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. Author manuscript; available in PMC: 2021 Feb 15.
Published in final edited form as: J Affect Disord. 2019 Nov 30;263:39–46. doi: 10.1016/j.jad.2019.11.130

Reciprocal associations between depression and screen-based sedentary behaviors in adolescents differ by depressive symptom dimension and screen-type

Jennifer Zink a, Shayan Ebrahimian a, Britni R Belcher a, Adam M Leventhal a,b
PMCID: PMC7035144  NIHMSID: NIHMS1545956  PMID: 31818794

Abstract

Background

The heterogeneity of depression and sedentary behavior complicates understanding of mechanisms underlying and interventions addressing comorbid depression and sedentary behavior in adolescents. This study investigated reciprocal associations between four depressive symptom dimensions and two types of screen-based sedentary behaviors in adolescents, and tested whether these associations differed by sex.

Methods

A school-based longitudinal cohort (N=2,717, baseline Mage=14.57 years) completed questionnaires during two assessments one year apart. Participants reported on levels of four depressive symptom dimensions (continuous) and television viewing and computer/videogame use (≥2 hours/day; yes vs. no).

Results

Reciprocal associations between computer/videogame use and higher negative affect symptoms were observed (computer/video games predicting negative affect, β=0.06, 95%CI 0.01–0.11, p=0.01; negative affect predicting computer/video games, OR 1.29, 95%CI 1.06–1.58, p=0.01). The relationship between baseline computer/videogame use and subsequent negative affect differed by sex (p=0.02), and was significant in girls (β=0.11, 95%CI 0.04–0.18, p=0.002), but not boys (β=0.004, 95%CI −0.06–0.07, p=0.90). Baseline computer/videogame use predicted somatic symptoms at follow-up (β=0.06, 95%CI 0.01–0.10, p=0.02), but the reverse association was non-significant. Higher baseline positive affect predicted computer/videogame use at follow-up (OR 1.16, 95%CI 1.02–1.31, p=0.02), but the reverse association was non-significant. Television viewing was unrelated to each depressive symptom dimension in either direction.

Limitations

Self-reports may be subject to recall errors and biases, and do not provide clinical diagnoses.

Conclusions

Associations are not uniform across screen-based sedentary behavior type, depressive symptom dimension, and sex. The association between computer/videogame use and negative affect symptoms may be reciprocal across time and particularly robust among girls.

Keywords: digital media, emotional health, bidirectional, youth

Introduction

Screen-based sedentary behaviors (e.g., television viewing, computer use) are common among youth and are associated with increased waist circumference, higher body mass index, and can prospectively increase risk for overweight/obesity and type 2 diabetes (Saunders et al., 2013; Thorp et al., 2011). Depressive symptoms in adolescents are also highly prevalent and can confer similar health risks (Assari et al., 2018; Hannon et al., 2013). Screen-based sedentary behaviors and depressive symptoms may be risk factors for overlapping negative health outcomes because they co-occur with one another (Hayward et al., 2016; Hume et al., 2011; Raudsepp, 2016). Moreover, emerging evidence suggests that screen-based sedentary behaviors and depressive symptoms may have reciprocal associations with one another—over time, depressive symptoms may increase the likelihood that one engages in screen-based sedentary behaviors and in turn, risk for depression continues to increase (Cushing et al., 2017; Gunnell et al., 2016; Houghton et al., 2018).

Despite the probable link between screen-based sedentary behaviors and depressive symptoms, discrepancies in the literature remain; which may be attributed to the inconsistent conceptualization of screen-based sedentary behavior across studies. Evidence from a population-based study suggests that total time spent engaged in screen-based sedentary behaviors is associated with depressive symptoms (Twenge and Campbell, 2018). However, in investigations where analyses are further stratified by screen-type, associations with depressive symptoms are oftentimes driven by behaviors such as computer use and videogame use, rather than television viewing (Goldfield et al., 2016; Maras et al., 2015). Thus, the strength of the association between screen-based sedentary behaviors and depressive symptoms can differ by screen-type and may depend on how screen-based sedentary behaviors are conceptualized in a given study.

