Booker et al. BMC Public Health (2018) 18:321
https://doi.org/10.1186/s12889-018-5220-4
RESEARCH ARTICLE
Open Access
Gender differences in the associations
between age trends of social media
interaction and well-being among
10-15 year olds in the UK
Cara L. Booker1* , Yvonne J. Kelly2 and Amanda Sacker2
Abstract
Background: Adolescents are among the highest consumers of social media while research has shown that their
well-being decreases with age. The temporal relationship between social media interaction and well-being is not
well established. The aim of this study was to examine whether the changes in social media interaction and two
well-being measures are related across ages using parallel growth models.
Methods: Data come from five waves of the youth questionnaire, 10-15 years, of the Understanding Society, the
UK Household Longitudinal Study (pooled n = 9859). Social media interaction was assessed through daily frequency
of chatting on social websites. Well-being was measured by happiness with six domains of life and the Strengths
and Difficulties Questionnaire.
Results: Findings suggest gender differences in the relationship between interacting on social media and wellbeing. There were significant correlations between interacting on social media and well-being intercepts and
between social media interaction and well-being slopes among females. Additionally higher social media
interaction at age 10 was associated with declines in well-being thereafter for females, but not for males.
Results were similar for both measures of well-being.
Conclusions: High levels of social media interaction in early adolescence have implications for well-being in later
adolescence, particularly for females. The lack of an association among males suggests other factors might be
associated with their reduction in well-being with age. These findings contribute to the debate on causality and
may inform future policy and interventions.
Keywords: Adolescents, Gender, Growth curve modelling, Longitudinal studies, Social media interaction, Well-being
Background
Rapid changes in technology have given rise to many important questions regarding their short- and long-term
effects on overall health and well-being. Television viewing expanded people’s exposure to new and different cultures and ideas; however up until recently, it has not
been an interactive medium. Thus it is especially important to explore, as this study does, whether there is a
long-term relationship between interacting on social
* Correspondence: cbooker@essex.ac.uk
1
Institute for Social and Economic Research, University of Essex, Wivenhoe
Park, Colchester CO4 3SQ, UK
Full list of author information is available at the end of the article
media and well-being among adolescents, as healthrelated behaviours and well-being levels track into adulthood [1–4]. The link between television viewing and
health outcomes such as increased obesity, fasting insulin and other markers of metabolic risk has been well
established leading many countries to establish guidelines for daily consumption [5]. More recently, technology has become more interactive, specifically with the
advent of social media websites and smartphone apps. A
recent report by the United Kingdom’s Office of Communications stated that adolescents aged 12-15 spend
more time online than they do watching television [6].
Additionally, adolescents in the United Kingdom (UK)
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Booker et al. BMC Public Health (2018) 18:321
are ranked in the bottom third on overall well-being in a
United Nations Children’s Fund report comparing several countries [7].
While social media allows for interaction between
people, it is still a sedentary activity that can be done in
a solitary environment. Conversely, social media are
often used in group settings. Whether done in isolation
or with friends, there may be risks to using social media,
which could lead to poorer physical and mental health
in adulthood [8, 9]. Risk factors such as social isolation
[10], low self-esteem [11, 12], increased obesity [13] and
decreased physical activity [14] may all contribute to
later life health issues. While some studies have shown a
negative relationship between interacting on social
media and well-being, there are others which show positive associations. High quality interactions [15–17], reduced social isolation [16, 18] or information seeking
[19] are all mechanisms through which well-being may
be increased with social media use.
More recently, research has focused on the patterns of
social media usage. There are different ways these patterns have been defined, Brandtzæg [16] identified five
types, sporadics, lurkers, socializers, debaters and
advanced. Others categorise users as active or passive
[20–22]. As research into the effects of social media use
and interaction has increased the theoretical framework
underlying the relationship with well-being have continued to be developed. Verdyun et al., [22] suggest that
the relationship operates differently for passive and active users. Active users may experiences an increase in
social capital and connectedness resulting in an increase
in well-being, however passive users may be more likely
to experience upward social comparison leading to a reduction in well-being [22]. A review of current literature
by Verduyn et al. [22] found mixed results for the passive mechanism while evidence for the active pathway
was stronger.[22]While much of the early evidence linking social media interaction and well-being was based
on cross-sectional data making causal inference impossible, evidence from longitudinal studies is increasing.
Recent longitudinal studies have reported longer term
associations between social media interaction and wellbeing with mixed results [22–25]. In a study of Belgian
adolescents, active private Facebook use, e.g. chatting or
sending personal messages, was indirectly associated
with lower depressed mood through increased perceived
friend support and decreased avoidant coping [20].
Recent reviews of studies have analysed the associations
between mental health and screen time or screen-based
media [11, 22, 26]. One review included all forms of
screen-based media and separated associations by type
of mental health indicator [11]. They found support for
a relationship of screen-based sedentary behaviours with
increased depressive symptoms, increased inattention,
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hyperactivity problems, decreased self-esteem and decreased well-being and quality of life [11]. Evidence of a
relationship with anxiety symptoms, internalising problems and eating disorder symptoms was inconclusive.
[11] A meta-analysis examined evidence from crosssectional and longitudinal studies separately with mixed
findings. Among cross-sectional studies, the findings
suggest a strong positive association between increased
screen time and depression risk [26]. However among
longitudinal studies’ findings suggest a negative,
although non-significant association [26]. Further investigation of the longitudinal studies included identifying
the quality of the studies, i.e. participant selection, measurement of constructs, methodology for addressing
study design issues, control of confounding and appropriate statistical methodology. Therefore when lower
quality studies were excluded increased screen time significantly predicted depression risk [26]. A limitation of
these reviews is that there is a conflation, in some cases,
of screen time with social media use or interaction on
social media. Social media use is conducted using a
screen, however there are features of social medial that
cannot be found in traditional screen time such as television viewing [16].
