ArticlePDF Available

WIRED: The impact of media and technology use on stress (cortisol) and inflammation (interleukin IL-6) in fast paced families *

Authors:

Abstract

This study examined how technology and media use affect stress (cortisol) and inflammation (interleukin IL-6) in dual earning parents and their adolescents. Sixty-two families reflected on their technology use the past week and collected saliva on two consecutive days that week. Technology use had the greatest effect on adolescents. Adolescents with greater phone use, general media exposure, and larger social networks via Facebook had a greater rise in their cortisol awakening response (CAR) and higher IL-6. Fathers' phone use and email were also associated with an increase in their CAR and IL-6. When bedtime technology use was high, greater general media use was associated with an increase in CAR for adolescents, but a decrease for fathers. Technology use did not significantly affect cortisol diurnal rhythm or mothers' biosocial markers. This study contributes empirical evidence of the physiological consequences of technology use among family members and provides potential theoretical explanations for future research. Published by Elsevier Ltd.
WIRED: The impact of media and technology use on stress (cortisol)
and inammation (interleukin IL-6) in fast paced families
*
Tamara D. Afifi
a
,
*
, Nicole Zamanzadeh
a
, Kathryn Harrison
a
, Micelle Acevedo Callejas
b
a
Department of Communication at the University of California Santa Barbara, USA
b
Department of Communication Studies at the University of Iowa, USA
article info
Article history:
Keywords:
Technology use
Media use
Cortisol
Immune system
Families
Adolescence
Parents
abstract
This study examined how technology and media use affect stress (cortisol) and inammation (interleukin
IL-6) in dual earning parents and their adolescents. Sixty-two families reected on their technology use
the past week and collected saliva on two consecutive days that week. Technology use had the greatest
effect on adolescents. Adolescents with greater phone use, general media exposure, and larger social
networks via Facebook had a greater rise in their cortisol awakening response (CAR) and higher IL-6.
Fathers' phone use and email were also associated with an increase in their CAR and IL-6. When
bedtime technology use was high, greater general media use was associated with an increase in CAR for
adolescents, but a decrease for fathers. Technology use did not signicantly affect cortisol diurnal rhythm
or mothersbiosocial markers. This study contributes empirical evidence of the physiological conse-
quences of technology use among family members and provides potential theoretical explanations for
future research.
Published by Elsevier Ltd.
In 2015, a Pew survey estimated that 68% of Americans owned a
smartphone and 45% owned tablets (Anderson, 2015). Although
people are increasingly dependent on these portable devices that
provide mobility, the majority of media is still consumed in the
home (Common Sense Media, 2013; Nathanson, 2015). Within the
family, parents experience the blurring of work and personal life
through the exibility that Internet-connected mobile devices offer
and often struggle to navigate these domains (Nam, 2014). Mean-
while, adolescents are exploring identity, completing schoolwork,
and developing and maintaining friendships via technology (boyd,
2015). Indeed, technology has become an inevitable and important
part of the fabric of most American families.
Despite the increased adoption of technology, research suggests
it has mixed effects on well-being. Technology can help family
members communicate efciently, multitask, regulate moods, and
facilitate social support (Carvalho, Francisco, &Relvas, 2015). For
adolescents, developing in the digital age has enabled increased
autonomy through easy access to peer networks, but it can also
increase parental monitoring (boyd, 2015). Similarly, while
technology can expedite communication and information sharing
that enhances positive emotional connections (Carvalho et al.,
2015), it can also contribute to surveillance and diminished face-
to-face communication that can fuel stress in families (McDaniel,
2015). Heavy technology use in particular has been associated
with depression, loneliness, anxiety, and narcissism (Rosen,
Whaling, Rab, Carrier, &Cheever, 2013).
Although scholars have begun to understand how technology
use affects individuals psychologically, much less is known about
how it affects them physiologically, especially within families
where multiple members are using technology simultaneously. The
current study addresses this void in the literature by focusing on
how technology use, and the type of technology used, affect bio-
logical stress responses (measured through cortisol) and immune
systems (measured through the pro-inammatory marker, inter-
leukin IL-6) of dual earning parents and their adolescents (ages
13e18). An affordances approach is used to explore distinctions in
both the role and the effects of technology on family members
stress. The impact of technology and media on stress in families is a
pressing issue given the increasing number of dual career house-
holds with adolescents in which all members tend to live fast-
paced lives (i.e., busy lifestyles replete with obligations of
balancing work and family and extracurricular activities) and
where everyone is wiredmost of their waking hours. We refer to
*
The researchers would like to thank the families who participated in the study
and the research assistants who helped with data collection.
*Corresponding author.
E-mail address: tafifi@comm.ucsb.edu (T.D. Afifi).
Contents lists available at ScienceDirect
Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
https://doi.org/10.1016/j.chb.2017.12.010
0747-5632/Published by Elsevier Ltd.
Computers in Human Behavior 81 (2018) 265e273
these families as fast-paced families or FPF.
1. Technology and media as tools that can affect stress
Media psychology provides a particularly useful theoretical
approach to technological effects (Rutledge, 2012, pp. 43e61).
Scholars who adopt a media psychology approach often model how
affordances of media and technology constrain and/or enhance
social perceptions, affect cognitive processing, and shape social,
emotional, and psychological outcomes. Importantly, these effects
also include physiological outcomes. Technology and media are
used here as overarching terms that include a multitude of devices,
as well as activities and new media that saturate the environment.
Through the attributes of portability and connection to the Internet,
technological devices afford perpetual contact and co-presence of
social networks and unprecedented amounts of information (Rice
&Katz, 2003). Therefore, these valuable tools can create tremen-
dous demands and ultimately stress that can affect the physiolog-
ical health of their users. In the current study, we focus on the
interconnections between one's social environment (e.g., family
and technology/media use) and biological stress responses (e.g.,
cortisol as a marker of stress and IL-6 as a marker of inammation
or immune strength) or what researchers often refer to as bioso-
cial markersof stress.
Technologies, such as the mobile phone, have become essential
tools for organizing and managing information, social coordination,
and maintaining relationships (Rice &Hagen, 2010, pp. 2e39). This
is likely particularly true for FPF who require management and co-
ordination of everyone's busy schedules. Portable devices connected
to the Internet (i.e., social media and chatting or messaging appli-
cations) can also connect people to their ofine social support net-
works and diverse online social support networks at multiple times
throughout the day. Riva, Ba~
nos, Botella, Wiederhold, and Gaggioli
(2012) also note that there are positive effects of technology on af-
fective quality, engagement/actualization, and connectedness. They
found that these technologies can facilitate positive emotions, allow
people to be more engaged and active in theirinterests via inducing
ow or total involvement, and increase social presence and social
capital. Yet, the multitude of new media and technology, which
afford people constant access to engaging information, entertain-
ment, and social connections, can lead to overload.
The technological advances that facilitate connection can also be
stressful for adults and adolescents if they overwhelm their ca-
pacity and/or desires for information and communication. For
instance, regardless of age, the pressure to be constantly available
to social networks alone can result in communication overload
(Stephens et al., 2017). Reinecke et al. (2016) found that across the
lifespan, people reported experiencing digital stress or digitally
caused strain. Their participants also engaged in communication
due to fears of missing out (FOMO) on information and the social
pressure to be responsive, which predicted anxiety, burnout, and
depression. However, taking an affordances approach, these expe-
riences of stress may also be linked to particular technological use
that are advantageous for people's goals at their particular life-
stages. Lee, Son, and Kim (2016) also found that social
networking site (SNS) use can result in information, communica-
tion, and technological overload, and later SNS fatigue. In both
studies, fatigue and stress existed across a range of ages, but was
highest among youth (29 or below). Adults, however, often report
feeling a technostressdue to new technologies embedded in
work life, including virtual ofces and increased exibility to work
from home (Jarvenpaa &Lang, 2005).
Technologies can also create stress among family members by
interfering with their relationships. Technoference,which is a
term for the experience relational partners have when technology
interrupts conversations and other important shared experiences,
has been found to produce technology-related conicts between
romantic partners and between parents and children (McDaniel,
2015). Conicts can inhibit one's quality of life and family re-
lationships and ultimately induce physiological stress (Kulhman,
Repetti, Reynolds &Robles, 2016). McDaniel and Coyne (2014)
found that the majority (70%) of romantic partners perceived
computers, mobile phones and television to sometimesor more
ofteninterfere with their relationship. More frequent technofer-
encehas also been associated with lower overall well-being (e.g.,
lower reported relationship satisfaction, greater depressive symp-
toms, and lower life satisfaction). Although largely studied among
romantic partners or parent-child dyads, Turkle (2011) remarks on
the emotional disconnect among family members at the dinner
table when their devices captivate them. Technology can interrupt
essential face-to-face interactions and produce lower life satisfac-
tion and technology-related conict and stress in families if it is not
used effectively.
Moreover, technology can interrupt, distract, or delay in-
dividuals' vital behaviors such as eating, exercising and sleeping,
which can increase stress. One of the most robust effects of tech-
nology and media usage is on sleep duration and sleep disturbances
(Cain &Gradisar, 2010). Sleep and quality of sleep are particularly
important to the body's recovery from daily stress. Yet, research
suggests that people's technology interfere with their very basic
and important functions. Thom
ee, Harenstam and Hadberg (2011)
discovered that high mobile phone usage was related to more
sleep disturbances and depression. Lemola, Perkinson-Gloor,
Brand, Dewald-Kaufmann, and Grob (2014) also discovered that
adolescents who owned a smartphone had later bed times. Further,
adolescents' reporting of late night usage of smartphones in bed
was associated with depressive symptoms. Overall, electronic me-
dia use was negatively correlated with sleep duration and posi-
tively correlated with disturbances and depression. By tampering
with sleep, technology might hinder the body's ability to recover
from stress and prepare for oncoming daily stressors. Technology's
ability to overload people with information and disrupt life,
including sleep, provide evidence for its role as a stressor that can
affect family members' biological stress responses.
2. Technology and physiological stress in families
To understand the impact of technology on physiological stress,
it is rst important to provide a brief overview of how the mind and
body respond to stress. In the face of a stressor, challenge or threat,
the body responds via the hypothalamic-pituitary-adrenal (HPA)
axis's secretion of glucocorticoids (Juster, McEwen, &Lupien, 2010).
