Electronic media use and symptoms of depression among adolescents in Norway

Annette Løvheim Kleppang; Anne Mari Steigen; Li Ma; Hanne Søberg Finbråten; Curt Hagquist

doi:10.1371/journal.pone.0254197

. 2021 Jul 7;16(7):e0254197. doi: 10.1371/journal.pone.0254197

Electronic media use and symptoms of depression among adolescents in Norway

Annette Løvheim Kleppang ^1,^*, Anne Mari Steigen ², Li Ma ^1,³, Hanne Søberg Finbråten ², Curt Hagquist ^1,⁴

Editor: Therese van Amelsvoort⁵

PMCID: PMC8263301 PMID: 34234359

Abstract

Background

The purpose of this study was to examine the association between electronic media use, including use of social media and gaming, and symptoms of depression, and whether gender or having friends moderated these associations.

Methods

This study was based on self-reported cross-sectional data from the Ungdata survey, conducted in 2018 by the Norwegian Social Research (NOVA) Institute in cooperation with seven regional drug and alcohol competence centres. The target group comprised 12,353 15–16 years old adolescents. Binominal logistic regression was used to analyse the association between electronic media use and symptoms of depression.

Results

The odds of having symptoms of depression were higher for those who used social media more than 3 hours per day (OR: 1.60, 95% CI: 1.43–1.80), compared to those who used social media 3 hours or less per day. Additionally, the odds of having symptoms of depression was higher for those who used more than 3 hours on gaming per day (OR: 1.57, 95% CI: 1.36–1.80), compared to those who used 3 hours and less on gaming per day after adjustment for potential confounders. There were no interaction effects between social media and gaming use with symptoms of depression. Neither were the associations between social media use and gaming with symptoms of depression moderated by gender or having friends.

Conclusions

The odds of having symptoms of depression were significantly higher for adolescents with a more frequent use of electronic media.

Background

Mental health problems are a growing concern worldwide [1], and studies on trends among adolescents have reported an increase across countries [2, 3]. The number of adolescents reporting symptoms of depression has also increased in Norway, with a higher number of girls reporting symptoms of depression than boys [4]. Depression represents a major public health challenge [5], and 19% of adolescents (aged 12–19) in Norway report that they are much affected by depressive symptoms [4]. The increasing prevalence of depressive symptoms signifies a need to identify modifiable risk factors.

The concerns about the heavy influence screen use have on children and adolescents are growing, and one of the concerns is related to mental health problems [6]. Adolescents nowadays spend more time on gaming and social media than previous generations [7]. In Norway, a recent survey reported that 66% of Norwegian adolescents aged 15–16 years spent more than 3 hours a day on screen-based activities (Bakken 2019). While girls are mostly active on social media, boys are mostly engaged in gaming [8].

Systematic reviews report that among adolescents, a higher level of electronic media use is associated with depressive symptoms and psychological distress [9, 10]. Additionally, several studies report associations between increased time spent on social media and higher levels of depression [11–13], while other studies question the association between time spent on social media and depressive symptoms [14–16]. Positive outcomes of social media use are also reported [17]. On the other hand, a longitudinal study among US adolescents found no association between increased time on social media and increased depression and anxiety [14].

Some studies on video gaming have reported no association between video gaming and symptoms of depression [11, 18, 19]. In contrast, a study among Canadian adolescents showed that both video gaming and computer use were associated with depressive symptoms [20]. In the most recent decades, both depressive symptoms and time spent on electronic media have been increasing simultaneously [4]. Therefore, it is of great importance to study the association between these trends. Moreover, different types of screen use behaviour may have different impacts on depressive symptoms [21].

Social media use, video gaming and computer use are more likely to be associated with depressive symptoms compared to television viewing [22]. Zink et al. [22], indicated in a recent systematic review of moderating variables, that screen-type influences the strength of the association between screen based sedentary behaviour and symptoms of depression and anxiety in adolescence. Some previous studies have not found any modifying effect of gender on the association between social media use and depression [12, 23], whereas a recent longitudinal study did find gender being a possible moderator of the associations between video game use and depressive symptoms [24]. Another study which used objective measures of video gaming found positive associations with wellbeing [25]. Social support is suggested as one of the protective factors potentially influencing the relationship between social media use and depression [26].

A gap in previous research is the lack of information on potential moderators of the association between electronic media use and depressive symptoms. The aim of this study is therefore to examine the association between electronic media use and symptoms of depression, and to study whether gender and having friends moderate the association between electronic media use and symptoms of depression.