A third recent review looked at two social media
usage components, overall usage of social networking
sites and types of social networking site use and their
associations with subjective well-being [22]. They conclude that cross-sectional studies provide a mixed message on overall usage and subjective well-being, while
longitudinal studies show more conclusively a decline
in subjective well-being as a result of using social networking sites [22]. A limitation to this review is that
the longitudinal studies sites used short follow-up
times, one to two weeks, which may not translate into
long-term effects. In their conclusions regarding types
of social networking sites use and subjective well-being
the authors suggest that passive use is associated with
lower subjective well-being while most studies cited
showed a positive association between active use and
subjective well-being [22].
Prior research shows that screen-based media interaction increases whilst well-being levels decrease
throughout adolescence and these changes differ by gender [6, 27, 28]. Many of the recent studies controlled for
gender and age, where appropriate, but did not look at
age or gender differences in screen-based media interaction or how associations with well-being might differ
with age and gender. In the meta-analysis conducted by
Liu et al., [26] gender and age moderation analyses were
conducted which showed a significant positive association for males and adolescents under the age of 14; no
significant associations were found for females or those
over the age of 14. This suggests that there might be
Booker et al. BMC Public Health (2018) 18:321
differences in the association between social media interaction and well-being by gender and across age groups.
The well-being measure used to examine the relationship between screen-based media and well-being might
also be a factor which contributes to the diverse and
sometimes conflicting results. Many studies have examined the associations between screen-based media and
negative markers of well-being such as depression,
socio-emotional difficulties and anxiety with mixed results [11, 20, 23, 29]. There have also been studies which
have examined positive markers of well-being, such as
happiness, self-esteem and quality of life, again with
mixed results [11, 27]. Findings from a study of UK
adolescents showed that interacting on social media for
more than 4 h was associated with more socioemotional difficulties, but not with lower levels of happiness suggesting that future research should investigate
whether the relationship between social media interaction and positive and negative markers of well-being
differs [27].
This study adds to the current literature by using longitudinal data from adolescents 10-15 years of age in the
UK. The primary aim of this study is to examine changes
in social media interaction and positive and negative
markers of well-being with age and to determine
whether any relationship exists between social media
interaction and well-being trajectories. A secondary aim
is to examine whether the social media interaction and
well-being relationships and trajectories differ by gender.
We also explore whether initial levels of well-being or
social media interaction are predictive of rates of change
in the other.
Methods
Participants
Respondents came from the youth panel of Understanding Society: the UK Household Panel Study (UKHLS).
UKHLS is a nationally representative longitudinal study
which interviews all household members annually
(2009/10-2014/15). A stratified, clustered sampling
scheme was used to identify primary sampling units.
Additional information on the sampling scheme and
data collection methods is available [30, 31]. All individuals 16 and older participated in the main survey while
the youth questionnaire was given to adolescents aged
10-15. Youth panel members self-completed a paperand-pencil survey. Verbal consent was required for
participation for all respondents. Written consent is only
required for requests to link administrative data to survey responses. Youth participation required the interviewer to ask the parent/guardian for their verbal
consent, and receive an affirmative response, and then
to ask the young person for their consent, at which point
the young person was free to agree or refuse. Ethical
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approval was obtained from the University of Essex Ethics Committee and the Oxfordshire Research Ethics
Committee (REC) A, REC reference OS/HO604/124.
In wave one, 4899 respondents participated in the
youth panel, this represents 74% of the invited 6627 adolescents [32]. As children reach the age of 10 they are
eligible to be included in the youth panel and at the age
of 16 they are eligible to enter the adult interview. Over
the first five waves of UKHLS, 9859 adolescents participated in the youth panel, participation in each wave
ranged from a low of 3656 in wave 5 to a high of 5014
in wave 2. The number of adolescents who participated
in just one wave was 3674; 2521 participated in two
waves, 1874 in three, 1280 in four waves and only 510
have participated in all 5 waves. Males comprised 51% of
the sample with 4990 individuals providing 11,073
person-age observations compared to 4869 females with
10,935 person-age observations.
Measures
Social media interaction: Two questions were used to
determine whether adolescents chatted via social media.
The first question asked “Do you belong to a social website such as Bebo, Facebook or MySpace?” and the second question “How many hours do you spend chatting
or interacting with friends through a social website like
that on a normal school day?” Responses for the latter
question were scored on a 5-point scale ranging from
“none” to “7 or more hours.” Responses were then
recoded so that those with no social networking profile
were coded as “no profile” and other responses were
recoded to “1 h or less”, “1-3 h” and “4 h or more”
category.
Well-being: Happiness and socio-emotional difficulties
reported by youth panel members were both used to
examine whether social media interaction is differentially
associated with positive and negative markers of wellbeing. Six questions covering different domains of life, i.e.
friends, family, appearance, school, school work and life as
a whole, were asked and scored on a 7-point Likert-type
scale. Factor analysis confirmed that all questions loaded
on to one factor, thus an overall happiness score was created with a range of 6-42 (Cronbach’s α = 0.77). Higher
scores indicated higher levels of happiness [33].