After the brain assesses that something is stressful, the
sympathetic-adrenal-medullary (SAM) releases adrenaline acti-
vating the HPA axis. Stimulating the production of corticotrophin
releasing hormone (CRH) leads the anterior pituitary to release the
adrenocorticotropic hormone (ACTH), which nally activates the
adrenal cortex leading to cortisol. Cortisol (and other hormones and
enzymes) energizes the body and prepares it to respond to an
impending stressor (e.g., ight or ghtresponses).
Cortisol also has a consistent diurnal pattern or cycle in healthy
children and adults (Saxbe &Repetti, 2010). A healthy diurnal
pattern has a high initial cortisol level that peaks approximately
30 min after waking. People need a certain level of cortisol after
they wake to combat the stress they face during their day (Munck,
Guyre, &Holbrook, 1984). The rise in cortisol that occurs approxi-
mately 30 min after waking is called the cortisol awakening
response or CAR (Adam et al., 2014). Nevertheless, too much or too
T.D. Afifi et al. / Computers in Human Behavior 81 (2018) 265e273266
little of a rise in CAR has been associated with poor physical and
mental health (see Fries, Dettenborn, &Kirschbaum, 2009). The
CAR is followed by a progressive reduction or steep drop in cortisol
throughout the morning with a nadir in the late afternoon, reaching
its lowest point at bedtime (Saxbe &Repetti, 2010). A smaller drop
in cortisol throughout the day or the atness of people's diurnal
rhythms is indicative of worse health (Doane et al., 2013).
The impact of stress on the HPA axis often depends upon the
nature of the stress and how the physiological stress responses are
measured. In the presence of acute perceived environmental
stressors, individuals often experience elevated levels of cortisol.
Chronic or recurring stress can comprise daily functioning of the
HPA axis (Juster et al., 2010). The HPA axis can become dysregulated
due to allostatic load, making the diurnal cortisol rhythm atter,
more sensitive to stress and challenge, poorer at recovery from
stressors, or non-responsive (McEwen, 1998).
Allostasis is the body's natural response to stress and is a dy-
namic process through which multiple biological systems respond
and adapt to environmental stressors (McEwen, 1998). This adap-
tation is necessary and important for humans to adapt to their
environment's demands. However, the body's ability to adapt
effectively to stress can be compromised when it is overstimulated.
Allostatic load is the cumulative wear and tearof chronic and/or
enduring stress on biological, physiological, and psychological
systems (Repetti, Robles, &Reynolds, 2011). Although we are not
measuring allostatic load in the current study, the overuse of
technology could contribute to acute and chronic stress and anxiety
through communication overload and technoference and manifest
in people's biological stress responses.
Chronic stress can weaken the body's immune system, making it
more susceptible to disease. Research shows that psychological
stress (from a wide range of sources, including conict, relationship
dissatisfaction, and loneliness) can dysregulate people's stress re-
sponses and inhibit their immune functioning (see Graham et al.,
2009). Stress can elevate the productive of proinammatory cyto-
kines, one of which is IL-6. Inammation is an essential way the
body responds to infection or injury, but the over production of it is
thought to be a crucial connection between stress and poor health
(Jaremka, Lindgren, &Kiecolt-Glaser, 2013). Chronic inammation
is predictive of a host of diseases and disorders, including type II
diabetes, obesity, cardiovascular disease, high blood pressure, and
premature aging (McEwen &Wingeld, 2003).
Different types of stress, however, seem to have different effects
on these responses. Environmental stress can affect the acute
functioning of the HPA axis, the diurnal variability of the system,
and the production of proinammatory cytokines (e.g., Desantis
et al., 2007). For the most part, too much stress tends to
contribute to the over production of IL-6, which then contributes to
even greater stress (Jaremka et al., 2013). Acute or more moderate
stress can exacerbate the diurnal variability of cortisol and often
produces a greater rise in CAR, whereas more severe and chronic
stress often produces a blunting of the diurnal variability and
cortisol awakening response (CAR) (Stetler &Miller, 2005).
Chronically high and chronically low cortisol levels have been
associated with psychological problems such as anxiety and
depression, whereas moderate levels are more indicative of adap-
tive functioning (Gordis, Granger, Susman, &Trickett, 2006).
Among anxious individuals, however, morning cortisol levels
should be exceedingly high and remain somewhat higher in the
evening (Doane et al., 2013). Although there is very little research
connecting technology use to cortisol or IL-6, Wallenius et al. (2010)
found that school age children (39 ten year olds and 3313 year olds)
who used greater amounts of technology the preceeding day (3 or
more hours) had a diminished or lower CAR the next morning. As
these authors speculated, too much technology use likely
overwhelmed the children's physiological stress system and its
ability to function properly. Long hours of technology use day after
day might function as a natural stressor that builds over time.
Correspondingly, technology can be used to facilitate connection
in physical proximity (Carvalho et al., 2015) or act as a tool for
isolation and separation even while in physical proximity (Turkle,
2011). In particular, the general shared experience of high tech-
nology use could produce communication overload, which should
be associated with higher stress among family members. But,
because of the lack of research, the exact nature of how technology
use affects cortisol patterns and IL-6 is unclear. Thus, we propose
the following research question:
RQ
1
: In what ways does the amount of overall technology used
by parents and adolescents during the week affect their CAR, IL-
6, and cortisol diurnal rhythms?
In addition, the types of technology and media parents and
adolescents use could produce different levels of stress. An affor-
dances theoretical framework provides a systematic approach to
exploring the characteristics of media that contribute to their social
and personal uses and effects. Specically, work-related email use
on mobile devices by parents could be most disruptive by dis-
placing parentstime and attention and most predictive of
technology-related stress (McDaniel, 2015). This might represent
communicative overload as an outcome of a lack of boundaries
between work and home (Jaavrenpa &Lang, 2005). Furthermore,
higher social media use (such as Facebook) affords higher social
connectivity, visibility, accessibility, edibility, persistence (of mes-
sages), searchability and social feedback (Treem &Leonardi, 2012,
pp. 143e189). Although these affordances have benets, they can
also be stressors (Fox &Moreland, 2015; Morin-Major et al., 2016).
Given the affordances literature or different functions of technol-
ogy, it is logical to assume that the forms of technology and media
use matter in their impact on cortisol and IL-6.
H1. Technology used (i.e., mobile phone usage, email usage, gen-
eral media use, or social network size on Facebook) by parents and
adolescents during the week will differ in their effects on cortisol
diurnal rhythms, CAR, and IL-6.
Finally, because research indicates that high technology use is
related with late night usage, sleep disturbances and decreased
sleep quality (Cain &Gradisar, 2010; Lemola et al., 2014), we hy-
pothesize the following:
H2. Overall technology/media use will interact with nighttime
screen/cell phone use to predict CAR, such that those with the
greatest technology/media use and nighttime screen/cell phone
use will have the highest CAR, and those with the lowest technol-
ogy/media use and nighttime screen/cell phone use will have the
lowest CAR.
3. Method
Families reected on their technology use the past week. They
also reported on their night time technology use, sleep, and
collected four saliva samples throughout the day on two consecu-
tive days in the middle of that week to test for cortisol and IL-6.
3.1. Participants
Sixty-two heterosexual couples (Mage ¼44.68) and one of their
adolescent children (ages 13e18 ; M¼14.8; 31 sons and 31
daughters) participated in this study. All the couples were married
T.D. Afifi et al. / Computers in Human Behavior 81 (2018) 265e27 3 267
(94%) except for four, who were cohabitating. Eight (13%) of the
couples were remarried. Most adolescents and parents were
Caucasian (adolescent n¼55 or 89%; parents ¼115 or 93%). The
median household income of the families was $113,000. Nine of the
parents had a high school degree (7%), 24 (19%) had an Associates'
Degree or some college, 47 (38%) had a Bachelor's degree, and 41
(33%) had an advanced degree (MA, Ph.D., MD). The parents had an
average of three children and worked an average of 44.7 h per
week. The adolescents spent an average of 11 h a week in extra-
curricular activities and had 4.07 social media accounts. Parents
reported 2.07 social media accounts.
3.2. Procedures
The families resided in the Midwest part of the U.S. They were
recruited through a university-wide email listserv at a research
university, advertisements sent to employees at large businesses,
Craigslist, and network sampling. Once the families called the
researcher, the researcher explained the study, screened members,
and gained verbal consent. Both parents needed to be employed full
time (min. of 40 h a week) and have two or more children still living
in their home. All parents needed to be in a romantic relationship
and living together and the adolescent needed to be currently
residing with them. Family members were excluded if they had a
health condition or were taking medications that could have
altered their hormones.
Data collection for each family lasted one week. Each family
member completed an initial survey on the same weekend, daily
diary logs the following Monday through Friday, and an exit survey
at the end of the week on the weekend. In this exit survey, par-
ticipants reported about their technology and media use during the
last week. All links to the online surveys and diary logs, as well as
reminders, were emailed to the family members individually each
day. The families choose two consecutive days in the middle of the
week to collect their saliva samples.
Upon consent, families were mailed details of the study, saliva
collection procedures, collection materials marked by days and
times, medical forms, and cold packets. Parents and adolescents
collected saliva by passively drooling into a small vial. Families
were asked not to drink alcohol or visit the dentist within 48 h
before their saliva collections. They were also instructed not to
exercise rigorously, consume caffeine, use illegal drugs, or smoke
on the days of collection. They were told not to brush their teeth,
eat, or drink anything an hour before their saliva collections. Saliva
samples were collected immediately upon waking in the morning
before their feet touched the oor, 30 min after waking, at noon (in
a private room, before lunch), and right before bedtime (resulting in
1488 saliva samples). The family was instructed to freeze their
saliva samples in their home freezer immediately after collection. If
they were at work or school, participants were asked to put their
sample in a freezer or use the frozen packet to keep it cold.
Several procedures were established to help ensure compliance
and accuracy in the data collection. Before the family began the
study, a researcher either called the family using regular voice
dialing or FaceTime (allowing the researcher to visually demon-
strate how to collect the saliva). The instructions were also written
in a letter, where the saliva collection was also visually depicted. A
wake-up text reminder was also sent to each family member the
morning of the saliva collections using their self-reported expected
wake-up times. Immediately after collecting every saliva sample,
the participants texted the researcher collected,providing a time
stamp. Each participant was compensated $35. The data were
collected during the academic year and not during a break or family
vacation. Upon retrieval from the family's home, the saliva samples
were immediately transferred to a freezer in the laboratory (20
C). The samples were then shipped overnight to Salimetrics where
they were frozen at 80
C until analyzed. All of the samples were
assayed for salivary cortisol in duplicate by enzyme immunoassay
(Granger et al., 2012). Intra- and inter-assay coefcients of variation
should be less than 10 and 15%, respectively. Cortisol is reported in
micrograms per deciliter (
m
g/dl).
