THE 191Written Response to Live PerformanceNameSection.docx

THE 191
Written Response to Live Performance
Name:
Section:
Date:
All answers should be written in complete sentences with proper
grammar, and all answers should be filled in within this
document. Submissions that are sent in that do not match this
format will be graded as a 0.
Question 1: ACTING
In the course of your watching and listening to Bat Boy, many
actors will take the stage and make acting choices while
performing the play that you have read (and watched). Describe
two moments in the play that stuck out to you and explain what
happened in that moment on stage ,and what about it was
working (or not) for you, using language from the course (i.e
Subtext, heightened language, inside out or outside in acting,
given circumstances, etc.) 20 pts- 40-70 words per answer.
A: (30pts) First Moment:
B. (30pts) Second Moment:
Question 2: DESIGN
We’ve discussed the wide range of design choices that have to
be made during the show (including sound, light, costume and
scenic design): Choose two moments from Bat Boy during
which the design element tells you something about a character,

mood, atmosphere or given circumstances. For each moment
you must 1) describe what the design did and what it told you 2)
Why was it effective? Each answer should be 40-70 words, 15
pts each
A: (20pts) First Moment:
B: (20pts) Second Moment
Question 3: REFLECTION
Now that you’ve seen Miami the production of Bat Boy! The
Musical, please reflect on A) What do you think was the
directorial concept for this play(refer to the chapter on directing
in your text book and your notes from class on Week 11), and
why do you think that? and b) What did the play (using the
concept you identified in A) mean to you? Each answer should
be 40-70 words, 15pts each.
(25pts)A):
(25pts)B):
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Family Disruption in Childhood and
Risk of Adult Depression

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CitationTitle:Family Disruption in Childhood and Risk of Adult
DepressionAuthors:Stephen E. Gilman, Sc.D., author
Ichiro Kawachi, M.D., Ph.D., author
Garrett M. Fitzmaurice, Sc.D., author
Stephen L. Buka, Sc.D., authorSource:American Journal of
Psychiatry: Official Journal of the American Psychiatric
Association. 160(5):939-946Publisher Information:American
Psychiatric Publishing, 2003.Publication
Year:2003Description:OBJECTIVE: The authors examined the
risk that family disruption and low socioeconomic status in
early childhood confer on the onset of major depression in
adulthood. METHOD: Participants were 1,104 offspring of
mothers enrolled during pregnancy in the Providence, R.I., site
of the National Collaborative Perinatal Project. Measures of
childhood family disruption and socioeconomic status were
obtained before birth and at age 7. Structured diagnostic
interviews were used to assess respondents’ lifetime history of
major depressive episode between the ages of 18 and 39.
Survival analysis was used to identify childhood risks for
depression onset. RESULTS: Parental divorce in early
childhood was associated with a higher lifetime risk of
depression among subjects whose mothers did not remarry as
well as among subjects whose mothers remarried. These effects
were more pronounced when accompanied by high levels of
parental conflict. Independent of the respondents’ adult
socioeconomic status, low socioeconomic status in childhood

predicted an elevated risk of depression. CONCLUSIONS:
Family disruption and low socioeconomic status in early
childhood increase the long-term risk for major depression.
Reducing childhood disadvantages may be one avenue for
prevention of depression. Identification of modifiable pathways
linking aspects of the early childhood environment to adult
mental health is needed to mitigate the long-term consequences
of childhood disadvantage.Document
Type:ArticleLanguage:English
English
EnglishISSN:0002-953X
1535-
7228DOI:10.1176/appi.ajp.160.5.939Availability:http://www.ps
ychiatryonline.org.ezproxy.snhu.edu/doi/full/10.1176/appi.ajp.1
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Contextualizing video game play: The
moderating effects of cumulative risk and parenting styles on
the relations among video game exposure and problem
behaviors

Detailed RecordTitle:Contextualizing video game play: The
moderating effects of cumulative risk and parenting styles on
the relations among video game exposure and problem
behaviors.Authors:Linebarger, Deborah L.. University of Iowa,
Iowa City, IA, US, [email protected]Address:Linebarger,
Deborah L., College of Education, University of Iowa, N278
Lindquist Center, Iowa City, IA, US, 52242,
[email protected]Source:Psychology of Popular Media Culture,
Vol 4(4), Oct, 2015. Special Issue: Video Games and Youth. pp.
375-396.NLM Title Abbreviation:Psychol Pop Media
CultPublisher:US : Educational Publishing
FoundationISSN:2160-4134 (Print)
2160-4142 (Electronic)ISBN:1-4338-2196-6
978-1-4338-2025-0Language:EnglishKeywords:hyperactivity,
video games, cumulative risk, parenting styles, attention
problemsAbstract:This study examined the direct and moderated
relations associated with video game exposure and child
behavior problems (i.e., hyperactivity, inattention). Research
linking video game exposure to these problem behaviors is
inconsistent likely because studies have used measures that
combine elements of both problems in the same scale and
because key contextual factors associated with video game play
(i.e., cumulative family risk, parenting styles, video game
content) are not included or have been covaried out. A
nationally representative group of US parents/caregivers of 788
preschoolers (2–5 years) and 391 school-age children (6–8
years) were interviewed by phone and asked to report their
child’s video game exposure via a 24-hr time diary,
demographic information, their parenting styles, and their
child’s hyperactivity levels and attention problems. Separate
regressions by age were conducted. Video game exposure was
directly associated only with increasing levels of hyperactivity
in preschool children, an effect reduced to nonsignificance
when parenting styles were covaried out. Adding cumulative
risk and parenting styles as moderators increased the amount of

variance accounted for across both the preschool and school-age
samples. Responsive parenting moderated the effects of video
game exposure for low-risk preschoolers’ and high-risk school-
age children’s hyperactivity levels and high-risk preschoolers’
and low-risk school-age children’s attention problems. In the
final set of models with video game exposure broken into
violent and nonviolent content, different patterns of effects and
larger effect sizes emerged across cumulative risk,
responsiveness, and nonviolent video game exposure. Violent
video game exposure was associated only with low-risk school-
age children’s hyperactivity levels. (PsycINFO Database Record
(c) 2018 APA, all rights reserved)Document Type:Journal
ArticleSubjects:*Behavior Problems; *Computer
Games; *Parenting
Style; *Recreation; *Exposure; Attention; HyperkinesisPsycINF
O Classification:Childrearing & Child Care
(2956)Population:Human
Male
FemaleLocation:USAge Group:Childhood (birth-12 yrs)
Preschool Age (2-5 yrs)
School Age (6-12 yrs)
Adulthood (18 yrs & older)Tests & Measures:MacArthur CDI
III Short Form
McCarthy Scales of Children’s Ability
National Household Education Survey
Assessment of Literacy and Language DOI: 10.1037/t14964-
000
Cumulative Risk Index DOI: 10.1037/t65678-000
Peabody Picture Vocabulary Test
Strengths and Difficulties Questionnaire DOI: 10.1037/t00540-
000Grant Sponsorship:Sponsor: U.S. Department of Education,
Corporation for Public Broadcasting, Public Broadcasting
System for the Ready to Learn initiative, US
Grant Number: cooperative agreement U295A05003
Recipients: No recipient indicatedMethodology:Empirical
Study; Interview; Quantitative StudyFormat

Covered:ElectronicPublication Type:Journal; Peer Reviewed
JournalPublication History:First Posted: Apr 6, 2015; Accepted:
Dec 3, 2014; Revised: Nov 20, 2014; First Submitted: Mar 3,
2014Release Date:20150406Correction
Date:20180412Copyright:American Psychological Association.
2015Digital Object
Identifier:http://dx.doi.org.ezproxy.snhu.edu/10.1037/ppm00000
69PsycARTICLES Identifier:ppm-4-4-375Accession
Number:2015-14739-001Number of Citations in Source:74
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Contextualizing Video Game Play: The Moderating Effects of
Cumulative Risk and Parenting Styles on the Relations Among
Video Game Exposure and Problem BehaviorsContentsVideo
Game ResearchEcological Approach to Understanding Potential
Effects of Video Game ExposureCumulative RiskParenting

