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University of Southern California Dissertations and Theses
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The role of social support in the relationship between adverse childhood experiences and addictive behaviors across adolescence and young adulthood
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The role of social support in the relationship between adverse childhood experiences and addictive behaviors across adolescence and young adulthood
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Content
The Role of Social Support in the Relationship Between Adverse Childhood Experiences and
Addictive Behaviors Across Adolescence and Young Adulthood
By
Christopher J. Rogers
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 ((HEALTH BEHAVIOR RESEARCH))
May 2022
Copyright 2022 Christopher J. Rogers
ii
Dedication
To my best friend and partner in life, Kimberly:
For always being there in the best and worst times and for supporting me in this journey.
I could not have done this without you. You are my favorite!
To my son, Macsen:
For always knowing how to distract me (it is good to be distracted from work sometimes).
I promise I will play video games with you after I submit this.
To Dave and Oatmeal: you know what you have done.
iii
Acknowledgements
I am so fortunate to have a dissertation committee chair who builds up, and fights for students.
Dr. Jennifer Unger, you have been and will continue to be an amazing mentor. Your generosity
with your time and energy is inspiring and I hope I have the privilege to working with you for
many years. Your kindness, feedback, positivity, and expertise has served to guide me and
develop me into the academic I want to become.
I am also so grateful for all of my committee members.
Dr. Myriam Forster, for mentoring and inspiring me since my undergraduate degree. Without
your guidance, support, and straight talk I would never have imagined I could get this far. You
have always had time for my questions and have talked me through so much. You have set a
high bar and a fast pace, and I hope I can keep running with you for a long time.
Dr. Steven Sussman, for being my first professor in my Doctoral Program and for always
encouraging me and watching out for me. You are a wonderful and unique person, and I will
never forget what you have taught me. I feel privileged to have worked with you.
Dr. Jessica Barrington-Trimis, for being such an inspiring role model and always making me
feel like I can make it. You have encouraged me to take on challenges and still be balanced.
Your encouragement and down to earth realism truly made me feel like a colleague.
Dr. Jane Steinberg, for your amazing kindness and policy expertise. You always have a positive
word to say when I meet with you and your feedback, timely advice, and empathy have been a
tremendous help through my program.
Finally, to my PhD cohort. You all have been an amazing sounding board, resource,
encouragement, and therapy group. I have made friends and colleagues for life and will stay in
the group chat till we all graduate.
iv
Table of Contents
Dedication ..................................................................................................................................................... ii
Acknowledgements ...................................................................................................................................... iii
List of Tables ................................................................................................................................................ v
List of Figures .............................................................................................................................................. vi
Chapter 1: Introduction ................................................................................................................................. 1
Addiction ................................................................................................................................................... 1
Childhood Trauma – Adverse Childhood Experiences ........................................................................... 13
Critical Age Groups ................................................................................................................................ 16
Subgroup Differences in the Relationship Between ACE and Addiction ............................................... 21
Social Support ......................................................................................................................................... 23
Gaps in the Literature .............................................................................................................................. 26
Overview Of the Studies ......................................................................................................................... 28
Chapter 2: Minority Status and the Relationship Between Adverse Childhood Experiences and Addictive
Disorders: The Moderating Role of Social Support .................................................................................... 32
Introduction ............................................................................................................................................. 32
Methods and Data ................................................................................................................................... 38
Results ..................................................................................................................................................... 44
Discussion ............................................................................................................................................... 52
Chapter 3: The Impact of Childhood Trauma on Alcohol and Drug Use Consequences Trajectories and
the Moderating Role of Social Support ....................................................................................................... 58
Introduction ............................................................................................................................................. 58
Methods and Data ................................................................................................................................... 63
Results ..................................................................................................................................................... 68
Discussion ............................................................................................................................................... 73
Chapter 4: Relationship Between Hours of Media Use and Internet Addiction Across Adverse Childhood
Experiences and Social Support. ................................................................................................................. 79
Introduction ............................................................................................................................................. 79
Methods and Data ................................................................................................................................... 84
Results ..................................................................................................................................................... 88
Discussion ............................................................................................................................................... 95
Chapter 5: Discussion ............................................................................................................................... 100
Summary of the Project ......................................................................................................................... 100
Overall Implications .............................................................................................................................. 101
Overall Limitations ............................................................................................................................... 102
Future Research Directions ................................................................................................................... 103
Concluding Remarks ............................................................................................................................. 105
References ................................................................................................................................................. 107
v
List of Tables
Table 1. Descriptive Statistics for Study 1 (N=62,142) .............................................................................. 45
Table 2. Correlation Matrix of Main Effects for Study 1 ........................................................................... 45
Table 3. Poisson Regression Models .......................................................................................................... 46
Table 4. Non Time-Dependent Descriptive Statistics for Study 2 (N=1,404 participants) ........................ 69
Table 5. Descriptive Statistics for Study 2 Over Time (N=6,348 observations) ........................................ 69
Table 6. Growth Curve Models. ................................................................................................................. 70
Table 7. Descriptive Statistics for Study 3 (N=1,166 participants) ............................................................ 90
Table 8. Correlation Matrix of Main Effects for Study 3 ........................................................................... 91
Table 9. Regression Model ......................................................................................................................... 92
vi
List of Figures
Figure 1. Conceptual Framework of the Three Proposed Studies .............................................................. 31
Figure 2. Study 1 Hypothesized Conceptual Model ................................................................................... 38
Figure 3. Two-Way Interaction (ACE*Support) on Alcohol and Drug Use Disorder ................................ 47
Figure 4. Two-Way Interaction (ACE*Support) on Problematic Gambling .............................................. 48
Figure 5. Three-Way Interaction (ACE*Support*Ethnicity) on Alcohol and Drug Use Disorder ............. 49
Figure 6. Three-Way Interaction (ACE*Support* Orientation) on Alcohol and Drug Use Disorder ........ 50
Figure 7. Three-Way Interaction (ACE*Support*Ethnicity) on Problematic Gambling ............................ 51
Figure 8. Three-Way Interaction (ACE*Support* Orientation) on Problematic Gambling ....................... 52
Figure 9. Study 2 Hypothesized Conceptual Model ................................................................................... 62
Figure 10. Problematic Alcohol and Drug Use Scores Over Time by ACE ............................................... 71
Figure 11. Problematic Alcohol and Drug Use Scores Over Time by ACE Paneled by Support ............... 73
Figure 12. Study 3 Hypothesized Conceptual Model ................................................................................. 84
Figure 13. Two-Way Interaction (Social Media*ACE) on Internet Addiction ........................................... 93
Figure 14. Three-Way Interaction (Gaming *ACE*Support) on Internet Addiction ................................. 94
vii
Abstract
Social support has been identified as an important tool in mental health interventions and has
been associated with many positive health outcomes. In contrast, adverse childhood experiences
have been among the most consistent and robust predictors of poor health outcomes including
addictive behaviors such as pharmacologic and behavioral addictions. The three studies
presented in this dissertation are exploring the role of social support as a moderator in the
relationship between adverse childhood experiences and addictive disorders and behaviors. To
do this, three distinct datasets are used: (study 1) a large regional sample of adolescents, (study
2) a cohort sample of Hispanic adolescents followed through young adulthood, and (study 3) a
cross-sectional sample of young adults in college. Results presented within the first study
indicate that for adolescents, social support can protect against the impact of adverse childhood
experiences on alcohol, drug, and gambling disorders and is particularly beneficial for ethnic and
sexual minorities. Results from the second study suggest adverse childhood experiences can
increase problematic alcohol and drug use as adolescents transition to young adulthood and that
social support during adolescence can lessen the impact of ACE on problematic use. The third
study identified that both adverse childhood experiences and higher use of social media and
online gaming are associated with increased internet addiction. Also, adverse childhood
experiences can exacerbate the relationships between hours of use and addiction. Social support
was found to be a buffer between the relationship of adverse childhood experiences and online
gambling, on internet addiction. This indicates that adverse childhood experiences may explain
some of the relationship between hours of use and addiction, and that social support may help to
protect against the negative consequences and addictive results of heavy use.
1
Chapter 1: Introduction
Addiction
Lacking a formal definition, consensus, or interpretation, early use of the word
“addiction” primarily described problematic psychoactive substance use, and was considered a
colloquial term rather than a diagnosis (Rosenthal & Faris, 2019). Arriving at a generally agreed
upon definition and acceptance of addiction as a condition has only come after much scrutiny
and debate. In the 3
rd
and 4
th
editions of the Diagnostic and Statistical Manual of Mental
Disorders (DSM), The American Psychiatric Association chose to omit addiction due to a lack of
agreement (American Psychiatric Association, 1994; American Psychiatric Association, 1980).
Over the past 20 years, addiction has evolved as a term to encompass issues beyond problematic
psychoactive substance use. Researchers have learned more about the brain and found that
certain behaviors and experiences activate the brain’s reward system similar to psychoactive
substances (Holden, 2001). Currently accepted definitions of addiction refer to the attempt to
achieve some appetitive effect and satiation through engagement in a behavior (Sussman &
Sussman, 2011). Consequently, the term has confidently reemerged in the diagnostic lexicon and
is included in the DSM-5, which changed the section on “Substance-Related Disorders” to the
more inclusive “Substance-Related and Addictive Disorders”(American Psychiatric Association,
2013).
A review of addiction studies found that five key elements are consistently seen: behavior
engagement to achieve appetitive effects, preoccupation with the behavior, temporary satiation,
loss of control, and suffering negative consequences (Sussman & Sussman, 2011). This helps to
further define the concept of addiction by identifying substances and behaviors that may be
engaged in to achieve satiation. A defining component of addiction is that continuing to engage
2
with the “addictive” behavior or substance inevitably leads to negative side effects (Sussman &
Sussman, 2011). Considering the broadened definition and the potential outcomes of addiction,
the current proposal intends to examine substances and other addictive behaviors beyond the use
of psychoactive substances and to further define the relationship of these addictions with
common risk factors across adolescence and young adulthood.
Theory and Science
To understand addiction, we must understand the biobehavioral and neurobiological
basis. In the late 1950s the discovery of dopamine and its properties as a neurotransmitter were
identified. It was not until many years later that methods such as brain imaging would enable
detailed observance of the correlation between dopamine and behavior (Di Chiara & Bassareo,
2007). Technological progress and further investigation into the function of neurotransmitters
have led researchers to better understand the relationship between the brain and addiction,
leading to the development of the brain disease model of addiction (Sussman & Ames, 2008;
Volkow et al., 2016). Essentially, the brain’s reward system provides motivation through
dopamine release and associative learning or conditioning in a Pavlovian framework (Volkow et
al., 2016). From an evolutionary standpoint, the system prioritizes actions and behaviors that are
life-sustaining by linking them with pleasure to encourage repeat behavior. As exposure to the
reward increases, dopamine may be released as an anticipatory response to cues (Schultz, 2002).
Connections to stimuli form that can illicit cravings for the substance or behavior and may
motivate further seeking of the action to achieve satiation (Weiss, 2005). As the motivational
attribute associated with a reward increases, the more effort a person is willing to put forth and
the more negative consequences the person is willing to endure to achieve appetitive effects
(Volkow et al., 2016). Eventually, neurotransmitter release may even become dependent on
3
continuance of the behavior and withdrawal symptoms can arise with cessation (Sussman, 2017).
This understanding of the brain disease model maps on to the five key elements of addiction
stated above (Sussman & Sussman, 2011). The reward circuit encourages engagement in the
behavior to achieve appetitive effects and the repeated reward may evolve into seeking behaviors
that lead to preoccupation with the behavior or action. As the action is engaged, there is a
temporary satiation; however, as the motivation to attain the reward increases there can be a loss
of control and a willingness to suffer negative consequences to achieve satisfaction.
Addiction: Substances and Beyond
Most addiction research has focused on the ingestion of substances, but there are clear
phenomenological and neurobiological parallels between substance use disorders and other
process addictions like gambling disorders, compulsive actions, sexual behaviors, and
problematic use of technology that includes smartphones, television and videos, video games,
and the Internet (Grant & Chamberlain, 2016; Sussman & Ames, 2008; Sussman et al., 2011;
Sussman & Moran, 2013; Sussman & Sussman, 2011). For example, many of the same
phenomenological addictive mechanisms and maladaptive behavioral patterns seen in alcohol
and drug use disorders are also found in process addictions, such as pathological gambling,
including an urge or craving state, loss of control, and a willingness to suffering negative
consequences (Clark et al., 2013; Demetrovics & Griffiths, 2012). Additionally, in both animal
models and human studies the same reward circuit response can be seen in striatal dopamine
release with gambling behaviors (Clark et al., 2013; Joutsa et al., 2012). Recognition of non-
pharmacological addictions has occurred slowly as evidence of the similarities in neurobiological
and psychological mechanisms emerged (Sussman, 2017). The DSM-5 was the first edition of
the diagnostic manual to move gambling disorders from impulse control disorders to the new
4
section of “Substance-Related and Addictive Disorders” (American Psychiatric Association,
2013). Given the establishment of this precedent, continued research is needed to investigate the
parallels between substance use disorders and other presumably addictive behaviors. It is
important to note that although the parallels between process addictions and substance use
addiction are similar, each behavior has both overlapping and unique features (Sussman, 2018),
and as such, risk and preventative factors already investigated in substance use should be tested
in other behaviors as they may operate differently, highlighting the need for continued research
across this newly classified array of addictions. If the full range of substance and behavioral
addictions are considered, an estimated 47% of the U.S. adult population may endure the
maladaptive signs of an addictive disorder in a 12-month period (Sussman et al., 2011) with
lifetime prevalence estimated to be even higher, further demonstrating the relevance of
continuing intervention, prevention, and research.
Technology and its Addictive Potential
There is no denying that, for many, modern technology has provided considerable
benefit. Specifically, the rapid expansion of the Internet has provided health benefits that include
increases in global access to health information, connection and communication, and a platform
for advocacy (Gatto & Tak, 2008; Levy & Strombeck, 2002). However, like any other activity,
technology in excess may become problematic, particularly if it begins to disrupt sleep, promote
social isolation, cause personal neglect, effect employment, and exacerbate other physical,
mental, and developmental issues (Andreassen et al., 2016; Emelin et al., 2013; Grüsser et al.,
2006; King et al., 2012; Mei et al., 2018). Early research exploring the addictive potential of
technology defined this phenomenon as a behavioral/non-chemical addiction that involves
human machine interaction (Griffiths, 1995). This definition has expanded to encompass a range
5
of technologies including gaming, social media use, pornography, smart phone use, and internet
use which, similar to substance related addictions, may involve a compulsion to continually
engage despite consequences to health—physical, mental, social, or otherwise (Pan et al., 2020;
Sharma et al., 2017; Sun & Zhang, 2020; Young, 2017; K. S. Young, 2009; Yu & Sussman,
2020).
As devices, content, and internet use become increasingly pervasive in daily life, there is
arguably an increase in potential for addictive behaviors because of nearly constant access to
activities like online gambling and video games. Researchers measuring internet and technology
addictions have noted that an overall increase in prevalence may simply reflect a pattern of
increasing human-machine interaction (Pan et al., 2020). Additionally, there may be unique
differences in specific technologies and use, given that advances such as the Internet may be a
medium to fuel other addictions (Starcevic & Aboujaoude, 2015), leading to suggestions that
future research not simply consider general technology or internet use but specific activities
(Starcevic, 2013) or possibly problematic use (Spada, 2014). Future research can deliver more
clarity to the current debate by incorporating activities not usually specified in past addiction
research, such as using a smart phone or computer to gamble, that would better help to determine
a distinction between addiction or medium (Griffiths et al., 2016). Regardless of stance,
researchers on both sides acknowledge that the addictive potential of technology is real and
given the increased integration of technology into contemporary life, addictive behaviors may
continue to increase. Given the conflict described, and the limited consensus, many who research
technology addictions have moved to assessing specific technology use behaviors or
“excessive/problematic” use of technology (Yellowlees & Marks, 2007). This is a trend also seen
6
in substance use studies with increasing amounts of research investigating the consequences of
use, excessive use, or problematic use (Grigsby et al., 2016; Macleod et al., 2004).
Studies in Specific and Problematic Technology Use
Technology, in its many modes and ever-expanding presence, affords numerous
opportunities for addictive potential. Some research has focused on the device such as computers
(Young et al., 1999), video games (D Griffiths et al., 2012), or even smart phones (Kwon et al.,
2013) and others have focused on potentially addictive domains of internet use such as cybersex,
cyber relationships, net compulsions, and information overload (Young et al., 1999).
Social Media
Since the emergence of early platforms in the 1990s (Edosomwan et al., 2011), social
media use in the United states has rapidly permeated the consumer market and culture. It is
estimated that over 80% of young adults engaged in use of social media platforms in 2014 and
just a few years later this estimate increased to over 90%. (Communications et al., 2016; Villanti
et al., 2017). Today social media use (social interaction through electronic platforms such as
Facebook, Instagram, TikTok, Twitter, YouTube, etc.) is ubiquitous and has become an
important element in shaping the developmental process for youth and young adults as they
interact with others (Berryman et al., 2018; Michikyan & Suárez-Orozco, 2016; Subrahmanyam
& Smahel, 2010). There are positive benefits that have stemmed from the emergence of social
media including exposure to current events, interpersonal connection, and enhancement of social
support networks (Communications et al., 2016; Riehm et al., 2019). However, there is growing
concern about the presence of social media in young peoples’ lives and the potential adverse
effects. These concerns are not unfounded given that a person’s online presence is connected to
their offline world and a potential factor in young peoples’ exploration and development of
7
identities, intimacy, and well-being (Michikyan & Suárez-Orozco, 2016). Given the many ways
to measure social media use, there have been some mixed results when researching potential
health drawbacks (Orben, 2020). Because of this, it may be more beneficial to assess how one
uses social media (Berryman et al., 2018) rather than simply measuring any use. The clearest
results emerge when assessing problematic use, high levels of use, and when assessing specific
content. A review assessing cyberbullying (much of which can occur on social media platforms)
identified that across studies the average percent of adolescents who were cyberbullied was over
20% (Hamm et al., 2015) and that there was a consistent relationship across studies between
cyberbullying and depression in young people. Above and beyond specific content, addictive use
of social media overall has been associated with lower self-esteem as well as lower life
satisfaction and well-being (Hawi & Samaha, 2017; Wartberg et al., 2020). Higher use (more
time using) of social media may also place adolescents and young adults at heightened risk for
poor sleep health, mental health issues, anxiety disorders, and other internalizing problems
(Graham et al., 2021; Lin et al., 2016; Riehm et al., 2019; Vannucci et al., 2017; Wong et al.,
2020). In consideration of the addictive potential of social media, youth who were restricted from
using social media had greater feeling of anxiety and restlessness when they were not able to
access messages on their social networking applications compared to their counterparts (Bashir
& Bhat, 2017). High use of social media has been associated with greater time distortion
resulting in underestimation of the amount of time on social media platforms and has also been
correlated with social media addiction scores (Turel et al., 2018).
Gaming and Online Gaming
Although “videogame addiction” has become a part of the public lexicon there is still
debate around whether it can actually be considered an addiction and how to distinguish
8
addiction from high engagement gamers (Brunborg et al., 2013; Spekman et al., 2013). However,
evidence has shown that for some, high engagement with videogames can result in much of the
same core criteria for addiction (relapse, withdrawal, conflict, salience, mood modification,
craving, and tolerance) (Brunborg et al., 2013) and that problematic gaming behavior is
associated with physical and social symptoms that are traditionally seen with substance addiction
(Spekman et al., 2013). Similar to gambling, some of the major distinctions between high
engagement gamers and those with gaming addiction can be seen in the psychological
motivation and the meaning and experience of gaming within their lives (Griffiths, 2010), with
some researchers suggesting that video game playing can be described as a non-financial form of
gambling (Griffiths, 1991). Again, making comparisons with gambling, the growth of online
gaming and the advent of new gaming devices, including portable systems and even smart
phones, has increased the availability and ease of access for all ages. Online video game play has
also added more complexity by including online social engagement, customization of online
worlds and characters, the advent of Massive Multi-user Online Role-Playing Games
(MMORPG), and even financial components with in-game purchases (Balakrishnan & Griffiths,
2018; Trepte et al., 2012; K. Young, 2009). Although some argue online gaming may result in
strong social ties and that these ties can have positive benefits (Trepte et al., 2012), others argue
that problematic gaming issues can be exacerbated with some players becoming preoccupied
with gaming, lying about use, losing interest in other activities, withdrawing from family and
friends to game, and using gaming as a means of psychological escape (Leung, 2004; K. Young,
2009). Studies comparing online vs. offline gamers identified that online gamers were more
likely to overuse, have interpersonal conflict, and deal with social isolation (Smohai et al., 2017).
