Theory Analysis 1The purpose of the assignment is to prepare stu.docx

Theory Analysis 1
The purpose of the assignment is to prepare students to read and
comprehend research articles in the field of communication
studies. Students will develop a deeper understanding of one
area of research for the main topic area.
Instructions:
1. Using the peer reviewed research article you found on video
games. Complete the areas below by providing:
a. a citation for the article in APA format
b. the purpose of the study
c. the type of study
d. the methodology used in the study
e. the results
f. a personal reaction
Student’s Name
COM520
Theory Analysis #
APA Citation:
Purpose of Study:
Type of Study:
Methodology:

Results:
Personal Reaction:
Running head: COMPANY NAME
1
21 ForEver
Company Description and SWOT analysis
LaTina Hamm
Strayer University
BUS 599
Dr. Andrea Banto
July 28, 2019
Company Description and SWOT analysis
21 ForEver was established to offer a healthy, all-natural
organic beverage supplement. It is essential to want to remain
youthful, as you get older, the need for more nutirients and a
much more active life style for the older generation is what is
most desired. Studies have proven the less active you become
once your are older, the greater the need for a boost. 21
ForEver offers just that! It’s invention was because of the
limited number of healthy beverages supplements available on
the market. 21 ForEver beverages are organic, all-natural with
with no pesticides, or artificial sweeteners, colors, or

preservatives. 21 ForEver has become a household name with
the introduction of "Very Berry" a new addition to 21 FoEver.
The growth of 21 ForEver has resulted in this wonderful
beverage being found in larger retail stores such as Whole
Foods Groceries, Wegmans, and Walmart. The future for 21
ForEver will launch its new vegetable beverage drink line that
will appeal to vegans.
Mission Statement
21 ForEver is a cost-effective NAB that is healthy and
guarantees that you will live a
much healthier life by incorporating this fantastic beverage
which offers more vegetables
compared to beverages in its class on the market today. 21
ForEver desires that consumers will
choose 21 ForEver because we pride our ability to be an organic
pesticide-free, all-natural
product. Free of artificial colors, flavors, and sweeteners. 21
ForEver stands proudly
behind its brand which helps to accomplish its mission
Trends in the industry
· 21 ForEver will market via the Pop-Up Shop Trend, where you
set temporary retail establisments.
· The desire to consume organic beverages have affected
supermarket chains. Although organic beverages are generally
high in comparison to sugary drinks, many consumers are
switching to organic to promote a healthier lifestyle ("Top 10
Natural & Organic Food Trends for 2019,"n.d.) 21 ForEver has
projections future growth rate:

· Have the products placed into the Fitness Centers and Gyms
Strategic position
The use of Pop-up marketing is an economic way to introduce
products to consumers
your procduct. Having a website is a must; a website is a great
marketing resource for
information about the product. Social media is a must
marketing tool, on all Social media
platforms and websites
Distribution
Production cost for organic food is usually much higher than
non organic foods therefore
Distribution for organic foods cost more. The care of nurturing
and feeding both the natural and
livestock is why the cost is more expensive than non organic
products.
Risks
Every business involves risk. 21 ForEver clients may decrease
as they may not be able to
afford the cost that is associated wih a healthy beverage. “A
risk asssessement helps to prepare
for and prevent threats to your success.(Secreats & Strategies).
The risk that the competitors
have established clients.

The idea that the Fitness Facility Golds Gym, may not be
receptive to marketing 21 Forever in its
facilities, the brand may not be a fit for the company.
SWOT Analysis
Strengths
· Beneficial to you health
· Organic
· Vegatable in a Drink
· New to the market
Weaknesses
· New Brand
· Small Business
· Limited Products
· Not established
Opportunities
· Increased Profits
· Pop-up Shop
· Expanding online partnreship by tapping into Amazon and
eBay can increase output and sales.
· Expand Products
Threats
· Competition
· A slow economy.
· Product marketing

· Marketing
References
Alton, Lary (2017). Entreprenaeur, Pop-up Shop Markeing: A
Quick How-to Guide, Small Business Heroes.
Retrieved from: https://www.entrepreneur.com/article/296064
Abrams, R. (2014). Successful Business Plan: Secrets &
Strategies (6th Ed.). Palo Alto, CA: Planning Shop.
Why is Organic food more expensive that Coventional food:
FAO (n.d.). Retrieved from : http://www.fao.org/organicag/oa-
faq/oa-faq5/en/
Top 10 Organic Food Trends for 2014. (n.d.). Retrieved from
http://livingmaswell.com/top-5-organic-food-trendsfor-2014
Assignment 4: Business Plan – Final
Due Week 10 and worth 150 points
This assignment consists of two sections: your final business
plan and your business plan financials. Note: You must submit
both sections as separate files for the completion of this
assignment.
You have completed all the necessary sections of your business
plan and will now create a final draft. Use any/all feedback you
have received to polish your plan to the point that you could
confidently show it to investors and potential partners or
customers.
Refer to the outline of a business plan beginning on page 399 of
the course text. Not all businesses will include all these
components in this order, but use the outline as a guide.
Specifically, your plan will not require the development,
milestones, and exit plan sections of the business plan.
Section 1: Business Plan – Final (MS Word or equivalent)
Construct a 10–30-page business plan. Note: Twenty pages are
sufficient for most businesses.
· Write a 1–3-page executive summary for your business plan,
in which you justify the following:

a. A clear and concise business concept
b. A thoroughly planned business concept
c. A capable management structure
d. A clear-cut market need
e. Significant competitive advantages for your business
f. Realistic financial projections
g. That investors have an excellent chance to make money
h. A realistic and developed exit plan
Note: Read Chapters 4 and 18 of the course text, Successful
Business Plan. Use the plan preparation worksheets on pages
58–61 and the sample executive summaries on pages 62–66 to
help guide you; choose to write either a synopsis summary or a
narrative summary and include highlights from each section of
your business plan.
· Combine all the sections stated below and revise your initial
business plan draft, which you submitted in Week 8, based on
feedback you have received.
· Executive summary
· Company description (Assignment 1)
· Industry analysis and trends (Assignment 1)
· Target market (Assignment 2)
· Competition (Assignment 2)
· Strategic position and risk assessment (Assignment 1)
· Marketing plan and sales strategy (Assignment 2)
· Operations plan (Assignment 3)
· Technology plan (Assignment 3)
· Management and organization plan (Assignment 3)
· Ethics and social Responsibility plan (Assignment 3)
· The Financials (Week 7 Discussion)
· Hint:
· The financial section of your business plan will be derived
from the previously completed financial worksheets.
· Note:
· The financials and the management description must spark
enough interest to convince a reader to continue. Enhance the
two mentioned sections to engage the reader.

