 MAAS Time 3
Factor Correlations
a
MAAS Time 1 1 .52** .32**
MAAS Time 2 .52** 1 .39**
MAAS Time 3 .35** .41** 1
Reliability Estimates
Cronbach’s alpha .91 .93 .89
Mean ITC (range) .61 (.48 - .70) .66 (.55 - .72) .71 (.66 - .74)
Mean IIC (range) .42 (.21 - .61) .47 (.29 - .63) .58 (.49 - .66)
Notes. **p <.01;
a
Lower left of diagonal is test-retest correlations between 15-item
and 6-item MAAS scales; Upper right of diagonal are parallel-forms correlations
between 8-item MAAS at Time 1/ Time 2 and 6-item MAAS at Time 3
ITC = item-total correlation; all ITC r’s significant at p<.01; IIC = inter-item
correlation; all IIC r’s significant at p<.01
Time interval between Time 1 and Time 2 = 3 months; Time interval between Time
2 and Time 3 = 10 months; Time interval between Time 1 and Time 3 = 13 months
52
Table 4. EFA results for convergent/ discriminant validity of the MAAS at time 3
Indicator Three-factor solution
1 2 3
MAAS
1 .72 -.02 .02
2 .82 .01 .04
3 .85 -.01 .05
4 .74 .02 -.05
5 .63 .01 -.14
6 .53 -.03 -.27
DIS-C
1 -.01 .01 .79
2 .03 .03 .74
3 -.06 .04 .56
4 -.24 -.03 .56
5 .04 -.01 .80
6 .19 .02 .49
UPPS-IBS
1 .02 .67 -.03
2 -.01 .70 -.03
3 -.04 .76 -.01
4 -.01 .78 .01
5 .02 .76 .04
6 .01 .76 .02
Notes. Eigenvalues ≥ 1 = 6.9, 3.4, 1.0; MAAS = Mindful Attention Awareness Scale,
DIS-C = Diagnostic Interview Schedule for Children; UPPS-IBS = UPPS Impulsive
Behavior Scale
53
Table 5. CFA goodness-of-fit indices to assess gender invariance of the 15-item MAAS at time 1
Nested tests
Model χ
2
df CFI TLI SRMR RMSEA
(90% CI)
Models
Compared
Δχ
2
Δdf ΔCFI
Configural
Females 1,237.18 87 .936 .92 .04 .07 (.06-.07)
Males 1,012.86 87 .941 .93 .04 .06 (.06-.07)
Measurement
M0 2,250.04 174 .938 .93 .04 .07 (.06-.07)
M1 2,288.78 188 .937 .93 .04 .07 (.06-.07) M1-M0 38.74** 14 -.001
M1P 2,262.89 184 .938 .93 .04 .07 (.06-.07) M1P-M0 12.85 10 .000
M2 2,430.30 198 .933 .93 .04 .07 (.06-.07) M2-M1P 167.41** 14 -.005
M2P 2,271.03 190 .938 .93 .04 .07 (.06-.07) M2P-M1P 8.14 6 .000
M3 2,374.20 205 .935 .93 .04 .06 (.06-.06) M3-M2P 103.17** 15 -.003
M3P 2,281.03 198 .938 .93 .04 .06 (.06-.07) M3P-M2P 10.00 8 .003
Structural
M4 2,294.98 191 .937 .93 .05 .06 (.06-.07) M4-M2P 23.95** 1 -.001
Notes. **p<.01; MAAS correlated measurement errors include: items 1 with 2, 4 with 5, and 12 with 14 ; P indicates
partial invariance for model; M1 = metric invariance; M2 = scalar invariance; M3 = uniqueness invariance
53
54
Table 6. CFA goodness-of-fit indices to assess gender invariance of the 6-item MAAS at time 3
Nested tests
Model χ
2
df CFI TLI SRMR RMSEA
(90% CI)
Models
Compared
Δχ
2
Δdf ΔCFI
Configural
Females 127.69 9 .978 .96 .02 .09 (.07-.10)
Males 105.15 9 .981 .97 .02 .08 (.07-.09)
Measurement
M0 232.84 18 .980 .97 .02 .08 (.07-.09)
M1 244.91 23 .979 .97 .03 .08 (.07-.08) M1-M0 12.07 5 -.001
M2 308.10 28 .974 .97 .04 .08 (.07-.08) M2-M1 63.19** 5 -.005
M2P 245.04 24 .979 .97 .03 .07 (.07-.08) M2P-M1 .13 1 .005
M3 256.73 30 .979 .98 .04 .07 (.06-.07) M3-M2P 11.69 6 .000
Structural
M4 246.21 25 .979 .98 .04 .07 (.06-.08) M4-M2P 1.17 1 .000
Notes. **p<.01; No measurement errors are correlated; M1 = metric invariance; M2 = scalar invariance; M3 =
uniqueness invariance
54
55
Table 7. Intercorrelations between the full and short MAAS and other factors to
asses nomological validity
Factor Time 1 Time 2 Time 3
15-item
MAAS
6-item
MAAS
15-item
MAAS
6-item
MAAS
6-item
MAAS
SCS .54** .55** .53** .53**
PSSE .02 .03 .12** .13** .08**
CESD -.40** -.41** -.48** -.47** -.52**
PSS -.54** -.54** -.53** -.51** -.59**
AQ -.29** -.29** -.33** -.33**
DIS-C -.65**
UPPS-IBS -.22**
Notes. **p<.01; MAAS = Mindful Attention Awareness Scale, SCS = Self-Control Scale,
PSSE = Perceived Social Self-Efficacy; CESD = Center for Epidemiologic Studies
Depression Scale; PSS = Perceived Stress Scale; AQ = Aggression Questionnaire; DIS-
C = Diagnostic Interview Schedule for Children; UPPS-IBS = UPPS Impulsive
Behavior Scale
56
Table 8. Incremental validity
a
of MAAS in relation to mental illness measures
Factor CESD PSS
B SE B β B SE B β
Time 1
MAAS
b
-.17 .01 -.23** -.26 .01 -.33**
SCS -.39 .02 -.37** -.45 .02 -.40**
PSSE -.11 .01 -.11** .03 .01 .03*
Time 2
MAAS
b
-.20 .01 -.30** -.25 .02 -.34**
SCS -.31 .02 -.34** -.40 .02 -.37**
PSSE -.06 .01 -.07** .01 .01 .01
Time 3
MAAS
c
-.23 .07 -.31** -.32 .12 -.35*
DIS-C .81 .08 .92** 1.11 .15 1.03**
UPPS-IBS .10 .02 .08** .12 .03 .08**
**p<.01, *p<.05;
a
Structural regression estimate is adjusted for other factors in
model;
b
15-item MAAS;
c
6-item MAAS
57
Chapter 2
Mindfulness moderates the effect of cigarette smoking intentions and refusal self-
efficacy on adolescent smoking frequency in the theory of planned behavior
2.1 Abstract
The mental capacity for mindfulness may influence the enactment of health
and risk behaviors by its bringing increased attention to and awareness of decision-
making processes underlying behavior. The present study examined the moderating
effect of mindfulness on the associations between direct predictors of adolescent
cigarette smoking (i.e., intentions to smoke (ITS) and smoking refusal self-efficacy
(SRSE))and smoking frequency in the theory of planned behavior. Self-reported data
from Chinese adolescents (N=5,287; M age=16.2, SD=0.7; 48.8% female) were
collected within 24 schools. Using Mplus software, smoking frequency was
regressed on latent factor interactions MAAS*ITS and MAAS*SRSE, after adjusting
for school clustering effects and demographic covariates. Both interaction terms
were significant in cross-sectional analyses and showed that high ITS predicted
higher smoking frequency among those low relative to high in mindfulness while
low SRSE predicted higher smoking frequency among those low relative to high in
mindfulness; however, interaction terms did not reach statistical significance in 3
and 13-month longitudinal prediction models. Our findings suggest that
mindfulness shields against decision-making processes that place adolescents at
risk for cigarette smoking and findings show promise for integrating mindfulness in
theories of health behavior.