9
Concern has grown for gaming addiction particularly among adolescents and young adults who
have grown up with videogames as a normal part of their formative lives.
Problematic Internet Use
Many times these addictive behaviors can cooccur, with one review estimating that the
amount of cooccurrence between addictions such as substance use, gambling, internet use, sex,
or exercise was about 23% across reviewed studies (Sussman et al., 2011). Sussman and
colleagues also identified that among youth, although 67.2% of the sample was non-addicted,
there was a class of youth dubbed the “Work Hard, Play Hard” group that was particularly
invested in addiction to love, sex, exercise, the Internet, and work (Sussman et al., 2014). This
provides evidence that researchers should not only assess specific behavioral addictions but also
consider the cooccurrence or cumulative potential across behaviors. Broadly, online gaming,
gambling, social media use, and pornography all are included in the idea that there may be an
aspect of internet use that is problematic. No one can deny the benefits that the Internet has
brought the world; however, for some the risks of overuse may also come at a price. Internet
addiction or problematic internet use can be broadly conceptualized as an inability to control
one’s use of the Internet which leads to negative consequences in daily life (Spada, 2014).
Interest in internet addictions has grown dramatically, with studies assessing excessive internet
users with non-excessive users, vulnerable groups of excessive internet use, psychometric
evaluations, case studies, and studies examining the relationship of internet addictions and other
behaviors (Widyanto & Griffiths, 2006). Young adults (Guillot et al., 2016; Sussman & Arnett,
2014) and adolescents (Kuss et al., 2013; Weinstein & Lejoyeux, 2010) may be at particular risk
for problematic internet use especially as younger generations are spending increasing amounts
of time online (Jorgenson et al., 2016; Young & De Abreu, 2010). Internet addiction has been
10
found to be associated with depression and other mental health issues, attention deficit
hyperactivity disorder, psychosocial maladjustment, social isolation, and other substance use
disorders (Guillot et al., 2016; Jorgenson et al., 2016; Kormas et al., 2011; Kuss et al., 2013;
Shaw & Black, 2008; Young & De Abreu, 2010). A review of worldwide studies from 1996 to
2018, that considered internet addiction, noted that the prevalence was increasing over time;
however, prevalence varied with different assessment tools (Pan et al., 2020). There are over 45
tools assessing internet addiction, but not all have been evaluated more than once in terms of
their psychometric properties (Laconi et al., 2014). One of the first and frequently used scales is
the Internet Addiction Test (Young, 2016) which has shown high internal consistence and
validity in a variety of populations (Frangos et al., 2012; Widyanto et al., 2011; Widyanto &
McMurran, 2004; Young, 1998). Internet addiction as assessed by the Internet Addiction Test
has been identified as being associated with general health issues, sleep problems, mental health
issues, work/school problems, and other health issues similar to those associated with gambling
disorders (Young, 2016). This may be further exacerbated with the increased prevalence of
smartphones, devices which can provide instant and wide-ranging access to applications that
allow users to watch videos, play video games, surf the Internet, access social networks, and
even gamble online (Samaha & Hawi, 2016). Worldwide, smartphones have become an
important part of typical daily life and many people feel inseparable from their smartphones,
with smartphone use increasing overall (Lepp et al., 2015; Samaha & Hawi, 2016). In addition to
internet addiction, problematic or maladaptive smartphone use has also been associated with
many physical, social, and mental health issues including higher perceived stress, lower life
satisfaction, lower physical activity, poor academic/work performance, and substance use
(Samaha & Hawi, 2016). Some studies have even identified that younger generations, including
11
young adults and particularly adolescents, have even greater prevalence of smartphone addiction
(Haug et al., 2015). Given the increased prevalence of internet and smartphone addiction among
young adults and adolescents and the wide spectrum of internet addictions, more research is
needed to consider all the preceding problematic online behaviors and the potential consequences
of these addiction.
Gambling and Online Gambling
Across the US, gambling is widely viewed as a socially acceptable form of recreation and
a majority of recreational participants can engage without consequence; however, for some
individuals gambling can be an addiction with negative physical, social, mental, and financial
consequences (Calado & Griffiths, 2016; Kourgiantakis et al., 2013). While first classified as
more of an obsessive-compulsive spectrum disorder, pathological gambling is now more
commonly considered a form of nonpharmacologic behavioral addiction and when identified in
young people can be associated with impulsivity issues similar to pharmacologic addictions
(Blanco et al., 2001; Chuang et al., 2017; Gainsbury, 2015). The growth and expansion of the
Internet marked a key shift for gambling. Previously an activity that was relegated to specific in-
person interaction, it became widely available through an easily accessed immersive interface
(Gainsbury, 2015). Internet or online gambling has had rapid growth across the world and is
continuing to increase (Gainsbury, 2015; Lycka, 2011). The expansion of availability and access
to internet gambling has generated concern about the potential increase in rates of disordered
gambling among adults and youth (Gainsbury & Wood, 2011; Gainsbury, 2015; Impact &
Commission, 1999). Evidence suggests that as availability of gambling opportunities increase
there may be a subsequent increase in gambling related problems and that the access provided by
internet gambling could even exacerbate this issue (Gainsbury, 2015). In addition to availability,
12
without physical money (cash) online gamblers find it easier to spend more money contributing
to the addictive potential and the consequences (Griffiths & Parke, 2002; McCormack &
Griffiths, 2012). At greatest risk are those who engage in gambling both online and off line, and
these higher rates of gambling involvement can be tied to higher problem gambling prevalence
(Wardle et al., 2011). For young adults and adolescents, the ease of access provided by online
gaming can also be detrimental. The immaturity of frontal cortical and subcortical
monoaminergic systems during normal neurodevelopment may contribute to adolescent
impulsivity and increase vulnerability to addictive behaviors such as problematic gambling
(Chambers & Potenza, 2003). Given the detrimental effects and ease of access there is good
cause of concern about young adults and adolescents gambling and the potential for behavioral
addiction.
Addiction and Substance Use Disorders
Substance abuse can be examined along a continuum with some being disease-free,
although they may still have maladaptive behaviors as normal functioning may be disrupted
(Sussman & Ames, 2008). Some individuals may progress to the end of the continuum and lose
control over use. As adaptive mechanisms fail there can be negative consequences of
engagement (Sussman & Ames, 2008). In studies assessing pharmacologic addictions there has
been an increase in attention to the deleterious effects of alcohol and other substances,
physically, mentally, and socially (Grigsby et al., 2016; Macleod et al., 2004). Similar to non-
pharmacologic addictive behaviors, general use of a substances (e.g., alcohol and drugs) does not
always infer negative consequences; however, problematic use (including abuse and
dependence) may. Given that risk for cooccurring addictions are high and that general use may
not indicate problematic use (Sussman et al., 2014; Sussman et al., 2011), it is important to better
13
understand pharmacologic addictions and non-pharmacologic addictive behaviors along with the
proliferation of problematic of use.
Although there are limitations and biases to the disease concept of addiction such as the
difficulty in verifying the existence of the disease and the considerations that use may be both a
symptom and a factor of the disease (Sussman & Ames, 2008), the DSM-4 and DSM-5 provide a
formal criteria used by clinicians and researchers to determine disordered use (American
Psychiatric Association, 1994, 2013). These diagnostic manuals use social, legal, and
environmental consequences of use to establish concepts of disorder. Given that there are many
circumstances that may exacerbate issues considered in these clinical criteria for diagnosis,
including physiologic processes, susceptibility to the effects, trauma and stress, and an
individual’s degree of behavioral regulation (Sussman & Ames, 2008), it is important to continue
to understand how these factors associated with diagnostic criteria.
Childhood Trauma – Adverse Childhood Experiences
Adverse childhood experiences (ACE) are highly correlated negative events occurring
before the age of 18, that include child maltreatment (e.g. sexual, physical, and verbal abuse) and
household dysfunction (e.g. parental divorce, substance use and household mental illness,
incarceration, and homelessness) (Felitti, Anda, Nordenberg, Williamson, Spitz, Edwards, Koss,
et al., 1998). ACE can have long-term effects on mental and behavioral health and are among the
most consistent and robust predictors of poor health outcomes, including pharmacologic
addictions and non-pharmacologic behavioral addictions (Forster, Rogers, Sussman, Yu, et al.,
2021; Forster, Rogers, Sussman, Watts, et al., 2021; Hughes et al., 2017; Ng & Wiemer-
Hastings, 2005; Sharma & Sacco, 2015), and individuals with a history of ACE tend to have
significantly more life course health problems that contribute to premature morbidity and
14
mortality (Hughes et al., 2017). ACE have been found to have a strong impact on health and
wellbeing (Felitti, Anda, Nordenberg, Williamson, Spitz, Edwards, Koss, et al., 1998). To add to
the concern, based on brain disease model research that draws attention to social and
environmental contributions to addiction, greater ACE in adolescence is likely to increase the
probability of later addiction (Lewis, 2018).
To maintain stability in times of stress (allostasis) the body uses systems such as the
endocrine and immune systems for short-term adaptation; however, chronic activation of these
systems may have detrimental consequences and contribute to allostatic overload (Danese &
McEwen, 2012). ACE can disrupt physiological pathways during development and may result in
cognitive and emotional impairment and a further increased allostatic load (Albott et al., 2018;
Danese & McEwen, 2012; Hughes et al., 2017; Pechtel & Pizzagalli, 2011), exacerbating an
already problematic response. These cognitive and emotional deficits can contribute to emotional
dysregulation and disrupted attachment, which can contribute to increased vulnerability for
maladaptive coping behaviors as well as increasing cravings for addictive behaviors that may
temporarily limit feelings of distress (Felitti & Anda, 2010; Forster et al., 2017; Gilbert, 2009;
Grant & Chamberlain, 2016; Pollak et al., 2000). Since the initial establishment of the ACE
framework by the seminal Kaiser Family Foundation study (Felitti, Anda, Nordenberg,
Williamson, Spitz, Edwards, Koss, et al., 1998), there has been a growing interest in the impacts
of ACE on addictive behaviors. Although considerable research has been conducted assessing
ACE and substance use behaviors as well as ACE and various health risk behaviors (Hughes et
al., 2017; Kalmakis & Chandler, 2015), more research is needed to assess the relationship
between ACE and other behaviors with addictive potential beyond substance use.
15
ACE and Addictive Behaviors
Compared to pharmacologic addictions, there have been significantly fewer studies
assessing the associations between ACE and other potentially addictive behaviors (Jackson et al.,
2021). Considering online behaviors specifically, recent research has shown that heavy digital
media use was three times higher among adolescents experiencing greater amounts of ACE
(Jackson et al., 2021) and that ACE has also been associated with problematic media use (Wilke
et al., 2020). Higher levels of ACE are associated with internet addiction in adolescents (Lee et
al., 2016) and young adults (EŞKİSU, 2021; Forster, Rogers, Sussman, Watts, et al., 2021; Lu et
al., 2020). Similarly, higher levels of ACE are associated with problematic smartphone use in
young adults (Forster, Rogers, Sussman, Yu, et al., 2021; Forster, Rogers, Sussman, Watts, et al.,
2021; Li et al., 2020). In adult populations, ACE has also been associated with
problematic/excessive online gaming (Grajewski & Dragan, 2020) and problematic/disordered
gambling (Felsher et al., 2010; Lane et al., 2016; Poole et al., 2017; Sharma & Sacco, 2015).
Compared to adult populations, far less research has explored problematic gambling specifically
in young adult and adolescent populations (Lane et al., 2016). One study in adolescents noted
differences seen between proportions of non-gamblers, social-gamblers, at-risk-gamblers, and
problematic gamblers across stressful life experiences; however, they were not modeled together
with other covariates and the differences were not further explored (Dickson et al., 2008).
Compared to studies assessing the frequency of use in pharmacologic addictions, far
fewer studies have assessed the association between ACE and the problematic substance use or
clinical disorder. Within young adults ACE has been shown to be associated with alcohol-related
negative consequences (e.g. Brief Young Adult Alcohol Consequences Questionnaire
(BYAACQ)(Kahler et al., 2005)) and alcohol related problems (e.g. Rutgers Alcohol Problem
16
Index (RAPI)(White & Labouvie, 1989) (Brett et al., 2018; Espeleta et al., 2018; Goldstein et al.,
2010; Shin et al., 2015; Shin et al., 2018). ACE has been associated with alcohol and drug
dependence in adulthood (Fuller-Thomson et al., 2016; Giordano et al., 2014; Koss et al., 2003;
LeTendre & Reed, 2017; Pilowsky et al., 2009). One study was found to be assessing the
association of ACE and substance use disorders in adolescents and concluded that exposure to
multiple ACE was associated with substance use disorders (Gomez et al., 2018). The
associations between ACE and clinical criteria for alcohol and drug disorders highlight the
importance of trauma-informed care, the need for more research in adolescent populations, and
the need for screening as well as research identifying effective strategies that may buffer this
relationship (Chandler et al., 2018).
Critical Age Groups
From birth through young adulthood cortical growth and remodeling continues, denoting
this period as integral to brain development, synaptic rearranging, and behavioral learning
(Crews et al., 2007). In adolescence specifically, much of the final development occurs which
can solidify behaviors and contribute to refinement of reasoning, goal and priority setting,
impulse control, and evaluating long and short term rewards (Crews et al., 2007). Adolescence is
a critical period with the potential for greater repercussions from pharmacologic addictions and
non-pharmacologic behavioral addictions, as these can be engrained into typical behavior and
establish lifelong habits (Crews et al., 2007; Essau & Delfabbro, 2008; Lewis, 2018; O'Connor,
1996; Sussman, 2007; Tervo-Clemmens et al., 2020). Although less brain development is
occurring post adolescence, young adulthood (a developmental period that has also been referred
to as emerging adulthood), is also a significant period for addiction research (Sussman & Arnett,
2014). In addition to the transformative, dynamic nature of this stage, emerging adulthood is
17
characterized by an increase in risk for addictive behaviors compared to other life stages (Bose et
al., 2018; Hedden et al., 2015). This is also a period of extended learning and experimentation, in
addition to an increased independence that can establish lifelong patterns of behavior and set a
precedent for relational and career trajectories (Sussman & Arnett, 2014). Many addictions are
established in either adolescence or young adulthood and can lead to life course morbidity and
mortality, making these periods integral for research, prevention, and treatment.
One of the ways that adolescence is structurally different than young adulthood with
regard to ACE is that adolescence is a period where there may be active ACE stressors. This has
both prevention and developmental implications. From a prevention paradigm ACE can be
identified and individuals may be removed from the exposure or taught strategies to mitigate the
effects of ACE stress. From a developmental standpoint it is possible that the earlier the ACE
stress can be mitigated the lower the “time dose” of ACE may be and thus the effects of ACE
may be partly attenuated. This is an important area for future research and more studies should
consider differences in childhood vs adolescence ACE exposure.
The Differential Impacts of ACE Across the Life Course
Adolescence
In the U.S. more than half of adolescents are ACE exposed and therefore may have
unique developmental needs that must be addressed by the health, education, and social
welfare systems that serve them (Soleimanpour et al., 2017). Within adolescence ACE
exposure can have implications specific to the adolescent period including increased risk for
poor learning in school and behavioral issues (Soleimanpour et al., 2017). During adolescence
there are challenges to capturing ACE information because adolescents may have
18
confidentiality concerns and concerns about mandatory reporting of information such as abuse
events. There is also a unique opportunity during adolescence for schools and school systems
to provide screening tools and trauma-informed interventions at a period where students are
mandated to attend school (Soleimanpour et al., 2017). This period is also particularly
challenging for some minority groups including sexual minorities who may face heightened
risks along with both ACE stress and discriminatory stressors (Soleimanpour et al., 2017).
These effects within sexual minority youth may be particularly pronounced in adolescent
populations relative to older populations (Schuler et al., 2018). In adolescence compared to
childhood, research has identified that impaired functioning in the prefrontal cortex may be
particularly detrimental in this period when social and cognitive demands increase (Croff &
Beaman, 2021). Adolescent populations experiencing ACE may have later life health effects
that can be expressed differently from the immediate effects of stress. Adversity in adolescents
results more in somatic concerns and acute mental and physical health issues compared to
chronic conditions in later life (Flaherty et al., 2013). Specifically, child maltreatment and
other ACE in adolescence is associated with depressed mood, anxiety, posttraumatic stress
disorder symptoms, risk-taking behavior, early pregnancy, eating disorders, weight problems,
substance use, sexually transmitted disease treatment, and suicide attempts (Bair-Merritt et al.,
2006; Boynton-Jarrett et al., 2008; Fiscella et al., 1998; Hussey et al., 2006; Margolin et al.,
2010; Mechanic & Hansell, 1989). Compared to adult populations there are substantially
fewer studies that examine the relationship between ACE and health in adolescents (Flaherty
et al., 2013).
About ACE and Addictions in Adolescence
19
In 2020, national prevalence estimates for past year illicit substance use in adolescents,
not including marijuana, was 11.4% for 12
th
grade, 8.6% for 10
th
grade, and 7.7% for 8
th
grade. Marijuana past year prevalence for 12
th
, 10
th
, and 8
th
grade was 35.2%, 28.0%, and
11.4%, and alcohol past year use was 55.3%, 40.7%, 20.5% (Johnston et al., 2021). Review of
past studies has also identified that up to 12.3 % of youth meet criteria for problem gambling
(Calado et al., 2017; Moreno et al., 2011). Although much less research has been seen
exploring the relationship between ACE and addiction in adolescents compared to adults,
ACE is associated with higher gambling and substance use during adolescence (Marchica et
al., 2020) and is also associated with substance use disorders, earlier drug and alcohol
initiation, and more severe drug use (S. R. Dube et al., 2006; Gomez et al., 2018).
Young Adulthood
Young adulthood, typically ranging from ages 18 to 29, is the period that marks the
transition from adolescence to adulthood. The risk and prevalence of substance use peaks during
this phase and substance use can interfere with achieving developmental milestones, research
that can inform effective prevention efforts targeting young adults is critical (Merline et al.,
2004). Although behaviors like alcohol (Shanta R Dube et al., 2006) and smoking initiation
(Anda et al., 1999) may begin during adolescence, childhood stressors can complicate successful
transitions to young adulthood, an already vulnerable period for substance use and related
negative outcomes (Davis et al., 2018). This disrupted development can set the stage for lifelong
patterns of maladaptive behaviors and as such, trauma informed prevention research specific to
this developmental period is necessary (Schulenberg et al., 2020b; Schwartz et al., 2005;
Sussman & Arnett, 2014). One of the ways that this period is distinct from adolescence is that
during this period many young adults gain new independence from households where they may
20
have previously experienced ACE. In addition to their newfound independence there are new
social and societal pressure including financial, schooling, and work expectations that they may
not have had to previously endure. These stressors can compound with prior stress from adverse
events and have an impact on young adult health. Specifically, in young adults ACE has been
associated with higher levels of depressive symptoms, ADHD symptoms, cigarette use, alcohol
use, marijuana use, and BMI, in addition to lower levels of fruit and vegetable intake, and sleep
(Windle et al., 2018). In young adults ACE is also associated with increased risk for poor health
generally as well as with early chronic conditions in young adulthood (Sonu et al., 2019).
