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RUNNING HEAD: PARENT DISTRACTION BY TECHNOLOGY
Technoference: Parent Distraction with Technology and Associations with
Child Behavior Problems
This is the author version of the following article: McDaniel, B. T., & Radesky, J. (2017). Technoference:
Parent distraction by technology and associations with child behavior problems. Child Development.,
which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/cdev.12822/full
This article may be used for non-commercial purposes in accordance with the Wiley Self-Archiving Policy
[ http://olabout.wiley.com/WileyCDA/Section/id-828039.html ].
Brandon T. McDaniel, Ph.D.a
Jenny S. Radesky, MDb
a Illinois State University; Department of Family and Consumer Sciences; Normal, IL.
b Division of Developmental Behavioral Pediatrics, University of Michigan Medical School,
Ann Arbor, MI
Corresponding Author: Brandon T. McDaniel, Department of Family and Consumer Sciences;
Campus Box 5060, Normal, IL. Email: btmcdaniel.phd@gmail.com. Phone: 309-438-5802.
Acknowledgments:
We would like to thank the families who participated in this research, as well as the research
assistants who made all of this recruitment and data collection possible. We would also like to
acknowledge the College of Health and Human Development, the Department of Human
Development and Family Studies, as well as the Bennett Pierce Prevention Research Center at
The Pennsylvania State University which awarded research funds to the first author to complete
this research. This research was also supported by the National Institute on Drug Abuse
(T32DA017629) and the National Institute of Child Health and Human Development
(F31HD084118). The content is solely the responsibility of the authors and does not necessarily
represent the official views of the National Institutes of Health.
PARENT DISTRACTION BY TECHNOLOGY 1
ABSTRACT
Heavy parent digital technology use has been associated with suboptimal parent-child
interactions, but no studies examine associations with child behavior. This study investigates
whether parental problematic technology use is associated with technology-based interruptions in
parent-child interactions, termed "technoference," and whether technoference is associated with
child behavior problems. Using parent reports from 170 U.S. families (child mean age = 3.04
years) and Actor Partner Interdependence Modeling, we found maternal and paternal problematic
digital technology use predicted greater technoference in mother-child and father-child
interactions; then, maternal technoference predicted both mothers’ and fathers’ reports of child
externalizing and internalizing behaviors. Results suggest that technological interruptions are
associated with child problem behaviors, but directionality and transactional processes should be
examined in future longitudinal studies.
Keywords: Parent media use; digital technology; smartphones; child behavior; parent-child
relationship; problematic phone use; parenting
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Technoference: Parent Distraction with Technology and Associations with
Child Behavior Problems
Emerging mobile and digital technologies such as smartphones, tablets, wearables, and
other mobile devices are now embedded throughout the daily lives of young children and their
families, with research evidence on their use and effects lagging behind their rate of adoption
(Radesky, Schumacher, & Zuckerman, 2015b). These multimodal devices, with their access to
unlimited Internet content, social contacts, work duties, information, and personal data, have
revolutionized the ways in which people interact with digital technology and with each other
(Katz, 2002; Campbell, Ling, & Bayer, 2014).
Despite the significant benefits that individuals reap from their use of technology, such as
increased social support (McDaniel, Coyne, & Holmes, 2012) and the ability to work from home
(Chesley, Slibak, & Wajcman, 2013), sociological and psychological research has highlighted
the potential for disruption of in-person social dynamics when mobile and digital technologies
are in use. This was initially described as ‘absent presence,’ or the act of being physically
present but having one’s mind elsewhere based on communication or content from mobile
phones (Gergen, 2002), followed by descriptions of new social norms allowing invasion of
portable devices into personal spaces (Ito, Okabe, & Matsuda, 2005; Campbell & Park, 2008).