3.3. Measures
3.3.1. Media and Technology Use Scale
An adapted version of the Media and Technology Use and Atti-
tudes scale (Rosen, Whaling, Carrier, Cheever &Rokkum, 2013)was
completed by the parents and adolescents at the end of the study.
We asked participants about the frequency in which they engaged
in each of the following activities in the last week: emailing (on all
devices; 4 items; adolescent
a
¼0.72, father
a
¼0.86, mother
a
¼0.86), phone use (including all smart phone use, calling, texting,
music, pictures, internet, etc.; 14 items; adolescent
a
¼0.89, father
a
¼0.91, mother
a
¼0.93), general media use (i.e., TV viewing,
gaming, media sharing, internet searching; 13 items; adolescent
a
¼0.86, father
a
¼0.87, mother
a
¼0.75), and social media use
(e.g., checked Facebook or other social networks,”“post status
updates”“post photos”“read postings; 9 items; adolescent
a
¼0.88, father
a
¼0.95, mother
a
¼0.95). These subscale items
were averaged and varied along the following anchors: 1 (never), 2
(once this past week), 3 (several times this past week), 4 (once a
day), 5 (several times a day), 6 (once an hour), 7 (several times an
hour), and 8 (always). Finally, we included the Facebooksubscale
that more specically asked about participantsnetworks on
Facebook. Participants were rst asked if they had a Facebook ac-
count (yes ¼1; no ¼0). They were then asked about their social
network on Facebook that ranged on a scale from 0 (none) to8 (751
or more) (e.g., How many total friends do you have on Facebook,
including friends you may not know?”“How many Facebook
friends do you know in person?adolescent
a
¼0.91, father
a
¼0.81, mother
a
¼0.68).
3.3.2. Night-Time Technology Use
Five items were created for this study to assess participants
reports of their typical technology use at bedtime in the initial,
entry survey. Participants indicated whether or not (i.e., indicated
yes ¼1orno¼0) they usually fall asleep at night looking at a
screen (or right after looking at a screen while still in bed) of some
sort (e.g., TV, laptop, cell phone, iPod, etc.),”“use your cell phone as
an alarm clock,”“silence your phone when you sleep (reverse
coded),”“check your cell phone in the middle of the nightand
receive instant notications on your cell phone (for email, texts,
apps, etc ) in general?
3.3.3. Hours of Sleep
Hours of sleep was a signicant control variable in the models.
Participants were asked in the diary logs the two days of their saliva
collections how many hours of sleep they got the night before. The
two evenings were averaged.
3.3.4. Physiological Measures
IL6 was measured with the wake-up saliva on day two of the
collection. For cortisol, the average across each of the time points
for the two collection days was calculated. CAR was measured as
the 30 min after waking up scoreminus the wakeup score.The
diurnal rhythm of cortisol was assessed across the four time points
(wake up, 30 min after waking up, noon, and bedtimedcoded 0, 1,
2, 3).
T.D. Afifi et al. / Computers in Human Behavior 81 (2018) 265e273268
4. Results
Means and standard deviations for the variables are provided in
Table 1. On average, the parents and adolescents reported moderate
levels of technology and media use. Facebook networks averaged
between 51e100to 101e17 5 friends. Most notably, mothers
reported using email and Facebook more than fathers and adoles-
cents and had a larger friendship network on Facebook. Fifty-eight
percent (n¼36) of adolescents, forty-eight percent (n¼30) of
fathers, and eighty-ve percent (n¼53) of mothers indicated they
had a Facebook account. The cortisol and IL6 scores were positively
skewed and were transformed with a natural log transformation.
Multilevel modeling (using SPSS MIXED) with a random inter-
cept was used to account for the nested nature of the data and the
growth curve for the diurnal rhythm of cortisol. This statistical
approach allowed for a detailed analysis of the effects of variables at
the level of the individual family member (father, mother, adoles-
centlevel 1), nested within a family (level 2). For each outcome, we
rst tested an unconditional or intercepts only model to obtain an
estimate of the intraclass correlation (ICC). If there was signicant
variance in the outcome variable, we then proceeded to build the
models by rst testing control variables and their interaction with
family role. Numerous control variables were examined (e.g.,
caffeine intake, number of children's activities, number of hours
working, mental health, income, education), but the only one that
was signicant was participants' averaged hours of sleep the nights
before their saliva collections. Therefore, this was the only control
variable included in the models. Finally, predictor variables were
entered, and interactions with family role were tested.
Separate intercepts for mothers and fathers were created by
inserting dummy codes for mothers and fathers into the model
instead of including a traditional intercept. The adolescent was the
omitted group in the dummy coding. The models were then run
again, making mothers the omitted group to determine all possible
combinations of results for family role. After these analyses were
complete, mothers, fathers and adolescents were then combined
into one variable with the intercept included in the xed effect (and
the random effect for the growth curve models). For the growth
curve models, separate slopes for mothers, fathers, and children
were also created by crossing them with time. The predictors were
grand mean centered.
4.1. Results for CAR
In the nal model for email use and CAR, there was a signicant
interaction for email use and fathers when compared to mothers
B¼0.01, 95%CI [0.001, 0.02], t(102.47) ¼2.24, p<0.05, but no
signicant interaction for email use and mothers when compared
to adolescents B¼0.01, 95%CI [-0.02, 0.01], t(61.26) ¼0.67, ns,or
email use for fathers when compared to adolescents B¼0.01, 95%CI
[-0.004, 0.02], t(59.15) ¼1.25, ns. This indicates that after control-
ling for the average number of hours slept at night, fathers who
reported greater email use had a greater rise in their CAR compared
to mothers. The random intercept variance remained signicant in
the models, Var ¼0.25e26, p<0.01. The test for whether these
family roles and email use were signicantly different from each
other was not signicant B¼0.007, 95%CI [-0.007, 0.008],
t(101.28) ¼0.20, ns.
In the nal model for phone use and CAR, there were no sig-
nicant interaction for phone use and fathers when compared to
mothers B¼0.004, 95%CI [-0.01, 0.01], t(95.54) ¼0.06, ns, no sig-
nicant interaction for phone use and mothers compared to ado-
lescents B¼0.005, 95%CI [-0.02, 0.01], t(60.54) ¼0.61, ns, and no
signicant interaction for phone use for fathers compared to ado-
lescents B¼0.01, 95%CI [-0.02, 0.003], t(108.39) ¼1.43, ns.
However, there was a main effect for adolescents for phone use at
p¼0.05, B¼0.01, 95%CI [-0.0003, 0.02], t(59.94) ¼1.95, indicating
that (controlling for sleep) as adolescents increased their phone
use, it corresponded with a slight rise in CAR. The random intercept
variance was signicant, Var ¼0.22-0.24, p<0.05.
In the nal model for general media use and CAR, there was a
signicant interaction for general media use for fathers when
compared to adolescents B¼0.02, 95%CI [-0.03, 0.002],
t(110.82) ¼2.31, p<0.05, a signicant interaction for general
media use for mothers compared to adolescents B¼0.02, 95%CI
[-0.03, -.0001], t(101.40) ¼2.02, p<0.05, but no signicant
interaction for general media use for fathers compared to mothers
B¼0.001, 95%CI [-0.02, 0.01], t(104.85) ¼0.13, ns. This indicates
that after controlling for the number of hours slept at night, ado-
lescents who reported greater general media use had a greater rise
in their CAR compared to mothers and fathers. There was also a
main effect for adolescents for general use B¼0.09, 95%CI [0.002,
0.17], t(59.49) ¼2.04, p<0.05, suggesting that as adolescents
increased their general media use, it corresponded with a rise in
CAR. The random intercept variance was signicant, Var ¼0.27e29,
p<0.001. The test for whether these family roles were signicantly
different than each other was signicant B¼0.01, 95%CI -0.02,
-.003], t(127.91) ¼2.65, p<0.01.
In the nal model for Facebook use and CAR, there was a sig-
nicant interaction for Facebook and mothers compared to ado-
lescents B¼0.02, 95%CI [-0.04, -.01], t(84.16) ¼2.70, p<0.01, but
no signicant interaction for Facebook for fathers compared to
adolescents B¼0.01, 95%CI [-0.03, 0.002], t(96.13) ¼1.65, ns,
and no signicant interaction for Facebook for fathers compared to
mothers B¼0.01, 95%CI [-0.01, 0.03], t(92.46) ¼1.17, ns. This in-
dicates that after controlling for the number of hours slept at night,
adolescents who reported greater Facebook use had a greater rise in
their CAR compared to mothers. There was also a main effect for
adolescents for Facebook B¼0.01, 95%CI [0.002, 0.02],
t(58.45) ¼2.49, p<0.05, suggesting that (controlling for sleep), as
adolescents increased their Facebook use, it corresponded with a
rise in CAR. The random intercept variance was signicant,
Var ¼0.27, p<0.01. The test for whether these family roles were
signicantly different than each other was signicant B¼0.01,
95%CI [-0.02, -.0009], t(142.02) ¼2.20, p<0.05.
Table 1
Descriptive statistics for predictor variables and IL6 and cortisol by family member.
Mothers Fathers Adolescent
MSDM SDMSD
Mean Hours Slept 6.99a 0.87 6.67b 0.98 7.44ba 1.03
Email Use 5.25ab 1.64 4.35bc 1.55 3.80ac 1.33
Phone Use 4.02 1.28 3.72a 1.28 4.30a 1.20
General Media Use 2.63a 1.11 2.66b 1.05 3.31ab 1.08
Social Media 3.56a 1.54 2.41ab 1.48 3.79b 1.49
Facebook 2.54ab 0.93 2.17b 1.22 2.04a 1.38
IL6 28.11 26.00 48.78 93.34 62.61 129.08
CAR 0.16þ0.10 0.10þ0.01 0.19þ0.04
Cortisol Wake 0.29 0.12 0.29 0.15 0.29 0.12
Cortisol 30 Min after Wake 0.45þ0.22 0.39aþ0.14 0.48a 0.16
Cortisol Noon 0.11a 0.07 0.14 þa 0.07 0.12þ0.06
Cortisol Bedtime 0.08 0.10 0.07 0.10 0.07 0.07
Note: IL6 and Cortisol scores are the raw scores before being transformed. Scale
anchors for email use to social media range from 1e8 [1 (never), 2 (once this past
week), 3 (several times this past week), 4 (once a day), 5 (several times a day), 6
(once an hour), 7 (several times an hour, and 8 (always)]. The specicFacebook
scale asked participants about their network on Facebook a scale from 0 (none) to 8
(751 or more) (e.g., How many total friends do you have on Facebook, including
friends you may not know?”“How many Facebook friends do you know in person?
