26. problems must be carefully written with
excellent grammar, correct usage of mathematical symbols, and
of course, with correct mathematics.
Each solution you submit for gold homework should represent
your best work. The
following rubric indicates how problems on the gold homework
will be scored.
Score Criteria
4 This is correct and well-written mathematics.
3 This is good work, yet there are some mathematical or writing
errors that need addressing.
2 There is some good intuition here, but there is at least one
serious flaw.
1 The grader didn’t understand this, but sees that you have
worked on it; come in for help!
0 You probably haven’t worked on this problem enough or you
didn’t submit any work.
Definition [Closure]. Let A ⊆ Rn be a set. The closure of A,
denoted A can be defined in three
different, but equivalent, ways; namely:
(i) A is the set of all limit points of A.
(ii) A is smallest closed set containing A; this means that if
there is another closed set F such
that A ⊆ F, then A ⊆ F .
(iii) A is the intersection of all closed sets containing A.
Definition [Interior]. Let A ⊆ Rn be a set. The set Å, called the
interior of A is the set of all points
x ∈ A such that there exists some � > 0 such that the
neighborhood V�(x) := {y ∈ R : |x−y| < �}
is contained in A.
27. Definition [Boundary]. Let A ⊆ Rn be a set. The set ∂A, called
the boundary of A. It is defined
by ∂A = A Å; that is, the boundary is the set of all points in
the closure that are not in the
interior.
1. Prove that the three definitions in the definition of closure
(above) are equivalent. That is,
show that (i) ⇔(ii) ⇔(iii). (For example, you could show (i) ⇔
(ii), and then show
(ii) ⇔(iii), or you could show that (i) ⇒ (ii) ⇒ (iii) ⇒ (i), or
some other order.)
2. Show that ∂A = A∩Ac. [Hint: Use the usual way to show set
equality; namely, choose an
element x in the left, and show it is in the right, then choose and
element x in the right, and
show it is in the left.]
3. Let F1,F2,F3, . . . be bounded, non-empty closed sets in Rn.
Suppose that they are decreasing
with respect to set inclusion, that is, suppose
F1 ⊇ F2 ⊇ F3 ⊇ ··· .
Prove that their intersection, F := ∩∞n=1Fn is closed, bounded,
and non-empty. [Hint: It is
essentially one line to show that F is closed and bounded. The
challenge is in showing that
F is non-empty. Use the Bolazno-Weierstrauss Theorem for
this.]
4. Let A ⊂ Rn be a non-empty compact set, ans suppose that B
is an open set and A ⊂ B.
Consider the “�-dilation” A� of A given by
28. A� := {y ∈ Rn : ‖x−y‖ < � for some x ∈ A} .
Show that there is an � > 0 such that A� ⊆ B. [Hint: Note that
for each x ∈ A, there is an
r > 0 such that neighborhood Vr(x) = {y ∈ Rn : ‖x−y‖ < r} ⊆ B.
Note that the set of all
these neighborhoods cover A. Now, use the definition of
compactness.]
Name:
Math 325 BRONZE Assignment # 19
Assigned: 2020.04.06
Due: 2020.04.10
Recall that you may be asked to present your solutions to
Bronze questions in class and that
Bronze Questions are to be turned in at the end of class.
For Friday, April 10 (Note the slightly extended due date.)
• Get 7–9 hours of good sleep each night. Sleep is the basis for
memory and creative
thought, so your sleep time should be as regular as possible and
absolutely non-
negotiable. A cold, pitch-black environment, with absolutely no
blue light can help. More
sleep tips can be found online.
Note: Losing even an hour of sleep can strongly impact your
immune system’s performance.
I Read Sections 6.1, 6.2, and 6.3 in Cummings.
29. • Bronze Questions
1. (Exercise 6.1(a) in Cummings) Use the �-δ definition of the
functional limit to prove that
limx→−2(4x + 3) = −5.
2. (Exercise 6.1(b) in Cummings) Use the �-δ definition of the
functional limit to prove that
limx→1
x3−1
x−1 = 3.
3. (Exercise 6.1(c) in Cummings) Use the �-δ definition of the
functional limit to prove that
limx→0 x
2 = 0.
4. (Exercise 6.1(d) in Cummings) Use the �-δ definition of the
functional limit to prove that
limx→2 x
3 = 8.
5. (Exercise 6.4(a) in Cummings) If limx→a f(x) and limx→a
g(x) both do not exist, can
limx→a[f(x) + g(x)] exist? Prove your result, or give a
counterexample.
6. (Exercise 6.11 in Cummings) Suppose that f : X → Y , and
that {Bα}α∈ I is a (possibly
uncountable) collection of subsets of Y . Prove that the pre-
image of the union is the
union of the pre-images, that is, prove that
30. f−1
(⋃
α∈ I
Bα
)
=
⋃
α∈ I
f−1 (Bα) .
Computers in Human Behavior 31 (2014) 305–313
Contents lists available at ScienceDirect
Computers in Human Behavior
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c
a t e / c o m p h u m b e h
Adolescent simulated gambling via digital and social media:
An emerging problem
0747-5632/$ - see front matter � 2013 Elsevier Ltd. All rights
reserved.
http://dx.doi.org/10.1016/j.chb.2013.10.048
⇑ Corresponding author. Address: School of Psychology, Level
4, Hughes Building,
The University of Adelaide, Adelaide, SA 5005, Australia. Tel.:
+61 8 83133740; fax:
+61 8 8303 3770.
31. E-mail address: [email protected] (D.L. King).
Daniel L. King ⇑ , Paul H. Delfabbro, Dean Kaptsis, Tara
Zwaans
School of Psychology, The University of Adelaide, Australia
a r t i c l e i n f o a b s t r a c t
Article history:
Available online 20 November 2013
Keywords:
Convergence
Pathological gambling
Social media
Adolescence
Addiction
Recently, there has been significant expansion in the range of
gambling activities supported by digital
technology. The convergence of gambling and digital media is
of particular concern with respect to the
immense potential for earlier age of gambling involvement, and
development of positive attitudes
and/or behavioral intentions toward gambling. This study
examined the prevalence of adolescent
involvement in a range of digital and social media gambling
activities, and the association between expo-
sure to, and involvement in, simulated gambling and monetary
gambling and indicators of pathological
gambling risk. A total of 1287 adolescents aged 12–17 years
were recruited from seven secondary schools
in Adelaide, South Australia. The results indicated that a
significant proportion of young people engage in
a range of simulated gambling activities via internet gambling
sites, social media, smartphone applica-
tions, and video-games. A logistic regression analysis showed
that adolescents with a history of engage-
32. ment in simulated gambling activities appear to be at greater
risk of endorsing indicators of pathological
gambling. These findings highlight the need for further research
on the potential risks of early exposure
to simulated gambling activities, as well as greater
consideration of the need for regulation and monitor-
ing of gambling activity via digital technologies.
� 2013 Elsevier Ltd. All rights reserved.