More recently, the concept of ‘technoference,’ defined as everyday interruptions in interpersonal
interactions or time spent together that occur due to digital and mobile technology devices, has
been introduced (McDaniel, 2015; McDaniel & Coyne, 2016a). Such interruptions may occur
during face-to-face conversations, routines such as mealtimes or play, or the perception of an
intrusion felt by an individual when another person interacts with digital technology during time
together. In adult romantic relationships, technoference has been associated with more conflict
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with one’s partner over technology use and poorer relationship satisfaction (McDaniel & Coyne,
2016a) as well as more negative perceptions of coparenting quality (McDaniel & Coyne, 2016b).
In a separate line of investigation, overuse of technology has been studied in terms of
Internet addiction and self-reported problematic mobile phone use behaviors (e.g., having
difficulty disconnecting, ruminating about possible messages received, etc.); both are associated
with mental health problems such as depression, anxiety, and social difficulties (e.g., Bianchi &
Phillips, 2005). Psychological correlates of problematic mobile technology use have included
anxious dependence in relationships (Cheever, Rosen, Carrier, & Chavez, 2014), poorer self-
regulation abilities and lower degree of mindfulness (Feldman, Greeson, Renna, & Robbins-
Monteith, 2011), susceptibility to the unconscious automaticity of mobile phone checking (Bayer
& Campbell, 2012; Drouin, Kaiser, & Miller, 2012), or perceiving social norms of needing to
answer calls or texts (Rainie & Keeter, 2006). However, none of these studies have specifically
examined these dynamics in the context of parenting.
In an attempt to define what constitutes ‘problematic’ media use for parents, several
studies have examined how parent digital technology use associates with quality and quantity of
parent-child interactions. Adding to a literature showing interruption of parent-child play by
background television (Kirkorian, Pempek, Murphy, Schmidt, & Anderson, 2009), recent studies
have suggested that parent mobile technology use around children is associated with fewer
parent-child interactions (Radesky, Miller, Rosenblum, Appugliese, Kaciroti, & Lumeng,
2015a), lower responsivity to child bids (Hiniker, Sobel, Suh, Sung, Lee, & Kientz, 2015), and
qualitative observations of parent hostility in response to child bids for attention (Radesky,
Kistin, Zuckerman, Nitzberg, Gross, Kaplan-Sanoff, Augustyn, & Silverstein, 2014).
Additionally, technological interruption during parenting has been associated with mothers'
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perceptions of lower coparenting quality (McDaniel & Coyne, 2016b). In interviews, children
describe feeling that parents should not use digital technology during family routines because of
their expectation that the parent be present and model good digital technology habits (Hiniker,
Shoenebeck, & Kientz, 2016). Parents echo this experience of discomfort with ‘absent presence’
when using digital technology around their children, describing it as “multitasking” that makes
them feel less effective in their parenting (Radesky et al., 2016).
However, this emerging literature on parent digital technology use and parent-child
relationships is limited by small sample sizes, assessment of parent digital technology use only
during brief episodes such as meals or playground visits, and none have specifically examined
child behavioral outcomes. Because of the hypothesized potential for technology to alter
parenting responsiveness, which is an important predictor of positive child social-emotional
outcomes (see Bornstein, Tamis-Lemonda, Hahn, & Haynes, 2008; Davidov & Grusec, 2006),
more research on parent digital technology use and child behavior is needed. The aim of the
present study was to examine cross-sectional associations between problematic parent digital
technology use (e.g., having trouble resisting the urge to check the device, using the device too
much, etc.), technoference (i.e., technology interference) in parent-child interactions, and child
behavior. We hypothesized that greater self-reported problematic digital technology use would
be associated with more frequent technoference in daily parent-child interactions (H1); and
greater reported technoference in daily parent-child interactions would be associated with greater
child externalizing and internalizing behavior (H2), as has been described in prior ethnographic
work (Radesky et al., 2014).
METHOD
Participants & Procedure
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Participants included mothers and fathers from 183 heterosexual couples with a young
child who took part in the Daily Family Life Project (McDaniel, 2016), a longitudinal study of
parenting and family relationships conducted from 2014 to 2016. Participants were recruited
through: (1) letters and phone calls to families who were part of a family research database in a
Northeastern U.S. state, (2) announcements on parenting websites and listservs, and (3) flyers in
the local community. To be eligible to participate, individuals had to speak English, be over age
18, be a parent of a child age 5 or younger, and currently live with their spouse/partner and child.
