Does Time Spent Online Affect Future Psychopathology in Adolescents?
Abstract
Abstract.Theoretical background: In cross-sectional studies, high levels of online time proved to be empirically related to a higher risk of online behavioral addictions, which in turn are cross-sectionally and longitudinally associated with psychopathology. First results indicated that online behavioral addictions could predict a higher psychopathological burden. Objective: We empirically examined whether online time is also a predictor of future psychopathology in youth. Methods: In a longitudinal study, we explored whether 249 adolescents (55.0 % girls, aged 15.31 years, SD = 1.78) were at increased risk for problematic Internet use at baseline (t1) and 12-month later (t2) using standardized questionnaires regarding psychopathology (SDQ), online behavioral addictions (CIUS), and time spent online. Results: In linear regression analyses, online time was not a statistically significant predictor of any psychopathological burden 12 months later (at t2), after controlling for gender, age, online behavioral addictions, and respective symptoms of psychopathology (all at t1). Discussion and conclusion: Time spent online does not seem to predict future psychopathological strain during adolescence.
Zusammenfassung.Theoretischer Hintergrund: Eine wachsende Zahl von Forschungsergebnissen zeigt, dass seit den Kontaktbeschränkungen und Restriktionen im Alltag im Zuge der COVID-19-Pandemie sowohl die Zeit, die online verbracht wird, als auch die psychische Belastung unter Jugendlichen erheblich zugenommen haben. Darüber hinaus wird die tägliche Onlinezeit (time spent online) mit einem höheren Risiko von Online-Verhaltenssüchten in Verbindung gebracht. Gleichzeitig zeigen Studien einen Zusammenhang zwischen Online-Verhaltenssüchten und internalisierenden sowie externalisierenden Problemen. Derzeit ist noch unbekannt, ob die tägliche Onlinezeit direkt ein höheres Maß an zukünftiger Psychopathologie vorhersagt. Fragestellung: Wir wollten den zeitlichen Zusammenhang zwischen der online verbrachten Zeit und der zukünftigen Psychopathologie (emotionale Probleme, Verhaltensprobleme, Beziehungsprobleme mit Gleichaltrigen und Hyperaktivität/ Aufmerksamkeitsprobleme) untersuchen. Methode: In einer Längsschnittstudie wurden 249 Jugendliche mit erhöhtem Risiko für problematische Internetnutzung (55.0 % Mädchen, Alter 15.31 Jahre, SD = 1.78) zu Beginn (t1) und nach 12 Monaten (t2) untersucht. Mit Hilfe von Korrelations- und linearen Regressionsanalysen wurden bivariate und multivariable längsschnittliche Zusammenhänge zwischen der täglichen Onlinezeit (in Stunden pro Tag) und der zukünftigen Psychopathologie untersucht. Ergebnisse: Die Ergebnisse zeigten, dass in linearen Regressionsanalysen die tägliche Onlinezeit kein signifikanter Prädiktor für verschiedene Symptome von Psychopathologie 12 Monate später war. Die Modelle wurden für Alter, Geschlecht, Symptome von Online-Verhaltenssüchten und entsprechenden Symptomen von Psychopathologie zur Baseline kontrolliert. Diskussion und Schlussfolgerung: Onlinezeit scheint keinen direkten, inkrementellen Einfluss auf die zukünftige Psychopathologie von Jugendlichen zu haben. Jedoch ist Onlinezeit assoziiert mit Online-Verhaltenssüchten, die wiederum assoziiert sind mit Psychopathologie, insbesondere Sozialverhaltensproblemen. Der Zusammenhang scheint also indirekt zu sein. Für künftige Studien zur Untersuchung des temporären Zusammenspiels der untersuchten Konstrukte wären messzeitpunktintensive Datenerhebungen (z. B. Ecological Momentary Assessments) hilfreich.
Excessive use of digital media is empirically related to a higher risk of online behavioral addictions (Durkee et al., 2012; Gentile, 2009; Kindt, 2019; Kindt et al., 2019; Király et al., 2020; Rehbein et al., 2010; Rumpf et al., 2014, 2020). Although online time does not define online behavioral addictions per se, several empirical findings from cross-sectional studies show that higher levels of online time are related to problematic use of the Internet and its applications (e. g., Wartberg et al., 2016).