Format your assignment according to these formatting
requirements:
· Cite the resources you have used to complete the exercise.
Note: There is no minimum requirement for the number of
resources used in the exercise.
· The paper must be typed, double-spaced, using Times New
Roman font (size 12), with 1-inch margins on all sides;
references must follow APA or school-specific format. Check
with your professor for any additional instructions.
· Include a cover page containing the title of the assignment, the
student’s name, the professor’s name, the course title, and the
date. The cover page and the reference page are not included in
the required page length.
Section 2: Business Plan Financials (MS Excel worksheets
bundled with course textbook)
1. For year one, submit a revised income statement, cash flow
projection, and balance sheet from the “Business Plan
Financials” Excel template based on your feedback from Project
Deliverable 3: Operations. Technology, and Management Plan
with Financials – Draft. You will submit the entire completed
and revised Excel worksheet.
The specific course learning outcome associated with this
assignment is:
· Construct a business plan with an executive summary that
justifies a clear concept, a management structure, a market
need, competitive advantages, and financial projections.
Contents lists available at ScienceDirect
Journal of Affective Disorders
journal homepage: www.elsevier.com/locate/jad

Research paper
Video game addiction in emerging adulthood: Cross-sectional
evidence of
pathology in video game addicts as compared to matched
healthy controls☆
Laura Stockdale⁎, Sarah M. Coyne
School of Family Life, Brigham Young University, 2086 JFSB,
Provo, UT 84602, United States
A R T I C L E I N F O
Keywords:
Video game addiction
Pathological gaming
Emerging adults
Internet gaming addiction
A B S T R A C T
Background: The Internet Gaming Disorder Scale (IGDS) is a
widely used measure of video game addiction, a
pathology affecting a small percentage of all people who play
video games. Emerging adult males are sig-
nificantly more likely to be video game addicts. Few
researchers have examined how people who qualify as video
game addicts based on the IGDS compared to matched controls
based on age, gender, race, and marital status.
Method: The current study compared IGDS video game addicts
to matched non-addicts in terms of their mental,
physical, social-emotional health using self-report, survey
methods.
Results: Addicts had poorer mental health and cognitive
functioning including poorer impulse control and ADHD
symptoms compared to controls. Additionally, addicts displayed

increased emotional difficulties including in-
creased depression and anxiety, felt more socially isolated, and
were more likely to display internet pornography
pathological use symptoms. Female video game addicts were at
unique risk for negative outcomes.
Limitations: The sample for this study was undergraduate
college students and self-report measures were used.
Conclusions: Participants who met the IGDS criteria for video
game addiction displayed poorer emotional,
physical, mental, and social health, adding to the growing
evidence that video game addictions are a valid
phenomenon.
1. Introduction
Video games have become a normative part of Western culture.
For
most video game players, video games are a harmless way to
relive
stress, socialize with peers, and spend time. Parents of
adolescents and
young adults frequently joke that their kids are "addicted" to
video
games, but this is hyperbole for most youth. However, there is
evidence
that for some individuals, video game play can interfere with
social
functioning and well-being. There is no universal definition of
addic-
tion, but Orford (2001) defined addiction as "a combination of
operant
reward, usually in the form of some powerful emotional change,
plus
wide cue elicitation of conditioned responses that assists
consumption
in one way or the other, operating within diverse social

contexts, be-
tween them constitute a powerful set of processes responsible
for the
amplification of a small and unremarkable liking into a strong
and
potentially troublesome attachment (p.22)." Hellman et al.
(2013)
further elaborate that a reward in this context can be anything
that is
pleasurable, and does not limit only to substances, but can
include re-
wards like gambling and video games. Therefore, addiction need
not be
limited only to substances, but can include any external stimuli
that
creates a "strong and potentially troublesome attachment."
Video game
use becomes pathological when this strong attachment damages
mul-
tiple levels of functioning such as family life, social
functioning, school
or work performance, or psychological functioning (Gentile et
al.,
2011).
1.1. Video game addiction
A nationally representative sample of 8–18-year-old youth in
the
United States found that approximately 8% of video game
players dis-
played pathological patterns of play (Gentile, 2009). In a
nationally
representative sample of 15–40-year-old participants in Norway
ap-

proximately 4.6% of video game players displayed pathological
pat-
terns of play and .6% met the criteria for true video game
addiction
(Mentzoni et al., 2011), suggesting that video game addiction is
a rare,
but valid phenomenon affecting a small percentage of video
game
players. In fact, formal features of video games may increase
the like-
lihood of developing addictive behaviors, similar to the formal
features
of slot machines increasing the likelihood of gambling
addiction. Pre-
vious researchers have argued that video games are exceptional
http://dx.doi.org/10.1016/j.jad.2017.08.045
Received 17 January 2017; Received in revised form 27 June
2017; Accepted 14 August 2017
☆ This research was funded by a LUROP fellowship awarded by
the Loyola University Chicago Office of the Provost.
⁎ Corresponding author.
E-mail address: [email protected] (L. Stockdale).
Journal of Affective Disorders 225 (2018) 265–272
Available online 18 August 2017
0165-0327/ © 2017 Elsevier B.V. All rights reserved.
T
http://www.sciencedirect.com/science/journal/01650327
http://www.elsevier.com/locate/jad

mailto:[email protected]
https://doi.org/10.1016/j.jad.2017.08.045
http://crossmark.crossref.org/dialog/?doi=10.1016/j.jad.2017.08
.045&domain=pdf
teachers because they increase in difficulty as players’ master
game
content and technique, present multiple ways of solving or
mastering a
problem, require repeated practice over multiple days, provide
rewards
for achievement, increase popularity by achieving success, and
provide
an adrenaline rush which excite learners (Gentile and Gentile,
2005).
These exceptional “teachers” have narrative and identity
features such
as being able to create an avatar playing in the game that looks
like the
player or how the player wishes they looked and constant
reward and
punishment features such as experience points, loss of life,
gaining
health, repairing items, difficult "bosses" at the end of a level,
instant
rewards, and the ability to instant replay a level, which all lead
to in-
creased difficulty disengaging from video games (King et al.,
2010).
These formal features which make video games excellent
teachers and
difficult to disengage from also increase the likelihood of
developing
addictive behaviors and tendencies.