Their spouse/partner also had to be willing to participate. Participants were emailed a survey
link through which they completed informed consent and a baseline online survey via Qualtrics.
Participants also completed follow-up assessments at approximately 1, 3, and 6 months. At
baseline, 98% (n = 360) of parents completed their survey.
In the present study, we utilized the baseline survey data of 333 of these 360 parents (168
mothers and 165 fathers from 170 families) who had completed child behavior rating scales; 11
families (n = 22 parents) who had a child younger than 1 year were excluded from the present
analysis, since the behavior rating items were not appropriate for infants. In addition, 3 mothers
and 3 fathers were missing behavior ratings, and 1 mother and 4 fathers did not respond to their
surveys and thus provided no baseline data at all. In our analytic sample of 333 parents, families
resided in the following U.S. regions: 53% Northeast, 16% Midwest, 16% South, and 15% West.
On average, mothers were 31.82 years old (SD = 4.22; range 22 to 42), and fathers were 33.34
(SD = 4.93; range 22 to 52). Most families (61%) had more than one child (M = 1.90, SD =
0.91), and the index child was 3.04 years old on average (SD = 1.24; Range = 1.0 to 5.5 years;
55% female). Most parents were Caucasian (92%), married (95%), and had at least a Bachelor’s
degree (73%). Median yearly household income was approximately $69,500 (M = $75,360, SD
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= $39,320), but ranged from no income to $250,000, with 20% of families reporting some form
of state or federal assistance (e.g., medical assistance, food stamps). Average relationship length
was 10.13 years (SD = 4.02), with 93% in a relationship of 5 years or longer.
Utilizing chi-squares and t-tests to examine potential demographic differences between
our analytic sample and those not in our analyses due to having a child under age 1 or who were
missing behavioral rating data, we found that parents in our sample were on average older (t
(360) = 2.18, p = .03), in a longer relationship (t (360) = 2.61, p = .009), and had more children (t
(360) = 3.63, p < .001); the samples were otherwise similar.
Measures
Parent problematic digital technology use. We assessed parent problematic digital
technology use using a 3-item self-report scale adapted from prior studies of problematic mobile
phone use (e.g., Derks & Bakker, 2014): (1) “When my mobile phone alerts me to indicate new
messages, I cannot resist checking them.” (2) “I often think about calls or messages I might
receive on my mobile phone.” (3) “I feel like I use my mobile phone too much.” Parents
responded on a 6-point scale ranging from 1 (strongly disagree) to 6 (strongly agree). Items
were averaged to produce an overall score (for mothers, M = 3.24, SD = 1.18; for fathers, M =
2.87, SD = 1.17) with higher scores indicating more problematic use (Cronbach's alpha = .74 for
mothers, .78 for fathers).
Technoference in parent-child relationships. Perceived technoference (i.e., technology
interference) in the mother-child relationship and in the father-child relationship was assessed via
mother and father self-report. Items were adapted from the Technology Device Interference
Scale (TDIS; McDaniel & Coyne, 2016a), which originally measured technoference in couple
relationships; for this study, we reworded the scale to refer to interactions with one's child.
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Parents were asked, "On a typical day, about how many times do the following devices interrupt
a conversation or activity you are engaged in with your child?" The 6 items on the scale included
the following devices: (1) cellphone/smartphone, (2) television, (3) computer, (4) tablet, (5)
iPod, and (6) video game console. Parents responded to each item on a 7-point scale ranging
from 0 (none) to 6 (more than 20 times). As this is a count measure and we expected there to be
variability (as opposed to consistency) within individuals' responses across these various devices
(i.e., some devices might interfere more than others), it was not appropriate to calculate
Cronbach's alpha. Items were averaged, with higher scores representing more frequent
technoference in the parent-child relationship; raw mean scores are reported in Table 1. We also
found that participants' scores were positively skewed (skewness values for the overall mean
score were 2.38 for mothers and 3.46 for fathers); therefore, we performed a square root
transformation on the overall mean scores, which resulted in scores that were more normally
distributed and more appropriate for analysis (skewness = -0.04 for mothers, 0.57 for fathers).