1. Introduction
1.1. Gambling and digital technology
In the last decade, there has been significant expansion in the
range of gambling activities supported by digital technology
(Grif-
fiths & Parke, 2010; King, Delfabbro, & Griffiths, 2010). The
‘‘con-
vergence’’ (Griffiths, King, & Delfabbro, 2013) of gambling
and
digital media is of particular interest and concern to
researchers,
regulators, and allied health practitioners because of its
potential
to increase the likelihood of young people developing an
interest
in gambling at an younger age (Derevensky, Sklar, Gupta, &
Mes-
serlian, 2010; King et al., 2010; Griffiths, King, & Delfabbro,
2012;
Phillips, Ogeil, & Blaszczynski, 2012). Although some earlier
re-
search suggested youth participation rates in online gambling
activities are usually lower than for terrestrial forms of
gambling
(Griffiths & Wood, 2007; Ipsos, 2009; Najman, Allen, Madden,
33. &
Brooks, 2008), more recent market research data (e.g., Casual
Connect., 2012; Church-Sanders, 2011) suggest that the
popularity
of online gambling is increasing rapidly. This growth has led to
concerns about the potential negative impacts on young people,
given that the ubiquity of these new activities allows them to
gam-
ble more covertly and unrestrictedly than was the case before
(Flo-
ros, Siomos, Fisoun, & Geroukalis, 2013).
Although much has been written about the increasing perva-
siveness of monetary gambling on digital media, another less
recognised concern relates to the growth of simulated gambling,
or gambling without the possibility of monetary reward (King
et al., 2010; Griffiths, King, & Delfabbro, 2012). Simulated
gambling
may be defined as a digitally simulated interactive gambling
activ-
ity that does not directly involve monetary gain but is otherwise
structurally identical to the standard format of a gambling
activity
due to its wagering features and chance-determined outcomes of
play. Although the boundaries between gambling and video-
gam-
ing are becoming increasingly blurred (for example, gaming
fea-
tures may be found in some gambling-like activities, and vice
versa), simulated gambling may be distinguished from many
forms
of video-gaming (e.g., shooting action games, role-playing
games)
because in video-games there is a clear relationship between
player strategy or actions and outcomes. Simulated gambling is
a
34. continually evolving mode of gambling that encompasses free-
to-play gambling games using virtual credits, smartphone and
so-
cial media apps, and hybrid video-game/gambling activities
with
monetisation features such those found in MMOs like
Runescape
(Delfabbro, King, Lambos, & Puglies, 2009; Griffiths & Wood,
2010; Johansson & Gotestam, 2004). Some of these activities
http://crossmark.crossref.org/dialog/?doi=10.1016/j.chb.2013.10
.048&domain=pdf
http://dx.doi.org/10.1016/j.chb.2013.10.048
mailto:[email protected]
http://dx.doi.org/10.1016/j.chb.2013.10.048
http://www.sciencedirect.com/science/journal/07475632
http://www.elsevier.com/locate/comphumbeh
306 D.L. King et al. / Computers in Human Behavior 31 (2014)
305–313
(e.g., gambling apps on Facebook) may be considered financial
be-
cause they allow players to purchase extra credit using real
money,
but they do not enable the player to ‘cash out’ winnings.
Simulated
gambling activities generally feature no age restriction or
barriers
to entry (King et al., 2010), employ inflated profit rates
(Sevigny,
Cloutier, Pelletier, & Ladouceur, 2005), and are presented as
youth-friendly entertainment (Griffiths & Parke, 2010). Further,
the emergence of gambling on video-gaming platforms has
blurred
the structural boundaries between gambling and gaming
35. activities
(Griffiths, 2008; Harper, 2007; King, Delfabbro, Derevensky, &
Grif-
fiths, 2012). For example, many forms of online video gambling
or
social media sites feature gambling, often for credits or points
paid
for with real money, and many internet gambling providers offer
free-play games that are rather like video games.
1.2. The risks of simulated gambling in adolescence
Potential problems related to simulated gambling may be par-
ticularly germane to young people for several reasons. The first
is
that young people are, by definition, developmentally immature
and not always able to appraise the riskiness of activities,
including
gambling (Delfabbro, Lambos, King, & Puglies, 2009; Hardoon
&
Derevenksy, 2002; Volberg, Gupta, Griffiths, Olasson, &
Delfabbro,
2010). Second, young people are particularly avid and savvy
con-
sumers of digital media and online services, including video
games,
laptops, tablets, and smartphones. Large-scale studies suggest
that
the average Australian adolescent spends about five hours per
day
engaged in digital media activities, including 2.5 h using the
Inter-
net (Australian Communications & Media Authority, 2007,
2008).
Most youth use Facebook to communicate and post information,
browse wikis, video tutorials, and other forums to create,
gather,
36. and share information, and visit sites such as eBay to buy and
sell
goods. Many acquired skills and knowledge of web
functionality
and online navigation may be transferable to use of online
gambling
activities and features. Additionally, the significant amount of
lei-
sure time spent on the Internet suggests there is significant
poten-
tial for exposure to gambling promotions, online gambling
activities, and assorted incentives to gamble (McMullan &
Kervin,
2012; Messerlian, Byrne, & Derevensky, 2004; Monaghan,
Dereven-
sky, & Sklar, 2008). Third, young people are often influenced
by psy-
chological and social factors (e.g., peer group pressure, the
desire to
conform, disillusionment, depression, low self-esteem, poor
emo-
tion regulation) that make isolated technology-based activities
par-
ticularly attractive to them Potenza et al. (2011). Current
national
and international evidence confirms that many young people
expe-
rience problems associated with online technology use
(Ferguson,
Coulson, & Barnett, 2011; Gentile, 2009; King, Delfabbro,
Griffiths,
& Gradisar, 2011; King, Haagsma, Delfabbro, Gradisar, &
Griffths,
2013b; Kuss, Griffiths, & Binder, 2013; Sletten,Torgersen, von
Soest,
Frøyland, & Hansen, 2010). Although the risks of excessive
37. online
social networking and video-gaming are well-documented, less
re-
search has examined whether simulated gambling activities can
give rise to similar social and psychological problems.
1.3. Research on adolescent gambling
Research studies of young people aged under 18 years suggest
that between 50% and 70% gamble at least once per year and
that
between 1% and 4% display behaviors consistent with a
gambling
pathology (Delfabbro, 2012; Hardoon & Derevenksy, 2002).
Patho-
logical gambling is usually associated with poorer social
relation-
ships and psychological functioning; a greater likelihood of
involvement in other high risk behaviors; and, poorer
educational
performance (Delfabbro & King, 2012). Adolescents with
gambling
problems are more likely to have peers and family who gamble,
have unrealistic views about the nature of gambling, and a
history
of gambling problems in their immediate family (Delfabbro,
2012).
An emerging but limited body of research suggests that simu-
lated gambling may co-occur with monetary gambling activity.
To
date the largest study of simulated gambling among youth has
been
conducted by Ipsos (2009), who surveyed 8598 adolescents
about
their gambling and ‘gambling-like’ behavior. Over 25% of
adoles-
38. cents had played in ‘money-free’ mode of gambling in the week
pre-
ceding the survey, with opportunities on social networking sites
four times more popular than those presented on real gambling
sites. Although the design of the study precluded statements of
cau-
sality, simulated gambling behavior was the strongest predictor
of
monetary gambling, and also significantly predicted at-risk
gam-
bling. Comparable findings have been reported in other studies
(By-
rne, 2004; Griffiths & Wood, 2007; Hardoon, Derevensky, &
Gupta,
2002). Griffiths and Wood (2007) surveyed 8017 adolescents
aged
12–15 years, and reported that 29% of adolescents who had
gam-
bled online also reported playing the free ‘demo’ games. Byrne
(2004) reported that young people with gambling problems were
significantly more likely to report online simulated gambling in
the past year than those without gambling problems. Hardoon
et al. (2002) reported that 25% of youth with serious gambling
prob-
lems and 20% of those at-risk for a gambling problem reported
play-
ing online using practice/trial sites. Similarly, research on adult
gamblers conducted by McBride and Derevensky (2009)
reported
that 77% of online gamblers (N = 563) reported playing
‘gambling-
like’ games (e.g., practice modes) in addition to monetary
gambling
on the Internet. Overall, it may be observed that the literature
on
youth simulated gambling is limited by: (a) its age of
39. publication
(i.e., older findings may not accurately reflect the current status
of
youth gambling given changes in the technological and social
con-
text of gambling), (b) the lack of studies conducted outside of
the
UK, and (c) the lack of detailed examination of a range of
gambling
activities available through digital and social media.