Methods

This study was based on data from 2018 retrieved from the Ungdata surveys conducted by the Norwegian Social Research (NOVA) institute in cooperation with regional drug and alcohol competence centres (KoRus). The Norwegian Directorate of Health, The Ministry of Children, Equality and Social Inclusion, and the Ministry of Justice and Public Security have financed the surveys. Ungdata is a repeated cross-sectional survey and have been conducted in many secondary schools all over the country since 2010. The Ungdata survey covers different aspects of young people’s lives, such as media use, physical activity, health issues, relationships with friends and parents, local environment, school issues, depressive symptoms, and wellbeing. In this study, we included adolescents of grade 10 (aged 15–16 years) from secondary schools all over the country. The web-based questionnaire was administered anonymously at school during a school hour with a teacher present available for questions. The parents received written information in advance through the school learning portal. Also, adolescents completed a consent form before participation and were informed that participation was voluntary. Altogether 12,353 adolescents in grade 10 participated, and the overall response rate in the secondary schools was 85% [27]. The study was conducted in line with the Declaration of Helsinki, and the data were analysed by independent researchers who did not participate in the data collection. The Norwegian Centre for Research Data (NSD) approved all ethical aspects of the study.

Table 1 gives the Ungdata Survey 2018 questions, response alternatives and variable definitions included in this study.

Table 1. Ungdata Survey 2018: Questions, response alternatives and variable definitions.

Questions	Response alternatives	Variable definitions
*Symptoms of depression*
During the past week, have you been affected by any of the following issues:
Felt that everything is a struggle (item 1)	Not been affected at all, not been affected much, been affected quite a lot and been affected a great deal.	Symptoms of depression ≥ 80th percentiles
Had sleep problems (item 2)		Symptoms of depression ≥ 80th percentiles
Felt unhappy, sad or depressed (item 3)
Felt hopelessness about the future (item 4)
Felt stiff or tense (item 5)
Worried too much about things (item 6)
*Social media use*
Think about what you do a normal day: How much time do you spend on the following things:	No time, < 30 minutes, 30 minutes—1 hour, 1–2 hours, 2–3 hours, < 3 hours	≤ 3 hours per day, > 3 hours per day
Social media (facebook, Instagram etc.)
**Gaming on computer/TV, telephone/tablets**
Think about what you do a normal day: How much time do you spend on the following things:	No time, < 30 minutes, 30 minutes—1 hour, 1–2 hours, 2–3 hours, < 3 hours	Gaming,: ≤ 3 hours per day, > 3 hours per day
gaming on computer/TV?		Gaming,: ≤ 3 hours per day, > 3 hours per day
*Friends*
Do you have at least one friend who you trust completely and who you can tell absolutely anything?	Yes definitely, yes I think so, I don´t think so and I have nobody I would call close online friends at the moment	Yes, no
*Gender*
Are you a boy or a girl?	Boy, girl
*Smoking*
Do you smoke?	I’ve never smoked, I used to smoke but I’ve stopped completely now, I smoke less than once a week, I smoke every week but not every day and I smoke every day.	No smoking, smoking
*Parents’ higher education*
Did your father and mother go to university or to a university college? Select one answer for each parent. If you are not in touch with one or both of your parents, then skip the question about that parent.	Yes, no	Both parents, One of the parents, None of the parents
*Family economy*
Financially, has your family been well off, or badly off, over the past years?	We have been well off the whole time, we have generally been well off, we have neither been well off nor badly off, we have generally been badly off, we have been badly off the whole time	Good economy, neither bad nor good economy, bad economy

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Measures

Symptoms of depression

Symptoms of depression were measured with six items. The items originate from the Depressive Mood Inventory [28] which was derived from the Hopkins Symptom Checklist—90 [28, 29]. The six items are used both as single items and to construct a scale that provides a total score for depressive symptoms [30, 31]. The adolescents were asked if during the past week they have been affected by any of the following: “Felt that everything is a struggle (item 1)”, “had sleep problems (item 2)”, “felt unhappy, sad or depressed (item 3)”, “felt hopelessness about the future (item 4)”, “felt stiff or tense (item 5)”, worried too much about things (item 6)”. The six questions have four response categories: “Not been affected at all (1)”, “not been affected much (2)”, “been affected quite a lot (3)” and “been affected a great deal (4)”.

The Rasch Measurement Theory [32, 33] has been used to examine the psychometric properties of the depressive symptoms scale in Ungdata from 2017 [34, 35] and in the present study. The scale shows good reliability (Person Separation Index: 0.82). The items work relatively well at a general level, except for item 2, ‘Sleeping problems’, which clearly misfit. The DIF-analysis indicated evidence of DIF for some items using ANOVA based on standardized residual of each person to each item. While there are some weaknesses, as a whole the symptoms of depression scale work well.

In the Rasch analysis the non-linear raw scores were transformed to person estimates on a linear interval logit scale on which each person is allocated a location (logit) value. In the present study, these person estimates were used in the statistical analysis. Lower values on the scale indicate a lower degree of symptoms of depression. For the purpose of this study, the symptoms of depression scale was dichotomized based on the latent symptoms of depression variable generated by the Rasch analysis. A cut off point, reported as logit value, was set at the 80th percentile (0.522), implying two categories on the scale: a) symptoms of depression (≥80th percentile) and b) no symptoms of depression (<80th percentile).