58
2.2 Introduction
Understanding the antecedents to adolescent cigarette smoking is an
essential task for tobacco prevention programming. Such efforts are especially
pertinent in developing countries where tobacco use continues to be one of the main
causes of morbidity and mortality (Lam et al., 2001). An approach that has
accumulated some success in explaining the antecedents to adolescent smoking
behavior is the theory of planned behavior (TPB). TPB is a testable social cognitive
model used to explain how the performance of a given behavior is predicted by
decision-making processes (Ajzen, 1991; Madden, Ellen & Ajzen, 1992). This model
posits that the enactment of behavior can be predicted by a person’s attitudes (i.e.,
the degree to which a person has a positive or negative appraisal of the specific
behavior and its consequences), subjective norms (i.e., the perceived social
pressures to perform the behavior based on opinions and behaviors of peers and
family members), and perceived behavioral control (i.e., the perception of control
over the successful completion of the behavior; synonymous with the construct of
self-efficacy; Bandura, 1977). These three factors indirectly predict performed
behavior through behavioral intentions (i.e., the amount of effort a person is
planning to exert in order to perform a behavior); however, self-efficacy can also
directly predict behavior when it is not fully under volitional control.
TPB is used extensively in the field of health behavior research, and has
specifically been used to explain adolescent smoking behavior (e.g., Guo et al., 2007;
Ma et al., 2008). This is for good reason considering that smoking initiation most
59
often develops during adolescence and coincides with the development of smoking
attitudes, norms, self-efficacy beliefs and intentions (Hesketh et al., 2001; Johnston
et al., 1998). Reviews and meta-analyses of the literature generally support the TPB
framework and suggest that behavioral intentions typically account for about 20-30
percent of the variance in health behavior outcomes (Armitage & Conner, 2001;
Hagger, Chatzisarantis & Biddle, 2002). Moreover, reviews indicate that self-efficacy
most often increases the explanatory power of TPB (Godin & Kok, 1996). However, a
substantial proportion of variance in health behavior remains unexplained by TPB
constructs. That is, TPB itself cannot explain why people sometimes do not perform
a behavior even when they report strong behavioral intentions to do so(e.g., Orbell
& Sheeran, 1998). Thus, a deeper understanding of TPB construct relationships may
be obtained by examining moderating factors that strengthen or weaken
associations between TPB constructs that are directly predictive of behavior (i.e.,
self-efficacy and behavioral intentions) and performed behavior.
Research into the exploration of moderators that elucidate the specific
conditions of TPB construct relationships is promising. For example, memory--the
ability to retain and recall pertinent information--is central to performing behavior
because forgetting to initiate behavior is one of the most common reasons for
inconsistency between intentions and performed behavior (Gollwitzer, 1999;
Sheeran & Orbell, 1999; Sheeran et al., 2005). Although one may intend to behave in
a certain manner, a faulty memory may simply result in forgetting to perform a
behavior. Consequently, researchers have suggested that improving peoples’
60
memory of their behavioral intentions can assist them in performing their intended
behavior (Aarts & Dijksterhuis, 2000). Moreover, memory scripts of previous
behaviors that are enacted repeatedly may lead to automatic behavioral reactions or
habits. These habits can interrupt the relationship between intentions and behavior
because environmental cues rather than deliberate intentions serve to automatically
elicit a behavioral response (Aarts, Verplanken & Knippenberg, 1998). Taken
together, it may follow then that a heightened awareness and recall of behavioral
intentions and reduced habitual behavioral responses may strengthen TPB
processes that directly predict performed behavior.
Mindfulness, defined as an enhanced attention to and awareness of present
moment experience (Brown & Ryan, 2003), may help translate behavioral
intentions and self-efficacy beliefs into performed behavior by improving awareness
of these decision-making factors and reducing habitual responding. Previous
research indicates that mindfulness is orthogonal to habitual responding because it
is an orientation of attention and awareness residing in the present moment and not
the past (Chatzisarantis & Hagger, 2007; Chatzisarantis & Hagger, 2005). Moreover,
to the extent that one is attentive and aware of momentary cognitions, opportunities
for correcting discrepancies between intended and performed behavior remain
within the field of awareness (Brown & Ryan, 2004). Similarly, it has been suggested
that mindful people, as compared to those less mindful, have a heightened
awareness of their behavioral routines and precursors driving behavior (Brown &
Ryan, 2003), and that because they pay greater attention to their behaviors they
61
have a greater ability to initiate or prevent the performance of a behavior
(Chatzisarantis & Hagger, 2007).
In support of the above mentioned effect of mindfulness on decision-making
and behavioral processes, studies have found that behavioral intentions predict
performed behavior among those who act with awareness but not among those who
act out of habit (Aarts et al., 1998; Verplanken, Aarts, Van Knippenberg & Moonen,
1998). These findings are corroborated in a study that formally tested the
moderating effect of mindfulness on the TPB intention-behavior link (Chatzisarantis
& Hagger, 2007). The study by Chatzisarantis found that mindful respondents were
not only more likely to enact their physical activity intentions than those less
mindful, and were also more likely exercise control over counterintentional binge
drinking habits than less mindful respondents. These findings support the notion
that heightened attention and awareness oriented to the present moment, rather
than habitual responding, predicts the performance of behavioral intentions. It is
postulated by (Chatzisarantis & Hagger, 2007) that mindfulness may strengthen
ones recall of behavioral intentions and may increase ones ability to remain focused
on performing those intentions given an increased capacity for attention and
awareness of decision-making processes in the present moment. Further research is
needed to verify this pioneering area of research with different populations.
The present study examines the moderating role of mindfulness on direct
predictors (i.e., smoking refusal self-efficacy and intentions to smoke) of adolescent
cigarette smoking in TPB. This research responds to a formal call for the integration
62
of mindfulness within established theories of health behavior (Black, 2010a;
Roberts & Danoff-Burg, 2010) and extends previous research that has tested the
moderating effect of mindfulness on the TPB intention-behavior link (Chatzisarantis
& Hagger, 2007). Our Hypothesis I contends that mindfulness strengthens the
positive relationship between intentions to smoke and cigarette smoking behavior.
As with Chatzisarantis & Hagger (2007), we seek to determine if mindfulness
strengthens the intention-behavior link; however, this is the first study to determine
if mindful people are more likely to enact unhealthy intentions to smoke. Hypothesis
II contents that mindfulness strengthens the inverse relationship between smoking
refusal self-efficacy and smoking behavior. Although no studies identified to date
have examined how mindfulness affects the relationship between self-efficacy and
performed behavior in TPB, studies have identified a positive relationship between
the development of mindfulness and various types of self-efficacy (Caldwell,
Harrison, Adams, Quin & Greeson, 2010; Greason & Cashwell, 2009; Oman, Hedberg,
Downs & Parsons, 2003). Therefore, in the context of smoking refusal self-efficacy
we use our same rationale provided for the effect of mindfulness on the intention-
behavior link whereby an increased awareness and attention should bring greater
awareness to self-efficacy cognitions that then help translate self-efficacy into
performed behavior.
63
2.3 Methods
Participants and Procedures
Data were collected as part of a longitudinal study conducted by
collaborating researchers from the Pacific Rim Transdisciplinary Tobacco and
Alcohol Use Research Center (TTAURC). The objective of the TTAURC project was to
investigate the determinants of health behavior among adolescents in Chengdu,
China. All consent procedures and survey instruments for this study were approved
by the Institutional Review Boards (IRBs) of the University of Southern California
and Chengdu, China Centers for Disease Control and Prevention. A total of twenty-
four schools (N=24) in Chengdu, China enrolled in the study. Within the 24 schools
that participated, there were a total number of 1,060 classes. A total of 338 classes
were randomly selected to participate in this study.
Parental consent forms were distributed to students within the selected
classrooms, and those students acquiring written or verbal parental consent and
giving personal assent completed a self-reported paper-and-pencil questionnaire in
their classroom during school hours. The students whose parent did not sign the
parental participation permission form and/or who did not actively assent were
excluded from the study. Participants voluntarily took part in the study and were
informed that they could discontinue their participation at any time. Classroom
teachers were not present during the survey period so that participating students
would feel confident about the confidentiality of their responses. The cross-sectional
64
data used in this study was obtained from baseline measurement of a larger project
that followed students from 10
th
to 12
th
grade.
Measures
Demographic data included respondent age, gender, parent education, and
type of school attended (regular or vocational) (see Table 9).
Mindful Attention Awareness Scale (MAAS; Brown & Ryan, 2003). The MAAS is
a 15-item single-dimension measure of trait mindfulness. The MAAS measures the
frequency of open and receptive attention to and awareness of ongoing events and
experience. Response options range from 1 (almost never) to 6 (almost always).
Items are reverse coded and higher mean scores indicate a greater degree of
mindfulness. To control for social desirability, respondents are instructed to
respond to the MAAS in a way that reflects their actual experience rather than in a
way they think their experience should be. Recent psychometric research indicates a
6-item short form of the MAAS has equivalent construct validity relative to the full
15-item MAAS among Chinese adolescents (short form Cronbach’s α = .89; range of
factor loadings = .71 -.81; mean inter-item r = .58; Black, in press). Other studies also
support the utility of such a shortened scale (Van Dam et al., 2010). Thus, the 6-item
short form MAAS from Black et al. (in press) was used in this study.
