Predicting Self-Reported Depression and Health Among Adolescents: Time Spent Online Mediated by Digital Skills and Digital Activities

Abstract

Although young people spend increasing amounts of time online, many gaps remain in the literature regarding the effect of time spent online on young people's development of well-being. We focus on the influence of time spent online on feelings of self-reported (a) depression and (b) health of adolescents. We also consider the mediating role of digital skills and digital activities, each of which is broken down into five dimensions. We collected data through a two-wave longitudinal online survey among 3,942 adolescents aged 12–17 years in six European countries (first wave [W1] = 2021; second wave [W2] = 2022). We specifically want to understand how feelings of self-reported depression and health at W2 are affected by the time spent online at W1, and how digital skills and digital activities at W2 mediate these relationships. Findings indicate a significant increase in feelings of depression and a decrease in self-reported health between W1 and W2. Regarding digital skills, information navigation and communication and interaction were linked to greater well-being (lower depression and greater self-reported health). Regarding digital activities, the development of social relationships online was linked to lower self-reported depression and greater health, while frequently using the Internet to look up information on physical or mental health issues was strongly linked to greater depression and lower health. We discuss the implications of these findings for practice and policy on young people's well-being.

Introduction

The link between using digital technologies and youths' psychosocial well-being is complex, as digital technologies can have both positive and negative effects on mental health and life satisfaction. In recent years, various studies have confirmed that excessive Internet use is strongly associated with feelings of depression and reduced subjective well-being among young people.^1–6 Although many studies—cross-sectional, experimental, longitudinal—have demonstrated this, a number of important gaps in the literature remain. For one, it is unclear to what extent certain factors play a mediating role in this relationship between time spent online and psychological, social, and physical types of well-being (simply referred to as well-being in this study). Particularly, longitudinal models, which allow for a better estimation of the role of possible mediators than cross-sectional ones, have only scarcely been used in this field.

A first potential mediator is digital skills. Many young people lack advanced and sometimes even basic digital skills, which impedes their participation in complex digital societies.^2,3 It seems that specific groups of young people, for example, the psychologically vulnerable and traditionally marginalized, are less likely to be able to take advantage of online opportunities.^5,6 Even more concerning is that they may also be less able to avoid more negative outcomes.² Fundamental in our approach is the assumption that the unequal distribution of outcomes represents the unequal distribution of digital skills. A recent systematic evidence review of the literature on digital skills highlights the need for longitudinal research on the mediating role of digital skills in youth well-being research.⁷ Previous cross-sectional studies have indicated (a) that heavy Internet users tend to have more digital skills and (b) that digital skills are linked to well-being in various ways—both positively and negatively.^2–4,7

A key shortcoming of many of these studies is that digital skills are conceptualized and measured through single concepts, even though the construct is multifaceted.^7,8 Earlier conceptualizations of digital skills used them with a focus on skills related to operating devices or installing software.⁸ As digital technologies evolved, it became clear that a narrow understanding of digital skills no longer represented the true nature of the construct. With the rise of large-scale search engines such as Google and Wikipedia, information navigation became (and continues to be) an important part of digital skills.⁹ With the emergence of social media, and the Web 2.0 more generally, two additional digital skills have become important: the ability to communicate with other users (e.g., comment sections on websites of traditional news outlets), and the creation (and production) of content on social media.⁹ This also includes the ability to critically evaluate the impact of interpersonal mediated communication and interactions on others.⁸

All skill dimensions have functional aspects (understanding technical functionalities and being able to use these) and critical aspects (understanding how and why devices and content are produced in certain ways). To participate fully in digital societies, being skilled in these main skill dimensions is indispensable. Based on Helsper et al.,⁸ we have conceptualized five dimensions of digital skills: (a) technical and operational skills, (b) programming,^a (c) information navigation and processing, (d) communication and interaction, and (e) content creation and production. Recent research conducted by Livingstone et al.¹⁰ has provided valuable insights into the significant role of digital skills in the well-being and experiences of depression among young people. Employing a qualitative approach, their study unveiled that among a sample of young people who reported mental health difficulties, proficient technical skills, including navigating digital platforms and employing a variety of tools, are pivotal in shielding them from potential risks linked to their online engagements.

