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Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study

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Previous research on associations between screen time and psychological well-being among children and adolescents has been conflicting, leading some researchers to question the limits on screen time suggested by physician organizations. We examined a large (n = 40,337) national random sample of 2- to 17-year-old children and adolescents in the U.S. in 2016 that included comprehensive measures of screen time (including cell phones, computers, electronic devices, electronic games, and TV) and an array of psychological well-being measures. After 1 h/day of use, more hours of daily screen time were associated with lower psychological well-being, including less curiosity, lower self-control, more distractibility, more difficulty making friends, less emotional stability, being more difficult to care for, and inability to finish tasks. Among 14- to 17-year-olds, high users of screens (7+ h/day vs. low users of 1 h/day) were more than twice as likely to ever have been diagnosed with depression (RR 2.39, 95% CI 1.54, 3.70), ever diagnosed with anxiety (RR 2.26, CI 1.59, 3.22), treated by a mental health professional (RR 2.22, CI 1.62, 3.03) or have taken medication for a psychological or behavioral issue (RR 2.99, CI 1.94, 4.62) in the last 12 months. Moderate use of screens (4 h/day) was also associated with lower psychological well-being. Non-users and low users of screens generally did not differ in well-being. Associations between screen time and lower psychological well-being were larger among adolescents than younger children.
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Preventive Medicine Reports
journal homepage: www.elsevier.com/locate/pmedr
Associations between screen time and lower psychological well-being
among children and adolescents: Evidence from a population-based study
Jean M. Twenge
a,
, W. Keith Campbell
b
a
San Diego State University, United States of America
b
University of Georgia, United States of America
ABSTRACT
Previous research on associations between screen time and psychological well-being among children and adolescents has been conflicting, leading some researchers
to question the limits on screen time suggested by physician organizations. We examined a large (n= 40,337) national random sample of 2- to 17-year-old children
and adolescents in the U.S. in 2016 that included comprehensive measures of screen time (including cell phones, computers, electronic devices, electronic games, and
TV) and an array of psychological well-being measures. After 1 h/day of use, more hours of daily screen time were associated with lower psychological well-being,
including less curiosity, lower self-control, more distractibility, more difficulty making friends, less emotional stability, being more difficult to care for, and inability
to finish tasks. Among 14- to 17-year-olds, high users of screens (7+ h/day vs. low users of 1 h/day) were more than twice as likely to ever have been diagnosed with
depression (RR 2.39, 95% CI 1.54, 3.70), ever diagnosed with anxiety (RR 2.26, CI 1.59, 3.22), treated by a mental health professional (RR 2.22, CI 1.62, 3.03) or
have taken medication for a psychological or behavioral issue (RR 2.99, CI 1.94, 4.62) in the last 12 months. Moderate use of screens (4h/day) was also associated
with lower psychological well-being. Non-users and low users of screens generally did not differ in well-being. Associations between screen time and lower psy-
chological well-being were larger among adolescents than younger children.
1. Introduction
A growing proportion of children and adolescents' leisure time is
spent with screens including smartphones, tablets, gaming consoles,
and televisions (Common Sense Media, 2015;Twenge et al., 2019),
raising concerns about the effect of screen time on well-being among
parents, health professionals, and educators (e.g., Kardaras, 2017).
These concerns have prompted physician organizations such as the
American Academy of Pediatrics (AAP) to recommend that parents
limit children's daily screen time, with specific time limits for preschool
children and a general suggestion of limiting time on screens for older
children and adolescents (Radesky and Christakis, 2016). In addition,
the World Health Organization recently decided to include gaming
disorder in the 11th revision of the International Classification of Dis-
eases (WHO, 2018).
Associations between screen time and poor health outcomes such
as obesity and lack of exercise have been well-documented (e.g.,
Chiasson et al., 2016;de Jong et al., 2013;Dumuid et al., 2017;
Poitras et al., 2017). However, research exploring associations be-
tween screen time and more psychological aspects of well-being
among children and adolescents has been inconsistent. Some studies
find significant associations between screen time and low well-being
(Babic et al., 2017;Page et al., 2010;Romer et al., 2013;Rosen et al.,
2014;Twenge et al., 2018a,b;Yang et al., 2013), while others find null
effects or even benefits with greater screen time (Granic et al., 2014;
Odgers, 2018;Przybylski and Weinstein, 2018;Valkenburg and Peter,
2009). Thus, some have suggested that more research is needed before
concluding that screen time limits are justified, arguing that valuable
physician appointment time should not be devoted to discussing
screen time without sufficient evidence for significant associations
with well-being (Przybylski and Weinstein, 2017, 2018). Some re-
searchers have made similar statements about the WHO characterizing
gaming disorder as a mental health issue, maintaining that associa-
tions between gaming and psychological well-being are not sub-
stantial or consistent enough to justify such a classification (Davis,
2018;van Rooij et al., 2018).
Theories and research on psychological well-being support the
notion of a broad concept including emotional stability, positive in-
https://doi.org/10.1016/j.pmedr.2018.10.003
Received 9 September 2018; Received in revised form 30 September 2018; Accepted 14 October 2018
Corresponding author at: Department of Psychology, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182-4611, United States of America.
E-mail address: jtwenge@mail.sdsu.edu (J.M. Twenge).
Preventive Medicine Reports 12 (2018) 271–283
Available online 18 October 2018
2211-3355/ © 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/BY/4.0/).
terpersonal relationships, self-control, and indicators of flourishing
(Diener et al., 1999;Ryff, 1995) as well as diagnoses of mood dis-
orders such as anxiety or depression (Manderscheid et al., 2010). Low
emotional stability, disrupted relationships, and low self-control have
all been implicated in greater morbidity and mortality (Graham et al.,
2017;Shipley et al., 2007;Shor et al., 2013;Turiano et al., 2015), and
mental health issues such as mood disorders are a significant risk
factor for morbidity and mortality, including via non-suicidal self-
harm behaviors, suicide attempts, and completed suicides (Hawton
et al., 2013;Murray et al., 2012).
In terms of prevention, establishing possible causes and outcomes of
low psychological well-being is especially important for child and
adolescent populations. Half of mental health problems develop by
adolescence (Erskine et al., 2015). Thus, there is an acute need to
identify factors linked to mental health issues that are amenable to
intervention in this population, as most antecedents (e.g., genetic pre-
disposition, trauma, poverty) are difficult or impossible to influence.
Compared to these more intractable antecedents of mental health, how
children and adolescents spend their leisure time is more amenable to
change.
To our knowledge, few if any previous studies have examined a
broad array of psychological well-being items in relation to screen time.
Moreover, although other studies have examined associations between
screen time and symptoms of anxiety and depression, no previous study
we know of has examined associations between screen time and actual
diagnoses of anxiety or depression or reports of professional treatment
for mental health issues. Furthermore, it is crucial for measures of
screen time to include not just television but more recently introduced
digital media including electronic gaming, smartphones, tablets, and
computers. In addition, studies using the same items to assess several
age groups of children and adolescents are rare, which is unfortunate as
Table 1
Approximate hours a day of screen use by age group, U.S., 2016.
2 to 5 6 to 10 11 to 13 14 to 17 d
TV and video games 1.46 (1.09) 1.53 (1.10) 1.80 (1.39) 1.89 (1.39) 0.34
Electronic devices 0.82 (0.96) 1.25 (1.11) 2.00 (1.40) 2.70 (1.53) 1.46
Total screen time 2.28 (1.72) 2.78 (1.95) 3.80 (2.36) 4.59 (2.50) 1.06
Note: 1. SDs in parentheses.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Approximate hours a day
Age in years
Total screen
!me
Electronic
devices
TV & video
games
Fig. 1. Hours per day spent on all screens, electronic devices, and TV and video games by individual years of age, U.S., 2016. Error bars are ± 1 SE.
J.M. Twenge, W.K. Campbell Preventive Medicine Reports 12 (2018) 271–283
272
Table 2
Well-being means by hours per day of total screen time (with controls) among age groups and d's comparing cells, U.S., 2016.