About ACE and Addictions in Young Adulthood
In 2020, national prevalence estimates for past year substance use in young adults ages
19-30 was 19.1% for any illicit drug (other than marijuana), 39.9% for marijuana, 82.1% for
alcohol and, 67% for past two week binge drinking (Schulenberg et al., 2020a). Considering
gambling, 68% of U.S. young adults have gambled in the past year, and 11% had gambled
more often than twice per week (Welte et al., 2008). Internet addictions are also of concern in
this population with the prevalence of problematic internet use, based on DSM-IV criteria for
substance use, ranging from 9.8% to 15.2% (Moreno et al., 2011).
Large numbers of young adults have exposure to adverse childhood experiences and
multiple ACE are particularly associated with addiction within this developmental period
(Shin et al., 2018). ACE has been associated with young adult substance use, polysubstance
use, and risky use of alcohol such as binge drinking (Davis et al., 2021; Forster et al., 2018).
ACE is also associated with addiction problems and later substance use disorders in young
adults (Leza, Siria, López-Goñi, & Fernández-Montalvo, 2021). Although there is much less
21
research, ACE has also been associated with problematic gambling in young adults (Felsher et
al., 2010) as well as problematic internet use and problematic use of other technologies such
as smartphones (Forster, Rogers, Sussman, Watts, et al., 2021).
Subgroup Differences in the Relationship Between ACE and Addiction
Since ACE research is still a growing field, there is far less subgroup research in ACE–
Health relationships compared to other risk factors for morbidity and mortality. This is especially
true for harder to reach subgroups, such as sexual minorities and some ethnic minority groups,
who typically less represented in community surveys. Reviews of ACE literature typically call
for disaggregation of demographic factors to further explain the nuance of the ACE–Health
relationships on a more intricate level (Carlson et al., 2020).
Minority stress theory is one model that seeks to explain the implications of an array of
stressors that are related to one’s minority status (e.g. racial/ethnic minority or sexual-minority)
that stem from stigma and prejudice (Meyer, 2003, 2012). The theory posits that minority
populations experience excess stress related to stigma and prejudice related to their
disadvantaged social position and that similar to ACE, these chronic stressors have been
associated with adverse health outcomes (Meyer, 2003, 2012). Given that minority stress and
ACE provide undue stress, it is important to consider populations that may experience multiple
sources of stress throughout their lives. These stressors may exacerbate the effects of ACE on
addictions.
Considering the effects of ACE on racial/ethnic minorities, more ACE is positively
associated with the likelihood of developing a substance use disorder (Bryant et al., 2020). The
differential exposure to stressors experienced by racial/ethnic minorities, such as those caused by
22
persistent discrimination or disadvantage on the basis of race, may also contribute to these
disparities (Strompolis et al., 2019). Prior research into specific racial/ethnic differences in the
relationship between ACE and substance use has identified that within young adults, higher ACE
was associated with an increase in the odds of past 30-day alcohol use for ethnic minorities but
not for Non-Hispanic White students, and increases in ACE were associated with a significant
increase in the odds of marijuana use among all groups, with the exception of African American
students (Forster et al., 2018). ACE was also associated with higher odds of illicit drug use
among all groups except for Hispanic, African American, or Multiracial respondents and
prescription medication misuse was also associated with increased ACE for non-Hispanic
Whites, Hispanics, and Asian/Pacific Islanders (Forster et al., 2018). Considering problem
drinking, more ACE was associated with higher probability of binge drinking for African
American/Black students and Hispanic students relative to their peers (Forster, Rogers, et al.,
2019). Race/ethnicity also moderated the relationship between ACE and heavy drinking with
African American/Black individuals about three times as likely to drink heavily compared to
other racial ethnic groups, and Hispanic individuals who experienced household challenges or
both ACE types were also more likely to report heavy drinking (Lee & Chen, 2017). Taken
together these results suggest that the relationship between ACE and substance use may vary
across racial ethnic background and that the effects of ACE may have a pronounced effect on
ethnic minority substance use behaviors and similarly by extension, the possibility of problem
gambling behaviors.
There are also differences in the relationship between ACE and gambling across racial/ethnic
populations. Broadly, ACE have been identified as increasing the risk for developing problem or
disordered gambling in adulthood, however, cultural differences and experienced stress may
23
further impact this relationship (Chee & Lui, 2021; Lane et al., 2016). However, research into
these effects is extremely limited and to my knowledge has not assessed ethnic differences in the
relationship between ACE and Problem Gambling.
Similar to racial/ethnic minorities, many sexual-minority populations, particularly younger
individuals, experience excess chronic stress related to stigma and prejudice around their sexual
minority status due to membership in a marginalized social group (Meyer, 2003, 2012). This
pathway from stress to morbidity is similar to ACE and can provide even more stress upon
sexual minority individuals at critical developmental stages such as childhood and adolescents.
These experiences, stressors, and internalized stigma may elevate the risk of addictions (Meyer,
2003, 2012). Prior research has shown a link between individuals living in a state with same-sex
marriage prohibition and an increased risk for substance use disorders; however, more recent
evidence has shown that legalization of same-sex marriage significantly reduced negative mental
health outcomes for sexual minority adolescents (Schuler et al., 2018). However, there are no
studies in adolescents that assess differences in the relationship between ACE and substance use
disorders or gambling by sexual orientation.
Social Support
Social support is a critical function of social relationships and the intention of support is
to aid or help which distinguishes supportive interactions from other negative interactions (Glanz
et al., 2008). Across studies and disciplines there is general consensus that social support
moderates stress and as such may also moderate the health consequences of stress (Jacobson,
1986). Even with this broad level of agreement, there is much less information about the causal
mechanisms for this change and some have sought to explain this through exploration of
24
different types of support. There are many different theories of stress, and many have identified
how social support may fit into this paradigm. Some of these models focus on needs that are met
with social support, transactions that occur when demands exceed resources, and transitions
between losses and gains that require a stress buffer (Jacobson, 1986). Many of these models are
tied to the idea that support, when provided in a timely manner, can attenuate stress.
Perceived Social Support
Perceived social support is a measure of how an individual perceives support from
different sources such as family members or peers and how those sources are available to
provide emotional, informational, instrumental, or appraisal support (Ioannou et al., 2019;
Jacobson, 1986). Simply put, it is the perceptions of help received from others, which has
consistently been identified as related to health and well-being (Ioannou et al., 2019; Siedlecki et
al., 2014; Uchino et al., 2012). Received support is the quantity of supportive behaviors received
by an individual (Haber et al., 2007). Both of these are interrelated; however, many times
perceptions relating to support received are based not on the actual number of times support is
given but specifically on the number of times it was received relative to the number of times they
have needed it (Melrose et al., 2015). Someone can have needs met but still not feel supported.
Social Support and Health
Social support, a complex construct that can take many forms, broadly is a network or set
of people who can provide psychological and material resources to assist in an individual’s
capacity to cope with stress (Cohen, 2004; Hendrick & Hendrick, 2000). Research concerning
support networks has long-noted an association between social relationships and health and less
isolated individuals have been identified as having a tendency to be more healthy (House et al.,
25
1988). Specifically, social support has been recognized as a positive factor in mental and
physical health outcomes and an important factor for mitigating the effects of trauma and stress
(Harandi et al., 2017; Sippel et al., 2015; Taylor, 2007). It has been proposed that seeking social
support may decrease maladaptive coping, suggesting that it can be an important aid in addiction
prevention, particularly in younger populations, decreasing the utilization of anger coping
(Galaif et al., 2003). Concordant with attachment theories, high social support may promote
behaviors that improve stress-regulation, increase confidence, decrease engagement in risky
behaviors, and promote healthy and effective coping strategies (Sippel et al., 2015). Specifically,
stable and supportive social relationships may buffer the activation of the stress response system
in childhood stress events and experiences by protecting noradrenergic systems and reducing
dysregulation of the hypothalamic pituitary adrenal axis and buffering serotonin transporter and
brain-derived neurotrophic factor gene polymorphisms (Ozbay et al., 2008; Sippel et al., 2015).
Social Support and Adverse Childhood Experiences
Research investigating the intersection of trauma, addiction and social support has
identified social support as a resiliency factor, buffering the relationship between trauma and
addictive behaviors (Caravaca-Sánchez & Wolff, 2020; Gros et al., 2016). However, there is
variability in results with some studies noting that after controlling for covariates, social support
was not enough to reduce the outcomes of trauma (Pinto et al., 2017). Specifically, social support
has been seen as a protective factor for substance use and substance use disorders in adults
(Caravaca-Sánchez & Wolff, 2020; Gros et al., 2016) as well as for gambling related problems
among adolescents (Hardoon et al., 2004).
26
Much of ACE, social support, and health research has been conducted on mental health
outcomes. Studies have shown that ACE exposed individuals with poor social support to be at
greater risk of depressive symptoms (Von Cheong et al., 2017), suicidal ideation (Wan et al.,
2019), and general mental health problems (Karatekin & Ahluwalia, 2020). Other health factors
have been investigated such as sleep. Study has identified that social support may attenuate the
relationship between ACE and sleep disturbances (Kalmakis & Chandler, 2015). One of the
reasons why I decided to consider this relationship in the context of addiction is that there is a
dearth of literature assessing social support as a moderator in the relationship between ACE and
substance use, gambling, and problematic internet use. One of the only studies I could identify
was one assessing problematic smartphone use and how extrafamilial social support may offset
the negative effects of household dysfunction in young adults (Forster, Rogers, Sussman, Watts,
et al., 2021). This study does provide some evidence that a similar relationship may be seen in
social support and other behaviors with addictive potential.
Despite the benefits seen in social support on the effects of trauma there is limited
research specifically assessing social support as a buffer in the relationship between ACE and
behaviors with addictive potential. Contextualizing this relationship across different age groups,
racial/ethnic groups, and other subpopulations can be critical to providing tailored treatment
approaches in prevention and cessation efforts for addictive behaviors.
Gaps in the Literature
1. There is a well-established association between ACE and substance use behaviors;
however, less is known about the relationship between ACE and other behaviors with an
addictive potential such as gambling and internet addiction.
27
2. In the body of literature exploring ACE and alcohol and drug use behaviors, there is
limited research focused on the relationship between ACE and clinical criteria for abuse
and dependence and no research assessing this in adolescent populations.
3. In addition to clinical criteria, there is limited research exploring the potential
relationship between ACE and the problematic use of alcohol and drug use and no
research assessing this longitudinally from adolescence through young adulthood.
4. Much of the research assessing the relationship between ACE and addictive behaviors
adopts a risk paradigm with far less research considering the protective effects of
environmental and developmental assets such as social support and whether they buffer
the ACE – addiction relationship.
5. Despite the benefits of social support evidenced in health research, little to no research
assesses the role of social support in the relationship between ACE and behaviors with
addictive potential such as gambling and problematic internet use. The same can be said
for the relationship between ACE and clinical criteria for substance use and problematic
use of substance use.
6. Due to lower power, explorations of the association of ACE and addictive behaviors as
well as the moderating effects of protective factors such as social support have not been
investigated stratified across at-risk population subgroups such as ethnic and sexual
minority youth, a critical component to establishing tailored prevention efforts.
28
Overview Of the Studies
This series of studies examined several aspects of the relationship between adverse
childhood experiences (ACE) and addictive behaviors beyond the conventional measures of
substance use frequency. Since these relationships are seen in typical substance use measures,
further confirmation of these associations would help to continue to establish similarities
between substance use addictions and other behaviors with addictive potential such as gambling,
problematic internet use, problematic alcohol and drug use, and alcohol and drug use disorders.
Given the substantial amount of evidence that has been established since the original ACE study,
it is imperative that we continue to investigate beyond the risk framework for the purpose of
identifying environmental and developmental assets that can buffer the ACE – addiction
relationship and be integrated into prevention and cessation models. Social support, widely seen
as critical protective factors in mental health outcomes has been identified as a potential buffer in
the ACE – addiction relationship (Hubbard, 2021; Uchino, 2006). Social support, however, has
been an inconstant moderator in these relationships (Forster et al., 2020; Hubbard, 2021). The
overarching goal of the current project is to explore the buffering effect of social support in the
ACE – addiction relationship across specific subpopulations including understudied age groups
(adolescents and young adults) and ethnic and sexual minorities. Exploration of these subgroups
may further clarify the inconsistencies and inform intervention and prevention efforts of not only
a potential critical asset, but also identify how a tailored approach may further bolster the
effectiveness of social support.
Study 1 Overview
The aims of the first study were to explore the protective effect of social support in the 1)
ACE – alcohol and drug disorder relationship and 2) ACE – problematic gambling relationship
29
across ethnic and sexual minority statuses in a large regionally representative population of
adolescents. This study used a sample of ninth and eleventh grade public high school students in
Minnesota (n=62,142) who completed the Minnesota Student Survey (MSS) in 2019 (Minnesota
Student Survey Interagency Team, 2019). To meet these aims, first, generalized linear models
regressed ACE on alcohol and drug use disorder criteria and problematic gambling criteria,
controlling for demographics (grade level, biological sex, and SES). A second series of indirect
effect regression models included an interaction term of ACE*social support. Finally, three-way
interaction term models assessed the relationship between ACE*social support by ethnic and
sexual minority status (e.g., Heterosexual, Bisexual or Pansexual, and Gay or Lesbian) to
identify differences in the relationship across subgroups.
Study 2 Overview
The aims of the second study were to longitudinally examine 1) the impact of ACE on
trajectories of problematic alcohol and drug use from adolescence to young adulthood and 2)
explore potential differences in the relationship between ACE and problematic use trajectories
across levels of social support in adolescence. This study used a cohort sample (n=1,404) of high
school students from predominantly Hispanic schools in Southern California who completed
surveys in the final year of high school and into young adulthood (survey years 2009, 2011,
2013, 2014, and 2016). Data are drawn from the Reteniendo y Entendiendo Diversidad para
Salud (RED) study data (Unger, 2018; Unger et al., 2009). To meet these aims, random linear
growth curve models, using SAS PROC MIXED, estimated problematic alcohol and drug use
criteria trends across time using random slopes, random intercepts, and the fixed effects of ACE
and demographic covariates. The first model included an interaction term to determine the cross-
level effect of ACE with time on alcohol and drug use consequences (ADUC). A second model
30
included a three-way interaction, and all lower interaction terms, ACE*Problematic Use*Social
Support.
Study 3 Overview
The aims of the third study were to examine the relationship between ACE, hours of
social media use and online gaming and problematic internet use among young adults and the
potential moderating effect of social support. This study used a sample of young adult college
student sample (n=1,166) from a large, diverse public university in Southern California in
(2020). Data are drawn from Student Use of Internet and Technology (SUIT) study. To meet
these aims, first, direct effects models assessed regressed problematic internet use on ACE,
social media hours, gaming hours, and social support. A second set of models included two-way
interactions between ACE*social media hours and ACE*gaming hours. Third, an interaction
term models assessed if these relationships may be moderated by social support by including a
three-way interactions (ACE* social media hours*social support) and (ACE*gaming
hours*social support).
31
Figure 1. Conceptual Framework of the Three Proposed Studies
32
Chapter 2: Minority Status and the Relationship Between Adverse Childhood Experiences
and Addictive Disorders: The Moderating Role of Social Support
Introduction
The prevalence of illicit drug use among adolescents in the United States (U.S.) has
increased since the early 1990s with 21.3% of 8
th
graders, 37.3 % of 10
th
graders, and 46.6% of
12
th
graders recently reporting any lifetime use in 2020 (Johnston et al., 2021). Although the
prevalence of alcohol use has declined since the early 1990s, alcohol is still the most prevalent
substance used with 25.6% of 8
th
graders, .46.4 % of 10
th
graders, and 61.5% of 12
th
graders
recently reporting any lifetime use (Johnston et al., 2021). Compared to other age groups,
adolescent populations may show fewer physical consequences of substance use because some
physical problems take time to develop; however, heavy substance use is a key risk factor for
dependence later in life (Sussman et al., 2008) and has been associated with negative
developmental and psychological changes in youth (Squeglia & Gray, 2016). Therefore,
investigating the consequences of alcohol and drug disorders in adolescents is an important line
of research (Czechowicz, 1988; Sussman et al., 2008). Although the implications of disordered
substance use in adolescents are serious, there are far more studies assessing frequency and risk
factors among adults than youth populations. Among U.S. adolescent substance users, it is
estimated that 7.3% meet the clinical definition (DSM-5 criteria) of substance abuse and 12.7%
meet the definition of substance dependence (Han et al., 2017), two dimensions of alcohol and
drug disorders. The DSM-5 has combined the elements of abuse (such as hazardous use, neglect,
legal issues, and social problems due to use) and dependance (such as withdrawal, tolerance
repeated attempts to quit, need for more, physical problems and activities given up due to use) to
indicate a general substance use disorder (American Psychiatric Association, 2013). Since youth
33
populations are at great risk for future deleterious effects of alcohol and drug use, researchers
have recommended an increased focus on adolescents who meet criteria for disorders, as well as
frequency of use (Han et al., 2017; Skala & Walter, 2013).
Adverse childhood experiences (ACE) are a set of highly correlated, early life stressors
that include child maltreatment (e.g., sexual, physical, and verbal abuse) and household
dysfunction (e.g., parental divorce, substance use and household mental illness, incarceration,
and homelessness) (Felitti, Anda, Nordenberg, Williamson, Spitz, Edwards, Koss, et al., 1998).
ACE are among the most consistent and robust predictors of poor health outcomes and premature
morbidity and mortality (Forster, Rogers, Sussman, Yu, et al., 2021; Forster, Rogers, Sussman,
Watts, et al., 2021; Hughes et al., 2017; Ng & Wiemer-Hastings, 2005; Sharma & Sacco, 2015).
ACE have been associated with substance-use frequency and problematic use across populations;
however, compared to research conducted among adult populations, there are fewer studies
examining the relationship between adverse childhood experiences (ACE) and health outcomes
in adolescent populations (Hughes et al., 2017), and even less focused specifically on alcohol
and drug use abuse or dependance (Hughes et al., 2017; Leza, Siria, López-Goñi, & Fernandez-
Montalvo, 2021). Although less studied, chronic adversity (ACE) in childhood is an important
risk factor in alcohol and drug disorders (Enoch, 2011) independent of a family history of use.
ACE may increase vulnerability to dependence through effects on the expression of genes within
the dopamine-reward pathway, and adolescent onset of alcohol and substance abuse may further
exacerbate these negative effects on the developing adolescent brain (Enoch, 2011). Although
ACE has been associated with an increased risk of adolescent substance use disorders (Gomez et
al., 2018), compared to adult populations very few studies have assessed the relationship
34
between ACE and alcohol and drug-use disorder criteria, such as abuse and dependence,
specifically in adolescents (Leza, Siria, López-Goñi, & Fernandez-Montalvo, 2021)
Similar to trends in drug use, the advent of online gambling (any form of gaming or
betting where you can wager and win money) has provided more accessibility, and given the rise
availability and social acceptance, it is no great surprise that youth gambling prevalence rates
have also increased (Calado et al., 2017; Griffiths et al., 2012). Up to 14% of adolescents are at
risk of developing gambling problems and up to 12% of youth are problem gamblers (Calado et
al., 2017; Shaffer & Hall, 1996). The immaturity of frontal cortical and subcortical
monoaminergic systems during normal neurodevelopment may contribute to adolescent
impulsivity and increase vulnerability to problem gambling (Chambers & Potenza, 2003).
Despite the increased vulnerability of adolescents to addictions, there are far fewer studies
assessing adolescent gambling than adult gambling. A review of the available data noted that
there is a need not only for investigation of risk factors but also for demographic differences,
particularly racial/ethnic differences (Blinn-Pike et al., 2010). The few studies that have
examined the associations between ACE and gambling in adolescents have found that the
number of ACE is associated with a higher probability of any gambling (Storr et al., 2012) and
problematic gambling (a subclinical form of gambling disorder defined by increases in negative
consequences) (Bristow et al., 2021).Given the limited research into the association between
ACE and DSM-5 criteria for problematic gambling, as well as ACE and DSM-5criteria for
alcohol and drug use disorder in adolescent populations, high quality research is needed to
explore these relationships that in adult populations.