Electronic media use

Electronic media use was measured using the two variables Social media use and Gaming. Social media use was measured by asking: “Think about what you do a normal day, how much time do you spend on the following things: social media (Facebook, Instagram etc.)”, and categorized as no time, < 30 minutes, 30 minutes– 1 hour, 1–2 hours, 2–3 hours and > 3 hours. Social media use was in the present study dichotomized as “more than 3 hours” and “3 hours or less”. Gaming was measured by asking: “Think about what you do a normal day, how much time do you spend on the following things: gaming on telephone/tablets or gaming on computer/TV”, and categorized as no time, < 30 minutes, 30 minutes– 1 hour, 1–2 hours, 2–3 hours and > 3 hours. Gaming was in the present study dichotomized as “more than 3 hours” and “3 hours or less”.

Friends

Friends were measured by asking: “Do you have at least one friend who you trust completely and who you can tell absolutely anything?” This was categorized as Yes, definitely, Yes, I think so, I don´t think so, and I have nobody I would call close online friends at the moment. This was in the present study categorized as “Yes, I have friends” and “No, I do not have friends”.

Smoking

Smoking was measured by asking: “do you smoke?” It was categorized as: I’ve never smoked, I used to smoke but I’ve stopped completely now, I smoke less than once a week, I smoke every week but not every day and I smoke every day. Smoking was dichotomized as”no smoking” and “smoking”.

Parents’ level of education and family economy

Parents’ level of education was measured separately for each parent by asking the following: “Did your father/mother go to university or to a university college?” Those young people not in touch with one or both parents, were asked to miss the question out. This was categorized as yes or no. Parental educational status was stratified as “no university education”, “one parent with university education” or “both parents with university education”. Family economy was measured as follows: “financially, has your family been well off, or badly off, over the past years?” This was categorized as: we have been well off the whole time, we have generally been well off, we have neither been well off nor badly off, we have generally been badly off, we have been badly off the whole time. Family economy was stratified as “good”, “neither bad nor good”, and “bad economy”.

Analysis

Descriptive contingency table and logistic regression analyses were conducted using SPSS 24.0 for Windows. In the descriptive analyses, the study population was stratified according to symptoms of depression and gender. Baseline characteristics were presented as proportions with 95% confidence intervals (CI) in each stratum. No overlap of the CI was considered significant at the 5% level.

Binomial logistic regression analysis was performed to examine the association between electronic media use (social media and gaming) and symptoms of depression, adjusted for possible confounding variables. A p-value of ≤ 0.05 was set as the level for statistical significance. Associations were presented as odds ratios (OR) with 95% CI, and with adjustments for friends, gender, smoking, parent’s education, and family economy.

Interaction analyses were used to examine whether gender and having friends affected the strength of the relationship between electronic media use and symptoms of depression. Similarly, potential interaction between social media use and gaming was examined. All potential interaction effects were examined using the Likelihood Ratio test (LR test), contrasting models with and without interaction terms. The main effect model included social media use, gaming, friends, gender, smoking, parents’ education and family economy as independent variables, and was tested against models including the following interactions; social media* gaming, social media *gender, social media *friends, gaming*gender, gaming*friends, and social media*gaming *gender. The incremental changes in log-likelihood between the main effect models and models including interactions were not significant, implying that the fit was not improved when applying interaction models. Therefore, only the main effect model is presented in the results section.

To check the robustness of our results, we tried dichotomizing the outcome variable regarding time spent on electronic media into more than two hours and two hours or less.

Results

Table 2 shows baseline characteristics of the study population aged 15–16 years in 2018, according to depressive symptoms and gender.

Table 2. Youth data surveys: Baseline characteristics of adolescents, aged 15–16 years in 2018, according to symptoms of depression and gender.