Facebook networks averaged between 51e100to 101e175friends). Corre-
sponding letters across rows indicate signicant differences between family
members for that variable at a minimum of p<0.05. þ¼p<0.10.
T.D. Afifi et al. / Computers in Human Behavior 81 (2018) 265e27 3 269
In the nal model for social media use and CAR, there were no
signicant interaction for social media use and fathers when
compared to mothers B¼0.003, 95%CI [-0.01, 0.01], t(99.81) ¼0.51,
ns, no signicant interaction for social media use and mothers
compared to adolescents B¼0.006, 95%CI [-0.01, 0.006],
t(108.04) ¼1. 0 0 , ns, and no signicant interaction for social media
use for fathers compared to adolescents B¼0.003, 95%CI [-0.01,
0.008], t(105.89) ¼0.49, ns. The random intercept variance was
signicant, Var ¼0.23e25, p<0.01.
4.2. Results for IL-6
In the nal model for email use and IL-6, there was no signicant
interaction for email use for fathers compared to mothers B¼0.09,
95%CI [-0.03, 0.20], t(103.32) ¼1.51, ns, no signicant interaction for
email use for mothers compared to adolescents B¼0.0007, 95%CI
[-0.13, 0.13], t(88.01) ¼0.01, ns, or email use for fathers compared to
adolescents B¼0.10, 95%CI [-0.05, 0.24], t(101.16) ¼1.40, ns.
However, there was a main effect for fathers and email use, indi-
cating that (controlling for sleep) the more fathers used their email,
the more their IL-6 increased. The random intercept variance was
not signicant, Var ¼0.05, ns.
In the nal model for phone use and IL-6, there was a signicant
interaction for phone use for fathers compared to adolescents
B¼0.18, 95%CI [0.01, 0.34], t(103.86) ¼2.15, p<0.05, no signicant
interactions for phone use for mothers compared to adolescents
B¼0.03, 95%CI [-0.12, 0.19], t(94.70) ¼0.44, ns, and the interaction
for phone use for fathers compared to mothers was approaching
signicance B¼0.13, 95%CI [-0.006, 0.28], t(95.31) ¼1.90, p¼0.06.
After controlling for sleep, adolescents who reported greater phone
use had a greater increase in their IL-6 compared to fathers. Fathers
who also reported greater phone use had a slightly greater increase
in their IL-6 compared to mothers. The random intercept variance
was not signicant, Var ¼0.07, ns. The test for whether these family
roles were signicantly different than each other was not signi-
cant B¼0.05, 95%CI [-0.04, 0.15], t(93.74) ¼1.01, ns.
In the nal model for general media use and IL-6, there was no
signicant interaction for general media use for fathers when
compared to mothers B¼0.07, 95%CI [-0.10, 0.25], t(103.21) ¼0.86,
ns, no signicant interaction for general media use and mothers
when compared to adolescents B¼0.04, 95%CI [-0.13, 0.22],
t(95.81) ¼0.47, ns, and no signicant interaction for general media
use and fathers when compared to adolescents B¼0.13, 95%CI
[-0.07, 0.32], t(105.93) ¼1.26, ns. Therefore, there were no signi-
cant effects for general media use on IL-6. The random intercept
variance was not signicant, Var ¼0.03, ns.
In the nal model for Facebook use and IL-6, there was a sig-
nicant interaction for Facebook for mothers compared to adoles-
cents B¼0.16, 95%CI [-0.32, 0.006], t(108.88) ¼2.06, p<0.05, an
interaction for Facebook and fathers compared to adolescents that
was approaching signicance B¼0.15, 95%CI [-0.32, 0.03],
t(91.14) ¼1.68, p¼0.09, but no signicant interaction for Facebook
for fathers compared to mothers B¼0.03, 95%CI [-0.22, 0.15],
t(91.77) ¼0.36, ns. This indicates that after controlling for the
number of hours slept at night, adolescents who reported greater
Facebook use had a greater increase in their IL6 compared to
mothers and fathers. There was also a main effect for adolescents
for Facebook B¼0.12, 95%CI [0.01, 0.23], t(54.21) ¼2.25, p<0.05,
suggesting that (controlling for sleep) as adolescents increased
their Facebook use, it corresponded with an increase in IL-6. The
random intercept variance was not signicant, Var ¼0.09-0.13, ns.
The test for whether these family roles were signicantly different
than each other was signicant B¼0.11, 95%CI [-0.21, -.01],
t(134.98) ¼2.35, p<0.05.
In the nal model for social media use and IL-6, there was no
signicant interaction for social media use for fathers when
compared to mothers B¼0.03, 95%CI [-0.10, 0.16], t(96.61) ¼0.40,
ns, no signicant interaction for social media use and mothers
when compared to adolescents B¼0.04, 95%CI [-0.17, 0.08],
t(100.39) ¼0.69, ns, and no signicant interaction for social media
use and fathers when compared to adolescents B¼0.002, 95%CI
[-0.14, 0.14], t(106.93) ¼0.03, ns. Therefore, there were no signi-
cant effects for social media use on IL-6. The random intercept
variance was not signicant, Var ¼0.04-0.05, ns.
4.3. Results for CAR and nighttime technology use and general
media use
The results also revealed a signicant three-way interaction for
general media use, night time technology use and fathers
compared to adolescents B¼0.01, 95%CI [-0.02, -.006],
t(54.42) ¼2.06, p<0.05, but no signicant interaction for general
media use, night time technology use and mothers compared to
adolescents B¼0.003, 95%CI [-0.009, 0.02], t(57.86) ¼0.49, ns. The
three-way interaction for general media use, night time technology
use and fathers compared to mothers was approaching signicance
B¼0.01, 95%CI [-0.03, 0.001], t(105.10) ¼1.83, p¼0.07. The
random intercept variance remained signicant in the nal models,
Var ¼0.26-0.27, p<0.01. Follow-up regression analyses, controlling
for sleep, were then conducted to examine the nature of the as-
sociation between general media use and CAR for fathers, mothers,
and adolescents. Night time use was broken up into low and high
levels based upon a mean split of the data. These analyses revealed
the when night time technology use was high, general media use
was associated with a signicant rise in CAR for adolescents,
b
¼0.34, t ¼2.39, p<0.05, but a signicant and sharp decrease in
CAR for fathers,
b
¼0.72, t ¼2.73, p<0.05, and no signicant
association for mothers,
b
¼0.06, t ¼0.25, ns. The associations
between general media use and CAR for adolescents,
b
¼0.16,
t¼0.56, ns, fathers
b
¼0.06, t ¼0.42, ns, and mothers
b
¼0.02,
t¼0.10, ns, were not statistically signicant when night time use
was low.
4.4. Results for diurnal rhythm
The results for the unconditional growth curve model indicated
that there was not sufcient variance across family members'
cortisol over time. Adding in technology as predictors also showed
a lack of convergence, suggesting that there was not enough dif-
ference or change in the family membersdiurnal rhythms.
5. Discussion
This study examined the physiological consequences of tech-
nology use in fast-paced families. Few studies have tested how
technology use is associated with people's biological stress markers
(i.e., cortisol) (for exceptions, see Brom et al., 2014; Gentile, Bender,
&Anderson, 2017; Heo et al., 2017; Morin-Major et al., 2016;
Wallenius et al., 2010). To our knowledge, this is the rst study to
assess the association between technology use and inammation.
This study is also important in that it accounts for individual family
members' technology use and biosocial markers as embedded
within larger technology use within the family system. The results
showed that technology affected family members differently,
largely as a function of the types of technology used. The most
evident nding was that technology had the greatest effect on
adolescents' CAR and IL-6 compared to mothers and fathers. Ado-
lescents with higher phone use, greater general media exposure,
and larger social network sizes via Facebook demonstrated a
greater rise in their CAR and higher rates of IL-6.
T.D. Afifi et al. / Computers in Human Behavior 81 (2018) 265e273270
Even though adolescents' technology use was most connected
to their biosocial stress markers, fathers were also affected. Fathers
who reported greater phone use had a slightly greater increase in
their IL-6 compared to mothers. Fathers who reported greater email
use had a stronger rise in their CAR compared to mothers. In
addition, the more that fathers used their email, the more they
experienced an increase in their IL-6. Finally, when night time
technology use was high, general media use was associated with a
signicant rise in CAR for adolescents, but a signicant decrease in
CAR for fathers. Technology use did not signicantly affect cortisol
diurnal rhythm nor did have any signicant effect on mother's
biosocial stress markers.
5.1. Interpreting the physiological consequences of technology use
Overwhelmingly, technology use affected adolescents' CAR
more than their parents. Interestingly, this technology use did not
inuence any of the family members' diurnal rhythms, but it
inuenced their CAR. Technology use alone was likely not powerful
enough to signicantly alter the slope of family members' cortisol
throughout the day, but it did affect their amount of cortisol pro-
duction in the morning. Adolescents' phone, general media use, and
Facebook social network size resulted in a greater rise in CAR for
adolescents than their parents. The only other signicant predictor
of a greater change in CAR for parents was for fathers, whose email
use was also associated with higher CAR than mother's email use.
Our results also revealed that when fathers used more general
media throughout the day and more technology at bedtime, it
diminished their CAR. But, the same pattern was predictive of a rise
in CAR for adolescents. This nding might indicate that using
technology right before bed and throughout the day could be
making fathers experience symptoms that emulate depression,
such as extreme fatigue. Adolescents, on the other hand, who
engage in the same patterns may be feeling an increase in anxiety
or stress. A rise in CAR is expected and healthy to combat daily
stressors (Fries et al., 2009). Too high or too low of a CAR, however,
is often predictive of poorer physical and mental health such as
anxiety and depressive symptoms (Fries et al., 2009). Although the
ndings on CAR are still debated, acute or more moderate stress
and anxiety is often predictive of a greater rise in CAR (Stetler &
Miller, 2005), which could be one explanation for rise in CAR for
the adolescents in our sample, whereas more severe and chronic
stress can lead to a blunting of the CAR (Stetler &Miller, 2005),
which might be indicative of fathersdrop in cortisol after waking.