None (0) < 1 h 1 h 2 h 3 h 4 h 5 h 6 h 7+ h d0 vs. 1 h d1 h vs. 4 h d1 h vs. 7+ h
Easy child (3 items)
2 to 5 (preschool) 4.42 (0.53) 320 4.33 (0.61) 749 4.37 (0.57) 1707 4.32 (0.61) 2687 4.29 (0.62) 1843 4.28 (0.63) 912 4.26 (0.66) 280 4.14 (0.73) 243 4.18 (0.67) 263 −0.09 −0.15* −0.33*
6 to 10 (elem.) 4.28 (0.65) 215 4.33 (0.63) 348 4.36 (0.63) 1457 4.36 (0.62) 3203 4.32 (0.63) 2187 4.33 (0.64) 1483 4.27 (0.65) 397 4.26 (0.63) 431 4.18 (0.69) 496 0.13 −0.05 −0.28*
11 to 13 (middle) 4.37 (0.65) 46 4.28 (0.72) 104 4.38 (0.66) 477 4.38 (0.60) 1621 4.35 (0.62) 1537 4.33 (0.63) 1464 4.33 (0.60) 525 4.21 (0.70) 566 4.15 (0.73) 895 0.02 −0.08 −0.33*
14 to 17 (h.s.) 4.36 (0.99) 24 4.49 (0.58) 80 4.54 (0.51) 370 4.46 (0.57) 1679 4.40 (0.57) 2488 4.35 (0.60) 2468 4.30 (0.66) 1118 4.20 (0.71) 1370 4.09 (0.77) 2547 0.33 −0.32* −0.61*
Curious
2 to 5 (preschool) 2.98 (0.12) 319 2.98 (0.14) 752 2.98 (0.12) 1716 2.98 (0.15) 2705 2.98 (0.16) 1853 2.96 (0.21) 919 2.96 (0.22) 280 2.95 (0.25) 245 2.96 (0.22) 266 0.00 −0.13* −0.15
6 to 10 (elem.) 2.94 (0.22) 215 2.94 (0.23) 349 2.95 (0.19) 1462 2.95 (0.22) 3225 2.93 (0.26) 2200 2.93 (0.29) 1488 2.88 (0.34) 401 2.88 (0.35) 434 2.88 (0.37) 504 0.05 −0.08* −0.30*
11 to 13 (middle) 2.88 (0.32) 47 2.91 (0.25) 105 2.91 (0.28) 480 2.88 (0.32) 1631 2.89 (0.30) 1537 2.86 (0.36) 1474 2.86 (0.36) 533 2.79 (0.46) 571 2.76 (0.47) 901 0.11 −0.15* −0.37*
14 to 17 (h.s.) 2.64 (0.58) 24 2.90 (0.26) 82 2.90 (0.30) 375 2.88 (0.31) 1691 2.86 (0.34) 2501 2.82 (0.39) 2485 2.79 (0.43) 1132 2.74 (0.48) 1371 2.71 (0.49) 2583 0.78* −0.21* −0.41*
No difficulty making friends
2 to 5 (preschool) 2.87 (0.35) 181 2.89 (0.31) 400 2.90 (0.30) 1112 2.91 (0.29) 1980 2.90 (0.32) 1405 2.86 (0.38) 707 2.81 (0.41) 204 2.85 (0.38) 191 2.84 (0.39) 209 0.10 −0.12* −0.19*
6 to 10 (elem.) 2.81 (0.42) 211 2.83 (0.40) 346 2.85 (0.38) 1454 2.85 (0.38) 3189 2.83 (0.41) 2176 2.82 (0.43) 1470 2.78 (0.48) 396 2.77 (0.45) 429 2.77 (0.47) 492 0.10 −0.07* −0.20*
11 to 13 (middle) 2.74 (0.53) 47 2.80 (0.45) 104 2.78 (0.47) 473 2.82 (0.43) 1620 2.78 (0.48) 1525 2.79 (0.44) 1464 2.82 (0.43) 522 2.70 (0.52) 569 2.66 (0.58) 888 0.08 0.02 −0.22*
14 to 17 (h.s.) 2.81 (0.49) 23 2.76 (0.48) 81 2.88 (0.36) 367 2.85 (0.40) 1678 2.82 (0.42) 2476 2.79 (0.46) 2464 2.76 (0.49) 1113 2.72 (0.51) 1355 2.66 (0.58) 2521 0.19 −0.20* −0.40*
Calm when challenged
6 to 10 (elem.) 2.46 (0.60) 211 2.42 (0.58) 344 2.48 (0.54) 1450 2.45 (0.56) 3190 2.39 (0.57) 2176 2.37 (0.60) 1465 2.32 (0.60) 394 2.35 (0.59) 428 2.32 (0.62) 491 0.04 −0.19* −0.29*
11 to 13 (middle) 2.62 (0.54) 47 2.54 (0.54) 104 2.56 (0.56) 475 2.57 (0.53) 1619 2.53 (0.56) 1521 2.51 (0.56) 1459 2.51 (0.56) 524 2.43 (0.60) 567 2.35 (0.62) 887 −0.11 0.09 −0.35*
14 to 17 (h.s.) 2.70 (0.58) 23 2.68 (0.56) 81 2.75 (0.45) 367 2.70 (0.49) 1677 2.66 (0.50) 2472 2.60 (0.54) 2462 2.55 (0.57) 1110 2.48 (0.60) 1352 2.45 (0.62) 2523 0.11 −0.29* −0.50*
Works to finish tasks started
6 to 10 (elem.) 2.71 (0.48) 211 2.66 (0.49) 345 2.72 (0.46) 1450 2.70 (0.48) 3182 2.65 (0.50) 2175 2.64 (0.52) 1465 2.58 (0.55) 392 2.61 (0.55) 430 2.57 (0.56) 491 0.02 −0.16* −0.31*
11 to 13 (middle) 2.75 (0.50) 47 2.79 (0.39) 104 2.72 (0.46) 474 2.72 (0.46) 1625 2.70 (0.47) 1522 2.67 (0.50) 1461 2.67 (0.50) 525 2.55 (0.57) 566 2.51 (0.59) 887 0.06 −0.10* −0.39*
14 to 17 (h.s.) 2.67 (0.49) 24 2.78 (0.45) 81 2.83 (0.37) 366 2.81 (0.39) 1675 2.76 (0.43) 2468 2.71 (0.47) 2455 2.66 (0.52) 1114 2.60 (0.57) 1352 2.54 (0.58) 2523 0.38 −0.26* −0.52*
Does not argue too much
6 to 10 (elem.) 2.66 (0.51) 209 2.64 (0.57) 346 2.67 (0.55) 1452 2.64 (0.58) 3193 2.60 (0.60) 2178 2.58 (0.61) 1467 2.56 (0.63) 393 2.58 (0.59) 430 2.48 (0.67) 490 0.04 0.16* −0.33*
11 to 13 (middle) 2.69 (0.56) 47 2.54 (0.55) 104 2.68 (0.55) 476 2.69 (0.54) 1621 2.63 (0.58) 1524 2.62 (0.59) 1465 2.61 (0.59) 526 2.54 (0.65) 569 2.47 (0.68) 887 −0.02 −0.10* −0.33*
14 to 17 (h.s.) 2.60 (0.66) 23 2.71 (0.55) 80 2.81 (0.46) 366 2.79 (0.46) 1681 2.73 (0.50) 2477 2.71 (0.53) 2461 2.68 (0.57) 1114 2.61 (0.60) 1354 2.52 (0.67) 2530 0.45 −0.19* −0.45*
Ever diagnosed with anxiety
11 to 13 (middle) 9.6% (0.29) 47 6.8% (0.25) 105 9.9% (0.30) 481 7.6% (0.26) 1634 10.0% (0.30)
1540
8.5% (0.28) 1477 9.3% (0.29) 532 11.2% (0.32) 573 12.2% (0.33) 904 0.01 0.05 0.07
14 to 17 (h.s.) 11.5% (0.32)
24
12.0% (0.33) 80 7.9% (0.26) 374 8.4% (0.28) 1698 9.7% (0.30) 2504 12.2% (0.33)
2489
13.4% (0.34)
1131
17.7% (0.38)
1374
18.1% (0.39)
2578
−0.13 0.13* 0.27*
Ever diagnosed with depression
11 to 13 (middle) 4.6% (0.21) 47 1.6% (0.12) 105 3.7% (0.19) 481 1.9% (0.14) 1629 4.1% (0.19) 1543 3.8% (0.19) 1479 4.3% (0.21) 534 5.4% (0.23) 573 7.2% (0.26) 906 −0.05 0.05 0.15*
14 to 17 (h.s.) 10.2% (0.30)
24
8.3% (0.28) 82 5.3% (0.23) 376 5.1% (0.23) 1700 6.3% (0.24) 2508 8.6% (0.28) 2493 8.8% (0.29) 1131 11.6% (0.32)
1379
12.7% (0.33)
2582
−0.20 0.12* 0.23*
Treated or needed to be treated by mental health professional, past 12 months
11 to 13 (middle) 7.6% (0.25) 47 10.4% (0.30)
104
13.5% (0.34) 480 10.5% (0.30)
1633
12.6% (0.33)
1539
12.2% (0.33)
1474
9.8% (0.30) 532 14.8% (0.36) 573 18.1% (0.39) 904 0.18 −0.04 0.12*
14 to 17 (h.s.) 25.8% (0.42)
24
15.9% (0.37) 82 9.8% (0.29) 374 11.5% (0.32)
1693
12.8% (0.34)
2496
14.1% (0.35)
2487
17.0% (0.38)
1128
20.7% (0.41)
1373
21.9% (0.41)
2578
−0.53 0.13* 0.30*
Took medication for psychological issue, past 12 months
11 to 13 (middle) 9.0% (0.29) 46 6.2% (0.24) 105 8.6% (0.28) 479 6.9% (0.25) 1623 8.5% (0.28) 1529 9.4% (0.29) 1473 9.1% (0.29) 529 12.4% (0.33) 571 13.3% (0.34) 894 −0.01 0.03 0.15*
14 to 17 (h.s.) 11.7% (0.32)
23
11.7% (0.32) 82 5.5% (0.22) 372 8.2% (0.28) 1686 8.6% (0.28) 2492 9.9% (0.30) 2481 12.1% (0.33)
1116
14.9% (0.36)
1366
16.1% (0.37)
2562
−0.27 0.15* 0.30*
Notes: 1. Within levels of screen time, numbers in each cell are: well-being means, SDs in parentheses, and n's. 2. d= effect size corresponding to difference in standard deviations. 3. * = t-test comparing cells significant