1.4. The current study
The vulnerability of adolescents may place them at greater risk
of problematic patterns of gambling via new and emerging
digital
and social media. On the one hand, it has been proposed that
early
exposure to gambling activities may condition a range of
‘‘safer’’
responses to gambling stimuli (e.g., smaller bet sizes,
infrequent/
social play), or develop knowledge about the chance-determined
nature of gambling, including the belief that one is very
unlikely
to win in the long-term. As Najman et al. (2008) state:
Practice play can affect the appeal of gambling games by
remov-
ing some of the mystery and excitement that surrounds previ-
ously unobtainable casino type games. By experimenting with
simulated casino games young people become accustomed to
them and become easily bored.
However, an alternative view is that simulated gambling activ-
ities may facilitate the transition to monetary forms of gambling
(McBride & Derevensky, 2009), and/or develop a behavioral
40. ten-
dency toward sustained gambling activity and riskier gambling
strategies (Bednarz, Delfabbro, & King, 2013). The research
litera-
ture on gambling convergence is currently limited with regard
to
explaining how and to what extent adolescent gamblers may be-
come involved in these new forms of gambling and gambling-
like
activities. However, it is well-documented that online gambling
service providers employ numerous strategies and techniques
out-
side the scope of current regulation to entice young players to
ini-
tiate and develop a familiarity with gambling (Derevensky et
al.,
2010; McBride & Derevensky, 2009; McMullan, Miller, &
Perrier,
2012).
D.L. King et al. / Computers in Human Behavior 31 (2014) 305–
313 307
Although there is currently no established theoretical model for
conceptualising risks of simulated gambling among youth,
expert
commentary and limited research evidence, as summarised
above,
suggests that simulated gambling in adolescence may act as a
‘‘gateway’’ activity that grooms a young person toward
transition
to higher-risk, monetary gambling activities. To examine this
pos-
sibility, this study aimed to assess: (a) the prevalence of
adolescent
41. involvement in a range of digital and social media gambling
activ-
ities, (b) the extent of the cross-over or association between
simu-
lated gambling and monetary gambling activities, and (c)
whether
simulated gambling exposure was associated with indicators of
pathological gambling risk.
2. Method
2.1. Design
This study employed a cross-sectional survey design. Fifty sec-
ondary schools in the outer metropolitan region of Adelaide,
South
Australia, were randomly selected from a comprehensive list of
public and private schools. Catholic schools were excluded due
to
barriers in obtaining ethical clearance. Each school principal
was
sent a letter and one-week follow-up email invitation to partici-
pate. The study was promoted as an investigation of ‘‘electronic
media use and mental health in young people’’ (see King,
Delfab-
bro, Zwwans, & Kaptsis, 2013a). Each participating school was
pro-
vided with an individualised summary report of findings, which
included an indication of the number of adolescents at-risk of
men-
tal health problems. In total, seven co-educational schools (4
pub-
lic, 3 private) provided consent to participate. Remaining
schools
either declined to participate (N = 20) or did not respond to the
invitations (N = 23). Data were collected from June to August
2012.
42. All participants provided informed consent and were free to
withdraw from the study at any time. The study was conducted
at each secondary school during class hours. Three of the
authors
(DLK, DK, and TZ) facilitated data collection at each of the
second-
ary schools. Upon obtaining consent, a teacher administered the
questionnaire to each student in the classroom. An online
version
of the questionnaire was available via Survey Monkey for those
schools with the requisite IT infrastructure. Completed surveys
were compiled and analyzed using SPSS for Windows (v18.0).
A to-
tal of 73 responses were excluded due to erroneous responses or
missing data. This study was approved by the Human Research
Ethics Subcommittee at the University of Adelaide, and the
Depart-
ment for Education and Child Development.
2.2. Participants
A total of 1287 high school students aged 12–17 years were re-
cruited. The gender distribution was 49.6% male and 50.4%
female.
The mean age was 14.9 years (SD = 1.5). Participants identified
as
Caucasian Australian (85.5%), Asian (6.8%), European (5.1%),
Aboriginal (1.6%), or Other (.9%). English was the primary
language
spoken at home by 95% of participants. Rates of ownership
and/or
home accessibility for various electronic media device were as
fol-
lows: mobile phone or smartphone (91%), portable music player
(89%), laptop (86%), video-gaming console (78%), personal
com-
43. puter (71%), and tablet devices (37%). The mean age at which
ado-
lescents had first used various electronic media devices
included:
(i) the Internet at 8.2 years old (SD = 2.3) (ii) video-games at
age
of 9.2 years (SD = 3.7), and (iii) mobile phone at age of 10.9
years
(SD = 2.1).
Table 1 presents a summary and chi-square analysis of demo-
graphic differences according to ‘at-risk’ gambling status. At-
risk
gambling referred to endorsing at least 1 indicator of
pathological
gambling. This classification method was consistent with
classifi-
cation employed in other studies of adolescent gamblers (e.g.,
Del-
fabbro & Thrupp, 2003). Although this inclusive classification
method may potentially over-classify cases where gambling is
un-
likely to be a significant issue, this method is often used in
evalu-
ating risk in youth mental health settings where low specificity
and higher sensitivity are prioritized. For example, a youth who
re-
ports fleeting thoughts of self-harm may not be considered to be
at
significant risk of suicide (or warrant a clinical diagnosis),
however
endorsement of this indicator may often be considered ‘at-risk’.
The results indicated that males and those participants of
Cauca-
sian Australian background were significantly more likely to
en-
44. dorse items related to pathological gambling, but these observed
effects were quite small according to Cohen’s (1992)
guidelines.
Gender and ethnicity were included as covariates in subsequent
multivariate analysis to account for the potentially confounding
role of these variables.
2.3. Materials
A standardised questionnaire assessed basic demographic infor-
mation (i.e., age, sex, school grade, cultural background, main
lan-
guage spoken at home), and aspects of electronic media use
(i.e.,
ownership and accessibility, frequency of use of each device in
a
typical week period over the previous 3-month period, function
and social context of media use, and age at which devices were
first
used). Additional questionnaires assessed gambling behavior
across a range of activities, as well as pathological gambling
and
mental health indicators.
2.3.1. Simulated and monetary gambling
Gambling activity was assessed by a 25-item questionnaire that
included questions about gambling with and without money. Gi-
ven the lack of guiding literature on assessment of gambling
across
a range of land-based (e.g., casino, public house) and digital or
on-
line (e.g., mobile phone, social media) environments, this
question-
naire was designed for the purpose of this study. However, some
of
45. its content was based on questions employed by Ipsos (2009)
and
Griffiths and Wood (2007). Adolescents indicated the frequency
of
involvement in the following gambling activities in the previous
12 months: card games (e.g., blackjack, poker, etc.), electronic
gaming machines, wagering on races or sports, lotteries, scratch
cards, or any other activity (i.e., ‘‘other’’). Frequency was
assessed
by a 5-point scale including: 1 = never, 2 = once or twice a
year,
3 = three times a year to monthly, 4 = two or three times per
month, and 5 = weekly. For each activity, participants indicated
whether they had: (1) played with money (i.e., financial
gambling),
(2) played without money involved (i.e., simulated gambling),
and
(3) for relevant activities (e.g., cards, gaming machines)
whether
they gambled via the Internet. For example, when asked to
indicate
involvement in card or table tables (item 1), participants
indicated
their frequency of involvement (using the 5-point scale) for
mone-
tary and then simulated play, and also whether this activity oc-
curred online.