These digital skills enable them to manage their online experiences and mitigate potential mental harm. Moreover, the study highlighted that the motivation to seek highly specialized or niche information related to mental health reflects a deeper interest and investment in digital skills that can empower young people in understanding and managing their mental health.¹⁰ While communicative skills can be supportive in fostering peer connections and facilitating the sharing of experiences, they also carry inherent risks when misused within fragile peer communities or by individuals with malicious intentions. These findings underscore the interdependence of digital skills with the development of psychosocial life skills among young people. This suggests that digital skills should not be viewed in isolation, but rather as intricately intertwined with broader facets of their lived experiences, including challenges related to mental health.¹⁰

In addition, as the Internet evolves into a complex and increasingly user-driven environment, there is a growing need to focus on the multidimensionality of activities that can be performed on it—a second potential mediator between time spent online and well-being. In this study, we highlight five aspects: (a) online learning through digital resources; (b) developing social relationships in digital settings; (c) entertainment; (d) content creation; and (e) using digital media to look up information regarding physical or mental health—key digital activities for young people today.⁸ Unsurprisingly, youth who spend more time online tend to spend more time doing (some of) these activities, and conversely, some of these activities have also been linked to youth well-being.²

Participating in online learning activities, such as accessing educational resources, attending virtual classes, or seeking informative content, has the potential to yield beneficial outcomes for youth well-being. These activities provide opportunities for intellectual growth, skill development, and academic achievement.^10,11 In addition, engaging with others through various social media platforms, online communities, or multiplayer gaming environments can have both positive and negative impacts on youth well-being. Positive interactions, such as forming supportive friendships, sharing experiences, and receiving social support, can enhance well-being by fostering a sense of belonging, connectedness, and emotional fulfilment.^11,12

Important to note is that digital skills and digital activities are two distinct concepts. While performing digital activities necessitates a certain skill level, the skills are implicit, while it is equally important to also include explicit measures of digital skills.⁸

In this study, we address key gaps in the literature by studying the mediating role of digital skills and digital activities in the longitudinal relationship between time spent online and well-being among adolescents in six European countries (N = 3,942) in a two-wave online survey study (Wave 1: 2021; Wave 2: 2022). See Figure 1 for an overview of the proposed model. The selection of the six European countries was based on the 2019 Digital Economy and Society Index, which considers diverse indicators reflecting digital capabilities.¹³

FIG. 1.

Theoretical model.

Data and Methodology

Data

We conducted a two-wave, online school-based survey study among adolescents aged 12–17 years in six European countries (Estonia, Finland, Germany, Italy, Poland, Portugal).^b For the first wave (W1), data were collected between April and November 2021 (N = 6,221). For the second wave (W2), data were collected 1 year later (N = 7,133). The initial students' response rate (W1) across all countries was 60.8 percent. There was dropout between W1 and W2 due to students being absent from class the day of data collection in W2, having moved away, having changed schools, having graduated secondary education, or because they have failed to pass examinations at the end of the school year, resulting in a slightly changed composition of classes. There were also a number of newcomers who had not participated in W1.

In this study, we focus on young people who participated in both rounds of the study (total N = 3,942; Estonia [n = 809], Finland [n = 623], Germany [n = 466], Italy [n = 674], Poland [n = 609], and Portugal [n = 761]—see Table 1 for an overview of the full sample). Data were collected in collaboration with secondary schools. We used a convenience sampling design but ensured that in each country, schools were selected based on their socioeconomic status to ensure diversity of participants. In each country, we selected and contacted schools located in different types of neighborhoods (less/more affluent), and, in countries with a segregating school system, we chose different types of schools. Although we did not impose strict quota on this distribution, the final sample for each country included participants from schools from lower-, middle-, and upper-class neighborhoods.

Table 1.

Descriptive Overview of the Sample (N = 3,942)

In percent			Total
Gender
Male			49.1
Female			49.5
Other			1.4

Mean scores (SE)	Wave 1	Wave 2	Total
Age			14.32 (1.23)
SES	2.82 (0.64)	2.82 (0.67)	2.82 (0.65)
Digital skills
Technical/operational	0.55 (0.31)	0.60 (0.32)	0.67 (0.31)
Programming	0.06 (0.24)	0.08 (0.26)	0.07 (0.25)
Information navigation/processing	0.35 (0.31)	0.37 (0.33)	0.36 (0.32)
Communication/interaction	0.65 (0.30)	0.66 (0.31)	0.65 (0.31)
Content creation/production	0.37 (0.33)	0.39 (0.35)	0.38 (0.34)
Digital activities
Online learning	2.64 (0.93)	2.70 (0.93)	2.67 (0.93)
Social relationships	3.58 (0.94)	3.64 (0.91)	3.61 (0.92)
Entertainment	4.08 (1.14)	4.13 (1.14)	4.10 (1.14)
Content creation	2.15 (1.31)	2.13 (1.25)	2.14 (1.28)
Health use	1.87 (1.03)	2.00 (1.05)	1.85 (1.04)
Time spent online	5.87 (1.97)	5.95 (1.81)	5.93 (1.90)

SE, standard error; SES, socioeconomic status.

