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BMC Psychiatry volume 24, Article number: 863 (2024)
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While the association between screen time (ST) and psychosocial well-being has been extensively examined, limited studies have investigated the dynamic patterns of ST, and their impact on subsequent psychosocial well-being among adolescents. Therefore, this longitudinal study aimed to examine the association between ST trajectories and the subsequent psychosocial well-being among Chinese adolescents.
Data were drawn from the Longitudinal Study of Adolescents’ Mental and Behavioral Well-being Research (Registration No. ChiCTR1900022032). The final analysis included 1480 participants who completed baseline and two follow-up surveys. Standardized measures were employed to assess ST and multiple psychosocial well-being, including depressive symptoms, anxiety, externalizing problems, and coping style. Group-based trajectory modeling and generalized linear mixed models were performed.
Over the two-year follow-up period, two distinct ST trajectories emerged: continued high (298 [20.1%]) and continued low (1182 [79.9%]). Compared with those in the continued low ST group, adolescents in the continued high group exhibited a higher likelihood of presenting depressive symptoms (β = 0.97, 95% CI = 0.43 ~ 1.50), anxiety symptoms (β = 0.29, 95% CI = 0.05 ~ 0.53), and emotional problems (β = 0.35, 95% CI = 0.22 ~ 0.48), and were less likely to demonstrate prosocial behavior or employ positive coping style. The stratified analysis demonstrated that the aforementioned associations only existed among female adolescents.
Persistent high exposure to ST was associated with an increased odds of emotional problems and a decreased probability of engaging in prosocial behavior and positive coping style, with particularly noteworthy effects observed among female adolescents. These findings underscore the importance of reducing ST exposure to improve the psychological well-being of adolescents.
Peer Review reports
Screen time (ST) refers to the duration individuals spend watching television, playing video games, and using mobile phones, tablets, and other electronic devices. The ST of children and adolescents has witnessed a surge in recent years, propelled by the rapid proliferation of digital devices, an increasing amount of extracurricular online courses, and the impact of the COVID-19 pandemic [1,2,3]. Experts have recommended that recreational screen time not exceed 2 h per day for children and adolescents [4,5,6]. However, large-scale researches indicate that nearly half of Chinese school-aged youth reported daily ST over 2 h [7, 8].
The deleterious effects of excessive screen activities on cognitive development and psychosocial well-being are concerning [9]. Among younger children, longer ST exposure significantly delayed their development during early childhood [1, 10,11,12]. As for adolescents, existing evidence showed that longer ST is largely associated with psychosocial problems including depressive symptoms [13, 14], anxiety [15], and externalizing problems [8]. Adolescence is a crucial transitional period of rapid biological and psychosocial growth, during which electronic technology is the most common way for adolescents to express themselves and connect with others [16]. Compared with younger children, adolescents are more likely to have their own smartphones or other hand-held devices [17], hence leading to a further amplification of ST. Longitudinal research also demonstrated that as age increased, more youth tend to engage in longer screen-based activities [18].
In terms of the dynamic nature of adolescents’ screen use [19, 20], it is crucial to examine ST trajectories longitudinally. This approach offers a more robust understanding of longitudinal screen use habits. However, very limited research has delved into how ST trajectories affect adolescents’ psychosocial well-being. A recent longitudinal study of Canadian adolescents found that increasing ST was significantly associated to anxiety within the same year, yet they failed to observe the lasting effects of ST on anxiety if increases in ST were not sustained [15]. A study among Swiss adolescents showed higher or increasing ST trajectories during adolescence were associated with depressive symptoms, anxiety, aggression, suicidal and self-injury behaviors in early adulthood, indicating that screen use patterns may have a long-term effect on people’s psychosocial well-being [21]. However, there are other studies that showed an inconsistent association between ST and psychosocial well-being among adolescents. Plackett and colleagues found non-significant association between time spent on social media and subsequent psychosocial difficulties (i.e., internalizing and externalizing problems) [22]. Tang’s review of longitudinal studies between 2005 and 2020 found some evidence supporting the deleterious effects of ST on psychosocial well-being, including depressive symptoms, anxiety, self-esteem, and other internalizing problems. However, the review also pointed out that the associations were inconsistent, with findings ranging from positive to nonsignificant and some effects varying substantially by sex or other factors [23].