Negative aspects of well-being were measured using
the Strengths and Difficulties Questionnaire (SDQ). The
SDQ is a validated instrument which screens for emotional and behavioural problems in children and adolescents aged 3-16 years [34]. The SDQ is comprised of 25
items; responses were ‘not true’, ‘somewhat true’ and
‘certainly true’. Twenty of these items covering hyperactivity/inattention, emotional symptoms, conduct problems and peer relationship problems are summed to
create a total difficulties score which ranges from 0 to 40
Booker et al. BMC Public Health (2018) 18:321
(Cronbach’s α = 0.67). Higher scores on the total difficulties score indicate worse well-being. SDQ total difficulties
scores of 20 or above indicate clinically relevant risk for
mental problems [35]. This cut-off was chosen so roughly
90% of the sample would fall in the normal or borderline
range and 10% would fall in the abnormal range [35]. The
distribution of the SDQ total difficulties scores was slightly
skewed, for both males (skewness = 0.56) and females
(skewness = 0.53). Happiness questions are asked annually,
however the SDQ is completed bi-annually.
Covariates: Control variables were chosen based on
the literature and previous analysis, conducted on the
same data, which showed independent associations between these variables and both screen-based media and
well-being [6, 27, 28, 36]. Parent- and household-level
covariates were included in this analysis. Marital status
was included as the parent-level covariate while
household-level covariates were highest educational attainment and household income. Covariates were also
included in the models as time-varying or timeinvariant, as appropriate. Ethnic group and mean household income were time-invariant while educational
attainment and marital status were time-variant. The
youth questionnaire only asked ethnic identity every
other year, thus some adolescents may not answer
these questions. Therefore we used the youth response to the ethnicity question where available, for
the 19% (n = 1847) with no ethnicity parent’s reporting of their own ethnic group identity was used instead. Ethnic group was coded as White British, Black
African/Caribbean, Asian, Other and Mixed. White
British was the reference group.
At each wave, previous month net income is reported
for the household. Household income was equivalised
for household composition using the Organisation for
Economic Co-operation and Development modified
equivalence scale [37] and then log transformed to create a more normal distribution. Due to missingness and
model convergence issues, income was averaged across
all waves the young person participated in.
Each parent reported their highest education qualification at each wave. The highest reported qualification of
either parent was used. Due to sample size in some of
the categories, General Certificate of Secondary Education (GCSE) and other qualification were combined so
that the categories were degree, other higher qualification, A-level, GCSE/Other qualification, no qualification;
degree was the reference category. GCSE are exams
taken at 16 years of age (school year 11) and A-levels are
exam taken at 18 years of age (school year 13). Each parent also reported their marital or cohabitation status
(referred to as partnership status) at each wave. Partnership status was dichotomised as partnered or not partnered with partnered as the reference category.
Page 4 of 12
Analysis
We estimated parallel latent growth curve models using
MPlus 7.3 [38] Well-being scores and social media interaction are repeated at each age and are modelled as distinct processes; the conceptual model is shown in Fig. 1.
Rather than model over time, we modelled by age.
Therefore these models do not measure change over
time within individuals but rather change by age averaged across individuals [39]. We estimated four models
two for happiness, one for females and one for males
and two for SDQ total difficulties, one for females and
one for males Linear growth parameters are estimated
for each process giving an intercept and a slope. Factor
loadings were fixed at zero at age 10, therefore the intercept is interpreted as either the well-being score or the
amount of time spent using social media at age 10.
Intercepts and slopes are allowed to covary across processes; additionally slopes of one process are regressed
on the intercept of the other process to estimate the potential reciprocal influence of social media interaction
and well-being as the panel members aged. All models
controlled for the young person’s ethnic group, parental
partnership status, highest educational attainment and
mean household income. Highest educational attainment
and marital status regression coefficients were set to be
equal across age to estimate the average effects of each
and to reduce any random fluctuations at each age. All
adolescents aged 10-15 in a household were given the
opportunity to complete a questionnaire; thus all models
were adjusted for clustering within households.
Results
The person-age distribution was similar and equal
within each gender; each age group consisted of 16-17%
of the overall sample (Table 1). A higher percentage of
fathers than mothers reported being partnered rather
than non-partnered, as resident fathers are more likely
to participate than non-resident fathers. The majority of
adolescents were White British (74%) with Asian as the
second largest ethnic group (11% for males and 12% for
females).
Table 1 shows that interacting on social media increased with age for both males and females. Females
used social media more than males, a pattern that continued throughout adolescence. At age 13, half of females were chatting for more than 1 h per day,
compared to one third of males. By the age of 15, 59% of
females and 46% of males were chatting for 1 or more
hours per day.
Well-being scores also differed by gender and age.