at p< .05. 4. For diagnoses, treatment, and medication, base rates were high enough for reliable comparisons only among the two older age groups.
J.M. Twenge, W.K. Campbell Preventive Medicine Reports 12 (2018) 271–283
273
age may be a significant moderator of associations between screen time
and psychological well-being.
The current research aims to examine associations between screen
time and a diverse array of measures of psychological well-being (in-
cluding emotional stability, relationships with caregivers, self-control,
diagnoses of mood disorders, and treatment of mental health issues)
among a large population-based survey of the caregivers of children
and adolescents ages 2 to 17 collected in 2016 in the U.S.
2. Method
2.1. Participants
Participants were the caregivers of 44,734 children and adoles-
cents 2 years of age and older in the U.S. in the National Survey of
Children's Health (NSCH) conducted in 2016 by the U.S. Census
Bureau. As many items on psychological well-being were asked only of
caregivers with children ages 2 and over, we restricted our analyses to
children ages 2 to 17.
Households were contacted by mail at random to identify those with
children or adolescents 17 years old or younger. In every household,
one child was randomly selected to be the subject of the survey. The
survey was administered either online or on paper, with an over-
sampling of children with special health care needs. The response rate
was 40.7%. Data are publicly available on the NSCH website.
We excluded children and adolescents with at least one of 8 major
conditions that might affect their day-to-day functioning: Autism,
blindness, cerebral palsy, deafness, Down Syndrome, developmental
delay, epilepsy, or intellectual disability (mental retardation), as these
were related to both well-being and to screen time. For example, among
14- to 17-year-olds, 33% of those with zero daily use of screens had one
of these conditions, compared to an overall 10.1% base rate. The sur-
vey's comprehensive screener for special health care needs could not be
used as it also includes those who receive any mental health care ser-
vices, which is a variable of interest. These exclusions resulted in a
sample nof 40,337.
Fig. 2. Mean score on the easy child index (1–5), by age and level of screen time, with controls, U.S., 2016. Error bars are ± 1 SE.
J.M. Twenge, W.K. Campbell Preventive Medicine Reports 12 (2018) 271–283
274
In the final sample, the children and adolescents were 49.8% male
and 50.2% female and were 71% White, 16% Hispanic, 6% Black, and
7% other. Family income was widely distributed, with 9% of children
below the 100% poverty level and 44% with family incomes at the
400% level or more. The sample was designed to be nationally re-
presentative of all U.S. children at these ages but underrepresents some
groups due to lower response rates.
We grouped children and adolescents into four categories based on
age that roughly correspond to educational levels: Preschoolers 2 to
5 years old (n= 9361), elementary schoolers 6 to 10 years old
(n= 10,668), middle schoolers 11 to 13 years old (n= 7555), and high
schoolers 14 to 17 years old (n= 12,753). These categories also cor-
respond to the structure of the survey, with some questions only asked
of the caregivers of preschool children and others asked only of children
6 years of age and up.
2.2. Measures
The survey asked two items about screen time. First, “On an average
weekday, about how much time does [child's name] spend in front of a
TV watching TV programs, videos, or playing video games?” Second,
“On an average weekday, about how much time does [child's name]
spend with computers, cell phones, handheld video games, and other
electronic devices, doing things other than schoolwork?” For both, re-
sponse choices were recoded to none = 0, less than an hour =0.5, an
hour = 1, 2 h = 2, 3 h = 3, and 4 or more hours = 5. For means, see
Table 1.
We added together the estimated number of hours spent on TV/
video games and on digital media devices to create a measure of total
screen time and recoded the results into 8 categories: None (no screen
time), < 1 h (0.01 to 0.99), 1 h (1.00 to 1.49), 2 h (1.50 to 2.49), 3 h
(2.50 to 3.49), 4 h (3.50 to 4.49), 5 h (4.50 to 5.49), 6 h (5.50 to 6.49)
and 7 h or more (6.50 and higher). Among the two older groups, very
few reported no screen time at all (n= 46 for 11- to 13-year-olds and
n= 24 for 14- to 17-year-olds), so these cells should be interpreted with
caution.
We examined all items in the NSCH survey that measured psycho-
logical well-being, broadly construed (see Supplemental material for
Fig. 3. Percentage not curious or interested in learning new things, by age and level of screen time, with controls, U.S., 2016. Error bars are ± 1 SE.
J.M. Twenge, W.K. Campbell Preventive Medicine Reports 12 (2018) 271–283
275
item wording including response choices). Most items did not correlate
highly enough to be combined into scales and are thus analyzed as
single items. The exceptions were three items measuring how easy the
child is and four items measuring self-control. We coded all items so
that higher scores indicated higher well-being.
2.3. Analysis plan
Analyses included controls for possible confounding variables: child
race (dummy variables for Black, Hispanic, and Other, with non-
Hispanic White as the comparison group), child sex, child age, house-
hold adults' highest grade completed (continuous, using the detailed
item including college education), family poverty ratio (a measure of
family income), and family structure (living with two biological/
adoptive parents vs. not). We did not weight analyses and did not re-
place missing data.
For items on a continuum, we report means in tables and percent
low in well-being in figures; categorical items (e.g., yes or no, such as
diagnoses of anxiety or depression) are reported as percentages in both.