Further questions assessed historical involvement in online
financial gambling and simulated gambling activities. One item
asked participants to indicate if they had ever tried to gamble
with
money on the Internet. Additional items asked whether the
participant had ever tried simulated gambling via: (1) free-play
or ‘practice’ modes online, (2) social networking site
applications
46. (e.g., Zynga Poker), (3) smart phone apps (e.g., Slotomania), (4)
video-game simulations or online games (e.g., Runescape).
These
items were scored using a dichotomous (yes/no) format. For
each
question, participants were asked to report the name of the
game,
website, or application, if able to recall. Adolescents were
asked to
indicate the proportion of their Internet time that was spent
Table 1
Demographic information of the total sample of adolescents
according to risk level of problem gambling.
N Total % No problem gambling (N = 910) At-risk/problem
gambling (N = 304) Group differences Effect size
n % n % v2 (df = 1) Sig. U
Sex/gender
Male 602 49.6 413 68.6 189 31.4
Female 612 50.4 497 81.2 115 18.8 25.7 .001 0.15
Grade
Junior (7–9) 580 47.8 446 76.9 134 23.1
Senior (10–12) 634 52.2 464 73.2 170 26.8 2.2 .136 0.04
School
Public 637 52.5 475 74.6 162 25.4
Private 577 47.5 435 75.4 142 24.6 0.1 .741 0.01
Ethnicity
Caucasian Australian 1038 85.5 796 64.8 242 35.2
47. Other 176 14.5 114 76.7 62 23.3 11.3 .001 0.10
Media accessibilitya
1–2 devices 398 32.8 307 77.1 91 22.9
3+ devices 816 67.2 603 73.9 213 26.9 1.4 .221 0.03
NB: Percentages refer to group/at risk ⁄ 100.
a Refers to access to electronic devices with online and
simulated gambling capabilities (i.e., personal computer, laptop,
mobile phone, and tablet).
308 D.L. King et al. / Computers in Human Behavior 31 (2014)
305–313
involved in gambling activities, and the number of video-games
that they currently played which featured simulated online gam-
bling. A copy of this measure is available by request to the
corre-
sponding author.
2.3.2. Pathological gambling
The Diagnostic Statistical Manual-IV-Multiple Response
Format
for Juveniles (DSM-IV-MR-J) is a 10-item tool that assesses
patho-
logical gambling among youth (American Psychiatric
Association,
2000). The scale examines the following dimensions of
pathologi-
cal gambling: cognitive salience, loss of control, escape, lies
and se-
crecy, chasing losses, tolerance, withdrawal, familial conflict,
and
school absenteeism. Items referred to financial gambling only.
All
items were scored using a 4-point Likert scale (1 = never, 2 =
48. some-
times, 3 = often, 4 = frequently) to enable greater sensitivity to
infrequent and/or emerging pathological gambling behaviors.
Although there has been long been debate regarding the validity
of assessment tools for youth gambling problems (see
Derevensky,
Gupta, & Winters, 2003; Ladouceur et al., 2000), this study’s
ap-
proach was consistent with large youth prevalence studies (e.g.,
the British Survey of Children and Gambling). Adolescents who
re-
sponded to at least one pathological gambling criteria with
‘‘often’’
were classified as ‘at-risk’, whereas endorsing 5 or more
criteria
was indicative of ‘probable pathological’ gambling status (Der-
evensky & Gupta, 2006). Internal consistency of the measure in
the current study was high (Cronbach’s alpha = .90).
2.3.3. Mental health status
The Revised-Children Anxiety Depression Scale (RCADS) is a
widely used instrument for assessing children’s symptoms
corre-
sponding to DSM-IV anxiety and major depressive disorders
(Chor-
pita, Yim, Moffitt, Umemoto, & Francis, 2000). The 47-item
scale
yields scores on six subscales: Separation Anxiety Disorder,
Social
Phobia, Obsessive Compulsive Disorder, Panic Disorder,
General-
ised Anxiety Disorder, and Major Depressive Disorder. Raw
scores
on each subscale are converted to T-scores (i.e., scores
standard-
ised according to gender and age). T-scores indicate normal
49. (<65), borderline (65–69), or clinically significant (70+)
symptom-
atology. The RCADS has demonstrated sound psychometric
proper-
ties in the Australian population (De Ross, Gullone, & Chorpita,
2002).
3. Results
3.1. Prevalence of simulated gambling
The first aim of this study was to examine the prevalence of
adolescent involvement in simulated gambling. Table 2 presents
a summary of adolescents’ involvement across a range of digital
and social media-based simulated gambling activities in the last
12 months. The most popular type of simulated gambling was
on-
line card games (11.9%), followed by electronic gaming
machines
(3.8%), and sports betting activities (3.2%). A chi-square
analysis
of non-pathological versus at-risk/pathological (henceforth ‘at-
risk’) gamblers indicated that all types of simulated gambling
were
significantly more prevalent than would be expected among at-
risk
gamblers. About 1 in 4 at-risk gamblers engaged in simulated
casi-
no card games (frequency [% of group]: once or twice a year
[38%],
three times a year to monthly [28%], two or three times per
month
[22%], and weekly [11%]), and 1 in 10 reported playing
simulated
electronic slot machine games (frequency [% of group]: once or
twice a year [54%], three times a year to monthly [14%], two or
three times per month [14%], and weekly [17%]). Overall, at-
50. risk
adolescent gamblers reported rates of participation in simulated
gambling activities at approximately 3 times or greater than
their
non-pathological gambling counterparts. The size of observed
ef-
fects was small to moderate (Cohen, 1992).
A substantial proportion of the sample (31.5%) reported past
involvement in at least one simulated gambling activity. About
25% of adolescents reported to have engaged in simulated gam-
bling in a video game, either as bonus feature of the video game
or as a virtual gambling experience (see King, Delfabbro, et al.,
2012). The majority of participants did not provide qualitative
feedback indicating specific simulated gambling activities. The
lim-
ited data highlighted the following games as containing
simulated
gambling: Grand Theft Auto (n = 35), Red Dead Redemption (n
= 16),
Xbox Live Poker (n = 10), Pokemon (n = 11), Runescape (n =
7), Fable 2
(n = 5), and Fallout: New Vegas (n = 3). To a lesser extent,
adoles-
cents reported engaging in simulated gambling via Facebook,
with
the most commonly identified applications being Zynga Poker
(n = 28) and Texas Hold’Em Poker (n = 9). Fewer adolescents
re-
ported to have engaged in simulated gambling via smartphone
apps (6.3%) and free-play or ‘demo’ modes of casino websites
(4.7%). The most frequently reported smartphone app was
Slotoma-
nia (n = 15), although several participants reported ‘‘iPhone
games’’
51. Table 2
Current and historical involvement in simulated gambling
activities according to risk level of problem gambling.