Happiness scores decreased for females from a high of
36.94 (95% Confidence Interval [95% CI] = 36.73, 37.15)
at age 10 to 33.33 (95% CI = 33.10, 33.57) at age 15. In
this sample, young women with clinically relevant SDQ
Booker et al. BMC Public Health (2018) 18:321
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Fig. 1 Conceptual parallel process growth model. Note SMI = Social Media Interaction; Double-headed arrows indicate correlations; Single-headed
arrows indicate regression paths. Parameter A = correlation between social media interaction and well-being intercepts; Parameter B = correlation
between social media interaction and well-being slopes; Parameter C = social media interaction slope regressed on social media interaction
intercept; Parameter D = Well-being slope regressed on well-being intercept; Parameter E = well-being slope regressed on social media interaction
intercept; Parameter F = social media interaction slope regressed on well-being intercept
scores had a happiness level 6.95 (95% CI = 6.31, 7.58)
points lower than young women who did not have
clinically relevant SDQ total difficulties scores, 1.42 of a
happiness standard deviation. The 3.44 (95% CI = 3.00,
3.89) point difference in happiness between female 10and 15-year olds is 0.70 of the total female happiness
standard deviation across all ages. With the exception of
the difference between ages 10 and 11 and ages 14 and
15, all levels of happiness were significantly different
from each other. Males showed a similar, albeit smaller,
reduction in happiness levels going from 36.02 (95% CI
= 35.80, 36.24) at age 10 to 34.55 (95% CI = 34.33, 34.78)
at age 15. This is equivalent to 0.30 standard deviates on
the happiness scale or one-quarter of the difference between young males with clinically and non-clinically
relevant SDQ scores. Young males aged 13 and older
were significantly less happy than both 10 and 11 year
olds while 12 year olds were significantly happier than
14 and 15 year olds. SDQ scores decreased for males,
but increased for females. At age 10 the average SDQ
score was 10.30 (95% CI = 9.94-10.66) and rose to 11.15
(95% CI = 10.83-11.46) at age 15. Average female SDQ
scores were significantly higher at ages 14 and 15 than
the scores at age 10, 11 and 12. Conversely, males had
an average SDQ score of 11.51 (95% CI = 11.15, 11.87) at
age 10 which decreased to 10.25 (95% CI = 9.92, 10.59)
at age 15. SDQ scores of males at ages 10 and 11 did not
differ from each other but were significantly higher than
the average scores of males aged 13, 14 and 15. While
the average score at age 10 was higher than the age 12,
there was no difference between the average age 11 and
age 12 scores.
Significant differences between genders at specific ages
were also observed. Ten and eleven year old females
were significantly happier and had lower SDQ scores
than males. These differences became non-significant at
age 12 and at age 13 males reported higher levels of happiness while the SDQ scores were non-significantly different. Fourteen and fifteen year old males on average
were significantly happier and had lower SDQ scores
than females.
Parallel growth model growth factor associations
The parameter estimates for the model intercepts,
slopes and growth factor associations are given in
Table 2. There were significant differences in the
models between males and females. In both the happiness and SDQ models, there were significant correlations between the intercept of social media interaction
and the intercept of each marker of well-being for females (Fig. 1, parameter A). These findings indicate
that increased social media interaction was correlated
with lower levels of happiness and higher levels of
socio-emotional difficulties at age 10. While the
Females
Males
Total
10 year olds
11 year olds
12 year olds
13 year olds
14 year olds
15 year olds
Total
10 year olds
11 year olds
12 year olds
13 year olds
14 year olds
15 year olds
(N = 4869)
n = 1664
n = 1794
n = 1841
n = 1853
n = 1884
n = 1847
N = 4990
n = 1711
n = 1824
n = 1907
n = 1938
n = 1861
n = 1750
Do not have
a profile/internet
access
23
50
38
23
13
10
8
28
55
43
30
17
13
10
Less than
1 h per day
38
39
41
42
37
34
33
45
36
43
48
51
48
44
1-3 h per day
30
10
18
29
37
39
43
22
7
12
19
26
31
36
4 or more
hours per day
10
1
3
7
13
17
16
5
2
2
2
6
8
10
Mother’s marital
status (% partnered)
76
80
76
75
76
76
76
78
79
79
78
77
77
77
Father’s marital
status (% partnered)
98
98
98
98
98
98
97
97
98
97
97
97
96
97
13
11
12
13
14
14
15
13
13
12
12
13
13
14
Social media interaction
Booker et al. BMC Public Health (2018) 18:321
Table 1 Social Media Interaction, Well-being and Socio-Demographic Variable Descriptives for 10-15 Year Old UK Young People by Gendera
Highest parental qualification
No qualification
b
13
12
13
13
14
14
14
13
11
12
13
14
13
15
GCSEc
29
28
30
30
29
29
28
29
30
30
29
28
29
28
A-leveld
17
19
18
17
17
16
16
18
17
19
19
17
18
17
Other higher
qualificaitone
11
12
10
11
11
11
11
11
12
11
11
11
11
11
Degree
16
18
17
17
16
16
15
16
16
16
16
16
16
16
Other qualification
Ethnicity
White British
74
74
Black
African/Caribbean
6
5
Asian
11
12
Other
4
4
Mixed
5
5
7.14
(0.43)
7.14
(0.43)
Mean log
household
income
Happiness scale
35.03 (5.14)
36.94 (4.32)
36.62 (4.42)
35.61 (4.88)
34.38 (5.17)
