Media Use, Sports Participation, and Well-Being in Adolescence: Cross-Sectional Findings From the UK Household Longitudinal Study

Abstract

Objectives. We investigated the relationship between selected types of screen-based media (SBM) use, total SBM use, sports participation, and markers of well-being.

Methods. Data came from the youth panel (n = 4899) of Understanding Society, the UK Household Longitudinal Study, conducted in 2009. Well-being was measured by the Strengths and Difficulties Questionnaire and markers of happiness in different life domains.

Results. The majority of young people used multiple types of SBM for at least 1 hour per day; only 30% participated in sports every day. Overall, young people with heavy SBM use were less happy than moderate users and more likely to have socioemotional difficulties. Chatting on social networking Web sites and game console use were associated with higher odds of socioemotional problems. Higher total SBM use was associated with lower odds of happiness and higher odds of socioemotional difficulties. Greater participation in sports was associated with higher odds of happiness and lower odds of socioemotional difficulties.

Conclusions. Further longitudinal research could inform future interventions to reduce sedentary behavior and encourage healthy lifestyles among young people.

Adolescence is a critical period for the development and establishment of behaviors and attitudes that continue into adult life.¹ Active behaviors can help to reduce poor health in later years, and sedentary behaviors may increase the risk of poor health. The association between sedentary behavior and markers of physical health is well established, but the relationship between sedentary behaviors and markers of mental well-being in young people has not been as extensively explored. Studies have found that more screen-based media (SBM) use was associated with lower likelihood of contentedness, poorer self-image, and physical aggression.^2–4 However, these studies did not take into account Internet use. Young people often use social networking Web sites as venues for socializing and communicating,⁵ and research has found positive associations between depressive symptoms and social networking.^5–7

Many mechanisms through which SBM use and markers of well-being may influence each other have been hypothesized. For example, SBM use has been associated with low self-esteem,^8,9 poor academic performance,^8,9 and obesity and decreased fitness.^9,10 These in turn could lead to decreased well-being. In addition, the content of SBM and reasons for use may have indirect effects on well-being. For example, increased violence and aggression,⁹ substance use,⁹ or sexual behaviour⁹ may lead to lower levels of well-being. Alternatively, moderate use of SBM for information gathering or social interaction may enhance well-being.¹¹

Another mechanism through which SBM use may be associated with well-being is described in the displacement hypothesis: participation in activities such as watching television or using social networks takes up time that otherwise would be spent being physically active or hanging out with friends in person.^4,12,13 This displacement of activities may have a negative effect on well-being if interactions with people with whom relationship ties are strong are replaced. If a replacement activity, such as television viewing, replaces physical activity, well-being may suffer, because studies have shown a positive relationship between physical activity and well-being.^3,14

Recent studies have shown that the effects of SBM use on markers of health and well-being differ by type of media, with increased television viewing producing the most consistent results.^15–17 We added to previous work by looking at use of social media sites and different gaming modes (computer games and game consoles). We also included participation in sports, because studies have shown an inverse relationship between SBM use and physical activity, along with an independent association of physical activity with well-being in young people.^4,10,15–21 Through the simultaneous examination of different SBM, we gained a more comprehensive picture of SBM use and well-being than was previously possible. Our objectives were to describe SBM use among UK youths and to examine the association of SBM use and sports participation with well-being.

METHODS

Data came from Understanding Society, the UK Household Longitudinal Study (UKHLS). The UKHLS is a nationally representative longitudinal study that began in 2009. We analyzed cross-sectional data from the first wave of the UKHLS. The survey used a 2-stage (stratified and clustered) sampling scheme to identify primary sampling units. The UKHLS also oversampled for ethnic minority groups. More detailed information on the sampling frame and data collection procedures is available in Burton et al.²² Verbal consent was required of the parent or responsible adult for each young person, aged 10 to 15 years, to fill out a 10-minute paper-and-pencil survey (n = 4899 youths living in 3656 households). The self-completion survey was administered while other interviews were taking place; however, parents were not present while the youth respondent completed the survey.