Another barrier to understanding the relationship between screen-based sedentary behaviors and depression is the heterogeneity of depressive symptoms—depression can manifest as any combination of distinct emotional, physical, cognitive, or social symptoms (Van Dam and Earleywine, 2011; Vanheule et al., 2008). Extant studies to date have utilized measures of depression that are based on a diagnosis or a symptom composite score that amalgamates the presence or levels of symptoms (Hoare et al., 2016). However, depression is unlikely to be a unitary phenotype and involves multiple different symptom expressions that may reflect distinct phenotypes, each of which may have some unique etiological causes (Hankin, 2006; Hasler et al., 2004). Thus, investigating the different symptom dimensions and their relationships with screen-based sedentary behaviors may shed light on the mechanisms linking the two.

Prior research has found that different depressive symptom dimensions uniquely predict the likelihood of engaging in other detrimental health behaviors (Leventhal et al., 2008). Only one cross-sectional study has looked at the different dimensions of depressive symptoms as they relate to sedentary behaviors, finding that interpersonal problems are most tightly linked with time spent sedentary compared to the other depressive symptom dimensions (Anton et al., 2006); which may partially be explained by the exposure to cyberbullying and the displacement of in-person social interactions that can occur as a result of engaging in screen-based sedentary behaviors (Sampasa-Kanyinga et al., 2014; Shen and Williams, 2011). However, it remains unknown whether the longitudinal and reciprocal associations between screen-based sedentary behaviors and depressive symptoms differ by depressive symptom dimension. Furthermore, previous studies indicate stronger sedentary behavior-depressive symptom associations among adolescent girls compared to boys (Ohannessian, 2009; Vancampfort et al., 2018). The rapid physiological and social changes that occur during puberty may put adolescent girls at particular risk for greater depressive symptoms and increased time spent in sedentary behaviors, compared to adolescent boys (Bacil et al., 2016; Skoog et al., 2016). Yet, no studies have explored whether sex moderates the screen-based sedentary behavior and depressive symptom dimension association, which is critical for sex-specific risk identification and intervention.

Therefore, the aim of the present study was to investigate if the four symptom dimensions of depression (i.e., negative affect, positive affect, somatic symptoms, and interpersonal disturbance) are uniquely and reciprocally associated with two types of screen-based sedentary behaviors (i.e., television viewing and computer/videogame use) in adolescents across a one-year period. We also examined if the associations differed by sex. Identifying the depressive symptom dimensions that are most tightly linked to specific forms of screen-based sedentary behaviors will inform targeted intervention strategies and elucidate the mechanisms linking these variables.

Methods

Participants and Procedures

The present analyses drew data from participants enrolled in the Happiness and Health (H&H) study. The H&H study’s primary purpose was to understand health behaviors and mental health in a large cohort of Los Angeles-area high school students (Leventhal et al., 2015). Forty schools with diverse demographic characteristics were recruited for the H&H study, and of these, 10 schools agreed to participate.

All ninth-grade students enrolled in a standard educational program in one of the participating schools in fall 2013 were eligible for the study. Prior to participation, students and their parents were required to provide written or verbal assent and consent, respectively. Data were collected via paper-and-pencil surveys at each time-point onsite in the students’ classrooms. Data in the present report involved two assessment waves—baseline (fall 2013, ninth grade) and one at 12-month follow-up (fall 2014, tenth grade). Students who were not onsite during the in-person data collection periods were given an abbreviated version of the survey (which omitted screen-based sedentary behavior variables) over the phone. All study procedures were approved by the University of Southern California institutional review board.