Intentions to Smoke (ITS). ITS measures respondent intentions to smoke
cigarettes at a future time. Two items used in previous research studies (Trinidad,
Unger, Chou, Azen & Johnson, 2004) were used to assess ITS, which included “At any
time during the next 12 months do you think you will smoke a cigarette” and “Do
65
you think you will be smoking cigarettes five years from now?” Response options
were given on a 4-point scale ranging from 1 (definitely yes) to 4 (definitely not).
Responses were reverse coded and higher scores indicate higher ITS.
Smoking Refusal Self-Efficacy (SRSE). SRSE measures the respondents
perceived ability to refuse cigarette use. Two items used in previous research
studies (Ma et al., 2008) were used to assess SRSE, which included “If one of your
best friends offered you a cigarette, would you smoke it?” and “If someone offers
you a cigarette, do you consider it impolite to refuse it?” Response options were
given on a 4-point scale ranging from 1 (definitely yes) to 4 (definitely not). Higher
scores indicate higher SRSE.
Current Cigarette Smoking. Frequency of current smoking was assessed with
the item: “During the past 30 days, on how many days did you smoke cigarettes?”,
which is a measure used in the Centers for Disease Control and Prevention (CDC)
Youth Risk Behavior Survey. Response options were coded on a 7-point scale
ranging from 1 (0 days) to 7 (all 30 days). Due to a positive skew, this variable was
log transformed to normalize its distribution, and higher scores indicate higher
smoking frequency.
Analyses
Data cleaning and descriptive statistics were conducted using SAS 9.2
software. Data were imported into Mplus version 5 and frequencies were cross-
examined between Mplus and SAS to assure correctness of transferred data. Mplus
is a structural equation modeling program that allows for the testing of latent factor
66
interactions. MAAS, SRSE, and ITS were modeled as latent factor interactions in the
prediction of adolescent smoking frequency (a measured indicator) as detailed in
(Muthen & Bengt, 1998). The unconditional means models for each dependent
variable indicated the presence of smoking behavior clustering within schools (ICC
>.02). Thus, our mediation models were hierarchical to account for students nested
within schools in order to obtain more accurate standard error estimates and
reduce Type I error rate (Krull & MacKinnon, 2001).
Considering that ad hoc procedures for handling missing data such as list-
wise deletion or mean substitution often result in biased parameter and/or
standard error estimates, our mediation modeling procedures used full information
maximum likelihood estimation (FIML) as implemented in Mplus to yield more
accurate estimates while adjusting for the uncertainty associated with the missing
data (Little & Rubin, 2002). The FIML estimation does not impute missing values but
directly estimates model parameters and standard errors using all available raw
data. The FIML estimator allowed our models to possibly use data from all 5,287
respondents.
2.4 Results
Demographics and Bivariate Correlations. Participants ranged in age from 14
to 20 years-old (M = 16.2, SD =0.7; see Table 9), and the ratio of males to females
was relatively equivalent (51.1% males). Over 45% of respondents ever smoked a
cigarette in their lifetime. Over 24% of respondents smoked in the past 30 days, and
almost 5% of the sample smoked daily. Table 9 shows that at T1, MAAS level was
67
significantly correlated with ITS (r = -.17, p < .01), SRSE (r = .14, p < .01), and log
smoking frequency (r = -.14, p < .01).
ITS-MAAS Interaction. In the covariate adjusted cross-sectional model, the
estimated equation derived from regressing log smoking frequency on ITS and
MAAS latent factor interaction terms was ŷ = .308+(-.045*MAAS)+(.413*ITS)+(-
.075*MAAS*ITS). The coefficient for the interaction term (B = -.073, SE = .016 , p <
.01) was statistically significant, which indicates the association between ITS and
smoking differs across levels of MAAS. Figure 2 shows estimated regression trends
for the smoking frequency outcome, which are exponents of the log scale to return
estimates to the original scale of measurement. The MAAS and ITS latent factors are
plotted in standard deviation units (MAAS 1 SD unit = 0.88; ITS 1 SD unit = 0.98).
MAAS shows an evident effect modification on the positive linear trend between ITS
and smoking frequency beyond the mean level of ITS. As such, the MAAS attenuates
the effect of high ITS on smoking frequency (e.g., if ITS is at +3 SD from its mean
then the B for MAAS at -1 SD = 5.79 and B for MAAS at +1 SD = 3.63).
SRSE-MAAS Interaction. In the covariate adjusted cross-sectional model, the
estimated equation derived from regressing log smoking frequency on SRSE and
MAAS latent factor interaction terms was ŷ =.327+(-.043*MAAS)+(-
.450*RSE)+(.112*MAAS*RSE). The coefficient for the interaction term (B = .112, SE =
.02 , p < .01) was statistically significant, which indicates the association between
SRSE and smoking differs across levels of MAAS. Figure 3 shows estimated
regression trends for the smoking frequency outcome, which are exponents of the
68
log scale to return estimates to the original scale of measurement. The MAAS and
SRSE latent factors are plotted in standard deviation units (MAAS 1 SD unit = .82;
SRSE 1 SD unit = .85). MAAS shows an evident effect modification on the positive
linear trend between SRSE and smoking frequency at values before the mean level
of SRSE. As such, the MAAS attenuates the effect of low SRSE on smoking frequency
(e.g., if SRSE is at -3 SD from its mean then the B for MAAS at -1 SD = 5.72 and B for
MAAS at +1 SD = 3.34).
2.5 Discussion
This study demonstrated the moderating effect of mindfulness on cigarette
smoking decision-making processes in the theory of planned behavior. An
overarching theme in this study was to continue a line of research that aims to
formally integrate the construct of mindfulness within established theories of health
behavior (Black, 2010a; Chatzisarantis & Hagger, 2007; Roberts & Danoff-Burg,
2010). Contrary to our directional Hypothesis I, the results from our study showed
that mindfulness did not strengthen the intention-behavior link for an unhealthy
behavioral outcome studied here as smoking. Rather, mindfulness had the opposite
effect by actually attenuating the positive association between ITS and smoking
frequency. Therefore, it appears that mindful people shield bad intentions from
unhealthy habits. Moreover, our findings yielded partial support for our directional
Hypothesis 2 whereby our results indicated mindfulness was protective of smoking
as it shielded low SRSE cognitions from translating into smoking behavior. However,
69
mindfulness did not strengthen the inverse SRSE-behavior link for adolescents at
higher levels of SRSE as we originally contended.
Our findings regarding Hypothesis I provide new and interesting evidence to
the growing area of research that examines the moderating influence of mindfulness
on theory- driven decision-making processes. For example, our findings both
challenge and extend existing evidence that suggests mindfulness strengthens the
intention-behavior link in TPB. Our results challenge previous research that has
indicated behavioral intentions predict performed behavior among those who act
with awareness but not among those who act out of habit (Aarts et al., 1998;
Verplanken et al., 1998), and that mindful respondents are more likely enact their
health promoting behavioral intentions than those less mindful (Chatzisarantis &
Hagger, 2007). Contrary to the notion suggesting that mindfulness strengthens the
intention-behavior link, we found that mindfulness attenuated this link in the
context of an unhealthy behavioral outcome. Thus, our study extends previous work
by showing that mindfulness may function not only to strengthen the intention-
behavior link in the context of a healthy behavioral outcome (e.g., physical exercise;
Chatzisarantis & Hagger, 2007) but also attenuate this link for an unhealthy
behavioral outcome (e.g., cigarette smoking as indicated in the current study).
The findings related to Hypothesis I might be explained by mindfulness
yielding an enhanced awareness of intentions and behaviors that are harmful to
one’s health. For example, adolescents are often provided with messages at school
and in the popular media that smoking causes disease and mortality. Therefore,
70
mindful adolescents may have greater awareness about the counterpoints (e.g., anti-
smoking attitudes, anti-smoking norms) of their intentions to smoke and
subsequently engage in the behavior less frequently than those who smoke from a
lack of awareness of these counterpoints and from habit. This notion may be at least
partially supported by a post hoc analysis of our data which indicated that
mindfulness has a significant inverse correlation with pro-smoking attitudes (r = -
.28, p < .01). Thus, mindfulness may strengthen ones recall of counterpoints to their
behavioral intentions and thus increase ones ability to remain focused on enacting
those counterpoints (i.e., refraining from smoking, reducing smoking frequency).