The tables report effect sizes (d, or difference in terms of standard de-
viations) as well as p-values for t-tests comparing means at different
levels of use. The text reports relative risk (RR) with 95% confidence
intervals (CIs) for dichotomized items.
We first examine items asked of caregivers of several ages of chil-
dren and then those asked only of caregivers of preschool children.
Given the curvilinear relationship between screen time and well-being
found in previous research (Przybylski and Weinstein, 2017;Twenge
et al., 2018b), we identified the inflection point at which the trend in
well-being moved from positive to negative to inform our analyses
(Simonsohn, 2017). Thus, we compare no use to low levels of use, low
use to moderate use, and low use to high use.
Fig. 4. Percentage who do not stay calm when challenged, by age and level of screen time, with controls, U.S., 2016. Error bars are ± 1 SE.
J.M. Twenge, W.K. Campbell Preventive Medicine Reports 12 (2018) 271–283
276
3. Results
3.1. Age differences in screen time
Total screen time averaged 3.20 h a day (SD = 2.40) and was pro-
gressively higher among older children, primarily driven by more time
spent on electronic devices (see Table 1 and Fig. 1). The largest increase
in screen time occurred between elementary school and middle school.
By high school (ages 14 to 17), adolescents spent 4 h and 35 min a day
with screens according to caregivers' reports.
3.2. Screen time and well-being
The association between screen time and well-being was not linear
and showed an inflection point at 1 h/day of use for most measures (see
Table 2 and Figs. 2–6). With one exception (the item on curiosity), well-
being did not differ significantly between those spending no time on
screens and those spending an hour a day. After an hour a day, how-
ever, increasing screen time was generally linked to progressively lower
psychological well-being. In terms of relative risk (RR), high users of
screens (7+ h/day) carried twice the risk of low well-being as low users
(1 h/day), including not staying calm (e.g., among 14- to 17-year-olds,
RR 2.08, 95% CI 1.72, 2.50), not finishing tasks (RR 2.53, CI 2.01,
3.20), not being curious (RR 2.72, CI 2.00, 3.71), and arguing too much
with caregivers (RR 2.34, CI 1.85, 2.97; see Figs. 2–6). High (vs. low)
users of screens were also described as more difficult to care for. Effect
sizes were generally larger among adolescents than among children.
In most cases, moderate users of screens (4 h/day) were also sig-
nificantly lower in well-being than low users (1 h/day), though with
lower effect sizes (see Table 2). Among 14- to 17-year-olds, moderate
users (vs. low users) were 78% more likely to not be curious (RR 1.78,
CI 1.30, 2.43), 60% more likely to not stay calm when challenged (RR
Fig. 5. Percentage who do not finish tasks started, by age and level of screen time, with controls, U.S., 2016. Error bars are ± 1 SE.
J.M. Twenge, W.K. Campbell Preventive Medicine Reports 12 (2018) 271–283
277
1.60, CI 1.32, 1.93), 66% more likely to not finish tasks they started (RR
1.66, CI 1.31, 2.11), and 57% more likely to argue too much with their
caregivers (RR 1.57, CI 1.24, 2.00; see Figs. 2–6). As with the com-
parisons between low and high use, differences in well-being between
low and moderate users were smaller among younger children than
among older adolescents.
3.3. Screen time and diagnoses of anxiety and depression
High users of screens were also significantly more likely to have
been diagnosed with anxiety or depression. Fourteen to 17-year-olds
spending 7+ h/day with screens (vs. 1h/day) were more than twice as
likely to ever have been diagnosed with depression (RR 2.39, 95% CI
1.54, 3.70) or anxiety (RR 2.26, CI 1.59, 3.22; see Fig. 7). High users of
screens were also more likely to have seen or needed to have been seen
by a mental health professional (RR 2.22, CI 1.62, 3.03), and more
likely to have taken medication for a psychological issue (RR 2.99, CI
1.94, 4.62; see Fig. 8) in the last 12 months. Moderate use was also
linked to a greater risk of depression (RR 1.61, CI 1.03, 2.52) and an-
xiety diagnoses (RR 1.52, CI 1.06, 2.18) among 14- to 17-year-olds,
though not among 11- to 13-year-olds.
3.4. Screen time and well-being items asked only of caregivers of preschool
children
We next examined the items asked only of caregivers of preschool
children. High users of screens were more likely to lose their temper,
less likely to calm down when excited, and less likely to switch tasks
without anxiety or anger (see Table 3 and Fig. 9). The largest effect size
appeared for self-control (d= −0.41), which included perseverance,
sitting still, completing simple tasks, and not becoming distracted; both
high and moderate users of screens displayed significantly lower self-
control than low users. In terms of relative risk, high (vs. low) users of
screens were twice as likely to often lose their temper (RR 1.99, CI 1.44,
Fig. 6. Percentage who argue too much with their caregivers, by age and level of screen time, with controls, U.S., 2016. Error bars are ± 1 SE.
J.M. Twenge, W.K. Campbell Preventive Medicine Reports 12 (2018) 271–283
278
2.77) and were 46% more likely to not be able to calm down when
excited (RR 1.46, CI 1.13, 1.88). Preschool children with moderate
screen use were also lower in well-being than those at low levels of use
(see Table 3). In terms of relative risk, moderate users (vs. low users)
were 30% more likely to not bounce back (RR 1.30, CI 1.15, 1.47) and
33% more likely to lose their temper (RR 1.33, CI 1.02, 1.72).
There were few significant associations between screen time and
social interaction items such as showing affection to caregivers, smiling
and laughing, playing well with others, or showing empathy for others
(see Table 3). However, several of these items (particularly showing
affection and smiling and laughing) suffered from an extreme lack of
variance with 95% of caregivers agreeing this was true of the child,
limiting their utility.
4. Discussion
Children and adolescents who spent more time using screen media
were lower in psychological well-being than low users. High users of
screens were significantly more likely to display poor emotion regula-
tion (not staying calm, arguing too much, being difficult to get along
with), an inability to finish tasks, lower curiosity, and more difficulty
making friends. Caregivers also described high users as more difficult to
care for and as lower in self-control. Among adolescents, high (vs. low)
users were also twice as likely to have received diagnoses of depression
or anxiety or needed treatment for mental or behavioral health condi-
tions. Moderate users were also significantly more likely than low users
of screens to be low in well-being and, among 14- to 17-year-olds, to
have been diagnosed with depression or anxiety or need mental health
Fig. 7. Percentage ever diagnosed with anxiety or depression, by age and level of screen time, with controls, U.S., 2016. Error bars are ±1 SE.
J.M. Twenge, W.K. Campbell Preventive Medicine Reports 12 (2018) 271–283
279
treatment. Non-users generally did not significantly differ in well-being
from low users of screens.
The AAP recommendations set specific screen time limits only for
children 5 years of age and younger. One set of researchers questioned
these limits based on null correlations between screen time and four
well-being items included in the 2011 NSCH (Przybylski and Weinstein,
2018). However, the more comprehensive set of well-being items in the
2016 NSCH produces significant associations between screen time and
well-being on 18 of 19 indicators, providing substantial support for
screen time limits. Notably, we found that the association between
screen time and low well-being was larger for adolescents than for
younger children, consistent with at least one earlier study (Rosen
et al., 2014). This suggests that the AAP and other organizations fo-
cused on public health might consider extending recommendations for
specific limits on screen time to preteens and teens.
It is worth speculating about why the associations between screen
time and psychological well-being were larger among adolescents.
One possibility is that adolescents, compared to younger children, are
considerably more likely to have social media accounts and to spend
more time online. Peer relationships are particularly important for
adolescents (Fuligni and Eccles, 1993), and if social media replaces
face-to-face interaction, that may have a negative impact on well-
being and mental health. Time spent on social media, gaming, and
online is more strongly correlated with low well-being than watching
TV/videos, and TV/videos are the more common screen activity for
younger children (Rosen et al., 2014). Unfortunately, associations
with well-being for TV vs. other screen activities cannot be de-
termined in this dataset as time spent on TV and electronic gaming
was included in the same item.