Measures

Screen-based sedentary behavior

Television viewing was measured utilizing the U.S. Youth Risk Behavior Surveillance System (YRBSS) item “On an average school day, how many hours do you watch TV?” with response options as: I do not watch TV on the average school day, less than 1 hour per day, 1 hour per day, 2 hours per day, 3 hours per day, 4 hours per day, 5 or more hours per day. Computer/videogame use was measured with the YRBSS item “On an average school day, how many hours do you play video or computer games or use a computer for something that is not school work? (Count time spent on things such as Xbox, PlayStation, an iPod, an iPad or other tablet, a smartphone, YouTube, Facebook or other social networking tools, and the Internet).” Response options included: I do not play video or computer games or use a computer for something that is not school work, less than 1 hour per day, 1 hour per day, 2 hours per day, 3 hours per day, 4 hours per day, 5 or more hours per day. Both screen-based sedentary behavior variables were each dichotomized as ≥2 hours/day vs. not, consistent with previous studies (Belanger et al., 2011; Houghton et al., 2015). This dichotomization cut point was also based on evidence from a meta-analysis (Liu et al., 2016) and from a recent population-based study among 14 to 17-year-olds that both suggest that rates of depression continuously increase once 2 or more hours of daily screen-based sedentary behaviors are accumulated (Twenge and Campbell, 2018). Items assessing screen-based sedentary behaviors from the YRBSS have demonstrated adequate psychometric properties in adolescents (Brener et al., 2002; Brener et al., 2013).

Depressive symptoms

Students completed the Center for Epidemiologic Studies Depression Scale (CES-D). This 20-item self-report questionnaire was designed for use in the general population to measure depressive symptoms during the past week (Radloff, 1977). Previous work has consistently demonstrated a four-factor structure of the CES-D across diverse samples, indicating four distinct depressive symptom dimensions: negative affect (e.g., I had crying spells, I thought my life had been a failure), positive affect (e.g., I felt hopeful about the future, I enjoyed life), somatic features (e.g., My sleep was restless, I had trouble keeping my mind on what I was doing), and interpersonal disturbance (e.g., I felt that people dislike me) (Shafer, 2006). Symptoms are rated on a four-point scale (ranging from zero to three); zero indicates a symptom occurring “rarely or none of the time (less than 1 day),” one indicates “some or a little of the time (1–2 days),” two indicates “occasionally or a moderate amount of the time (3–4 days),” and three indicates symptoms occurring “most or all of the time (5–7 days).” Each subscale score was treated as continuous, with higher scores indicating more symptoms. The CES-D has demonstrated strong psychometric properties such as internal consistency and divergent validity in adolescents (Garrison et al., 1991). Cronbach’s alpha for the entire CES-D scale at baseline was 0.82; and for each baseline CES-D factor separately, it ranged from 0.78 to 0.90.

Covariates and descriptive characteristics

Baseline covariates were selected based on potential for confounding screen-based sedentary behavior-depression associations (Gunnell et al., 2016; Maras et al., 2015). Therefore, the demographic characteristics of sex (dichotomous), age (continuous), race (categorical), ethnicity (dichotomous), and highest parental education (categorical), which served as a proxy for socioeconomic status (SES), were included in all models. Additionally, body mass index (BMI) percentile based on self-reported height and weight, YRBSS self-reported daily physical activity, and school at baseline were included as covariates.

Statistical Analysis

Descriptive analysis

Descriptive statistics of the study sample were performed, and logistic regression models were used to determine whether participant characteristics were associated with missing data at baseline or follow-up (yes vs. no). Confirmatory factor analysis (CFA) assessed if the factor structure of the CES-D items at baseline and follow-up in our sample were consistent with previous works (Shafer, 2006; Williams et al., 2007). The negative affect and somatic features factors are each typically indicated by seven items, while positive affect is indicated by four items, and the interpersonal disturbance factor by two items. To assess model fit, the comparative fit index (CFI) and the root mean square error of approximation (RMSEA) indices were used, as they are more appropriate for assessing model fit in large samples compared to the likelihood ratio χ2 test statistic. CFI values >0.90 and RMSEA values <0.05 indicate good model fit for the data (Rigdon, 1996; Taasoobshirazi and Wang, 2016).

Primary analysis

To test the association between baseline screen-based sedentary behavior and CES-D subscale scores one year later, each baseline screen-based sedentary behavior variable (e.g., television viewing and computer/videogame use) was entered as a separate predictor of each continuous CES-D subscale score (e.g., negative affect, positive affect, somatic symptoms, and interpersonal disturbance) at one-year follow-up, controlling for the respective baseline CES-D subscale score in linear regression models. Then a set of models with both screen-based sedentary behaviors as simultaneous predictors, adjusting for the a priori covariates mentioned above, and all CES-D subscale scores at baseline were run. To test sex as a moderator, the television viewing by sex and computer/videogame use by sex interaction terms were tested in separate, fully adjusted models.