The analytic sample represented 74% of the invited sample of 6627 persons.²³ Nonrespondents were more likely than respondents to be from an ethnic minority group and to live in a rental property, and they had lower household incomes and larger households. Respondents and nonrespondents also displayed regional differences (all differences, P < .001). No gender or age differences were observed, and nonrespondents were no more likely than respondents to live with single parents.

Measures

Screen-based media use.

We used 4 questions to assess SBM use. We determined chatting on social networking Web sites by the question, “How many hours do you spend chatting or interacting with friends through a social Web site like that on a normal school day?” (A previous question gave examples of social Web sites as Bebo, Facebook, and MySpace.)

We assessed game console use by the question, “How many hours do you spend playing games on a games console on a normal school day?” We determined computer game use by the question, “How many hours do you spend using the computer at home for playing games on a normal school day?” We assessed watching TV by the question, “How many hours do you spend watching TV, including video and DVDs, on a normal school day?”

Response options for the SBM use questions were the same 5-point scale, ranging from none to 7 or more hours. We combined responses into 3 categories because of small cell sizes: less than an hour, 1 to 3 hours, and 4 or more hours. We created an index of total SBM use on a normal school day by summing all items. This scale ranged from 4 to 12, with higher numbers indicating more hours spent using SBM.

Sports participation.

We ascertained sports participation from the question, “How many days in a usual week do you play sports, do aerobics or do some other keep fit activity?” Response categories were on a 6-point scale ranging from never or hardly ever to every day. We combined the lowest 2 participation categories as less than once per week. Correlations of SBM use and sports participation variables are given in Table A (available as a supplement to the online version of this article at http://www.ajph.org).

Markers of well-being.

We determined happiness with life from 6 questions on how young people felt about school work, appearance, their family, friends, school, and life as a whole. Each question had 7 emoticon faces expressing feelings ranging from completely happy to not at all happy. We combined the items into an overall happiness scale (Cronbach α = 0.77), with higher scores indicating greater happiness.²⁴

Socioemotional difficulties were self-reported on the Strengths and Difficulties Questionnaire (SDQ), an instrument used to screen for behavioral and emotional problems in children aged 3 to 16 years.²⁵ The 25 SDQ items are scored on a 3-point Likert scale and can be summed into 5 subscales; a total difficulties score is created by summing 4 subscales (Cronbach α = 0.79). Scores from 20 to 40 points on the total difficulties score indicate risk of clinically relevant socioemotional difficulties.²⁶

Only 2% of youths were in the top decile of happiness and in the clinical range of the SDQ. The correlation between the happiness scale and the SDQ total difficulties score was −0.51 (P < .05).

Covariates.

We scored parents' highest educational qualification level as a 6-point categorical variable with responses ranging from no qualification to degree. We used the highest qualification achieved by either parent. We determined household income from the total gross previous month household income, equivalized for household composition with the Organisation for Economic Co-operation and Development modified equivalence scale²⁷ and then recoded into quintiles for the analysis. Both parental educational qualification and household income were provided by the parent(s) in the adult interview of the UKHLS.

We combined age into 2 groups for descriptive purposes: 10 to 12 years and 13 to 15 years. We included age in models as a continuous variable. Gender was a dichotomous variable.

Analysis

We conducted all statistical analyses in Stata MP version 12.²⁸ We used weights to take account of the unequal probability of being sampled. Both outcome scales were highly skewed toward the end of the distribution, indicating better well-being. We therefore dichotomized scores to identify the top decile of the happiness scale (scores ≥ 41) and those above the criterion for clinical risk on the SDQ scale. We estimated logistic regression models for happiness and socioemotional difficulties with Stata’s SVY commands to adjust standard errors for the clustering of young people within households.