Adolescents are also more likely than younger children to have their
own smartphone (Rosen et al., 2014), which allows the use of tech-
nology in more situations. This may increase the possibility of Internet
addiction, excessive gaming, or problematic social media use, which
has been linked to low well-being (Satici and Uysal, 2015). It may also
increase the impact on sleep, as smartphones may be brought into the
bedroom or even the bed, with negative impacts on sleep duration and/
Fig. 8. Percentage receiving mental health treatment and percentage taking medication for psychological issues during the last 12 months, by age and level of screen
time, with controls, U.S., 2016. Error bars are ± 1 SE.
J.M. Twenge, W.K. Campbell Preventive Medicine Reports 12 (2018) 271–283
280
or sleep quality (Twenge et al., 2017). Smartphones may also be used
during face-to-face social interactions, which may negatively impact
those interactions and blunt their usually positive impact on well-being
(Dwyer et al., 2018).
Due to the cross-sectional design of the study, it is not possible to
determine if screen time leads to low well-being, low well-being leads
to screen time, or both. However, several longitudinal studies have
found that increases in recreational screen time precede lower psy-
chological well-being among children and adolescents (Allen and Vella,
2015;Babic et al., 2017;Hinkley et al., 2014;Kim, 2017) as well as
among adults (Kross et al., 2013;Schmiedeberg and Schröder, 2017;
Shakya and Christakis, 2017). In addition, experiments have shown that
the presence of smartphones can lower enjoyment during social inter-
actions (e.g., Dwyer et al., 2018;Kushlev et al., 2017) and that ab-
staining from social media use for one week can increase well-being
(Tromholt, 2016). In other studies, the relationship appears to be re-
ciprocal, with screen time and well-being each causing the other
(Gunnell et al., 2016). These studies suggest that at least some of the
causation moves from screen time to lower well-being. Regardless of
the direction of causation, however, these associations have meaningful
clinical implications for screening and intervention. For example, an
assessment of screen time may help providers identify children and
adolescents at higher risk for mental health issues and broach the topic
of the possible role of screen time in mental health among these in-
dividuals.
These data are limited by several factors. First, screen time was
reported by caregivers and not the children or adolescents themselves.
This likely resulted in underestimates of screen time and may have
unknown interactions with reports of well-being. The well-being mea-
sures may be influenced by caregivers' perceptions and may under-
report issues that children do not disclose to their parents. This is likely
to be less of an issue for the items on diagnoses of anxiety and de-
pression and reports of taking medication. In addition, informant re-
ports are often considered a strength in study design, as in some cases
observers can provide more accurate information than is possible via
self-report (Connelly and Ones, 2010); that is especially true for
younger children. Second, the survey assessed only weekday screen
time, and screen time may be higher on weekends. However, previous
research found similar associations with well-being for weekday and
weekend use of screen media (Przybylski and Weinstein, 2017).
Weekday screen time is also likely to vary less and thus may produce a
more reliable estimate. Third, the survey items combined TV and
electronic games into one question, which allowed only an analysis of
total screen time and not any differentiation between legacy media (TV)
and digital media (electronic games, Internet, social media, etc.).
Fourth, although the Census Bureau attempted to recruit a re-
presentative sample, the response rate was not 100% and some groups
(such as Black Americans) are under-represented relative to their per-
centage of the total U.S. population in the final sample.
In summary, these results show a negative association between
screen time and psychological well-being among children and ado-
lescents. Across a diverse array of well-being measures, including
measures of self-control, relationships with caregivers, emotional
stability, diagnoses of anxiety and depression, and mental health
treatment, psychological well-being was progressively lower from 1 h
a day of screen time to 7 or more hours a day of screen time, parti-
cularly among adolescents. The significant association between screen
time and well-being may have important clinical implications for the
mental and physical health of children and adolescents and for de-
veloping guidelines for specific screen time limits for older children
and adolescents.
Funding
This study received no funding.
Table 3
Well-being means on items asked only of caregivers of preschool children among 2- to 5-year-olds by hours per day of total screen time (with controls) and d's comparing cells, U.S., 2016.
None (0) < 1 h 1 h 2 h 3 h 4 h 5 h 6h 7+ h d0 vs. 1 h d1 h vs. 4 h d1 h vs. 7+ h
Affectionate 2.98 (0.14)
321
2.95 (0.21)
754
2.96 (0.19)
1714
2.96 (0.20)
2704
2.96 (0.20)
1857
2.94 (0.26)
917
2.93 (0.27)
281
2.95 (0.23)
243
2.93 (0.34)
266
−0.11* −0.10* −0.14
Smiles and laughs 2.98 (0.11)
322
2.98 (0.14)
755
2.98 (0.12)
1715
2.98 (0.15)
2705
2.99 (0.12)
1858
2.98 (0.18)
919
2.96 (0.24)
280
2.97 (0.19)
246
2.98 (0.18)
266
0.00 0.00 0.00
Bounces back 2.74 (0.44)
321
2.70 (0.49)
751
2.73 (0.46)
1708
2.72 (0.48)
2701
2.72 (0.49)
1857
2.64 (0.56)
915
2.63 (0.57)
281
2.68 (0.52)
246
2.68 (0.55)
265
−0.01 −0.18* −0.11
Does not lose temper 3.05 (0.53)
181
3.05 (0.48)
400
3.05 (0.53)
1113
3.03 (0.51)
1987
2.99 (0.53)
1406
2.96 (0.57)
709
2.89 (0.59)
205
2.82 (0.68)
190
2.89 (0.68)
210
0.00 −0.16* −0.29*
Can calm down when excited 3.09 (0.60)
180
3.00 (0.57)
397
3.00 (0.61)
1112
3.02 (0.61)
1984
2.99 (0.62)
1403
2.98 (0.62)
710
2.85 (0.65)
205
2.81 (0.68)
191
2.86 (0.68)
210
−0.15 −0.03 −0.23*
Switch tasks without anxiety or
anger
3.49 (0.56)
182
3.44 (0.54)
400
3.49 (0.55)
1114
3.48 (0.56)
1986
3.41 (0.58)
1404
3.40 (0.60)
711
3.38 (0.57)
205
3.32 (0.57)
190
3.39 (0.63)
211
0.00 −0.16* −0.20*
Task self-control (4 items) 3.16 (0.37)
177
3.10 (0.42)
394
3.08 (0.40)
1107
3.06 (0.40)
1980
3.00 (0.42)
1396
2.98 (0.40)
704
2.96 (0.47)
201
2.86 (0.49)
189
2.91 (0.46)
207
−0.21* −0.25* −0.41*
Plays well with others 3.35 (0.54)
180
3.39 (0.53)
399
3.41 (0.54)
1114
3.39 (0.55)
1985
3.35 (0.55)
1407
3.35 (0.57)
711
3.27 (0.66)
203
3.34 (0.61)
190
3.40 (0.61)
210
0.11 −0.11* −0.01
Empathy 3.24 (0.70)
182
3.32 (0.66)
399
3.32 (0.66)
1115
3.30 (0.67)
1989
3.27 (0.70)
1409
3.25 (0.70)
711
3.23 (0.74)
205
3.27 (0.68)
191
3.31 (0.71)
211
0.12 −0.10* −0.01
Notes: 1. Within levels of screen time, numbers in each cell are: well-being means, SDs in parentheses, and n's. 2. d= effect size corresponding to difference in standard deviations. 3. * = t-test comparing cells significant
at p< .05.
J.M. Twenge, W.K. Campbell Preventive Medicine Reports 12 (2018) 271–283
281
Conflict of interest statement
The authors declare that there are no conflicts of interest.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.pmedr.2018.10.003.