To test the association in the opposite direction, each baseline CES-D subscale score was entered as a separate predictor of each screen-based sedentary behavior at follow-up (controlling for the respective baseline screen-based sedentary behavior) in logistic regression models. Next, all four baseline CES-D subscale scores were entered as simultaneous predictors of each screen-based sedentary behavior at follow-up, adjusting for a priori covariates and both screen-based sedentary behavior variables at baseline. To test sex as a moderator, an interaction term between sex and each baseline CES-D subscale score was entered into the fully adjusted models separately. If any interaction terms were significant, stratified analyses were conducted.

Intraclass correlations for school effects on baseline CES-D subscale scores, television viewing, and computer/videogame use ranged from 0.002 to 0.027, indicating minimal impact of nesting of data by school on standard error estimates and permitting the modeling of school as a fixed effect in all models. Participants with data unavailable for any screen-based sedentary behavior or CES-D variable at either time point were excluded from the analyses. Respondents with missing demographic or covariate information (except for sex) were included and addressed using multiple imputation (note available N for each covariate in Table 1) (Toutenburg, 1990). For the primary analyses of the reciprocal associations between screen-based sedentary behavior and CES-D subscale scores, two-tailed p-values are presented. All analyses were conducted in SASv9.4.

Table 1.

Descriptive statistics of the sample at baseline (N=2,717)

N (%) or Mean (SD)
Female 1510 (55.58%)
Age (mean [SD])a 14.57 (0.40)
Race/Ethnicityb
American Indian 24 (0.89%)
Asian 486 (18.11%)
African American 115 (4.28%)
Hispanic/Latino 1295 (48.25%)
Native Hawaiian/Pacific Islander 112 (4.17%)
White 433 (16.13%)
Other/More Than One Race 179 (6.67%)
Can’t Choose/Don’t Know 40 (1.49%)
Highest Parental Education
8th grade or less 98 (3.61%)
Some high school 222 (8.17%)
High school graduate 394 (14.5%)
Some college 441 (16.23%)
College graduate 753 (27.71%)
Advanced degree 467 (17.19%)
Don’t know 342 (12.59%)
BMI Percentile (mean [SD])c 59.15 (29.48)
Days/Week Physically Active (mean [SD])d 4.75 (2.03)
2+ Hours/Day Television Viewing 1302 (47.92%)
2+ Hours/Day Computer/Videogame Use 1565 (57.60%)
Depressive Symptoms (mean [SD])
CES-D Negative Affect 0.63 (0.71)
CES-D Positive Affect 2.06 (0.81)
CES-D Somatic Features 0.70 (0.61)
CES-D Interpersonal Disturbance 0.69 (0.82)
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a

N=2716

b

N=2684

c

N=2260

d

N=2706

Results

Study sample

Of the 4100 9th grade students among the 10 participating high schools, 3874 (94.5%) assented, of whom 3396 (87.7%) parents provided consent. Baseline data were collected in 3383 (99.6%) students and follow-up data were collected in 3282 (96.6%) students one year later. One hundred and forty-two participants were absent during the onsite data collection at either wave and were given the abbreviated surveys which omitted the screen-based sedentary behavior questions. Of the remaining 3140 participants who completed the full survey at both waves, 423 (13.5%) students did not provide full data for any of the CES-D subscales, screen-based sedentary behavior, or sex variables at either wave, leaving an analytic sample of (N=2717) for this report. There were small differences between cohort enrollees included (N=2717) vs. excluded (N=666) by age, sex, race, and parental education. Moreover, those included in the analytic sample were less likely to have fewer than 2-hours of daily television viewing vs. more (OR 0.78, 95%CI 0.66–0.93), and more likely to have higher positive affect subscale scores (OR 1.37, 95%CI 1.24–1.51). See the supplemental text provided for a more detailed comparison of the cohort enrollees to the analytic sample.