Simulated gambling N % No Problem gambling (N = 910) At-
risk or problem gambling (N = 304) Group differences Effect
size
n % of Group n % of Group v2 (df = 1) Sig. U
Current usea
Card games 145 11.9 71 7.8 74 24.3 59.3 .001 0.22
EGMs 46 3.8 14 1.5 32 10.5 53.6 .001 0.21
Sports betting 39 3.2 18 2.0 21 6.9 20.9 .001 0.13
Racing 24 2.0 9 1.0 15 4.9 18.3 .001 0.12
Other 29 2.4 10 1.1 19 6.2 25.9 .001 0.15
Historical use
Free play or demo modes 55 4.7 16 1.8 39 13.3 64.8 .001 0.24
Facebook apps 117 9.6 48 5.5 69 23.5 79.9 .001 0.26
Smartphone apps 77 6.3 24 2.7 53 18.1 84.3 .001 0.27
Video-game features 314 25.9 179 20.4 135 46.1 73.8 .001 0.25
a Refers to activity in the past 12 months.
D.L. King et al. / Computers in Human Behavior 31 (2014) 305–
313 309
which may have included this app. Adolescents tended to report
accessing free-play casino activities via the website Pokerstars
(www.pokerstars.com) (n = 6). A chi-square analysis examining
nor-
mal versus at-risk gamblers indicated that a history of
involvement
52. in all types of simulated gambling was significantly more
prevalent
among at-risk gamblers as compared to non-pathological gam-
blers. Rates of simulated gambling via smartphone apps were
over
6 times more prevalent among at-risk gamblers than non- patho-
logical gamblers. The size of these effects was small to
moderate.
3.2. Co-occurrence of simulated gambling and monetary
gambling
The second aim of this study was to measure the association be-
tween simulated gambling and monetary gambling activities.
Ta-
ble 3 presents a summary of participants’ monetary gambling
activities and features of pathological gambling, according to
level
of involvement in simulated gambling. In the overall sample,
the
most prevalent types of monetary gambling were scratch tickets
(15.3%), card games (9.4%), and wagering on races (10.4%).
Fewer
adolescents (2.3%) reported to have had tried any form of
gambling
directly involving money on the Internet at least once in the
past. A
Table 3
The co-occurrence of simulated gambling and monetary
gambling activity.
N % No simulated gambling (N = 1050)
n % of Group
Monetary gamblinga
53. Card games 114 9.4 79 …
REVIEW
CURRENTOPINION New age technology and social media:
adolescent
psychosocial implications and the need for
protective measures
Copyright
www.co-pediatrics.com
Jay Shah, Prithwijit Das, Nallammai Muthiah, and Ruth
Milanaik
Purpose of review
In recent years, breakthroughs and advancements in new age
technology have revolutionized the way
children communicate and interact with the world around them.
As social media platforms such as
Facebook, Instagram, and Snapchat continue to grow in
popularity, their usage has raised concerns about
their role and impact on adolescent development and behavior.
This review examines the psychosocial
implications of social media usage on youth outcomes related to
body image, socialization, and
adolescent development. It discusses ways that clinicians and
parents can effectively safeguard their
children from the potential threats posed by digital media while
providing a fact sheet for parents that
addresses these concerns and summarizes recommended
strategies to combat them.
Recent findings
While social media platforms continue to experience surges in
popularity, mounting evidence suggests
54. significant correlations between their usage and adolescent
mental health and behavioral issues. Increased
social media usage has been linked to diminished self-esteem
and body satisfaction, elevated risk of cyber-
bullying, heightened exposure to pornographic material, and
risky sexual behaviors.
Summary
Given how new age technology is steadily permeating everyday
life, greater efforts are needed to inform
adolescent users and their families about the negative
consequences of social media usage. Pediatricians
and parents must take cautionary measures to reduce
psychosocial risks and ensure the online safety of
children.
Keywords
cyber-bullying, new age technology, online safety, pornography,
social media
Division of Developmental and Behavioral Pediatrics, Steven
and Alex-
andra Cohen Children’s Medical Center of New York, Northwell
Health
System, Lake Success, New York, USA
Correspondence to Ruth Milanaik, DO, Division of
Developmental and
Behavioral, Pediatrics, Steven and Alexandra Cohen Children’s
Medical
Center of New York, 1983 Marcus, Ave, Suite 130, Lake
Success, NY
11042, USA. Tel: +1 516 633 7416; e-mail: [email protected]
Curr Opin Pediatr 2019, 31:148–156
DOI:10.1097/MOP.0000000000000714
INTRODUCTION
56. � New age technologies have given rise to novel forms
of digital media and networking that have made
communication and socialization faster and easier for
children across the world.
� Online social media exposure and usage can have
detrimental consequences on the psychosocial well
being and development of adolescents ranging from
body image issues and risky sexual behaviors to social
anxiety and other mental health conditions.
� Clinicians, parents, and families can use the online
safety fact sheet provided to address ways to ensure
the safety of children on digital media such as
Facebook, Instagram, and Snapchat.
New age technology and social media Shah et al.
drawbacks of new age digital devices and media
platforms on adolescent psychosocial development
are areas of concern. This review article is organized
into three main sections. First, we explore the nega-
tive psychosocial implications of social media usage
on teenage users. Next, we discuss some of the most
popular social media platforms and how they are, at
present, used by adolescents. Finally, we offer sug-
gestions for parents and pediatricians to better mon-
itor adolescent social media usage to protect
adolescent psychological health and encourage
social well being.
NEGATIVE IMPLICATIONS OF SOCIAL
MEDIA USAGE
Social media usage can lead to a number of negative
psychosocial consequences on adolescent self-
esteem, body image, and identity, and also raise
58. It is, however, well established that the intro-
duction of new age technology, such as television
media, significantly impacts adolescent self-esteem
and body image [5,9,10]. Moreover, greater time
spent on social media has consistently been corre-
lated to decreased body satisfaction [11]. However,
the directionality of the relationships between body
image and self-esteem and social media use is gen-
erally unclear [11,12
&
,13
&&
]. Studies have suggested
that social media may serve as a superficial method
of increasing self-esteem in adolescents. In other
words, teenagers who already have low self-esteem
appear to be predisposed to finding validation or
achieving positive social relations through social
media [14,15]. It is also well established that those
with protective personality traits (e.g. happiness,
high purpose in life) are less likely to engage in
negative social comparison through social media
[4,6]. Other studies argue that increased social
media use increases the risk of exposure to phenom-
ena like cyber-bullying, which, in turn, can influ-
ence an adolescent’s self-esteem [16]. Thus,
selection bias likely influences the way adolescents
use and respond to social media.
Identity validation
During the transition from childhood to adulthood,
59. teenagers often seek to exert autonomy and to cul-
tivate their personal identities [9]. For these reasons,
adolescents use electronic media to experiment with
their identities and receive feedback from peers.
Even though the use of platforms such as Facebook
appears to decline as adolescents get older, approxi-
mately 50% of teens experiment with their identi-
ties on the Internet, seeking their friends’ reactions
to their posts [7,17]. Ultimately, social media use
might serve to strengthen peer groups and validate
identities. Thus, social media websites, like Face-
book or Instagram, can be used by individuals of
any sex to display qualities they believe their friends
will perceive as ‘attractive.’ With the ease in upload-
ing pictures depicting clothing style and person-
alities online, adolescents appear to use social
r Health, Inc. All rights reserved.
rved. www.co-pediatrics.com 149
Office pediatrics
media to exaggerate their desired appearance. Nota-
bly, without peers’ immediate responses to posts
such as those on Instagram and Facebook, adoles-
cents’ self-esteem tends to decrease [7,17].
Image enhancement and body image
Beauty trends are constantly reinforced through
social media networks. Often, these social media
outlets will utilize image editing tools such as Adobe
Photoshop to alter images to fit beauty standards.