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Supplementary resource (1)

... Monet tutkimukset ovat osoittaneet, että lapset, jotka viettävät paljon aikaa älylaitteidensa parissa, voivat kokea monenlaisia fyysisiä ja henkisiä terveysongelmia (34,35). On huolestuttavaa, että ylenpalttinen laitteiden käyttö on yhteydessä lasten mielialahäiriöihin, kuten masennukseen ja ärtyisyyteen (36,37), ja voi aiheuttaa lapselle mielialanvaihteluita sekä tunteiden säätelyn vaikeuksia (35). ...
... Monet tutkimukset ovat osoittaneet, että lapset, jotka viettävät paljon aikaa älylaitteidensa parissa, voivat kokea monenlaisia fyysisiä ja henkisiä terveysongelmia (34,35). On huolestuttavaa, että ylenpalttinen laitteiden käyttö on yhteydessä lasten mielialahäiriöihin, kuten masennukseen ja ärtyisyyteen (36,37), ja voi aiheuttaa lapselle mielialanvaihteluita sekä tunteiden säätelyn vaikeuksia (35). Pitkäkestoinen ruutuaika voi myös heikentää vuorovaikutus-ja ongelmanratkaisu taitoja lapsien käyttäessä älylaitteita entistä enemmän viihteen ja sosiaalisen vuorovaikutuksen välineinä (38). ...
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Älypuhelimet edustavat teknisen kehityksen huippua; nämä taskukokoiset laitteet pitävät sisällään tietokoneen, valokuva- ja videokameran, GPS-navigointijärjestelmän, pääsyn sosiaalisiin verkostoihin, kalenterit, lääketieteelliset sensorit ja pelit. Näiden laitteiden käyttö on olennaisesti muuttanut kommunikointitapaamme, päivittäisiä rutiinejamme ja tiedonhankintaamme tarjoten samalla uusia mahdollisuuksia vuorovaikutukseen ja oppimiseen. Puhelimet ovat tuoneet käyttöömme lukuisia sovelluksia ja palveluita, jotka sujuvoittavat arkeamme. Ne ovat tehneet yhteydenpidosta ja päivittäisten askareidemme hallinnasta helpompaa. Voimme vuorokauden ajasta riippumatta pitää yhteyttä ihmisiin eri puolilla maailmaa viestien, puheluiden ja videopuheluiden avulla. Sosiaalisen median sovellukset, kuten Facebook, Instagram ja WhatsApp, ovat luoneet myös uusia kanavia itseilmaisuun ja identiteetin muodostamiseen erityisesti nuorison keskuudessa. Älypuhelinsovellukset voivat tarjota terapeuttista hyötyä samoin kuin viihdettäkin, mutta toisaalta niiden runsas käyttö voi myös aiheuttaa ongelmia hyvinvoinnillemme.
... Namun, bagi siswa yang menggunakan gadget lebih dari 3 jam sehari, risiko dampaknya menjadi lebih besar. Penggunaan gadget lebih dari 2 jam sehari dapat menyebabkan gangguan konsentrasi dan emosional pada anak-anak menurut (Twenge & Campbell, 2018 /jurnal.rocewisdomaceh.com/index.php/wisdom e-ISSN : 3031-9366 Vol. 2, No. 1, Thn. 2025 https://doi.org/10.71275/wisdom.v2i1.77 ...
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In this modern era, gadgets have become an inseparable part of everyday life. The rapid development of software on gadgets allows us to access various applications for communication, entertainment, and learning. Gadget use is no longer limited to a certain age, so many children, including students at SDN Bandar Kidul 01 Kota Kediri, use gadgets in their daily activities. The problem that arises is how often the intensity of gadget use by elementary school students is and the impact it has on their lives. This research aims to analyze the duration of gadget use and its impact on students at SDN Bandar Kidul 01 Kediri City, with a focus on academic, health, and social aspects. The research was carried out through an outreach approach and filling out questionnaires by students as research subjects. The research results show that the average student uses gadgets for 1-2 hours per day, with the main aim of playing games and watching videos for entertainment. Negative impacts include decreased focus on learning for students, health problems such as eye fatigue for students, and reduced social interaction for students. On the other hand, the use of gadgets for educational applications is reported to support students' learning process. These results emphasize the importance of supervision from parents and teachers to regulate the use of gadgets wisely. With good time management, negative impacts can be minimized and the benefits of gadgets in supporting education can be optimized.
... Las plataformas fomentan la comparación constante a través de imágenes, logros y estilo de vida, lo que puede llevar a sentimientos de insuficiencia y la necesidad de estar constantemente conectado (Irmer & Schmiedek, 2023)TikTok, and YouTube. El uso excesivo de redes sociales y la constante búsqueda de validación pueden contribuir a problemas de salud mental como ansiedad, depresión y baja autoestima, especialmente durante este período crucial de desarrollo personal y social (Twenge & Campbell, 2018). De otra parte, la naturaleza efímera del contenido y la generación constante de nuevos desafíos crean el temor a perderse experiencias significativas, que podrían estar disfrutando los demás, lo que es especialmente pronunciado en niños y adolescentes, que pueden sentirse excluidos si no están conectados constantemente (Przybylski et al., 2013;Oberst et al., 2017)but it has also been shown that these users can suffer from negative psychological consequences when using these sites excessively. ...
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Resumen El objeto de este trabajo es cuestionarse si el Derecho Penal habría de intervenir o no en el control de los riesgos derivados del diseño adictivo de las redes sociales y plataformas, a la vista de las conclusiones aportadas por la literatura científica especializada. Se concluye afirmativamente sobre esta cuestión, sin perjuicio de la tutela administrativa que pueda también darse, formulando los rudimentos de una propuesta político-criminal incriminadora, articulada en dos pilares fundamentales. El primero de ellos es la preponderancia de dos principios fundamentales: el cri-terio de precaución, frente a riesgos muy importantes o sistémicos, y el principio de responsabilidad pro víctima, que prioriza el control del riesgo ante la posibilidad de los daños y riesgos que la literatura científica conecta al diseño adictivo de las redes sociales y plataformas. Como segundo pilar, considerar estos diseños adictivos fuera del riesgo permitido con relación a los menores, sirviéndonos del paragüas que proporciona ya el art. 5 del Reglamento IA de la UE y centrándonos muy par-ticularmente, en la responsabilidad de las plataformas que usan tales herramientas de IA para conseguir la atención y la constante y compulsiva conexión del usuario. Palabras clave Adicción a redes sociales, IA persuasiva, algoritmos adictivos, responsabilidad penal por diseño algorítmico adictivo Abstract The purpose of this work is to examine whether Criminal Law should intervene in the regulation of risks associated with the addictive design of social networks and platforms, in light of the findings provided by specialized scientific literature. This analysis reaches an affirmative conclusion on the matter, without prejudice to the administrative oversight that may also apply, and outlines the foundational elements of a criminal policy framework based on two fundamental pillars. The
... Furthermore, systemic interventions aimed at regulating social media usage and promoting digital well-being are essential for addressing social mediainduced depression at a broader level. Policy initiatives to limit screen time and promote offline activities can help mitigate the adverse effects of excessive social media use on mental health (Twenge & Campbell, 2018). Moreover, collaboration between policymakers, educators, and technology companies is needed to develop guidelines and best practices for responsible social media usage that prioritize user well-being over engagement metrics. ...