We estimated 3 models for each outcome, happiness and socioemotional difficulties, giving a total of 6 models: (1) bivariate models with 1 type of SBM use at a time (logistic regression) or total SBM use as the independent variable (linear regression); (2) multivariate models that controlled for age, gender, highest parental educational qualification, and household income; and (3) multivariate models that simultaneously adjusted for all other markers of SBM use (except total SBM use), sports participation, and all control variables. We used the Wald test to estimate the combined effect of individual SBM use categories on each measure of well-being. We included a household composition variable in a sensitivity analysis with no changes to the results, so we did not include it in the final analysis. The reference categories were less than 1 hour per day for SBM use and every day for sports participation. Because the literature shows gender and age differences in SBM use, we tested 2-way interactions of markers of SBM use (or total SBM use) with gender and age, with adjustment for sociodemographic characteristics.

RESULTS

The sample was evenly split by gender and age (Table 1). More than 20% (weighted percentage) of the sample had parents with degree qualifications. Less than 40% of the sample chatted on social Web sites or played on game consoles and less than half used computer games for more than 1 hour per day. More than 75% of young people reported watching television for more than an hour per day. Slightly less than 30% the sample reported participating in sports every day. The mean score for total SBM use was 6.32 (SD = 1.50).

TABLE 1—

Distribution of Sociodemographic and Well-Being Measures Among Youths Aged 10–15 Years: UK Household Longitudinal Study, 2009

Variable	No. (Weighted %)
Gender
Male	2436 (51.50)
Female	2463 (48.50)
Age, y
10	800 (16.15)
11	829 (16.50)
12	798 (16.43)
13	851 (17.08)
14	798 (16.24)
15	823 (17.60)
Highest parental educational qualification
None	571 (9.65)
Other qualificationa	252 (4.89)
GCSEb	1026 (21.51)
A levelc	960 (20.32)
Other higher qualificationd	774 (16.73)
Degree	1260 (26.90)
Equivalized income quintiles
1 (lowest)	1128 (20.00)
2	994 (20.01)
3	947 (20.02)
4	919 (20.00)
5 (highest)	870 (19.97)
Chatting on social Web sites, h/d
< 1	3165 (63.93)
1–3	1407 (30.03)
≥ 4	288 (6.04)
Playing computer games on school day, h/d
< 1	2548 (51.99)
1–3	1920 (39.53)
≥ 4	403 (8.48)
Using game console on school day, h/d
< 1	3220 (64.53)
1–3	1367 (29.26)
≥ 4	288 (6.22)
Watching TV on school day, h/d
< 1	1183 (24.42)
1–3	2898 (60.13)
≥ 4	795 (15.45)
Sports participation, d/wk
< 1	321 (6.38)
1–2	935 (18.60)
3–4	1309 (27.12)
5–6	872 (18.41)
7	1428 (29.48)

Note. GCSE = General Certificate of Secondary Education. Sample size was n = 4899.

^aE.g., Certificate of Secondary Education, skills certifications, apprenticeships, clerical qualification.

^bExams taken at age 16 (year 11).

^cExam taken at age 18 (year 13).

^dE.g., teaching, nursing or diploma certifications–qualifications.

Prevalences of SBM use, sports participation, and markers of well-being by gender, age group, household income quintile, and highest parental educational qualification are shown in Tables B to E (available as supplements to the online version of this article at http://www.ajph.org).

Young people who participated in more sports spent less time using SBM. For example, 55% of youths who participated in sports at least 5 days and 45% of young people who participated in sports 1 day or less per week played computer games for 1 hour per day or less (data not shown). Correlations between SBM use measures, physical activity, and total SBM use can be found in Table A (available as an online supplement).

Happiness

Young people who chatted on social Web sites between 1 and 3 hours per day were about half as likely to be happy as those who chatted for less than 1 hour per day; respondents who chatted for more than 4 hours per day had 57% lower odds of happiness (Wald χ² [2,n = 4764] = 36.97; P < .001; Table 2, model 1). We observed similar patterns for young people who spent more time on computer games (Wald χ² [2,n = 4764] = 36.97; P < .001), game consoles (Wald χ² [2,n = 4779] = 8.77; P = .01), and watching television (Wald χ² [2,n = 4778] = 9.89; P = .007). Some individual associations were weaker and in some cases nonsignificant. For example, playing computer games for 1 to 3 hours per day was associated with lower happiness; however, the same level of television viewing was not. These estimates were somewhat attenuated by the addition of sociodemographic covariates, but patterns were unchanged and still statistically significant, with 1 exception (model 2).