Concerning online behavioral addictions, the greatest evidence is available for gaming disorder, which was included in ICD-11 as a new diagnosis (ICD-11 code: 6C51) in the behavioral addictions chapter (Leo & Lindenberg, 2021; Lindenberg & Holtmann, 2021; World Health Organization, 2018). A growing body of research found evidence for addictive social networking use, which can be classified as other specified behavioral addiction (6C5Y) (Brand et al., 2020). Less is known about the addictive use of streaming services such as Netflix or YouTube. Therefore, addictive behaviors in this context must be classified as unspecified behavioral addiction (6C5Z). It was recently reported that, during the COVID-19 pandemic, the rate of online behavioral addiction increased parallel to an increase in online time (DAK Gesundheit, 2021). However, it is unclear to what extent these findings were primarily caused by the pandemic-related restrictions and whether they remain valid after the measures have been lifted. Neumann et al. (2022) found supporting evidence for the direct relationship between increased online time and increased risk of online behavioral addiction development.
Online behavioral addictions in adolescents are highly comorbid with both internalizing and externalizing problems (King et al., 2013; Lindenberg, Szász-Janocha et al., 2017; Müller et al., 2015; Wartberg & Kammerl, 2020). Some longitudinal studies show internalizing and externalizing problems to predict online behavioral addictions (Cho et al., 2013; Ko et al., 2009; Strittmatter et al., 2016), but in the first empirical surveys, it was also observed that vice versa online behavioral addictions predict higher psychopathological burden (e. g., Wartberg et al., 2019). Multiple directions of action between psychological disorders are possible, and it is probably a complex interplay of developing processes (Leo, Kewitz, Wartberg & Lindenberg, 2021). Existing internalizing disorders increase the risk of developing behavioral addiction symptoms 1 year later, which in turn maintains and reinforces internalizing disorder symptomatology (Leo et al., 2021). Dysfunctional emotion regulation is discussed as a transdiagnostic mechanism (Lindenberg et al., 2020; Wartberg & Lindenberg, 2020; Wartberg et al., 2021). The question is whether excessive online use can be understood as a conditioned, dysfunctional emotion regulation strategy that serves two functions: (1) to compensate for an existing gratification deprivation and (2) to be used as avoidance behavior to immediately reduce anxiety and depressive feelings, while in the long term maintaining the symptomatology (Leo & Lindenberg, 2021; Lindenberg et al., 2020, 2022).
Time spent online among 12- to 19-year-old adolescents in Germany has increased from 134 minutes (2011) to 241 minutes (2021) per day over the past 10 years (Medienpädagogischer Forschungsverbund Südwest, 2021). During the first lockdown of the COVID-19 pandemic in the spring of 2020, within a very short time, online use among children and adolescents increased by 75% on weekdays compared to daily online times in the fall of 2019 (Thomasius, 2020). Hence, from a clinical perspective, it is of upmost importance to understand whether and what risks to the mental health of children and adolescents emerge with increased use of digital media. The most popular online activities among children and adolescents are playing video games, using social networks, and streaming movies and video clips (Medienpädagogischer Forschungsverbund Südwest, 2021). Use of other online applications such as online pornography, online shopping, and online gambling also appear but seem to play a minor role among children and adolescents. In clinical practice, therefore, research focuses on screen time activities, including playing video games, using social media, and streaming.
In contrast to studies on the relationship between screen time and online behavioral addictions, little is known about the direct effect of online time on psychopathology in children and adolescents (such as emotional problems, conduct problems, hyperactivity/inattention, and peer relationship problems) and prosocial behavior. To date, it is unclear if and how online time directly causes psychological harm. A large, population-based, cross-sectional U.S. study (Twenge & Campbell, 2018) examined 2- to 17-year-old children and adolescents and found a negative association between screen time and mental health: Higher online time was associated with lower well-being and higher emotional, social, and attentional problems, although the temporal relationship remained unclear in this study. Assuming that intensive use of digital media (and correspondingly high screen times) serves as a maladaptive, avoidant emotion-regulation strategy, it can be supposed that high levels of online time could affect future psychopathology because it is used to avoid unpleasant situations, tasks or emotions.
This longitudinal study examined a sample of adolescents at increased risk for problematic Internet use to determine the temporal association between online time and future symptoms of psychopathology. We wanted to empirically investigate whether online time is a predictor of different aspects of future psychopathology (emotional problems, conduct problems, hyperactivity/attentional problems, and peer relationship problems) and prosocial behavior in youth. For this reason, we explored the following research question: Does online time predict future psychopathology over 12 months in adolescents?
Methods
Procedure
The data were collected within the longitudinal PROTECT study (Lindenberg et al., 2022) in 33 schools in three German federal states (Baden-Württemberg, Hesse, and Rhineland-Palatinate) during regular school hours. We obtained the permission of the Ethics Committee of the University of Education Heidelberg (Az.: 7741.35 – 13) and Regional Council (Az.: 71c2 – 6499.25). Informed written consent was obtained from all adolescents and their legal guardian(s). A detailed description of the sampling procedure may be found in the study protocol (Lindenberg, Halasy et al., 2017).