StylesModerating Effects of Different Contextual FactorsAge-
Related
DifferencesMethodParticipantsDesignProcedureDemographic
MeasuresCumulative RiskParenting StyleChild Ability
CovariateVideo Game ExposureOutcome MeasuresAnalytic
ApproachResultsModel 1: Does Video Game Exposure Predict
Child Behavior Problems and Do the Patterns Differ for
Hyperactivity and Inattention?Model 2: After Including
Parenting Styles as Covariates, Does Video Game Exposure
Predict Child Behavior Problems and Do the Patterns Differ for
Hyperactivity and Inattention?Model 3: Do Cumulative Risk
and Parenting Styles Moderate the Relations Between Video
Game Exposure and Child Behavior Problems?Model 4: Do the
Patterns Observed for Overall Video Game Exposure,
Cumulative Risk, and Parenting Styles Vary When Video Game
Exposure Is Divided Into Content
Categories?DiscussionParenting StylesVideo Game Content
EffectsLimitationsConclusionReferences
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By: Deborah L. Linebarger
University of Iowa;
Acknowledgement: This project was supported by a cooperative
agreement between the U.S. Department of Education, the

Corporation for Public Broadcasting, and the Public
Broadcasting System for the Ready to Learn initiative, PR#
U295A05003. However, these contents do not necessarily
reflect the opinions or represent the policy of the Department of
Education. You should not assume endorsement by the Federal
Government as well.
Recent research linking video game exposure to child behavior
problems including attention and hyperactivity problems are
inconsistent (Ferguson, 2011; Parkes, Sweeting, Wight, &
Henderson, 2013; Swing, Gentile, Anderson, & Walsh, 2010).
Swing et al. (2010) found that 9-year-old US children’s
attention problems in the classroom were positively associated
with increasing time spent playing video games (i.e., exposure)
although the magnitude of the effect was small (β = .10). The
scale used to measure attention problems in this study consisted
of three items that combined elements of both inattention and
hyperactivity/impulsivity (e.g., difficulty staying on task, often
interrupts other children’s work). Parkes et al. (2013) found no
relation between U.K. 7-year-olds’ video game exposure and a
combined screening measure of hyperactivity and attention
problems (i.e., Strengths and Difficulties Questionnaire).
Ferguson (2011) also found no relation between Hispanic 10- to
14-year-olds’ video game exposure and attention problems
although the estimates used to calculate video game exposure
were fairly imprecise both in total exposure and in the
categorization of video game content. Children were asked to
recall the total number of hours they spent playing video games
during a week and whether the games they played were violent
or not.
One explanation for the small effect found by Swing et al.
(2010) and the null findings reported by Parke et al. (2013) is
related to how outcomes were measured. In these two studies,
items tapping both inattention and hyperactivity were included.
Although there is likely overlap in these two behaviors,
emerging evidence suggests differentiation in the
neuropsychological mechanisms associated with each behavior

problem (Martel & Nigg, 2006; Sonuga-Barke, 2003).
Specifically, regulation problems and cognitive control deficits
are hypothesized to lead to inattention whereas reactive or
motivational control problems are hypothesized to lead to
hyperactivity (Martel & Nigg, 2006). Each behavior problem is
then differentially predictive of adolescent and adult outcomes
including academic problems for those exhibiting greater
inattention (Breslau, Lane, Sampson, & Kessler, 2008; Duncan
et al., 2007) and substance abuse, juvenile delinquency, and
other externalizing problems for those exhibiting greater
hyperactivity (Elkins, McGue, & Iacono, 2007). The null
findings reported by Ferguson (2011) could be a function of the
data collection method. Asking for a global estimate of the
hours of video games played assumes that respondents are able
to accurately provide an estimate of use in a relatively short
amount of time. In a series of time use studies, researchers have
found that adults often overestimated their and their children’s
time spent in socially desirable activities (e.g., reading to their
children, working out, number of hours worked) and
underestimated their time spent in socially undesirable
activities (e.g., drinking, TV watching; Juster & Stafford, 1985;
Robinson, 1985; Robinson & Godbey, 1997). Finally, other
research indicates that older children have difficulty accurately
estimating their own time use (Comstock & Scharrer, 1999).
Increasing measurement error reduces the accuracy of the
predictive models and likely underreports effects, an issue when
most media exposure/outcomes studies find relatively small
effects.
Video Game Research
Researchers have proposed different theories to explain the
relations between video game exposure and behavior problems.
Considering each theory in light of the neuropsychological
mechanisms described above suggests that it might be possible
to more clearly predict whether and how video games would be
associated with specific behavior problems. The first theory

relates to the fast pace of video games. It is argued that this fast
pace increases arousal during and after exposure due to frequent
shifts in attention and the continual renewal of the orienting
response (Huizinga, Nikkelen, & Valkenburg, 2013). Over time,
children habituate to these arousal levels leading to changes in
the child’s baseline arousal level that require ever and greater
stimulation. When such stimulation is absent, children will be
more likely to exhibit inattention or hyperactivity when faced
with less arousing situations (Lang, Zhou, Schwartz, Bolls, &
Potter, 2000). Research investigating this hypothesis with TV
indicates that fast pace is unrelated to attention problems
specifically (Anderson, Huston, Schmitt, Linebarger, & Wright,
2001) although Lillard and Peterson (2011) have found that
watching a 9-min clip from a fast-paced program (i.e., Sponge
Bob Squarepants) caused short-term deficits in executive
function, a set of skills that comprises both hyperactivity and
attentional components, compared with children who either
watched a more slowly paced educational program or drew a
picture.
The second hypothesis proposes that frequent video game
playing affects the way that a child’s attentional style develops
such that the child scans and shifts attention to onscreen content
more frequently rather than selecting and focusing attention for
any period of time (Huizinga et al., 2013). Spending more time
scanning and shifting attention and less time selecting and
focusing attention may make it especially challenging to attend
to tasks like reading or homework that require the selecting and
focusing attentional style (Jensen, Martin, & Cantwell, 1997).
Although it is unclear how each media effects hypothesis might
differentially relate to hyperactivity and inattention, the
differentiated neuropsychological mechanisms coupled with the
different hypotheses does provide potential explanatory
mechanisms for different patterns of effects. Therefore, one
purpose of this study is to test the effects of video game
exposure to evaluate similarities and differences across both
hyperactivity and inattention problems.

In addition to these medium-specific based explanations, it is
also likely that the specific content of a video game (e.g.,
violence, role-playing, first-person shooter, educational
messages) would influence particular outcomes. Content-based
theories of TV effects have typically provided better
explanations of observed relations compared with medium-
specific theories (Anderson & Bushman, 2001). Research
investigating the relation between exposure to specific video
game content and attention is mixed and typically conducted
with children age seven years and older. Visual-perceptual
attention skills are enhanced when children and adults play
more action-based video games (i.e., 1st/3rd person point of
view, heavy action, fast-paced typically featuring violent
themes like Call of Duty or Halo; Dye & Bavelier, 2010; Oei &
Patterson, 2013). Playing matching games, spatial memory
games, and hidden object games are associated with stronger
visual search skills and spatial memory (for the latter two game
types; Oei & Patterson, 2013). In contrast, research that defines
attention as the ability to concentrate and not become easily
distracted has correlationally and longitudinally linked violent
video game exposure to attention problems (Gentile, Swing,
Lim, & Khoo, 2012) although the effect disappears when initial
attention problems were controlled. Research examining
hyperactivity and specific video game content has similarly
linked action-based video game exposure to better inhibitory
control in an adult sample (Oei & Patterson, 2013). In the
present study, exposure was divided into nonviolent content and
violent content using ratings provided by the Entertainment
Software Rating Board (i.e., ESRB).
Ecological Approach to Understanding Potential Effects of
Video Game Exposure
Also missing from studies examining exposure and behavior
problems is an examination of the ecological contexts in which
video game play is situated in young children’s lives and how
these contexts might moderate the relationships observed