Teenagers who, perhaps, are not consciously aware of
the body alterations made in commercial photos may
60. become more self-conscious of their body image. This
in turn can decrease self-esteem and body satisfac-
tion, especially among adolescent girls [8,18]. Ulti-
mately, internalization of beauty standards places
teenagers at increased risk for developing body image
concerns, engaging in weight-modification behav-
ior, and potentially developing eating disorders
[8,19]. Many adolescents, especially girls, are pres-
sured by society to fit certain beauty standards and
turn to easily accessible image-editing software to
alter their images before posting them on social
media. In fact, 28% of girls between the age of 8
and 18 admit to editing their photos to make them-
selves look more attractive prior to posting them
online [8]. Furthermore, social media platforms such
as Instagram facilitate editing by offering the option
to apply filters – preset color-enhancing software that
alter the appearance of original images.
Cyber-bullying
New age technology has also provided a new
medium for bullying. Cyber-bullying is defined as
the willful and repeated harm inflicted through the
use of computers, cell phones, and other electronic
devices [20]. This type of bullying can take place on
various technological platforms, such as text mes-
sages, chats, and social media sites. Examples of
cyber-bullying include: sending messages to harass
and threaten victims, spreading rumors with the
intent to humiliate, and impersonating a victim
in an attempt to damage their reputation [21]. Vic-
tims of bullying were most often targeted for looks
(55%), body shape (37%), and race (16%) [22].
The anonymity of cyber-bullying starkly distin-
guishes it from more traditional forms of bullying.
62. As growing access to new age technology raises
the threat of cyber-bullying, examining the impact of
cyber-bullying on mental health becomes increas-
ingly important [29]. Cyber-bullying has been asso-
ciated with heightened risks of depression, paranoia,
anxiety, and suicide [30]. According to a meta-analy-
sisof 34 studies, traditionalbullying increased suicide
ideation by a factor of 2.16, whereas cyber-bullying
increased it by a factor of 3.12 [31]. It is well docu-
mented that cyber-bullying also increases risk of
substance abuse among adolescents, suggesting that
they are abusing drugs as a coping mechanism to
their bullying experience [30,32,33].
Pornography
Pornography includes material containing explicit
depictions of sexual activity and behaviors.
Although pornographic material was formerly con-
fined to print media forms and videotapes, the
Internet has allowed pornography to expand into
the digital realm and reach a significantly wider
population. Today, the majority of adolescents are
exposed to Internet pornography before the age of
18 [34]. According to one study, 93.2% of boys and
62.1% of girls surveyed had viewed pornographic
media before age 18 [34]. Greater technological
capabilities, greater internet access, and rapid mar-
keting strategies by pornographic companies have
all contributed to the rise in exposure to pornogra-
phy [35,36
&
]. Online pornography is often the first
source of sex education for many sexually maturing
63. adolescents [37,38].
Health, Inc. All rights reserved.
Volume 31 � Number 1 � February 2019
New age technology and social media Shah et al.
Adolescence is a period during which children
begin to explore and understand sexuality. As the
adolescent brain is still developing, making them
increasingly susceptible to external influences,
exposure to pornography during this time can have
negative impacts on psychological development
[39]. Viewing pornography during adolescence
may involuntarily result in teenagers internalizing
unrealistic expectations about their own physical
body appearance [40
&&
,41], which, when unmet, can
decrease self-esteem and confidence [41]. The odds
of sexually aggressive behavior increase with expo-
sure to violent pornography: one study even cited a
six-fold increase [42,43]. Studies also suggest that
frequent pornography viewing is associated with
higher incidences of depressive symptoms, reduced
emotional bonding with caregivers, increased
aggression at school, and lower levels of social inte-
gration [44].
Frequent pornographic viewing might also lead
to false perceptions and distorted views of sexual
intercourse and relationship dynamics. Researchers
in Sweden found that adolescents who sought sex-
64. ually explicit material were more likely to act out
porn in their relationships by emulating extreme
sexual behaviors portrayed in porn [45]. In fact, a
study by Owens et al. [44] found that repeated
pornographic exposure was linked to a recreational
and casual attitude towards sex, much like eating
and drinking. Similarly, other studies have found
that men who visit porn sites frequently are more
likely to view sex as a mere physical act and perceive
women as sex objects [46].
Studies have also suggested that increased expo-
sure to pornography among adolescents may be
associated with riskier sexual behaviors [36
&
]. In a
2009 cross-sectional study of New York City adoles-
cents aged 12–22, those who reported visiting sexu-
ally explicit websites were more likely to have had
multiple lifetime sexual partners, have had more
than one sexual partner in the past 3 months, have
engaged in anal sex, or have used alcohol or other
drugs during their last sexual encounter [47]. In
agreement with this finding, exposure to pornogra-
phy has a greater influence on an adolescent’s deci-
sion to have sex than do parents, religion, or schools
combined [48].
Sexting
Sexting is a phenomenon that has risen with the
advent of texting. By definition, sexting is sending
or receiving images or videos that are sexually sug-
gestive in nature [49]. Some definitions also include
text messages that are sexually explicit. Adolescents
66. tures, updates on activities, and locations, which
are organized by the platform into a scrollable page
of information (‘news feed’). Users can ‘like’ or
share their friends’ statuses and even react to them
with icons called ‘emojis’ (short for emoticons),
which are small digital and visual representations
of ideas and emotions, varying from happy faces to
images of food.
Because online material is now often presented
concisely and simply, adolescents can easily culti-
vate a general understanding of the latest political,
social, and scientific news through platforms like
Facebook. Interacting with information-disseminat-
ing platforms, which often dually function as social
media tools, can encourage adolescents to critically
reflect on the discrete events unfolding in the world
around them [53].
Social media platforms, such as Facebook, also
allow individuals with specific physical conditions,
such as diabetes, to form support groups and
collective forums as an outlet for emotional, moral,
financial, and informational support. Evidence
from a diabetes group on Facebook with 30 000
r Health, Inc. All rights reserved.
rved. www.co-pediatrics.com 151
Office pediatrics
international users demonstrated the prowess of
social media to connect people regardless of lan-
guage and cultural barriers [54].
67. Despite these benefits, it remains crucial for
parents to be aware of how Facebook could nega-
tively impact children. In 1998, the Children’s
Online Privacy Protection Rule (COPPA) was issued
to protect the privacy and personal information of
children under the age of 13 [55]. However, reports
reveal that more than half of children use social
media by the age of 10 [56]. Although Facebook
complies with COPPA by setting the minimum
age to create a profile at 13, in 2011, it was reported
that 7.5 million Facebook users were under the age
of 13 [57]. As a result, children are becoming more
vulnerable to threats including exposure to inappro-
priate graphic and sexual content, increased contact
with strangers, and cyber-bullying.
Additionally, studies have suggested that post-
ing images or status updates to Facebook is associ-
ated with an increased perception of social support
[58–62]. The ability of Facebook ‘friends’ to ‘like’
and comment on their friends’ activities and posts
via text or emojis results in this perceived social
support. Although these responses can yield the
impression of robust social support, a lack of
response can have a detrimental effect on adolescent
self-esteem [17].
Instagram
Instagram is a photo-sharing platform launched in
2010 that allows users to share pictures with friends.
Much like with Facebook, Instagram users can
choose to ‘follow’ other users, and Instagram’s soft-
ware will organize friends’ pictures into a scrollable
‘feed’ of posts. Friends have the option to ‘like’
posted pictures. Each ‘like’ adds to a visible, quanti-
fied ‘like-count’ at the bottom of each picture.
69. tally-altered pictures paint an unrealistic represen-
tation of one’s lifestyle, putting additional pressure
on friends to match that lifestyle. This might con-
tribute to a phenomenon referred to as a ‘fear of
missing out,’ or FOMO, where individuals feel regret
because they feel others are having more positive
experiences from which they are absent [65
&&
]. Ulti-
mately, FOMO may contribute to feelings of anxiety
and depression among teenage users of the platform
[66].