A total of 51 schools agreed to participate, with 342 classes participating in both waves, either in-person in school (applying to the majority of classes) or online at home (in distance education due to school closings during the COVID-19 pandemic). Researchers were present during the data collection in both waves, either in-person in class or digitally when data were collected in distance education. Informed consent was obtained from participants and their legal guardians according to the national regulations. The survey was translated into the official languages of the countries under study by professional translators and back-translated by native speakers. After thoroughly comparing the original and back-translated versions, we identified no discrepancies regarding the meaning of the questions.

Measures

To provide an answer to our research questions, we measured the following constructs^14–19:

- Feelings of depression (6 items)

- Self-reported health (1 item)

- Digital skills (25 items)

- Digital activities (11 items)

- Time spent online (1 item)

For a detailed overview of the different measures used in the study, see the Supplementary Data. All items were presented in the exact same way in both waves of the study.

Data analysis

We first conducted paired-samples t-tests to study whether depression and self-reported health scores differed between W1 and W2. We then conducted a cross-lagged panel structural equation model with weighted least squares estimation (see Fig. 1 for the model) to investigate whether time spent online at W1 prospectively predicted feelings of depression and self-reported health at W2, and whether these relationships were mediated by digital skills and digital activities at W2. To summarize, we estimated (a) associations between time spent online at W1 and feelings of depression, and self-reported health at W2; (b) associations between time spent online at W1 and digital skills and digital activities at W2; and (c) associations between digital skills and digital activities at W2 and depression and health indicators at W2.

Aside from the W1 depression, W1 health, and W1 Internet use controls (Fig. 1), we also controlled for age, gender, socioeconomic status, and country fixed effects through dummy variables. See Supplementary Table S1 for an overview of Pearson correlations between key study variables. All analyses were conducted using SAS version 9.4. Model fit for the cross-lagged analysis was evaluated using state-of-the art guidelines.^20,21 We used maximum likelihood estimation with standard errors.²²

Results

There was a significant increase in feelings of depression among participants between W1 and W2 (t = −6.23, p < 0.001): participants reported greater feelings of depression at W2 (mean [M] = 2.03, standard deviation [SD] = 0.68) than at W1 (M = 1.97, SD = 0.68). In the same vein, self-reported health decreased between waves (t = 12.90, p < 0.001), from a score of 3.56 (out of 5) at W1 to 3.33 at W2. Internet use increased somewhat between waves (t = −2.33, p = 0.020): participants spent more time on the Internet during a regular weekday at W2 (M = 5.95, SD = 1.81) than at W1 (M = 5.87, SD = 1.96). Post hoc testing showed that the reported increase in feelings of depression between W1 and W2 was not found among Finnish and Portuguese participants. The decrease in self-reported health and the increase in Internet use were reported in all countries (Table 2).

Table 2.

Paired-Samples t-Tests for Self-Reported Depression, Self-Reported Health, and Time Spent Online

	Mean W1 (SD)	Mean W2 (SD)	t-score	p
Self-reported depression	1.97 (0.68)	2.03 (0.68)	−6.23	0.000
Self-reported health	3.56 (1.05)	3.33 (1.12)	12.90	0.000
Time spent online	5.87 (1.96)	5.95 (1.81)	−2.33	0.020

SD, standard deviation; W1, first wave; W2, second wave.

The proposed theoretical model in Figure 1 provided a good fit to the data (root-mean-squared error of approximation [RMSEA] = 0.07, goodness of fit index [GFI] = 0.94, comparative fit index [CFI] = 0.87, Akaike information criterion [AIC] = 2,613.65, χ² = 2,265.65, df = 151). Deleting nonsignificant paths yielded a model with slightly improved model fit (RMSEA = 0.07, GFI = 0.94, CFI = 0.88, AIC = 2,606.80, χ² = 2,286.80, df = 165).

In this model, W1 Internet use was a significant predictor of both W2 depression (β = 0.04, p < 0.01) and W2 self-reported health (β = −0.05, p < 0.01). Regarding digital skills, only information navigation/processing skills at W2 were negatively linked to depression at W2 (β = −0.03, p < 0.05). Regarding digital activities, (trying to) engage in social relationships was linked to lower depression (β = −0.03, p < 0.05), but looking up information regarding health problems (either physical or mental) was strongly associated with greater feelings of depression (β = 0.18, p < 0.001).