Minority stress theory is one model that seeks to explain the implications of an array of
stressors that are related to one’s minority status (e.g., racial/ethnic minority or sexual-minority)
35
that stem from stigma and prejudice (Meyer, 2003, 2012). The theory posits that minority
populations experience excess stress related to stigma and prejudice related to their
disadvantaged social position and that similar to ACE, these chronic stressors have been
associated with adverse health outcomes (Meyer, 2003, 2012). Belonging to a minority group
such as a racial/ethnic minority or a sexual minority, may be an additional stressor beyond ACE,
and it is important to consider populations that may experience multiple sources of stress over
the life course such as ACE and being a member of a minority group. These stressors can pose
an important risk within these populations in two distinct ways. First racial/ethnic minorities and
sexual minorities disproportionately experience more ACE and second, the effects of ACE on
racial/ethnic minorities and sexual minorities may be more impactful given the additional
stressors (Andersen & Blosnich, 2013; Bryant et al., 2020). The differential exposure to stressors
experienced by racial/ethnic minorities, such as those caused by persistent discrimination or
disadvantage on the basis of race likely contribute to these disparities (Bernard et al., 2021;
Strompolis et al., 2019). Broadly, ACE have been identified as increasing the risk for developing
addictions such as disordered gambling in adulthood; however, cultural differences and
sociocultural stressors may further exacerbate this relationship (Chee & Lui, 2021; Lane et al.,
2016). Research into these effects is extremely limited and current literature has not assessed
ethnic differences in the relationship between ACE and alcohol and drug disorders or gambling
disorders among adolescents.
One of the methodological challenges in determining the prevalence and associations
between ACE and health among sexual minority populations is that many of these studies
combine gay/lesbian and bisexual populations to preserve statistical power, despite a burgeoning
literature demonstrating that many health risk indicators differ between gay/lesbian and bisexual
36
groups (Andersen & Blosnich, 2013) and that sexual minorities (gay, lesbian, bisexual) report the
highest prevalence of comorbid substance use and mental health disorders relative to non-sexual
minority populations (McCabe et al., 2020). Overall, sexual minority individuals tend to report
elevated rates of substance use behaviors and disorders relative to heterosexuals with minority
stress theorized to contribute to these disparities (Schuler et al., 2018). This effect may be
particularly pronounced for younger populations (Schuler et al., 2018), and evidence suggests
that bisexual individuals may have particularly elevated rates of substance use relative to both
heterosexuals and gay/lesbian individuals (Schuler et al., 2018). Combined, these experiences,
stressors, and internalized stigma can elevate the risk for addictive behaviors (Meyer, 2003,
2012). Given that sexual minority youth often experience increased social stress due to prejudice,
discrimination, harassment, and victimization, the increased stress and pressure may in part
explain the disproportionate use of substances within these populations (Lowry et al., 2017).
However, despite the increased prevalence of ACE, the research has not fully explored the
association between sexual orientation, ACE, and addictive behaviors such as substance use or
gambling.
From a prevention and treatment perspective, identifying factors that contribute to
resilience and protective factors that may play a role in buffering the ACE-addiction
relationships is important. However, not all children who have been exposed to maltreatment
will go on to develop psychopathology (Enoch, 2011) and research has identified several key
ingredients of resilience. Social support, a network or set of people who can provide
psychological and material resources to assist in an individual’s capacity to cope with stress
(Cohen, 2004; Hendrick & Hendrick, 2000), is one positive asset across a spectrum of mental
and physical health outcomes (Uchino, 2006) that can mitigate the effects of trauma and stress
37
(Harandi et al., 2017; Sippel et al., 2015; Taylor, 2007). It has been proposed that seeking social
support may decrease maladaptive coping, suggesting that it can be an important aid in addiction
prevention, particularly in younger populations, decreasing the utilization of maladaptive coping
(Galaif et al., 2003). In adolescent populations, social support can promote positive mental health
outcomes even for ACE exposed individuals (Forster et al., 2020) while in adult populations,
social support can moderate the relationship between ACE and addictive behaviors (Caravaca-
Sánchez & Wolff, 2020; Gros et al., 2016), including substance use (Hubbard, 2021) and
gambling-related problems (Hardoon et al., 2004). Despite the benefits of social support for the
effects of trauma, there is limited research assessing whether social support can offset the
negative effects of ACE for behavioral addictions (Pinto et al., 2017) particularly in adolescents.
In fact, thus far, no study has assessed the potential protective effects of social support in the
relationship between ACE and alcohol and drug disorders in adolescents, highlighting a critical
need for research and study. To continue to build effective prevention and cessation programs,
identifying protective factors is key, and exploring the moderating effect of social support in this
relationship may provide a critical intervention opportunity that can be applied in early life-
phases before disordered use is establish and carries forward over the life course. To identify a
tailored prevention approach, differences in the moderating effect of social support across
demographic subgroup is an important line of inquiry; especially across racial/ethnic groups, age,
and sexual orientation.
Study Aims
To address the gaps in the literature, the current study explored 1) the impact of ACE on
alcohol and drug disorder and problematic gambling behaviors; 2) the buffering effect of social
support on these relationships; and 3) whether these associations vary across racial/ethnic and
38
sexual minority statuses in a large regionally representative populations of adolescents. We
hypothesize that H1) adolescents with a history of ACE will have a higher likelihood of meeting
criteria for an alcohol and drug disorder and H2) adolescents with a history of ACE will have a
higher likelihood of meeting criteria for problematic gambling compared to adolescents with no
ACE. Considering the buffering effects of social support, we hypothesize that the relationship
between ACE and criteria for H3) alcohol and drug disorders and H4) problematic gambling will
be moderated by social support, specifically the ACE- behavior relationship will be attenuated by
social support. Finally, we hypothesize that the moderating effect of social support will operate
differently across H5) ethnic and H6) gender identity minority groups, specifically the impact of
social support on the ACE- behavior relationship will be stronger for ethnic and sexual
minorities. Due to the novelty of this final three-way assessment of this final set of relationships,
we did not established a priori directionality in the hypothesis 5 and 6.
Figure 2. Study 1 Hypothesized Conceptual Model
Methods and Data
Participants and Procedures
Data are from participants of the 2019 Minnesota Student Survey (MSS). The MSS has
been surveying students since 1989 in regular public-school districts, charter schools, tribal
39
schools, nonpublic schools, alternative learning centers and juvenile correctional facilities
(Minnesota Department of Education, 2020). Students in fifth, eighth, ninth and eleventh grades
are surveyed. The survey is anonymous and asks students questions about activities, opinions,
behaviors, and experiences. School district participation is voluntary; however, in 2019 over
81% of regular public-school districts across the state chose to participate. The survey was
administered online (Minnesota Department of Education, 2020). The Institutional Review
Board (IRB) approved all study procedures. Sexual orientation questions were only asked of
middle and high school students, so the sample is restricted to eighth, ninth, and eleventh grade
students. Finally, the sample was restricted to only those who provided complete data on model
variables and covariates (n=62,142).
Measures – Dependent Variables
Problematic gambling criteria (dependent variable) was assessed with the three-item
Brief Adolescent Gambling Screen (BAGS) (Stinchfield et al., 2017). Response options included
0=“never,” 1=“sometimes,” 2=“many times,” and 3=“all the time,” and were scored from 0 to 3.
The final score is a sum of the 3 variables ranging from 0 to 9. All three questions were preceded
by “During the last 12 months, how often have you…” The questions included “hidden your
gambling/betting from your parents, other family members, or teachers,” “felt that you might
have a problem with gambling/betting,” and “skipped hanging out with friends who do not
gamble/bet to hang out with friends who do gamble/bet.” The scale has been found to be reliable
in adolescent populations (Stinchfield et al., 2017).
Alcohol and Drug Abuse was derived from 4 items from the DSM-4 criteria for alcohol
abuse and was extended to include both alcohol and drug use (American Psychiatric Association,
1994). All four questions were preceded by “During the last 12 months…” Questions included
40
“have you continued to use alcohol or drugs even though you knew it was hurting your
relationships with friends or family,” “how many times have you missed work or school, or
neglected other major responsibilities because of alcohol or drug use,” “how many times have
you driven a motor vehicle after using alcohol or drugs,” and “how many times have you hit
someone or become violent while using alcohol or drugs.” Responses were dichotomized to
1=yes and 0=no. The final index measuring a gradient of substance abuse was summed with a
range of 0 (no elements of alcohol and drug abuse criteria) to 4.
Alcohol and Drug Dependence was derived from 7 items from the DSM-4 criteria for alcohol
dependence and was extended to include both alcohol and drug use (American Psychiatric
Association, 1994). All seven questions were preceded by “During the last 12 months…”
Questions included “have you found that you had to use a lot more alcohol or drugs than before
to get the same effect,” “have you tried to cut down on your use of alcohol or drugs but
couldn’t.” “how many times have you spent all or most of the day using alcohol or drugs, or
getting over their effects,” “how many times have you given up important social or recreational
activities like sports or being with friends or relatives to use alcohol or drugs or to get over their
effects,” “how many times have you used so much alcohol or drugs that the next day you could
not remember what you had said or done,” “how many times have you used more alcohol or
drugs than you intended to,” and “how many times has alcohol or drug use left you feeling
depressed, agitated, paranoid, or unable to concentrate.” Responses were dichotomized to 1=yes
and 0=no. The final index measuring a gradient of substance abuse was summed with a range of
0 (no elements of alcohol and drug dependence criteria) to 7.
Alcohol and Drug Use Disorder (dependent variable) was derived from the DSM–5
criteria for alcohol use disorder where 4 of the alcohol abuse items were combined with 7 of the
41
alcohol dependence items (American Psychiatric Association, 2013). The final variable of
Alcohol and Drug Use Disorder was created in the same way, combining the edited (changed to
include alcohol and drugs) 4 abuse items and 7 of the dependence items. The final index ranged
from 0 (no criteria for disorder) to 11.
Measures – Independent Variables
Self-reported Adverse Childhood Experiences was an index created by summing up 5
types of household disfunctions (incarceration, alcohol misuse, substance misuse, mental illness,
and domestic violence) and 3 types of childhood maltreatment (physical abuse, verbal abuse, and
sexual abuse) (Felitti, Anda, Nordenberg, Williamson, Spitz, Edwards, Koss, et al., 1998).
Household disfunction questions had response options of 1=“yes” and 0=“no.” Household
disfunction questions included “have any of your parents or guardians ever been in jail or
prison,” “do you live with anyone who drinks too much alcohol,” “do you live with anyone who
uses illegal drugs or abuses prescription drugs,” “do you live with anyone who is depressed or
has any other mental health issues,” “have your parents or other adults in your home ever
slapped, hit, kicked, punched, or beat each other up.”
Childhood maltreatment questions had response options of 1=“yes” and 0=“no.” Physical
abuse, and verbal abuse were each assessed with 1 question and sexual abuse was assessed with
2 questions. If a respondent responded yes to either of the sexual abuse questions they were
classified as having sexual abuse. Childhood maltreatment questions included “has a parent or
other adult in your home ever hit, beat, kicked or physically hurt you in anyway,” “does a parent
or other adult in your home regularly swear at you, insult you or put you down,” “has anyone
who was not a relative/family member ever pressured, tricked, or forced you to do something
sexual or done something sexual to you against your wishes,” and “has any relative/family
42
member ever pressured, tricked, or forced you to do something sexual or done something sexual
to you.” Each indicator was coded as 0=no and 1= yes, and the final ACE index was calculated
by summing both the 5 types of household disfunction and 3 types of childhood maltreatment.
The final ACE measure ranged from 0 (no ACE) to 8. The ACE index has been reliability
measured in adolescent populations (Duke & Borowsky, 2018).
Measures – Moderators
Social support was measured with four items that assessed perceived support by different
networks of support (Minnesota Department of Education, 2020). All questions were preceded
with “How much do you feel…” Questions included other adult relatives care about you,”
“friends care about you,” “teachers/other adults at school care about you,” and “adults in your
community care about you.” Response options included 1=“not at all,” 2=“a little,” 3=“some,”
4=“quite a bit,” and 5=“very much.” The items were summed to create a final measure of
support.
Sexual Orientation was assessed by asking, “How do you describe yourself?” Response
options included: “heterosexual,” “bisexual,” “gay or lesbian,” “pansexual,” “queer,” “I do not
describe myself in any of these ways,” and “I am not sure what this means.” This is collapsed to
three levels: 0=heterosexual, 1=bisexual or pansexual, 2=gay or lesbian. Further disaggregation
would create groups that are too small for the model estimates and those specifying, “I do not
know,” were removed.
Race/Ethnicity was assessed by asking, “How do you describe yourself?” Response
options included: “American Indian or Alaskan Native,” “Asian or Asian American,” “Black,
African or African American,” “Hispanic or Latino/Latina,” “Native Hawaiian or Other Pacific
Islander,” and “White.” Since this was a “select all that apply” variable, the final variable
43
included: 0= White only, 1=American Indian or Alaskan Native only, 2=Asian or Asian
American only, 3=Black, African or African American only, 4=Hispanic or Latino/Latina only,
5=Native Hawaiian or Other Pacific Islander only, and 6=more than one race/ethnicity. The
respondents reporting “Native Hawaiian or Other Pacific Islander only” were collapsed into the
“Asian or Asian American” group to form an Asian /Pacific Islander category due to groups that
would be too small for the model estimates.
Measures – Covariates
Grade level was assessed by asking, “What is your grade in school right now?” Options
included: 5
th
, 8
th
, 9
th
, and 11
th
. Biological sex was assessed by asking, “What is your biological
sex?” Response options were female and male. Socioeconomic status (SES) was assessed with a
proxy variable asking, “Do you currently get free or reduced-price lunch at school?” Response
options were yes and no.
Statistical Analysis
First, two generalized linear models regressed 1) alcohol and drug use disorder criteria,
and 2) problematic gambling on ACE, while controlling for demographics (social support, grade
level, biological sex race/ethnicity, sexual orientation, and SES). Generalized linear models with
a Poisson link function were used to account for the count-based dependent variables. Second,
set of indirect effects regression models included an interaction term of ACE*social support and
all lower order terms and covariates. The final series of indirect effects regression models
included three-way interactions ACE*social support*ethnicity and a separate model for
ACE*social support* sexual orientation. To visualize the change seen across the variables PROC
PLM was used to create figures that map the predicted probabilities of alcohol and drug use
disorder criteria and problematic gambling across levels of ACE. Interaction term models
44
included all lower order terms. Patterns of missing data were explored to determine potential
biases. All statistical tests were performed using SAS v9.4 with a type one error rate of 0.05.
Results
The final models included 62,142 eighth, ninth, and eleventh grade students in Minnesota
who participated in the school-based survey and who provided complete data on model variables
and covariates (Table 1). The study was predominantly non-Hispanic White (73%) with the next
highest racial/ethnic category endorsed being multiple races (8%). The sample was evenly split
by sex at birth with 52% of the sample identifying as female. The majority of participants were
heterosexual (80%), followed by bisexual (7%) and gay/lesbian (5%). The average age of the
sample was 15 years old (SD=1.13). Nearly half (48%) of the sample reported at least one ACE
and 7% experienced 4 or more ACE. The most frequently reported ACE was household mental
illness (27%) followed by verbal abuse (15%). At least 15% endorsed at least one of the criteria
questions for alcohol and drug disorder and 3% endorsed at least one of the criteria questions for
problematic gambling. In bivariate analyses (Table 2), ACE was inversely correlated with social
support and positively correlated with both alcohol and drug disorder criteria and problematic
gambling criteria.
45
Table 1. Descriptive Statistics for Study 1 (N=62,142)
Variable Frequency Percent
Race/Ethnicity
American Indian or Alaskan Native only 662 1.07%
Asian or Asian American only 3,867 6.22%
Black, African or African American only 3,506 5.64%
Hispanic or Latino/a only 3,320 5.34%
Native Hawaiian or Other Pacific Islander only 97 0.16%
White only 45,406 73.07%
Multiple races (checked more than one) 5284 8.51%
Sex at Birth
Male 29,595 47.62%
Female 32,463 52.24%
No Answer 84 0.14%
Sexual Orientation
Heterosexual 49,434 79.55%
Bisexual or Pansexual 4,527 7.28%
Gay or Lesbian Queer 2,921 4.70%
I don’t know/I do not want to say 5,260 8.46%
Socioeconomic Status Proxy (Free or reduced-price lunch)
No 47,009 75.65%
Yes 15,133 24.35%
Variable Mean Std Dev
Adverse Childhood Experiences 0.98 1.39
Social Support 15.07 3.52
Age in Years 15.51 1.13
Alcohol and Drug Disorder Criteria 0.46 1.45
Problematic Gambling Criteria 0.06 0.48
Notes: Std Dev= Standard Deviation, Min=Minimum, Max=Maximum
Table 2. Correlation Matrix of Main Effects for Study 1
ACE Support A & D Disorder
Gambling
ACE 1 -0.42*** 0.32***
0.11***
Support 1 -0.20***
-0.08***
A & D Disorder 1
0.20***
Gambling
1
Notes: vales represent the Pearson’s correlation coefficient, *p<0.05, **p<0.01, ***p<0.001
Poisson regression was used to regress substance use disorder criteria and problematic
gambling criteria on model main effects and covariates. Direct effects models indicated that for
every additional ACE there was a significantly higher (0.369, 95%CI=0.362, 0.376 and 0.288,
95%CI=0.269, 0.306) incident rate of substance-use disorder criteria and problematic gambling
46
criteria respectively while controlling for social support, SES, sex, sexual orientation, age, and
race/ethnicity. Social support showed inverse effects with every additional unit of support score
associated with a significantly (-0.066, 95%CI=-0.082, -0.056) and (-0.062, 95%CI=-0.070, -
0.055) lower incident rate of substance-use disorder criteria and problematic gambling criteria
respectively while controlling for ACE, SES, sex, orientation, age, and race (see Table 3).
Table 3. Poisson Regression Models
Substance Use Disorder Criteria
N=57,133
Problematic Gambling
Criteria
N=57,953
Direct Effect Models IRR (95% CI) IRR (95% CI)
Intercept -4.098*** (-4.280, -3.916) -2.252*** (-2.706, -1.798)
ACE 0.369*** (0.362, 0.376) 0.288*** (0.269, 0.306)
Support -0.066*** (-0.082, -0.056) -0.062*** (-0.070, -0.055)
Sexual Orientation
Heterosexual -ref- -ref-
Bisexual/Pansexual 0.035 (-0.002, 0.071) -0.004 (-0.124, 0.117)
Gay/Lesbian/Queer -0.144*** (-0.194, -0.095) 0.205** (0.083, 0.328)
SES -0.215*** (-0.244, -0.185) -0.064 (-0.137, 0.009)
Race
White Only -ref-
Native American/Alaskan 0.216*** (0.131, 0.301) 0.612*** (0.406, 0.818)
Asian or Pacific Islander -0.467*** (-0.533, -0.401) 0.279*** (0.151, 0.407)
Black/African American -0.191*** (-0.253, -0.129) 0.927*** (0.823, 1.032)
Hispanic 0.098*** (0.044, 0.151) 0.681*** (0.564, 0.798)
Multiple Races/Other 0.120*** (0.082, 0.157) 0.393*** (0.295, 0.491)
Female -0.097*** (-0.122, -0.072) -1.586*** (-1.663, -1.508)
Age 0.259*** (0.248, 0.270) 0.045** (0.018, 0.072)
Two Way Interaction Models IRR (95% CI) IRR (95% CI)
ACE 0.112*** (0.092, 0.132) 0.1632*** (0.112, 0.2145)
Support -0.107*** (-0.111, -0.102) -0.0794*** (-0.089, -0.070)
ACE*Support 0.017*** (0.001, 0.015) 0.008*** (0.005, 0.011)
Three Way Interaction Models IRR (95% CI) IRR (95% CI)
Race Interactions Model
ACE*Support*White -ref- -ref-
ACE*Support*ANAI -0.009* (-0.016, -0.001) -0.016 (-0.033, 0.001)
ACE*Support*API -0.011** (-0.020, -0.003) -0.013* (-0.025, -0.001)
ACE*Support*Black -0.006* (-0.011, -0.001) 0.001 (-0.008, 0.008)
ACE*Support*Hispanic 0.005 (-0.001, 0.011) 0.0013 (-0.011, 0.014)
ACE*Support*Multi -0.001 (-0.005, 0.003) -0.0055 (-0.015, 0.004)
Sexual Orientation Interactions Model
ACE*Support*Heterosexual -ref- -ref-
ACE*Support*Bisexual/Pansexual -0.008*** (-0.011, -0.004) -0.0033 (-0.0156, 0.009)
ACE*Support*Gay/Lesbian/Queer -0.017*** (-0.021, -0.012) -0.0102* (-0.0195, -0.0009)
Notes: P<0.05*, P<0.01**, P<0.001***, IRR=incident rate ratio, 95% CI= 95% Confidence Interval, all models controlled
for SES, Sex, Age, and Race, Interaction term models also controlled for all lower order terms and interactions.