between video game play and child behavior problems. A
number of environmental factors reflecting broader ecological
categories like settings, economic resources, parenting, parent
education, and other cultural and linguistic factors surround
where and how children experience media. Human development
is always development in context (Bronfenbrenner & Morris,
2006; Ceci & Hembrooke, 1995; Luscher, 1995; Moen, Elder, &
Luscher, 1995). Contexts can simultaneously limit and facilitate
performance, depending upon the nature of the context. For
instance, more educated parents, greater economic resources, or
high-quality parenting might buffer any negative effects
associated with video game exposure whereas poorly educated
parents, fewer economic resources, or poor-quality parenting
might exacerbate negative effects (Burchinal, Campbell, Bryant,
Wasik, & Ramey, 1997; Sameroff, Seifer, Barocas, Zax, &
Greenspan, 1987). A second purpose of this study was to test
for the moderating roles of two ecological contexts: the home
environment created by varying levels of cumulative
sociodemographic risk (Sameroff & Feise, 2000) and the
parent–child relationship characterized by different parenting
styles (Baumrind, 1991; Maccoby & Martin, 1983).Cumulative
Risk
Research indicates that poor developmental outcomes including
child behavior problems increase as the number of
sociodemographic risk factors accumulate (Ackerman, Izard,
Schoff, Youngstrom, & Kogos, 1999; Burchinal et al., 1997;
Jones, Forehand, Brody, & Armistead, 2002; Sameroff, Bartko,
Baldwin, Baldwin, & Seifer, 1998; Sameroff & Feise, 2000;
Sameroff et al., 1987). Specifically, low maternal education,
single-parent status, poverty, maternal age at birth, minority
status, and number of children living in the house have been
individually linked to poorer academic and social outcomes
(Burchinal et al., 1997). The individual effects of any one
sociodemographic risk are amplified as the presence of these
risks accumulates leading to substantial disadvantage (Lima,
Caughy, Nettles, & O’Campo, 2010; Sameroff et al., 1987).

Children living in high-risk environments are more likely to
exhibit behavior problems compared with children living in
low-risk environments (Essex et al., 2006; Pingault et al.,
2011). Much of the influence of these sociodemographic risks
on child behavior problems is likely transmitted through
parenting (Burchinal, Roberts, Zeisel, Hennon, & Hooper, 2006;
Ceballo & Hurd, 2008). As risks accumulate, the ability of a
parent to maintain a positive emotional climate in the home is
markedly diminished (Arnold & Doctoroff, 2003; Conger et al.,
1992; Conger, Ge, Elder, Lorenz, & Simmons, 1994;
Trentacosta et al., 2008).Parenting Styles
Broad patterns of parenting, referred to as parenting styles, are
composed of a complex set of behaviors that function to create a
particular emotional climate in a child’s home (Baumrind, 1991;
Darling & Steinberg, 1993; Maccoby & Martin, 1983).
Parenting styles have been conceptualized along two different
continua: responsiveness/warmth and consistency/behavioral
control. Children with parents who are appropriately responsive,
sensitive to their children’s cues, and warm exhibit fewer
attention problems and decreased levels of hyperactivity
(Healey, Flory, Miller, & Halperin, 2011). In contrast, parents
who exhibit disorganized and unpredictable parenting tend to
create environments where discipline is inconsistently applied
and rules may or may not be followed. Children in these
families are more likely to exhibit higher levels of hyperactivity
and more attention problems (Gardner, 1989; Nigg, Hinshaw, &
Huang-Pollack, 2006; Stormshak, Bierman, McMahon, Lengua,
& Conduct Problems Prevention Research Group,
2000).Moderating Effects of Different Contextual Factors
Although cumulative risk and parenting styles are both
hypothesized to moderate the relations between video game
exposure and child behavior problems, it is unclear how each
construct, individually or in combination, will exert influence.
For instance, researchers using an approach similar to the one
proposed here (i.e., cumulative risk and parenting quality as
moderators) found significant and positive associations between

educational TV viewing and executive function in a high-risk
sample of elementary schoolchildren, especially when their
parents exhibited higher levels of warmth and responsiveness
(Linebarger, Barr, Lapierre, & Piotrowski, 2014). In contrast,
the low-risk elementary schoolchildren in this sample exhibited
poorer executive function when they were exposed to increasing
levels of background TV and had parents who exhibited less
consistency in their parenting. Collectively, the different
neuropsychological pathways identified for hyperactivity and
attention problems, combined with different associations linked
to cumulative risk and exposure, highlight the importance of
examining how exposure is situated in a child’s life and whether
different contextual factors are associated with differing
patterns of effects (Bronfenbrenner & Morris, 2006).
Age-Related Differences
One final complication involves examining the role that age-
related differences might play in whether and how parenting and
video game exposure are linked to hyperactivity and attention
problems. In this sample, it was possible to test whether effects
were the same or different for preschoolers (i.e., 2–5 years old)
compared with school-age children (i.e., 6–8 years old). Several
lines of research would suggest that this age split is important
to consider. First, there is rapid developmental change between
birth and age 5 across a variety of academic and social
behaviors including impulse control and attention. By age 5,
foundational capacities for directing attention and controlling
impulses are in place. As children enter formal schooling, they
are shifting from this rapid developmental phase to refining and
learning to efficiently deploy attention and inhibit impulses
(Hardy et al., 2007; Moriguchi & Hiraki, 2013). Although the
rapid phase of development is diminishing, it is also possible
that formal schooling, with its significant cognitive and social
demands, could exacerbate behavior problems. Second, video
game exposure varies between preschoolers and school-age
children. These different play patterns are likely the result of

the types of content typically available for or preferred by each
age group as well as a better developed ability to navigate the
complexities associated with playing video games (e.g., motor
skills, ability to read, understanding of how video games work).
Consequently, parenting styles, cumulative risk, and video game
exposure that might have been harmful, helpful, or unrelated to
behavior in preschoolers could manifest differential effects in a
school-age population.
The purposes of this study are fourfold and will be examined
separately for preschoolers and school-age children. First, the
direct relations between exposure and child behavior problems
will be tested independently for hyperactivity and attention
problems. Next, parenting styles will be included as covariates
in each model. Third, cumulative risk and parenting styles will
be evaluated as moderators of any relations between total video
game exposure and child behavior problems. Finally,
differential patterns associated with violent and nonviolent
video game content will be tested.
MethodParticipants
Participants were 788 parents/primary caregivers of a child
between 2 and 5 years old (preschoolers) and 391
parents/primary caregivers of a child between 6 and 8 years old
(school-age). An additional 298 parents/primary caregivers were
not included in the analysis because their child was under 2
years of age and the outcomes of interest could not be collected
for that age-group. Table 1 reports demographic, cumulative
risk, video game exposure, parenting styles, and outcomes
descriptives for the full sample of preschoolers and school-
agers as well as the age cohorts split by cumulative risk status.
Descriptive Statistics for Full Sample and Split by Risk Status
for Demographics, Cumulative Risk, Video Game Exposure,
Parenting Styles, and Child Behavior ProblemsDesign
Institutional review board approval was obtained from the
University of Pennsylvania prior to a private survey research

firm administering a rolling cross-sectional survey using a
disproportionate stratified random digit dialing procedure
between January and March, 2009. The response rate of 39.1%
was similar to other nationally representative surveys conducted
with parents of young children (e.g., 40%; see Lapierre et al.,
2012, for further details regarding survey
implementation).Procedure
The survey firm administered eligibility screening and informed
consent prior to parents completing the 50-min survey.
Questions ranged from household demographics, to a 24-hr time
use diary, and an assessment of the child’s behavioral
functioning. Parents were compensated US$25 when using a
landline (∼96%) and US$50 when using a cellphone. No
demographic differences were found between those contacted
via landline and those contacted via cellphone.Demographic
Measures
Parents were asked a series of questions regarding child and
family characteristics. See Table 1 for full descriptive statistics
on all measures.Cumulative Risk
A cumulative risk index modeled after Sameroff et al. (1998)
was created using six demographic variables. Risks were
dichotomized into 0 = no risk present and 1 = at risk. The
following were included in the index:
Child’s racial/ethnic background
Children whose parents identified them as Latino/a, African
American, Hispanic, American Indian, or other were coded as
minority status and coded at risk.
Children in the household
Children living in families with four or more children in the
home were considered at risk.
Maternal age
Children whose mothers were younger than 18 years at the time
of their child’s birth were coded at risk.
Maternal education
Children whose mothers reported having less than a high school
diploma were coded at risk.