	Total (N = 11836)			Boys (N = 6133)			Girls (6169)
	No symptoms of depression	Symptoms of depression	Total	No symptoms of depression	Symptoms of depression	Total	No symptoms of depression	Symptoms of depression
	(N = 9528), n (%; 95% CI)	(N = 2308), n (%; 95% CI)	(N = 5777), n (%; 95% CI)	(N = 5181), n (%; 95% CI)	(N = 596), n (%; 95% CI)	(N = 6013), n (%; 95% CI)	(N = 4320), n (%; 95% CI)	(N = 1693), n (%; 95% CI)
Electronic media use
*Social media (N = 11633)*
> 3 hours	2346 (25.0; 24.2–25.9)	958 (42.4; 40.4–44.3)	1027 (18.2; 17.2–19.2)	862 (17.0; 15.9–18.0)	165 (28.9; 25.2–32.7)	2263 (38.1; 36.9–39.3)	1480 (34.7; 33.2–36.1)	783 (46.8; 44.4–49.2)
*Gaming (N = 11423)*
> 3 hours	1812 (19.7; 18.9–20.5)	507 (22.8; 21.1–24.6)	1655 (29.8; 28.6–31.0)	1419 (27.2; 26.2–29.7)	236 (42.0; 37.9–46.1)	654 (11.2; 10.4–12.0)	389 (9.3; 8.4–10.2)	265 (16.2; 14.4–18.0)
Confounding variables
Friends (N = 11780)
Yes	8448 (89; 88.0–89.3)	1880 (81.0; 79.8–83.0)	5744 (89.8; 89.0–90.6)	4704 (91.1; 90.4–91.9)	455 (78.2; 74.8–81.5)	5332 (89.0; 88.2–89.2)	3921 (91.1; 90.2–91.9)	1411 (83.6; 81.8–85.4)
*Smoking N = 11836*
No smoking	8847 (92.9; 92.3–93.4)	1967 (85.2; 83.7–86.6)	5218 (90.3; 89.6–91.1)	4748 (91.6; 90.9–92.4)	470 (78.9; 75.3–82.0)	5635 (94.0; 93.1–94.3)	4073 (94.3; 93.6–95.0)	1481 (87.5; 85.9–89.1)
Smoking	681 (7.1; 6.6–7.7)	341 (14.8; 13.3–16.2)	559 (9.7; 8.9–10.4)	433 (8.4; 7.6–9.1)	126 (21.1; 17.0–24.4)	459 (7.6; 7.0–8.3)	247 (5.7; 5.0–6.4)	212 (12.5; 11.0–14.1)
*Parents’ higher education N = 10164*
Both parents	4982 (60.7; 59.7–61.8)	1059 (54.1; 51.9–56.3)	2952 (59.3; 57.9–60.6)	2675 (60.0; 58.5–61.4)	277 (53.5; 49.2–57.8)	3066 (59.5; 58.2–60.9)	2294 (61.6; 60.0–63.1)	772 (54.3; 51.7–56.8)
One of the parents	1731 (21.1; 20.2–22.0)	452 (23.1; 21.2–25.0)	1040 (20.9; 19.8–22.0)	928 (20.8; 19.6–22.0)	112 (21.6; 18.1–25.2)	1139 (22.1; 21.0–23.3)	801 (21.5; 20.2–22.8)	338 (23.8; 21.5–26.0)
Neither parents	1494 (18.2; 17.4–19.0)	446 (22.8; 20.9–24.7)	988 (19.8; 18.7–21.0)	859 (19.3; 18.1–20.4)	129 (24.9; 21.2–28.6)	944 (18.3; 17.3–19.4)	631 (16.9; 15.7–18.1)	313 (22.0; 19.8–24.2)
*Family economy N = 11672*
Good economy	7653 (81.4; 80.6–82.2)	1471 (64.9; 62.9–66.8)	4600 (80.5; 70.5–81.6)	4214 (82.2; 81.1–83.2)	386 (66.1; 62.3–69.9)	4495 (76.0; 74.9–77.0)	3422 (80.5; 79.3–81.7)	1073 (64.4; 62.2–66.7)
Neither bad nor good	1405 (14.9; 14.2–15.7)	547 (24.1; 22.4–25.9)	869 (15.2; 14.3–16.1)	734 (14.3; 13.4–15.3)	135 (23.1; 19.7–26.5)	1074 (18.1; 17.2–19.1)	666 (15.7; 14.4–16.8)	408 (24.5; 22.4–26.6)
Bad economy	346 (3.7; 3.3–4.1)	250 (11.0; 9.7–12.3)	243 (4.3; 3.7–4.8)	180 (3.5; 3.0–4.0)	63 (10.8; 8.3–13.3)	349 (5.9; 5.3–6.5)	165 (3.9; 3.3–4.5)	184 (11.1; 9.6–12.6)

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Symptoms of depression coded as ≥ 80th percentiles, no symptoms of depression coded as < 80th percentiles.

Overall, a significantly higher proportion of the girls (28.2%) had symptoms of depression compared with the boys (10.3%). In gender subgroups, those with symptoms of depression reported significantly poorer family economy, more smoking, fewer friends and that their parents had lower education, compared with the rest of the study population.

Among adolescents who spent more than 3 hours per day on gaming and social media, a higher proportion reported symptoms of depression compared with adolescents using 3 hours and less per day. There were small gender differences. Of those who reported using social media more than 3 hours per day, a higher proportion of the girls (46.8% compared to 34.7%) and a higher proportion of boys (28.9% compared to 17.0%) reported symptoms of depression. Of those who reported gaming more than 3 hours per day, a higher proportion of the girls (16.2% compared to 9.3%) and a higher proportion of boys (42.0% compared to 27.2%) reported symptoms of depression.

Table 3 present binominal logistic regression of symptoms of depression in relation to electronic media use and other factors.

Table 3. Binary logistic regression analysis of symptoms of depression in relation to social media use and gaming, controlling for possible confounders factors (Ungdata 2018, adolescents aged 15–16 years).

	Analysis 1, social media use	Analysis 2, gaming	Analysis 3, social media use and gaming
Variables	AOR (95% CI)	AOR (95% CI)	AOR (95% CI)
Social media use
3 hours or less	1 (ref)		1 (ref)
More than 3 hours	1.60 (1.43–1.80)		1.51 (1.34–1.70)
Gaming
3 hours or less		1 (ref)	1 (ref)
More than 3 hours		1.57 (1.36–1.80)	1.38 (1.19–1.59)

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Symptoms of depression coded as ≥ 80th percentiles, no symptoms of depression coded as < 80th percentiles. OR: odds ratio; 95% confidence interval. AOR: adjusted for gender, friends, smoking, parent’s higher education and family economy.