Our findings for Hypothesis II are the first to implicate the effect of
mindfulness on the association between self-efficacy cognitions and performed
behavior in TPB. This is an important finding considering that lack of smoking
refusal self-efficacy reduces adolescents ability to refrain from smoking (Conrad,
Flay & Hill, 1992), and because smoking refusal self-efficacy is a main target of
adolescent smoking prevention programs (Flay, 1985). Based on the theoretical
notions used to explain the impact of mindfulness on the intention-behavior link,
one would think that mindful people would be more likely to enact their smoking
refusal self-efficacy beliefs because they are more aware of these cognitions and can
focus more attention on enacting them. However, although mindfulness exerted a
protective effect on smoking frequency as predicted, this occurred only among those
reporting low SRSE. These findings indicate that mindful adolescents who are at risk
for smoking because of their low SRSE have a reduced rate of smoking. Our findings
71
suggest that mindfulness has a protective role in the context of unhealthy behavior
rather than having a health promoting role as has been found with physical exercise
intentions in previous work (Chatzisarantis & Hagger, 2007). It remains unclear
exactly why mindfulness did not strengthen the SRSE-behavior link in TPB among
adolescents reporting high levels of SRSE. This may be best interpreted by
acknowledging that those with high SRSE already report very low smoking
frequency (as evident in Figure 3), and this phenomenon may suggest a floor effect
whereby those at the higher levels of SRSE report very low smoking frequency
leaving no room for sensitivity to subgroup differences in mindfulness. Although no
known previous research has examined the impact of mindfulness on the
relationship between self-efficacy and performed behavior in TPB, this appears to
be a promising area of further investigation.
Certain limitations in our study require comment. First, our sample consisted
of Chinese adolescents, which implies that our findings may not generalize to
adolescents in other countries. We recommend that studies replicate our proposed
model with data from adolescents and adults representing various socio-cultural
and ethnic characteristics. Second, our study relied on the self-report data that may
be vulnerable to respondent bias; however, research have shown little discrepancy
between self-reports and biochemical assessments of adolescent cigarette smoking
(Stacy et al., 1990) and respondents were continually assured that their responses
were confidential prior to questionnaire completion. It is also possible that social
desirability bias may have led respondents to over report mindfulness and under
72
report smoking thus bolstering an inverse relationships between these two
variables.
Conclusion
In summary, results from this study indicate that mindfulness moderates
both the intention-behavior link and the self-efficacy-behavior link in theory of
planned behavior in the context of cigarette smoking among adolescents. Our
results demonstrate that mindfulness helps shield high intentions to smoke from
translating into smoking behavior and also helps shield low smoking refusal self-
efficacy from translating into smoking behavior. These findings are promising in
that they highlight the important effects of mindfulness within an established
decision making theory of health behavior.
73
Table 9. Demographic characteristics of sample (N=5,287)
Variable M SD N % Range
Gender
Female 2,583 48.9
Male 2,704 51.1
Age 16.2 0.7 14-20
Parent Education 4.1 1.6 1-7
a
Lifetime Smoking
Yes 2,426 45.8
No 2,871 54.2
Past 30-day
Smoking
0 days 3,977 75.8
1-2 days 350 6.7
3-5 days 151 2.9
6-9 days 138 2.6
10-19 days 200 3.8
20-29 days 184 3.5
all 30 days 248 4.7
Notes.
a
Response to the measure “Have you ever tried cigarette smoking, even a
few puffs?”
74
Table 10. Descriptive statistics and correlations for study variables
Variable M (SD)
a
Range MAAS ITS SRSE Smoke
MAAS 4.4 (1.1) 1-6 1.0
ITS 1.8 (1.0) 1-4 -.17 1.0
SRSE 3.0 (1.0) 1-4 .14 -.75 1.0
Smoke 0.3 (0.6) 0-1.95 -.14 .70 -.64 1.0
Notes. r > .05 are significant at p < .01; Lower left of diagonal are T1
intercorrelations,
a
descriptive is for T1; MAAS = Mindful Attention Awareness Scale;
ITS = Intention to Smoke, SRSE = Smoking Refusal Self-Efficacy, Smoke = log of past
30-day smoking frequency
75
Figure 2. Moderating effect of the MAAS on the association between ITS and
smoking
Notes. MAAS = Mindful Attention Awareness Scale;
a
in standard deviation units;
estimates are exponents of the log scale to return estimates to the original scale of
measurement
0
1
2
3
4
5
6
7
-3 -2 -1 0 1 2 3
Smoking Frequency
a
Intentions to Smoke
-1
0
1
a
MAAS
76
Figure 3. Moderating effect of the MAAS on the association between SRSE and
smoking
Notes. MAAS = Mindful Attention Awareness Scale;
a
in standard deviation units;
estimates are exponents of the log scale to return estimates to the original scale of
measurement
0
1
2
3
4
5
6
7
-3 -2 -1 0 1 2 3
Smoking Frequency
a
Smoking Refusal Self-Efficacy
-1
0
1
a
MAAS
77
Chapter 3
Testing the indirect effect of trait mindfulness on adolescent cigarette smoking
through negative affect and perceived stress mediators
3.1 Abstract
Mindfulness refers to an enhanced attention to and awareness of present
moment experience. This study examined how trait mindfulness, as measured with
the Mindfulness Attention Awareness Scale (MAAS), might influence adolescent
cigarette smoking frequency through its impact on depressive affect, anger affect,
and perceived stress mediators. Self-reported data from Chinese adolescents
(N=5,287; M age=16.2, SD=0.7; 48.8% female) were collected within 24 schools. The
product of coefficients test was used to determine significant meditation paths.
Results from baseline cross-sectional data indicated that trait mindfulness had a
significant indirect effect on past 30-day smoking frequency through depressive
affect, anger affect, and perceived stress mediators. Results from 13-month follow-
up data indicated these indirect effects remained significant for depressive affect
and perceived stress but not anger affect. Findings from this study suggest that
heightening mindfulness among adolescents may indirectly reduce cigarette
smoking perhaps by improving affect regulation competencies.
78
3.2 Introduction
Adolescence is a key developmental stage when maladaptive behaviors such
as cigarette smoking are initiated. Over 91% of adults who have ever smoked on a
daily basis initiated smoking before age 20, and 77% of these adults became regular
smokers in their adolescent years (USDHHS, 1994). The initiation of maladaptive
behaviors in adolescence is not surprising considering the transitional nature of this
pubescent period, often characterized by a heightened exposure to life stressors and
negative affect (Jessor, 1993). Consequently, many adolescents smoke in an effort to
self-regulate feelings of negative affect and stress. Theoretical models of affect
regulation and substance use (Colder & Chassin, 1993; Tomkins, 1966; Wills, 1986)
suggest that smoking, paradoxically experienced as both a fast-acting stimulant and
relaxant, is used as a coping response to reduce under stimulation arising from
negative affect and also to dampen overstimulation arising from stress arousal
(Gilbert, 1979; Leventhal & Cleary, 1980). Thus, adolescents may smoke to self
regulate burdensome feelings such as stress, depression, and anger.
Empirical findings provide some evidence for an affect regulation model of
adolescent smoking. For example, when asked why they continue to smoke, the
majority of adolescent smokers report using cigarettes to relax (CDC, 1994).
Similarly, among adolescent smokers attending a primary care clinic, 72% reported
stress relief as their most common reason for progression from experimental
smoking to becoming a regular smoker, and 33% indicated smoking helped them
cope with their problems (Siqueira, Diab, Bodian & Rolnitzky, 2000). In addition, the
79
level of perceived stress was lowest in never-smokers and higher in experimenters,
suggesting a dose-response relationship. Some studies comprised of school-based
samples also support an affect regulation model of smoking (e.g., Castro, Maddahian,
Newcomb & Bentler, 1987; Dugan, Lloyd & Lucas, 1999; Skara, Sussman & Dent,
2001; Sussman et al., 1993). Wills (1986) found, among an ethnically diverse sample
of adolescents from three junior high schools, subjective stress to prospectively
predict increases in smoking at one year follow-up, and reverse causation was not
supported by these data. Byrne et al. (1995) sampled 6,410 Australian adolescents
from various socioeconomic backgrounds and found that stress was associated with
both present smoking status and smoking onset from a previously non-smoking
status.