Single parent status
Children who were living in families where there was only one
adult caregiver in the home were considered at risk.
Socioeconomic status
An income-to-needs ratio was calculated based on 2009 federal
poverty guidelines by asking parents their average household
income and their family size (Federal Register, 23 January
2009). Children whose income-to-needs ratio was less than 2.0
were considered at risk.Parenting Style
Based on Baumrind’s conceptualization of parenting styles
(1991), two subscales of the original 62-item questionnaire
(Robinson, Mandleco, Olsen, & Hart, 1995) were administered
to all participants: degree of parenting responsiveness and
degree of parenting inconsistency (lack of follow through).
Each was measured via seven items using a 5-point Likert scale:
1 (never) to 5 (always; e.g., how often does parent find it
difficult to discipline the child: α = .73, inconsistency; how
often does parent praise the child: α = .83,
responsiveness).Child Ability Covariate
Based on prior research suggesting relations among parenting
quality, early language and reading skills, and the outcomes
examined here (Bernier, Carlson, & Whipple, 2010), vocabulary
size (children between 2 and 3 years old), language skills
(children between 3 and 6 years old), and early literacy skills
(children between 6 and 8 years old) were included as
covariates in all models.
Vocabulary skills
Vocabulary skills for preschool children between 2 and 3 years
old were assessed via the MacArthur CDI III short form
(MCDI), a 100-item vocabulary production checklist. Raw
scores were converted to percentile ranks accounting for both
sex and age differences. Validity estimates were calculated
using the McCarthy Scales of Children’s Ability (.47–.56); the
Peabody Picture Vocabulary Test (.41–.49); and conversational
language samples (.26–.42; Feldman et al., 2005). Three- to 5-
year-olds’ language skills were based on 10 questions adapted

from the Assessment of Literacy and Language (ALL;
Lombardino, Lieberman, & Brown, 2005). These items asked
parents to report their child’s vocabulary knowledge, language
complexity, and articulation ability. Exploratory factor analysis
supported a one-factor model for these 10 items with adequate
internal reliability (Cronbach’s alpha = .77).
Literacy skills
Literacy skills for children between 6 and 8 years old were
based on a series of questions about the child’s phonological
and phonemic awareness abilities and their early reading skills
(e.g., does your child read simple stories? Chapter books?).
Items were selected from the ALL (Lombardino et al., 2005)
and the National Household Education survey (O’Donnell,
2008). A one-factor model was found through an exploratory
factor analysis. The scale’s internal reliability estimate was
adequate (Cronbach’s alpha = .91).Video Game Exposure
A 24-hr time diary adapted from the Panel Study of Income
Dynamics was administered to capture the duration of all of the
child’s activities from the previous weekday or weekend day (or
most recent typical day if previous day was atypical). Total
video game exposure was operationalized as the total number of
hours of video game play parents reported their child did during
the previous 24 hr. In addition, when parents reported their
children playing video games, interviewers asked them what the
title of the video game was. Each title was then categorized as
containing any violence or no violence using content codes
supplied by the Entertainment Software Rating Board (ESRB,
2014).Outcome Measures
Two subscales (i.e., hyperactivity, attention problems) were
used from the widely used parent-report measure Behavior
Assessment System for Children (BASC-2; Reynolds &
Kamphaus, 2004). The BASC-2 has sound psychometric
properties (internal consistency = .90 to .91; test–retest = .84)
and is able to discriminate between groups of children with
preexisting clinical diagnoses (Sullivan & Riccio, 2006).
Convergent validity is reported with The Achenbach System of

Empirically Based Assessment (.71–.83); the Conner’s Rating
Scales (.51–.78); and the Behavior Rating Inventory of
Executive Function (.83, global executive function composite).
Questions were answered using a 4-point Likert scale (i.e., 1
(never) to 4 (almost always)). Two separate versions of the
BASC-2 Scales were administered based on the child’s age:
preschool (2–5 years old) and school-age (6–8 years old).
Summed raw scores were converted to T scores (M = 50, SD =
10). Higher scores on each subscale were indicative of more
behavior problems (i.e., greater levels of hyperactivity or more
attention difficulties). Specifically, scores ranging from 60 to
69 were considered at risk for maladjustment while scores 70 or
higher were considered clinically significant for behavior
problems.Analytic Approach
Multiple linear regression models were computed within the
survey module of STATA 12.0 to include the survey weight
correction thereby eliminating problems arising from incorrect
standard error estimates (see Lapierre et al., 2012, for details).
Weights were included to adjust for differing probabilities in
the likelihood of selection for all survey respondents as well as
to account for coverage gaps and nonresponse biases in the
survey frame. Weights were poststratified along several
dimensions using estimates from the 2009 Census American
Community Survey.
To address the research questions, four sets of regression
models were computed separately by age (i.e., preschool, 2–5
years old; school-age, 6–8 years old), each building upon the
last. First, the direct associations between video game exposure
and hyperactivity and inattention were computed with
demographic covariates included (Models 1a/b). Next, parenting
styles (i.e., responsive, inconsistent) were included as
individual covariates to determine whether removing the
variance associated with parenting changed any relations
between exposure and outcomes (Models 2a/b). The third set of
models involved first splitting the sample into low-risk and
high-risk groups based on previous research indicating

significant differences in relations across low- and high-risk
(Linebarger et al., 2014; Sameroff et al., 1998). Then, each
model included interaction terms between both parenting styles
and total video game exposure to test the moderating effects of
parenting styles (Models 3a–d). Interaction terms were created
by first centering the independent variable and the two
moderators. Table 2 provides information about exposure and
both behavior problems by a median split for responsiveness
and consistency (i.e., high/low) for age-groups. The fourth set
of models were constructed the same way as Models 3a–d;
however, video game exposure was split into violent content
and nonviolent content and interaction terms were created for
both parenting styles and both exposure categories (Models 4a–
d). When significant interactions between parenting style and
video game exposure were found, simple effects slopes were
calculated for the mean and ±1 standard deviation for
responsiveness. Graphs were created using these responsiveness
values and three levels of video game exposure: none, 30 min,
and 60 min.
Means for Video Game Exposure, Hyperactivity, and Attention
Problem Split by Age and Parenting Style
High risk was operationalized as 2 or more cumulative risk
factors (Sameroff et al., 1998). Although Models 3 and 4
involved splitting children into low- and high-risk samples,
when each regression was computed, the original continuous
risk variable was used; therefore, in the low-risk regressions,
risk was either 0 or 1 whereas in the high-risk regressions, risk
ranged from 2–6 risks. Tables 3–8 provide detailed regression
results.
Regression Results Predicting Preschool Behavior Problems
From Video Game Exposure Without (Model 1) and With
(Model 2) Parenting Style Included as a Covariate
Regression Results Predicting School-Age Behavior Problems

From Video Game Exposure With Cumulative Risk and
Parenting Style Included as Moderators (Model 3)
From Video Game Exposure Split by Content With Cumulative
Risk and Parenting Style Included as Moderators (Model 4)

Figure 1. Low-risk preschoolers’ hyperactivity levels by
parental responsiveness and nonviolent video game exposure.
Figure 2. High-risk preschoolers’ attention problems by parental
responsiveness and nonviolent video game exposure.
Figure 3. Low-risk school-age children’s attention problems by
Figure 4. High-risk school-age children’s hyperactivity levels
by parental responsiveness and nonviolent video game exposure.
ResultsModel 1: Does Video Game Exposure Predict Child
Behavior Problems and Do the Patterns Differ for Hyperactivity
and Inattention?
Hyperactivity
Each hour of video game exposure was associated with a 2.36-
point increase in hyperactivity scores in preschoolers with the
model, accounting for 8.6% of the variance in the outcome
(Table 3). Video game exposure was unrelated to hyperactivity
in school-age children although the overall model accounted for
16.2% of the variance (Table 4).