Additionally, as an integral component of interpersonal functioning within psychosocial well-being, maladaptive coping style and social maladjustments stemming from excessive ST received limited attention [24]. Engagement in screen activities often signifies an inclination towards a negative avoidant coping style, which plays a vital role in psychosocial well-being [24]. According to existing evidence, positive/negative coping style is a significant mediator between screen-based activities and mental health [24, 25]. Therefore, research focusing on the association between ST and comprehensive psychosocial well-being among Chinese adolescents warrants further investigation.
To address these research gaps, this longitudinal study among Chinese adolescents aims to investigate the associations of ST trajectories with subsequent psychosocial well-being, including depressive symptoms, anxiety, externalizing problems, sleep quality, and coping style.
Data were derived from the Longitudinal Study of Adolescents’ Mental and Behavioral Well-being Research in Guangzhou, China (Registration No. ChiCTR1900022032, Registration Date: 2019/03/21). A multi-stage, stratified cluster, random sampling method was used to recruit participants from six junior high schools and four senior high schools in four districts of Guangzhou. The junior high school in China is 7th grade to 9th grade, and the senior high school is 10th to 12th grade. After finishing junior high school, adolescents will enter different senior high school, and adolescents who finish senior high school will enter different colleges, which makes it hard to follow up. As a result, to extend follow-up period and avoid the loss of follow-up, only the first graders of the junior high schools (7th grade, junior grade one) and the senior high schools (10th grade, senior grade one) were included. The data collection procedure has been described elsewhere [26]. At baseline (January to April 2019), 1,957 participants (wave 1; response rate: 99.03%) were eligible, 1831 students were followed up at wave 2 (October to December 2019; retention rate: 93.56%), and 1738 completed the follow-up at wave 3 (October to December 2020; retention rate: 88.81%). A total of 1480 participants were included in the final analysis with the complete dataset for ST exposure in all three waves (Fig. 1). The mean (SD) age of the final sample was 13.6 (1.5) years at baseline, with 741 (50.1%) of them being male adolescents and 739 (49.9%) being females. Self-reported questionnaires were completed without teachers present during all three surveys to reduce information bias and protect privacy. Participants completed the questionnaires in classrooms with the presence of trained investigators, who ensured the completeness and logical consistency of each questionnaire. Written informed consent was distributed to the participants and legal guardians, and those who agreed to participate were included. The study was performed in accordance with the Declaration of Helsinki, and obtained ethical approval from Sun Yat-sen University School of Public Health Institutional Review Board (Ethics Number: L2017060).
The flowchart of the study participants
Legend: Inclusion and exclusion procedure of the study participants
Screen time was designed and calculated based on previous studies [8, 27] using six questions as follows: “How long do you spend on electronic devices (e,g., for entertainment, including computer, smartphone, tablet, video game)/television/Internet (for entertainment and academic use) in a typical day of weekdays/weekends during the last month?” Participants reported the mean hours and minutes per day. The daily mean ST for each item (i.e., electronic devices/television/Internet) was calculated as follows: (weekday ST*5 + weekend ST*2)/7. The total daily mean ST was calculated by summing STs for all three items.
Depressive symptoms were measured by the Center for Epidemiology Scale for Depression (CES-D) in Chinese, which has shown good reliability and validity in Chinese adolescents [28, 29], and the Cronbach’s alpha was 0.89 in this study. The respondents were asked to rate the frequency of 20 depressive symptoms by selecting one of four response options ranging from “0 = rarely or none of the time” to “3 = most or all of the time” over the past week. The total scores range from 0 to 60. Higher scores reflect more severe depressive symptoms.
Anxiety symptoms were assessed by the Generalized Anxiety Disorder Scale-7 (GAD-7) in Chinese, which has been validated and extensively utilized in Chinese studies [30, 31], and the Cronbach’s alpha was 0.89 in this study. The respondents were asked to rate the frequency of seven anxiety symptoms during the last two weeks, with the response options given on a 4-point scale from 0 to 4. The seven items’ total scores range from 0 to 21, with higher scores indicating more severe anxious symptoms [32].