Regarding self-reported health, greater communication/interaction skills were linked to a more positive view of participants' overall health (β = 0.04, p < 0.05). Engaging in online learning (β = 0.05, p < 0.01) and social relationships (β = 0.05, p < 0.01) on digital media were also linked to more positive views. However, spending more time on digital media for entertainment (β = −0.05, p < 0.01) and health use (β = −0.15, p < 0.01) was linked to a more negative evaluation of participant's health compared with their peers. Indirect effects also indicated a strengthening of the adverse effect of excessive Internet use (through digital skills and digital activities) on well-being (Table 3).

Table 3.

Cross-Lagged Model for Self-Reported Depression and Health at the First Wave and the Second Wave

	Self-reported depression W2	Self-reported health W2
Direct effects
Self-reported depression W1	0.53^*** (0.01)	—
Self-reported health W1	—	0.41^*** (0.02)
Time spent online W1	0.04^** (0.02)	−0.05^** (0.02)
Digital skills
Technical/operational	ns	ns
Programming	ns	ns
Information navigation/processing	−0.03^* (0.01)	ns
Communication/interaction	ns	0.04^* (0.02)
Content creation/production	ns	ns
Digital activities
Online learning	ns	0.05^** (0.02)
Social relationships	−0.03^* (0.02)	0.05^** (0.02)
Entertainment	ns	−0.05^** (0.02)
Content creation	ns	ns
Health use	0.18^*** (0.02)	−0.15^*** (0.02)
Indirect effects
Time spent online W1	0.02^*** (0.02)	−0.02^*** (0.00)

p < 0.05; ^**p < 0.01; ^***p < 0.001.

Note: Standardized coefficients reported. RMSEA = 0.07; GFI = 0.94; CFI = 0.88; AIC = 2,606.80; χ² = 2,286.80; df = 165.

AIC, Akaike information criterion; CFI, comparative fit index; GFI, goodness of fit index; ns, not significant; RMSEA, root-mean-squared error of approximation.

Discussion

The main aim of this study was to study the prospective link between time spent online and well-being (operationalized through feelings of depression and self-reported health) among youth, with digital skills and digital activities as potential mediators of this relationship. The main takeaway from this study is that, for the 12- to 17-year-olds in our data set, time spent online is negatively associated with youth well-being, with greater feelings of depression and lower self-reported health. In addition, digital skills and digital activities only partially mediate this relationship. Scoring highly on information navigation skills was linked to lower depression, while high scores on communication and interaction skills linked to greater self-reported health. Similar results can be found for digital activities: online learning (self-reported health only) and developing social relationships online were positively linked to well-being.

Taken together, these findings indicate that youths who make greater use of the Internet to build or cultivate connections with others (i.e., by keeping in touch with real-life friends) and who frequently use it for learning purposes (i.e., using YouTube videos to develop skills in unrelated hobbies) or have greater information navigation skills (i.e., knowing how to find relevant information) experience fewer drawbacks from (excessive) Internet use. In digital skills as well as digital activities, we found that social aspects (i.e., being skilled in communication and interaction, frequently using the Internet for interacting with others online, and often using the Internet for establishing and maintaining social relationships) are most relevant as mediators between time spent online and self-reported well-being.

This is not surprising considering that establishing intimate relationships with peers and strengthening ties with peers rather than family are among the most important developmental tasks of adolescents,^23,24 which they also address online.^25,26 Thereby, our results confirm the assumed relevance of digital communication skills for this particular age group. Other dimensions of digital skills, programming skills, skills in content creation and production, as well as technical and operational skills do not seem to contribute as mediators to young people's well-being.

Our study has innovated the field by showing that for young people, digital skills and digital activities have a mediating role between time spent online and well-being. Previous research on the influence of digital skills revealed contradicting results due to diverse measurement (above all, single concepts of digital skills instead of multifaceted concepts). Differentiating between five dimensions of digital skills, our research shows that communication/interactions skills as well as information/navigation skills predict youth well-being (higher skills predict lower depression and better health).

Regarding digital activities, previous research found that online learning activities are beneficial for youth well-being,^10,11 while engagement with others online can have a positive or negative effect.^11,12 Our study confirms the beneficial effect of online learning for adolescents: online learning activities reduce the negative effect of excessive Internet use. Furthermore, our study shows that using the Internet for making new friends and for maintaining existing social relationships with friends and family has a positive effect on well-being.