Descriptive statistics

Table 1 describes the analytic sample at baseline; the mean age was 14.57 years old, with over half (55.58%) of the sample being female. 48.25% of the sample was of Hispanic/Latino ethnicity and the mean BMI percentile was 59.15. On average, the participants in our sample reported being physically active for at least 60 minutes 4.75 days per week; with 65.0% of participants reporting being active for at least 60 minutes 5 or more days per week. Our sample reported being more physically active compared to a nationally-representative sample of United States youth (Katzmarzyk et al., 2018). 47.92% of participants reported watching television for 2-hours or more daily (mean daily television viewing reported was approximately 1.70 hours) and 57.60% of participants engaged in 2-hours or more of computer/videogame use daily (mean daily computer/videogame use reported was approximately 2.19 hours). The prevalence of each form of screen-based sedentary behavior in our sample are comparable to other samples of youth (Thomas et al., 2019). Each of the CES-D subscale scores ranged 0 to 3, with the average of each subscale score ranging from 0.63 to 2.06 (higher values indicating more symptoms).

The prevalence of 2-hours or more of daily television viewing was not different between boys and girls at baseline and follow-up. Two hours or more of daily computer/videogame use was more prevalent among boys compared to girls at baseline and follow-up. However, the negative affect, somatic features, and interpersonal disturbance subscale scores were higher in girls compared to boys at baseline and follow-up. Lastly, positive affect was higher in boys compared to girls at both time points. See Table 2 for a more detailed description of screen-based sedentary behaviors and continuous CES-D subscale scores by sex.

Table 2.

Descriptive statistics (N[%] or mean[SD]) of screen-based sedentary behaviors and CES-D subscale scores by sex at baseline and follow-up (N=1510 females, N=1207 males).

Female Male P-value Female Male P-value
Baseline Follow-up
Television Viewinga 713 (47.22) 589 (48.80) 0.41d 656 (43.44) 497 (41.18) 0.23d
Computer/Videogame Useb 833 (55.17) 732 (60.65) 0.004d 772 (51.13) 710 (58.82) <0.0001d
Negative Affectc 0.79 (0.77) 0.41 (0.55) <0.0001e 0.87 (0.81) 0.47 (0.63) <0.0001e
Positive Affectc 1.93 (0.82) 2.22 (0.76) <0.0001e 1.88 (0.83) 2.14 (0.85) <0.0001e
Somatic Featuresc 0.80 (0.64) 0.58 (0.54) <0.0001e 0.87 (0.73) 0.56 (0.62) <0.0001e
Interpersonal Disturbancec 0.82 (0.87) 0.53 (0.72) <0.0001e 0.82 (0.90) 0.56 (0.79) <0.0001e
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a

Television viewing for 2 or more hours daily versus not.

b

Computer/videogame use for 2 or more hours daily versus not.

c

Continuous CES-D subscale score.

d

P-value derived from Pearson’s chi-square test.

e

P-value derived from independent samples t-test.

Confirmatory factor analysis

Supplemental Table 1 presents the standardized factor loadings from the CFA at baseline and follow-up. The average factor loading was 0.72 and ranged from 0.35 to 0.92. The baseline CFA model (CFI=0.96, RMSEA=0.049) and the follow-up CFA model (CFI= 0.95, RMSEA=0.059) both demonstrated good fit for the data, indicating that the CES-D factor structure was consistent with previous works at both time-points in the study sample.

The relationship between baseline screen-based sedentary behavior and subsequent CES-D subscale scores

Baseline daily television viewing for 2-hours or more (vs. less) was unrelated to each continuous CES-D subscale score one year later in the unadjusted and adjusted models. Baseline daily computer/videogame use for 2-hours or more (vs. less) was related to each depressive symptom dimension except for positive affect at follow-up in the unadjusted models; only the associations with subsequent negative affect (β=0.06, 95%CI 0.01–0.11, p=0.01) and somatic symptoms (β=0.06, 95%CI 0.01–0.10, p=0.02) persisted after adjustment for covariates (Table 3).

Table 3.