TABLE 2—

Logistic Regression of Youth Happiness on Screen-Based Media Use and Sports Participation: UK Household Longitudinal Study, 2009

Variable	Happy,a %	Model 1: Bivariate, OR (95% CI)	Model 2: Multivariate Adjusted for Confounders,b OR (95% CI)	Model 3: Multivariate Fully Adjusted,c OR (95% CI)
Chatting on social Web sites, h/d
< 1 (Ref)	12.91	1.00	1.00	1.00
1–3	6.68	0.48 (0.37, 0.62)	0.61 (0.47, 0.81)	0.69 (0.52, 0.92)
≥ 4	5.98	0.43 (0.26, 0.72)	0.61 (0.36, 1.05)	0.78 (0.44, 1.36)
Playing computer games on school day, h/d
< 1 (Ref)	13.20	1.00	1.00	1.00
1–3	8.23	0.59 (0.47, 0.73)	0.62 (0.50, 0.77)	0.75 (0.59, 0.95)
≥ 4	6.03	0.42 (0.26, 0.68)	0.56 (0.35, 0.91)	0.74 (0.44, 1.22)
Using game console on school day, h/d
< 1 (Ref)	11.65	1.00	1.00	1.00
1–3	8.63	0.72 (0.56, 0.91)	0.68 (0.52, 0.88)	0.77 (0.59, 1.00)
≥ 4	8.11	0.67 (0.41, 1.11)	0.71 (0.42, 1.20)	0.92 (0.55, 1.54)
Watching TV on school day, h/d
< 1 (Ref)	12.39	1.00	1.00	1.00
1–3	10.64	0.84 (0.67, 1.06)	0.83 (0.66, 1.04)	0.91 (0.72, 1.16)
≥ 4	7.54	0.58 (0.41, 0.81)	0.53 (0.37, 0.76)	0.64 (0.44, 0.93)
Sports participation, d/wk
< 1	5.95	0.36 (0.21, 0.59)	0.40 (0.24, 0.67)	0.43 (0.25, 0.74)
1–2	6.98	0.42 (0.30, 0.58)	0.44 (0.31, 0.62)	0.46 (0.33, 0.64)
3–4	8.18	0.50 (0.38, 0.66)	0.50 (0.38, 0.67)	0.52 (0.39, 0.70)
5–6	11.82	0.75 (0.57, 1.00)	0.75 (0.57, 1.00)	0.77 (0.58, 1.02)
7 (Ref)	15.10	1.00	1.00	1.00

Note. CI = confidence interval; OR = odds ratio.

^aTop decile of happiness with life scale.

^bAge, gender, highest parental educational qualifications, and household income.

^cAll confounders included in model 2 and all other screen-based and sport participation indicators.

We observed a linear relationship between greater sports participation and happiness. This relationship was slightly attenuated with the addition of sociodemographic covariates but remained statistically significant (Wald χ² [4,n = 4706] = 36.15; P < .001). We found a significant interaction between game console use and age (Wald χ² [2,n = 4710] = 7.30; P = .03). As age increased, the odds of happiness increased for youths who played console games 1 to 3 hours per day. No other interactions were significant (not shown).

In the simultaneously adjusted model (Table 2, model 3), chatting on social Web sites and sports participation remained significantly associated with happiness. Young people who chatted on social Web sites for 1 to 3 hours per day were about two thirds as likely to be happy as those who spent less than 1 hour on social Web sites (Wald χ² [2,n = 4640] = 6.54; P = .04). Only young people who spent 4 or more hours viewing television had significantly lower odds of happiness than other respondents (Wald χ² [2,n = 4640] = 5.79; P = .06).