Child externalizing and internalizing behavior problems. Parents completed the
items that make up the internalizing (36 items) and externalizing scales (24 items) of the Child
Behavioral Checklist (CBCL; Achenbach & Rescorla, 2000). These items concern their child
now or within the past two months on a 3-point scale ranging from 0 (not true, as far as you
know) to 2 (very true or often true). Internalizing consists of items such as “whining,” “sulks a
lot,” and “feelings are easily hurt.” Externalizing consists of items such as “can’t sit still,
restless, or hyperactive,” “easily frustrated,” and “temper tantrums or hot temper.” Items were
summed to produce separate mother and father ratings of internalizing and externalizing child
behavior (Cronbach’s alpha for internalizing = .90 for mothers, .88 for fathers; alpha for
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externalizing = .92 for mothers, .93 for fathers). We then converted these raw sum scores to
normed externalizing and internalizing t-scores for analysis (Achenbach & Rescorla, 2000).
Potential confounding variables. Parents reported their age, educational attainment,
marital status, race/ethnicity, family composition, household income, and child’s age, gender,
and health at baseline. They also reported their child’s daily duration of screen media use, as
well as measures of coparenting quality, depressive symptoms, and parenting stress.
Parents rated how much time, on a typical day, their child spent using screen media
devices across 8 items (e.g., computer, TV, smartphone, tablet, video games) on an 11-point
scale ranging from 0 (None) to 10 (7 or more hours). Items were summed to produce an overall
child screen use score (Cronbach's alpha = .77 for mothers, .76 for fathers).
As this sample consists of two-parent families, we controlled for coparenting quality—or
how well parents work together in rearing their child as a parenting team (e.g., Feinberg et al.,
2012). Coparenting quality has been shown to predict child behavior problems (e.g., Murphy,
Jacobvitz, & Hazen, 2015); therefore, controlling for the potential influences of coparenting
lends further weight to any potential associations we may find between technoference and child
behavior. We assessed coparenting quality with an established measure, the Coparenting
Relationship Scale (CRS; Feinberg et al., 2012) which consists of 35 items (e.g., "When I'm at
my wits end as a parent, partner gives me extra support I need" and "My partner undermines my
parenting") that assess various dimensions of coparenting such as support, undermining, and
agreement on a 7-point scale ranging from 0 (not true of us) to 6 (very true of us). After reverse
coding negatively worded items, all items were averaged to produce an overall coparenting score
with higher scores indicating parent perceptions of higher quality coparenting (Cronbach's alpha
= .94 for mothers, .93 for fathers).
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Depressive symptoms were measured utilizing the validated Center for Epidemiologic
Studies Depression Scale (CES-D; Radloff, 1977). Participants rated how often they experienced
20 symptoms (e.g., "I felt depressed" and "I felt sad") in the past week on a 4-point scale ranging
from 0 (rarely or none of the time, less than 1 day) to 3 (most or all of the time, 5 to 7 days).
Items were averaged to produce an overall depression score (Cronbach's alpha = .90 for mothers,
.80 for fathers). We controlled for depressive symptoms as depressed mood has been associated
with quality of parent-child interactions (e.g., Elliston et al., 2008; McDaniel & Teti, 2012) and
greater technoference in couple relationships (McDaniel & Coyne, 2016a).