Associations between television viewing or computer/videogame use at baseline and each depression symptom at follow-up (N=2,717)

Unadjusteda Adjustedb
β (95%CI) P-value β (95%CI) P-value
Negative Affectc
Television Viewingd −0.001 (−0.05–0.05) 0.97 −0.01(−0.06–0.04) 0.69
Computer/Videogame Usee 0.06 (0.01–0.11) 0.02 0.06 (0.01–0.11) 0.01
Positive Affectc
Television Viewingd 0.01 (−0.05–0.07) 0.77 0.02 (−0.03–0.08) 0.40
Computer/Videogame Usee −0.05 (−0.10–0.02) 0.16 −0.03 (−0.09–0.02) 0.26
Somatic Symptomsc
Television Viewingd 0.02 (−0.03–0.06) 0.48 −0.001 (−0.05–0.05) 0.98
Computer/Videogame Usee 0.06 (0.01–0.11) 0.02 0.06 (0.01–0.10) 0.02
Interpersonal Disturbancec
Television Viewingd 0.05 (−0.002–0.11) 0.06 0.04 (−0.02–0.10) 0.15
Computer/Videogame Usee 0.06 (0.01–0.12) 0.03 0.04 (−0.02–0.10) 0.19
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a

Models included baseline respective screen-based sedentary behavior and baseline respective CES-D subscale score as predictors.

b

Models included baseline television viewing and baseline computer/videogame use as simultaneous predictors, adjusted for age, sex, race/ethnicity, SES, school, physical activity, BMI percentile, baseline negative affect, baseline positive affect, baseline somatic symptoms, and baseline interpersonal disturbance.

c

Continuous CES-D subscale score.

d

Television viewing for 2 or more hours daily versus not.

e

Computer/videogame use for 2 or more hours daily versus not.

The relationship between baseline CES-D subscale scores and subsequent screen-based sedentary behavior

Each continuous CES-D subscale score at baseline was unrelated to subsequent 2-hours or more (vs. less) daily television viewing one year later in the unadjusted and adjusted models. Baseline interpersonal disturbance was associated with computer/videogame use at follow-up in the unadjusted models; while the unadjusted association between baseline negative affect and computer/videogame use at follow-up was marginal (OR 1.12, 95% CI 1.00–1.26, p=0.06). After controlling for covariates, negative affect was positively associated with subsequent computer/videogame (OR 1.29, 95%CI 1.06–1.58, p=0.01). Baseline positive affect was also positively associated with subsequent engagement in 2-hours or more daily computer/videogame use (OR 1.16, 95%CI 1.02–1.31, p=0.02), while baseline somatic symptoms and interpersonal disturbance were unrelated to computer/videogame use at follow-up in the adjusted models (Table 4).

Table 4.

Associations between each depression symptom dimension at baseline and television viewing or computer/videogame use at follow-up (N=2,717)

Unadjusteda Adjustedb
OR (95%CI) P-Value OR (95%CI) P-Value
Negative Affectc
Television Viewingd 1.03 (0.92–1.16) 0.59 1.05 (0.86–1.29) 0.63
Computer/Videogame Usee 1.12 (1.00–1.26) 0.06 1.29 (1.06–1.58) 0.01
Positive Affectc
Television Viewingd 0.95 (0.86–1.05) 0.34 0.99 (0.88–1.13) 0.94
Computer/Videogame Usee 1.04 (0.94–1.15) 0.42 1.16 (1.02–1.31) 0.02
Somatic Symptomsc
Television Viewingd 0.95 (0.83–1.09) 0.47 0.84 (0.69–1.02) 0.08
Computer/Videogame Usee 1.05 (0.92–1.20) 0.46 0.89 (0.73–1.09) 0.26
Interpersonal Disturbancec
Television Viewingd 1.04 (0.94–1.15) 0.42 1.07 (0.93–1.23) 0.33
Computer/Videogame Usee 1.11 (1.00–1.23) 0.04 1.10 (0.96–1.26) 0.15
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OR= odds ratio.

a

Models included baseline respective CES-D subscale score and baseline respective screen-based sedentary behavior as predictors.

b

Models included baseline negative affect, baseline positive affect, baseline somatic symptoms, and baseline interpersonal disturbance as simultaneous predictors, and adjusted for age race/ethnicity, SES, school, physical activity, BMI percentile, baseline television viewing, and baseline computer/videogame use.

c

Continuous CES-D subscale score.

d

Television viewing for 2 or more hours daily versus not.

e

Computer/videogame use for 2 or more hours daily versus not.