Regarding the theoretical framework of development in adolescence, our results lead to assume that online communication with friends as well as digital communication skills contribute to fulfilling important developmental tasks in adolescence—strengthening peer relations and establishing intimate peer relations^24,26—and thereby are able to reduce negative effects of time spent online on well-being.

Moreover, our findings also suggest that youths who frequently use the Internet for entertainment or to look up information regarding their physical or mental health reported lower well-being. This does not mean that the Internet cannot be highly useful as a source of health information for young people. However, excessively using the Internet for this purpose may indicate that young people do not have an offline outlet for their physical or mental issues, perhaps because of stigma, stereotypes, or other fears (i.e., see concerns of lesbian, gay, bisexual, transgender and queer-youth²⁷).²⁸ Exclusively depending on the Internet for information may also have other drawbacks: youths may misdiagnose certain symptoms, thereby perceiving certain health issues as more (or less) serious than they actually are. With this, we encourage policy makers, parents, educators, and youth alike to continue working toward giving young people safe spaces that enable them to discuss their physical or mental health issues with guardians or medical professionals.

Although our findings are novel, given the partial mediation of Internet use by digital skills and digital activities, there are various avenues for further research on this topic. In this study, we paid relatively little attention to youths' family contexts. Parenting strategies, peer and other family support systems, self-efficacy, and civic engagement are just some of the additional factors that have been found to relate to Internet use, well-being, and digital skills in different studies.^2,4,26,29 To better understand these complex relationships, it is imperative to further study how the family context can encourage or discourage young people's digital engagement.²⁹ For example, when young people feel comfortable discussing their digital activities, concerns, and challenges with their family members in a supportive family environment, it can provide emotional support and help them navigate potential negative experiences online.^1,2,4,29

This family support can buffer against feelings of isolation, anxiety, and depression, thereby promoting overall well-being.^1,6 Conversely, parental monitoring and guidance in the digital realm might mitigate potential risks and promote healthy digital behaviors.²⁶ Actively involved parents who monitor their children's digital activities, set boundaries, and provide guidance on responsible technology use have been shown to reduce the likelihood of encountering harmful online content or engaging in excessive or problematic digital behaviors.²⁹ Consequently, this proactive parental involvement contributes to lower levels of stress, anxiety, and depression among young people.²⁹

Furthermore, we acknowledge a potential limitation arising from the inherent ambiguity of the self-reported health question, which may have introduced variability in participants' responses due to differing interpretations of the multifaceted concept of “health.” In addition, we have presented findings from a two-wave longitudinal study, but it would be beneficial to have more long-term studies (with additional waves) to provide a finer-grained approach to understanding the ways in which the relationship between time spent online and youth well-being changes over time.^5,6 We also acknowledge that the data collection for W1 took place during the COVID-19 pandemic, while W2 data collection occurred after the pandemic. The significant changes that occurred during this time may have contributed to the observed changes in depression and self-reported health at W2.³⁰

It is possible that the associations between Internet use at W1 and depression and health at W2 could be influenced by the types of Internet activities, particularly during the pandemic when Internet use might have been more focused on socializing and education due to COVID-19 restrictions. Finally, it is important to acknowledge that the observed link between seeking health information online and indicators of well-being (both measured at W2), as well as the association between online social relationships and depression, may be influenced by pre-existing health concerns, feelings of depression, or social difficulties. Therefore, caution should be exercised in drawing causal conclusions from these findings.

While our study provides valuable insights into the predictive power of time spent online and cross-sectional associations between digital skills, digital activities, and indicators of well-being, it is important to consider alternative explanations and potential confounding factors that could contribute to the observed relationships. Expanding longitudinal designs or experimental approaches would be beneficial in determining the directionality of these associations and better understanding the underlying mechanisms. Notwithstanding these avenues for further research, our study provides valuable first insights into the mediating role of digital skills and digital activities between time spent online and well-being among young people in Europe.

Notes

Programming is considered to be a minor skill with considerably lower skill levels across all groups of young people.⁸

Approval for this study was obtained from the KU Leuven Social and Societal Ethics Committee (case number G-2019 11 1813).

Footnotes

Authors' Contributions

D.D.C.: Conceptualization, methodology, formal analysis, writing—original draft. L.d.: Conceptualization, writing—review and editing, supervision, project administration, funding acquisition. N.W.: Conceptualization, writing—review and editing, supervision, project administration.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

The research reported here is part of the project Youth Skills (ySKILLS) and received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 870612.

Supplementary Material

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Supplementary Material

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