Attention problems
Video game exposure was unrelated to attention problems in
both preschoolers (Table 3) and school-age children (Table 4),
with the models accounting for 10.1% and 16.0% of the
variance, respectively.Model 2: After Including Parenting
Styles as Covariates, Does Video Game Exposure Predict Child
Behavior Problems and Do the Patterns Differ for Hyperactivity
and Inattention?
Hyperactivity
Once parenting styles were included as covariates, video game
exposure was no longer associated with hyperactivity scores in
preschoolers (Table 3). The association between exposure and
hyperactivity was also not significant for school-age children
(Table 4). The inclusion of both parenting styles to the models
explained an additional 13.8% of the variance in preschoolers’
hyperactivity scores and 11.3% in school-agers’ scores.
Attention problems
Video game exposure was unrelated to attention problems in
both preschoolers (Table 3) and school-age children (Table 4).
The inclusion of both parenting styles to the models explained
an additional 8.9% of the variance in preschoolers’ attention
scores and 4.0% in school-agers’ scores.Model 3: Do
Cumulative Risk and Parenting Styles Moderate the Relations
Between Video Game Exposure and Child Behavior Problems?
Low-risk preschoolers
Responsive parenting moderated the relation between exposure
and hyperactivity levels, increasing the amount of variance
accounted for by 4.2% (Tables 5). Parenting did not moderate
the relation between exposure and attention problems. Parental
consistency also did not moderate the exposure/hyperactivity
relationship.

At low and mean levels of video game exposure, children whose
parents were more responsive exhibited lower levels of
hyperactivity compared with children whose parents were
average in responsiveness or low in responsiveness. Further,
increasing levels of exposure were associated with concomitant
increases in hyperactivity levels. For children whose exposure
levels were 1 standard deviation above the mean, there were no
differences associated with responsiveness; that is, all children
exhibited significantly higher levels of hyperactivity.
High-risk preschoolers
and attention problems, increasing the amount of variance
accounted for by 3.3% but was unrelated to hyperactivity levels
in high-risk preschoolers (Tables 5). Consistency did not
moderate the exposure/behavior problems relationship.
In general, children with low levels of exposure to video games
exhibited more attention problems. As exposure increased,
attention problems decreased. When video game exposure was
high, children with more responsive parents exhibited better
attention. Conversely, when video game exposure was low,
children with less responsive parents exhibited better attention.
There were no differences in attention problems associated with
responsiveness for children whose video game exposure was
average.
Low-risk school-agers
and attention problems increasing the amount of variance
in low-risk school-agers (Tables 6). Consistency did not
moderate the exposure/behavior problems relationship.
exhibited more attention problems. As exposure increased,

attention problems decreased. When video game exposure was
average or high, children with more responsive parents
exhibited better attention. There were no differences in
attention problems associated with responsiveness for children
whose video game exposure was low.
High-risk school-agers
and hyperactivity levels, increasing the amount of variance
in high-risk school-agers (Tables 6). Parental consistency was
unrelated to the exposure/behavior problems relations. Although
parenting styles did not moderate the relation between exposure
and attention problems for high-risk school-age children, there
was a direct effect of exposure on attention problems. As
exposure increased, attention problems increased by 2.40 points
per hour.
exhibited higher levels of hyperactivity. As exposure increased,
hyperactivity decreased. When video game exposure was low or
average, children with more responsive parents exhibited higher
hyperactivity levels. There were no differences in hyperactivity
associated with responsiveness for children whose video game
exposure was high.Model 4: Do the Patterns Observed for
Overall Video Game Exposure, Cumulative Risk, and Parenting
Styles Vary When Video Game Exposure Is Divided Into
Content Categories?
Low-risk preschoolers
Responsive parenting moderated the relation between video
game exposure and both hyperactivity and attention problems in
low-risk preschoolers, increasing the variance in each model by
4.6% and 0.2%, respectively (Tables 7). Consistency did not
moderate the exposure/behavior problems relationship. Because
the increase in variance accounted for in the attention model
was quite small and nonsignificant, the interactions model will
not be discussed further.

As nonviolent video game exposure increased, all children’s
hyperactivity levels were higher (Figure 1). At no and 30-min
exposure, children’s hyperactivity scores fell within the normal
range. Children with no exposure whose parents were more
responsive exhibited the lowest levels of hyperactivity, 4 points
lower than their peers whose parents were the least responsive
(just under half a standard deviation). Hyperactivity scores for
children with at least 1 hr of exposure to nonviolent video
games the previous day were considered in the clinically
significant range for all children regardless of responsiveness;
that is, all low-risk preschoolers who played at least 1 hr of
nonviolent video games the previous day were reported to
exhibit hyperactivity behaviors that put them at risk for
maladjustment problems related to hyperactivity. Children
whose parents were highly responsive obtained scores that were
2.5 points worse than their peers whose parents were the least
responsive (i.e., about ¼ standard deviation difference).
Figure 1. Low-risk preschoolers’ hyperactivity levels by
High-risk preschoolers
game exposure and both hyperactivity and attention problems in
high-risk preschoolers, increasing the variance in each model by
1.2% and 4.4%, respectively (Tables 7). Consistency did not
moderate the exposure/behavior problems relationship. Because
the increase in variance accounted for in the hyperactivity
model was small and nonsignificant, this interactions model will
not be discussed further.
As nonviolent video game exposure increased, all children’s
attention problems were lower (Figure 2). Children with no
exposure had attention problem scores in the clinically
significant range for maladjustment problems. In contrast, high-

risk preschoolers with 30 min or more of nonviolent video game
exposure during the previous day obtained attention problem
scores in the normal range. At 30 min of exposure, children
with highly responsive parents obtained scores 4 points better
than their peers with the least responsive parents (i.e., nearly ½
standard deviation better) whereas at 60 min of exposure,
children with highly responsive parents obtained scores 8 points
better than their peers with the least responsive parents (i.e.,
nearly 1 standard deviation better).
Figure 2. High-risk preschoolers’ attention problems by parental
responsiveness and nonviolent video game exposure.
Low-risk school-agers
game exposure and attention problems only in low-risk school-
agers increasing the variance by 11.2% (Tables 8). Consistency
did not moderate the exposure/behavior problems relations.
Although parenting did not moderate the relation between
exposure and hyperactivity problems for low-risk school-age
children, there was a direct effect of exposure on hyperactivity
levels. As violent video game exposure increased, hyperactivity
levels increased by 2.31 points per hour.
Attention problem scores were within the normal range for all
low-risk school-age children regardless of responsiveness and
nonviolent video game exposure although children who had no
exposure had higher attention problem scores compared with
their peers with 30 or more min of exposure (Figure 3). In
addition, children whose parents were highly responsive
obtained attention problem scores that were between 4 and 6.5
points better than their peers whose parents were the least
responsive.