Moderation by sex

Sex moderated the association between baseline daily 2-hours or more (vs. less) computer/videogame use and follow-up continuous negative affect score (interaction β=−0.12, 95%CI −0.2-−0.02, p=0.02). Baseline computer/videogame use was unrelated to subsequent negative affect in boys (β=0.004, 95%CI −0.06–0.07, p=0.90), while this association was positive and significant in girls (β=0.11, 95%CI 0.04–0.18, p=0.002). No other significant moderation by sex emerged.

Discussion

Our findings suggest that reciprocal associations are non-uniformly extended across all depressive symptom dimensions and are specific to computer/videogame use and in some cases, female sex. Specifically, we found that reciprocal associations between screen-based sedentary behavior and depression were only significant between computer/videogame use and the negative affect depressive symptom dimension in girls, while television viewing was unrelated to each depressive symptom dimension. A recent longitudinal study of adolescent girls found that depressive symptoms measured by the CES-D were associated with subsequent sedentary behaviors, but not vice versa (Raudsepp and Vink, 2019); this study may not have detected reciprocal associations between depressive symptoms and sedentary behaviors because each depressive symptom dimension and each form of sedentary behavior were not differentiated from one another (Raudsepp and Vink, 2019).

The device-specific associations (e.g., television viewing vs. computer/videogame use) observed in our study suggests that sitting alone (which can occur during both, computer/videogame use and television viewing) may not be the primary explanation for the link between computer/videogame use and affective depressive symptom dimensions. In other words, computer/videogame use-driven negative affect may not be explained by physiological mechanisms (e.g., reduced synthesis of serotonin and dopamine resulting from lack of physical movement) (Lopresti et al., 2013; Young, 2007). Instead, our results suggest that psychosocial mechanisms are more likely to explain the computer/videogame use-affect link. Computer use and videogame playing are often perceived as enjoyable by adolescents (Sáinz and López-Sáez, 2010; Vernadakis et al., 2014), whereby a preference for these behaviors may be created. However, the perceived enjoyment of computer/videogame use may not be sufficient to protect against the negative affective consequences of such behaviors. A majority (82%) of adolescents with access to a computer/the internet utilize these technologies to go on social media sites (Lenhart et al., 2010); moreover, those with smartphones commonly use these devices to access social media internet applications without being bound to a computer (Yildiz Durak, 2018). Thus, the potential emotional consequences of social media use among adolescents should be considered in the context of the present study. “Passive” social media use (e.g., looking at peers’ social media profiles without direct communication) is associated with more depressive symptoms as compared to “active” social media use (e.g., posting statuses and pictures, liking and commenting on peers’ posts) (Frison and Eggermont, 2017; Verduyn et al., 2015). The negative affective consequences of “passive” social media use can likely be attributed to the upward social comparison (e.g., feelings of envy or inferiority, feelings of being a failure) that is commonly experienced during “passive” social media use (Frison and Eggermont, 2017; Nesi and Prinstein, 2015; Steers et al., 2014; Verduyn et al., 2015). Social media may further be linked to negative affect because of its association with loneliness (Wang et al., 2018); it is believed that social media may be related to feeling lonely because it increases perceptions of social isolation and threatens feelings of belonging (Primack et al., 2017; Tobin et al., 2015).

Consistent with previous studies, our results demonstrated that sex moderates the associations between computer/videogame use and depressive symptoms; associations are in some cases specific to girls, but not to boys, despite boys reporting more computer/videogame use than girls (Desai et al., 2010; Rottenberg et al., 2014; Suchert et al., 2015). Epidemiological studies have showed that girls, relative to boys, are more vulnerable to affective disturbance in early adolescence (Breslau et al., 2017; Hyde et al., 2008; Marcotte et al., 2002), which may be explained by fluctuations in female reproductive hormones (Hyde et al., 2008). However, a more likely explanation for the sex differences observed in our study could be that adolescent girls have more attentional biases and experiential sensitivities to negative stimuli while engaging in computer/videogame use relative to adolescent boys (Yang et al., 2018). The negative emotional effects of computer/videogame use may be exacerbated in girls, which can be demonstrated through the self-image concerns and reduced self-esteem that result from computer use in girls, but not boys (Bennett et al., 2005; Golan et al., 2014; Suchert et al., 2015). Therefore, cognitive biases that may be more prevalent among adolescent girls can make computer/videogame use more harmful for girls, relative to boys. However, it is also possible that the online environment is actually more hostile and threatening for girls, and that sex differences are not completely due to differences in perceived experiences. For example, adolescent girls have reported being exposed to more unintentional negative online content (sexual content, slander) compared to adolescent boys (Park, 2009). Thus, the emotional detriment of computer/videogame use among adolescent girls, specifically, may be attributed a number of a psychosocial factors.