1.2. Risk factors for video game addiction
Young adult males have been shown to be at the greatest risk
for
video game addiction possibly due to the flexible work/study
hours
associated with the higher education typical during this age
range,
living outside of the home for the first time, and increased
autonomy
(King et al., 2012b; Young, 1998). Time spent playing video
games,
poor social competence (Gentile et al., 2011), poor impulse
control,
increased sensation seeking, increased narcissistic personality
traits
(Griffiths et al., 2012), high state and trait anxiety (Mehroof
and
Griffiths, 2010) and previous truancy and few leisure activities
(Rehbein et al., 2010) are all risk factors to developing video
game
addictions and in fact are risk factors to most addictive
behaviors. In
adolescents, being from a single parent home is a risk factor for
de-
veloping a video game addiction (Rehbein and Baier, 2013),
likely due
to lack of monitoring and increased time spent playing video
games. A
series of studies by Dong et al., (2010, 2013) found executive
func-
tioning problems in response to a color word stroop task in
video game
addicts, further reflecting the importance of poor impulse
control and
behavioral inhibition in video game addiction.

1.3. Outcomes of video game addiction
Video game addiction has been associated with a variety of
negative
psychological and social outcomes including decreased life
satisfaction,
loneliness, social competence (Lemmens et al., 2009), poorer
academic
achievement, increased impulsivity (Gentile, 2009), increased
aggres-
sion (Griffiths et al., 2012), and increased depression and
anxiety
(Mentzoni et al., 2011). It is important to note that time spent
playing
video games alone was not associated with these negative
social,
emotional, and psychological outcomes and that these negative
out-
comes are specifically related to video game addiction
(Brunborg et al.,
2014). Some research suggests that some of the negative
consequences
of pathological gaming can be negated if gamers are able to
disconnect
from the gaming world. For example, Gentile et al. (2011)
found that
depression, anxiety, and social phobias all improve when
adolescents
stop being a pathological gamer. Similarly, Cognitive
Behavioral
Therapy (CBT), a therapeutic approach that teaches people to
recognize
emotions and thought processes associated with addictions and
learn
coping skills to correct these cognitions, has been relatively

effective at
treating and preventing relapse of video game addictions
(Griffiths and
Meredith, 2009).
1.4. Purpose of the current study
Though much research has examined the risks and outcomes of
video game addiction, all of the previously mentioned studies
failed to
compare video game addicts to age and gender matched healthy
con-
trols and instead compare addicts to the general population.
Comparing
video game addicts to the general population fails to take into
account
subtle differences in mental, social, physical, and emotional
health
outcomes that vary by gender, ethnicity, age, and marital status.
For
example, racial-ethnic minority populations display
significantly higher
rates of obesity (Carroll et al., 2008; Paeratakul et al., 2002)
and
married people display lower rates of depression (Inaba et al.,
2005).
Thus, comparing a racial-ethnic minority or married video game
addict
to the general population may compound outcomes and falsely
attri-
bute differences in health outcomes to video game addiction.
Similarly,
the previous studies did not use measures of social and
psychological
functioning recommended by leading health organizations. This

study
seeks to further lend support to the potential validity of the
IGDS as a
measure of video game addiction by assessing the relationship
between
participants whose IGDS scores would qualify them as video
game ad-
dicts and how this classification is associated with poorer
emotional,
social, mental, and physical health. Therefore, the goal of the
present
study is to compare video game addicts to healthy controls that
are
matched on age, race, gender, and marital status on measures of
phy-
sical, social, mental, and emotional health recommended by the
Na-
tional Institute of Mental Health, the U.S. Department of Health
and
Human Services, and the World Health Organization. This study
will
also assess comorbidity between IGD video game addiction,
substance
use, and other online addictions. Previous researchers have
shown high
comorbidity between substance addiction and addictions to
other sub-
stances (Dani and Harris, 2005), gambling addiction and
tobacco use
(McGrath and Barrett, 2009), and gambling addiction and
substance
use and abuse (Lorains et al., 2011), and the comorbidity
between
addiction and psychiatric disorders (Kessler et al., 2008; Stein
et al.,
2001). However, few researchers have examined video game

addiction
and potential comorbidity with substance use, gambling, and
internet
pornography use.
We hypothesize that IGD video game addicts will display
poorer
social, emotional, physical, and mental health than matched
non-ad-
dicts. We also hypothesize that IGD video game addicts will
display
increased comorbidity between video game addiction and other
ad-
dictive behaviors as compared to matched non-addicts.
2. Method
2.1. Participants
1205 young adults (mean age = 20.32, SD age = 4.17; 48.85%
male, 50.15% female, all participants reported their gender)
who re-
ported playing video games were recruited from two large
universities
in the United States, one in a large urban setting in the Midwest
and one
in the Mountain West. Of the 1205 young adults screened, 87
met the
criteria for video game addiction (approximately 7%). The 87
video
game addicts (mean age = 20.80, SD age = 2.18; 68% male, 15%
female; 78.3% Non-Hispanic White, 6.6% African American,
2.8%
Latino, 5.7% Asian, and 8.5% Other; 85% single, 15% married)
were
matched on geographical location, age, sex, ethnicity, and

marital
status to non-addicts. This results in a final sample of 174
addicts and
non-addicts.
2.2. Procedures
Participants were recruited through university online systems
for
introductory psychology courses and were given class credit
required
for course completion for completing an online study.
Participants
completed an online survey through Qualtrics which took
approxi-
mately one hour to complete. They were specifically told that
the
purpose of the study was to examine media and behavior and
that they
must have played video games to participate. All participants
gave
implied consent and all procedures and materials were approved
by
both universities internal review boards.
L. Stockdale, S.M. Coyne Journal of Affective Disorders 225
(2018) 265–272
266
2.3. Measures
2.3.1. Video game addiction
Video game addiction was measured using the Internet Gaming