Table 3 presents the odds ratios of depressive symptoms by social media (column 1), gaming (column 2) and social media use and gaming (column 3) adjusted for potentially confounding variables. In the multivariate model where gender, friends, smoking, parents’ higher education and family economy were adjusted, the odds of having symptoms of depression were 1.60 times higher for those who use social media more than 3 hours per day, compared to those who use social media 3 hours and less per day. Additionally, the odds of having symptoms of depression were higher for those who spent more than 3 hours on gaming per day (OR: 1.57, 95% CI: 1.36–1.80), compared to those who spent 3 hours and less on gaming per day. These odds were little changed when the two variables were entered simultaneously. There was no interaction effect with gender and friends according to the likelihood ratio test.

To check the robustness of our results, we tried dichotomizing the outcome variable regarding time spent on electronic media into more than two hours and two hours or less. The results showed that adolescents who spent more than two hours on social media every day had higher odds of depressive symptoms than those who spent two hours or less. Further, adolescents who spent more than two hours in gaming had higher odds of depressive symptoms compared to those who spent two hours or less on the same activity.

Discussion

This study examined the association between electronic media use, including use of social media and gaming, and symptoms of depression among adolescents in Norway, and whether gender or having friends moderated these associations. Our results based on multivariate analyses showed that symptoms of depression was significantly associated with adolescents’ frequent use of social media and gaming after adjustment for having friends, gender, smoking, parents higher education and family economy.

These results are consistent with many previous studies [9, 10]. However, some studies reported no association between gaming and depressive symptoms [11, 18, 19]. Some other studies found a protective effect of video gaming on depression [36]. Moreover, using objective measures of video gaming a recent cross-sectional study found a positive relationship between video gaming and affective well-being [25]. The inconsistent results may depend on the purpose of playing the videogame, the content and the type [22].

There were no interaction effects between use of social media and gaming. This means that the strength of the association between social media use and symptoms of depression was not stronger among frequent game players than among non-frequent gameplayers. Inversely, the association between gaming and symptoms of depression is as strong among frequent social media users as among non-frequent social media users. These patterns also held when the analyses were repeated separately for boys and girls. Moreover, the analyses did not show any interactions between electronic media use and having a friend. According to Zink et al.[22], previous studies provide inconsistent results for moderating effect of gender, friendship, physical activity, social context, cultural characteristics, age, parental factors, and no moderating effect of socioeconomic status.

Another study showed that sleep mediated the association between screen time (eg, social messaging, web surfing, TV/movie watching, and gaming) and depressive symptoms among adolescents, but they did not examine interaction effects [37].

The discrepancy in the results may be explained by that the adolescents spend more time on electronic media use compared to previous generations [7]. Additionally, the discrepancy may reflect the increasing availability of screen based devices, and that type and features of online activities might be associated with mental health differently [38]. A longitudinal study among Norwegian adolescent showed a discrepancy in the results due to measurement methods, self-reported screen time was associated with mental distress while objectively measured sedentary behaviour was not [39].

To test the robustness of our results, we dichotomized the outcome variable into more than two hours and two hours or less. The estimated results show that those who spent more than two hours on social media or gaming every day have higher odds of depressive symptoms than those who spent two hours or less. The results are consistent with the results presented in this study, signaling that spending longer time on electronic media use is associated with higher odds of having symptoms of depression, all else equal.

The contribution of this study lies in at least two aspects. First, there is a lack of knowledge about the association between electronic media use and depressive symptoms among adolescents in the Nordic countries. Based on the most recent Norwegian data, this study will enrich our relevant knowledge of the situation in the Nordic area. Second, findings of this study may guide policy development regarding mental health issues of children and adolescents.

The cross-sectional study design does not allow conclusions about the direction of the association, i.e., whether excessive social media use caused depressive symptoms or if adolescents with previous mental health problems are overrepresented among excessive media users. Alternatively, the association may work in both directions.

Strength and limitations

A major strength of this study is the large sample size in combination with a high response rate. The data used in this study were collected in 2018 and provide an up-to-date description of key aspects of young people’s lives. Limitations of this study exist. First of all, this is a cross-sectional study, which precludes inferences about causal relationships. Depressive symptoms may not only work as an outcome but may also act as an exposure that is hampering electronic media use. Furthermore, the use of self-reported measures may have led to unidentified misclassifications or measurement errors. As far as we know, no evaluation has been performed in the Ungdata Survey regarding self-reporting of electronic media use. Furthermore, the associations found between electronic media use and depressive symptoms might be influenced by other factors that were not controlled for in the present study. For example, our data do not include information regarding the content of social media and gaming. We had no information about what adolescents were doing on social media or what types of games that they were playing. Thus, our results should be interpreted with caution.

Conclusions

Adolescents with a more frequent use of electronic media was associated with significantly higher odds of having symptoms of depression. Our results did not vary by gender or having friends or not. From a public health perspective, we hope these results can contribute to the discussion regarding the relationship between electronic media use and mental health. Future research should aim at including variables that consider the context and content of electronic media use–and whether and how these factors might influence the relationship between electronic media use and symptoms of depression.