With the pressure to post perfect pictures on
Instagram, there has been a recent rise in the ‘Fin-
sta,’ a ‘fake’ Instagram. These are alternative
accounts on the same platform that users create
for a smaller circle of friends, where the user is less
concerned about which pictures they post and how
many likes they receive [67]. In other words, Insta-
gram represents the life users want to show the
world while ‘Finsta’ depicts their real lives.
Snapchat
Snapchat is a unique photo-sharing app in which
users can take pictures and short videos, referred to
as ‘snapshots’ or ‘snaps,’ and send them to people on
their friend lists. Users can preset a view time rang-
ing from 1 to 10 s on each snap. Snapchat will notify
a user when he/she receives a snap; once that user
clicks the notification, the picture will expire after
the preset time.
Snapchat, like Instagram, offers a ‘story’ feature,
70. which allows users to share snaps with not just
selected individuals, but rather every individual
on their friend list. Although the pictures have a
finite time limit, the receiver can use their smart-
phone’s features to screenshot the picture and save
the image to their phone’s photo library. Users will
receive notifications when others take screenshots
of their snaps, but it is also possible to evade sending
this notification. Additionally, it may be possible
that others are present when a user opens a received
Health, Inc. All rights reserved.
Volume 31 � Number 1 � February 2019
New age technology and social media Shah et al.
‘snap,’ allowing unintended viewers to see the snap.
Along with providing real-time updates, Snapchat
allows friends to identify a user’s location through
geotags, special location-based borders and images,
or the newer ‘snap map’ that displays users’ loca-
tions on a physical map. The user’s location appears
on the snap map by default unless ‘ghost mode’ in
enabled. Through ghost mode, a user can mask his
or her location and prevent Snapchat friends from
locating them on the map. For these reasons,
parents should be cautious about how their children
interact with Snapchat. Any snap that is sent can be
saved, sent to others, and potentially compromise
the location of an adolescent.
Tinder
Social media has also transformed the realm of
dating with the introduction of apps, such as Tinder.
Tinder – an interactive dating app – allows individ-
71. uals who are mutually interested in each other to
connect. Until June 2016, adolescents aged 13–17
were allowed to create Tinder accounts; in fact, there
were over a million users in that age range [68].
Although that age range is now banned from using
Tinder, adolescents can falsify their age and can still
be active on the app.
Tinder presents others’ profiles to the user based
on perceived compatibility and location. The cen-
tral feature of Tinder is ‘swiping’ on a user’s profile:
swiping right indicates interest in that person,
whereas swiping left indicates a desire to continue
searching for users. One can also view users’ profiles
to come to a better decision. If both users swipe right
on each other, they ‘match’ and then can
begin chatting.
GroupMe
Launched in 2010 by Microsoft, GroupMe is a group
messaging app that allows users to send messages to
large groups of people rather than sending individ-
ual messages to friends. GroupMe is accessible both
online and as an app on mobile devices. Because it
relies on Internet connection to send and receive
messages, GroupMe overcomes the limits of texting:
users can communicate even in places without
phone service and can maintain contact across
international borders. However, while group mes-
sages may make communication easier, they also
pose a potential for groupthink – a set of psycho-
logical effects associated with group decision-mak-
ing which discourages nonconformity, creativity,
and disagreement with the group – putting pressure
on individuals to do things they might not be
individually inclined to do [69].
73. Parents are encouraged not only to monitor
their children’s chats, but also to talk to their …
Mini-Review
The Impact of Social Media on the Sexual and Social Wellness
of Adolescents
Lisa M. Cookingham MD, Ginny L. Ryan MD, MA *
Department of Obstetrics & Gynecology, University of Iowa
Carver College of Medicine, Iowa City, IA
a b s t r a c t
For most adolescents in the United States, the use of social
media is an integral part of daily life. While the advent of the
Internet has
enhanced information dispersal and communication worldwide,
it has also had a negative impact on the sexual and social
wellness of
many of its adolescent users. The objective of this review is to
describe the role of social media in the evolution of social
norms, to illustrate
how online activity can negatively impact adolescent self-
esteem and contribute to high-risk adolescent behaviors, to
elucidate how this
activity can result in real-world consequences with life-long
results, and to provide guidance regarding social media use for
those who care
for adolescents. Although research is now aimed at use of social
media for positive health and wellness interventions, much work
needs to
be done to determine the utility of these programs. Adolescent
healthcare providers are important contributors to this new field
of study
and must resolve to stay informed and to engage this up-and-
74. coming generation on the benefits and risks of social media use.
Key Words: Adolescent health, Sexuality, Social media, Internet
Introduction
Adolescence is a time of self-discovery, increased social
independence, and transformation into a unique individual.
While peers, parents, and educators have a direct impact on
adolescents during daily face-to-face interactions, Internet-
based entities are playing an increasingly large role during
this critical life stage.1 Internet use for social purposes has
increased dramatically over recent years, with 95% of US
adolescents between the ages of 12 and 17 regularly ‘going’
online, and 80% participating in some type of social media
website.2
Social networking sites (SNS) are a relatively new phe-
nomenonandincreasingly popularamong adolescents.These
are websites that permit social interaction among users3 and
allow users to create online profiles that may (or may not)
represent the user's real-life identity. Users personalize pro-
file pages with images, audio, and text, and can designate
‘friends’ and otherrelationships. Thesewebsitesare attractive
to adolescents because they allow for individualized self-
promotion as well as inclusion into a group that may not be
attainable in physical reality. During a time when it is as
important to be unique as it is to fit in, SNSs allow adolescents
to manufacture an image they want the world to see.
One model proposed to explain how adolescents inte-
grate media into their development of self is the Media
Practice Model.4 This model assumes 3 key features in un-
derstanding the effect of media on adolescents: (1) that
most media use is active or interactive; (2) that media use
The authors indicate no conflicts of interest.
* Address correspondence to: Ginny L. Ryan, MD, MA,
Department of Obstetrics &
75. Gynecology, University of Iowa Carver College of Medicine,
200 Hawkins Dr, 31332
PFP, Iowa City, IA 52242; Phone: (319) 384-9170; fax: (319)
384-9367
E-mail address: [email protected] (G.L. Ryan).
1083-3188/$ - see front matter � 2015 North American Society
for Pediatric and Adole
http://dx.doi.org/10.1016/j.jpag.2014.03.001
and its effects are in an active reciprocal relationship with
the user; and (3) that the adolescent's current and evolving
sense of identity is the basis for how media is chosen and
applied in daily life.4 The ‘media diet’ chosen by the
adolescent, therefore, is a reflection of who they believe
they are and who they want to be.4 While an SNS may seem
to provide the ideal venue for adolescent identity explora-
tion without committing to real-world consequences, this
model supports the notion that SNS behavior truly reflects
real-life behaviors or intent.
As this model suggests, social media use may have a sig-
nificant impact on the social and sexual well-being of ado-
lescents. Many adolescents display limited self-regulation
and judgment skills that are not yet fully mature, which lead
to risky behaviors, especially on SNSs.5 Adolescents can fall
easy prey to the ‘online disinhibition effect,’ meaning that
personal details and private information are more readily
released into the public domain than they would be face-to-
face interactions due to the dissociative anonymity SNSs
provide.6 SNSs provide an all too attractive outlet for
adolescents during a time in development where self-
expression and validation are important, and this expres-
sion may translate into risky social and sexual behavior.