We observed a 24% decrease in the odds of happiness for every unit increase in total SBM use (Table 3).These odds became slightly larger with the addition of sociodemographic covariates. The addition of sports participation had little further attenuating effect.

TABLE 3—

Logistic Regression of Youth Happiness on Total Screen-Based Media Use and Sports Participation: UK Household Longitudinal Study, 2009

Variable	Model 1: Bivariate, OR (95% CI)	Model 2: Multivariate Adjusted for Confounders,a OR (95% CI)	Model 3: Multivariate Fully Adjusted,b OR (95% CI)
Total screen-based media use	0.76 (0.69, 0.83)	0.80 (0.72, 0.87)	0.81 (0.74, 0.89)
Sports participation, d/wk
< 1			0.44 (0.26, 0.75)
1–2			0.46 (0.33, 0.65)
3–4			0.53 (0.40, 0.71)
5–6			0.78 (0.58, 1.04)
7 (Ref)			1.00

Note. CI = confidence interval; OR = odds ratio. Youth happiness was defined as scoring in the top decile of happiness with life scale.

^aAge, gender, highest parental educational qualifications, and household income.

^bAll confounders included in model 2 and sports participation.

Socioemotional Difficulties

Socioemotional difficulties and happiness showed similar associations: higher SBM use was generally associated with higher odds of socioemotional difficulties (Table 4). However, we found 3 unique correlations. Young people who reported spending 1 to 3 hours chatting on social Web sites (Wald χ² [2,n = 4784] = 18.26; P = .001) or watching television (Wald χ² [2,n = 4798] = 6.87; P = .03) were no more likely than those who spent less time in these activities to experience socioemotional difficulties. Young people who chatted on social Web sites or played computer or console games 4 or more hours per day were at least twice as likely to have socioemotional difficulties as those who spent less than an hour per day in these pursuits. The effect of the highest level of television viewing was much less pronounced.

TABLE 4—

Logistic Regression of Socioemotional Difficulties on Screen-Based Media Use and Sports Participation: UK Household Longitudinal Study, 2009

Variable	Socioemotional Difficulties,a %	Model 1: Bivariate, OR (95% CI)	Model 2: Multivariate Adjusted for Confounders,b OR (95% CI)	Model 3: Multivariate Fully Adjusted,c OR (95% CI)
Chatting on social Web sites, h/d
< 1 (Ref)	7.37	1.00	1.00	1.00
1–3	8.07	1.10 (0.86, 1.42)	1.36 (1.03, 1.79)	1.32 (0.99, 1.76)
≥ 4	15.20	2.25 (1.55, 3.27)	3.03 (2.01, 4.59)	2.38 (1.47, 3.84)
Playing computer games on school day, h/d
< 1 (Ref)	6.60	1.00	1.00	1.00
1–3	8.79	1.36 (1.07, 1.74)	1.33 (1.04, 1.70)	1.09 (0.84, 1.42)
≥ 4	13.67	2.24 (1.58, 3.18)	2.38 (1.64, 3.44)	1.33 (0.87, 2.03)
Using game console on school day , h/d
< 1 (Ref)	6.26	1.00	1.00	1.00
1–3	10.54	1.76 (1.38, 2.25)	1.72 (1.30, 2.29)	1.63 (1.23, 2.17)
≥ 4	15.11	2.67 (1.83, 3.89)	2.73 (1.80, 4.15)	2.21 (1.45, 3.39)
Watching TV on school day, h/d
< 1 (Ref)	6.92	1.00	1.00	1.00
1–3	7.90	1.15 (0.87, 1.53)	1.12 (0.84, 1.50)	1.10 (0.82, 1.49)
≥ 4	10.51	1.58 (1.11, 2.24)	1.48 (1.03, 2.12)	1.22 (0.83, 1.77)
Sports participation, d/wk
< 1	17.49	2.95 (1.98, 4.40)	3.48 (2.31, 5.25)	3.62 (2.38, 5.50)
1–2	8.90	1.36 (0.97, 1.90)	1.56 (1.09, 2.21)	1.59 (1.11, 2.28)
3–4	7.79	1.18 (0.86, 1.61)	1.33 (0.96, 1.85)	1.39 (1.00, 1.92)
5–6	6.30	0.94 (0.64, 1.36)	0.99 (0.69, 1.46)	1.05 (0.72, 1.54)
7 (Ref)	6.70	1.00	1.00	1.00

Note. CI = confidence interval; OR = odds ratio.