These ndings suggest that the physiological impacts of technology
on families depends on who is using it, how it is used, and why it is
used.
Unlike adults, adolescents have reported using an array of
different types of social media, including Facebook. Adolescents use
Facebook to keep up with social networks (Frison &Eggermont,
2016) and maintain a mediated social identity (Oeldorf-Hirsch,
Birnholtz, &Hancock, 2017). Dening their role in society and
developing an identity are central goals at their age (boyd, 2015).
But, this can produce social stress. Social stress is a pervasive and
common type of stress for adolescents, which can have far reaching
physiological effects (Finnel &Wood, 2016). Because of the
continual access to others' personal information, adolescents might
become overwhelmed with managing the array of emotions from
otherslives, monitoring their place in their social network and
experiencing social comparisons.
Our results suggest that the consequences of being wirednot
only inuence CAR, but also extend to inammation. Although no
results were found for email and general media use for adolescents
or parents, adolescents who reported greater phone use and
Facebook use experienced greater IL-6. Pro-inammatory
cytokines, such as IL-6, have been associated with poorer physical
and mental health in families (Graham et al., 2009). Over-
production of IL-6 has been correlated with cardiovascular disease,
cancer, psychiatric disorders, and post-traumatic stress disorders
(Carpenter et al., 2010; Cohen, Doyle, &Skoner, 1999). Given that
this is only a one week study with families, however, additional
research is necessary that can track adolescentstechnology use
and its association with biosocial markers over the life course.
5.2. Affordances as a theoretical explanation
Even though we did not examine why family members used
certain technology or media, the research on media affordances
(Fox &Moreland, 2015; Rice et al., 2017; Treem &Leonardi, 2012,
pp. 143e189) could shed light on our ndings. Fathers were more
affected physiologically by increased email use than mothers. Fa-
ther's email use may be bringing work into the home and thus
introducing stress. However, adolescents' physiology and technol-
ogy use tells a different story. Even though adolescents were born
with these technologies, overall media consumption generally ap-
pears to be more physiologically stressful for adolescents than their
parents. Furthermore, not all media use had this effect. Unlike
parents, adolescents were unaffected by email. One speculation for
this is that email is not as important in adolescents' lives, especially
for maintaining their friendships, compared to other forms of
technology. Moreover, Facebook social network size, rather than
social media use in general, had an effect on CAR and IL-6 for ad-
olescents. These ndings seem to indicate that it is the reason for
technology use above and beyond technology use itself, which has
an effect on physiological stress.
These ndings contribute to developing theoretical models of
technology effects, such as the affordances framework. For
instance, the mobility of devices (i.e., portability) may extend fa-
thers' capacity to engage in work related media such as email, but
this affordance of availability and accessibility might diminish
work-home boundaries and facilitate communication overload
(Jarvenpaa &Lang, 2005; Reinecke et al., 2016; Schrock, 2015). This
perspective could be applied to the other family members. Mothers'
CAR and IL6 were not signicantly affected by technology use,
which could indicate that they are better adapted to the volume of
communication that technology have facilitated than fathers and
adolescents. This requires further investigation. Yet, the role of
portability might also explain the greater physiological effects
experienced by adolescents compared to parents, especially as it
relates to social network sizes via Facebook (Morin-Major et al.,
2016; Schrock, 2015). Adolescentssocial networks might increase
the number of relationships they have beyond a size that is perhaps
maintainable. This supports other ndings that demonstrate that
people sometimes experience fatigue and stress because of SNS
(Fox &Moreland, 2015; Lee et al., 2016).
Adolescents who are in the process of exploring and developing
their social environment and identity might also be affected by the
content afforded by technology, such as the increased capacity for
selective self-presentation (Rice et al., 2017). Selective self-
presentation may combine with other affordances, such as
increased access and visibility in one's social network, to facilitate
social comparison. Content can be selectively chosen to enhance
self-presentation, creating perceptions that one's peers are happier,
more social, and more attractive. Through social comparisons, ad-
olescents can experience sensations of loneliness (Best, Manktelow,
&Taylor, 2014), dissatisfaction (Tiggemann &Slater, 2013) and
anxiety such as FOMO (Rosen et al., 2013a). The communication
within social networks could facilitate adolescents' social stress by
creating unachievable norms that precipitate adolescent's feelings
of inadequacy.
T.D. Afifi et al. / Computers in Human Behavior 81 (2018) 265e27 3 271
Similarly, the content of social network messages could produce
stress by facilitating emotional contagion (Fox &Moreland, 2015).
Hancock, Gee, Ciaccio, and Lin (2008) found that the emotional
content of the messages shared by people's social networks inu-
ence the mood of messages that people share. This might be
especially true of adolescents who are exploring social networks,
experiencing hormonal changes, and facing the challenges of
school and peers as they prepare for college and enter their rst
romantic relationships (boyd, 2015). Peer networks' message con-
tent could increase exposure to negative affect and increase ado-
lescents' stress.
5.3. Final thoughts
The ndings of the current study must be set within its limita-
tions. Because of the intensity and cost of the data collection, the
sample included a small group of families from the Midwest. Our
ndings need to be tested with a larger sample and in more diverse
parts of the United States, and the globe more broadly. Because the
current sample is not randomly generated, we cannot generalize
our ndings within or outside the United States. Different effects of
technology and media use on diurnal rhythm might be realized
with a larger, randomly selected, international sample and one that
is collected over a longer period of time. In addition, our sample
size might have also affected our power to detect signicant dif-
ferences. We decided to continue to report ndings that were
approaching signicance (p<0.10) given that signicance levels are
arbitrarily created by researchers, are likely a reection of sample
size, and are of practical and theoretical importance. We were also
unable to examine differences in technology/media use and
biosocial markers for boys and girls because of our limited sample
size. However, this is an important direction for future research
given that research suggests that technology and media use and
decision making often differ depending upon the sex and age of the
adolescent (e.g., Fedorowicz, Vilvovsky, &Golibersuch, 2010).
Finally, although different technology and media uses were
compared in this study, the reasons for using these technologies are
speculative and based on previous affordances research. Future
research should evaluate distinctions among family members
needs, uses and gratications of media and technology use and how
they affect biosocial stress markers. In addition, gaming was
measured with a couple of items within the general media use
subscale. However, given its popularity with adolescents and adults
alike, researchers might compare its inuence on biosocial markers
to other media and technology. Even with these limitations, the
current study provides foundation information on the effect of
technology use in families on cortisol and immune strength. Our
results suggest that adolescents, in particular, might be most at risk
for the effects of being wiredin a fast paced world.
Funding
Funding for this study was provided by the University of Iowa.
References
Adam, E. K., Vrshek-Schallhorn, S., Kendall, A. D., Mineka, S., Zinbarg, R. E., &
Craske, M. G. (2014). Prospective associations between the cortisol awakening
response and rst onsets of anxiety disorders over a six-year follow-up - 2013
Curt Richter award winner. Psychoneuroendocrinology, 44,47e59. https://
doi.org/10.1016/j.psyneuen.2014.02.014.
Anderson, M. (2015). Technology device ownership: 2015. Pew Research Center.
http://www.pewinternet.org/2015/10/29/technology-device-ownership-2015/.
Best, P., Manktelow, R., & Taylor, B. (2014). Online communication, social media and
adolescent wellbeing. Children and Youth Services Review, 41,27e36.
boyd, d (2015). It's complicated: The social lives of networked teens. New Haven, CT:
Yale University Press.
Brom, C., Buchtov
a, M.,
Sisler, V., D
echt
erenko, F., Palme, R., & Glenk, L. M. (2014).
Flow, social interaction anxiety and salivary cortisol responses in serious
games: A quasi-experimental study. Computers &Education, 79,69e100.
https://doi.org/10.1016/S0161-813X(14)00128-4.
Cain, N., & Gradisar, M. (2010). Electronic media use and sleep in school-aged
children and adolescents: A review. Sleep Medicine, 11,735e742. https://
doi.org/10.1016/j.sleep.2010.02.0 06.
Carpenter, L. L., Gawugas, C. E., Tyrka, A. R., Lee, J. K., Anderson, G. M., et al. (2010).
Association between plasma IL-6 response to acute stress and early-life
adversity in healthy adults. Neuropsychopharmacology, 35,2617e2623.
Carvalho, J., Francisco, R., & Relvas, A. P. (2015). Family functioning and information
and communication technologies: How do they relate? A literature review.
Computers in Human Behavior, 45,99e108. https://doi.org/10.1016/
j.chb.2014.11.037.
Cohen, S., Doyle, W. J., & Skoner, D. P. (1999). Psychological stress, cytokine pro-
duction, and severity of upper respiratory illness. Psychosomatic Medicine, 61,
175 e180 .
Common Sense Media. (2013). Zero to eight: Children's media use in American 2013.
New York, NY: Common Sense Media.
Desantis, A., Adam, E., Doane, L., Mineka, S., Zinbarg, R., & Craske, M. (20 07). Racial/
ethnic differences in cortisol diurnal rhythms in a community sample of ado-
lescents. Journal of Adolescent Health, 41,3e13.
Doane, L. D., Mineka, S., Zinbarg, R. E., Craske, M., Grifth, J. W., & Adam, E. K. (2013).
Are atter diurnal cortisol rhythms associated with major depression and
anxiety disorders in late adolescence? The role of life stress and daily negative
emotion. Development and Psychopathology, 25, 629e642. https://doi.org/
10.1017/S0954579413000 060.
Fedorowicz, Jane, Vilvovsky, Sonia Gantman, & Golibersuch, Andrew J. (2010).
Gender differences in Teenagers' elective use of computer technology. Com-
munications of the Association for Information Systems, 27. Article 3. Available at:
http://aisel.aisnet.org/cais/vol27/iss1/3.
Finnell, J. E., & Wood, S. K. (2016). Neuroinammation at the interface of depression
and cardiovascular disease: Evidence from rodent models of social stress.
Neurobiology of Stress, 4,1e14. https://doi.org/10.1016/j.ynstr.2016.04.001.
Fox, J., & Moreland, J. J. (2015). The dark side of social networking sites: An
exploration of the relational and psychological stressors associated with Face-
book. Computers in Human Behavior, 45,168e176. https://doi.org/10.1016/
j.chb.2014.11.083.
Fries, E., Dettenborn, L., & Kirschbaum, C. (2009). The cortisol awakening response
(CAR): Facts and future directions. International journal of Psychophysiology, 72,
67e73. https://doi.org/10.1016/j.ijpsycho.2008.03.014.