47
Two-way interaction models (ACE*Support) assessed the potential for the moderating
effect of social support on the relationship between ACE and addictive criteria to test hypothesis
H3 and H4. There was support for both hypotheses with models indicating that the relationship
between ACE and substance use disorder criteria as well as ACE and problematic gambling both
significantly varied by levels of social support. Considering the alcohol and drug-disorder
criteria model, predicted substance use disorder criteria, based on model results, showed that
those with higher ACE have significantly higher substance use disorder criteria at all levels of
support; however, for those at the highest level of ACE the slope less steep (see Figure 3).
Figure 3.Model Results for the Two-Way Interaction (ACE*Support) on – Alcohol and
Drug Use Disorder Criteria
48
Considering the problematic gambling criteria model, results suggest that youth with
higher ACE will have significantly higher problematic gambling criteria at all levels of support;
however, with more support they will have fewer problematic gambling criteria (see Figure 4).
Figure 4. Model Results for the Two-Way Interaction (ACE*Support) on – Problematic
Gambling Criteria
Three-way interaction models (ACE*social support*ethnicity and ACE*social support*
sexual orientation) assessed if the moderating effect of social support on the relationship between
ACE and addictive criteria may differ across ethnicity or sexual orientation (H5 and H6). There
was support for both hypothesis with models indicating social support moderated the relationship
49
between ACE and substance use disorder criteria as well as ACE and problematic gambling and
both associations significantly varied by ethnicity and sexual orientation.
Among those with lower ACE, the number of substance use disorder criteria are similar
for all races when support is low; however, more support for those with higher ACE appears to
be most impactful for those that report they are Asian, Black/African American, or Native
American (see Figure 5). Differences were also present across sexual orientation. At lower ACE,
the number of substance use disorder criteria are similar for all sexual orientation groups when
support is low; however, more support for those with higher ACE appears to be most impactful
for students who are gay/lesbian/queer but there was also significant protection for those who are
bisexual/pansexual although the impact is not as prominent (see Figure 6).
Figure 5. Model Results for the Three-Way Interaction (ACE*Support*Ethnicity) on –
Alcohol and Drug Use Disorder Criteria
50
Figure 6. Model Results for the Three-Way Interaction (ACE*Support* Sexual
Orientation) on – Alcohol and Drug Use Disorder Criteria
At lower ACE, the number of problematic gambling criteria are similar for all races when
support is low; however, more support for those with higher ACE appears to be most impactful
for those who are Asian as they have the greatest shift and remain significant (see Figure 7).
51
Figure 7. Model Results for the Three-Way Interaction (ACE*Support*Ethnicity) on –
Problematic Gambling Criteria
There were also differences across sexual orientation. At lower ACE, the number of
substance use disorder criteria are similar for all orientations when support is low; however,
more support for those with higher ACE appears to be most impactful for those who are
gay/lesbian/queer as they have the greatest shift (see Figure 8).
52
Figure 8. Model Results for the Three-Way Interaction (ACE*Support* Sexual
Orientation) on – Problematic Gambling Criteria
Discussion
This study was one of the first to assess the associations between ACE and both
substance and gambling addictions—as well as the potential moderating effect of social support
and variations across racial/ethnic and sexual orientation groups in adolescents. One of the
strengths of this study is the large number of respondents which allows for subgroup analysis.
This is particularly beneficial for identifying differences across groups that tend to be
underpowered in many other datasets such as adolescent ethnic and sexual minorities. To that
end, this is the first to consider the moderating effect of social support and the potential
differences seen across ethnicity and sexual orientation. The benefit of this line of research is that
53
these results present a novel look at social support as a buffer for the impact of ACE on addictive
disorders and how that buffering effect may operate differently in specific groups. Addictive
disorders may affect academic performance, professional development, and general health and
wellbeing across the life course, making it a critical public health priority for research and
intervention.
All study hypotheses were supported including distinct differences in effects across
demographic subgroups. Consistent with similar literature assessing general substance use and
addictive behaviors (cites), ACE exposure increased the likelihood of meeting more criteria for
an alcohol and drug disorder and for problematic gambling. There is substantial evidence that
ACE are a risk factor for addictive behaviors in all life stages; however, this study highlights the
impact of ACE on clinical criteria for disordered behaviors among adolescents. This is an
important distinction because ACE exposed individuals who are already beginning to engage in
problematic or disordered addictive behaviors in middle school or high school and early
problematic behaviors, particularly pharmacologic disorders, have been associated with negative
developmental and psychological differences in youth (Squeglia & Gray, 2016) setting the stage
for life-long health effects as these youth transition to a period of young adulthood with even
more autonomy and where many critical life-decisions may be made.
The second contribution of the current study was the identification of the moderating
effect of social support, which may help to buffer the negative impact of ACE on addictive
behaviors in adolescents. Although ACE may place youth at a disadvantage, increasing the
likelihood of addictive disorders, social support may attenuate this impact reducing the effects of
ACE on both clinical alcohol and drug use disorder criteria and problematic gambling criteria.
This provides additional confirmation for the use of social support in adolescent interventions to
54
prevent addictive behaviors. However, it is important to note that the study did identify distinct
differences of how this buffering effect operates across subgroups within the sample.
Intervention studies are needed to further determine how support could be administered for the
best outcomes and if the effects of ACE can be offset by different types of support.
Overall, there is a clear protective effect of support in the relationship between ACE and
clinical criteria for addictive behaviors although the degree of protection was not equal across
ethnic and sexual orientation subgroups confirming the hypothesis 5 and 6. On average, social
support may buffer the effects of ACE on alcohol and drug disorder and problematic gambling
for most adolescents; however, the attenuating effect is strongest for Asian Americans,
Black/African American, and Native Americans. This highlights that social support may be of
particular benefit in mitigating the effects of childhood trauma on addictive disorders for
racial/ethnic minority adolescents. This however does not mean that that social support will not
also benefit racial/ethnic majority adolescents as evidenced by the significant two-way models.
Like the effects seen across racial/ethnic subgroups, the strength of the moderation of social
support also varied across sexual orientation. Specifically, the attenuating effect is strongest for
adolescents who identify as gay/lesbian or queer. Although the effects of social support may
benefit adolescents or any sexual orientation, sexual orientation minority adolescents such as
those who identify as gay/lesbian/queer or bisexual/pansexual may benefit the greatest from the
mitigating effects of social support on the relationship between ACE and addictive disorders.
The results of this research are consistent with other studies that have identified social
support as beneficial in that support can mitigate the risk for addictive behaviors. Such findings
suggest that intervention and prevention research and services should consider the benefits of
social support in adolescent populations. This is particularly true for those who may be at a
55
greater disadvantage from early childhood trauma. Support services, as well as bolstering
relationship skills to improve the quality of support, could be used in interventions and
prevention efforts to help mitigate the impact of ACE and subsequent health outcomes such as
addictive behaviors including disordered substance use and problematic gambling. Helping
adolescents develop positive relationships to cope with trauma is a promising component of
future prevention work for at-risk youth. This is especially true for individuals who may also be
racial/ethnic or sexual minorities. This research highlights the intersectionality between ACE and
multiple minority statuses that may also provide stress similar to that of ACE. Given that
minority stress and ACE provide undue stress, it is important to consider populations that may
experience multiple sources of stress throughout their lives. The current study is in line with this
concept highlighting the importance of positive tools such as social support in potentially
reducing the effects of multiple sources of stress in youth.
Future Research
The current study considered the potential moderating effects of social support in the
relationship between ACE and addictive disorders in adolescents as well as the differential
effects seen across minority subpopulations. Although this large regional data provides robust
data for subgroup analysis, the data was still cross-sectional in nature and future research needs
to assess the lasting longitudinal effect of these relationships. Future research should also
consider potential differences in sources of support. This may be a critical component of future
intervention given that support from school staff or friends may operate differently and
leveraging these differences may provide a stronger effect on reduction of problematic alcohol
and drug use.
56
Limitations
First, although this is a state representative sample, the data is not representable to all
states. The sample is obtained from a Midwestern state and is only representable to samples with
a similar demographic profile; however, given the size and regionality, one could reasonably
extend these results to other populations. Second, these data are based on self-reported ACE and
substance use, and although the inclusion of biomarkers would provide a more definitive report
of misuse, self-reported substance use has been found to be highly accurate under confidential
survey conditions such as the present study (Harrison & Hughes, 1997). Third, ACE was
assessed retrospectively. However, much of the ACE research has been conducted
retrospectively and even studies challenging retrospective versus prospective reports note that
retrospective reports provide a meaningful addition to the literature and are validly associated
with other subjective measures (Reuben et al., 2016). Fourth, we cannot definitively anchor ACE
to a specific time-point in childhood; however, the survey specifies events that occurred prior to
age 18. Because of this, we are not able to assess whether the timing of ACE occurred prior to
any high school trajectory changes or prior to substance use initiation.
Conclusions
Results from this study comport with research highlighting the impact of ACE on
addictive disorders including alcohol use, drug use, and gambling as early as adolescence. This
study builds upon prior results and provides preliminary evidence of the benefits of social
support for youth with a history of ACE. Moreover, we highlight that the mitigating effects of
social support for this population may be particularly salient for racial/ethnic or sexual
minorities. Social support in high school is a critical component of future intervention and
57
prevention efforts seeking to combat early problematic addictive behaviors in turn curbing many
lasting negative health consequences of establishment of adolescent addictive disorders.
58
Chapter 3: The Impact of Childhood Trauma on Alcohol and Drug Use Consequences
Trajectories and the Moderating Role of Social Support
Introduction
Patterns of alcohol and drug use and subsequent negative outcomes across the life course
are mostly heterogeneous in the U.S. population; although some individuals use alcohol and drug
use without serious consequences, misuse is a major public health concern (HSER et al., 2009).
For a subset of users, functioning becomes disrupted over time by maladaptive behaviors
(Sussman & Ames, 2008) and as adaptive mechanisms fail, there are a spectrum of negative
consequences from use (Sussman & Ames, 2008). Individuals’ dependence on alcohol or drugs
often persists across the lifespan and consequences including mortality, morbidity, criminality,
and lost productivity take a toll on the individual and society. The annual health, crime, and lost
productivity costs associated with substance use disorders in the US are estimated to be $740
billion(Centers for Disease Control and Prevention, 2019; National Institute on Drug Abuse,
2017; US Department of Health Human Services, 2014). Studies of longitudinal patterns of
substance use and treatment have identified cycles of cessation and relapse; however, compared
to cross-sectional research, there is far less longitudinal evidence and much of the cohort
research has focused on frequency and pattern of use (HSER et al., 2009). Research that focuses
on substance use consequences and the context that shapes substance use patterns is needed,
particularly research examining the critical events and factors that contribute to change over time
and that attenuate negative problems of use (HSER et al., 2009). Reviews assessing alcohol and
drug use consequences suggest that continued research would improve our understanding of
factors that increase harmful consequences and those that can limit negative outcomes
(Blanchard et al., 2003; Grigsby et al., 2016; Palmer et al., 2012; Pedersen et al., 2016). These
59
consequences can include missed work or classes, poor academic or work performance, injuries,
sexual assaults, overdoses, memory blackouts, changes in brain function, cognitive deficits, and
even death (Blanchard et al., 2003; Grigsby et al., 2016; Palmer et al., 2012; Pedersen et al.,
2016; White & Labouvie, 1989). Given that the risk for co-occurring addictions are high and the
general use may not indicate problematic use (Sussman et al., 2014; Sussman et al., 2011), it is
important to better understand pharmacologic addictions along with problematic use. One
instrument that uses consequences/problems as an indicator of potential problematic alcohol use
is the Rutgers Alcohol Problem Index (RAPI) (White & Labouvie, 1989). This scale was initially
developed to identify a series of indicators for problematic use of alcohol in adolescents,
although many have applied this to populations beyond adolescents and have started to expand it
to include drug use as well. Epidemiologic evidence suggests that the prevalence of drug use and
abuse increases over the course of adolescence and peaks in young adulthood and that
understanding patterns of problematic use across this period are necessary; however, there is
limited longitudinal research assessing these trajectories within this life period (Palmer et al.,
2009).
Adverse childhood experiences (ACE) are highly correlated negative events occurring
before the age of 18, that include child maltreatment (e.g., sexual, physical, and verbal abuse)
and household dysfunction (e.g., parental divorce, substance use and household mental illness,
incarceration, and homelessness) (Felitti, Anda, Nordenberg, Williamson, Spitz, Edwards, Koss,
et al., 1998) When examined as a set of highly correlated events, ACE are consistent predictors
of poor health outcomes, including dysregulated engagement in addictive behaviors (Forster,
Rogers, Sussman, Yu, et al., 2021; Forster, Rogers, Sussman, Watts, et al., 2021; Hughes et al.,
2017; Ng & Wiemer-Hastings, 2005; Sharma & Sacco, 2015). Individuals with a history of ACE
60
tend to have significantly more life course health problems and negative outcomes known to
contribute to premature morbidity and mortality (Hughes et al., 2017). ACE can disrupt
physiological pathways during development, result in cognitive and emotional impairment, and
strain individuals’ coping capacity (Albott et al., 2018; Danese & McEwen, 2012; Hughes et al.,
2017; Pechtel & Pizzagalli, 2011), exacerbating the risk for maladaptive coping behaviors.
Trauma related cognitive and emotional deficits can contribute to emotional dysregulation and
disrupted attachment, which increase vulnerability for maladaptive coping behaviors, especially
addictive behaviors that can temporarily limit feelings of distress (Felitti & Anda, 2010; Forster
et al., 2017; Gilbert, 2009; Grant & Chamberlain, 2016; Pollak et al., 2000). Since the seminal
study introducing the adverse childhood experiences (ACE) framework by the Kaiser Family
Foundation (Felitti, Anda, Nordenberg, Williamson, Spitz, Edwards, Koss, et al., 1998), there
has been a growing interest in the impact of ACE on behavioral health. ACE have been
associated with alcohol and drug use frequency; with alcohol-related negative consequences
among young adults (e.g., Brief Young Adult Alcohol Consequences Questionnaire
(BYAACQ)(Kahler et al., 2005)) and alcohol related problems (e.g., Rutgers Alcohol Problem
Index (White & Labouvie, 1989) (Brett et al., 2018; Espeleta et al., 2018; Goldstein et al., 2010;
Shin et al., 2015; Shin et al., 2018) and with consequences of other drug use such as marijuana
(Vilhena-Churchill & Goldstein, 2014). Given the importance of the life course perspective and
the periods of adolescence and young adulthood in relation to addiction, research is needed to
advance the understanding of problematic use of alcohol and drug use and the impact of ACE
longitudinally from adolescence through young adulthood. Assessing the ACE- substance use
consequences relationship by exploring differential patterns over time will provide crucial
information for prevention and treatment efforts.
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From a prevention and treatment perspective, identifying resilience and mediating factors
that may play a role in buffering the ACE-addiction relationships given that not all youth with a
history of ACE develop psychopathology (Enoch, 2011). Social support, a network or set of
people who can provide psychological and material resources to assist in an individual’s capacity
to cope with stress (Cohen, 2004; Hendrick & Hendrick, 2000) has been identified as a positive
factor in mental and physical health outcomes that can mitigate the effects of trauma and stress
(Harandi et al., 2017; Sippel et al., 2015; Taylor, 2007; Wang et al., 2018). Social support can
decrease maladaptive coping and promote behaviors that improve stress-regulation, increase
confidence, decrease engagement in risky behaviors, and promote healthy and effective-coping
strategies (Sippel et al., 2015), which are all important for addiction prevention (Galaif et al.,
2003). For adolescents specifically, social support can limit dysregulation in the face of
traumatic stressors (Ozbay et al., 2008; Sippel et al., 2015) and promote positive mental health
outcomes, even for ACE exposed individuals (Forster et al., 2020). Despite the benefits of social
support among trauma exposed populations, there is limited research specifically assessing social
support in the context of ACE and behaviors with addictive potential. Overall, the benefits of
social support in buffering the effects of stress have been well documented (Cohen & Wills,
1985; Maymon & Hall, 2021; Taylor, 2011; Wang et al., 2021). However, only few studies have
investigated the intersection of trauma, addiction, and social support and found that social
support is a key ingredient of resilience for adults (Caravaca-Sánchez & Wolff, 2020; Gros et al.,
2016); although others have not (Pinto et al., 2017). Ongoing research with adolescent and young
adult populations is needed to investigate the ACE-substance use consequences relationship and
whether social support can offset the downstream effects of ACE in behavioral health. To this
end and considering the dearth of studies assessing the potential buffering role of social support
62
in the relationship between ACE and problematic alcohol and drug use in adolescents or
longitudinally through young adulthood, the current study explored the differential patterns of
ACE-alcohol and drug use trajectories over time.
Study Aims
Specifically, the current study sought to longitudinally examine 1) the impact of ACE in
increasing trajectories of past 30-day problematic alcohol and drug use from adolescence to
young adulthood; and 2) the potential protective effects of social support in the ACE -
problematic alcohol and drug use trajectories from adolescence to young adulthood. We
hypothesize that H1) at baseline higher ACE will be associated with higher problematic alcohol
and drug use and higher support will be associated with lower problematic alcohol and drug use
We also hypothesize that H2) Higher ACE exposure in young adults will have a greater increase
in problematic alcohol and drug use over time, compared to young adults who were not ACE
exposed; and H3) the differences in trajectories of problematic alcohol and drug use over time by
ACE exposure will be moderated by social support; for example, those with higher adolescent
social support will have similar trajectories as non-ACE exposed students and not for those with
low social support.
Figure 9. Study 2 Hypothesized Conceptual Model
63
Methods and Data
Participants and Procedures
Participant information was derived from Project Reteniendo y Entendiendo Diversidad
para Salud (RED), a longitudinal cohort study designed to assess acculturation and substance use
patterns among Hispanic/Latino adolescents enrolled public high schools in Southern California
(Unger, 2018; Unger et al., 2009). Adolescents who were initially enrolled attended one of the
eight randomly selected high schools in the Los Angeles area with student bodies that were at
least 75% Hispanic (as indicated by data from the California Board of Education). Investigators
visited classrooms of all eight high schools and distributed parental consent and youth assent
forms. The first survey wave occurred in 2005 with 9
th
grade students and then repeated in 10
th
and 11
th
grade. To extend the study beyond high school into young adulthood, the cohort was re-
contacted in 2011. This study led to five post-high school waves of data collection that occurred
in 2011, 2013, 2014, 2016, and 2018. The Institutional Review Board (IRB) approved all study
procedures. Alcohol and drug use consequences were measured in the final high school survey
and in the first four young adulthood surveys. The sample was restricted to 1) only respondents
who provided data on ACE (n=1,404), collected retrospectively in 2013; and 2) those who used
(lifetime) alcohol or (lifetime) drugs at some point in any wave; and 3) provided alcohol and
drug use consequences data on at least one of the surveys where it was collected (2009, 2011,
2013, 2014, and 2016). Some of the original cohort (N=3,218) was lost to follow-up, with the
final analytic sample comprised of 1,404 participants with data from six survey waves.