33.56 (5.47)
33.33 (5.24)
35.27 (4.83)
36.02 (4.63)
35.87 (4.71)
35.53 (4.73)
35.04 (4.85)
34.67 (5.04)
34.55 (4.84)
SDQ total difficulties
10.61 (5.60)
11.30 (5.74)
9.83 (5.42)
10.33 (5.61)
10.62 (5.59)
11.35 (5.83)
11.15 (5.28)
10.65 (5.69)
11.51 (5.89)
11.05 (6.03)
10.59 (5.66)
10.40 (5.63)
10.16 (5.37)
10.25 (5.43)
b
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Abbreviation: GCSE, General Certificate of Secondary Education; SDQ, Strengths and Difficulties Questionnaire; UK, United Kingdom
Social media Interaction, parental marital status, parental qualification and parental ethnicity are percentages; Mean log household income, happiness and SDQ total difficulties are means and standard deviations
Other qualifications include CSE, skills certifications, apprenticeships, clerical qualifications, etc.;
c
GCSE = exams taken at age 16 (year 11)
d
A Level exam taken at age 18 (year 13)
e
Examples of other higher qualifications are teaching, nursing or diploma certifications /qualifications
a
Booker et al. BMC Public Health (2018) 18:321
Page 7 of 12
Table 2 Parameter estimates
Happiness
SDQ total difficulties
Females (n = 4765)
Males (n = 4903)
Females (n = 4762)
Males (n = 4901)
PE
95% CI
PE
95% CI
PE
95% CI
PE
95% CI
Well-being Intercept
36.55
(33.32, 39.78)
38.40
(35.20, 41.61)
13.57
(8.86, 18.27)
10.44
(5.54, 15.34)
Well-being Slope
1.65
(− 0.01, 3.31)
0.76
(−1.00, 2.52)
1.69
(0.44, 2.93)
0.78
(− 0.40, 1.95)
SMI intercept
0.00
SMI slope
0.44
Model Intercepts
0.00
(− 0.32, 1.20)
0.00
0.47
0.00
(− 0.26, 1.20)
− 0.17
(− 0.71, 0.36)
0.11
(− 0.46, 0.68)
(− 0.19, − 0.01) −0.02
(− 0.10, 0.07)
0.18
(0.08, 0.27)
0.10
(0.01, 0.19)
(− 0.15, 0.12)
0.26
(0.09, 0.43)
0.17
(−0.03, 0.36)
Growth factor associations
Path A: Intercept SMI < −-> Intercept WBa − 0.10
Path B: Slope SMI < −-> Slope WB
a
− 0.23
(−0.36, − 0.09)
Path C: Slope SMI < −- Intercept SMI
−0.08
(− 0.13, − 0.04) −0.14
(− 0.17, − 0.11) −0.08
(− 0.13, − 0.04) −0.14
(− 0.17, − 0.11)
Path D: Slope WB < −- Intercept WB
−0.08
(− 0.12, − 0.04) −0.07
(− 0.11, − 0.03) −0.10
(− 0.15, − 0.05) −0.08
(− 0.12, − 0.03)
−0.02
Path E: Slope WB < −- Intercept SMI
−0.06
(− 0.13, 0.01)
−0.004
(− 0.05, 0.04)
0.10
(0.004, 0.19)
0.03
(−0.03, 0.10)
Path F: Slope SMI < −- Intercept WB
−0.01
(− 0.03, 0.001)
−0.01
(− 0.02, 0.002)
0.01
(− 0.01, 0.02)
− 0.001
(− 0.01, 0.01)
Model fit
Loglikelihood
−43,001.75
−42,831.53
−31,170.95
−31,064.01
AIC
86,111.50
85,771.06
62,449.90
62,236.02
BIC
86,289.24
86,121.94
62,799.19
62,586.86
Abbreviations: AIC Akaike Information Criterion, BIC Bayesian Information Criterion, 95% CI 95% Confidence Interval, PE Raw parameter estimate,
SMI Social Media Interaction, WB Well-being; <−- regression; <−-> correlation
a
Coefficients are correlations
happiness and social media interaction intercepts were
uncorrelated in males, there was a significant correlation between the two intercepts in the SDQ model,
Correlation parameter (r) =0.10 (95% CI = 0.01, 0.19).
Parameter B, the correlation between the slopes of social media interaction and well-being was significant for
females only. In both cases, an increase in social media
interaction was correlated with a decline in happiness,
r = − 0.23 (95% CI = − 0.36, − 0.09) and an increase in
SDQ score, r = 0.26 (95% CI = 0.09, 0.43).
For both males and females, the intercept of social
media interaction was associated with the social
media slope (Parameter C) and the well-being intercept was associated with the well-being slope
(Parameter D). The associations were negative for
both happiness and SDQ total difficulties. These
findings indicate that adolescents with high levels of
social media interaction at age 10 have less steep
trajectories (slower rate of change) with age than
those who interacted less social media at age 10.
The happiness model correlation estimate for males
is r = − 0.14 (95% CI = − 0.17, − 0.11) and females is
r = − 0.08 (95% CI = − 0.13, − 0.04). Parameter
estimates for the SDQ model were similar (Table 2).
Similarly, high levels of happiness or a low level of
socio-emotional difficulties at age 10 were associated
with smaller changes in the respective marker of
well-being with age (Parameter D).
Finally, there was only one significant association for
Parameter E, the association between the social media
interaction intercept and the SDQ slope. For females, increased interaction on social media at age 10 was associated with greater increases in SDQ with age, path
coefficient = 0.10 (95% CI = 0.004, 0.19). The association
approached significance (p-value = 0.07) in the happiness
model for females, coefficient = − 0.06 (95% CI = − 0.13,
0.01). There were no significant associations for
Parameter F, the slope of social media interaction
regressed on the well-being intercept, however in the
happiness models the female (p-value = 0.07) association
approached significance.
Parallel growth model covariate parameter estimates
Table 3 provides the associations of the covariates with
the well-being and social media variables. There was no
association between parental education and happiness
for females. However lower levels of parental education
were associated with lower levels of happiness for males.
In the SDQ models, there was a dose-response relationship between parental education and their child’s SDQ.