^aDefined as scoring in the top decile of Strengths and Difficulties Questionnaire.

^bAge, gender, highest parental educational qualifications, and household income.

^cAll confounders included in model 2 and all other screen-based and sport participation indicators.

Adjustment for sociodemographic covariates resulted in an amplification of the odds ratios for more chatting on social Web sites and more use of computer games and game consoles. For example, the odds ratio for young people who chatted on social Web sites for more than 4 hours per day increased with adjustment (Wald χ² [2,n = 4718] = 27.77; P < .001). We also saw amplification for all levels of sports participation except 5 to 6 days per week.

Gender, age, and socioeconomic characteristic differences in SBM use may help to explain the observed amplification (Tables B–E, available as online supplements). We found 3 significant interactions: television viewing with gender and with age and game console use with age. Adolescent girls who watched television 1 to 3 hours per day were 57% less likely than adolescent boys to have socioemotional difficulties (Wald χ² [2,n = 4730] = 10.12; P = .01). Increased age was associated with decreased odds of socioemotional difficulties for those who watched television (Wald χ² [2,n = 4707] = 16.07; P < .001) or used game consoles (Wald χ² [2,n = 4732] = 9.08; P = .01) for more than 1 hour per day.

Models that simultaneously adjusted for all SBM use and sociodemographic covariates showed attenuation of the estimates derived from the multivariate models that adjusted for confounders only. Use of computer games and television viewing were no longer independently associated with socioemotional difficulties.

The odds of socioemotional difficulties increased about 27% per unit increase in total SBM use (Table 5). The addition of sociodemographic covariates had little effect, and sports participation had no attenuation or amplification effect.

TABLE 5—

Logistic Regression of Socioemotional Difficulties on Total Screen-Based Media Use and Sports Participation: UK Household Longitudinal Study, 2009

Variable	Model 1: Bivariate, OR (95% CI)	Model 2: Multivariate Adjusted for Confounders,a OR (95% CI)	Model 3: Multivariate Fully Adjusted,b OR (95% CI)
Total screen-based media use	1.27 (1.18, 1.37)	1.29 (1.19, 1.39)	1.29 (1.19, 1.39)
Sports participation, d/wk
< 1			3.54 (2.34, 5.36)
1–2			1.55 (1.09, 2.21)
3–4			1.37 (0.99, 1.89)
5–6			1.04 (0.71, 1.52)
7 (Ref)			1.00

Note. CI = confidence interval; OR = odds ratio. Socioemotional difficulties defined as scoring in the top decile of Strengths and Difficulties Questionnaire.

^aAge, gender, highest parental educational qualifications, and household income.

^bAll confounders included in model 2 and sports participation.

DISCUSSION

We found that young people who were heavier SBM users were less likely to be happy and more likely to have socioemotional difficulties than youths who spent less time engaged with SBM. Participation in sports was positively associated with happiness and negatively associated with socioemotional difficulties. More than half of UK youths used SBM for at least 1 hour per day, and close to 50% participated in sports at least 5 times per week. The patterns of sports participation were in line with the Health Survey for England, with slight gender differences.²⁹

The relationship of SBM use with well-being appeared to be equal but opposite for the 2 well-being outcomes. Generally, using any medium between 1 and 3 hours per day was associated with lower odds of happiness; however, with the exception of computer gaming and television viewing, more than 4 hours of use was not significantly associated with lower happiness after adjustment for confounders, and in the fully adjusted model, only the television viewing association remained significant. Although these findings might be attributable to a lack of power to detect differences, this is unlikely because we did not observe similar patterns of nonsignificance for socioemotional difficulties. Thus the associations with certain types of SBM use may differ for different markers of well-being. The attenuation of the odds ratios in the final models with all SBM use variables included points to the covariation of these variables. SBM can be and often are used simultaneously, for example, television viewing and chatting on social Web sites. Thus the total SBM use index may have provided a more complete picture of the association with markers of well-being, showing that an increase in total SBM use was associated with reduced well-being.