The PROTECT study focused on adolescents at-risk of developing online behavioral addictions. Thus, all participants were screened before study enrollment (n = 422). The PROTECT study aimed to investigate the effectiveness of a school-based preventive measure against online behavioral addictions in a randomized controlled design. This analysis includes only the control group. We excluded six cases (n = 6) that were already in young adulthood (i. e., aged older than 19 years, in contrast to all other, clearly younger participants). Thus, we included n = 249 adolescents at increased risk for online behavioral addictions with complete datasets. This study used paper-pencil questionnaire data from the baseline assessment (t1) and the 12-month follow-up assessment (t2). The data were collected by trained psychologists during regular school hours.
Participants
Participants were N = 249 adolescents at increased risk for online behavioral addictions (55.0 % female, mean age =15.31 years, SD = 1.78; grades 7 – 12 from 15 German secondary schools). By the defined inclusion and exclusion criteria, participants showed elevated risk of online behavioral addictions as assessed by a sum score of 20 or higher on the Compulsive Internet Use Scale (CIUS; Meerkerk et al., 2009; M = 26.21, SD = 5.59). The average daily online time was 4.53 hours (SD = 2.23).
Measures
We used the Strengths and Difficulties Questionnaire (Goodman, 2003; Goodman et al., 2003; Koglin et al., 2007) to assess psychopathology. The SDQ includes 25 items scored on a 3-point scale. It allows calculation of a total difficulties score (range: 0 – 40, which is often used as a measure of general psychopathological burden). We used the four problem scales (5 items each): emotional problems, conduct problems, hyperactivity/attentional problems, and peer relationship problems (higher scores indicate higher pathology); the fifth scale assesses prosocial behavior, i. e., higher scores indicate lower pathology. In the investigated sample, we observed the following reliability coefficients (Cronbach’s α): emotional problems (t1) = 0.70, emotional problems (t2) = 0.76, conduct problems (t1) = 0.47, conduct problems (t2) = 0.48, hyperactivity (t1) = 0.62, hyperactivity (t2) = 0.72, peer relationship problems (t1) = 0.45, peer relationship problems (t2) = 0.51, prosocial behavior (t1) = 0.65 and prosocial behavior (t2) = 0.73.
Furthermore, we applied the German version of the Compulsive Internet Use Scale (Wartberg et al., 2014) to measure problematic Internet use. The CIUS consists of 14 questions with a 5-level response format, assessing the five symptoms loss of control, preoccupation, withdrawal symptoms, dysfunctional coping, and conflicts. A sum value (range: 0 to 56) can be determined by adding all 14 items. The German version of the Compulsive Internet Use Scale showed good psychometric properties in a representative sample of adolescents (Wartberg et al., 2014). In this analysis, we observed reliability coefficients (Cronbach’s α) for the CIUS of 0.51 at t1 and 0.83 at t2. The CIUS also contains additional questions about the average online time on a typical day during the week and on weekends (a mean value was calculated over the 5 weekdays and 2 weekend days). In addition, demographic data (gender, age etc.) were assessed.
Statistical Analyses
We calculated the means, standard deviations, and proportions. Furthermore, we conducted correlation analyses and multiple linear regression analyses. The dependent variables in the multiple regression analyses were (A) emotional problems at t2, (B) conduct problems at t2, (C) hyperactivity/inattention at t2, (D) peer relationship problems at t2, and (E) prosocial behavior at t2. The explanatory variables were the same in all multivariable analyses (gender, age, online behavioral addictions, time spent online, and the respective subscale (all measured at t1). All statistical analyses were conducted with SPSS (version 25.0, IBM, 2017, New York, USA).
Results
Bivariate Analyses
A correlation matrix for all included variables is shown in Table 1. Many associations were obtained in the correlation analyses; however, we did not observe statistically significant bivariate correlations between online time (at t1) and any aspect of psychopathology at t2 (see Table 1).
Multivariable Analyses
Emotional Problems
In the multiple linear regression analysis, female gender and emotional problems at t1 were statistically significant predictors for emotional problems at t2 (see Table 2), while online time (at t1) did not predict emotional problems 1 year later.
Conduct Problems
In the multiple linear regression analysis, conduct problems at t1 were a statistically significant predictor for conduct problems 1 year later (see Table 3), while online time (at t1) did not predict conduct problems at t2.