In both the happiness and SDQ models, all levels of parental educational attainment were associated with increased social media interaction for both males and
females compared to adolescents whose highest parental
achievement was at degree level. Having an unpartnered
mother was associated with lower well-being for both
Booker et al. BMC Public Health (2018) 18:321
Page 8 of 12
Table 3 Covariate parameter estimatesa,b
Happiness
Females (n = 4765)
SDQ total difficulties
Males (n = 4903)
Females (n = 4762)
Males (n = 4901)
Well-beingc
No qualification
0.24 (− 0.22, 0.69)
− 0.47 (− 0.90, − 0.03)
0.71 (0.10, 1.30)
1.50 (0.91, 2.10)
GCSE/Other qualification
0.11 (− 0.24, 0.46)
− 0.40 (− 0.73, − 0.06)
0.60 (0.15, 1.05)
0.90 (0.44, 1.36)
A-level
− 0.21 (− 0.66, 0.20)
− 0.29 (− 0.67, 0.08)
0.43 (− 0.09, 0.95)
0.80 (0.27, 1.32)
Other higher Qualification
0.02 (− 0.43, 0.46)
− 0.52 (− 0.97, − 0.08)
0.21 (− 0.38, 0.80)
0.45 (− 0.15, 1.04)
− 0.81 (− 1.12, − 0.51)
− 0.69 (− 0.98, − 0.39)
0.51 (0.12, 0.91)
0.88 (0.47, 1.29)
−1.52 (−2.52, − 0.52)
− 0.60 (− 1.35, 0.15)
1.37 (0.23, 2.49)
0.77 (− 0.28, 1.81)
Degree (Ref)
Unpartnered Mother
Partnered Mother (Ref)
Unpartnered Father
Partnered Father (Ref)
Social Media Interactionc
No qualification
0.85 (0.60, 1.10)
0.87 (0.62, 1.12)
0.85 (0.59, 1.11)
0.88 (0.61, 1.14)
GCSE/Other qualification
0.84 (0.65, 1.03)
0.62 (0.43, 0.80)
0.85 (0.66, 1.04)
0.62 (0.42, 0.81)
A-level
0.56 (0.33, 0.78)
0.59 (0.38, 0.81)
0.57 (0.35, 0.79)
0.60 (0.38, 0.82)
Other higher Qualification
0.60 (0.36, 0.84)
0.45 (0.22, 0.68)
0.61 (0.37, 0.85)
0.45 (0.22, 0.69)
0.41 (0.26, 0.56)
0.32 (0.17, 0.48)
0.41 (0.26, 0.56)
0.32 (0.16, 0.48)
0.12 (− 0.37, 0.61)
0.37 (− 0.06, 0.80)
0.15 (− 0.34, 0.63)
0.37 (− 0.07, 0.82)
Degree (Ref)
Unpartnered Mother
Partnered Mother (Ref)
Unpartnered Father
Partnered Father (Ref)
Well-being Intercept
Black African/Caribbean
1.06 (0.38, 1.74)
1.14 (0.25, 2.04)
−1.72 (−2.65, − 0.79)
−1.38 (−2.66, − 0.11)
Asian
0.46 (− 0.16, 1.07)
0.80 (0.25, 1.35)
− 0.91 (− 1.75, − 0.08)
−1.56 (− 2.39, − 0.72)
Other Ethnicity
0.84 (0.003, 1.68)
− 0.15 (− 1.08, 0.79)
− 0.37 (− 1.62, 0.88)
− 0.37 (− 1.62, 0.89)
Mixed Ethnicity
− 0.67 (− 1.55, 0.20)
0.42 (− 0.34, 1.17)
−0.10 (− 1.16, 0.96)
−0.83 (− 1.80, 0.14)
0.11 (−0.33, 0.54)
−0.28 (− 0.71, 0.16)
−0.57 (− 1.20, 0.07)
0.04 (− 0.63, 0.70)
Black African/Caribbean
−0.05 (− 0.27, 0.17)
0.004 (− 0.25, 0.26)
−0.02 (− 0.26, 0.21)
−0.20 (− 0.51, 0.11)
Asian
0.18 (0.01, 0.36)
0.31 (0.15, 0.47)
−0.16 (− 0.37, 0.05)
−0.12 (− 0.34, 0.09)
Other Ethnicity
− 0.14 (− 0.41, 0.13)
0.14 (− 0.09, 0.36)
−0.28 (− 0.56, − 0.002)
−0.02 (− 0.31, 0.26)
Mixed Ethnicity
0.04 (− 0.24, 0.32)
−0.15 (− 0.37, 0.08)
0.08 (− 0.15, 0.32)
0.11 (− 0.13, 0.34)
0.06 (−0.06, 0.17)
0.20 (0.10, 0.31)
−0.03 (− 0.17, 0.12)
−0.03 (− 0.18, 0.11)
White (Ref)
Mean Log Household Income
Well-being Slope
White (Ref)
Mean Log Household Income
Social Media Interaction Intercept
Black African/Caribbean
−0.04 (− 0.44, 0.36)
0.07 (− 0.53, 0.68)
−0.04 (− 0.44, 0.36)
0.06 (− 0.54, 0.67)
Asian
−0.81 (−1.14, − 0.48)
−0.47 (− 0.80, − 0.13)
−0.80 (− 1.13, − 0.47)
−0.48 (− 0.81, − 0.14)
Other Ethnicity
−0.04 (− 0.50, 0.42)
0.22 (− 0.27, 0.71)
−0.01 (− 0.47, 0.45)
0.21 (− 0.28, 0.70)
Mixed Ethnicity
−0.53 (− 1.00, − 0.06)
−0.08 (− 0.57, 0.41)
−0.53 (− 1.00, − 0.07)
−0.09 (− 0.57, 0.40)
−0.34 (− 0.58, − 0.10)
−0.16 (− 0.41, 0.09)
−0.36 (− 0.60, − 0.12)
−0.16 (− 0.41, 0.09)
White (Ref)
Mean Log Household Income
Booker et al. BMC Public Health (2018) 18:321
Page 9 of 12
Table 3 Covariate parameter estimatesa,b (Continued)
Happiness
Females (n = 4765)
SDQ total difficulties
Males (n = 4903)
Females (n = 4762)
Males (n = 4901)
Social Media Interaction Slope
Black African/Caribbean
−0.05 (− 0.17, 0.06)
−0.02 (− 0.14, 0.10)
−0.06 (− 0.17, 0.06)
−0.03 (− 0.15, 0.09)
Asian
− 0.27 (− 0.37, − 0.17)
−0.10 (− 0.18, − 0.03)
−0.27 (− 0.37, − 0.17)
−0.11 (− 0.19, − 0.04)
Other ethnicity
−0.12 (− 0.26, 0.01)
−0.10 (− 0.20, 0.004)
−0.17 (− 0.27, − 0.03)
−0.09 (− 0.19, 0.01)
Mixed ethnicity
0.13 (0.01, 0.26)
0.01 (−0.09, 0.11)
0.14 (0.02, 0.27)
0.01 (−0.09, 0.10)
0.08 (0.02, 0.14)
0.05 (−0.004, 0.11)
0.09 (0.03, 0.15)
0.06 (−0.002, 0.11)
White (Ref)
Mean log household income
Abbreviation: GCSE General Certificate of Secondary Education, SDQ Strengths and Difficulties Questionnaire
a
Other ethnicity = Gypsy or Irish Traveller, Other White, Any other Asian background, Any other Black background, Arab, Any other ethnic group
b
GCSE exams taken at age 16 (year 11); A Level exam taken at age 18 (year 13); Examples of other higher qualifications are teaching, nursing or diploma
certifications /qualifications; Other qualifications include CSE, skill s certifications, apprenticeships, clerical qualifications, etc.
c