Figure 3. Low-risk school-age children’s attention problems by
High-risk school-agers
game exposure and hyperactivity levels only in high-risk
school-agers, increasing the variance by 3.1% (Tables 8).
Consistency did not moderate the exposure/behavior problems
relations. Although parenting did not moderate the relation
between exposure and attention problems for high-risk school-
age children, there was a direct effect of exposure on attention
problems. As nonviolent video game exposure increased,
attention problems increased by 2.80 points per hour.
In general, children with low levels of exposure to nonviolent
video games exhibited higher hyperactivity levels (Figure 4). As
exposure increased, hyperactivity levels decreased. For high-
risk school-age children with 30 min or no exposure,
hyperactivity scores were in the at risk and clinically significant
range, respectively. Only children with 60 min or more of
nonviolent video game exposure obtained hyperactivity scores
within the normal range. Children with no exposure whose
parents were also highly responsive obtained hyperactivity
scores that were nearly 4 points worse when compared with
their peers whose parents were the least responsive (i.e., nearly
½ standard deviation). There were no differences in
hyperactivity scores associated with responsiveness for children
who were exposed to 60 min or more of nonviolent video
games.
Figure 4. High-risk school-age children’s hyperactivity levels
by parental responsiveness and nonviolent video game exposure.
Discussion
Video game exposure was directly associated only with
increasing levels of hyperactivity in preschool children, an
effect reduced to nonsignificance when parenting styles were
covaried out of the model, a finding consistent with previous

research (Parkes et al., 2013). No direct relations without or
with parenting styles included as covariates were observed
between preschoolers’ exposure and attention problems or
school-agers’ exposure and both hyperactivity and attention
problems. Adding cumulative risk and parenting styles as
moderators did increase the amount of variance accounted for
across both the preschool and school-age samples. Responsive
parenting moderated the effects of video game exposure for
low-risk preschoolers’ and high-risk school-age children’s
hyperactivity levels and high-risk preschoolers’ and low-risk
school-age children’s attention problems. In the final set of
models with video game exposure broken into violent and
nonviolent content, different patterns of effects and larger
effect sizes emerged across cumulative risk, responsiveness, and
nonviolent video game exposure. Violent video game exposure
was associated only with low-risk school-age children’s
increasing hyperactivity levels. These results indicate that an
ecological approach which considers multiple and interacting
child and family factors be used while investigating media
effects (see Barr & Linebarger, 2010; Jordan, 2004; Linebarger
et al., 2014; Linebarger & Vaala, 2010).Parenting Styles
Parenting plays a central role in the development and
consolidation of the regulatory functions of attention and
hyperactivity (Nigg et al., 2006). These skills are rapidly
developing during early childhood. Parenting consistency
functioned as a main effect in the majority of models tested,
predicting lower levels of hyperactivity and fewer attention
problems. When parents were more inconsistent and
unpredictable, children’s behavior problems increased similarly
without any additional buffering or exacerbation linked to video
game exposure.
On the other hand, moderating effects were observed with
responsiveness. Research involving children with clinical
diagnoses of ADHD documents that responsive parenting is
particularly relevant to externalizing problems including
hyperactivity and impulsivity (Johnston, Murray, Hinshaw,

Pelham, & Hoza, 2002; Rothbaum & Weisz, 1994). The ability
to monitor and sensitively respond to challenging behavior is
more difficult when a child displays impulsive, disorganized,
and poorly regulated behavior (Johnston et al., 2002). In
responsive households, parents are more sensitive to their
child’s cues thereby contributing to a child’s sense of trust in
and understanding that his or her needs will be met. This trust,
in turn, facilitates the development of a child’s appropriate
behavioral responses and effective coping styles leading to
competence in self-regulating (Landry, Smith, & Swank, 2003).
Responsiveness moderated the video game exposure/behavioral
problems relations differently depending on cumulative risk and
only for nonviolent video game exposure. As nonviolent video
game exposure increased, low-risk preschoolers’ hyperactivity
levels increased. Preschoolers whose parents were highly
responsive had the lowest levels of hyperactivity when they also
had no exposure to nonviolent video games when compared with
preschoolers whose parents were the least responsive. The
reverse was true for these low-risk preschoolers at 60 min of
nonviolent video game exposure; that is, preschoolers with
highly responsive parents had the highest levels of hyperactivity
compared with their peers with the least responsive parents.
One explanation for these differences may be that low-risk
preschoolers, particularly those with highly responsive parents,
who spend a lot of time playing nonviolent video games are
spending less time with their parents or less time engaged in
cognitively stimulating activities that have been traditionally
associated with positive developmental outcomes (e.g., more
book reading). In this study, low-risk preschoolers who played
video games the previous day spent 18 min reading versus 25
min spent reading by low-risk preschoolers who did not play
any video games the previous day. These low-risk preschool
video game players were also exposed to 3.41 hr of background
TV the previous day compared with 2.71 hr of background TV
for those who did not play. Previous research indicates that
higher levels of exposure to background TV for preschoolers is

associated with worsening executive function skills (Linebarger
et al., 2014). It is important to note that the time diary used in
this study only measured exposure during the prior 24 hr and, as
such, it is difficult to know whether those who played video
games did so on a more regular basis and for longer than their
peers who did not play during the prior 24 hr. Additional
detailed and extensive diary studies are needed to investigate
this more carefully.
High-risk preschoolers’ attention problem scores decreased as
their exposure to nonviolent video games increased. No
exposure was associated with behavior problem scores
indicative of attention maladjustment regardless of parenting
responsiveness. In contrast, attention problems scores were
within the normal range for high-risk preschoolers when they
were exposed to 30 or more minutes of nonviolent video games
during the prior 24 hr. Further, high-risk preschoolers whose
parents were also highly responsive had the fewest attention
problems, between 4 and 8 points (about ½ to ¾ of a standard
deviation) better than their peers whose parents exhibited
average or low levels of responsiveness. Children who are high-
risk due to sociodemographic characteristics such as living in
poverty with less educated parents and often in single-parent
homes tended to exhibit more behavior problem symptomology
compared with their low-risk peers (Galéra et al., 2011).
Although the direction of the effects between nonviolent video
game exposure and attention problems is unclear, these results
provide intriguing evidence that nonviolent video game
exposure plays some role in the development of attentional
skills in these high-risk preschoolers’ lives. For instance, video
games might provide some type of structure and environmental
input that may be lacking in high-risk homes where material and
educational resources are limited and parenting, while warm and
responsive, was also more likely to be inconsistent. Video
games are structured so that players are constantly operating at
the outer edge of their competence, with continual challenges
(Gee, 2007). The key is that these challenges are difficult but

not undoable. In addition, video games offer immediate
feedback and potential rewards that likely encourage the child
to continue playing, improving task performance that, in turn,
contributes to greater advancements within the game. With
appropriate scaffolding, video game content can provide a
bridge between what a child can do currently and new
competencies. Similarly, appropriate parental scaffolding, a
skill more often found with responsive parents (Landry, Miller-
Loncar, Smith, & Swank, 2002), has been linked to stronger
executive function skills (to which impulse control contributes)
in preschoolers (Landry et al., 2002).
Both high- and low-risk school-age children’s behavior
problems scores were lower as nonviolent video game exposure
increased. Specifically, hyperactivity levels were lower and in
the normal range for high-risk school-age children when their
exposure levels topped 60 min whereas all low-risk school-age
children’s attention problems scores were within the normal
range regardless of exposure although children with no exposure
obtained the poorest attention scores.Video Game Content
Effects
Not all relations between video game exposure and behavior
problems were moderated by responsiveness. Two models
evidenced only main effects of video game exposure although
cumulative risk differentiated the results. Low-risk school-age
children’s hyperactivity scores were 2.31 points higher (∼¼
standard deviation; β = .15) for every hour of violent video
games played whereas high-risk school-age children’s attention
problems scores were 2.80 points higher (∼¼ standard
deviation; β = .28) for every hour of nonviolent video games
played. The research investigating the relations between violent
video game exposure and behavior problems are mixed (see
recent meta-analyses by Anderson & Bushman, 2001; Ferguson
& Kilburn, 2009) likely due to a combination of poor
measurement of behavior problems (e.g., combining attention
and hyperactivity items in the same scale; Swing et al., 2010)
and lack of precision in the exposure estimates (specifically for