The Pittsburgh Sleep Quality Index (PSQI) was used to evaluate sleep quality and disturbances in the last month. The Chinese version of the PSQI has been validated in a Chinese adolescents-based study [33], and the Cronbach’s alpha was 0.77 in the present study. The PSQI includes 19 items constituting seven components of sleep: sleep duration, sleep latency, sleep efficiency, sleep disturbances, subjective self-assessed sleep quality, daytime dysfunction, and use of sleeping medication. The sum of the scores for these seven components yields one global score with a range of 0–21 points, in which higher scores indicate worse sleep quality [34].
The self-reported version of the Strengths and Difficulties Questionnaire (SDQ) was also adopted in this study, and the Chinese version of SDQ has been validated and utilized in the Chinese population [35]. The SDQ comprises five subscales with five items each, generating scores for emotional problems, conduct problems, hyperactivity, peer problems, and prosocial behavior. A total difficulties score ranging from 0 to 40 was summed by the scores within all subscales except prosocial behavior [36]. Higher scores suggest greater emotional and behavioral problems, while a lower score of the prosocial subscale indicates more significant prosocial problems. The Cronbach’s alphas of the total difficulties scale and prosocial scale were 0.74 and 0.75 in this study.
Coping style was evaluated by the Simplified Coping Style Questionnaire (SCSQ) [37], which was developed based on the Ways of Coping Questionnaire by Folkman and Lazarus [38] according to Chinese culture. The SCSQ has been validated and widely used in Chinese adolescents [39], and the Cronbach’s alpha was 0.83 in this study. The SCSQ includes two dimensions that assess the positive and negative coping styles. The respondents were asked to rate each item on a 4-point scale based on the frequency they adopted the positive or negative coping style in their daily life, with higher scores indicating greater positive/negative coping.
Demographic factors included age, sex, living arrangement, household socioeconomic status (HSS), one child or not, relationships with classmates and teachers, perceived academic pressure, parents’ education level, and ever smoking a cigarette at least once. In addition, based on previous researches [40, 41], physical activity is a significant influence factor on psychosocial well-being, and the association of screen-based activity and people’s health condition is independent of physical activity. As a result, self-reported physical activity (PA, hours per week) was included as a covariate in this study.
The analysis was restricted to participants with the complete dataset for ST exposure in all three waves. As for other variables, a total of 2.9% (43 of 1480) of total items were missing and were assumed to be missing at random, and thus were imputed with the multiple imputations of chained equations (MICEs) method using the baseline characteristics [42]. We created 5 imputed data sets and pooled the results using the command “mi estimate” in the Stata.
Considering ST may change over time and differ among individuals, the group-based trajectory modeling was used to classify participants into subgroups with similar ST exposure patterns during follow-up, based on identifying heterogeneous longitudinal polynomial trajectories [20, 43]. We implemented this technique using TRAJ in Stata [43]. Goodness-of-fit and model adequacy indexes, such as the Bayesian information criterion (BIC), were used to select the best model.
Baseline participant characteristics were summarized in total participants and by ST trajectories using mean (standardized deviation, SD) or median (interquartile range, IQR) for continuous variables and frequency and percentages for categorical variables. Generalized linear mixed models were conducted to test the association of ST trajectory groups with the psychosocial well-being score among adolescents, with the mean effect size evaluated using inverse probability weighting. To test the robustness and potential variations in different subgroups, we repeated all analyses stratified by sex (male and female adolescents). Furthermore, we conducted weighted generalized linear mixed models with logit links to explore factors associated with ST trajectory group and the probability of group membership weighted these regressions to account for measurement error introduced by the probabilistic nature of group assignment [44]. In addition, we conducted sensitivity analyses by repeating all analyses using the complete data set (1437 participants) without multiple imputations. All statistical analyses were performed with Stata (version 17.0, StataCorp LLC, Texas, USA). Statistical significance was set at P ≤ 0.05, and 2-sided tests were used.
Among the 1480 adolescents included in the analyses, the mean (SD) age at baseline was 13.6 (1.5) years, 50.1% of them were male adolescents, 44.5% and 47.4% of their mothers and fathers had a college or above education level, respectively, and 1.0% of them reported ever smoking (Table 1). No significant differences in the distributions of covariates were observed between those included in this analysis and all participants in the original longitudinal study (eTable 1 in the supplement).