Adolescent smoking is also linked with negative affect, including both
depressive (Brook, Schuster & Zhang, 2004; Patton et al., 1998; Windle & Windle,
2001) and anger affect (Forgays, Forgays, Wrzesniewski & Bonaiuto, 1993; Johnson
& Gilbert, 1991; Seltzer & Oechsli, 1985; Siqueira et al., 2000). Covey and Tam
(1990) identified a strong cross-sectional association between depressive affect and
number of cigarettes smoked among eleventh graders even after adjusting for peer,
sibling, and parent smoking, worry and other covariates. Kandel and Davies (1986)
surveyed public high school youth at age 15 to 16 and nine years later and found
depressive symptoms were predictive of heavy smoking. Results from a national
survey of adolescents in the U.S. indicated that depressive symptoms increased the
rate of smoking initiation by 13% and 19% for adolescents reporting low and high
80
depressive symptoms, respectively (Escobedo, Reddy & Giovino, 1998). Data from
an ethnically diverse school-based sample of adolescents indicated that intense
anger was associated with both initiation and maintenance of smoking (Johnson &
Gilbert, 1991). Similarly, Siqueira et al. (2000) found anger was associated with a
greater likelihood to experiment with smoking as well as current smoking status.
Identifying factors that may reduce smoking attributable to the self-
regulation of affect and stress is critical for developing effective smoking prevention
programs. Mindfulness, an enhanced attention to and awareness of present moment
experience (Brown & Ryan, 2003), is one protective factor that may play a role in
cigarette smoking behavior as it appears to be positively associated with affect-
regulation competencies. For example, trait mindfulness, as measured with the
Mindful Attention Awareness Scale (MAAS), has been shown to have strong inverse
associations with negative affect and stress indicators and positive associations with
indicators of behavioral regulation and emotional wellbeing (e.g., Brown & Ryan,
2003; among adults, MAAS correlates with depressive affect = -.37, anger affect r = -
.41, and stress symptoms = -.46). Emerging evidence from fMRI studies conducted
among adults also suggest trait mindfulness plays a role in self-regulation processes
that are identifiable in specific brain regions (Creswell, Way, Eisenberger &
Lieberman, 2007; Herwig, Kaffenberger, Jäncke & Brühl, 2010).
Studies examining the association between trait mindfulness and
psychosocial factors among adolescents are scant. In one cross-sectional study,
Marks, Sobanski & Hine (2010) found that trait mindfulness attenuated the
81
association between life hassles and symptoms of depression and stress among
Australian high school students. A recent cross-sectional study (Black et al, in press)
conducted among Chinese adolescents found medium- to- large- size correlations
between trait mindfulness and depressive affect (r = -.40), anger affect (r = -.29),
and perceived stress (r = -.54), which were similar to those findings garnered from
adult samples (Brown & Ryan, 2003). Black et al. (in press) also showed trait
mindfulness explained variance in negative affect/stress measures beyond other
psychosocial constructs, suggesting the incremental validity of trait mindfulness.
The primary aim of the current study is to evaluate mediation models that
test the influence of mindfulness on adolescent smoking through depressive affect,
anger affect, and perceived stress mediators. Based on the affect regulation model of
smoking, we hypothesized that mindfulness would be inversely related to smoking
behavior through its attenuating effect on negative affect and perceived stress
mediators. This aim addresses a main gap in mindfulness research, which is a lack of
understanding of the mechanisms linking trait mindfulness to health behavior in
naturalistic contexts. A secondary aim of this study is to verify the trait nature of
mindfulness over time as measured with the Mindful Attention Awareness Scale
(MAAS; Brown & Ryan, 2003). A trait is defined here as a distinguishable
characteristic that differs between individuals, but is relatively stable within
individuals over time. Although mindfulness, as operationalized with the MAAS, is
discussed as a trait characteristic in the extant literature, no longitudinal data
support this claim. Therefore, verifying the trait nature of mindfulness with the use
82
of growth curve modeling methods is important to contextualize the findings
resulting from the primary aim of this study mentioned above. Based on previous
convention, we hypothesized that the MAAS would function as a stable trait
characteristic across time.
3.3 Methods
Participants and Procedures
Data were collected as part of a longitudinal study conducted by
collaborating researchers from the Pacific Rim Transdisciplinary Tobacco and
Alcohol Use Research Center (TTAURC). The objective of the TTAURC project was to
investigate the determinants of health behavior among adolescents in Chengdu,
China. All consent procedures and survey instruments for this study were approved
by the Institutional Review Boards (IRBs) of the University of Southern California
and Chengdu, China Centers for Disease Control and Prevention. A total of twenty-
four schools (N=24) in Chengdu, China enrolled in the study. Within the 24 schools
that participated, there were a total number of 1,060 classes. A total of 338 classes
were randomly selected to participate in this study.
Parental consent forms were distributed to students within the selected
classrooms, and those students acquiring written or verbal parental consent and
giving personal assent completed a self-reported paper-and-pencil questionnaire in
their classroom during school hours. The students whose parent did not sign the
parental participation permission form and/or who did not actively assent were
excluded from the study. Participants voluntarily took part in the study and were
83
informed that they could discontinue their participation at any time. Classroom
teachers were not present during the survey period so that participating students
would feel confident about the confidentiality of their responses. The same
participants completed surveys in their respective classroom from 10
th
to 12
th
grade
for a total of five waves of data collection. This study examines the data specifically
from wave 2, wave 3, and wave 5 because the measures of interest were collected
during these waves. For clarity purposes, these waves of data collection are referred
to as Time 1, Time 2 and Time 3, respectively, in the current study.
Measures
Demographic data included respondent age, gender, parent education, and
type of school attended (regular or vocational) (see Table 11).
Mindful Attention Awareness Scale (MAAS; Brown & Ryan, 2003). The MAAS is
a 15-item single-dimension measure of trait mindfulness. The MAAS measures the
frequency of open and receptive attention to and awareness of ongoing events and
experience. Response options range from 1 (almost never) to 6 (almost always).
Items are reverse coded and higher mean scores indicate a greater degree of
mindfulness. To control for social desirability, respondents are instructed to
respond to the MAAS in a way that reflects their actual experience rather than in a
way they think their experience should be. Recent psychometric research indicates a
6-item MAAS has equivalent and perhaps stronger evidence for construct validity
relative to the full 15-item MAAS among Chinese adolescents (6-item Cronbach’s α =
.89; Black, in press). Other studies also support the utility of a shortened scale (Van
84
Dam, Earleywine & Borders, 2010). Thus, the 6-item MAAS from Black et al. (in
press) was used in this study.
Mediators. Three items from the Center for Epidemiologic Studies Depression
Scale (CESD; Radloff, 1977; Cronbach’s α = .90 among Asian adolescents-Yang,
Soong, Kuo, Chang & Chen, 2004; current study Cronbach’s α = .87) were used to
assess depressive affect (e.g., On how many of those days [referencing the past 7
days] did you have trouble shaking off sad feelings?). Three-items from the
Aggression Questionnaire anger subscale (AQ; Buss & Perry, 1992; Cronbach’s α =
.89 among Asian adolescents-Ang & Yusof, 2005; current study Cronbach’s α = .81)
were used to assess the affective component of anger (e.g., “I have trouble
controlling my temper”). Six items from the Perceived Stress Scale (PSS; Cohen,
Kamarck & Mermelstein, 1983; Cronbach’s α = .81 among Asian adolescents-Xie et
al., 2006; current study Cronbach’s α = .86) were used to assess respondent
perceptions of life situations as stressful in the past month (e.g., In the last month,
how often were you unable to control the important things in your life?). High mean
scores for each instrument indicated higher endorsement of each construct.
Current Cigarette Smoking. Frequency of current smoking was assessed with
the item: “During the past 30 days, on how many days did you smoke cigarettes?”,
which is a measure used in the Centers for Disease Control and Prevention Youth
Risk Behavior Survey. Response options were coded on a 7-point scale ranging from
85
1 (0 days) to 7 (all 30 days). Due to a positive skew, this variable was log
transformed to help normalize its distribution.
Analyses
Data cleaning and descriptive statistics were conducted using SAS 9.2
software. Data were imported into Mplus version 5 and frequencies were cross-
examined between Mplus and SAS to assure correctness of transferred data. The
unconditional means models for each dependent variable indicated the presence of
smoking behavior clustering within schools (ICC >.02). Thus, our mediation models
were hierarchical to account for students nested within schools in order to obtain
more accurate standard error estimates (Krull & MacKinnon, 2001).