We observed higher average total SBM use scores among young people with lower sports participation, results also found in other studies with similar age groups.³⁰ Although our findings suggest displacement of sports participation with social media, the relationship between SBM use and well-being did not change with the addition of sports participation. Therefore, the association between SBM use and well-being may be mediated through some other mechanism, such as the quality of peer interactions. The displacement of high-quality time spent with peers with whom adolescents have strong social ties with low-quality interactions with online peers with weaker ties may result in lower overall quality or quantity of relationships, negatively affecting well-being.³¹

It has been shown that elements of well-being track throughout the life course.^32,33 A vast literature focuses on health-related behaviors and measures of well-being, but the links between sedentary behaviors, including SBM use, and young people’s well-being are not so clearly established.³⁴ It is generally accepted that SBM use is an integral part of young people’s lives. Furthermore, it has been reported that social network Web site use promotes face-to-face contact rather than leading to social isolation.³⁵ Heavy social network Web site use possibly renders young people vulnerable to unwanted attention, harassment, and the temptation to imitate risky behavior reported by peers, leading to socioemotional difficulties.^36,37 Parental supervision is important, because studies suggest that it ameliorates negative impacts on youth well-being.³⁸

Limitations

We drew on a large representative sample of UK young people to describe their SBM use and well-being. However, the data were cross-sectional, and causal inferences cannot be drawn. We modeled odds of well-being, but changes in well-being may lead to changes in SBM use behaviors and sports participation. We did not have data on use of social networking through different technology, such as computers, mobile phones, MP3 players, or tablets. The data were self-reported, which could have implications for the findings if the estimates of the number of hours per day spent on the activities were imprecise. For example, the slight gender differences in sports participation between the UKHLS and the Health Survey for England could be attributable to reporting methods. In the Health Survey for England, a parent answered questions about sports participation for children younger than 12 years, whereas all young people aged 10 to 15 years self-reported sports participation in the UKHLS.

The UKHLS measure did not ask young people to distinguish between physically more active (e.g., football, tennis, running) and less active (yoga, archery, darts) sports or between formal and informal sports participation. We did not know about weekend sports participation, what percentage of games played on consoles were active rather than sedentary, when SBM use happened during the day, SBM use outside of the home, or the content of the SBM. Although the addition of family structure type did not change the findings, it is possible that parenting style or other family processes affected the amount of SBM use and, in turn, well-being. Finally, the response options given for measuring the explanatory and outcome variables could have biased the reporting of these behaviors.

Although we created an index of total SBM, we were not able to specify total hours per day and could not examine use above and below expert and government recommendations. Recommendations vary by country and type of SBM; some countries only specify amount of television viewing, and others recommend limits on total SBM use.³⁹ Regardless of the medium, most recommend using SBM for 2 hours or less per day.³⁹

Conclusions

SBM use is becoming increasingly important in the lives of today’s young people, and the tools and technology used to access the Internet and play games are ever changing. Greater use of computers, smart phones, and tablets for games and access to social Web sites contribute to more sedentary lifestyles, which may have implications for health and well-being in later life. Future studies should expand the types of SBM use, provide more detailed estimations of hours per day, explore whether young people are active participants, and investigate the longer-term effects of SBM use in adolescence.

The development of health-related practices and behaviors begins in adolescence. These behaviors can be shaped by the circumstances in which adolescents live and are reinforced by prevailing social norms. Research has shown clear continuities in health behaviors and well-being from adolescence into later life, and our findings can help inform future longitudinal studies to better understand the mechanisms through which SBM use and well-being influence each other and what the later-life health outcomes may be.