Disorder Scale (IGD; Lemmens et al., 2015). The IGD is a nine
question
self-report measure of addiction that covers criteria described in
the
DSM-V, including preoccupation, tolerance, withdrawal,
persistence,
escape, deception, displacement, and conflict regarding video
game
use. Participants answer yes or no to the nine questions in
regards to
their gaming behavior in the last 12 months. Participants who
respond
yes to five or more items are classified as addicts. Participants
who
played video games, but responded yes on two or fewer
questions were
used as controls. Previous research has shown the IGD to be a
reliable
and valid measure of video game addiction (Lemmens et al.,
2015, α =
.93). Example items include "In the last 12 months have you
hidden the
time you spend on games from others?" and "In the last 12
months have
you played games so that you would not have to think about
annoying
things?"
2.3.2. Attention-deficit/Hyperactivity
Attention-deficit/Hyperactivity (ADHD) symptoms were
measured
using the World Health Organization Adult ADHD Self-Report
Scale
(ASRS; Kessler et al., 2004). The ASRS is an eighteen-item

measure of
ADHD symptoms that can be administered verbally or through
survey.
Participants select how frequently statements are true for them
on a
five-point scale from 1 = never to 5 = very often. Higher scores
are
indicative of more symptoms associated with ADHD. Previous
re-
searchers have shown the ASRS to be a reliable and valid
measure of
ADHD symptoms for the general population (Kessler et al.,
2007; α =
.72). Example items include "How often do you have trouble
wrapping
up the fine details of a project, once the challenging parts have
been
done?" and "How often do you find yourself talking too much
when you
are in a social situation?"
2.3.3. Cognitive functioning
Cognitive functioning was measured using the Neuro-QOL
(quality
of life) Cognitive Functioning short form (Cella et al., 2012).
The
Neuro-QOL was developed by the National Institute of
Neurological
Disorders and Stroke and has been shown to be reliable for
clinical
populations and the general public in order to assess multiple
domains
of quality of life (α = .85–.96, Cella et al., 2012). The cognitive
functioning subscale contains eight questions and participants
answer

four questions on a five-point scale of 1 = never to 5 = very
often
(several times a day) and four questions on a scale of 1 = none
to 4 =
cannot do. Higher scores are indicative of more cognitive
difficulties.
Example items include "In the past 7 days I had to read
something
several times to understand it" and " How much difficulty do
you cur-
rently have reading and following complex instructions (e.g.,
directions
for a new medication)?"
2.3.4. Mental health
Mental health was measured using the mental health subscale of
the
PROMIS Global Health Scale (Hayes et al., 2009). The mental
health
subscale includes four items and range on a five-point scale
from 1 =
excellent to 5 = poor, with higher scores being indicative of
poorer
mental health. Example items include "In general, how would
you rate
your mental health, including your mood and ability to think?"
and "In
general, please rate how well you carry out your usual social
activities
and roles (This includes activities at home, at work, and in your
com-
munity, and responsibilities as a parent, child, spouse,
employee,
friend, etc.)". The mental health subscale of the PROMIS
Global Health

scale has been shown to be a reliable and valid measure of
mental
health (α = .86, Hayes et al., 2009).
2.3.5. Physical health
Physical health was measured using the physical health subscale
of
the PROMIS Global Health scale (Hayes et al., 2009). PROMIS
is a
system of questionnaires developed by the U.S. Department of
Health
and Human Services intended to monitor and evaluate physical,
social,
and emotional health in adults and children and can be used
with the
general population and individuals living with chronic
conditions. The
physical health subscale includes four items and range on a
five-point
scale from 1 = excellent to 5 = poor, with higher scores being
in-
dicative of poorer physical health. Example items include "In
general
how would you rate your physical health?" and How would you
rate
your fatigue on average (scale none to very severe)?" The
physical
health subscale of the PROMIS Global Health scale has been
shown to
be a reliable measure of physical health (α = .81, Hayes et al.,
2009).
2.3.6. Somatic disturbance
Somatic disturbances were measured using the Neuro-QOL
Sleep

Disturbances-Short Form. This eight-item measure is designed
to mea-
sure difficulty sleeping in the general population and as a result
of
neurological disorders or conditions. Participants answer on a
five-
point scale from 1 = never to 5 = always, with higher scores
being
indicative of greater somatic difficulties. Example items include
"In the
past 7 days I had to force myself to get up in the morning" and
"In the
past 7 days I had trouble falling asleep." This measure has been
shown
to be a reliable and valid measure of somatic difficulties (α=
.93, Perez
et al., 2007).
2.3.7. Body Mass Index (BMI)
BMI was measured as a proxy of physical health. Participants
re-
ported their height in feet and inches and their weight in
pounds. The
National Heart, Lung, and Blood Institute's online BMI
calculator was
used to computer each participants BMI
(http://www.nhlbi.nih.gov/
health/educational/lose_wt/BMI/bmicalc.htm).
2.3.8. Anxiety
Anxiety was measured using the PROMIS Emotional Distress-
Anxiety-Short Form 8a. This scale is an eight-item scale
designed to

measure anxiety in the general population and in clinical
samples.
Participants answer on a five-point scale of 1 = never to 5 =
always,
with higher scores being indicative of increased anxiety.
Example items
include "In the past 7 days I have felt uneasy" and "In the past 7
days I
have felt tense." The PROMIS Anxiety Short Form 8a has been
shown to
be a reliable and valid measure of anxiety (α =.95, Pilkonis et
al.,
2011).
2.3.9. Depression
Depression was measured using the PROMIS Emotional
Distress-
Depression-Short Form 8a. This scale is an eight-item scale
designed to
measure depression in the general population and in clinical
sample.
Participants answer on a five-point scale of 1 = never to 5 =
always
with higher scores being indicative of increased depressive
symptoms.
Example items include "In the past 7 days I have felt depressed"
and "In
the past 7 days I felt I had no reason for living." This measure
has been
shown to be a reliable and valid measure of depressive
symptoms (.93,
Pilkonis et al., 2011).
2.3.10. Positive affect and well-being
Positive affect and well-being were measured using the Neuro-