Mediation path analysis was conducted in Mplus, which is a program that
allows all regression equations in the mediation model to be estimated
simultaneously. Mediation is the scenario where an independent variable (X) causes
an intervening variable (M), which in turn causes the dependent variable (Y). In our
study, we tested the following hypothesized pathway: trait mindfulness (X)
inversely predicts negative affect and perceived stress mediators (M), which in turn
reduce the impact of M on smoking behavior (Y). Path models controlled for
baseline score on the mediator and outcome variables, and adjusted for age, gender,
parent education, school type, and treatment condition. Significance tests for
mediation were conducted using the product of coefficients tests (MacKinnon,
Lockwood, Hoffman, West & Sheets, 2002). According to this procedure, a variable
can be tested as a mediator by dividing the estimate of the product of paths a*b by
86
its corresponding standard error and comparing this value to a standard normal
distribution to determine significance. The product of these two parameters a*b is
the mediated or indirect effect, and the coefficient c relating the X variable to the Y
variable adjusted for the mediator is the nonmediated or direct effect. Relative to
other tests of mediation, the product of coefficients tests appears to be one of the
best tests among several methods for testing mediation in terms of having the most
power and accurate Type 1 error rates (MacKinnon et al., 2002). A pattern of results
that indicates statistically significant indirect effects but not direct effects
represents the strongest demonstration of a mediation effect (Kline, 2005). Percent
mediated was calculated by dividing the total effect by the indirect effect.
To asses the trait behavior of mindfulness over time, a latent growth model
(LGM) was tested in Mplus to determine the growth in mean MAAS level across a
13-month period. The MAAS was assessed at three time points including baseline
(T1) and 5 (T2) and 13 (T3) month follow-up periods. Initially, the intercept and
slope growth factors were estimated in a simplified model without covariates. Then,
growth estimates were regressed on gender, age, parent education, and school type.
Considering that ad hoc procedures for handling missing data such as list-
wise deletion or mean substitution often result in biased parameter and/or
standard error estimates, our mediation modeling procedures used full information
maximum likelihood estimation (FIML) as implemented in Mplus to yield more
accurate estimates while adjusting for the uncertainty associated with the missing
data (Little & Rubin, 2002). The FIML estimation does not impute missing values but
87
directly estimates model parameters and standard errors using all available raw
data.
3.4 Results
Demographics and Bivariate Correlations. T1 participant ages range from 14
to 20 years-old (M = 16.2, SD =0.7; see Table 11), and the ratio of males and females
is relatively equivalent. Over 45% of respondents report ever smoking a cigarette in
their lifetime. Over 24% of the sample report smoking in the past 30 days, and
almost 5% of the sample report daily smoking. Table 12 shows that at T1, MAAS
level is inversely and significantly correlated, in medium to large magnitude, with
the T1 mediators PSS (r = -.47), CESD (r = -.37), and AQ (r = -.25) and in smaller
magnitude with T1 smoking frequency (r = -.14). Significant and inverse
correlations hold between T1 MAAS and T2 PSS (r = -.35), CESD (r = -.29), and AQ (r
= -.19) . T1 MAAS level has a small yet significant correlation with T3 smoking
frequency (r = -.07).
Mediation Findings. Table 13 provides the cross-sectional mediation path
coefficients; all constructs in this model were measured at T1. Significant indirect
effects linking MAAS to the smoking outcome are found for PSS (indirect β = -.046, p
< .01), CESD (indirect β = -.040, p < .01), and AQ (indirect β = -.026, p < .01). Total
and indirect effect estimates indicate full mediation for all three models. Table 4
provides the longitudinal mediation path coefficients. Baseline measures were
assessed at T1, mediators were assessed at T2 (5 months post-baseline), and the
smoking outcome was assessed at T3 (13 months post-baseline). Significant indirect
88
effects linking MAAS to the smoking outcome included PSS (indirect β = -.009, p <
.01) and CESD (indirect β = -.005, p < .05). Marginally significant indirect effects
were found for AQ (indirect β = -.003, p < .06). Total and indirect effect estimates
indicate full mediation for all models. To test the potential for reverse causation,
each of the three longitudinal mediation models was analyzed by switching the
position of the mediator (M) and predictor (X). Results from these models indicate
no significant indirect effects, suggesting lack of empirical support for reverse
causation.
MAAS Latent Growth. Figure 4 illustrates individual trajectories of
respondent mean MAAS level over time. The x-axis is the data collection wave
(month 0=T1; month 5=T2; month 13=T3) and the y-axis is the mean MAAS level
reported by the respondent. Table 3 provides the latent growth estimates for the
MAAS. In the reduced model without covariates, the intercept is 4.095, which is the
average initial MAAS level for the total sample. There is a very slight positive mean
linear growth in MAAS level over time (B = .012, p < .01). There is a substantial
range of individual differences around the average initial MAAS level (B = 1.685, p <
.01), but there is not a significant range of individual differences in the rate of linear
increases in MAAS level over time (B = .008, ns).
The negative covariance in the intercept and slope parameters (B = -.073, p <
.01) indicates those who report high MAAS levels at T1 tend to have a slight
decrease in mean MAAS scores over time, while those who report low levels of
MAAS at T1 tend to report a slight increase in MAAS level over time. The LGM
89
results for the adjusted model indicates males have lower initial MAAS levels, and
age and parent education are related to higher initial MAAS levels. All the
demographic covariates measured have little influence on the rate of change of
MAAS levels over time (range of B for covariates = -.003 to .01).
3.5 Discussion
This study, conducted in a naturalistic school-based setting with Chinese
adolescents incorporated trait mindfulness within an affect regulation model of
substance use, and verified the trait nature of mindfulness when measured with the
MAAS. The results of this study are consistent with our initial predictions in several
respects. First, findings from our mediation analyses suggest mindfulness was
inversely association with adolescent smoking behavior through its influence on
negative affect and perceived stress mediators. Findings from both cross-sectional
and longitudinal data supported our prediction that trait mindfulness indirectly
reduces smoking frequency by its attenuating effect on negative affect and
perceived stress indicators. Our findings provide initial support for models that
propose mindfulness protects against adolescent maladaptive behavior through is
attenuating influence on negative affect and stress, perhaps through affect
regulation competencies (Hede, 2010; Herwig et al., 2010). Further support for the
hypothesized direction of our models was obtained by tests for reverse causation,
which indicated no significant indirect effects on smoking when negative
affect/stress measures were modeled as the predictor and trait mindfulness as the
mediator.
90
Our findings corroborate previous research which supports an inverse
relationship between trait mindfulness, as measured with the MAAS, and depressive
affect, anger affect, and stress among adult (Brown & Ryan, 2003) and adolescent
samples (Marks et al., 2010; Black 2010). Our findings extend this literature by
proposing a testable model of trait mindfulness and affect regulation, which
elucidates one mechanism whereby mindfulness may protect against adolescent
smoking behavior. This study also addresses recent discussion regarding the
integration of mindfulness within models of health behavior (Black, 2010).
Moreover, this study adds to previous research by being one of the first to use
longitudinal data to support a prospective relationship between trait mindfulness
and negative affect/stress among adolescents in a naturalistic school-based setting.
Second, using latent growth modeling, we found the MAAS behaved as a trait
characteristic across a 13 month period; data indicated high between-subjects
variability in the initial level of MAAS scores, but low within- and between- subjects
level of change in MAAS scores over time. Moreover, demographic variability in age,
parent education, and school type had little influence on the trajectory of MAAS,
suggesting that mindfulness may be a unique characteristic that may be most
sensitive to mindfulness training alone and not intrapersonal factors. Previous
research suggests that trait mindfulness is highest among those that practice
mindfulness skill-building techniques (Brown & Ryan, 2003). Overall, our growth
modeling results support the conceptualization of mindfulness, as measured with
the MAAS, as a trait characteristic that is relatively stable over time. This specific
91
finding adds to previous literature (Brown & Ryan, 2003) by elucidating the growth
estimates of mindfulness using longitudinal data, which allows for stronger
inferences about the trait nature of the MAAS.
Certain limitations in our study require comment. First, our sample consisted
of Chinese adolescents, which implies that our findings may not generalize to
adolescents in other countries. We recommend that studies replicate our proposed
model with data from adolescents and adults in various countries. Second, our
mediation model did not capture a direct measure of affect regulation (e.g., Negative
Mood Regulation Expectancies scale; Catanzaro & Mearns, 1990), which limits a
more direct interpretation of the actual self-regulation mechanisms leading
mindfulness to attenuate negative affect and smoking. Future studies should directly
measure affect regulation competencies and determine if these competencies
mediate the relationship between trait mindfulness and negative affect/stress
indicators. This area of research is promising considering recent brain imaging
studies that have suggested increased affect regulation capacity among those with
increased levels of trait mindfulness (Creswell et al., 2007; Herwig et al., 2010).