Finally, we also controlled for parenting stress which we measured using 27 items from
the Parenting Stress Index (PSI; Abidin, 1995). Following other scholars in the field (e.g.,
Feinberg et al., 2010; Leavitt et al., 2016; Maas et al., 2015), we chose to use 27 items from the
36-item PSI Short Form due to lower factor loadings on 9 of the items, as was found by Abidin
(1995). Items were averaged to produce an overall stress score (Cronbach's alpha = .90 for
mothers, .92 for fathers). Parenting stressors are common in parents with young children (Crnic
& Low, 2002) and often predict poor family functioning (Cummings & Davies, 1994; Gelfand,
Teti, & Radin Fox, 1992). Feeling stressed could hypothetically lead parents to use digital
technology devices as a potential means of escape (Radesky et al., 2016) as well as to allow
children to more frequently use digital technology (Pempek & McDaniel, 2016).
Data Analysis
We first examined the associations between our study variables using bivariate
correlations in SPSS. Then, using Actor Partner Interdependence Modeling (APIM; Kenny,
Kashy, & Cook, 2006), we tested a structural equation model (SEM) of (H1) mother and father
problematic digital technology use predicting technoference in mother-child and father-child
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interactions, which (H2) technoference then predicted mother and father reports of child
behavior problems. One model was tested for externalizing behavior, and one model was tested
for internalizing behavior (Figures 1 and 2). The models were tested utilizing AMOS (Arbuckle
& Wothke, 1999), and both mother and father variables were entered. Standardized estimates
are shown for the models in Figures 1 and 2. Mother and father ratings of parenting stress and
coparenting quality were also controlled in the models. Other potential confounders including
depressive symptoms, family income, parent education, marital status, race/ethnicity, child age,
child gender, child health status, and child screen use were also entered, but were removed from
the final models as results did not change significantly. Any missing data were handled using
full information maximum likelihood estimation. As access to and use of digital technology by
adults and children varies by child age and family socioeconomic status (e.g., Wartella et al.,
2013), we also examined potential moderation of path estimates in our final models by child age,
parent education, and family income.
RESULTS
Descriptive data and bivariate correlations for study variables are presented in Table 1.
On average, mothers and fathers perceived about 2 devices as interfering in their interactions
with their child at least once or more on a typical day, and only 11% of participants reported that
technoference did not occur. Furthermore, 17% of participants reported that technoference
occurred 1 time, 24% reported 2 times, and 48% reported 3 or more times on a typical day. On
average, 40% of mothers and 32% of fathers stated that they used digital technology (specifically
their mobile phone) in problematic ways (score of 3.5 or higher). Mothers perceived their phone
use as more problematic than fathers perceived their own use (t (162) = 3.15, p < .01). No
significant mean differences were found between mothers and fathers on other study variables. In
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our sample, 4% of parents’ ratings of children met or exceeded the clinical cut-off (t-score of 70
or above; Achenbach & Rescorla, 2000) for externalizing behavior and 3% for internalizing
behavior.
As expected, parent problematic digital technology use and technoference were
correlated (see Table 1). Additionally, in fathers, higher perceived problematic digital
technology use was associated with greater internalizing behavior, higher income, more child
screen time, and greater parenting stress; however, mothers’ problematic digital technology use
was not associated with any other variables besides technoference. In both mothers and fathers,
technoference in parent-child activities was associated with greater internalizing behavior and
more child screen time. Furthermore, technoference in parent-child activities was associated with
greater externalizing behavior as reported by mothers and worse perceptions of coparenting,
depressive symptoms, and parenting stress as reported by fathers.
The model predicting child externalizing behavior with technoference and parent
problematic digital technology use fit the data well (χ² (18) = 15.83, ns; RMSEA = .00; CFI =
.99) as did the model for internalizing behavior (χ² (18) = 11.74, ns; RMSEA = .00 CFI = .99).
As hypothesized (H1), greater mother and father problematic digital technology use significantly
predicted their perceptions of greater technoference in their own interactions with their child (for
mothers, β = .35, p < .001; for fathers, β = .39, p < .001). We also found support for hypothesis
2 in that greater technoference in the mother-child relationship significantly predicted greater
child externalizing behavior as reported by both mothers (β = .20, p < .001) and fathers (at the
trend level, β = .12, p = .06). Unexpectedly, technoference in the father-child relationship did
not predict greater externalizing behavior. Similar results also appeared for internalizing
behavior, adding further support for our hypothesis 2: Greater technoference in the mother-child
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relationship significantly predicted greater child internalizing behavior as reported by both
mothers (β = .16, p < .01) and fathers (β = .14, p < .05), but again technoference in the father-
child relationship did not predict internalizing behavior.