Frison, E., & Eggermont, S. (2016). Exploring the relationships between different
types of Facebook use, perceived online social support and adolescents'
depressed mood. Social Science Computer Review, 34,153e171.
Gentile, D. A., Bender, P. K., & Anderson, C. A. (2017). Violent video game effects on
salivary cortisol, arousal, and aggressive thoughts in children. Computers in
Human Behavior, 70,39e43. https://doi.org/10.1016/j.chb.2016.12.045.
Gordis, E. B., Granger, D. A., Susman, E. J., & Trickett, P. K. (2006) . Asymmetry be-
tween salivary cortisol and
a
-amylase reactivity to stress: Relation to aggressive
behavior in adolescents. Psychoneuroendocrinology, 31,976e987. https://
doi.org/10.1016/j.psyneuen.2006.05.010.
Graham, J. E., Glaser, R., Loving, T. J., Malarkey, W. B., Stowell, J. R., & Kiecolt-
Glaser, J. K. (2009). Cognitive word use during marital conict and increases in
proinammatory cytokines. Health Psychology, 28,621e630. https://doi.org/
10.1037/a0015208.
Granger, D., Fortunato, C., Beltzer, E., Virag, M., Bright, M., & Out, D. (2012). Focus on
methodology: Salivary bioscience and research on adolescence: An integrated
perspective. Journal of Adolescence, 35, 1081e1095. https://doi.org/10.1016/
j.adolescence.2012.01.005.
Hancock, J. T., Gee, K., Ciaccio, K., & Lin, J. M. (2008). Im sad youre sad: emotional
contagion in CMC. In Proceedings of CSCW 08: ACM Conference on computer
supported cooperative work (pp. 295e298). https://doi.org/10.1145/
1460563.1460611.
Heo, J.-Y., Kim, K., Fava, M., Mischoulon, D., Papakostas, G. I., Kim, M.-J., Jeon, H. J.
(2017). Effects of smartphone use with and without blue light at night in
healthy adults: A randomized, doubleS-blind, cross-over, placebo-controlled
comparison. Journal of Psychiatric Research, 87,61e70. https://doi.org/10.1016/
j.jpsychires.2016.12.010.
Jaremka, L., Lindgren, M., & Kiecolt-Glaser, J. (2013). Synergistic relationships among
stress, depression, and troubled relationships: Insights from psychoimmunol-
ogy. Depression and Anxiety, 30, 288e296.
Jarvenpaa, S., & Lang, K. (2005). Managing the paradoxes of mobile technology.
Information Systems Management, 22,7e23.
Juster, R. P., McEwen, B. S., & Lupien, S. J. (2010). Allostatic load biomarkers of
chronic stress and impact on health and cognition. Neuroscience and Biobe-
havioral Reviews, 35,2e16. https://doi.org/10.1016/j.neubiorev.2009.10.002.
Kuhlman, K. R., Repetti, R. L., Reynolds, B. M., & Robles, T. F. (2016). Change in
parent-child conict and the HPA-axis: Where should we be looking and for
how long? Psychoneuroendocrinology, 68,74e81. https://doi.org/10.1016/
j.psyneuen.2016.02.029.
Lee, A. R., Son, S. M., & Kim, K. K. (2016). Information and communication tech-
nology overload and social networking service fatigue: A stress perspective.
Computers in Human Behavior, 55,51e
61. https://doi.org/10.1016/
j.chb.2015.08.011.
Lemola, S., Perkinson-Gloor, N., Brand, S., Dewald-Kaufmann, J. F., & Grob, A. (2014).
T.D. Afifi et al. / Computers in Human Behavior 81 (2018) 265e273272
Adolescents: Electronic media use at night, sleep disturbance, and depressive
symptoms in the smartphone age. Journal of Youth and Adolescence, 44,
405e418. https://doi.org/10.1007/s10964-014-0176-x.
McDaniel, B. T. (2015). Technoference: Everyday intrusions and interruptions of
technology in couple and family relationships. In C. J. Bruess (Ed.), Family
communication in the age of digital and social media. New York: Peter Lang
Publishing.
McDaniel, B. T., & Coyne, S. M. (2014). Technoference: The interference of tech-
nology in couple relationships and implications for women's personal and
relational well-being. Psychology of Popular Media Culture, 5.https://doi.org/
10.1037/ppm0000 065. Advance online publication.
McEwen, B. S. (1998). Protective and damaging effects of stress media- tors. New
England Journal of Medicine, 338,171e179.
McEwen, B. S., & Wingeld, J. C. (20 03). The concept of allostasis in biology and
biomedicine. Hormones and behavior, 43,2e15.
Morin-Major, J. K., Marin, M. F., Durand, N., Wan, N., Juster, R. P., & Lupien, S. J.
(2016). Facebook behaviors associated with diurnal cortisol in adolescents: Is
befriending stressful? Psychoneuroendocrinology, 63, 238e246. https://doi.org/
10.1016/j.psyneuen.2015.10.005.
Munck, A., Guyre, P. M., & Holbrook, N. J. (1984). Physiological functions of gluco-
corticoids in stress and their relations to pharmacological actions. Endocrine
Review, 5,25e44. https://doi.org/10.1210/edrv-5-1-25.
Nam, T. (2014). Technology use and work-life balance. Applied Research in Quality of
Life, 9,1017e1040. https://doi.org/10.1007/s11482-013-9283-1. article.
Nathanson, A. I. (2015). Media and the family: Reections and future directions.
Journal of Children and Media, 9,133e139. https://doi.org/10.1080/
17482798.2015.997145.
Oeldorf-Hirsch, A., Birnholt, J., & Hancock, J. (2017). Your post is embarrassing me:
Face threats, identity, and the audience on Facebook. Computers in Human
Behavior, 73,92e99. https://doi.org/10.1016/j.chb.2017.03.030.
Reinecke, L., Aufenanger, S., Beutel, M. E., Dreier, M., Quiring, O.,
Stark, B., Dreier, M. (2016). Digital stress over the life span: The effects of
communication load and internet multitasking on perceived stress and psy-
chological health impairments in a German probability sample. Media Psy-
chology, 0,1e26. https://doi.org/10.1080/15213269.2015.1121832.
Repetti, R. L., Robles, T. F., & Reynolds, B. (2011). Allostatic processes in the family.
Development and Psychopathology, 23,921e938. https://doi.org/10.1017/
S095457941100040X.
Rice, R. E., Evans, S. K., Pearce, K. E., Sivunen, A., Vitak, J., & Treem, J. W. (2017).
Organizational media affordances: Operationalization and associations with
media use. Journal of Communication, 67,106e130 .
Rice, R. E., & Hagen, I. (2010). Young adults' perpetual contact, social connectivity,
and social control through the internet and mobile phones. In C. Salmon (Ed.),
Communication yearbook, 34. London: Routledge.
Rice, R. E., & Katz, J. E. (2003). Comparing internet and mobile phone usage: Digital
divides of usage, adoption, and dropouts. Telecommunications Policy, 27,
597e623.
Riva, G., Ba~
nos, R. M., Botella, C., Wiederhold, B. K., & Gaggioli, A. (2012). Positive
Technology: Using interactive technologies to promote positive functioning.
Cyberpsychology, Behavior, and Social Networking, 15,69e77. https://doi.org/
10.1089/cyber.2011.0139.
Rosen, L. D., Whaling, K., Carrier, L. M., Cheever, N. A., & Rokkum, J. (2013a). The
media and technology usage and attitudes Scale: An empirical investigation.
Computers in Human Behavior, 29, 2501e2511. https://doi.org/10.1016/
j.chb.2013.06.006.
Rosen, L. D., Whaling, K., Rab, S., Carrier, L. M., & Cheever, N. A. (2013b). Is Facebook
creating iDisorders? The link between clinical symptoms of psychiatric dis-
orders and technology use, attitudes and anxiety. Computers in Human Behavior,
29, 1243e1254. https://doi.org/10.1016/j.chb.2012.11.012.
Rutledge, P. B. (2012). Arguing for media psychology as a distinct eld. In K. E. Dill
(Ed.), The Oxford handbook of media psychology. Oxford, UK: Oxford University
Press.
Saxbe, D., & Repetti, R. L. (2010). For better or worse? Coregulation of couples'
cortisol levels and mood states. Journal of Personality and Social Psychology, 98,
92e103.
Schrock, A. R. (2015). Communicative affordances of mobile media: Portability,
availability, locatability and multimediality. International Journal of Communi-
cation, 9, 1229e1246. https://doi.org/10.1177/0094306111425016k.
Stephens, K. K., Mandhana, D. M., Kim, J. J., Li, X., Glowacki, E. M., & Cruz, I. (2017).
Reconceptualizing communication overload and building a theoretical foun-
dation. Communication Theory, 3, 269e289. https://doi.org/10.1111/comt.12116.
Stetler, C., & Miller, G. (20 05). Blunted cortisol response to awakening in mild to
moderate depression: Regulatory inuences of sleep patterns and social con-
tacts. Journal of Abnormal Psychology, 114,697e705.
Thom
ee, S., H
arenstam, A., & Hagberg, M. (2011). Mobile phone use and stress, sleep
disturbances, and symptoms of depression among young adults - a prospective
cohort study. BMC Public Health, 11,1e11. https://doi.org/10.1186/1471-2458-11-
66.
Tiggemann, M., & Slater, A. (2013). NetGirls: The Internet, Facebook, and body image
concern in adolescent girls. International Journal of Eating Disorders, 46,
630e633.
Treem, J. W., & Leonardi, P. M. (2012). Social media use in organizations: Exploring
the affordances of visibility, editability, persistence, and association. In
Communication Yearbook (Vol. 36)New York, NY: Routledge.
Turkle, S. (2011). Alone together: Why we expect more from technology and less from
each other. New York, NY: Basic Books.
Wallenius, M., Hirvonen, A., Lindholm, H., Rimpela, A., Nygard, C., Saarni, L., et al.
(2010). Salivary cortisol in relation to the use of information and communica-
tion technology (ICT) in school-aged children. Psychology, 1,88e95. https://
doi.org/10.4236/psych.2010.12012.
T.D. Afifi et al. / Computers in Human Behavior 81 (2018) 265e27 3 273
... A flatter diurnal cortisol slope has been associated with several physical and mental health outcomes e.g., obesity, depression, and externalizing symptoms 21 , while a higher cortisol awakening response has been associated with higher general life stress and prior-day feelings of sadness and being overwhelmed 23,24 . Few studies have investigated the relationship between the recreational use of screen media and biomarkers of stress [25][26][27][28][29] , but experimental studies are warranted. ...