Hyperactivity/Inattention
In the multiple linear regression analysis, hyperactivity/inattention at t1 was a statistically significant predictor for hyperactivity/inattention at t2 (see Table 4), while online time (at t1) did not predict hyperactivity/inattention 1 year later.
Peer Relationship Problems
In the multiple linear regression analysis, peer relationship problems at t1 were a statistically significant predictor for peer relationship problems 1 year later (see Table 5), while online time (at t1) did not predict peer relationship problems at t2.
Prosocial Behavior
In the multiple linear regression analysis, prosocial behavior at t1 was a statistically significant predictor for prosocial behavior at t2 (see Table 6), while online time (at t1) did not predict prosocial behavior 1 year later.
Discussion
Online time does not predict future psychopathology over 12 months in adolescents. Cross-sectional correlation analyses showed that higher levels of online time were consistently related to higher symptoms of online behavioral addictions (small effects at t1 and t2). Moreover, higher symptoms of behavioral addictions were associated with higher levels of psychopathology, specifically conduct problems (small effects at t1 and t2). Less consistently, online behavioral addictions were associated with higher levels of emotional problems (medium effect only at t2), higher levels of hyperactivity/inattention (small effect at only t2), and increased peer relationship problems (small effect only at t2). In addition, the association between higher levels of online time and higher levels of psychopathology was also inconsistent. We found significant associations between higher levels of online time and a) increased peer relationship problems and b) reduced prosocial behavior only at t2 (small effects).
The finding that higher levels of online time were associated with higher symptoms of online behavioral addictions is in line with several previous studies (Durkee et al., 2012; Gentile, 2009; Kindt, 2019; Kindt et al., 2019; Király et al., 2020; Rehbein et al., 2010; Rumpf et al., 2014, 2020, Wartberg et al., 2016). The second finding, which underlines the association between online behavioral addictions and conduct problems in adolescents, has less frequently been investigated but has also been reported previously (e. g., Lindenberg, Szász-Janocha et al., 2017). However, we were surprised that the direct associations between online time and psychopathology were inconsistent. Moreover, in contrast to our assumption, online time did not predict future psychopathology after controlling for baseline psychopathology, age, gender, and symptoms of online behavioral addictions.
The findings suggest that there might be far stronger predictors for psychopathology than time spent online. Symptoms of online behavioral addictions seem to be associated with psychopathology but not online time per se. In other words, online time probably does not directly cause psychological harm. However, results indicate that it might have indirect effects since online time was associated with higher levels of addictive online behavior symptoms, which in turn were consistently associated with increased symptoms of conduct disorders. Less consistently, symptoms of online behavioral addictions were also associated with increased peer relationship problems and reduced prosocial behavior, although the temporal relationship remains unclear. We assume that adolescents with symptoms of online behavioral addictions invest less time in building and maintaining relationships with their peers and thus miss out on important learning opportunities for building social skills. Vice versa, adolescents with less rewarding social interactions might have stronger social reward deprivations which they might try to compensate for through highly rewarding internet activities.
Beyond the fact that online time did not predict future psychopathology, and that the cross-sectional effects between online behavioral addiction, online time, and future psychopathology were small and inconsistent, some important limitations restrict the generalizability of the study. We observed regression tendencies to the mean over 1 year in many variables. Often found in studies with at-risk samples, these effects must be considered in the interpretation. Another limitation concerns the validity of recording adolescents’ daily online time. In our study, we used self-report questionnaires to capture daily time spent online, and, so far, we know little about this procedure’s validity. There is still no gold standard for objectively measuring online time. Even alternatives such as capturing screen time via digital devices directly create other problems because of the variety of digital devices and applications used. In our sample, the average amount of online time per day was 4.53 hours, which is slightly above the German average. This reported average appears realistic because our sample consisted of prescreened adolescents with elevated symptoms of online behavioral addictions (CIUS > = 20, i. e., the upper 36 % of the total screened population). However, to date we know little about the validity of self-reported daily online time because this has hardly been cross-validated with objective measures such as screen times on electronic devices. And from a developmental psychopathology perspective, online behavior (such as time spent online) may change rapidly within a time of 12 months, which might also contribute to the inconsistent findings.
Overall, the results of this study indicate that the investigation of longitudinal effects of online time on the psychological well-being of children and adolescents might benefit from more sophisticated longitudinal designs, such as ecological momentary assessments. At the same time, more research is needed to understand the possibly differential functionality and the associated risks of digital media use among different groups of adolescents. The findings do not allow us to conclude how many hours of online time per day are harmless, but they do show that higher levels of online time are associated with higher levels of online behavioral addictions, which are in turn associated with higher symptoms of conduct problems and probably also increased peer relationship problems and reduced prosocial behavior.
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