We also examined whether the model results held when utilizing only mobile
technoference (e.g., phones, tablets, iPods) as opposed to all of the technoference items. After
entering the mobile technoference variable in the models in the place of overall technoference,
the models still fit the data well for externalizing (χ² (18) = 14.96, ns; RMSEA = .00; CFI = .99)
and internalizing (χ² (18) = 10.72, ns; RMSEA = .00; CFI = .99), and our results remained
significant. In other words, greater mother and father problematic mobile technology use
significantly predicted their perceptions of greater mobile technoference in their own interactions
with their child (for mothers, β = .34, p < .001; for fathers, β = .49, p < .001). Moreover, greater
mobile technoference in the mother-child relationship significantly predicted greater child
externalizing behavior as reported by both mothers (β = .17, p < .01) and fathers (β = .13, p =
.04) and significantly predicted greater child internalizing behavior as reported by both mothers
(β = .18, p < .01) and fathers (β = .17, p < .01).
To explore potential differences in the strength of these associations by child age, parent
education, and family income, we utilized a multigroup structural equation modeling approach in
AMOS. In this approach, the model fit is compared between a model where all paths are allowed
to vary freely across groups and a model where all paths are constrained to be equal across
groups. If a significant difference is found in the model fit, this suggests that differences exist in
some of the path estimates between the groups. We split our moderator variables into groups as
follows: child age (1 = age 3 and up, 0 = younger than 3), parent education (1 = Bachelor’s
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degree or higher, 0 = less than a Bachelor’s degree), and family income (1 = higher than
$69,500, 0 = less than or equal to $69,500; family income was split at the median).
No differences were found in model fit for child age or father education. However, the
externalizing model with estimates constrained to be equal across groups showed worse fit with
mother education (Δχ² (19) = 35.93, p = .01) and family income (Δχ² (19) = 38.10, p < .01). The
same occurred for the internalizing model with mother education (Δχ² (19) = 35.41, p = .01) and
family income (Δχ² (19) = 37.83, p < .01). In terms of moderation of main model paths, the
association between fathers’ ratings of technoference and fathers’ ratings of externalizing child
behavior was stronger in families with lower maternal education (z = -1.98, p < .05), although
the path was not significant in either group. In terms of family income, the association between
mothers’ ratings of technoference and mothers’ ratings of externalizing behavior was stronger in
families with higher income (β = .33, p < .001) as compared to those with lower income (β = .03,
p = .78; z = 2.42, p < .05). No other significant differences emerged in our main model paths.
DISCUSSION
Our study is the first to show significant associations between parent self-perceptions of
problematic digital technology use, perceived technoference in parenting, and reported child
behavioral difficulties. Perceived technoference in mother-child interactions was associated with
externalizing and internalizing behavior as rated by both mothers and fathers. The fact that
technoference in mother-child interactions also related to fathers' reports of child behavior lends
further weight to the current results, as this indicates that the results are not likely due to single
reporter bias.
Surprisingly, technoference in father-child interactions was not associated with reports of
externalizing or internalizing behavior by either parent. This discrepancy between findings
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related to maternal versus paternal digital technology use is interesting and could be explained by
several mechanisms. First, fathers may be less reliable reporters of their own digital technology
use during parent-child activities; however we believe this is less likely given that fathers’ self-
reported problematic digital technology use was significantly correlated with their reports of
technoference. Another explanation is that children co-regulate their emotions and behavior
differently with their mothers and fathers (Lunkenheimer, Olson, Hollenstein, Sameroff, &
Winter, 2011), and thus may have differential reactions to changes in maternal versus paternal
responsiveness. It is also possible that children simply spent more time with their mothers on a
daily basis in our sample, so there were a greater number of opportunities for technoference in
mother-child activities as compared with fathers. In this study sample, 45% of mothers worked
30 hours or more per week versus 82% of fathers; therefore fathers may have taken part in fewer
activities with their children overall as compared with mothers.