QOL
Positive Affect and Well-Being-Short Form (Salsman et al.,
2013). This
nine-item measure takes into account feelings of hope, worth,
happi-
ness, and satisfaction in general and with life. Participants
respond on a
five-point scale from 1 = never to 5 = always with higher scores
being
indicative of more positive affect and greater well-being.
Example items
include "Lately I felt hopeful" and "Lately my life had
purpose." This
measure has been shown to be a reliable and valid measure of
positive
affect and well-being (α = .94, Salsman et al., 2013).
(2018) 265–272
267
http://www.nhlbi.nih.gov/health/educational/lose_wt/BMI/bmic
alc.htm
http://www.nhlbi.nih.gov/health/educational/lose_wt/BMI/bmic
alc.htm
2.3.11. Aggression
Aggression was measured using the Short-Form Buss Perry
Aggression Questionnaire (BPAQ-SF; Buss and Perry, 1992).
This
twelve-item measure takes into account physical and verbal
aggression,

anger, and hostility. Participants respond on a seven-point scale
of 1 =
extremely uncharacteristic of me to 7 = extremely characteristic
of me
with higher scores being indicative of more aggression.
Example items
include "There are people who have pushed me so far that we
have
come to blows" and "Sometimes I fly off the handle for no good
reason."
This short-form has been shown to be a reliable and valid
measure of
aggression (α = .83, Diamond and Magaletta, 2006).
2.3.12. Hypermasculinity
Hyper-masculinity was measured using the Hypermasculinity
Inventory-Revised (HMI-R, Peters et al., 2007). This 27-item
measure
assesses over subscription to cultural norms and values that
emphasize
masculinity and "machismoism" while diminishing norms
associated
with compassion, kindness, gentleness, and meekness.
Participants an-
swer questions on a ten-point scale (scale items changed from
statement
to statement, e.g. for the statement "After I have gone through a
really
dangerous experience" 1 = my knees feel weak and a shake all
over and
10 = I feel high and for the statement "Call me a name" 1 = I'll
pretend
not to hear you and 10 = I'll call you another with higher scores
being
indicative of higher hyper-masculinity. Example items include

"After I
have gone through a really dangerous experience my knees feel
weak
and I shake all over (or I feel high)" and "I like dependable cars
and
faithful lovers (or fast cars and fast lovers)." The HMI-R has
been shown
to be a reliable and valid measure of hyper-masculinity (α= .90,
Peters
et al., 2007).
2.3.13. Companionship
Companionship was measured using the PROMIS
Companionship-
Short Form. This four-item scale has participants answer on a
five-point
scale of 1 = never to 5 = always with higher scores being
indicative of
more feelings of companionship. Example items include "Do
you have
someone with whom to have fun?" and "Can you find
companionship
when you want it?" This has been shown to be a reliable and
valid
measure of companionship and social health (Tucker et al.,
2014).
2.3.14. Emotional support
Emotional support was measured using the PROMIS Emotional
Support-Short Form 4a scale. This four-item scale has
participants an-
swer on a five-point scale of 1 = never to 4 = always with
higher
scores being indicative of increased emotional support. Example

items
include "I have someone who will listen to me when I need to
talk" and
"I have someone to talk with when I have a bad day." This
measure has
been shown to be a valid and reliable measure of emotional
support in
the general population and clinical populations (Tucker et al.,
2014).
2.3.15. Social isolation
Social isolation was measured using the PROMIS Social
Isolation
Short Form 4a. This four-item scale has participants answer on
a five-
point scale of 1 = never to 5 = always with higher scores being
in-
dicative of increased social isolation. Example items include "I
feel left
out" and "I feel isolated from others." This measure has been
shown to
be a valid and reliable measure of social isolation (Tucker et
al., 2014).
2.3.16. Tobacco use
Tobacco use was measured using a self-report version of the
Adult
Tobacco Survey (ATS; King et al., 2012a) problem tobacco use
section.
Participants answered fourteen yes or no questions regarding
their to-
bacco use with higher scores being indicative of greater
difficulty be-
cause of tobacco. Example items include "Did you ever have

times when
you smoked even though you promised yourself you wouldn't?"
and "Did
tobacco ever cause you any physical problems like coughing,
difficulty
breathing, lung trouble, or problems with your heart or blood
pressure?"
2.3.17. Drug use
Drug use was measured using the Drug Abuse Screening Test
(DAST-
10; Skinner, 1982), developed by a cross-cultural collaboration
by the
World Health Organization. This is a ten-item screening tool
that can be
self-administered. Participants respond yes or no regarding their
drug
use in the past 12 months (not including tobacco and alcohol
use).
Higher scores are indicative or more problematic drug use.
Example
items include "Are you always able to stop using drugs when
you want
to?" and "Have you engaged in illegal activities in order to
obtain
drugs?"
2.3.18. Alcohol use
Alcohol use was measured using the self-report version of the
Alcohol Use Disorders Identification Test (AUDIT, Saunders et
al.,
1993) a measure developed through the World Health
Organization.