Average effects across age - covariate regressions were held to be equal across ages
males and females. Compared to adolescents who lived
with a partnered mother, those living with an unpartnered mother interacted on social media more; the effect
size was the same for both males and females in the happiness and the SDQ models. Living with an unpartnered
father was associated with worse well-being for females
only; there were no significant associations for males.
Time-invariant associations were mixed and should be
interpreted with caution due to the aggregation of ethnic
groups. Black African/Caribbean adolescents had better
well-being at age 10 compared to White British adolescents. Asian (Indian, Bangladeshi or Pakistani) males
had higher levels of happiness at age 10 and both Asian
males and females showed a greater increase in happiness with age when compared to their White British
counterparts. Asian adolescents also had lower levels of
socio-emotional difficulties at age 10 compared to White
British adolescents. Asian adolescents used social media
less at age 10 and their increase in use with age was
slower than for White British adolescents. In the SDQ
model, males from higher income households had
greater increases of happiness with age compared to
those from lower income households. In both well-being
models, females in higher income households interacted
on social media less at age 10; however their interaction
increased more from 10 to 15 years more than adolescents in lower income households.
Discussion
The results from this study showed that social media
interaction increases with age and happiness decreases
with age for both males and females. While socioemotional difficulties decreased with age for males, they
increased for females. The parallel growth models
showed stark differences by gender, although the
patterns were similar between the two measures of wellbeing. Worse well-being was associated with greater social media interaction at age 10 and the changes over
time were also associated for females. Of most importance, greater interaction on social media at age 10 was
associated with worsening socio-emotional difficulties
with age among females. The findings for males showed
that social media interaction and levels of well-being at
age 10 were associated with their changes with age; however there were no cross-associations. Meaning that initial levels of well-being or social media interaction was
not associated changes in interacting on social media or
well-being levels, respectively. Only, social media interaction and SDQ scores were associated at age 10 in the
SDQ model.
The findings indicate that well-being at older ages
among females is associated with how much they interacted on social media at age 10; this was not the case for
males. This is one of the first studies to show such stark
differences between social media interaction and wellbeing between males and females. Many studies control
for gender and do observe a significant gender main effect; however they do not test for gender interactions or
stratify by gender [25, 40, 41]. In a cross-sectional analysis of UK adolescents, Brodersen et al., [42] found that
the emotional symptoms subscale of the SDQ was associated with sedentary behaviour for females but not for
males. Verduyn et al. [22] have offered potential pathways through which active and passive social media
interaction may impact well-being, social capital and
upward comparison. It is possible that as adolescent
females age there is an increase in upward social
comparison leading to decreases in well-being. While
Verduyn et al. [22] do not theorise on the effects of
active use on upward social comparison, it is possible active use is also associated with upward social comparison. Thus there may be a mediating role of upward
social comparison on the relationship between social
media interaction and well-being among females as they
age. It is possible that by only controlling for gender and
looking across age, these studies are masking the true
Booker et al. BMC Public Health (2018) 18:321
relationships between social media interaction and wellbeing as they might differ by gender.
The male models did show that both happiness and
socio-emotional difficulties decreased with age, however
if these reductions are not associated with social media
interaction what other factors could be responsible?
Many studies have shown that social media interaction
is higher among females than males while males are
more likely to participate in gaming, either via computer
or console [25, 27, 41, 42]. As gaming has become as
interactive as social media, it is possible that greater associations between gaming and well-being might be
found for males than females. Preliminary analysis on
this sample suggests this, data not shown.