As this is a cross-sectional analysis, it is important not to assume directionality between
technoference and externalizing or internalizing child behavior. In recent in-depth interviews,
parents reported having more difficulty multitasking between children and their mobile device,
making it more difficult to read and respond to child cues and effectively manage difficult child
behavior (Radesky et al., 2016). This concept is supported by naturalistic mealtime observations
of children escalating their behavior in order to get the attention of their mobile-device using
caregivers, who then sometimes responded with anger or frustration (Radesky et al., 2014). An
alternative explanation for our findings is that mothers who perceive their children as more
behaviorally dysregulated may use digital technology during parent-child activities as a means of
withdrawal (Nakamura, 2015), taking a break from difficult social interactions so that they can
lower their stress levels. In qualitative interviews, many stay-at-home mothers reported using
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digital technology as a way to “escape” the boredom or frustrations of childrearing, or to regulate
their own emotions or arousal (Radesky et al., 2016). However, it is important to note that our
current results remained significant after controlling for parent depression or stress levels.
It is also possible that greater parent digital technology use is a marker of other parent or
household characteristics that independently predict digital technology use and child behavioral
problems, such as greater family dysfunction (Hinkley, Verbestel, Ahrens, et al., 2014). To
account for this we adjusted for numerous other household characteristics and coparenting
quality, and again our results remained. Nonetheless, it is possible that problematic digital
technology use is a marker of an unmeasured parent characteristic such as anxiety (e.g., Cheever,
Rosen, Carrier, & Chavez, 2014) or emotion regulation difficulties (e.g., Feldman et al., 2011).
As parent and child digital technology use differ depending on child age and family
socioeconomic factors (e.g., Wartella et al., 2013), we explored whether the strength of the
associations in our model would differ depending on such factors. Of particular note, mothers’
ratings of technoference in higher income households (as compared with those in lower income
households) were linked more strongly with mothers’ ratings of externalizing behavior. This
difference was not due to higher income families having access to a greater number of devices;
in post-hoc analyses we found no significant difference by family income in the number of
devices in the home, technoference, problematic digital technology use, or child externalizing
behavior. It is possible that the variance in child externalizing behavior in low-income
households is driven by other factors, such as stressful life events. We suggest that further
research is needed with larger, more diverse samples to better understand the potential
differences in these associations by income and other factors.
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It is perhaps premature to draw implications from this study for clinical practice, but our
findings contribute to a growing literature showing associations between greater digital
technology use and potential relationship dysfunction (e.g., McDaniel & Coyne, 2016a;
McDaniel & Coyne, 2016b) or changes in interpersonal interactions (Przybylski & Weinstein,
2013). Although some professional societies such as Zero To Three and the American Academy
of Pediatrics now recommend “unplugged” family time, it has not yet been tested whether
manipulating digital technology use during parent-child activities leads to improvements in child
behavior.
A primary limitation of this study was the use of parent self-reports of digital technology
use and child behavior; objective assessments of child behavior and parent/child digital
technology use would reassure us that observed associations are not due to reporter bias.
However, self-report methods allow examination of this topic in larger sample sizes and from
both parents, which were limitations of prior studies. Moreover, although effects were generally
small in size, the agreement of both maternal and paternal report of greater child behavioral
symptoms with greater maternal technoference provides some support to our findings. Future
studies should consider using methods such as video coding of child behavior during parent-
child activities.
Although this study was limited by its cross-sectional design and having a primarily
Caucasian, fairly-educated sample, its findings are a first glance at complex family processes
around rapidly adopted digital technologies. We were able to demonstrate that even low and
seemingly normative amounts of technoference were associated with greater child behavior
problems, which may have great public health relevance. Future, larger-scale and more diverse
studies should continue to examine whether associations between parent technoference and child
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behavioral problems depend on other contextual influences such as parenting style, sensitivity, or
family stressors. Yet, we hope that our results can be a springboard for future research into both
the specific cascades of parent-child interactions that underlie these associations, as well as
longitudinal transactional relations between difficult child behavior, family digital technology
use, and parenting.