correlational studies). In addition, the size of the observed
effects tends to be small, accounting for less than 10% of the
variance and usually less than 5%. More time spent playing
violent video games likely displaced low-risk school-age
children’s time spent with parents and in activities that have
been traditionally and positively associated with low-risk
children’s development (e.g., reading, cognitively stimulating
materials and experiences; Bradley, Corwyn, Burchinal,
McAdoo, & Garcia Coll, 2001; Bradley, Corwyn, McAdoo, &
Coll, 2001). Other research has found similar results for school-
age children (Wright et al., 2001).
Most of the significant effects found in this study were
associated with nonviolent video game exposure especially in
combination with responsiveness likely because all children in
this study who played video games were more likely to play
nonviolent ones (3 min to every 1 min of violent content in
preschool; 1.67 min to every 1 min of violent content in school-
age). As described above, exposure to violent video games was
associated only with low-risk school-age children’s
hyperactivity scores. Video games coded as violent consisted of
titles mainly in the ESRB category E (generally suitable for
everyone) and E10 (suitable for children 10 years and older).
Violent content descriptors for these games included “minimal
cartoon, fantasy, or mild violence” (ESRB, webpage
http://www.esrb.org/ratings/ratings_guide.jsp; Lego Batman or
Lego Star Wars, various Super Mario Cart/Party games, and a
variety of Wii Sports contact games such as Wii Play and Tony
Hawk).Limitations
There are several limitations to the presented findings. First,
this study is a cross-sectional correlational study that relies on
parental report. In no way is it possible to determine the
direction of the effects. Although exposure may cause behavior
problems, it is equally likely that behavior problems existed and
exposure followed. Or, the relationship could be reciprocal.
Research indicates that problematic video game use is higher
among children with clinical ADD/ADHD diagnoses although

time spent playing does not differ (Mazurek & Engelhardt,
2013). Perhaps video games engage these children in different
ways. In addition, parents of children with higher levels of
hyperactivity and more attention challenges may allow their
children to use video games as a way to manage difficult
behavior (Gadow & Sprafkin, 1993). Second, the exposure
measure used (a time diary recording all activities during the
previous 24 hr including duration and title of any media
activities) was only able to provide estimates of the previous
day’s use. Consequently, just 17.5% of the sample reported any
video game use the prior day, a percentage consistent with
recent estimates of daily use (i.e., 17%; Commonsense Media,
2011). These data underestimate all children’s use of video
games and likely underestimate the associations reported here.
Conclusion
Collectively, the results highlight the multiple and
interdependent contextual factors that are associated with child
behavior problems including the home environment, the quality
of the parent–child relationship, and exposure to different
genres of video games. Total video game exposure did directly
predict higher levels of hyperactivity in the preschool sample,
just one of four direct associations tested. Covarying out
parenting variables reduced this direct relation to
nonsignificance. More importantly, dividing exposure into
nonviolent and violent content and including parenting styles
and cumulative risk as moderators offered a clearer picture of
the results; accounted for a greater percentage of the variance in
child behavior problems; and helped to uncover effects absent
or minimal in other studies when such variables were not
included (Swing et al., 2010), were covaried out (Parkes et al.,
2013), or were collected with a great deal of imprecision
(Ferguson, 2011). Future research would benefit from methods
that measure exposure and outcomes with greater precision,
designs and analyses that include multiple contextual factors
like those tested here (i.e., cumulative risk, parenting styles,

different video game content), and analyses that allow these
factors to vary and interact (Bronfenbrenner & Morris, 2006).
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Submitted: March 3, 2014 Revised: November 20, 2014
Accepted: December 3, 2014
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Infant Developmental Outcomes: A
Family Systems Perspective
Detailed RecordTitle:Infant Developmental Outcomes: A Family
Systems Perspective. Authors:Parfitt, Ylva1
Pike, Alison1
Ayers, Susan2Source:Infant & Child Development.
Jul/Aug2014, Vol. 23 Issue 4, p353-373. 21p. Document
Type:ArticleSubject Terms:*MENTAL health -- Evaluation
*MOTOR ability
*COGNITIVE testing
*CONCEPTUAL structures (Information theory)
*INFANT development
*INTERVIEWING
*LANGUAGE acquisition
*LONGITUDINAL method
*MENTAL illness
*OBSERVATION (Scientific method)
*PARENT & child
*PUERPERIUM
*RESEARCH funding
*STATISTICAL hypothesis testing

*STATISTICS
*VIDEO recording
*DATA analysis
*MULTIPLE regression analysis
*FAMILY systems theory
*INTER-observer reliability
*DESCRIPTIVE statistics
*ADULTS
*CHILDREN
RESEARCH evaluationGeographic Terms:GREAT
BritainAuthor-Supplied Keywords:couple's relationship
infant characteristics
infant development
parent–infant relationship
parent-infant relationship
parental mental healthNAICS/Industry Codes:621330 Offices of
Mental Health Practitioners (except Physicians)Abstract:The
aim of the current study was to examine whether parental
mental health, parent-infant relationship, infant characteristics
and couple's relationship factors were associated with the
infant's development. Forty-two families took part at three time
points. The first, at 3 months postpartum, involved a video
recorded observation (CARE-index) of parent-infant
interactions. At 5 months postpartum, in-depth clinical
interviews (the Birmingham Interview of Maternal Mental
Health) assessed parental mental health and parental perceptions
of their relationship with their infant, their partner and their
infant's characteristics. Finally, the Bayley Scales III was
carried out 17 months postpartum to assess the infants'
cognitive, language and motor development. A higher mother-
infant relationship quality was significantly associated with
more optimal language development, whilst a higher father-
infant relationship quality was associated with more advanced
motor development. Additionally, maternal postnatal post-
traumatic stress disorder had a negative impact on the infant's
cognitive development, whilst maternal prenatal depression was

associated with a less optimal infant's language development.
The largest prediction was afforded by parental perceptions of
their infant's characteristics. The findings indicate that such
perceptions may be crucial for the infant's development and
imply that negative internal parental perceptions should be
considered when assessing risk factors or designing
interventions to prevent negative child outcomes. Copyright ©
2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
Copyright of Infant & Child Development is the property of
John Wiley & Sons, Inc. and its content may not be copied or
emailed to multiple sites or posted to a listserv without the
copyright holder's express written permission. However, users
may print, download, or email articles for individual use. This
abstract may be abridged. No warranty is given about the
accuracy of the copy. Users should refer to the original
published version of the material for the full abstract.
(Copyright applies to all Abstracts.)Author Affiliations:1School
of Psychology, University of Sussex, Brighton UK
2School of Health Sciences, City University London, London
UKISSN:1522-7227DOI:10.1002/icd.1830Accession
Number:97320098Publisher Logo:
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Prenatal tobacco exposure:
Developmental outcomes in the neonatal period
Detailed RecordTitle:Prenatal tobacco exposure: Developmental
outcomes in the neonatal period.Authors:Espy, Kimberly
Andrews. Department of Psychology, University of Nebraska,
Lincoln, NE, US, [email protected]
Fang, Hua. Department of Psychology, University of Nebraska,
Lincoln, NE, US
Johnson, Craig. Department of Psychology, University of
Nebraska, Lincoln, NE, US
Stopp, Christian. Department of Psychology, University of
Nebraska, Lincoln, NE, US
Wiebe, Sandra A.. Department of Psychology, University of
Alberta, Edmonton, AB, Canada
Respass, Jennifer. Department of Psychology, University of
Nebraska, Lincoln, NE, USAddress:Espy, Kimberly Andrews,
Office of Research, University of Nebraska-Lincoln, 303
Canfield Administration Building, Lincoln, NE, US, 68588-
0443, [email protected]Source:Developmental Psychology, Vol
47(1), Jan, 2011. pp. 153-169.NLM Title Abbreviation:Dev
PsycholPublisher:US : American Psychological
AssociationISSN:0012-1649 (Print)
1939-0599 (Electronic)Language:EnglishKeywords:longitudinal
modeling, prenatal tobacco exposure, self-
regulationAbstract:Smoking during pregnancy is a persistent
public health problem that has been linked to later adverse
outcomes. The neonatal period—the first month of life—carries
substantial developmental change in regulatory skills and is the
period when tobacco metabolites are cleared physiologically.
Studies to date mostly have used cross-sectional designs that