Third, our study relied on the self-report data that may be vulnerable to respondent
bias; however, research have shown little discrepancy between self-reports and
biochemical assessments of adolescent cigarette smoking (Stacy et al., 1990), and
respondents were continually assured that their responses were confidential prior
to questionnaire completion.
92
Our findings have implications for practical application. Although the MAAS,
as a measure of trait mindfulness, produces relatively stable scores over time,
mindfulness can nevertheless be heightened through specific mindfulness practices
(Brown & Ryan, 2003; Shapiro, Brown & Biegel, 2007) such as Mindfulness-Based
Stress Reduction (MBSR). Mindfulness-based interventions are increasingly being
adapted for and delivered to adolescents, and have shown promise to reduce some
adolescent maladaptive behaviors (see reviews by Black, Milam & Sussman, 2009;
Burke, 2010). However, no research to date has reported outcomes for mindfulness
based programs for adolescents in China. Therefore, programs such as MBSR that
use techniques to heighten mindfulness may perhaps prove useful to substance
abuse prevention programs targeted to adolescents in school-based settings in
China and other countries. To verify this notion, future research trials are needed to
determine if mindfulness-based interventions can increase mindfulness among
adolescents in naturalistic school-based settings, and to determine if these
interventions bolster affect regulation competencies and reduce various substance
use behaviors.
In summary, the results from the current study indicate that trait
mindfulness is associated with a reduction in adolescent smoking behavior through
its attenuating influence on negative affect and stress indicators in both cross-
sectional and longitudinal models. Further, this study provides evidence that the
MAAS functions as a trait measure of mindfulness among adolescents across a 13
month period. Mindfulness can be cultivated with practice, and this may suggests
93
that future studies should examine the effects of mindfulness-based interventions
on affect regulation competencies and substance use among adolescents in
naturalistic school-based settings.
94
Table 11. Demographic characteristics of Chinese adolescents at T1 (N=5,287)
Variable M SD N % Range
Gender
Female 2,583 48.9
Male 2,704 51.1
Age 16.2 0.7 14-20
Parent Education 4.1 1.6 1-7
Lifetime Smoking
Yes 2,426 45.8
No 2,871 54.2
Past 30-day
Smoking
0 days 3,977 75.8
1-2 days 350 6.7
3-5 days 151 2.9
6-9 days 138 2.6
10-19 days 200 3.8
20-29 days 184 3.5
all 30 days 248 4.7
Notes.
a
Response to the measure “Have you ever tried cigarette smoking, even a few
puffs?”
95
Table 12. Bivariate correlations across multiple time points
Variable MAAS PSS CESD AQ Smoking
a
MAAS -.35 -.29 -.19 -.07
PSS -.47 .43 .20 .03
CESD -.37 .60 .15 .06
AQ -.25 .31 .26 .06
Smoking -.14 .10 .13 .12
M (SD)
b
4.4 (1.1) 2.5 (0.9) 2.0 (0.9) 2.5 (1.1)
Range 1-6 1-5 1-4 1-5
Notes. r > .05 are significant at p < .01; Lower left of diagonal are T1
intercorrelations; Upper right of diagonal are T1 with T2 intercorrelations,
a
except
for the smoking variable which is measured at T3;
b
descriptive is for T1; MAAS =
Mindful Attention Awareness Scale; CESD = Center for Epidemiologic Studies
Depression Scale; PSS = Perceived Stress Scale; AQ = Aggression Questionnaire-
Anger; Smoking = past 30-day smoking frequency
96
Table 13. Cross-sectional mediation analysis for past 30-day smoking frequency
Model Path B SE 95% CI for B β %
med.
a
R
2
PSS Model
MAAS-->PSS -.368 .013 (-.394, -.343) -.463**
PSS--> Smoking .073 .012 (.049, .096) .099**
MAAS-->Smoking -.082 .014 (-.109, -.056) -.140**
Indirect effect -.027 .004 (-.036, -.018) -.046** 100
Model R
2
for PSS .236
Model R
2
for
Smoking
.312
CESD Model
MAAS-->CESD -.292 .001 (-.314, -.270) -.366**
CESD-->Smoking .080 .015 (.054 .105) .108**
MAAS-->Smoking -.086 .015 (-.115, -.057) -.147**
Indirect effect -.023 .004 (-.031, -.015) -.040** 100
Model R
2
for CESD .139
Model R
2
for
Smoking
.218
AQ Model
MAAS-->AQ -.253 .016 (-.283, -.222) -.253**
AQ-->Smoking .061 .007 (.048, .074) .103**
MAAS-->Smoking -.094 .014 (-.121, -.067) -.160**
Indirect effect -.015 .002 (-.019, -.011) -.026** 100
Model R
2
for AQ .077
Model R
2
for
Smoking
.218
Notes. **p<.01, *p<.05, †p<.06; MAAS = Mindful Attention Awareness Scale; CESD =
Center for Epidemiologic Studies Depression Scale; PSS = Perceived Stress Scale;
CESD = ; AQ = Aggression Questionnaire;
a
% mediated = indirect effect / total effect;
Each model adjusted for age, gender, parent education, school type, mediator score,
treatment exposure, and smoking frequency at baseline. Hierarchical models
account for students nested within schools.
97
Table 14. Longitudinal mediation path analysis for past 30-day smoking frequency
Model Path B SE 95% CI for B β %
med.
a
R
2
PSS Model
MAAS-->PSS -.147 .016 (-.115, -.179) -.178**
PSS--> Smoking .034 .010 (.014, .055) .048**
MAAS-->Smoking .002 .011 (-.019, .023) .003
Indirect effect -.005 .002 (-.002, -.008) -.009** 100
Model R
2
for PSS .221
Model R
2
for
Smoking
.476
CESD Model
MAAS-->CESD -.129 .013 (-.104, -.155) -.165**
CESD-->Smoking .025 .009 (.007, .043) .033**
MAAS-->Smoking -.002 .010 (-.022, .019) -.003
Indirect effect -.003 .003 (-.011, .000) -.005* 100
Model R
2
for CESD .187
Model R
2
for
Smoking
.475
AQ Model
MAAS-->AQ -.079 .019 (-.041, -.116) -.082**
AQ-->Smoking .019 .007 (.005, .033) .031*
MAAS-->Smoking -.004 .010 (-.024, .016) -.007
Indirect effect -.001 .001 (-.007, .000) -.003† 100
Model R
2
for AQ .193
Model R
2
for
Smoking
.475
Notes. **p<.01, *p<.05, †p<.06; MAAS = Mindful Attention Awareness Scale; CESD =
Center for Epidemiologic Studies Depression Scale; PSS = Perceived Stress Scale;
CESD = ; AQ = Aggression Questionnaire;
a
% mediated = indirect effect / total effect;
Each model adjusted for age, gender, parent education, school type, mediator score,
treatment exposure, and smoking frequency at baseline. Hierarchical models
account for students nested within schools. Baseline measures assessed at 0
months, mediator assessed at 3 months post-baseline, and outcome assessed at 13
months post-baseline.
98
Figure 4. Randomly selected individual trajectories for mean MAAS scores (N=25)
Notes. Discontinued lines indicate missing data
Mean MAAS Score
Measurement Wave (Months)
99
Table 15. Linear growth estimates of mean MAAS scores across 13 month period
Growth Factors B SE p
Unadjusted Model
Mean
a
Intercept 4.095 .020 **
Slope .012 .002 **
Variance
b
Intercept 1.685 .050 **
Slope .008 .001 **
Covariance -.073 .004 **
Covariate Adjusted Model
Mean
Intercept 1.803 .078 **
Slope .012 .008
Variance
Intercept .908 .037 **
Slope .004 .001 **
Covariance -.037 .003 **
Covariates
c
Intercept
Gender (Male) -.096 .019 **
Age .154 .004 **
Parent Education .041 .007 **
School Type .009 .007
Slope
Gender (Male) .009 .002 **
Age -.003 .001 **
Parent Education .006 .001 **
School Type .010 .001 **
Notes. **p<.01;
a
intercept = average initial MAAS level, slope = linear growth in
average MAAS level;
b
intercept = range of individual differences around the average
initial MAAS level, slope = range of individual differences in the rate of linear
increases in MAAS level over time;
c
estimates from intercept and slope growth
factors regressed on covariates.