Data Availability

The data and materials from the Ungdata Surveys are stored in a national database administered by NOVA. The data are available for research purposes upon application. For request of the data, please contact ungdata@oslomet.no. Further information about the study and the questionnaires can be found on the web page https://www.nsd.no/nsddata/serier/ungdata_eng.html.

Funding Statement

The authors received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0254197.r001

Decision Letter 0

Therese van Amelsvoort

20 Apr 2021

PONE-D-20-34067

Electronic media use and symptoms of depression among adolescents in Norway

PLOS ONE

Dear Dr. Kleppang,

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Reviewer #1: The article presents original data from a large sample and results are in acordance with previous studies.

1. More detailed information should be made available about the scale used to assess mental health symptoms, as it appears from the manuscript that this was a 90 itens questionnaire. HSCL has also a 10 and a 25 questions version, correct? This is particularly important as 3 out of the six questions used to define the outcome variable could also be interpreted as signs of anxiety (itens 2, 5 and 6), not being specific to depression. Please clarify.

2. If more information was gathered on mental health symptoms, it would be interesting to see the association between attentional problems and gaming, which is frequently discussed in the literature.

3. A few small corrections in the text:

- In Table 1, response alternatives to social media use and gaming have the "> sign" inverted (>3 hours)

- In Table 1, parents higher education, the word parents is misspelled

- In Table 1, the variable definition does not match the response alternatives in social media use and gaming (times per day vs hours per day).

- Line 105-106: the word derived appears twice

- Line 222: reference style

4. In the logistic regression (table 3), the ORs of depressive symptoms for both gender and economic status are much higher than those for social media use or gaming. I would recommend, in the discussion, commenting/highlighting the importance of <<multiple factors="">> that possibly mediate, moderate or confound the association between screen media use and mental health issues.

5. Why did the authors predict an interaction effect between gaming and social media use? Have you run separate models for boys and girls? Based on the literature, I would expect that the strength of the relationship between electronic media use and symptoms of depression would be affected by socioeconomic status

6. Exchanging text, audio or video messages with friends through WhatsApp, Messenger or similar apps was included as social media use? (sorry, I don't know if these are used in Norway). Did the question presented to the adolescents mention plataforms other than Facebook and Instagram?</multiple>

Reviewer #2: Overall, this is a very clear and concise study on the associations of electronic media use and depression in adolescents. The results imply that more time using social media or video games is associated with a greater depression risk. The paper is really well-written, easy to follow, and addresses simple but important research questions - congratulations on such a nice manuscript to read!

My main reservations about this paper are the lack of adjustment for several key confounding variables (highlighted below) and the unnecessary dichotomisation of the exposure and outcome variables. I explain these points below, along with some other more minor additions:

Introduction

- Page 3 final paragraph, it's worth mentioning a meta-analysis by Liu et al 2016 on screen time and depressive symptoms in young people found no association between video gaming and depression (but did with other forms of screen time) in a sub group analysis - these seems to contrast with your Zink reference

- Liu M, Wu L, Yao S. Dose-response association of screen time-based sedentary behaviour in children and adolescents and depression: a meta-analysis of observational studies. Br J Sports Med [Internet]. 2016 Oct 1 [cited 2019 Sep 17];50(20):1252–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26552416

- There was also a recent longitudinal study that showed protective associations between video game use and depressive symptoms, which did find gender as a possible moderator (as I believe did Liu et al) in contrast to lines 71-72:

- Kandola A, Owen N, Dunstan DW, Hallgren M. Prospective relationships of adolescents’ screen-based sedentary behaviour with depressive symptoms: the Millennium Cohort Study. Psychol Med [Internet]. 2021 Feb 19 [cited 2021 Mar 1];1–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/33602369

- Another recent (cross sectional) study which used objective measures of video gaming found protective associations with wellbeing, which seems relevant here:

- Johannes, N., Vuorre, M., & Przybylski, A. K. (2020). Video game play is positively correlated with well-being. Royal Society Open Science.

Methods:

- Do you have data on response rates and/or attrition/missing data for the survey? It would be helpful for assessing the extend of selection bias

- Table 1 could probably go in the supplementary materials

- Electronic media use - why dichotomise these variables? Could the authors provide some clear justification for this. I would advise against it here, you lose a lot of information this way and these individual categories represent different usage patterns that warrant investigating e.g., under an hour or 1-2 hours

- Depressive symptoms - again I'd argue against dichotomising here. Depressive symptoms exist on a continuum in reality not categorically. This is particularly relevant here as you're using a depression scale that isn't widely used as a screening tool for depression. You also sacrifice statistical power e.g., see Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med [Internet]. 2006 Jan 15 [cited 2019 May 29];25(1):127–41. Available from: http://doi.wiley.com/10.1002/sim.2331