The AUDIT is a ten-question assessment of problematic alcohol
use.
Example items include "How often do you have six or more
drinks on
one occasion?" and "Have you or someone else been injured as a
result
of you drinking?" Higher scores on the AUDIT are indicative of
more
problematic alcohol use.
2.3.19. Pornography Use
Pornography use was measured using the Cyber Pornography
Use
Inventory-9 (CPUI; Grubbs et al., 2010). The CPUI is a nine-
item
questionnaire used to assess problematic internet pornography
use.
Participants answer on a dichotomous yes/no scale with higher
scores
being indicative of more problematic internet pornography use.
Ex-
ample items include "Even when I do not want to use
pornography
online, I feel drawn to it" and "I have put off important
priorities to view
pornography." The CPUI has been shown to be a reliable and
valid
measure of problematic internet pornography use in religious
and
nonreligious populations (α = .83, Grubbs et al., 2010).
2.3.20. Gambling
Pathological gambling was assessed using an adapted version of
the

DSM IV Checklist for addiction (Kessler et al., 2008).
Participants an-
swer ten questions on a dichotomous scale regarding their
gambling
behavior in the last 12 months. Higher scores are indicative of
greater
difficulties. Example items include "Do you rely on others to
provide
money to relieve a desperate financial situation caused by
gambling?"
and "Do you need to gamble with increasing amounts of money
in order
to achieve the desired excitement?"
3. Results
In order to compare IGD video game addicts to matched healthy
controls in terms of physical, emotional, social, mental health,
and
comorbidity of substance use and internet addictions, and take
into
account potential gender differences and interactions, a series of
2 ×
2MANOVAs were conducted in SPSS version 22. The means
and stan-
dard deviations and the video game addiction main effects for
the
MANOVAs for each major variable are reported in Table 1 and
Fig. 1.
Gender interactions are only noted and explored when
significant. Main
effects for gender are not reported here but can be obtained by
con-
tacting the first author.
3.1. Mental health

A one-way MANOVA was conducted to examine the effect of
video
game addiction status on ADHD symptoms, cognitive
functioning, and
global measures of mental health. The overall MANOVA for
video game
status was significant (Wilk's Λ = .83, F (3, 204) = 13.60, p<
.001,
η2p = .17). Follow-up ANOVAs revealed that video game
addicts ex-
perienced greater ADHD symptoms (F (1, 209) = 17.11, p<
.001, η2p
(2018) 265–272
268
= .08), poorer cognitive functioning (F (1, 209) = 37.46, p<
.001, η2p
= .15), and scored worse on global measures mental health (F
(1, 209)
= 20.63, p< .001, η2p = .09). Together these results suggest that
video game addicts display poorer cognitive functioning and
mental
health compared to non-addicts.
3.2. Physical health
video
game addiction status on physical health, BMI, and somatic
difficulties.

The overall MANOVA for video game status was significant
(Wilk's Λ=
.80, F (3, 203) = 16.91, p< .001, η2p = .20). Follow-up
ANOVAs
revealed that video game addicts did not differ from non-addicts
in
their physical health (F (1, 209) = 1.85, p = .18, η2p = .009), or
BMI
(F (1, 209) = .39, p = .53, η2p = .002), but addicts did display
sig-
nificantly more somatic difficulties (F (1, 209) = 51.06, p< ;
.001, η2p
= .20).
The gender X addiction status interaction was significant
(Wilk's Λ
= .94, F (3, 203) = 4.46, p = .005, η2p = .06). A series of 2 × 2
ANOVA was conducted to examine the effects of video game
addiction
and gender on physical health (see Table 1). There was no
difference
between addicts and non-addicts on a global measure of
physical health
(F (1, 209) = 4.15, p = .14, η2p = .01) or BMI (F (1, 209) = .01,
p =
.91, η2p< .001). However the interaction between gender and
video
game addiction status was significant for somatic difficulties (F
(1, 209)
= 9.32, p = .003, η2p = .04). Probing post-hoc independent
samples t-
tests were conducted to examine the direction of the interaction.
A t-test
examining the effect of gender on somatic difficulties for
addicts re-
vealed that female addicts displayed significantly more somatic

diffi-
culties than male addicts (t (55.93) = − 4.81, p< .001, d = 1.03;
addict male mean = 18.81 SD = 4.49; addict female mean =
23.88 SD
= 5.33). A t-test examining gender differences in somatic
difficulties in
non-addicts revealed no significant differences in somatic
difficulties
between males and females (t (49.25) = − .47, p = .64, d = .15;
male
mean = 16.00 SD = 4.16 female mean = 16.53 SD = 5.86).
Taken together these results suggest that video game addicts did
not
report poorer physical health than non-addicts, but that female
addicts
may be uniquely at risk for negative physical health outcomes
and sleep
disturbances.
3.3. Emotional health
video
game addict status on anxiety, depression, aggression, and
positive af-
fect and well-being. The overall MANOVA for video game
status was
significant (Wilk's Λ = .81, F (4, 201) = 12.18, p< .001, η2p =
.20).
Table 1
Means, standard deviations, and ANOVA results for
comparisons between addicts and
non-addicts.

Addicts Non-Addicts
M SD M SD F df Effect size
Mental Health
ADHD symptoms*** 6.07 3.21 3.97 2.73 17.11 1, 206 .08
Cognitive
functioning***
21.30 5.42 16.98 4.64 39.39 1, 208 .15
Global measure of
mental health*
10.81 3.24 8.70 2.98 4.01 1, 208 .09
Physical Health
Global measure of
physical health
8.25 1.60 7.95 1.47 2.74 1, 205 .009
BMI 23.90 4.10 23.56 3.64 .39 1, 205 .002
Somatic
difficulties***
20.39 5.30 16.12 4.75 52.36 1, 208 .20
Emotional Health
Anxiety*** 21.18 6.89 16.29 5.63 35.71 1, 207 .15
Depression*** 17.58 7.00 12.76 5.26 36.96 1, 207 .15
Aggression*** 3.22 .86 2.75 .82 17.50 1, 207 .08
Positive affect and
well-being***
33.79 5.90 37.60 5.94 22.03 1, 207 .10