Adolescents were classified into 2 trajectory groups (continued high and low trajectory groups) based on their total ST at three waves (Fig. 2), and the optimal group-based trajectory model was selected according to the model fit indexes (eTable 2 in the supplement). The continued high trajectory group (n = 298 [20.1%]) demonstrated a higher average baseline ST (mean: 6.01 h/d, SE: 0.10) and exhibited a stably high exposure level, and the continued low trajectory group (n = 1182 [79.9%]) was characterized by a relatively stable amount of ST (eFigure 1 and eTable 3 in the supplement).
Screen time trajectories of the two groups. Legend: Screen time trajectories was conducted with group-based trajectory model using total screen time in three waves during follow-up
Table 2 demonstrates that ST trajectories were associated with all psychological well-being outcomes in wave 3 in the unadjusted model (Model 1). In Model 2, after adjusting for covariates and depressive symptoms scores at baseline, compared to the continued low group, adolescents in the continued high group were significantly related to a higher level of depressive symptoms (β = 0.97, 95%CI = 0.43 ~ 1.50). Similar results were observed for anxiety symptoms and emotional problems at wave 3, after adjusting for covariates and corresponding outcomes at baseline (e.g., β = 0.29, 95%CI = 0.05 ~ 0.53 for anxiety symptoms, Model 2 in Table 2). As for coping style, those in the continued high group were less likely to have a positive coping style or prosocial behaviors (β < 0, P < 0.05). Similar results were observed in Model 3, where all covariates in Model 2 and all psychosocial well-being variables at baseline were adjusted.
Tables 3 and 4 show the results stratified by sex. Among male adolescents, when adjusting for covariates and corresponding outcomes (Model 2), compared to the continued low group, the continued high group was positively associated with only peer problems (β = 0.13, 95% CI = 0.03 ~ 0.24, Model 2 in Table 3). Among female dolescents, the continued high group was more likely to have depressive symptoms, anxiety symptoms, poor sleep quality, and emotional problems (e.g., β = 1.78, 95%CI = 0.95 ~ 2.62 for depressive symptoms, Model 2 in Table 4) and was less likely to have peer problems, prosocial behavior and positive coping style (e.g., β = −1.98, 95%CI = −2.63 ~ −1.32 for positive coping style, Model 2 in Table 4). Robust results were observed when all psychosocial well-being variables at baseline were adjusted (Model 3 in Tables 3 and 4).
As shown in eTable 4 and eTable 5 in the supplement, age, living with single parent, average relationship with teachers, ever smoking, depressive symptoms, poor sleep quality, PA, hyperactivity, peer problem, conduct problem, and negative coping style at baseline were related to higher odds of being in the continued high ST group (all adjusted odds ratios [AORs] > 1, P < 0.05). Students with above-average academic pressure, parents with higher education, and positive coping styles were at a decreased risk of being in the continued high ST group (all AORs < 1, Ps < 0.05). With the effect size (AOR in eTable 5 and parameter value in eTable 6) and the values for each participant’s baseline characteristics, the probability of belonging to the identified trajectory group for every participant can be calculated using the equation stated in eTable 6 in the supplement.
Moreover, sensitivity analysis was conducted using the complete original data (1437 participants) without multiple imputations, and the results consistently supported our main findings (eTables 7–10 in the supplement).
In this longitudinal study, two patterns of ST trajectory (i.e., the continued high trajectory group and the continued low group) were identified based on participants’ total daily ST exposure during the two-year follow-up. In terms of psychosocial well-being, we observed robust unfavorable associations of continued high ST exposure over two years with subsequent emotional and prosocial well-being. In addition, stratified results showed that the aforementioned associations were only significant in female adolescents but not in males.
Our results supported that adolescents with high levels of ST were at higher risk of presenting with depressive and anxiety symptoms. In line with previous study, higher-level ST users (over 7 h/day, compared to 1 h/day users) were at higher risk of having emotional problems or being diagnosed with depression or anxiety disorders [17]. Li et al. also found that adolescents with persistent Internet addiction were at a higher risk of presenting with depressive symptoms subsequently [45]. Another research indicated that adolescents with increasing or high levels of ST trajectories demonstrated higher levels of psychosocial difficulties (e.g., depression, anxiety, and suicidal ideation) in early adulthood [21]. These changes may be attributable to the adverse influence of the Internet and social media overuse on social behaviors. Persistently high levels of screen-based activity may lead to inadequate time and opportunities for offline social communications, further leading to social isolation and withdrawal, and increasing the odds of emotional and prosocial problems [24, 46]. Another theory of excessive ST causing psychosocial difficulties is upward social comparison. Indulging in excessive screen-based activity, especially social media use, lower adolescents’ self-esteem and self-satisfaction, therefore triggering the upward social comparison effect and resulting in negative affections like depression and anxiety [13].