We also found that, among the entire sample, certain baseline screen-based sedentary behaviors were associated with the somatic depressive symptom dimension one year later, which can encompass symptoms such as restless sleep and trouble concentrating. Screen-based sedentary behaviors such as computer and mobile phone use, but not television viewing, before bed can reduce sleep duration and quality (Lemola et al., 2015). It is believed that these forms of screen-based sedentary behaviors before bed can shorten sleep duration by displacing sleep (Cain and Gradisar, 2010), while simultaneously reducing sleep quality by increasing pre-sleep emotional and cognitive arousal (Gregory et al., 2008). For example, emotional and thoughtful communication using mobile phones (e.g., text messaging) before bed may induce sleep disturbances (Munezawa et al., 2011). Moreover, it is estimated that one third of adolescents are woken up in the middle of the night by phone notifications (Cain and Gradisar, 2010). Modern forms of screen-based sedentary behaviors that immediately send notifications to users (e.g., computers and mobile phones) may also hinder attentional functioning; constant distractions that require frequent attentional shifts and multitasking can result in difficulties concentrating, particularly during adolescence, a period of rapid neurocognitive development (Chen and Yan, 2016; Giedd, 2004). Thus, screen-based sedentary behaviors that send user notifications may be particularly impactful for the somatic symptom dimension (e.g., sleep disturbance and trouble concentrating) of depression in youth.

Although the present study was longitudinal and was conducted in a diverse sample of adolescents, our findings should be interpreted in light of some limitations. Reports of depressive symptoms via CES-D were used rather than a clinical assessment of depression. Similarly, self-reported sedentary behaviors were assessed rather than more objective measures of sedentary time; moreover, participants only reported on leisure-time sedentary behaviors on weekdays and sedentary behaviors such as homework were not captured. However, previous studies among adolescents suggest that time spent in non-leisure sedentary behaviors (homework, reading) are unrelated to depressive symptoms cross-sectionally and longitudinally (Gopinath et al., 2012; Hamer et al., 2016). We also did not take into account the content that the participants were exposed to when engaging in screen-based sedentary behaviors; similarly, active versus passive screen-based sedentary behaviors were not differentiated, which may uniquely contribute to depressive symptom outcomes (Hallgren et al., 2018). Future studies should consider clinically assessing the distinct depressive manifestations while using accelerometers in accordance with ecological momentary assessment to measure a wider range of sedentary behavior types; doing so will result in a more nuanced understanding of the sedentary behavior-depression link. Lastly, casual inferences cannot be drawn in the current study due to the observational nature of the study design.

Conclusions

This study of a cohort of Los Angeles high school students found that associations between screen-based sedentary behaviors and depressive symptoms are not uniform across sedentary behavior type, depressive symptom dimension, and sex. The association between computer/videogame use and negative affect symptoms may be reciprocal across time and particularly robust among girls. These findings indicate that research and clinical approaches to addressing depression-sedentary behavior comorbidity that operationalize depressive symptoms and screen-based sedentary behaviors as composite variables may mask relevant heterogeneity within depression and sedentary behavior that contributes to their association. Therefore, future research should continue to assess the unique contributions of different types of screen-based sedentary behaviors to each depressive symptom dimension separately in order to identify the most vulnerable populations and to optimize intervention strategies addressing depression-sedentary behavior comorbidity.

Supplementary Material

1

Highlights.

  • Associations are non-uniform across screen-type and depressive symptom dimension

  • Reciprocal associations are specific to negative affect and computer/videogame use

  • Computer/videogame use among girls, but not boys, was related to subsequent negative affect

Acknowledgments

Funding Sources

This work was supported by the National Institute on Drug Abuse (R01-DA033296) and the National Cancer Institute (T32 CA009492).

Footnotes

Conflict of Interest

The authors report no conflicts of interest.

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