76. Well before the advent of SNS popularity, adolescents
have been vulnerable to negative outcomes from poor
sexual choices. Adolescents are the highest risk group for
contracting a sexually transmitted infection (STI)7 and
nearly 3 million adolescents are infected annually.8 Several
common practices contribute to this high risk of contracting
an STI, including: concurrent sexual partners, multiple
sexual partners, and lack of consistent condom use.9 We are
now beginning to see that social media may be increasing
these risky sexual behaviors and decreasing the overall
social and sexual wellness in adolescents.
scent Gynecology. Published by Elsevier Inc.
Delta:1_given name
Delta:1_surname
mailto:[email protected]
http://crossmark.crossref.org/dialog/?doi=10.1016/j.jpag.2014.0
3.001&domain=pdf
http://dx.doi.org/10.1016/j.jpag.2014.03.001
L.M. Cookingham, G.L. Ryan / J Pediatr Adolesc Gynecol 28
(2015) 2e5 3
Impact on Self-Esteem
In this digital age, popularity is measured by how many
‘friends’ or ‘likes’ are collected on a SNS. Social media
encourage adolescents to compete for attention in order to
increase their ‘likes’ and enhance their self-worth. If a ‘post’
or a ‘pic’ doesn't garner enough comments, the adolescent is
encouraged to ‘share’ it to make it more newsworthy. Bolder
and more daring behavior is rewarded when the audience
applauds the actions of the performer, and the cycle per-
petuates. These seemingly innocuous online behaviors can
be quite damaging themselves, and they are easily trans-
lated into a risky offline reality.10
77. While individual conduct can damage self-esteem, so too
can the actions of an online adversary or ‘cyberbully.’ This
era's equivalent of a schoolyard bully, a cyberbully is some-
one who deliberately uses social media to perpetuate false,
humiliating, or malevolent information about another in-
dividual.3 Similar to traditional offline bullying, studies have
shown cyberbullying can lead to depression, anxiety, severe
isolation, and poor self-esteem for the bullied individual.11
Cyberbullying can be even more pervasive, however,
because SNSs provide a forum any time of the day or night
for anyone and everyone to see.12 Perhaps not surprisingly, it
has also been shown that individuals who participate in
cyberbullying are more likely to participate in offline
bullying.13
Changing Social Norms and Promotion of High-Risk Behavior
Social norms that evolve over time and are peculiar to a
culture and behaviordeemed unacceptable 50 years agomay
now be conventional. Psychological theorists suggest
behavior is strongly influenced by the perception of peers'
actions, whether or not this is the reality. As such, subjective
norms contribute significantly to behavioral intentions and
subsequent actions.14 Research supports the normative in-
fluence that social media, specifically SNSs, have on today's
adolescents. It has been suggested, for example, that SNSs
mayactually serve as a “media super-peer” by endorsing and
establishing social and behavioral norms of an adolescent's
peers.15,16 If an adolescent believes that her peers are
participating in a particular behaviordeven high-risk
behaviordshe is more likely to participate in it as well
because it is perceived as ‘normal.’
Much research is being done to highlight the influence of
SNSs on evolving social norms and promotion of high-risk
78. behavior. In a recent study assessing the relationship be-
tween the perception and the reality of high-risk sexual
behavior among peers using SNSs, the authors found that
adolescents consistently over-report high-risk sexual
behavior and under-report protective behaviors of their
peers.17 This suggests that adolescents overestimate their
peers' high risk behaviors.17 Another study demonstrated
that adolescents who viewed SNS photos with minimal or no
sexually suggestive content perceived that their peers were
participating in safer sex practices, such as condom use, and
reported that it would influence their future behavior to do
the same.14 In the same study, adolescents who viewed
sexually-suggestive SNS photos perceived that their peers
were having sex without protection or with strangers, and
they were more likely to report personal engagement in
these same high-risk behaviors.14 These findings suggest
that high-risk behavior displayed on SNSs may encourage
similar high-risk behavior in others and simultaneously
endorse such behavior as ‘normal.’
While high-risk behavior by adolescents is not new,
SNSs allow for a new manifestation of this behavior that
has been labeled “self-exploitation” by some.18 This refers to
the “creation and distribution of explicit or inappropriate”
materialdphotos, comments, suggestionsdon SNSs, social
media websites, other Internet sites, or through personal
cell phones.18 There are several specific types of self-
exploitation common to adolescent SNS profiles. In a cross-
sectional study evaluating risk behavior promotion on SNSs,
for example, 54% of profiles were found to contain 1 or more
references to a high-risk behavior such as sexual activity,
substance abuse, or violence.15 These practices may open
the door for similar behavior in both online and offline
relationships.
Studies show it is common for adolescents to self-report
79. high-risk sexual behavior on personal SNS profiles, with
references to sex displayed on 24% of profiles reviewed in 1
study.15 Other adolescents may not directly reference sexual
behavior but will partake in a practice known as ‘sexting.’
This refers to the sending, receiving, or forwarding of
sexually explicit messages, photographs, images, or videos
via the Internet, a cell phone, or another digital device.3 One
survey found that 20% of adolescents between 13-19 years
old have sent or posted a nude or semi-nude photo or video
of themselves to another adolescent.19 In a more recent
longitudinal study, the authors reported that 28% of their
subjects had received a ‘sext’ and 57% had been asked to
send a ‘sext.’20 More disconcerting was the finding that
male and female adolescents who engaged in sexting were
more likely to have had sex, and that sexting was associated
with high-risk sexual behaviors in females (this association
was not seen in males).20
Adolescents also engage in risk-taking related to sub-
stance abuse and SNS profiles have become a popular site for
the promotion of this behavior. In a study examining the
prevalence of risky behaviors displayed on an SNS, substance
abuse was the most frequently cited high-risk behavior, with
41% of profiles having some reference to alcohol, tobacco, or
drug use.15 A more recent study measuring online and offline
influences on adolescent smoking and alcohol use demon-
strated that exposure to SNS images of partying or drinking
increased both smoking and alcohol use in study subjects.21
These data again highlight the concern that online behavior
can readily translate into real world behavior and potential
repercussions.
Offline Consequences of Online Behavior
While the cost of risky online behavior is clearly high
80. when it comes to social and sexual health, there are also
potential legal ramifications. Laws originally created to
protect children are being used to criminalize them as por-
nographers in many states.22 One disturbing illustration in-
volves a 14-year-old girl who posted nude photos of herself
L.M. Cookingham, G.L. Ryan / J Pediatr Adolesc Gynecol 28
(2015) 2e54
on a SNS and was subsequently charged with possession and
distribution of child pornography.23 Another example in-
volves a teen who received unsolicited explicit photos of his
girlfriend via text message and then mass-e-mailed the nude
photos after their breakup to “get back at her.”24 This teen
was subsequently convicted of transmitting child pornog-
raphy and labeled a sex offender.24 While it seems right that
some punishment should be incurred for such unwise and
often hurtful decisions, few adolescents are aware that the
act of simply hitting ‘send’ can cause serious ramifications.
Inappropriate online behavior can lead to lifelong
repercussions, whether or not the actions are prosecuted.
Images and commentary posted on SNSs arefreelyaccessible
and leave a digital footprint, allowing college admission
committees and employers to pre-screen their applicants.25
More distressing than the potential negative impact of SNS
personal disclosure on professional success is the fact that
sexual predators troll SNSs for vulnerable adolescents who
don't understand the effects of haphazard Internet use.
While recent studies suggest that sexual solicitation is more
likely to occur between 2 adolescents (versus an adult
soliciting an adolescent), the threat very much exists.25
Utilization of Social Media for Education