- Can the authors state how these confounding variables were selected? There are many potential confounding variables that aren't included here, e.g., household income/deprivation, time in physical activity/sedentary behaviour, alcohol use, parental mental health or another marker of genetic mental health risk, overall physical health or disability

Results:

- Table 2: There is quite a lot going on in this table. I suggest shortening the variable names for each characteristics to just 'social media', 'video game' etc. And only comparing across gender or depressive symptoms rather than both

- Table 3: I am unclear what is being presented here. It looks like these were all separate univariate models in the way they are presented here. Presumably this is the main analysis where you ran social media and gaming as exposure variables (in separate models? or mutually adjusted?) with depression as the outcome, adjusted for the confounders? If so, I suggest first including the crude (unadjusted) estimates then the fully adjusted estimates - only showing the ORs for the exposures (i.e., gaming and social media). The ORs for the confounding variables can be misleading for some readers as they are commonly misinterpreted, see this paper for a clear explanation of this as the 'Table 2 fallacy':

- Westreich, D., & Greenland, S. (2013). The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. American journal of epidemiology, 177(4), 292-298.

- I'd also include a sentence clarifying there was no interactions with gender and friends with the appropriate statistics for indicating this (rather than indicating only in the methods)

Discussion

- The first couple of sentence can be moved down or removed, you just need a clear sentence clarifying the aim of this study followed swiftly by the main findings

- Lines 227-228, again worth mentioning this contrasts with the studies I mentioned to include in the discussion finding protective associations of video gaming on mental health - I also think this merits some discussion, why do the authors think these results contrast?

- lines 253 - include any sensitivity analysis in the methods and results, not in the discussion alone

- Strengths and limitations needs a bit of expansion, this is an important part of the paper for guiding future research. I'd mention the sample size as a key strength. Weaknesses to consider include:

- the self-report exposure measures that are subject to recall and social desirability biases - worth noting the Johannes paper mentioned above used objective measures and found the opposite association.

- exposure data is only focusing on time, whereas contextual factors are likely to be equally as important to its relationship with depression e.g., what type of video games were participants playing? what were they using social media to do? Interact with friends or scroll through newsfeeds aimlessly?

- no data on television - which is the most studied domain of screen time in adults

- The depressive symptoms are measured using a not particularly well validated tool, which could introduce additional measurement error

- There are also several confounding variables missing that I mention above as limitations

- Conclusion is also a little short, id include a sentence stating the implications/future directions for research

**********

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Reviewer #1: Yes: Renata Kieling

Reviewer #2: Yes: Aaron Kandola

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PLoS One. 2021 Jul 7;16(7):e0254197. doi: 10.1371/journal.pone.0254197.r002

Author response to Decision Letter 0

2 Jun 2021

Reviewer #1: The article presents original data from a large sample and results are in accordance with previous studies.

1. More detailed information should be made available about the scale used to assess mental health symptoms, as it appears from the manuscript that this was a 90 items questionnaire. HSCL has also a 10 and a 25 questions version, correct? This is particularly important as 3 out of the six questions used to define the outcome variable could also be interpreted as signs of anxiety (itens 2, 5 and 6), not being specific to depression. Please clarify.

We have added more text in the manuscript to clarify (page 6 under Measures).

Some of the items measuring depressive symptoms in the HSCL-10 are similar to the depressive symptom scale, but the wording on some of the items are different. We have chosen not to use items measuring anxiety in the current study. A reason for that is that anxiety items from the HSCL-10 is not in the main module in Ungdata, only in one of the optional modules which some schools have chosen not to include.

2. If more information was gathered on mental health symptoms, it would be interesting to see the association between attentional problems and gaming, which is frequently discussed in the literature.

We agree that this could be interesting to examine. However, according to the aim of our study we chose to focus on symptoms of depression in the current study and use the validated depressive symptoms scale. Please see the explanation above.

1. A few small corrections in the text:

- In Table 1, response alternatives to social media use and gaming have the "> sign" inverted (>3 hours)

- In Table 1, parents higher education, the word parents is misspelled

- In Table 1, the variable definition does not match the response alternatives in social media use and gaming (times per day vs hours per day).

- Line 105-106: the word derived appears twice

- Line 222: reference style

Thank you, the changes are made.

4. In the logistic regression (table 3), the ORs of depressive symptoms for both gender and economic status are much higher than those for social media use or gaming. I would recommend, in the discussion, commenting/highlighting the importance of <> that possibly mediate, moderate or confound the association between screen media use and mental health issues.

Table 3 is revised after comments from the other reviewer. In the method section (page 9), the moderator analysis in the present study is described and further discussed on page 14, line 253-264.

Zink et. al. (2020) indicated in a recent systematic review of moderating variables that screen-type influences the strength of the association between screen based sedentary behaviour and symptoms of depression and anxiety in adolescence. Therefore, we did interaction analysis to examine if the strength of the association between social media use and depressive symptoms was affected by gaming. We performed separate analysis for boys and girls, we did not find anything new, and the results for boys and girls are consistent with the results presented in this study.