Measures – Dependent Variables
Self-reported Problematic Alcohol and drug use was assessed with a modified version of
the Rutgers Alcohol Problem Index (White & Labouvie, 1989) adapted for general drug use.
64
Seven items were prefaced with the following statement, “Different things happen to people
while they are drinking alcohol or using other drugs or because of their alcohol drinking or use
of other drugs. How many times has each of these things happened within the last month due to
drinking or drug use?” Questions included “not able to do your work or study for a test?,” “got
into fights with other people (friends, relatives, strangers)?,” “neglected your responsibilities?,”
“felt that you needed more alcohol (or drugs) than you use to in order to get the same effect?,”
"felt that you had a problem with alcohol or drug use?,” “kept drinking or using drugs when you
promised yourself not to?,’ and “felt physically or psychologically dependent on alcohol or
drugs.” Response options included 1=“never,” 2=“sometimes,” 3=“often,” and 4=“more than
five times.” The final items were summed. The questions were asked in the final year of high
school (survey 3) and then the first four post-high school surveys (Survey 4-7). The final variable
ranged from (7 to 28) with a Cronbach’s Alpha showing good internal consistency (α=0.820).
Measures – Independent Variables
Time was accounted for in the models as a continuous variable starting with 1=3
rd
survey,
2=4
th
survey, 3=5
th
survey, 4=6
th
survey, 5=7
th
survey. Nine self-reported Adverse Childhood
Experiences were assessed with items measured retrospectively in the second post-high school
wave of data collection. Items included child maltreatment (e.g., physical, sexual, and verbal
abuse) and household dysfunction (e.g., parental partner violence, incarceration, alcohol misuse,
illicit substance use, mental illness, and divorce). The maltreatment questions were prefaced
with, “While you were growing up, that is your first 18 years of life, how often did a parent,
step-parent, or adult living in your home…” in line with the original ACE measure (Felitti, Anda,
Nordenberg, Williamson, Spitz, Edwards, & Marks, 1998). Response options were dichotomized
to 1=“yes” and 0=“no”. Physical abuse was assessed with two items and endorsement of abuse
65
was coded with a report of yes to either question: “Push, grab, slap, or throw something at you?”
or “Hit you so hard that you had marks or were injured?” Verbal abuse was assessed with two
items and endorsement of abuse was coded with a report of yes to either question: “Swear at you,
insult you, or put you down?” or “Threaten to hit you or throw something at you, but didn’t do
it?” Sexual abuse was assessed with four items and endorsement of abuse was coded with a
report of yes to either question: “Touch or fondle your body in a sexual way?” or “Have you
touched their body in a sexual way?” or “Attempt to have any type of sexual intercourse with
you (oral, anal, or vaginal)?” or “Actually, have any type of sexual intercourse with you (oral,
anal, or vaginal)?” Household disfunction items asked participants if, before they turned 18 years
old, they lived with anyone who was mentally ill, misused substances, was incarcerated, or was
physically violent with their spouse/partner. Response options were 1=“yes,” or 0=“no.”
Physically violent with their spouse/partner was assessed with four items and endorsement of
abuse was coded with a report of yes to either question: “Push, grab, slap, or throw something at
her?” or “Kick, bite, hit her with a fist, or hit her with something hard?” or “Repeatedly hit her
over at least a few minutes?” or “Threaten her with a knife or gun, or use a knife or gun to hurt
her?” Household mental illness was assessed with two items and endorsement of illness was
coded with a report of yes to either question: “Was anyone in your household depressed or
mentally ill?” or “Did anyone in your household attempt to commit suicide?” The final four
household dysfunction items household problematic alcohol use, household drug use, household
divorce, physically violent spouse/partner, and household incarceration were assessed with “Did
you live with anyone who was a problem drinker or alcoholic?”, “Did you live with anyone who
used street drugs?”, “Were your parents ever separated or divorced?”, “While you were growing
up, that is, in your first 18 years of life, did anyone in your household go to prison? ACE items
66
were recoded (0=no and 1=yes) and then summed to create an index of childhood adversity
(range 0-9).
Measures – Moderators
Social Support was assessed with the Multidimensional Scale of Perceived Social
Support scale (Zimet et al., 1988). Response options included 1=“strongly disagree,”
2=“somewhat disagree,” 3=“agree,” 4=“strongly agree.” There was a total of 12 items with 4
items about support from a “special person,” 4 items about support from family, and 4 items
about support from friends. The statements are listed below. The final support measure was a
sum of the 4 “special person,” items and the 4 “friends” items. Items included “there is a special
person who is around when I am in need,” “there is a special person with whom I can share my
joys and sorrows,” “I have a special person who is a real source of comfort to me,” "my
FRIENDS really try to help me,” “I can count on my FRIENDS when things go wrong,” “I have
FRIENDS with whom I can share my joys and sorrows,” "in my life, there is a special person
who cares about my feelings,” and “I can talk about my problems with my FRIENDS.”
Measures – Covariates
Demographic covariates included sex, nativity, and socioeconomic status. Sex was
assessed with one item asking, “What is your sex?” Response options included female and male,
with female as the reference group. Nativity was measured with one question; “In what country
were you born?” Response options were “U.S.” and “other,” with other as the reference group.
Nativity is an important confounder to control for, given that ACE prevalence may differ based
on nativity. This study created a standardized index to represent SES that has been validated in
this population (Unger et al., 2009; Unger et al., 2014) and included parent’s education rated on a
6-point scale ranging from ‘‘8th grade or less’’ to “advanced degree;” a ratio of the number of
67
rooms per person in the home captured by dividing the number of people in the house by the
number of rooms in the house; and the U.S. census median household income in the respondent’s
zip code provided. The index also included dichotomous measures of eligibility for free/reduced
price lunch at school (1 = no, 0 = yes), homeownership (1 = family owns its home, 0 = family
rents home from a landlord), presence of a computer in the home (1 = yes, 0 = no), presence of a
gaming console in the home (1 = yes, 0 = no), and availability of the Internet at home (1 = yes, 0
= no). To weight each indicator equally, items were standardized to a mean of 0 and a standard
deviation of 1 and summed (Unger et al., 2009; Unger et al., 2014). Dummy coded variables for
schools were included to control for any school level differences. Random effects for schools
were not included because of low intraclass correlation coefficients (ICC) of students nested
within schools (<0.03). Due to the limited variability in age, it was not included as a covariate
(all students entered the study as first-year students). Race was not included because the sample
was restricted to students who identified as Hispanic.
Statistical Analysis
Univariate and bivariate analyses describe the sample and patterns in missing data.
Means plots were calculated to graph the problematic alcohol and drug use score trends across
time. Random linear growth curve models, using SAS PROC MIXED, estimated the problematic
alcohol and drug use trends across time using random slopes, random intercepts, and the fixed
effects of ACE and demographic covariates. Models were iteratively assessed to determine
appropriateness of model parameters beginning with empty models and subsequently adding
random effects of time and differing covariance patterns. The final models with the best fit were
random linear models that included the random intercepts and slopes for time, as well as the
fixed effects of time invariant independent variable (ACE), and the time invariant baseline model
68
covariates (SES, sex, nativity, and school). After the final best fit models were assessed,
interaction terms were included in models to assess the cross-level effect of moderators with time
on problematic alcohol and drug use. The first model included an interaction term (ACE*time) to
determine the cross-level effect of ACE with time on problematic alcohol and drug use. The
second interaction term model included a three-way interaction with the including ACE* time
*Social Support, along with all covariates and lower order interaction terms, to see if there were
differences in the ACE*time interaction by social support. This approach can determine if
differences in the trajectories of average problematic alcohol and drug use scores over time,
potentially exacerbated by ACE, could be moderated by early social support in high school. To
visualize interaction effects, the final model’s predicted values were stratified by levels of ACE
and plotted with 95% confidence intervals using the SAS PROC PLM procedure with panels of
different social support levels from low to high support. To address missing data across waves,
maximum likelihood estimation of mixed models allow for data that are missing at random in the
dependent variable (Allison, 2012; Molenberghs & Kenward, 2007). Therefore, the sample was
restricted to participants with complete data on ACE and alcohol or any substance across any
survey wave, but still allowed for missing data on other variables. All statistical tests were
performed using SAS v9.4 with a type one error rate of 0.05.
Results
The final analytic sample was comprised of 1,404 Hispanic participants who provided
ACE and problematic alcohol and drug use data on at least one time point. For this analytic
sample, 88% were U.S.-born and just over half (59%) were female. At the third wave of high
school data collection (the first wave used for the study), the average age was 16.47 (SD=0.39)
and at the fourth young adult wave, the average age was 23.87 (SD=0.42). The average ACE was
69
2.75 (2.19) with 58% of the sample reporting verbal abuse, 51% physical abuse, 37% parental
divorce, 30% household alcohol use, 25% parental intimate partner violence, 22% household
mental illness, 22% household incarceration, 17% household drug use, and 16% sexual abuse.
Table 4. Non Time-Dependent Descriptive Statistics for Study 2 (N=1,404 participants)
Variable Frequency Percent
Nativity
U.S. Born 1228 87.78%
Not U.S. Born 171 12.22%
Sex at Birth
Male 570 40.60%
Female 834 59.40%
Variable Mean Std Dev
Adverse Childhood Experiences 2.75 2.19
Social Support (in adolescence) 26.02 4.01
Socioeconomic Status (in adolescence) -0.28 3.70
Notes: Std Dev= Standard Deviation, Min=Minimum, Max=Maximum
Table 5. Descriptive Statistics for Study 2 Over Time (N=6,348 observations)
HS Survey
3
YA Survey
1
YA Survey
2
YA Survey
3
YA Survey
4
Problematic Alcohol and Drug Use
Scores
8.28 (2.23) 7.98 (2.32) 8.13 (2.58) 8.13 (2.23) 8.25 (2.45)
Notes: HS=High School, YA=Young Adulthood
Within the sample, the of past 30-day problematic alcohol and drug use score was 8.3
(SD=2.2) at the third high school wave, 7.9 (SD=2.3) at the first young adulthood wave, 8.1
(SD=2.6) at the second young adulthood wave,8.1 (SD=2.3) at the third young adulthood wave
and, 8.4 (SD=2.5) at the fourth young adulthood wave. The growth curve analysis was used to
estimate participants’ trajectory of problematic alcohol and drug use scores from the final high
school year through emerging adulthood, 5 waves of data collection.
70
Table 6. Growth Curve Models.
MODEL 1
Problematic Alcohol and Drug Use-
ACE*Time Interaction model
MODEL 2
Problematic Alcohol and Drug
Use -ACE*Time*Support
model
Variance Components Parameter Estimate (SE) Parameter Estimate (SE)
UN1,1 1.830*** (0.1678) 1.803*** (0.167)
UN2,1 -0.1483** (0.0513) -0.141** (0.051)
UN2,2 0.1521*** (0.023) 0.151*** (0.023)
Time 3.357*** (0.080) 3.359*** (0.081)
Fixed Effects Parameter Estimate (SE) Parameter Estimate (SE)
Intercept 7.995** (0.353) 6.6568 (0.623)
Time -0.044 (0.032) 0.309 (0.216)
ACE
0.159*** (0.026)
In
0.637*** (0.173)
Support -0.025* (0.011) 0.025 (0.226)
Sex 0.514 *** (0.092) 0.513*** (0.092)
Time*ACE 0.019* (0.009) -0.103 (0.061)
Time* Support -Not Included- -0.018 (0.008)
ACE*Support -Not Included- -0.018** (0.006)
Time*ACE*Support -Not Included- 0.005* (0.002)
Notes: P<0.05*, P<0.01**, P<0.001*** The models also controlled for SES, Nativity, and School.
Across all models, there was significant variance in random slopes; however, there was
only significant variance in random intercepts for models 1 and 2. Based on model 1, when
assessing the fixed linear effect of ACE on substance use at baseline, ACE was a significant
predictor for differences in problematic alcohol and drug use scores. Those with higher ACE had
significantly higher problematic alcohol and drug use scores at baseline (β=0.159, 95%CI=0.11,
0.21). When assessing the fixed linear effect of social support in high school on problematic
alcohol and drug use scores at baseline, those with higher support had significantly lower levels
of problematic alcohol and drug use at baseline (β=-0.025, 95%CI=0.05, 0.01) supporting H1).
Sex was also significant; at baseline, males were expected to have higher levels of problematic
alcohol and drug use than females (β=0.514, 95%CI=0.33, 0.69).
71
Hypothesis: H2) the cross-level effects of ACE
In model 1 the interaction term between time and ACE was positive and significant,
indicating that the linear rate of change over time differed across levels of ACE for problematic
alcohol and drug use (β=0.02, 95%CI=0.01, 0.03). At baseline, those with higher ACE start with
more problematic alcohol and drug use and then continue to increase at a greater rate than those
with lower levels of ACE. At higher levels of ACE, there is a steeper trajectory of problematic
alcohol and drug use scores, meaning that on average over time, those with higher ACE will end
up with more problematic alcohol and drug use than respondents with lower ACE (see Figure.
11). In model 1, social support is also significant indicating that at baseline those with more
support have lower problematic alcohol and drug use.
Figure 10. Model Problematic Alcohol and Drug Use Scores Over Time by ACE
72
Hypothesis: H3) the cross-level effects of ACE by Support
In model 2, the three-way interaction term between time and ACE and social support was
positive and significant, indicating that the linear rate of change over time differs across levels of
ACE for problematic alcohol and drug use and that this relationship operates differently across
levels of support. This finding suggests that support may moderate the effects of ACE in
problematic use over time (β=0.005, 95%CI=0.001, 0.009). Across all levels of support, those
with higher ACE start with higher problematic alcohol and drug use scores; however, the effect
of ACE is much more pronounced among individuals with less support. Respondents who had
low levels of social support in high school and higher ACE started with more problematic
substance use in their baseline scores than those with more social support, regardless of ACE
level. There were also changes over time across ACE and support. Although respondents with
the lowest support and high ACE start with the highest problematic use scores, all ACE groups
begin to regress to the mean over time; however, those with high ACE still have higher amounts
of substance use than those with no ACE. For those with the highest level of support, although
all ACE levels begin at a lower level, those with more ACE appear to increase in problematic
alcohol and drug use over time, and those with lower ACE appear to decrease in problematic
alcohol and drug use over time. It is important to note that those with the highest level of ACE
and the highest level of support will still end with lower problematic alcohol and drug use scores
compared to those with the lowest support and higher levels of ACE (see Figure. 12).
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Figure 11. Model Problematic Alcohol and Drug Use Scores Over Time by ACE Paneled by
Social Support
Discussion
This study is one of the first to assess the impact of ACE longitudinally in problematic
alcohol and drug use from adolescence through young adulthood and assess the potential
moderating role of social support in high school. Negative consequences of substance use
including problematic alcohol and drug use can be an indicator of risk for substance use
disorders and may impact academic performance, professional development, and general health
and wellbeing across the life course, making it a critical public health priority.
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There was evidence supporting the hypothesis that H1) at baseline, every additional ACE
was associated with higher problematic alcohol and drug use scores in high school, suggesting
that ACE exposed individuals using alcohol or drugs in high school will be at a disadvantage.
There was also evidence supporting the hypothesis H2) that H1) adolescents with higher ACE
exposure will have a greater increase in problematic alcohol and drug use into young adulthood
compared to those who were not ACE exposed. Adolescent use of alcohol and drugs a key
indicator for problematic use later in life, but early problematic use has been associated with
negative developmental and psychological differences in youth (Squeglia & Gray, 2016).
Moreover, this can lead to continued or worsening consequences as these youth transition to
young adulthood where they will have even more autonomy and will make many critical life
decisions. The cross-level effects seen with ACE illustrate that those with higher ACE have
differential trajectories though young adulthood compared to those with lower ACE or no ACE.
Specifically, not only do ACE-exposed individuals start with higher problematic alcohol and
drug use scores, but problematic use continues over time while their peers with low to no ACE,
who start lower at baseline, appear to decrease their problematic use over time. Youth and young
adults with high ACE exposure have an increasing likelihood of future problematic alcohol and
drug use highlighting the potential persistent impact of ACE on problematic use. In contrast,
non-ACE exposed individuals appear to have a decreasing likelihood of young adult problematic
alcohol and drug use.
Given the persistence of this change over time, it is important to identify potential protective
factors that can reduce problematic alcohol and drug use or attenuate change over time. We
identified that strong social support in high school, as early as 9
th
grade, was associated with
lower problematic alcohol and drug use at baseline. There was also evidence supporting H3), that
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the differences in trajectories of problematic alcohol and drug use over time by ACE exposure
will be moderated by social support. At very low levels of social support, there are large
differences in problematic substance use across ACE. Students with high ACE have much higher
problematic use scores at baseline, and over time, among high ACE experiencing individuals
problematic use reduces but among lower ACE experiencing individuals problematic use scores
increase with all groups regressing; however, those with higher ACE are still significantly higher
at wave 5 compared to non-ACE exposed individuals. In other words, low social support may
exacerbate the effects of ACE for problematic use of alcohol and drugs creating even greater
disparities at baseline between those who are ACE and non-ACE exposed. The changes over
time for this low support group suggest that even the non-ACE exposed individuals with limited
social support may eventually be at risk for problematic use in high school. In contrast, at very
high levels of social support, there are few baseline differences in problematic alcohol and drug
use scores (everyone starts much lower) and over time end lower than individuals with low
levels of social support. However, it is important to note that the increases in problematic use
among ACE-exposed individuals are still much lower for those with support compared to those
with low support. Both the baseline and over time differences across levels of support
demonstrate that fostering social support in high school may be an intervention and prevention
opportunity for programs targeting adolescents and young adults.
In sum, these findings underscore the importance of intervention and prevention research
and services for youth with ACE. Given the prevalence of ACE and the cost of associated health
issues, providing adolescents and young adults the tools, training, and support necessary to
manage traumatic stress, as well as encouraging the development of positive relationships to
coping with their trauma, should be a public health priority. These results are in line with other
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research that has shown that cultural assets and support systems may in fact mitigate the negative
effects of ACE for youth and young adult health and well-being (Brown & Shillington, 2017;
Chatterjee et al., 2018; Forster, Davis, et al., 2019; Karatekin & Ahluwalia, 2020; Robertson et
al., 2010).
Future Research
The current study recognizes the potential moderating effects of early high school social
support, and the critical differences support can make for baseline substance use. Social support
can be a moderator in the ACE-problematic use relationship; however, future research that
examines social support across the continuum of adolescence and young adulthood would add to
the understanding of the moderating affects. Studies using longitudinal designs that assess
support at different time points would help identify key periods at which support-interventions
could reduce ACE-related negative patterns of substance use behaviors. Future research should
also consider examining differences in the sources and types of support people receive. This may
be critical given that support from school staff or friends is likely to operate differently from
family and community. Understanding these differences and benefits will improve the
effectiveness of prevention efforts for young people.
Limitations
First, these data are self-reported ACE and substance use; however, self-reported
substance use has been found to be highly accurate under confidential survey conditions such as
the present study (Harrison & Hughes, 1997). Second, ACE was assessed retrospectively.
However, much of the ACE research has been conducted retrospectively and even studies
challenging retrospective verses prospective reports suggest that retrospective reports provide a
meaningful addition to the literature and are validly associated with other subjective measures
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(Reuben et al., 2016). Third, we cannot definitively anchor ACE to a specific time-point in
childhood; however, the survey specifically refers to events that occurred prior to age 18.
Similarly, we were not able to assess whether the timing of ACE occurred prior to any high
school trajectory changes or prior to substance use initiation. Fourth, like other longitudinal
studies, there was a considerable amount of attrition and the study used the maximum likelihood
estimations built into the SAS PROC MIXED procedure which produces unbiased parameter
estimates by accounting for all included data and other model covariates, even with missing data
on the dependent variable (Allison, 2012; Molenberghs & Kenward, 2007). Fifth, participants
that were excluded due to attrition or not providing information on ACE and substance use may
represent an especially vulnerable subset of the sample, such that the results may only provide a
preliminary understanding of these relationships. Finally, the sampling design restricted the
sample to Hispanic/Latinx individuals. This does provide needed information in a critical
minority population; however, it is not generalizable to all populations.