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Background:-Social media offering opportunities for connectivity, information sharing, and entertainment. However, alongside the benefits, there is a negative impact of social media use on mental health, particularly concerning depression and anxiety. This study determined the consequences of social media on the onset of depression and anxiety among teachers from junior schools and students from their families and relatives. Methods:-This is a cross-sectional survey applied on the teachers (n=263) from junior schools and students (n=99) from their families and relatives from Qassim population of Saudi Arabia. A standardized instrument was applied to quantify the social media usage. Depression and anxiety levels were quantified using the Patient Health Questionnaire–9 (PHQ-9), the Generalized Anxiety Disorder Questionnaire–7 (GAD-7), respectively. Results:-Among teachers, a majority displayed minimal depression (55.1%), with some showing mild (26.6%), moderate (10.3%), and severe depression (8.0%). Students also exhibited 56.6% minimal depression, followed by mild (16.2%), moderate (17.2%), and severe depression (10.1%). Regarding anxiety, teachers reported minimal (39.9%), mild (40.3%), moderate (12.5%), and severe (7.2%) anxiety. Similarly, students displayed anxiety levels as minimal (48.5%), mild (29.3%), moderate (10.1%), and severe (12.1%). The levels of depression and anxiety among studied teachers and students were not statistically comparable (p>0.05). Conclusion:-This is the first study evaluating the effects of social media usage on the onset of depression and anxiety among teachers in junior schools and students from their families and relatives. A majority of both teachers and students experienced minimal levels of depression and anxiety due to their social media usage. Overall, these findings suggest that teachers from junior schools and students from their families in Qassim did not exhibit significant impacts of severe depression and anxiety from their social media use.
... Twenge (2017), who aims to provide explanations on Generation Z, or 'iGen' as he calls it, by utilising the findings of research conducted on millions of young people and his own field studies, states that the viewing habits of Generation Z have changed drastically. It is seen that internet-based viewing is on the rise among Generation Z members, 60% of the generation members accept the Youtube platform as a TV platform, watching video streaming service content replaces the act of watching television, and that television is not a useful tool for them (Twenge & Campbell, 2018;Dimock, 2019;Akşehirlioğlu & Erkınay, 2023;Söğütlüler & Başer, 2023, p. 814). This situation is undoubtedly important for communication sciences and needs to be examined in order to provide predictions for the future of the field. ...
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The concept of parasocial interaction describes viewers’ one-sided bonds with media figures. Since 1956, it has been extensively studied in psychology, sociology, communication sciences, and especially psychiatry. This study was carried out to examine with the aim of investigating the interaction of young individuals with the characters in video streaming series by taking into account the viewing tendencies. Four different scales including parasocial interaction (PSI), parasocial breakup (PBS), perceived attributes of the favorite character (PAFC) and perceived attributes of the least desired character (PALDC) scale were applied to male and female video streaming services users (n=381) aged 18-27; IBM SPSS Statistics 22.0 software was used for statistical procedures. It was observed that the participants mostly preferred foreign video streaming series over domestic ones, had more parasocial interactions with foreign video streaming series characters, female participants scored more on all scales compared to male participants, and their viewing time was higher. It was concluded that male participants tended more to have parasocial interactions with ‘strong’ and ‘physically attractive’ characters, while female participants tended more to have parasocial interactions with ‘strong’ and ‘positive social behaviour’ characters. Male participants described the characters they disliked as ‘cunning’, ‘repulsive’ and ‘ruthless’, while female participants described them as ‘selfish’, ‘repulsive’ and ‘ruthless’. Parasocial breakups were observed when characters with whom participants had established parasocial interactions left the series. Through the data obtained, issues related to the character creation process were discussed, and it was predicted that the results would contribute positively to the competition of domestic productions with global productions.
... Social media, while being a widely used communication tool among young people today, can also have significant effects on mental health (Primack et al., 2017;Twenge & Campbell, 2018). There is an increasing awareness that social media platforms can have both positive and negative effects on mental health (Odgers & Jensen, 2020). ...
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This mixed-methods study examines the impact of childhood trauma and family dynamics on mental health and the development of social media use disorder (SMUD) among university students. Through a combination of quantitative and qualitative methods, this research offers a comprehensive investigation into how early life experiences influence social media addiction and the mediating role of mental health. The independent variable is childhood experiences, the dependent variable is social media use disorder, and mental health serves as the mediating variable. The study, conducted in 2024 with a sample of 330 university students, employs correlation analysis and mediation testing, with bootstrapping used to evaluate the significance of indirect effects. The results demonstrate that mental health issues, such as depression, anxiety, and stress, mediate the relationship between childhood experiences and social media use disorder. Structural Equation Modeling (SEM) further reveals that adverse childhood experiences contribute to mental health challenges in adulthood, which in turn exacerbate social media addiction. Qualitative findings offer rich insights into how social media affects daily routines, family interactions, and psychological well-being, revealing its central role in the participants’ lives. Moreover, this study highlights how childhood experiences shape social media use patterns. These findings significantly advance the understanding of the complex interplay between early life trauma, mental health, and social media use, addressing critical gaps in the literature on digital addiction.
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Background: Inflammation has been linked to an increased risk of depression, but there is limited and conflicting research on the role of inflammatory markers in adolescent depression. The purpose of this study was to examine associations between cytokines TNF-α, IL-1β, IL-6, and IL-8 and depression among a community-based sample of adolescents (13–19 years of age). Methods: Salivary samples were self-collected by adolescents for assay of cytokines. The Patient Health Questionnaire-9 (PHQ-9) was used to measure depressive symptoms and clinical depression, where a score ≥11 indicated the threshold for experiencing clinical depression. Multiple linear and logistic regression models were used to examine the relationships between cytokines and depression, adjusting for age, sex, ethnicity, income, and body mass index. Results: The mean age of the 83 participants was 15.86 years. Eight participants screened positive for depression; the mean depressive symptom score was 5.11. Higher levels of IL-6 (Coef = 1.33, p < .001) and IL-8 (Coef = 0.69, p = .025) were associated with more frequent depressive symptoms while higher levels of TNF-α (OR = 2.50, p = .002), IL-1β (OR = 1.98, p = .001), and IL-8 (OR = 2.44, p = .008) were associated with greater odds of meeting criteria for clinical depression. Conclusions: Future research should focus on factors that induce higher cytokine levels and the mechanisms underlying their effects on depression. Cytokines assessed in this study may ultimately have implications as methods for depression screening or targets for biologic interventions to prevent and treat adolescent depression.
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This book chapter delves into the profound significance of technology in shaping individual and collective happiness in the contemporary era. Drawing on insights from multidisciplinary research and empirical evidence, the chapter explores both the positive and negative dimensions of technology's impact on well-being. The discourse begins by examining historical perspectives on the relationship between technological progress and happiness, highlighting seminal works such as Easterlin's Paradox. The chapter navigates through the transformative changes brought about by technology, from enhancing social connectivity to revolutionizing mental health care and education. It probes the nuanced dynamics of social media and digital communication, acknowledging both their potential to foster meaningful connections and their role in exacerbating feelings of isolation and comparison. Furthermore, the chapter addresses the challenges posed by techno stress and explores strategies for coping with its detrimental effects. The potential of technology to support mental health is illuminated through discussions on innovative interventions and virtual therapy platforms. Additionally, the concept of digital detox is examined, shedding light on the benefits of intentional breaks from technology in promoting well-being. Overall, this comprehensive exploration underscores the pivotal role of technology in shaping the landscape of human happiness in the digital age, while also acknowledging the need for ongoing research and informed strategies to optimize its positive impact.
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Studies have produced conflicting results about whether digital media (the Internet, texting, social media, and gaming) displace or complement use of older legacy media (print media such as books, magazines, and newspapers; TV; and movies). Here, we examine generational/time period trends in media use in nationally representative samples of 8th, 10th, and 12th graders in the United States, 1976–2016 (N = 1,021,209; 51% female). Digital media use has increased considerably, with the average 12th grader in 2016 spending more than twice as much time online as in 2006, and with time online, texting, and on social media totaling to about 6 hr a day by 2016. Whereas only half of 12th graders visited social media sites almost every day in 2008, 82% did by 2016. At the same time, iGen adolescents in the 2010s spent significantly less time on print media, TV, or movies compared with adolescents in previous decades. The percentage of 12th graders who read a book or a magazine every day declined from 60% in the late 1970s to 16% by 2016, and 8th graders spent almost an hour less time watching TV in 2016 compared with the early 1990s. Trends were fairly uniform across gender, race/ethnicity, and socioeconomic status. The rapid adoption of digital media since the 2000s has displaced the consumption of legacy media.