Notably, we found that persistently high levels of ST exposure over two years was significantly associated with compromised prosocial behavior and positive coping style, which were supported by previous studies [24, 47]. Fitzpatrick et al. found a significant negative effect of more Internet and media exposure on prosocial behavior (e.g., helping others) in two years [47]. One explanation is that screen-based activities may reduce adolescents’ time engaging in real-person interaction (e.g., peer friendship and parent–child connection), during which empathy and prosocial skills develop [48]. In regards to coping style, previous evidence also proposed that maladaptive coping, including avoidant coping (e.g., denial, substance use, or self-blame) and inflexible coping, represents a maladjustment consequence of problematic screen-based activity [24]. The excessive screen-based activity itself reflects a negative avoidant behavior to avoid facing stress [24]. Adolescents who spend too much time on screen-based activities or are addicted to the Internet have a greater propensity to use avoidant and inflexible coping methods [24]. However, our result did not observe a significant association between ST and negative coping style. It suggested that other factors, rather than excessive ST might influence negative coping style. Our result herein preliminarily demonstrated the harmful influence of persistent screen overuse on positive coping style. Our main findings indicated that persistent and excessive use of screen-based devices might negatively impact adolescents’ psychosocial well-being. It is recommended that parents take proactive steps, such as supervising their children’s recreational ST, in line with the suggested limit of 2 h per day. Schools and educational institutions are also encouraged to minimize the amount of time required for online assignments, ensuring that students can complete their work within an appropriate ST limit (e.g., less than 2 h/day).
It is of particular note that in our stratification analyses, we observed that except for peer problems, the aforementioned associations remained significant in female adolescents but not in males, which extended the evidence that female adolescents were more likely to have compromised psychosocial well-being if characterized as heavy users of screen-based devices compared to males [49, 50]. Except for the biological and hormonal differences that contributed to females’ vulnerability to psychosocial difficulties [51], the divergent association of males’ and females’ screen-based activity with psychosocial well-being might be attributed to the content of screen-based activity they engaged in. Female adolescents spend more time on social media, which, according to the upward social comparison hypothesis, could bring anxiety about body image or popularity, lower individuals’ self-esteem, and trigger emotional problems [13, 50]. On the other hand, Chodorow’s theory indicated that females were prone to adopt an internalizing and maladaptive coping method, including suppressing their emotions, self-blame, hiding feelings, and escaping, which deteriorate psychosocial well-being [49, 52]. Therefore, sex differences should be cautiously noticed when discussing the association between screen-based activities and psychosocial well-being. Further, targeted preventive strategies and intervention for psychosocial difficulties among male and female adolescents separatly are recommended.
Additionally, the current study explored the influence factors associated with ST trajectories. Parental attainment of higher education level (i.e., above middle school) protected adolescents from being classified in the continued high ST group, which aligns with Trinh’s study, where the inverse association between parental education level and ST trajectory was found among children [19]. Furthermore, we observed that compared to adolescents with good relationships with their teachers, those who have average or poor relationships had higher odds of being in the continued high ST group. Relationship with teachers is a part of school-related social support, and is beneficial for adolescents’ mental and behavioral health [53]. Additionally, our results supported previous evidence that psychosocial well-being at baseline was associated with ST exposure level [54], and extended the evidence that a positive coping style protected against longer ST exposure, while a negative coping style predicted it [55]. These associations potentially reflected a bidirectional association of ST trajectories and psychosocial well-being, and reinforce the relationship between ST trajectories and future psychosocial well-being. On the other hand, our study illustrated a positive association between longer PA and continued high level of ST exposure, which is opposite to the traditional assumption of an inverse relationship [56, 57]. Nonetheless, PA and screen-based activity can coexist [58]. Adolescents with high levels of PA can also engage in a high level of ST [59]. These results indicated that adolescents who have the above characteristics may be more vulnerable to presenting excessive ST habit than others. They may require more attention from parents and teachers, especially adolescents who have already demonstrated poor psychosocial well-being.