... Another pathway may be that passive social media use affects well-being negatively through social comparison 36 . Finally, Afifi et al. suggest that engagement with screen media devices may possibly disrupt and displace fundamental behaviors such as eating, exercising, and sleeping, which may induce stress 29 . This is the first trial exploring the effect of limiting screen media use on daily levels of cortisol and cortisone. ...
Article
Full-text available
Studies have linked higher digital screen use with poorer mental health. However, there is limited experimental evidence to suggest a causal relationship. In this trial, we aimed to investigate the effects of limiting recreational digital screen use on mental well-being, mood, and biomarkers of stress in healthy young and middle-aged adults. We randomly allocated 89 families (including 164 adults) to participate in an extensive screen media reduction intervention or control. Participants in the intervention group were instructed to decrease their recreational screen use to less than 3 hours/week/person. Intervention compliance was assessed using applications and tv-monitors. Overall subjective mental well-being and mood, and collected daily biomarkers of stress (salivary cortisol and cortisone) was assessed at baseline and 2-week follow-up. Reducing recreational digital screen use resulted in significantly improved self-reported well-being and mood in adults allocated to the intervention compared to control. We observed no intervention effects for biomarkers of stress. (ClinicalTrials.gov: NCT04098913, 23/09/2019).
... Los perjuicios de un uso intensivo de la tecnología son variados y afectan a diferentes dimensiones físicas, socioemocionales y fisiológicas. Como menciona Desmurget (2020: p. 12), "todos los pilares del desarrollo se ven afectados desde lo somático es decir el cuerpo con consecuencias para la maduración cardiovascular o el desarrollo de obesidad hasta lo emocional con agresividad y depresión entre otras secuelas pasando por lo cognitivo o sea lo intelectual con efectos sobre el lenguaje y la concentración, entre otros aspectos" (Afifi et al., 2018;Bellissimo et al., 2007;Ciccarelli et al., 2015;Crowley et al., 2015;Figueiro & Overington, 2016;Fowler & Noyes, 2017;Gao et al., 2015;Gebremariam et al., 2013;Harris et al., 2015;Morin-Major et al., 2016;Woo et al., 2016;World Health Organization, 2015). ...
Article
This article presents an investigation that analyzes teachers' knowledge of data protection procedures and ethical and responsible use of technology within the parameters established by the European Union for the sustainable use of technology in Education. The study has been approached from a double quantitative and qualitative approach that aims to assess the knowledge and the importance that teachers give to data protection procedures, copyright, the ethical and safe use of digital devices in the processes of teaching-learning and the importance of teacher training in these four previous dimensions. The results show that training is the only determining variable to promote sustainable teaching. Likewise, among other relevant aspects, the teachers consider that they do not have sufficient knowledge of the protocols and regulations for the use of digital devices and programs to guarantee data protection and the rights of minors in digital environments. In this sense, it is also considered that the unsustainable use of digital devices can cause different psycho-emotional damages in students that should be worked on transversally in the different school subjects to promote its prevention. Este artículo presenta una investigación en la que se analiza el conocimiento del profesorado sobre procedimientos de protección de datos y uso ético y responsable de la tecnología dentro los parámetros establecidos por la Unión Europea para un uso sostenible de la tecnología en la Educación. El estudio se ha abordado desde un doble enfoque cuantitativo y cualitativo que pretende valorar el conocimiento y la importancia que otorga el profesorado a los procedimientos de protección de datos, de derechos de autor, al uso ético y seguro de los dispositivos digitales en los procesos de enseñanza-aprendizaje y a la importancia de la formación del profesorado en estas cuatro dimensiones anteriores. Los resultados muestran que la formación es la única variable determinante para promover una didáctica sostenible. Asimismo, entre otros aspectos relevantes, el profesorado considera que no tiene un conocimiento suficiente de los protocolos y las normas de uso de dispositivos y programas informáticos para garantizar la protección de datos y los derechos del menor en entornos digitales. En este sentido, también se considera que el uso poco sostenible de los dispositivos digitales puede provocar en los estudiantes diferentes perjuicios psicoemocionales que deberían trabajarse de forma transversal en las diferentes áreas para fomentar su prevención.
... These hypotheses are based on the literature outlining the short-term effects of smartphone use on psychological stress (Hunter et al., 2018;Melumad and Pham, 2021) and on the literature examining the impact of technology use on physiological stress (Riedl, 2012;Afifi et al., 2018). ...
Article
Full-text available
Texting while walking (TWW) is a dangerous behavior that can lead to injury and even death. While several studies have examined the relationship between smartphone use and stress, to our knowledge no studies have yet investigated the relationship between stress and TWW. The objective of the present study was to investigate this relationship by examining the effects of stress on TWW, the effects of TWW on subsequent stress, and the effect of stress on multitasking performance. A total of 80 participants completed two sequential tasks in a laboratory while they walked on a treadmill and responded to a biological motion stimulus imitating the movement of another pedestrian. In the unrestricted task, participants were given the choice to use their personal phones. In the controlled task, they carried a text conversation with a research assistant while they walked and responded to the stimulus. Stress was measured via questionnaire and saliva collection for measure of cortisol (a stress hormone) before and after each task. Results show that greater psychological stress and cortisol variations were associated with a greater number of phone uses during the unrestricted task. Greater phone use during the unrestricted task was associated with lower subsequent psychological stress in women and total time of phone use was correlated with subsequent cortisol levels. Stress measured before the controlled task had no effect on multitasking performance, but participants with moderate performance were those with the highest cortisol levels. Our results suggest that stress could be a precursor to TWW and that it could affect a pedestrian’s ability to stay safe when using their smartphone.
... We suggest, scholars revisit some of the concepts of technostress which is referred to the stress caused by an excessive use of technology (Afifi et al., 2018;Mair et al., 2015). Techno-stress due to work (Mair et al., 2019;Tarafdar et al., 2017) and personal (Hawk et al., 2019;Lee et al., 2016;Wang et al., 2015;Zhang et al., 2016) use of technology has been analyzed and assessed extensively in information systems and management literature. ...
... Thus, instead of focusing on producing good results, employees spend most of their time frequently experiencing fear of job loss due to automation. Previous studies have concluded that people reporting elevated levels of technostress are more likely to suffer the psychological strains of diminished commitment, struggle to flourish and display signs of languishing (Tarafdar and Stich, 2021), have poor self-esteem (Korzynski et al., 2021), and dissatisfaction with the IT system (Tams et al., 2020), harmful psychological responses, and burnout (Afifi et al., 2018), and their wellbeing is negatively affected. It is, therefore, proposed that technostress has a negative influence on workplace flourishing. ...
Article
Full-text available
The remote working environment is characterised by excessive use of new technology and work activities that extend to personal time. It is expected of each employee to balance multiple roles whilst maintaining maximum performance and individual wellbeing; however, without adequate support from an organisation, employees languish instead of flourish. The current study applied a model to investigate the combined effect of technostress, work–family conflict, and perceived organisational support on workplace flourishing for higher education employees. The study followed a cross-sectional quantitative research framework. Data were collected from a sample of 227 academic and support staff employees from a selected residential University in South Africa. The results indicated that technostress through perceived organisational support and through work–family conflict influences workplace flourishing. No direct significant effect was reported between technostress and workplace flourishing. Technostress, work–family conflict, and perceived organisational support combined explained 47% variance in workplace flourishing. Perceived organisational support displayed the strongest direct effect on workplace flourishing, and technostress is a strong determinant of work–family conflict, which then mediates the relationship between technostress and workplace flourishing. The study concluded that providing organisational support and creating policies favourable to work–life balance assist employees in managing techno-overload, techno-invasion, and techno-complexity (technostress) better and enhance workplace flourishing. Although employees struggle in the remote working context with demands imposed by techno-overload, techno-invasion, and techno-complexity, the results indicate that perceived organisational support and balanced work life act as job resources that enhance emotional, psychological, and subjective wellbeing (workplace flourishing).
... These changes in technology use in private settings have been abrupt and disruptive, demanding a high level of resilience and adaptability from users. First of all, an excessive use of technology may cause technostress (Afifi et al., 2018;Maier et al., 2015), which is defined as a situation of stress experienced by an individual due to their use of technology (Brod, 1984;Tarafdar, Qiang, Ragu-Nathan, & Ragu-Nathan, 2007;Salo, Pirkkalainen, Chua, & Koskelainen, 2022). The academic literature has established that technology platforms used within occupational contexts can induce technostress (Chandra et al., 2019;Maier et al., 2019). ...
Article
The COVID-19 pandemic forced most individuals to work from home. Simultaneously, there has been an uptake of digital platform use for personal purposes. The excessive use of technology for both work and personal activities may cause technostress. Despite the growing interest in technostress, there is a paucity of research on the effects of work and personal technology use in tandem, particularly during a crisis such as the COVID-19 pandemic. Using a sample of 306 employees, this paper addresses this research gap. The findings highlight how both work and personal digital platforms induce technostress during the enforced remote work period, which in turn increases psychological strains such as technology exhaustion and decreases subjective wellbeing. Study results also show that employees with previous remote working experience could better negotiate technostress, whereas those with high resilience experience decreased wellbeing in the presence of technostress-induced technology exhaustion in the enforced remote work context.
... The millennial generation makes extensive use of technology in the fields of information and communication. Social media technology, such as Facebook, Twitter, and Instagram, is frequently regarded as a means of communication (Afifi et al., 2018). ...
Article
Full-text available
This study investigated the communication style used by an Instagram account to engage millennials. A qualitative approach with document study was used. This shows Instagram posts are part of the personal documents that can be analyzed based on types of communication styles. The source of data was the Folkative Instagram account which has approximately 6000 postings with a hundred thousand likes and thousands of comments. In this study, ten captions were taken as the data. The data were analyzed through data collection, data reduction, data display, and conclusion drawing. The result found three communication styles were used by Folkative namely structuring style, equalitarian style, and controlling style. To sum up, Folkative engages the millennials through structuring style, equalitarian style, and controlling style.