Social Health
Companionship 15.62 3.31 16.43 3.43 2.57 1, 207 < .001
Emotional support 16.69 3.71 17.30 3.11 2.37 1207 .003
Social isolation*** 11.44 3.48 9.76 3.02 13.25 1208 .003
Hypermasculinity
Hypermasculinity 4.23 1.05 4.15 1.04 1.64 1, 210 .008
Comorbidity
Pornography
addiction*
4.50 2.37 2.81 2.17 5.84 1,70 .08
Note: ** p<.01.
* p< .05.
*** p< .001.
Fig. 1. Means and standard deviations for addicts and non-
addicts. Note: * p< .05; ** p<.01; ***p< .001 for differences
between addicts and non-addicts.
(2018) 265–272
269
Follow-up ANOVAs revealed that video game addicts displayed
in-
creased anxiety (F (1, 207) = 35.71, p< .001, η2p = .15),
depression
(F (1, 207) = 36.96, p< ;.001, η2p = .15), increased aggression
(F (1,
207) = 17.50, p< .001, η2p = .08), and decreased positive affect
and

well-being (F (1, 207) = 22.03, p< .001, η2p = .10).
Taken together these results suggest that video game addicts
report
poorer emotional health than non-addicts.
3.4. Social health
video
game addict status on feelings of social isolation,
companionship, and
emotional support. The overall MANOVA for video game status
was
significant (Wilk's Λ = .95, F (3, 205) = 4.77, p = .003, η2p =
.07).
Follow-up ANOVAs revealed that video game addicts did not
differ
from non-addicts in terms of feelings of companionship (F (1,
210) =
2.57, p = .11, η2p = .01) and emotional support (F (1, 210) =
2.37, p
= .13, η2p = .01), but addicts did report feeling significantly
more
socially isolated (F (1, 210) = 14.08, p< .001, η2p = .06).
3.5. Hypermasculinity
An ANOVA was conducted to examine the effect of video game
addict status and gender on hypermasculinity. The main effect
of video
game addiction (F (1, 210) = 1.64, p = .20, η2p = .008, or
gender was
not significant (F (1, 210) = .33, p = .56, η2p = .002, but there
was a
significant interaction (F (1, 210) = 4.26, p = .04, η2p = .02).

Probing
post-hoc independent samples t-tests were conducted to examine
the
direction of the interaction. For non-addicts, males were
significantly
more hypermasculine than females (t (80.90) = 2.08, p = .04, d
= .42;
male mean = 4.27 SD = 1.10, female mean = 3.87 SD = .83).
For
addicts, there was no significant difference between males and
females
in terms of hypermasculinity (t (62.67) = −1.03, p = .31, d =
.22;
male mean = 4.15 SD = 1.04, female mean = 4.38 SD = 1.08).
These
results suggest that female addicts have hypermasculinity scores
similar
to male addicts and nonaddicts.
3.6. Video game addiction and comorbidity with other
addictions
Finally, we examined the comorbidity of video game addiction
with
other substance use and behavioral addiction. Due to the
universities
surveyed there were low rates of tobacco, drug, alcohol, and
gambling
behaviors in addicts and non-addicts. Thus, only pornography
use was
examined. Video game addicts showed higher levels of
problematic
pornography use (F (1, 70) = 5.84, p = .02, η2p = .08) and was
no
interactions with gender.

4. Discussion
In line with previous research, approximately 7% of the young
adults who played video games met the IGD criteria for video
game
addiction (Gentile, 2009), with males being more likely to be
video
game addicts than females (King et al., 2012b; Young, 1998). In
gen-
eral, video game addicts reported poorer mental, physical, and
emo-
tional heath and being a female video game addict placed
individuals at
particular risk for certain negative outcomes. Video game
addiction was
also comorbid with problematic internet pornography use.
4.1. Mental health
Video game addicts, regardless of gender, displayed increased
ADHD symptoms, poorer overall cognitive functioning, and
poorer
mental health. Previous researchers have shown that poor
impulse
control (Li et al., 2009) and cognitive functioning (Bickel et al.,
2014)
are risk factors for addiction. It is argued that poor cognitive
control
and impulsivity make it more difficult for individuals to
disengage from
rewarding and arousing stimuli, making individuals more likely
to
continuously seek and abuse such stimuli. The current study
lends
support for these findings by showing poor impulse control and

cog-
nitive functioning in video game addicts. This finding also adds
support
to IGD being used as a measure of video game addiction and
that video
game addiction may have similar risk factors to other
addictions.
However, it is impossible to tell from the current data the
direction on
effects. It is possible that video game addiction leads to
biological
changes in the neural network that weaken cognitive functioning
and
impulse control. Future researchers should examine the
development of
video game addiction and how this relates to cognitive
functioning and
overall mental health. The little work that has been done
suggests that
poor impulse control and executive functioning are risk factors
for the
development of video game addiction (Ko et al., 2013).
4.2. Physical health
In general, being a video game addict was not related to poorer
physical health. Video game addicts were no different than
controls in
terms of their body mass index or self-report measures of
physical
health. Video game players are frequently portrayed in the
media as
over-weight, socially isolated, individuals who play games
alone in a
darkened room (Sukkau, 2012). However, the current study does
not

support this pervasive stereotype. Video game addicts appear to
be
physically healthy and as physically active as their non-addict
peers.
Dispelling this stereotype is important as it might help draw
attention to
the reality of video game addiction. It may be more difficult for
video
game addicts to recognize their own pathology when they do not
see
themselves in the same light as a stereotypical video game
addict
(Koordeman et al., 2010). Changing this stereotype may make it
easier
for video game addicts to recognize their own pathology.
Video game addiction was related to increased somatic
difficulties.
Previous studies have reported increased sleep disturbances and
diffi-
culties in pathological gamers (Gentile et al., 2011). It is likely
that
video game play is directly interfering with sleep and sleep
patterns in
this population. Adding to previous research, this study
identified that
female video game addicts were at particular risk for somatic
difficul-
ties. Males are significantly more likely to be video game
addicts than
females and it is possible that female video game addicts feel
more
shame and need to hide their behavior than males. Previous
research
has shown that it is more socially acceptable for males to play
video

games and this social acceptance makes male video game play
more
public (Lucas and Sherry, 2004). It is possible that female video
game
addicts feel a greater need to hide their behavior and as a result
spend
more time playing during nighttime hours. Conversely, it is
theoreti-
cally possible that females with somatic difficulties are more
likely to
seek video games as a way to deal with their sleep disturbances.
Future
researchers should look at these relationships longitudinally.
4.3. Emotional health
Video game addicts consistently displayed poorer emotional
health
than non-addicts. Video game addicts reported more anxiety,
depres-
sion, aggression, and lower positive affect and well-being.
Previous
researchers have repeatedly shown addictions are related to
poorer
emotional health and in particular depression and anxiety are
fre-
quently comorbid in addiction (Bruchas et al., 2011). Addiction
has also
been associated with poorer life-satisfaction and overall
measures of
well-being (Murphy et al., 2005). This pattern in video game
addiction
further supports the validity of the IGDS as a measure of video
game
addiction and pathological video game use, and adds to the
growing