One of the strengths of this study is that we took into account the trajectory of screen-based activities by measuring them within multiple time points during follow-up, which may better assess the long-term influence of screen-based activities on psychosocial well-being among adolescents. Besides, our study has a reasonable response rate and retention rate, which could reduce selection bias. However, several limitations should be noticed when interpreting the current results: 1) Due to school transfer and graduation, times of follow-up were limited to 3 waves, where only two trajectories were fitted using group-based trajectory modeling. In future research, multiple follow-ups are recommended to improve ST trajectories. 2) This study only investigated students who were present at school on the research day and did not include students absent from school. However, excessive and addictive screen-based activities and psychosocial difficulties may be more common among those students. 3) The ST we assessed in the current study was based on self-report. It could cause information bias since adolescents may misremember or underestimate the precise ST. Nonetheless, self-report ST has been widely used in previous studies [60], and studies using objective measures like accelerometer and parents-report ST have observed similar results as our study, indicating that self-report is a validated method to assess ST [60, 61]. In our future study, we are planning to include parent-report ST in our questionnaire or use wearable electronic devices to capture a more precise value of ST. 4) Although the missing rate of data on psychosocial well-being and covariates was low (2.9% of the total analytic participants 1480), it is possible that the missing not at random might exist, which could cause underestimated associations.
This longitudinal study demonstrated that continued high ST exposure is associated with more emotional problems, and compromised prosocial behaviors and positive coping style, whereas female adolescents have much closer associations between ST trajectories and poor psychosocial well-being than male. These findings suggest that proper guidance and supervision are necessary to help adolescents manage ST in their daily routines. Moreover, intervention strategies aimed at reducing persistent excessive ST exposure may be beneficial for improving adolescents’ psychosocial well-being.
The data necessary to reproduce the analyses presented here are available from the corresponding author upon reasonable request (Dr. Lan Guo, guolan3@mail.sysu.edu.cn).
Screen time
Center for Epidemiology Scale for Depression
Generalized Anxiety Disorder Scale-7
Pittsburgh Sleep Quality Index
Strengths and Difficulties Questionnaire
Simplified Coping Style Questionnaire
Household socioeconomic status
Physical activity
Standardized deviation
Interquartile range
Bayesian information criterion
Multiple imputations of chained equations
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The authors gratefully acknowledge the contribution of the Guangzhou primary and high school health promotion center and its participating schools.
This study was supported by Natural Science Foundation of Guangdong Province (grant No. 2021A1515111184, 2023A1515012255), National Natural Science Foundation of China (grant No. 82473652, 82304168). The funding sources had no involvement in study design, collection, analysis and interpretation of data, writing of the report and the decision to submit the article for publication.
Wanxin Wang and Weiqing Jiang these authors contributed equally to this work.
Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, No.74 Zhongshan Rd 2, Guangzhou, Guangdong, 510080, China
Wanxin Wang, Weiqing Jiang, Liwan Zhu, Ciyong Lu, Yanzhi Li, Wenjian Lai & Lan Guo
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LG and CYL contributed to the study conception and design. Material preparation, data collection and analysis were performed by WXW, WQJ, LG, LWZ, YZL, and WJL. The first draft of the manuscript was written by WXW, LG and WQJ. LWZ, CYL, YZL, and WJL revised it critically for important intellectual content. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Correspondence to Lan Guo.
Written informed consents was obtained from the participants and their legal guardians. The study was performed in accordance with the Declaration of Helsinki, and obtained ethical approval from Sun Yat-sen University School of Public Health Institutional Review Board (Ethics Number: L2017060).
Written informed consents was obtained from the participants and their legal guardians with detailed information on this research and related publications. In addition, all data was deidentified at the first time before further analysis.
The authors declare no competing interests.
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Wang, W., Jiang, W., Zhu, L. et al. Screen time trajectories and psychosocial well-being among Chinese adolescents: a longitudinal study. BMC Psychiatry 24, 863 (2024). https://doi.org/10.1186/s12888-024-06329-3
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DOI: https://doi.org/10.1186/s12888-024-06329-3
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