Article
Social media connects people in a myriad of ways, yet when prevented from staying connected, an experience of missing out on information and events perceived to be integral to one's well-being may ensue. Relatedness, a core construct of self-determination theory, is a primary influencer of motivation, and therefore being cut off from others has a negative impact on one's quality of life. Across diverse groups of people, social media is utilized for a variety of purposes directly related to connectedness, which implies inherent differences in how one's fear of missing out (FoMO) manifests in everyday life. This study employed the previously validated Fear of Missing Out Scale (FoMOS) with a nonclinical sample of African American and Caucasian college students in the United States, with a particular focus on validity of the measure with an African American cohort due to a lack of empirical evidence pertaining to with this demographic. Factor analyses yielded inconsistent findings from the FoMOS initial validation study, and results indicated differences in self-reported FoMO between the two racial groups. We speculate that observed group-based differences are at least partially the result of how individuals from each group understood and internalized the conceptual meaning of FoMO, supporting a hypothesis of practical differences in how anxieties related to missing out manifest. This may be due to individual or group-based differences in motivations for, or general purpose of, using social media.
Article
What happens when our world, filled with burgeoning stressors, is conditioning us to subsist in a state of allostatic load (A-Load) due to chronic over-activation of our sympathetic nervous systems [1] – [4] ? What happens when our world is also teeming with technological goods and services that allow us to self-medicate through coping mechanisms that detract our brains with repeated dopamine releases? What are the long-term effects of these self-enslaving behaviors if, over time, they result in decreases in the dopamine receptors expressed in our brains [5] , [6] ? What happens when our dopamine-inducing digital activities are stamped in our brains as the easier reward paths to be sought and we subsequently lose the drive for real-world activities [5] , [7] ? When we allow ourselves to be habitually bombarded with stress, hedonism, distractions, social comparisons, and multitasking, we are negatively impacting the chemicals in our brain. Are we becoming a physiologically enervated technologically enslaved society (PETS)? Are we allowing ourselves to be perpetually tethered like PETS on digital leashes?
Article
Full-text available
Social media fatigue is a subjective sense of physical and mental exhaustion, lassitude, and irritation, caused by social media use. The current research explored the association between individual differences in attachment styles and the experience of fatigue resulting from extensive social media use. Two studies examined the association between adult attachment style and Facebook fatigue, and the mediating role of stressors related to social media use, self-esteem, and self-concept clarity. The results of the first study (N = 264) revealed an association between attachment anxiety and Facebook fatigue that was mediated by Facebook social comparison and Facebook anxiety. In the second study (N = 294), attachment anxiety was also associated with Facebook fatigue and was mediated by fear of missing out and Facebook anxiety, and these mediation effects were moderated by self-concept clarity. The findings indicate that the experience of social-media fatigue varies in accordance with specific user characteristics. Additionally, they Illustrate the impact of social media use on mental health, and emphasize the need to create a user experience that takes into account the stressors associated with social media use.
Article
Full-text available
This study reconceptualizes communication overload and builds a theoretical foundation to understand how this phenomenon applies in contemporary life. We build theory by relying on past research and using a Q-method to capture the subjective perspectives of people who experience communication overload. In our refinement of this abstract concept, we identified seven dimensions composing communication overload. The dimensions included: compromising message quality, having many distractions, using many information and communication technologies, pressuring for decisions, feeling responsible to respond, overwhelming with information, and piling up of messages. Our reconceptualization integrates disparate research, links the availability–expectation–pressure pattern to overload, and elaborates on communication quality, quantity, and generalized perceptions of feeling overwhelmed. The resulting formative theoretical model sets the stage for additional theorizing and empirical studies.
Article
Full-text available
A large body of evidence has emerged linking stressful experiences, particularly from one's social environment, with psychiatric disorders. However, vast individual differences emerge in susceptibility to developing stress-related pathology and may be due to distinct differences in the inflammatory response to social stress. Furthermore, depression is an independent risk factor for cardiovascular disease, another inflammatory-related disease, and results in increased mortality in depressed patients. This review is focused on discussing evidence for stress exposure resulting in persistent or sensitized inflammation in one individual while this response is lacking in others. Particular focus will be directed towards reviewing the literature underlying the impact that neuroinflammation has on neurotransmitters and neuropeptides that could be involved in the pathogenesis of comorbid depression and cardiovascular disease. Finally, the theme throughout the review will be to explore the notion that stress-induced inflammation is a key player in the high rate of comorbidity between psychosocial disorders and cardiovascular disease.
Article
While Facebook is a popular venue for sharing information about ourselves, it also allows others to share information about us, which can lead to embarrassment. This study investigates the effects of shared face-threatening information on emotional and nonverbal indicators of embarrassment using an experiment (N = 120) in which pairs of friends posted about each other on Facebook. Results show that face-threatening information shared by others produces a powerful emotional and nonverbal embarrassment response. However, it is not the content of the face-threatening post that produces this effect. Rather, the level of embarrassment depends primarily on whether that information violates the individual’s identity and if they perceive that unknown members of their audience can see it. In response, individuals were most likely to joke about the post, although those were most embarrassed were more likely to delete it. These results inform our understanding of how the process of embarrassment works online. The emotional embarrassment response is similar to offline, but is affected by the features of these sites, such as a large, invisible audience, and the need for ideal self-presentation. This finding has important implications for treating online social networks and their effects to be as “real” as those offline.
Article
The concept of affordances has been increasingly applied to the study of information and communication technologies (ICTs) in organizational contexts. However, almost no research operationalizes affordances, limiting comparisons and programmatic research. This article briefly reviews conceptualizations and possibilities of affordances in general and for media, then introduces the concept of organizational media affordances as organizational resources. Analysis of survey data from a large Nordic media organization identified six reliable and valid organizational media affordances: pervasiveness, editability, self-presentation, searchability, visibility, and awareness. Eight media scales based on frequency of use of 10 media within each of three organization levels were differentially associated with these affordances. The conceptualization, measurement approach, and results from this study provide the foundation for considerable future organizational communication and ICT research.
Article
An experiment investigated the effects of violent content in video games on two physiological indicators of the fight-or-flight response (cortisol and cardiovascular changes) and on accessibility of aggressive thoughts in children. Participants played a randomly assigned violent or nonviolent video game, rated the game on several dimensions, and did a word completion task. Results showed that the violent video game increased cortisol and (for boys) cardiovascular arousal (relative to baseline) more than did the equally exciting nonviolent game. The violent game also increased the accessibility of aggressive thoughts. The cortisol findings in particular suggest that playing a violent video game may activate the sympathetic nervous system and elicit a fight-or-flight type response in children. Theoretical implications and future research are discussed.
Article
Smartphones deliver light to users through Light Emitting Diode (LED) displays. Blue light is the most potent wavelength for sleep and mood. This study investigated the immediate effects of smartphone blue light LED on humans at night. We investigated changes in serum melatonin levels, cortisol levels, body temperature, and psychiatric measures with a randomized, double-blind, cross-over, placebo-controlled design of two 3-day admissions. Each subject played smartphone games with either conventional LED or suppressed blue light from 7:30 to 10:00PM (150 min). Then, they were readmitted and conducted the same procedure with the other type of smartphone. Serum melatonin levels were measured in 60-min intervals before, during and after use of the smartphones. Serum cortisol levels and body temperature were monitored every 120 min. The Profile of Mood States (POMS), Epworth Sleepiness Scale (ESS), Fatigue Severity Scale (FSS), and auditory and visual Continuous Performance Tests (CPTs) were administered. Among the 22 participants who were each admitted twice, use of blue light smartphones was associated with significantly decreased sleepiness (Cohen's d = 0.49, Z = 43.50, p = 0.04) and confusion-bewilderment (Cohen's d = 0.53, Z = 39.00, p = 0.02), and increased commission error (Cohen's d = −0.59, t = −2.64, p = 0.02). Also, users of blue light smartphones experienced a longer time to reach dim light melatonin onset 50% (2.94 vs. 2.70 h) and had increases in body temperature, serum melatonin levels, and cortisol levels, although these changes were not statistically significant. Use of blue light LED smartphones at night may negatively influence sleep and commission errors, while it may not be enough to lead to significant changes in serum melatonin and cortisol levels.
Article
Each new communication medium provides different combinations and levels of way to facilitate and/or constrain social connections. These different patterns of connectivity in turn both represent and influence forms of social control. In particular, the Internet and mobile phones are fostering a sense of perpetual contact, the potential for pervasive, personal, and portable communication. This chapter considers how these aspects of perpetual contact moderate the influence of Internet and mobile phone usage on aspects of social connectivity (constructing identity, fostering and changing group and network relations, and displaying social relations—both membership and sharing) and in turn on aspects of social control (dependency, balancing self and group, managing coordination and multitasking, navigating family relations, blurring public and private space, and engaging privacy and surveillance). These issues are particularly fluid and salient to young users, so the chapter reviews relevant research from around the world on use of these new media by teenagers and young adults.
Article
The use of social media technologies—such as blogs, wikis, social networking sites, social tagging, and microblogging—is proliferating at an incredible pace. One area of increasing adoption is organizational settings where managers hope that these new technologies will help improve important organizational processes. However, scholarship has largely failed to explain if and how uses of social media in organizations differ from existing forms of computer-mediated communication. In this chapter, we argue that social media are of important consequence to organizational communication processes because they afford behaviors that were difficult or impossible to achieve in combination before these new technologies entered the workplace. Our review of previous studies of social media use in organizations uncovered four relatively consistent affordances enabled by these new technologies: Visibility, persistence, editability, and association. We suggest that the activation of some combination of these affordances could influence many of the processes commonly studied by organizational communication theorists. To illustrate this point, we theorize several ways through which these four social media affordances may alter socialization, knowledge sharing, and power processes in organizations.
Article
Objective: The purpose of this study is to assess the role of psychological stress in the expression of illness among infected subjects and to test the plausibility of local proinflammatory cytokine production as a pathway linking stress to illness. Methods: After completing a measure of psychological stress, 55 subjects were experimentally infected with an influenza A virus. Subjects were monitored in quarantine daily for upper respiratory symptoms, mucus production, and nasal lavage levels of interleukin (IL)-6. Results: Higher psychological stress assessed before the viral challenge was associated with greater symptom scores, greater mucus weights, and higher IL-6 lavage concentrations in response to infection. The IL-6 response was temporally related to the two markers of illness severity, and mediation analyses indicated that these data were consistent with IL-6 acting as a major pathway through which stress was associated with increased symptoms of illness. However, this pattern of data is also consistent with increases in IL-6 occurring in response to tissue damage associated with illness symptoms. Conclusions: Psychological stress predicts a greater expression of illness and an increased production of IL-6 in response to an upper respiratory infection.