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We greatly appreciate the care and thought that is evident in the ten commentaries that discuss our debate paper, the majority of which argued in favor of a formalized ICD-11 gaming disorder. We agree that there are some people whose play of video games isrelated to life problems. We believe that understanding this population and the nature and severity of the problems they experience should be a focus area for future research. However, moving from research construct to formal disorder requires a much stronger evidence base than we currently have. The burden of evidence andthe clinical utility should be extremely high because there is a genuine risk of abuse of diagnoses. We provide suggestions about the level of evidence that might be required: transparent and preregistered studies, a better demarcation of the subject areathat includes a rationale for focusing on gaming in particular versus a more general behavioral addictions concept, the exploration of non-addiction approaches, and the unbiased exploration of clinical approaches that treat potentially underlying issues such as depressive mood or social anxiety first. We acknowledge there could be benefits to formalizing gaming disorder, many of which were highlighted by colleagues in their commentaries, but we think they do not yet outweigh the wider societal and public health risks involved. Given the gravity of diagnostic classification and its wider societal impact, we urge our colleagues at the WHO to err on the side of caution for now and postpone the formalization.
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In nationally representative yearly surveys of United States 8th, 10th, and 12th graders 1991–2016 (N = 1.1 million), psychological well-being (measured by self-esteem, life satisfaction, and happiness) suddenly decreased after 2012. Adolescents who spent more time on electronic communication and screens (e.g., social media, the Internet, texting, gaming) and less time on nonscreen activities (e.g., in-person social interaction, sports/exercise, homework, attending religious services) had lower psychological well-being. Adolescents spending a small amount of time on electronic communication were the happiest. Psychological well-being was lower in years when adolescents spent more time on screens and higher in years when they spent more time on nonscreen activities, with changes in activities generally preceding declines in well-being. Cyclical economic indicators such as unemployment were not significantly correlated with well-being, suggesting that the Great Recession was not the cause of the decrease in psychological well-being, which may instead be at least partially due to the rapid adoption of smartphones and the subsequent shift in adolescents’ time use.
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Background: The purpose of this systematic review was to examine the relationships between sedentary behaviour (SB) and health indicators in children aged 0 to 4 years, and to determine what doses of SB (i.e., duration, patterns [frequency, interruptions], and type) were associated with health indicators. Methods: Online databases were searched for peer-reviewed studies that met the a priori inclusion criteria: population (apparently healthy, 1 month to 4.99 years), intervention/exposure and comparator (durations, patterns, and types of SB), and outcome/health indicator (critical: adiposity, motor development, psychosocial health, cognitive development; important: bone and skeletal health, cardiometabolic health, fitness, risks/harm). The quality of the evidence was assessed by study design and outcome using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. Results: Due to heterogeneity, meta-analyses were not possible; instead, narrative syntheses were conducted, structured around the health indicator and type of SB. A total of 96 studies were included (195,430 participants from 33 countries). Study designs were: randomized controlled trial (n = 1), case-control (n = 3), longitudinal (n = 25), longitudinal with additional cross-sectional analyses (n = 5), and cross-sectional (n = 62). Evidence quality ranged from "very low" to "moderate". Associations between objectively measured total sedentary time and indicators of adiposity and motor development were predominantly null. Associations between screen time and indicators of adiposity, motor or cognitive development, and psychosocial health were primarily unfavourable or null. Associations between reading/storytelling and indicators of cognitive development were favourable or null. Associations between time spent seated (e.g., in car seats or strollers) or in the supine position, and indicators of adiposity and motor development, were primarily unfavourable or null. Data were scarce for other outcomes. Conclusions: These findings continue to support the importance of minimizing screen time for disease prevention and health promotion in the early years, but also highlight the potential cognitive benefits of interactive non-screen-based sedentary behaviours such as reading and storytelling. Additional high-quality research using valid and reliable measures is needed to more definitively establish the relationships between durations, patterns, and types of SB and health indicators, and to provide insight into the appropriate dose of SB for optimal health in the early years.
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Young people who are already struggling offline might experience greater negative effects of life online, writes Candice Odgers.
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There is little empirical understanding of how young children's screen engagement links to their well-being. Data from 19,957 telephone interviews with parents of 2- to 5-year-olds assessed their children's digital screen use and psychological well-being in terms of caregiver attachment, resilience, curiosity, and positive affect in the past month. Evidence did not support implementing limits (< 1 or < 2 hr/day) as recommended by the American Academy of Pediatrics, once variability in child ethnicity, age, gender, household income, and caregiver educational attainment were considered. Yet, small parabolic functions linked screen time to attachment and positive affect. Results suggest a critical cost–benefit analysis is needed to determine whether setting firm limits constitutes a judicious use of caregiver and professional resources.
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In two nationally representative surveys of U.S. adolescents in grades 8 through 12 (N = 506,820) and national statistics on suicide deaths for those ages 13 to 18, adolescents’ depressive symptoms, suicide-related outcomes, and suicide rates increased between 2010 and 2015, especially among females. Adolescents who spent more time on new media (including social media and electronic devices such as smartphones) were more likely to report mental health issues, and adolescents who spent more time on nonscreen activities (in-person social interaction, sports/exercise, homework, print media, and attending religious services) were less likely. Since 2010, iGen adolescents have spent more time on new media screen activities and less time on nonscreen activities, which may account for the increases in depression and suicide. In contrast, cyclical economic factors such as unemployment and the Dow Jones Index were not linked to depressive symptoms or suicide rates when matched by year.
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Using a field experiment and experience sampling, we found the first evidence that phone use may undermine the enjoyment people derive from real world social interactions. In Study 1, we recruited over 300 community members and students to share a meal at a restaurant with friends or family. Participants were randomly assigned to keep their phones on the table or to put their phones away during the meal. When phones were present (vs. absent), participants felt more distracted, which reduced how much they enjoyed spending time with their friends/family. We found consistent results using experience sampling in Study 2; during in-person interactions, participants felt more distracted and reported lower enjoyment if they used their phones than if they did not. This research suggests that despite their ability to connect us to others across the globe, phones may undermine the benefits we derive from interacting with those across the table.
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Study objectives Insufficient sleep among adolescents carries significant health risks, making it important to determine social factors that change sleep duration. We sought to determine whether the self-reported sleep duration of U.S. adolescents changed between 2009 and 2015 and examine whether new media screen time (relative to other factors) might be responsible for changes in sleep. Methods We drew from yearly, nationally representative surveys of sleep duration and time use among adolescents conducted since 1991 (Monitoring the Future) and 2007 (Youth Risk Behavior Surveillance System of the Centers for Disease Control; total N = 369,595). Results Compared to 2009, adolescents in 2015 were 16%–17% more likely to report sleeping less than 7 h a night on most nights, with an increase in short sleep duration after 2011–2013. New media screen time (electronic device use, social media, and reading news online) increased over this time period and was associated with increased odds of short sleep duration, with a clear exposure–response relationship for electronic devices after 2 or more hours of use per day. Other activities associated with short sleep duration, such as homework time, working for pay, and TV watching, were relatively stable or reduced over this time period, making it unlikely that these activities caused the sudden increase in short sleep duration. Conclusions Increased new media screen time may be involved in the recent increases (from 35% to 41% and from 37% to 43%) in short sleep among adolescents. Public health interventions should consider electronic device use as a target of intervention to improve adolescent health.
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This study examined the Big Five personality traits as predictors of mortality risk, and smoking as a mediator of that association. Replication was built into the fabric of our design: we used a Coordinated Analysis with 15 international datasets, representing 44,094 participants. We found that high neuroticism and low conscientiousness, extraversion, and agreeableness were consistent predictors of mortality across studies. Smoking had a small mediating effect for neuroticism. Country and baseline age explained variation in effects: studies with older baseline age showed a pattern of protective effects (HR<1.00) for openness, and U.S. studies showed a pattern of protective effects for extraversion. This study demonstrated coordinated analysis as a powerful approach to enhance replicability and reproducibility, especially for aging-related longitudinal research.