literature that pathological video game use is a small, but real
per-
centage of video game players. Video game addiction in
particular has
been associated with increased aggression in previous research
(Gentile, 2009; Gentile et al., 2011). The results of this study
replicate
these previous results.
(2018) 265–272
270
4.4. Hypermasculinity
Furthermore, female video game addicts displayed hyper-
masculine
tendencies and gender atypical behavior. It is possible that
gender
stereotypical females are less likely to play video games.
However, it is
also possible that frequently playing video games changes
scripts and
schemes regarding gender for females. Previous research has
shown
that video games frequently portray women as sexualized and
ag-
gressive (Dill and Thill, 2007) and that playing video games
with sex-
ualized females lead to decreased self-efficacy in women
(Behm-
Morawitz and Mastro, 2009). Similarly, research has shown that
women may experience greater increases in aggressive behavior

after
playing video games than men (Eastin, 2006). Therefore, it is
possible
that repeatedly playing video games leads women to be more
ag-
gressive and display more hyper-masculinity. It is important to
note
that very little research has been conducted specifically
examining fe-
male gamers and female video game addicts. Future research
should
address this limitation.
4.5. Social health
Video game addicts displayed mixed results regarding social
health
and functioning. Addicts did not report any difference in social
support
or feelings of companionship compared to non-addicts. Addicts
feel that
they have people to turn to when they need it and that they have
friends
and a support system. However, it is unclear from these
measures if
these social relationships are online with other video game
addicts, or
face-to-face relationships. Future research should specifically
examine
friendships and social support systems in video game addicts.
Conversely, addicts did report feeling more isolated than non-
addicts.
This is supported by past research that has shown that social
isolation is
a risk factor for the development of addiction (Lovic et al.,
2011)

Likewise, internet addiction has been associated with increased
social
isolation and feelings of loneliness (Yao and Zhong, 2014).
Video game
addiction may follow similar patterns to internet addiction,
where ad-
dicts report many friends and online relationships, but these
relation-
ships cannot replace face-to-face contacts in terms of reducing
feelings
of loneliness and isolation. Taken together these results suggest
that
video game addicts may be at risk for some markers of poorer
social
health and functioning compared to non-addicts who play video
games.
4.6. Comorbidity of addiction
Most the sample did not report using drugs, tobacco, alcohol, or
gambling. Thus, comorbidity could not be assessed in this
sample.
However, video game addicts did show increased symptoms of
pro-
blematic internet pornography use compared to non-addicts.
There is a
vast body of literature that suggests that addicts of one type are
at in-
creased risk for developing addictions in other areas (Bruchas et
al.,
2011). The results of the current study lend support to this
notion. It is
likely that addiction is a result of underlying cognitive,
biological,
psychological, social, and emotional difficulties that place
people at

increased risk for addiction. These difficulties, such as poor
impulse
control, seem to be the same in video game addiction.
The present study contributes to the growing body of literature
on
video game addiction by using measures of health outcomes re-
commended by leading health organizations and by matching
addicts to
age, gender, race-ethnicity, and marital status controls. Most
past re-
search on video game addiction has compared addicts to the
greater
population, thus potentially confounding health outcomes and
falsely
attributing them to video game addiction. The current study
addresses
this limitation in past research and clearly identifies video game
ad-
diction as a significant risk factor for poorer emotional,
physical,
mental, and social health. The disparities were found using
measures
recommended by leading health organizations.
4.7. Limitations
While the current study adds vital information to the growing
body
of literature regarding video game addiction, it is not without
limita-
tions. The current study employed self-report measures of all
behavior
and was cross-sectional. Individuals may not be accurate
reporters of
their own behavior and self-report measures may lead to biased

re-
porting. However, it should be noted that people typically
underreport
negative behaviors so it is possible that using other measured
would
lead to increased effects. The cross-sectional nature of the
current study
does not allow for longitudinal examinations and does not allow
for
examining causation. Future researchers should examine video
game
addiction longitudinally to better understand risk and protective
factors
to the development of video game addiction. Perhaps the biggest
lim-
itation of the current study is the use of undergraduate college
students.
While college students are known to spend a significant amount
of time
playing video games (Stockdale et al., 2015) and may be at
increased
risk for developing pathological video game play (King et al.,
2012b;
Young, 1998), this population is educated and relatively
functional and
may have better overall health. Future researchers should
examine
video game addiction in a non-college population.
4.8. Conclusions
Even with the above noted limitations the present study lends
fur-
ther support to IGDS as a valid measure of pathological video
game use
and potentially as a measure of video game addiction.

Significant re-
search attention needs to be given to video game addiction to
improve
understanding of risk factors to development, appropriate
treatment
and intervention strategies, and biological effects of video game
ad-
diction. Interest needs to be invested in female video game
addicts to
understand the unique risk factors and treatment options for
female
addicts.
Limitations
The current study employs self-report, cross-sectional data with
undergraduate college students.
Acknowledgement
We would like to that the Loyola University Office of the
Provost for
funding this research.
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X/sbref54Video game addiction in emerging adulthood: Cross-
sectional evidence of pathology in video game addicts as
compared to matched healthy controlsIntroductionVideo game
addictionRisk factors for video game addictionOutcomes of
video game addictionPurpose of the current
studyMethodParticipantsProceduresMeasuresVideo game
addictionAttention-deficit/HyperactivityCognitive
functioningMental healthPhysical healthSomatic
disturbanceBody Mass Index (BMI)AnxietyDepressionPositive
affect and well-
beingAggressionHypermasculinityCompanionshipEmotional
supportSocial isolationTobacco useDrug useAlcohol

usePornography UseGamblingResultsMental healthPhysical
healthEmotional healthSocial healthHypermasculinityVideo
game addiction and comorbidity with other
addictionsDiscussionMental healthPhysical healthEmotional
healthHypermasculinitySocial healthComorbidity of
addictionLimitationsConclusionsLimitationsAcknowledgementR
eferences

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