Conclusion
ACE can have a persistent impact on problematic alcohol and drug use as seen in the
trajectories of youth with a history of co-occurring ACE. Compared to youth who experience
fewer or no ACE, this vulnerable subset of students is at greater risk for problematic use as early
as age 14 that persists through young adulthood. However, and importantly for prevention and
intervention efforts, the moderating effect of social support during high school can attenuate the
effect of ACE for problematic use. This early reduction of problematic alcohol and drug use may
present a lasting benefit and limit the negative effects of ACE and the impact of problematic use.
The results of this study emphasize the potential benefits of social support during high school in
reducing problematic alcohol and drug use and attenuating the impacts of ACE on these
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relationships, disrupting the risk trajectories among Hispanic youth and emerging adults.
Meaningful and supportive personal relationships have tremendous value and, it may be
advantageous to promote relationship building via campus programs, mentorships, or other
intervention.
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Chapter 4: Relationship Between Hours of Media Use and Internet Addiction Across
Adverse Childhood Experiences and Social Support.
Introduction
The rapid expansion of the internet has provided health benefits that include increases in
global access to health information, connection and communication, and a platform for advocacy
(Gatto & Tak, 2008; Levy & Strombeck, 2002). However, like any other activity, excessive
internet use may become problematic, particularly if it begins to disrupt sleep, promote social
isolation, cause personal neglect, affect employment, and exacerbate other physical, mental, and
developmental issues (Andreassen et al., 2016; Emelin et al., 2013; Grüsser et al., 2006; King et
al., 2012; Mei et al., 2018). One of the early challenges of internet research was how to
operationalize risky use. Some studies have used frequency/time of use and others have adapted
scales that identify criteria for problematic use or addictive use. Researchers measuring internet
addictions have noted that an overall increase in prevalence may simply reflect a pattern of
increasing human-machine interaction (Pan et al., 2020) and given this growth in use, many
researchers have moved to measuring risky or problematic use over frequency. Internet addiction
or problematic internet use can be broadly conceptualized as an inability to control one’s use of
the internet which leads to negative consequences in daily life (Spada, 2014). Although there is
considerable variability in the way internet addiction is operationalized, the pooled estimates
across 113 studies approximate that the adult proportion of the world qualifying for internet
addiction is 7.02% (95%CI=6.09%–8.08%) (Pan et al., 2020).
Interest in the addictive potential of the internet has grown dramatically with studies
assessing excessive internet users vs. non-excessive users, vulnerable groups of excessive
internet users, psychometric evaluations, case studies, and studies examining the relationship of
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internet addictions and other behaviors (Widyanto & Griffiths, 2006). Problematic internet use
has been found to be associated with depression and other mental health issues, attention deficit
hyperactivity disorder, psychosocial maladjustment, social isolation, and substance use disorders
(Guillot et al., 2016; Jorgenson et al., 2016; Kormas et al., 2011; Kuss et al., 2013; Shaw &
Black, 2008; Young & De Abreu, 2010). A review of worldwide studies from 1996 to 2018 that
considered problematic internet use, noted that the prevalence was increasing over time,
however, prevalence varied with different assessment tools (Pan et al., 2020). There are over 45
tools assessing internet addiction and problematic internet use (Laconi et al., 2014) and one of
the first and most frequently used scales is the Internet Addiction Test (Young, 2016), which has
shown high internal consistency and validity in a variety of populations (Frangos et al., 2012;
Widyanto et al., 2011; Widyanto & McMurran, 2004; Young, 1998). Internet addiction as
assessed by the Internet Addiction Test has been associated with general health issues, sleep
problems, mental health issues, work/school problems, and other health issues similar to those
associated with gambling disorders (Young, 2016). This may be further exacerbated with the
increased prevalence of smartphones, devices that can provide instant and wide-ranging access to
applications that allow users to watch videos, play video games, surf the internet, access social
networks, and even gamble online (Samaha & Hawi, 2016).
Internet addiction is measured similarly to pharmacologic addictions by assessing the
negative factors associated with use, which distinctly separates the term from other internet use
descriptors such as “excessive” or “heavy” that only measure the quantity of use (Kurniasanti et
al., 2019; Sheldon et al., 2021; Weinstein & Lejoyeux, 2010). There are studies assessing
addiction and studies assessing frequency of use; however; there are far fewer available studies
investigating the differences in these groups and how patterns between heavy and addictive use
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may vary with respect to online media modalities such as social media compared to online
gaming (Sheldon et al., 2021). Given that for many internet-users time spent online is increasing,
it is important to further investigate factors that may moderate the relationship between heavy
and addictive use, particularly for common activities like social media and gaming. Young
adulthood (a developmental period that has also been referred to as emerging adulthood), is a
significant period for addiction research (Sussman & Arnett, 2014). In addition to the
transformative and dynamic nature of this stage, young adulthood is characterized by an increase
in risk for addictive behaviors when compared to other life stages (Bose et al., 2018; Hedden et
al., 2015). This is also a period of extended learning and experimentation, in addition to an
increased independence that can establish lifelong patterns of behavior and set a precedent for
relational and career trajectories (Sussman & Arnett, 2014). Many addictions are established in
young adulthood and can lead to life-course morbidity and mortality, making these periods
integral for research, prevention, and treatment. Young adults (Guillot et al., 2016; Sussman &
Arnett, 2014) may be at particular risk for problematic internet use especially as younger
generations have grown up with the internet and have been spending increasing amounts of time
online (Jorgenson et al., 2016; Young & De Abreu, 2010).
Adverse childhood experiences (ACE), highly correlated stressors occurring before the
age of 18, such as child maltreatment (e.g., sexual, physical, and verbal abuse) and household
dysfunction (e.g., parental divorce, substance use and household mental illness, incarceration,
and homelessness) (Felitti, Anda, Nordenberg, Williamson, Spitz, Edwards, Koss, et al., 1998),
are among the most consistent and robust predictors of poor health outcomes, including
dysregulated engagement in addictive behaviors (Forster, Rogers, Sussman, Yu, et al., 2021;
Forster, Rogers, Sussman, Watts, et al., 2021; Hughes et al., 2017; Ng & Wiemer-Hastings,
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2005; Sharma & Sacco, 2015). Individuals with a history of ACE tend to have significantly more
life-course health problems that contribute to premature morbidity and mortality (Hughes et al.,
2017). ACE can disrupt physiological pathways during development and may result in cognitive
and emotional impairment and a further increased allostatic load (Albott et al., 2018; Danese &
McEwen, 2012; Hughes et al., 2017; Pechtel & Pizzagalli, 2011), exacerbating an already
problematic response. These cognitive and emotional deficits can contribute to emotional
dysregulation and disrupted attachment, which can contribute to increased vulnerability for
maladaptive coping behaviors as well as increasing cravings for addictive behaviors that may
temporarily limit feelings of distress (Felitti & Anda, 2010; Forster et al., 2017; Gilbert, 2009;
Grant & Chamberlain, 2016; Pollak et al., 2000). Since the initial establishment of the ACE
framework by the seminal Kaiser Family Foundation study (Felitti, Anda, Nordenberg,
Williamson, Spitz, Edwards, Koss, et al., 1998), there has been a growing interest in the impacts
of ACE on addictive behaviors. Compared to pharmacologic addictions, considerably less
research has been conducted assessing the associations between ACE and online behaviors
(Jackson et al., 2021). Researchers have recently shown that heavy digital media use was three
times higher among adolescents experiencing greater amounts of ACE (Jackson et al., 2021) and
that ACE has also been associated with problematic media use (Wilke et al., 2020) (Ozbay et al.,
2008; Sippel et al., 2015). To date, no studies have assessed whether or not ACE may moderate
the relationship between heavy use and addictive use. Assessing this across different online
media such as social media and gamming can further explore the specificity of this potential
relationship. Social support has been identified as a resiliency factor, buffering the relationship
between trauma and addictive behaviors (Caravaca-Sánchez & Wolff, 2020; Gros et al., 2016).
However, there is variability in results with some studies noting that after controlling for
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covariates, social support was not enough to reduce the outcomes of trauma (Pinto et al., 2017).
Despite the benefits of social support on the effects of trauma, there is limited research
specifically assessing social support as a buffer in the relationship between ACE and internet
addiction. International research has found that in adolescents, social support was associated with
lower probability of internet addictions (Gunuc & Dogan, 2013; Wu et al., 2016), and that family
support may mediate the relationship between maltreatment and internet addiction (Lo et al.,
2021). One U.S. study of young adults found that the relationship between ACE and smart phone
addiction was moderated by social support; however, it did not moderate the relationship
between ACE and problematic internet use (Forster, Rogers, Sussman, Watts, et al., 2021).
Given the limited studies that have assessed this relationship specifically in young adulthood,
and because there is limited research assessing this relationship, further exploration of the
potential moderating effect of social support in the relationship between ACE and problematic
internet use is needed. This may be particularly of interest given the rise in online activities.
There are mixed results about the benefits or harms of online activities and the potential that they
themselves may be a form of support. One of the ways that this may be further explained is by
bettering understanding these relationships in the context of the specific modality of internet use.
For example, social media or online gaming can be beneficial to some and potentially provide
social support but for others can be a source of potential for addiction.
Study Aims
To understand the relationship between ACE, online behaviors (social media and online
gaming), and internet addiction, the current study: 1) assessed the direct relationship between
ACE and hours of social media use and online gaming on internet addiction, while controlling
for social support and other covariates; 2) the potential interaction between ACE and these online
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behaviors on internet addiction; and 3) whether social support buffers the relationship between
ACE, online behaviors, and internet addiction. We hypothesized that H1) the more time a
respondent spends on online behaviors, the higher internet addiction scores will be; H2) the more
ACE experienced the higher internet addiction scores will be; H3) the relationship between
internet addiction and online behaviors will differ across levels of ACE, such that those with
more ACE and higher time spent online using social media use and online gaming will have
higher internet addiction scores compared to those with lower ACE. Finally, to assess the role of
social support, H4) the potential ACE-online behaviors interaction on internet addiction will vary
across levels of social support. Due to the novelty of this final three-way assessment of this final
set of relationships, we did not establish a priori directionality in hypothesis 4.
Figure 12. Study 3 Hypothesized Conceptual Model
Methods and Data
Participants and Procedures
Participant information was derived from the Student Use of Internet and Technology
(SUIT) study, a cross-sectional survey assessing internet and technology use in young adults
(Forster, Rogers, Sussman, Watts, et al., 2021). The participants were undergraduate and
graduate students at California State University, Northridge. Classrooms were randomly selected
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and invited to participate in the electronic survey. After professors approved, an email containing
a link to the survey was sent to students. Recruitment began in October 2020 while the
university-maintained distance learning due to the COVID-19 pandemic. Approximately 60% of
classrooms that were invited agreed to participate and recruitment continued until a sufficient
sample size was obtained for analysis. Prior to participation, students were informed of the risks
and benefits of the study and all participation was voluntary. The Institutional Review Board
(IRB) approved all study procedures. The final analytic sample was (N=1,166) students.
Measures – Dependent Variables
Internet addiction was assessed with the 20-item Internet Addiction Test. The Internet
Addiction Test assesses characteristics (e.g., salience, excessive use, neglect of work,
anticipation, lack of control, neglect of social life) that are consistent with pharmacological
addictions (Young, 2016). Items include “How often do you neglect household chores to spend
more time online?”, “How often do your grades or schoolwork suffer because of the amount of
time you spend online?”, “How often do you try to cut down the amount of time you spend
online and fail?”, and “How often do you try to hide how long you’ve been online?” Response
were 0=Not applicable, 1=Rarely, 2=Occasionally, 3=Frequently, 4=Often, and 5 = Always
(Cronbach’s alpha = 0.92). Items were summed to create a final score where higher values
indicated greater internet addiction and scores between 80 and 100 being severe levels of Internet
addiction.
Measures – Independent Variables
Eight self-reported Adverse Childhood Experiences items, specifically household
dysfunction (e.g., parental partner violence, incarceration, alcohol misuse, illicit substance use,
mental illness, and divorce), were included. The household dysfunction questions were prefaced
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with, “Before the age of 18…” in line with the original ACE measure (Felitti, Anda,
Nordenberg, Williamson, Spitz, Edwards, & Marks, 1998). Response options were 1=“yes” and
0=“no.” Questions included were “Did you ever have to stay in a shelter or somewhere not
intended as a place to live?”, “Did any of your parents or guardians ever go to jail or prison?”,
“Did you live with anyone who drinks too much alcohol?”, “Did you live with anyone who used
illegal drugs or abuses prescription drugs?”, “Did your parents or other adults in your home ever
slap, hit, kick, punch or beat each other up?”, “Did a household member attempt suicide?”, “Was
a household member depressed or mentally ill?”, and “Were your parents ever separated or
divorced?” ACE items were then summed to create an index of childhood adversity (range 0-8).
Hours per week of each online activity were used as independent variables in the models.
Questions were asked to identify the average number of “days per week” and activity was
performed and “hours per day.” Questions asked, “on average, how many days per week do
you…”and “on average, how many hours a day do you…” This was repeated for online gaming
(“play video games”), gambling (“gamble for money online”), social media (“on social media
such as: Facebook, Twitter, Instagram, Snapchat, etc.”), sexually explicit content (“watch
sexually explicit content i.e., pornography”), dating (“dating or relationship website
applications”), and shopping (“spend time online shopping”). To calculate the number of hours
per week the “days per week” of each activity was multiplied by the “hours per day” for that
activity.
Measures – Moderators
Social Support in adolescence was assessed with the Multidimensional Scale of
Perceived Social Support (Zimet et al., 1988). Response options included 1=“strongly disagree,”
2=“somewhat disagree,” 3=“agree,” 4=“strongly agree.” There was a total of 11 items with 4
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items about support from a “special person,” 4 items about support from family, and 3 items
about support from friends. The final support measure was a sum of the 4 “special person,” items
and the 3 friend items. The 4 special person questions included: “There is a special person who is
around when I am in need,” “There is a special person with whom I can share my joys and
sorrows,” “I have a special person who is a real source of comfort to me,” and “In my life, there
is a special person who cares about my feelings.” The 3 friend questions included: “My
FRIENDS really try to help me,” “I can count on my FRIENDS when things go wrong,” and “I
have FRIENDS with whom I can share my joys and sorrows.” The 11 items were summed to
create a perceived social support score with a range of 11-44.
Measures – Covariates
Age was a continuous variable and was assessed with a single question asking: “How old
are you?” Sex was assessed with a single question asking: “What sex were you assigned at birth,
such as on an original birth certificate?” Race/Ethnicity was assessed with a single question
asking: “How do you usually describe yourself?” and coded non-Hispanic white=0, non-
Hispanic black=1, Hispanic/Latino=2, Asian/Pacific Islander=3, and other=4. The survey asked
which school within the college the students were attending was asked, coded as “Arts, Media,
Communication, Business and Economics, Education; Humanities, and Social & Behavioral
Sciences,” “Health & Human Development,” “STEM (Science & Mathematics; Engineering and
Computer Science,” and “Graduate, International and Midcareer Education.” As a proxy for
socioeconomic status two questions assessing food insecurity were used: “Within the past 12
months, I was worried whether my food would run out before I got money to buy more” and
“Within the past 12 months, the food I bought just didn't last and I didn't have money to get
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more.” Response options were “Yes” and “No.” If the respondent said yes to either question,
they were classified as food insecure, indicating low SES.
Statistical Analysis
First, a multivariable linear regression model regressed ACE and average weekly hours
of online behaviors on internet addiction. Specifically, the model estimated the impact of ACE,
hours of gaming, hours of social media on internet addiction scores while controlling for social
support, sex, age, ethnicity, and other online behaviors (hours of pornography, gambling,
shopping, and dating-app use). All online behaviors were planned as moderators; however, due
to low prevalence some were only included as covariates. Second, a batch of indirect effects
linear regression models included an interaction term of ACE*social media and ACE *gaming.
The final batch of indirect effects regression models included three-way interactions ACE*social
media*social support and a separate model for ACE*gaming*social support. To visualize the
change seen across the variables PROC PLM was used to create figures that map the predicted
probabilities of internet addiction across levels of ACE and online behaviors. These figures were
all paneled across levels of the social support to visualize significant pattern differences. Listwise
deletion of missing responses was used, resulting in a final model sample size of n=1,166.
Interaction term models included all lower order terms. All statistical tests were performed using
SAS v9.4 with a type one error rate of 0.05.
Results
The final analytic sample included 1,166 undergraduate and graduate college students
from a large ethnically diverse urban public university in Southern California. The sample was
ethnically diverse with 49% reporting Hispanic/Latino, 23% non-Hispanic White, 13% multi-
ethnic, 11% Asian/Pacific Islander, and 4% Black/African American. The sample was majority
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female (81%%), and the largest proportion of respondents were students in the College of Health
and Human Development. Considering the different online behaviors, the most frequently used
online behaviors were social media and online gaming. The average age of the sample was 23
years old. Half (55%) of the sample was ACE exposed (reporting at least one ACE) and 8% of
the sample had experienced 4 or more ACE. The most frequently reported ACE was parental
divorce (32%) followed by was household mental illness (30%) and household over drinking
(17%) (see table 7).
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Table 7. Descriptive Statistics for Study 3 (N=1,166 participants)
Variable Frequency Percent
Race/Ethnicity
Non-Hispanic white 267 22.9%
Non-Hispanic Black / African American 39 4.2%
Hispanic/Latino 571 49.0%
Asian/Pacific Islander 133 11.4%
Multiethnic / Other 146 12.5%
Sex at Birth
Male 218 18.7%
Female 948 81.3%
Food Insecurity (SES Proxy)
Yes 407 34.9%
No 759 65.1%
Schools within College
Arts, Media, Communication, Business and Economics,
Education; Humanities, and Social & Behavioral Sciences
166 14.2%
Health & Human Development 888 76.2%
STEM (Science & Mathematics; Engineering and Computer
Science
88 7.5%
Graduate, International and Midcareer Education 24 2.1%
Variable Mean Std Dev
Internet Addiction Score 30.56 15.20
Adverse Childhood Experiences 1.21 1.50
Social Support 11.28 2.43
Age (in years) 23.66 4.44
Online Gaming
Average Hours per Week 3.98 9.57
Online Gambling
Average Hours per Week 0.13 2.70
Social Media
Average Hours per Week 24.71 19.40
Sexually Explicit Content
Average Hours per Week 0.73 3.04
Online Dating
Average Hours per Week 0.23 1.18
Online Shopping
Average Hours per Week 2.85 5.70
Notes: Std Dev= Standard Deviation, Min=Minimum, Max=Maximum
Considering internet addiction, 34% of the sample scored mild internet addiction and
11% were moderate to severe internet addiction. Considering the basic bivariate correlations,
internet addiction was inversely correlated with social support and directly correlated with ACE,
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gaming, social media, pornography, dating apps and shopping. Social support was also inversely
associated with gaming and ACE was also directly associated with pornography (see table 8).
Table 8. Correlation Matrix of Main Effects for Study 3
ACE Support Gaming Gambling Social Sexual Dating Shopping
ACE 1 -0.12*** -0.02 -0.02 0.01 0.01 0.08** 0.03
Support 1 -0.08** -0.03 -0.06* -0.05 -0.10** -0.06*
Gaming 1 0.03 0.01 0.25*** -0.03 -0.01
Gambling 1 0.07* 0.07* 0.01 -0.02
Social 1 0.08** 0.08** 0.25***
Sexual 1 0.07* 0.03
Dating 1 0.01
Shopping 1
Notes: vales represent the Pearson’s correlation coefficient, *p<0.05, **p<0.01, ***p<0.001
A linear model was used to regress internet addiction scores on model main effects and
covariates. The direct effects model indicated that for every additional ACE there was a
significant (1.43, 95%CI=0.91, 1.96) increase in the internet addiction scores while controlling
for support, SES, sex, age, and race and other online behaviors. For every additional hour of
social media use per week there was a significant (0.21, 95%CI=0.17, 0.25) increase in the
internet addiction scores while controlling for support, ACE, SES, sex, age, and race and other
online behaviors. Similarly, for every additional hour of online gaming per week there was a
significant (0.21, 95%CI=0.12, 0.30) increase in the internet addiction scores while controlling
for support, ACE, SES, sex, age, race, and other online behaviors. Higher social support scores
were significantly associated with (-0.42, 95%CI=-0.73, -0.11) lower internet addiction scores
while controlling for ACE, SES, sex, age, race, and other online behaviors.