PARENT DISTRACTION BY TECHNOLOGY 18
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Table 1. Bivariate Correlations and Descriptives for Study Variables for Fathers (above diagonal) and Mothers (below diagonal)
Prob.
Tech.
Use
Tech.
Interfere
Ext.
Beh.
Int.
Beh.
Income
Eductn
Parent
age
Child
age
Child
screen
time
Cop.
Dep.
Parenting
stress
Problematic digital
technology use
.23**
.41***
.00
.20**
.18*
.06
-.08
-.05
.17*
-.11
.09
.20**
Technology interference
.33***
.28***
.12
.26***
.07
-.05
-.05
-.02
.45***
-.32***
.18*
.32***
Externalizing Behavior
.11
.27***
.59***
.72***
-.10
.02
-.01
.02
.05
-.34***
.30***
.55***
Internalizing Behavior
.08
.25***
.68***
.61***
-.11
-.05
-.05
.14
.12
-.32***
.37***
.58***
Income
.08
.00
-.05
-.08
--
.25**
.28***
-.01
.02
.01
-.17*
.02
Education
.13
.05
-.12
-.08
.35***
.43***
-.06
.04
-.08
.01
.04
.11
Parent age
-.02
-.07
.05
.00
.38***
.19*
.72***
-.04
-.04
.01
-.03
-.01
Child age
-.11
-.01
.08
.21**
.00
-.20*
-.02
--
.28***
.05
.06
.00
Child screen time
-.05
.41***
.25***
.23***
-.06
-.22**
-.10
.33***
.66***
-.15
.08
.12
Coparenting
-.08
-.09
-.21**
-.20**
.00
.06
-.06
-.03
-.19*
.41***
-.41***
-.49***
Depression
.12
.11
.18*
.22**
-.19*
-.12
-.12
-.09
.14
-.43***
.29***
.53***
Parenting stress
.08
.13
.53***
.54***
-.03
.01
-.07
-.02
.12
-.43***
.49***
.47***
Mothers
Mean
3.24
0.55
47.79
46.11
75.36
5.33
31.82
3.04
6.20
4.99
11.12
2.05
Std. Dev.
1.18
0.41
10.40
10.48
39.32
1.65
4.22
1.24
5.45
0.77
8.79
0.57
Fathers
Mean
2.87
0.47
47.91
46.76
75.36
5.07
33.34
3.04
6.81
4.97
10.24
2.06
Std. Dev.
1.17
0.56
10.94
10.84
39.32
1.73
4.93
1.24
5.59
0.75
8.49
0.59
Note. *p < .05, **p < .01, ***p < .001. N = 168 mothers and 165 fathers (from 170 families). Correlations for fathers are presented above the
diagonal and for mothers below the diagonal. Correlations between fathers and mothers are bolded, italicized, and on the diagonal. Income is in
$1,000 units, and Externalizing and Internalizing scores are in normed t-score units.
PARENT DISTRACTION BY TECHNOLOGY 26
Figure 1. Model of parent problematic digital technology use predicting technoference during
parent-child interactions which finally predicts child externalizing behavior. Standardized
estimates are reported. Parenting stress and coparenting quality were also controlled in the model
(paths not displayed here). Entering demographics, depression, and child age and media use as
controls did not change the results and were therefore omitted from the final model. Note: *** p
< .001, ** p < .01, * p = .06.
Figure 2. Model of parent problematic digital technology use predicting technoference during
parent-child interactions which finally predicts child internalizing behavior. Standardized
estimates are reported. Parenting stress and coparenting quality were also controlled in the model
(paths not displayed here). Entering demographics, depression, and child age and media use as
controls did not change the results and were therefore omitted from the final model. Note: *** p
< .001, ** p < .01, * p < .05.