Following the reviewer’s suggestion, we have conducted new interaction analysis to examine if the strength of the association between electronic media use and symptoms of depression would be affected by socioeconomic status. The incremental changes in log-likelihood between the main effect models and models including interactions were not significant implying that the fit was not improved when applying interaction models.

The national Ungdata used in the present study mentioned only Facebook, Instagram and etc.

Introduction

- Another recent (cross sectional) study which used objective measures of video gaming found protective associations with wellbeing, which seems relevant here:

- Johannes, N., Vuorre, M., & Przybylski, A. K. (2020). Video game play is positively correlated with well-being. Royal Society Open Science.

Thank you for addressing this, new references are added, and changes are made in the manuscript (page 3 and 4).

Methods:

- Do you have data on response rates and/or attrition/missing data for the survey? It would be helpful for assessing the extend of selection bias

We agree and have added the participation rate for secondary school (85%). Additionally, in table 2 you can see how many adolescents who have answered on the questions used in this study.

- Table 1 could probably go in the supplementary materials

We have chosen to keep the table, because we think it clarify the text.

We understand your advice. Adolescents nowadays spend more time on online gaming and social media than previous generations, and recent studies have used the same cut off value <3h per day (Boshi et al 2020 & Kleppang et al 2021).

Nonetheless, to address this comment, we have taken the following actions.

1) In the Analysis section, we added information regarding testing robustness of our results (see page 10).

2) At the end of the Results section, we added information regarding results of our robustness test (see page 13).

3) In the section of Discussion, we discussed the results from the robustness test (see page 15).

The scale has been used in The Municipal Youth Surveys (Ungdata) since 2010. A psychometric analysis of the scale has been done using Rasch measurement theory, and the dichotomising of the depression scale has been used in previous studies. We have added text and ref. to clarify in the method section on page 6.

Existing studies have shown that SES and smoking are associated with depression. Additionally, friendship have been reported to be inversely associated with depression. Hence, we used parent’s higher education, family economy, gender and having friends as confounding variables.

Results:

We agree and have shortened the variable names.

- Westreich, D., & Greenland, S. (2013). The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. American journal of epidemiology, 177(4), 292-298.

We agree and have made the changes in the table.

- I'd also include a sentence clarifying there was no interactions with gender and friends with the appropriate statistics for indicating this (rather than indicating only in the methods)

We agree and have added this in the results on page 13.

Discussion

- The first couple of sentence can be moved down or removed, you just need a clear sentence clarifying the aim of this study followed swiftly by the main findings

We agree and have moved down the sentences to the introduction part (page 13, line 231-235).

We agree and have added the articles in the discussion.

- lines 253 - include any sensitivity analysis in the methods and results, not in the discussion alone

We agree and have added this both under method and results section.

We agree and have added the following: The present study’s major strengths were the large sample size combined with a high response rate.

We agree and have added this as a limitation

We agree and have added this as a limitation.

- no data on television - which is the most studied domain of screen time in adults

Since the present study have examined adolescents, we chose to focus on electronic media use.

- The depressive symptoms are measured using a not particularly well validated tool, which could introduce additional measurement error

The scale has been validated in the present study and in previous studies (see page 7, line 120-125)

We have added that the scale has been validated using Rasch Measurement Theory (see page 15).

- There are also several confounding variables missing that I mention above as limitations

We have added this as a limitation

- Conclusion is also a little short, id include a sentence stating the implications/future directions for research

We agree and have added sentence to clarify (see page 15)

PLoS One. doi: 10.1371/journal.pone.0254197.r003

Decision Letter 1

Therese van Amelsvoort

23 Jun 2021

Electronic media use and symptoms of depression among adolescents in Norway

PONE-D-20-34067R1

Dear Dr. Kleppang,

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PLoS One. doi: 10.1371/journal.pone.0254197.r004

Acceptance letter

Therese van Amelsvoort

28 Jun 2021

PONE-D-20-34067R1

Electronic media use and symptoms of depression among adolescents in Norway

Dear Dr. Kleppang:

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Data Availability Statement

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PERMALINK

Electronic media use and symptoms of depression among adolescents in Norway

Annette Løvheim Kleppang

Anne Mari Steigen

Li Ma

Hanne Søberg Finbråten

Curt Hagquist

Roles

Abstract

Background

Methods

Results

Conclusions

Background

Methods

Table 1. Ungdata Survey 2018: Questions, response alternatives and variable definitions.

Measures

Symptoms of depression

Electronic media use

Friends

Smoking

Parents’ level of education and family economy

Analysis

Results

Table 2. Youth data surveys: Baseline characteristics of adolescents, aged 15–16 years in 2018, according to symptoms of depression and gender.

Table 3. Binary logistic regression analysis of symptoms of depression in relation to social media use and gaming, controlling for possible confounders factors (Ungdata 2018, adolescents aged 15–16 years).

Discussion

Strength and limitations

Conclusions

Data Availability

Funding Statement

References

Decision Letter 0

Therese van Amelsvoort

Roles

Author response to Decision Letter 0

Decision Letter 1

Therese van Amelsvoort

Roles

Acceptance letter

Therese van Amelsvoort

Roles

Associated Data

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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