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Table 9. Regression Model
Internet Addiction Score Model N=1,166
Direct Effect Models Beta (95% CI)
Intercept 38.03*** (31.53, 44.53)
ACE 1.43*** (0.91, 1.96)
Social Media Hours 0.21*** (0.17, 0.25)
Gaming Hours 0.21*** (0.12, 0.3)
Shopping Hours 0.16* (0.03, 0.30)
Pornography Hours 0.58*** (0.31, 0.85)
Dating App Hours 0.15 (-0.44, 0.74)
Online Gambling Hours 0.05 (-0.24, 0.33)
Social Support -0.42** (-0.73, -0.11)
Two-Way Interaction Models Beta (95% CI)
ACE* Social Media Hours 0.04** (0.02, 0.07)
ACE* Gaming Hours 0.01 (-0.05, 0.06)
Three-Way Interaction Models Beta (95% CI)
ACE* Social Media Hours*Social Support -0.01 (-0.001, 0.01)
ACE* Gaming Hours*Social Support -0.04* (-0.07, -0.01)
Notes: P<0.05*, P<0.01**, P<0.001***, 95% CI= 95% Confidence Interval, all models controlled for SES, Sex,
Age, and Race, and other online behaviors (Shopping, Pornography, Dating App, and Gambling). Interaction
term models also controlled for all lower order terms and interactions
Two-way interaction models (ACE*online behaviors) assessed whether the relationship
between online behaviors and internet addiction differed by ACE to test hypothesis H3. The
relationships did not vary by ACE for both tested behaviors. There was no difference in the
relationship between hours of online gaming and interest addiction across ACE. Those with
higher mean hours of online gaming per week were associated with high internet addiction and
the relationship was stable across levels of ACE. Since this relationship was not significant, the
predicted values were not graphed. There was a difference in the relationship between hours of
social media use and interest addiction across ACE. Predicted internet addiction scores, based on
model results, showed that those with more social media use per week have higher internet
addiction scores and those with higher ACE have increasing steeper slopes. Those with high
ACE and high social media use were at greatest risk of higher internet addiction compared to
those with lower ACE and lower social media use (see Figure 14).
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Figure 13. Model Results for the Two-Way Interaction (Social Media*ACE) on – Internet
Addiction Scores
Three-way interaction models (ACE*online behaviors*Social Support) assessed whether
the online behaviors-ACE interaction was moderated by support to test hypothesis H4. The
interactions did not vary by support for both tested behaviors. In contrast to the two-way
interaction models, there was no different social media-ACE interaction across levels of support.
The significantly differing slopes that were seen in the two-way interaction term models for
social media were consistent across levels of support indicating that the relationship was not
moderated by support. Since this relationship was not significant, the predicted values were not
graphed. Also, in contrast to the two-way interaction term models, there was a difference in the
social media-ACE interaction across levels of support. Although overall, there is no difference in
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the slopes of internet addiction across gaming hours by ACE, but when stratified by social
support, there were significant differences. At low levels of support, there are differences in
slopes with higher slopes of internet addiction as gaming hours increased for those with higher
ACE compared to those with lower ACE. However, when respondents have higher levels of
social support, the slopes overlap and flatten indicating that social support is attenuating the
effects of both ACE and gaming on internet addiction (see Figure 15).
Figure 14. Model Results for the Three-Way Interaction (Gaming *ACE*Support) on –
Internet Addiction Scores
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Discussion
Although some early research has identified that ACE may have an impact on internet
addiction, the research is still limited and based on the current review of the literature, there is no
research that has assessed the impact of specific modalities of internet behaviors on internet
addiction across levels of ACE and support to assess potential moderating effects. This study
provides a preliminary novel exploration of these associations and the differences for hours spent
online gaming and using social media. Investigating these research aims may provide insight into
the buffering effects of social support upon risk factors for internet addiction. Additionally, this
study further explores the relationship between heavy use of different media and internet
addiction. Given the increase of interest in internet and technology addictions, as well as the
growing integration of internet and technology into our lives, this is an important line of research
for future public health study and intervention.
There was evidence in support of the first two direct effects hypotheses indicating that
more time spent per week on online behaviors was associated with internet addiction and that
those with higher ACE would also experience more internet addiction. These results comport
with prior research identifying both ACE and higher levels of social media and gaming use as
risk factors for internet addiction. The novel aspects of this study include identifying differing
patterns in the associations between ACE, support, and online behaviors.
The two-way interaction term models showed that the relationship between hours spent
using social media and internet addiction was different for those with higher ACE compared to
those with lower ACE. Specifically, for those with higher ACE, hours spent using more social
media were associated with more internet addiction; however, for those who were not ACE
exposed the salience of increased social media use was not as impactful indicated by a
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moderated slope. These results suggest that for ACE-exposed individuals, more time spent on
social media may produce more of the negative effects of heavy internet use (leading to internet
addiction) compared to individuals with lower ACE. This highlights the complexity of online
interactions. Although some can gain support from online socialization, buffering the role of
online behaviors and addiction, for others heavy use may become problematic. These outcomes
imply that ACE may play a role in this distinction exacerbating the impact of increased social
media use on elements of internet addiction. These same results were not seen with heavy online
gaming. For those participating in online gaming, the increasing relationship between hours
spent gaming online and internet addiction was consistent across all levels of ACE. This
relationship suggests that increasing online gaming may be a risk factor for internet addiction
and the problems associated with heavy internet use regardless of ACE. These results infer
interventions for the prevention or treatment of internet addiction should pay particular attention
to individuals participating in heavy online gaming and/or individuals who are also ACE
exposed.
To support future prevention and intervention efforts, the current study also tested the
buffering effects of social support. Similar to the differential results seen in the third hypothesis,
the results for the fourth hypothesis also varied by online behavior, further distinguishing the
pattern. The three-way interaction term models identified that the interaction seen between hours
spent using social media and ACE on internet addiction was not moderated by social support.
This means that social support would not attenuate the ACE effects seen as social media use
increased. This may be due in part to the ubiquitous use of social media within the sample. Over
90% of the sample reported some weekly use of social media. This may also be a function of the
idea that some may consider social media an element of social support while others may not. For
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online gaming, there were significant three-way interactions found, and the relationship between
hours spent online gaming and ACE on internet addiction was moderated by social support. At
low levels of support, the same interaction was seen in online gaming similar to social media.
Those with high ACE have a greater risk of increased internet addiction as the amount of gaming
increased compared to those with lower ACE. This shows that although across the entire sample
the two-way interaction between ACE and gaming was not significant, ACE does have a strong
impact on internet addiction among those with low support and ACE. Future interventions may
benefit from considering how for those with higher support, the steeper slopes of internet
addiction were attenuated. Although more gaming may increase the internet addiction scores,
some of it is the same for all ACE groups and is significantly less than for those with low
support. This underscores the benefits of social support in buffering not only the ACE effects on
internet addiction, but also the effects of high use of online games.
The results of this research are in line with other studies that have identified social
support as a beneficial buffer of risk factors for addictive behaviors. In concordance with prior
research, these findings suggest that intervention and prevention research and services should
consider the benefits of social support in college populations who may be at risk for negative
consequences of internet addiction. This is also true for those who may be at a greater
disadvantage from early childhood trauma. College support services, counseling services, and
campus interventions, as well as training, may be used in interventions and prevention efforts to
help buffer the potential negative effects of heavy internet use and internet/technology
addictions.
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Future Research
The current study only provides a preliminary look at associations with internet
addiction. These results will need to be confirmed with further study and in larger samples.
Future research should also consider other online behaviors given they have a high enough
prevalence and if these behaviors may cluster in patterns that can help to further explain these
relationships.
Limitations
First, these data are based on self-reported ACE and was assessed retrospectively;
however, much of the ACE research has been conducted retrospectively, and even studies
challenging retrospective vs. prospective reports note that retrospective reports provide a
meaningful addition to the literature and are validly associated with other subjective measures
(Reuben et al., 2016). Second, we cannot definitively anchor ACE to a specific time-point in
childhood, but the survey specifies events that occurred prior to age 18. Because of this, we are
not able to assess whether the timing of ACE occurred prior to any internet use patterns that may
have started in adolescence. Third, the data are cross-sectional and as such are limited in support
of causal conclusion. Finally, the sampling design restricted the sample to young adults at a large
public university that is in an urban diverse community. This does provide needed information
but is not generalizable to all populations.
Conclusions
Results from this study are in line with prior research emphasizing the impact of ACE
and internet use on internet addiction. This study adds to the research by assessing the
relationship between hours of online gaming and social media use with the moderating effect of
social support on these specific online behaviors, in addition to the effects of ACE on internet
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addiction. Both adverse childhood experiences and higher use of social media and online gaming
are associated with increased internet addiction and adverse childhood experiences can also
exacerbate the relationships between these hours of use and addiction. Social support was found
to be a buffer between the relationship of adverse childhood experiences and online gambling,
lowering the risk for internet addiction. The study outcomes not only indicate that adverse
childhood experiences may explain some of the relationship between hours of use and addiction
but also that social support may help to protect against the negatives consequences and addictive
results of heavy use in a young adult college population. The online presence of young adults
may continue to grow as online technologies further integrate into their lives. Social support can
help to attenuate the effects of ACE on internet addiction as heavy use becomes more prevalent.
Social support in college populations can be a critical component of future intervention and
prevention efforts seeking to combat the negative consequences of internet addiction.
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Chapter 5: Discussion
Summary of the Project
The overall goal of this dissertation is to assess the moderating effect of social support on
the relationship between ACE and addictive behaviors. This was accomplished through three
separate studies. The first study explored the impact of ACE on alcohol and drug disorder and
problematic gambling behaviors and the buffering effect of social support in these relationships
across ethnic and sexual minority statuses in a large regionally representative population of
adolescents. The second study longitudinally examined the impact of ACE in trajectories of past
30-day problematic alcohol and drug use from adolescence to young adulthood; and potential
differences in the relationship between ACE and problematic alcohol and drug use trajectories
across levels of social support in adolescence and young adulthood. The third study assessed the
relationship between ACE, online behaviors, and problematic internet use in young adults and
tested if social support may buffer the relationship between ACE and online behaviors on
internet addiction. All three studies used ACE, a measure of social support and some addictive
behavior outcomes and they also all employed similar analytic strategies, employing regression
analysis and interactions to test potential effect modification.
Although some of the associations assessed in the studies have been evaluated in other
populations, one of the unique elements of this combined project was explorations of these
connections across both adolescence and young adults with unique datasets and approaches. The
three studies that offer assessment over a series of different datasets and across time points that
provide a more comprehensive understanding of the impact of ACE and the potential of social
support as a moderator. The studies in context integrate cross-sectional analyses separately in
adolescence and young adults and then for comparison, longitudinally across both populations.
101
One of the benefits of this approach is that these results provide a spectrum of analysis to fill the
research gaps.
In addition to the variation in statistical approaches the full project represents a variety of
different populations. The data are from an ethnic specific dataset (Hispanic), data from a diverse
urban public university, and data from a collection of Midwestern schools. These provide
regional and cultural context to the overall goals of assessing the relationship between ACE and
behaviors with addictive potential and the potential moderating impact of social support. Along
with the population differences the full project offers an assessment of the proposed relationships
across unique life periods. The results of these studies that were assessed in both adolescence and
young adulthood help to inform prevention and cessation efforts in both of these important
transitional periods that are critical to lifelong habit formation. Finally, the project allows for the
assessment of social support at different times within the adolescent-young adulthood window.
When compared across studies, this provides an additional element relevant for prevention and
intervention. The benefits of support in buffering the impact of ACE can be seen across different
time periods, ethnic and regional samples, and with support provided in adolescence or young
adulthood. These results taken in sum demonstrate the robustness of social support as a
moderator in the relationship between ACE and addictive behaviors.
Overall Implications
The results from all three studies show that social support is a robust moderator between
the relationship of ACE and addictive behaviors. Social support can be provided either in
adolescents or in young adulthood and the effect can be beneficial, although the more proximal
the support (closer to the period of addiction) the greater the potential effect. In addition to the
overall benefits of support across these samples there are also specific unique groups that benefit.
102
Racial/ethnic and sexual minorities may particularly benefit from the buffering effect social
support may provide, helping to reduce the impact of ACE on addictions. Additionally, social
support is a consistent stress buffer across both adolescents and young adults.
The results of this research are in line with other studies that have identified social
support as a beneficial buffer of risk factors for addictive behaviors. In concordance with prior
research, these findings suggest that intervention and prevention research and services should
consider the benefits of social support in adolescent and young adult populations and that this is
especially true for those who may be at a greater disadvantage from adverse childhood
experiences. Support services in schools and colleges, as well as training, may be used in
interventions and prevention efforts to help mitigate the impact of ACE and the subsequent
health outcomes such as engaging with addictive behaviors including disordered substance use,
problematic gambling, and internet addiction. Helping youth and young adults develop positive
relationships to cope with trauma is a key component of future prevention work and the current
study is in line with this concept highlighting the importance of positive tools such as social
support in potentially reducing the effects of adverse stressors and promoting life source health.
Overall Limitations
All three studies are only representable to their respective samples and samples with
similar demographic profiles. The first study was also a regional sample and as such is
representative to a larger sample population. One of the benefits of the project in total is that
these relationships are being tested in different samples and this allows for assessing the
consistency of support and ACE as associated with addictive behaviors. The second limitation
that was consistent across all studies was that ACE was assessed with retrospective self-reported
measures. However, much of the ACE research has been conducted retrospectively through self-
103
report and even studies challenging retrospective versus government record-based reporting note
that retrospective reports provide a meaningful addition to the literature and are validly
associated with other subjective measures (Reuben et al., 2016). Given the way ACE is typically
measured we cannot definitively anchor ACE to a specific time-point in childhood but all
questions across studies followed the survey guidelines of the original ACE study and preceded
all questions with a statement that specified events that occurred prior to age 18. Similarly, the
addictive outcomes were all also self-reported measures. Although the inclusion of biomarkers,
journals, or other confirmatory tests would provide a more definitive report of abuse the
measures used in these studies have been used across many addiction studies and have been
shown to be consistent in many populations including adolescents and young adults. When all
three studies are taken as a whole there is another limitation that arises regarding the consistency
of variables across studies. Although each study captures social support and ACE, the way they
are operationalized in each study is different. This makes comparison of these measures across
studies challenging and across study comparisons should be limited. The final limitation that was
consistent across studies was the survey sampling methodology. All studies included non-
probability samples and as such are limited by sampling biases. Despite these overlapping
limitations the studies provided consistent results across multiple outcomes, populations, and
time points. The project benefits from this observed consistency in that even with the limitations
across studies there is stability in the effects seen building greater evidence towards causality
even though complete causal conclusion cannot be derived from these samples.
Future Research Directions
Across all studies social support both in adolescents and young adulthood was a mostly
consistent moderator of the negative effects of ACE on addictive behaviors; however, this is only
104
a single picture of support. Even though the measure of support used in this study have been well
tested in populations the current studies used social support as a single indicator. More fine grain
detail of support would further benefit intervention and prevention efforts. Future research into
these effects should consider the different sources of support. It may be that one source friend
verses family verses teacher may have differential effects. This specificity can help to further
inform intervention efforts by providing specific support systems to build in youth in young
adults.
Although one of the three studies was able to assess subgroup differences in the
interactions more research into how ACE effects different demographic group is needed in future
research into the ACE-addictive behavior relations. This may provide more data for a targeted
approach to prevention and intervention and to identify not only those at greater risk but who
may benefit most from interventions such as social support. Additionally, only one study was
able to assess these effects longitudinally. There is limited longitudinal ACE research data and
even less using support as a moderator. Continued longitudinal research is needed to further
assess the risk of ACE and the potential for support not only be moderate the effects but also if
there are any other factor that may mediate these relationships.
Finally, another area for future research is to assess clustering of behaviors. Using data
driven processes and analytics to establish patters and clusters in the outcomes may help to
further explain some of the relationships seen. These addictive behaviors explored in this project
share similarities with regard to their ability to elicit some of the same core criteria for addiction
(relapse, withdrawal, conflict, salience, mood modification, craving, and tolerance).
Understanding how these behavior pattern across populations and by ACE can further identify
additional risk behaviors to consider for intervention based on current addiction profiles. These
105
studies provide a preliminary look at these relationships and many of them are novel and need
further exploration.
Concluding Remarks
In review, the benefits of social support in youth and young adults can be extended to
buffering the effects of adverse childhood experiences on addictive behaviors both
pharmacological and non-pharmacological. These benefits are especially salient for minority
individuals and are consistent across adolescence and young adulthood. Social support for
adolescents can protect against the impact of adverse childhood experiences on alcohol, drug,
and gambling disorders and is particularly beneficial for ethnic and sexual minorities. Adverse
childhood experiences can also increase problematic alcohol and drug use as adolescents
transition to young adulthood and social support can lessen this impact. Social support can also
buffer between the relationship of adverse childhood experiences and online gambling, on
internet addiction. This not only indicates that adverse childhood experiences may explain some
of the relationship between hours of use and addiction, but also that social support may help to
protect against the negative consequences and addictive results of heavy use.
The impact of ACE on addictions is pervasive and can continue throughout adolescence
and young adulthood, underscoring this as a critical point of public health research and cause for
determining the factors, such as social support, which can mitigate the effects of ACE. This
research can contribute to the body of work that is used for trauma-informed programming in
both high school and college systems. When it comes to internet behaviors, individuals with
childhood trauma and increased online gaming time may especially benefit from interventions
that employ social support as a means of reducing internet addictions. These findings can inform
106
future studies on the development of the addiction interventions and future research may extend
these results to even more behaviors with addictive potential.
107
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Abstract (if available)
Abstract
Social support has been identified as an important tool in mental health interventions and has been associated with many positive health outcomes. In contrast, adverse childhood experiences have been among the most consistent and robust predictors of poor health outcomes including addictive behaviors such as pharmacologic and behavioral addictions. The three studies presented in this dissertation are exploring the role of social support as a moderator in the relationship between adverse childhood experiences and addictive disorders and behaviors. To do this, three distinct datasets are used: (study 1) a large regional sample of adolescents, (study 2) a cohort sample of Hispanic adolescents followed through young adulthood, and (study 3) a cross-sectional sample of young adults in college. Results presented within the first study indicate that for adolescents, social support can protect against the impact of adverse childhood experiences on alcohol, drug, and gambling disorders and is particularly beneficial for ethnic and sexual minorities. Results from the second study suggest adverse childhood experiences can increase problematic alcohol and drug use as adolescents transition to young adulthood and that social support during adolescence can lessen the impact of ACE on problematic use. The third study identified that both adverse childhood experiences and higher use of social media and online gaming are associated with increased internet addiction. Also, adverse childhood experiences can exacerbate the relationships between hours of use and addiction. Social support was found to be a buffer between the relationship of adverse childhood experiences and online gambling, on internet addiction. This indicates that adverse childhood experiences may explain some of the relationship between hours of use and addiction, and that social support may help to protect against the negative consequences and addictive results of heavy use.
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Rogers, Christopher J.
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Core Title
The role of social support in the relationship between adverse childhood experiences and addictive behaviors across adolescence and young adulthood
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Keck School of Medicine
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Doctor of Philosophy
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Preventive Medicine (Health Behavior Research)
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2022-05
Publication Date
03/03/2024
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addictive behaviors,adolescence,adverse childhood experiences,Alcohol,drug,Gambling,internet addiction,OAI-PMH Harvest,social support,young adulthood
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Tags
addictive behaviors
adverse childhood experiences
drug
internet addiction
social support
young adulthood