The personal and household characteristics of the adolescents produced interesting findings. Levels of wellbeing were better among Black African/Caribbean and
Asian adolescents and changes in happiness were greater
in Asians. This finding of better well-being of ethnic
minority adolescents in the UK has been found elsewhere [43, 44]. A new finding of this study is that Asian
adolescents chatted on social media less and their
increase with age was lower than White British adolescents. Finally there was an association between social
position and social media interaction in that adolescents
from households with lower education or income had
higher levels of interacting on social media and among
females lower income was associated with more social
media interaction at age 10, which has been replicated
with US adolescents [45] but not in the UK [46].
There are several strengths of this study. It uses longitudinal data from a nationally representative sample.
We were able to estimate models separately by gender
showing significant differences in growth factor associations. This study controlled for several time-invariant
and time-varying covariates. The associations between
those covariates and the intercept and slopes of social
media interaction and well-being intercepts and slopes
differed. Associations also varied by age. Finally, the
questions included in this study only assess one form
of active social media interaction, i.e. chatting, and
does not assess other forms of active interaction, nor
passive interaction. So while we cannot examine differences between active and passive use, we are able to
look at longitudinal effects of active use. Active interaction implies content contribution or creation while
passive interaction includes reading but not commenting on posts. Thus active social media interaction may
lead to increased feelings of connectedness and thus
better well-being. The findings from this study contradict this hypothesis as well as previous findings [22].
There are limitations, however, the social media question asks specifically about interaction on a normal
school day and not social media interaction during the
Page 10 of 12
weekend or when not in school, which might be
higher. Thus the findings may be underestimated.
Additionally, there are no questions on patterns or reasons that adolescents interact with social media. Recent studies have identified typologies of use and have
examined how patterns of use are associated with wellbeing. [16, 20–22]. Future waves of UKHLS ask about
weekend use and should be compared to weekday use.
While UKHLS is longitudinal, it was not possible to
use parallel latent growth curve models to examine
within individual changes in the social media interaction and well-being relationship due to the replacement nature of the youth questionnaire and the long
data collection period, 2 years, which did not allow for
creation of cohorts. Use of a longitudinal study without
these issues should enable further examination of
changes over time within individuals.
Conclusions
Advances in technology have resulted in increases in
sedentary behaviour and, in the past, solitary activities.
However with the creation of social media it is possible
to interact with others while still being separate.
Adolescents are increasingly engaged in social media
and the long-term effects on well-being are not fully
known. Some studies suggest that interacting on social
media might reduce social isolation; however there are
others which have come to opposite conclusions. The
findings of this study show gender differences in that
greater social media interaction at age 10 was associated with lower levels of well-being at later ages among
females. The lack of significant associations among
males, suggests that other factors are associated with
the reduction of well-being during adolescence. Future
studies should examine what these factors could be.
Social media interaction increases with age during
adolescence and the current generation is not expected
to reduce their use once they enter adulthood. It is
therefore important to educate adolescents, specifically
females, and their parents on the consequences of high
levels of use at young ages on their future well-being,
not just in later adolescence but in adulthood as well.
Abbreviations
CI: Confidence Interval; GCSE: General Certificate of Secondary Education;
SDQ: Strengths and Difficulties Questionnaire; UK: United Kingdom;
UKHLS: UK Household Longitudinal Study
Acknowledgements
Not applicable.
Funding
Dr. Booker was supported by the UK Economic and Social Research
Council (ESRC): Understanding Society: The UK Longitudinal Household
Study (RES-586-47-0001); Understanding Society, the UK Longitudinal
Studies Centre (RES-586-47-0002) and the Research Centre on
Micro-Social Change (MiSoC) (award no. RES-518-28-001). Professors Kelly
Booker et al. BMC Public Health (2018) 18:321
and Sacker were supported by the ESRC International Centre for
Lifecourse Studies in Society and Health (ES/J019119/1). Understanding
Society is funded through the following ESRC grants: Understanding
Society: The UK Longitudinal Household Study (RES-586-47-0001);
Understanding Society, the UK Longitudinal Studies Centre
(RES-586-47-0002). The ESRC provided funding for the collection of
UKHLS data; however they were not involved in the study design,
analysis, interpretation of the data or writing up of this paper.
The decision to submit this paper for publication was at the
discretion of the authors.
Page 11 of 12
8.
9.
10.
11.
12.
Availability of data and materials
UKHLS data is available from the UK Data Service
https://discover.ukdataservice.ac.uk/catalogue/?sn=6614. Data documentation
is available from the Understanding Society website
https://www.understandingsociety.ac.uk/documentation.
13.
Author’s contributions
CB conducted the data management, helped with the design of the
analytical scheme, conducted the analysis and prepared the manuscript. AS
and YK provided input on the analytical scheme. AS assisted with analytical
issues and reviewed and provided input on the manuscript. YK assisted with
variable construction and reviewed the manuscript. All authors read and
approved the final manuscript.
14.
Ethics approval and consent to participate
Ethical approval was obtained from the university ethics committee and the
National Research Ethics Service. Adults 16 and older provided written
consent to participate. For adolescents aged 10-15, parental or responsible
adult verbal consent was required and obtained for participation.
17.
Consent for publication
Not applicable.
19.
Competing interests
The authors declare no competing interests.
20.
Publisher’s Note
21.
15.
16.
18.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
22.
Author details
1
Institute for Social and Economic Research, University of Essex, Wivenhoe
Park, Colchester CO4 3SQ, UK. 2ESRC International Centre for Lifecourse
Studies in Society and Health, Department of Epidemiology and Public
Health, University College London, 1-19 Torrington Place, London WC1E 6BT,
UK.
23.
24.
Received: 24 October 2016 Accepted: 26 February 2018
25.
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