100
Figure 5. MAAS linear growth model adjusted for covariates
Notes. i = latent intercept; s = latent slope; c = covariates; e = measurement error;
T1, T2, T3 = time points;
i s
MAAS
T1
MAAS
T2
MAAS
T3
c
1 1
1 0
5 13
e e e
101
Figure 6. Meditation analysis conceptual path model
Notes. MAAS = Mindful Attention Awareness Scale; CESD = Center for Epidemiologic
Studies Depression Scale; PSS = Perceived Stress Scale; CESD = ; AQ = Aggression
Questionnaire. Three separate path models were estimated for the three mediating
variables; mediators are depicted in one box for ease of interpretation.
MAAS
PSS
CESD
AQ
Smoking
Frequency
a b
c
102
Conclusion
Pioneering research into the empirical realm of what is currently being called
mindfulness science is promising (Black, 2010b), and this area is gaining national
attention. For example, the U.S. National Institutes of Health (NIH) currently funds
83 open research trials on mindfulness and mindfulness-based interventions.
However, the empirical history of mindfulness science is relatively new with
empirical publications just beginning to grow steadily over the past two decades
(Black, 2010c; Black 2010d). During the brief course of this research history, the
vast majority of evidence of the salutatory effects of mindfulness is based on
findings garnered from adult populations, and only recently has research delved in
the examination of mindfulness among adolescence. This dissertation study fits
specifically within a small pool of literature examining mindfulness among youth
and is the first to examine dispositional mindfulness among Chinese adolescents. In
the following conclusion of this dissertation the findings of the three dissertation
studies are placed within the context of the extant research literature on
mindfulness among adolescents. Then implications for this dissertation research are
discussed in the context of adolescent smoking prevention in China, and finally
broad limitations in mindfulness science are noted.
As mentioned, a small yet growing area of research has started to document
the effects of mindfulness among younger populations (see review Black et al.,
2009). The available research literature on mindfulness on adolescence can be
categorized into three main areas. The first category is inclusive of a set of five
103
studies that have examined the effects of mindfulness based intervention (MBIs) on
physiological outcomes; these outcomes have been indices of cardiovascular
functioning or sleep (Barnes, Davis, Murzynowski & Treiber, 2004; Barnes,
Gregoski, Tingen & Treiber, 2010; Barnes, Pendergrast, Harshfield & Treiber, 2008;
Britton et al., 2010; Gregoski, Barnes, Tingen, Harshfield & Treiber, 2010). The
second and broader category includes 13 studies that pertain to the psychosocial
and behavior effects of MBIs (Beauchemin, Hutchins & Patterson, 2008; Biegel,
Brown, Shapiro & Schubert, 2009; Flook et al., 2010; Lee, Semple, Rosa & Miller,
2008; Liehr & Diaz, 2010; Napoli, Krech & Holley, 2004; Schonert-Reichl & Lawlor,
2010; Semple, Lee, Rosa & Miller, 2010; Singh et al., 2011; Singh et al., 2007; Singh et
al., 2010a; Singh et al., 2010b; Zylowska et al., 2008). This collection of studies notes
general improvements in attention, affect and compliant behavior as well as other
health indices during and following programs aiming to enhance mindfulness.
The third category, which is most relevant to this dissertation, includes only
one empirical investigation that reports on a new area of recent exploration
pertaining to dispositional mindfulness (operationalized with the MAAS) occurring
in naturalistic settings and its associations with indices of mental and behavioral
heath. In this single study (Marks, Sobanski & Hine, 2010) sampled 317 youth ages
14 to 19 in a high school setting and assessed dispositional mindfulness with the
MAAS (Brown & Ryan, 2003). Results from this study showed mindfulness had
significant and inverse cross-sectional correlations with rumination (r = -.60),
depression (r = -.48), anxiety (r = -.43) and stress (r = -.56). Moreover, this study
104
found that mindfulness attenuated the positive relationship between life hassles and
several psychological ailments including depression, anxiety, and stress.
The current dissertation comprises only the second investigation of
dispositional mindfulness among adolescents. This dissertation advances current
scientific knowledge in this area because it is the first to: (a) yield evidence to
suggest the MAAS (Brown & Ryan, 2003) can be a valid measure to capture
dispositional mindfulness among Chinese adolescents, and this study yielded a valid
6-item short scale of the MAAS, (c) support the notion that mindfulness can protect
against adolescent smoking behavior through its protective effect on negative affect
and stress mediators, (b) provide evidence that mindfulness can moderate
important decision-making processes conceptualized in the theory of planned
behavior, and this study showed that mindfulness can shield against decision-
making processes that place youth at risk for smoking behavior, and (c) support the
notion that mindfulness can protect against adolescent smoking behavior through
its protective effect on negative affect and stress mediators. As such, this
dissertation plays an essential role in advancing our initial understanding of
dispositional mindfulness in the context of adolescent health.
This dissertation also has implications for cigarette smoking prevention
efforts targeted to Chinese adolescents. Overall, findings from this dissertation
suggest that mindfulness is protective against adolescent smoking frequency in the
context of both cognitive and affective frameworks. For example, it appeared that
mindfulness indirectly reduced smoking frequency through its protective effect on
105
negative affect and perceived stress. Moreover, it appeared that those with high
relative to lower levels of mindfulness were shielded against smoking-related
cognitions that placed them at risk for smoking. Therefore, one implication resulting
from this work is the consideration that enhancing mindfulness among youth may
protect them from more dangerous levels of smoking behavior (i.e., higher levels of
smoking). Studies in the United States show that school- and clinic-based programs
that aim to enhance mindfulness (e.g., mindfulness based stress reduction; MBSR)
are relatively easy to administer and are typically well-accepted by adolescents and
their parents as well as by school-teachers and allied health professionals. Thus,
using evidence-based smoking prevention programming with added mindfulness
content may enhance mindfulness and subsequently prevent smoking behavior
among Chinese youth to a greater extent. More research is needed to determine the
feasibility and acceptability of mindfulness based interventions among Chinese
youth and to determine the efficacy of mindfulness based programs on substance
use prevention among this population.
As with all new and expanding fields of research, the field of mindfulness
science currently has a many limitations that may challenge the validity of current
research findings both presented in this dissertation and in the broader research
literature. Two of these major limitations include the lack of a standard definition of
mindfulness and a lack of large scale studies that include representative samples of
respondents. In this dissertation study, mindfulness is clearly stated to be
operationlized as a individual disposition that remains relatively stable across time.
106
However, mindfulness has been conceptualized in many different ways such as: (a)
a state of consciousness derived from meditation practice, (b) a type of meditation
practice itself (i.e., mindfulness meditation), (c) an innate trait-like disposition
comprised of heightened attention and awareness. Although there is much overlap
in these conceptualizations of mindfulness (i.e., characterized by attention and
awareness oriented to the present moment), much more synthesis and clarification
is needed in order to increase the validity and interpretability of mindfulness
science. Efforts to provide such a landmark definition of mindfulness are currently
underway (A Chiesa, DS Black, A Serretti and P Malinowski, in preparation) and
these efforts may strengthen our understanding of the current empirical evidence
on mindfulness science and guide its future research direction.
In summary, it is clear that the popularity of mindfulness science is growing
among children and adolescents. Therefore, much research is needed among large
samples of youth representing diverse ethnic and socio-demographic populations in
order to replicate the findings resulting from this dissertation research. It has
recently been articulated that mindfulness may have an important role in health
behavior research and theory (Black, 2010a) and this dissertation yields some of the
first empirical accounts that such suggestions may have scientific merit.
107
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Asset Metadata
Creator
Black, David S.
(author)
Core Title
The effects of mindfulness on adolescent cigarette smoking: Measurement, mechanisms, and theory
School
Keck School of Medicine
Degree
Doctor of Philosophy
Degree Program
Preventive Medicine (Health Behavior)
Publication Date
03/22/2011
Defense Date
03/11/2011
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
Adolescent,affect,China,cognition,mindfulness,OAI-PMH Harvest,psychometrics,smoking
Place Name
China
(countries)
Language
English
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Electronically uploaded by the author
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Advisor
Sussman, Steven Y. (
committee chair
), Brooks, Devon (
committee member
), Milam, Joel (
committee member
), Riggs, Nathaniel (
committee member
), Rohrbach, Luanne (
committee member
)
Creator Email
dave_blizac@yahoo.com,davidbla@usc.edu
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https://doi.org/10.25549/usctheses-m3690
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UC1446540
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etd-Black-4356 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-444742 (legacy record id),usctheses-m3690 (legacy record id)
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etd-Black-4356.pdf
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444742
Document Type
Dissertation
Rights
Black, David S.
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Repository Name
Libraries, University of Southern California
Repository Location
Los Angeles, California
Repository Email
cisadmin@lib.usc.edu
Tags
affect
cognition
mindfulness
psychometrics
smoking