limit characterizing potential impacts of prenatal tobacco
exposure on the development of key self-regulatory processes
and cannot disentangle short-term withdrawal effects from
residual exposure-related impacts. In this study, pregnant
participants (N = 304) were recruited prospectively during
pregnancy, and smoking was measured at multiple time points,
with both self-report and biochemical measures. Neonatal
attention, irritable reactivity, and stress dysregulation were
examined longitudinally at three time points during the first
month of life, and physical growth indices were measured at
birth. Tobacco-exposed infants showed significantly poorer
attention skills after birth, and the magnitude of the difference
between exposed and nonexposed groups attenuated across the
neonatal period. In contrast, exposure-related differences in
irritable reactivity largely were not evident across the 1st month
of life, differing marginally at 4 weeks of age only. Third-
trimester smoking was associated with pervasive, deleterious,
dose–response impacts on physical growth measured at birth,
whereas nearly all smoking indicators throughout pregnancy
predicted level and growth rates of early attention. The
observed neonatal pattern is consistent with the neurobiology of
tobacco on the developing nervous system and fits with
developmental vulnerabilities observed later in life. (PsycINFO
Database Record (c) 2016 APA, all rights reserved)Document
Type:Journal ArticleSubjects:*Neonatal
Development; *Prenatal Exposure; *Self-Regulation; *Tobacco
SmokingMedical Subject Headings (MeSH):Attention; Child
Development; Female; Follow-Up Studies; Humans; Infant,
Newborn; Irritable Mood; Male; Pregnancy; Prenatal Exposure
Delayed Effects; Prospective Studies; TobaccoPsycINFO
Classification:Developmental Psychology
(2800)Population:Human
Male
FemaleLocation:USAge Group:Childhood (birth-12 yrs)
Neonatal (birth-1 mo)
Adulthood (18 yrs & older)Tests & Measures:Woodcock–

Johnson III Brief Intellectual Ability Assessment
Conners Adult ADHD Rating Scale, Short
Neonatal Temperament Assessment
Brief Symptom Inventory DOI: 10.1037/t00789-000Grant
Sponsorship:Sponsor: National Institutes of Health
Grant Number: R01 DA014661; DA023653; DA015223;
MH065668; HD050309
Recipients: No recipient indicatedMethodology:Empirical
Study; Interview; Quantitative StudyFormat
Covered:ElectronicPublication Type:Journal; Peer Reviewed
JournalPublication History:First Posted: Nov 1, 2010; Accepted:
Jun 11, 2010; Revised: Jun 3, 2010; First Submitted: May 26,
2008Release Date:20101101Correction
Date:20110117Copyright:American Psychological Association.
2010Digital Object
Identifier:http://dx.doi.org.ezproxy.snhu.edu/10.1037/a0020724
PMID:21038943PsycARTICLES Identifier:dev-47-1-
153Accession Number:2010-22322-001Number of Citations in
Source:94
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By: Kimberly Andrews Espy
Department of Psychology and Office of Research, University
of Nebraska—Lincoln;
Hua Fang
of Nebraska—Lincoln
Craig Johnson
Christian Stopp
Sandra A. Wiebe
Department of Psychology, University of Alberta, Edmonton,

Alberta, Canada
Jennifer Respass
Acknowledgement: This research was supported in part by
National Institutes of Health Grants R01 DA014661, DA023653,
DA015223, MH065668, and HD050309. We gratefully
acknowledge the participating families, hospital staff, and
project personnel who made this work possible.
Approximately 20% of women acknowledge smoking during
pregnancy in the United States (National Survey on Drug Use
and Health; Office of Applied Studies, 2005), which results in
at least 500,000 prenatally tobacco-exposed infants annually.
Smoking during pregnancy is substantially more prevalent than
prenatal use of alcohol or illicit drugs. For most women,
smoking is a daily habit that, when pregnant, results in a regular
dosing pattern to the fetus. As such, prenatal tobacco exposure
carries broad risk for harm and potential morbidity (Koren,
1993; Slotkin, 1998b).
Tobacco contains a number of chemically active compounds.
Nicotine appears to be the predominant contributor to the
impact on growth and behavior of children exposed during
pregnancy. Nicotine is a powerful vasoconstrictor that reduces
the flow of available nutrients and oxygen to the developing
fetus. Indeed, exposure-related reductions in birth weight have
been reported in the literature for several decades. Besides birth
weight, prenatal tobacco exposure is also associated with dose-
dependent reductions in body length and head size (e.g., Hardy
& Mellits, 1972; Rantakallio, 1983; Roza et al., 2007; Vik,
Jacobsen, Vatten, & Bakketeig, 1996). These exposure-related
physical growth differences at birth usually resolve by the
infant's first birthday (Conter, Cortinovis, Rogari, & Riva,
1995; Day et al., 1992; Hardy & Mellits, 1972). The physical
growth deficits and the associated tobacco-exposure–related
increase in perinatal complications both contribute to, but do

not completely account for, a greater risk for attention-
deficit/hyperactivity disorder (Nigg & Breslau, 2007; Szatmari,
Saigal, Rosenbaum, Campbell, & King, 1990; Willoughby,
Greenberg, Blair, Stifter, & Family Life Investigative Group,
2007).
Although largely ignored for decades, nicotine is also a
psychoactive compound that acts directly on the brain. Nicotine
activates nicotinic acetylcholine receptors that are situated on
dopamine neurons in the striatum and noradrenergic neurons in
the locus coeruleus (Lichtensteiger et al., 1982) and are present
as early as eight weeks gestation (Hagino & Lee, 1985). In
elegant preclinical work in nonhuman animals, prenatal tobacco
exposure has been found to disrupt the timing of cholinergic
synaptic activity during key developmental periods, to alter
receptor-mediated processes controlling cell replication and
differentiation (Slotkin, 1998a), and to result in abnormal
neuronal reactivity (Landmesser, 1994; Navarro, Seidler,
Whitmore, & Slotkin, 1988; Seidler, Levin, Lappi, & Slotkin,
1992; Slotkin, Lappi, & Seidler, 1995), including the disruption
of developing dopaminergic circuits (Azam, Chen, & Leslie,
2007). When administered prenatally, nicotine reduces postnatal
dopaminergic activity in the ventral tegmental area, nucleus
accumbens, and striatum (Chen, Parker, Matta, & Sharp, 2005;
Muneoka et al., 1997; Slotkin, 1998b), with a corresponding
reduction in D2 dopamine receptors (S. A. Richardson & Tizabi,
1994). Serotonergic systems are affected similarly, as prenatal
tobacco exposure disrupts paroxetine binding to the 5-HT
transporter (Levin & Slotkin, 1998). These disruptions persist
well after nicotine exposure has ceased (McFarland, Seidler, &
Slotkin, 1991), suggesting that prenatal nicotine exposure alters
cell development programs in an irreversible manner (Slotkin,
1998b) that is not attributable solely to the hypoxic effects of
nicotine on the central nervous system (Slotkin, Greer, Faust,
Cho, & Seidler, 1986).
Given the strong link between alterations of the dopaminergic
and serotonergic brain systems and developmental

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