How self-esteem and resilience mediate the association between screen time and depression symptoms in Chinese adolescents

doi:10.21203/rs.3.rs-3232916/v1

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How self-esteem and resilience mediate the association between screen time and depression symptoms in Chinese adolescents

https://doi.org/10.21203/rs.3.rs-3232916/v1

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Objectives

To clarify the associations between screen time and depression symptoms among Chinese adolescents, as well the mediating roles of self-esteem and resilience.

Methods

A total of 1,624 primary and middle school students aged 9 to 16 years-old from five schools in China were recruited by stratified cluster sampling. The Center for Epidemiologic Studies Depression Scale Revised (CESD-R), Self-Esteem scale (SES), Connor-Davidson Resilience Scale (CD-RISC), and Screen time questionnaire were applied. The data was analyzed by SPSS 26.0 and Amos 26.0.

Results

Screen time was positively associated with depression symptoms (p < 0.001). Self-esteem partially mediated this association (p < 0.05) with a mediation rate of 33.63%, and resilience (p < 0.05) mediated this association with a mediation rate of 6.46%. Self-esteem and resilience formed a mediating chain between screen time and adolescents depression symptoms (p < 0.05) with a mediation rate of 3.15%.

Conclusions

Longer screen time was significantly associated with increased risk of depression symptoms among Chinese adolescents, and self-esteem and resilience partially mediated this association. This evidence provides potential targets for preventive interventions against adolescent depression.

Biological sciences/Psychology

Health sciences/Health care

Screen time

Depression

Self-esteem

Resilience

Adolescent

Chinese

Faced with increasing autonomy and exploration of self-identity, adolescents experience a dramatic transition between childhood and adulthood that places them at high-risk for mental health conditions². Depression symptoms often appear before or during adolescence and are a leading cause of mental health disorders among adolescents ¹¹. Adolescent depression symptoms can hinder academic performance, social and cognitive development and lead to high-risk behaviors such as substance abuse¹ and suicide³. In China, the prevalence of adolescent depression symptoms ranges from 19.85–43.7% ^46,68. In 2020, the national Chinese Mental Health Development Report showed that 24.6% of Chinese adolescents had depression symptoms. The results indicated that depression should be considered as a major public health problem for Chinese adolescents ^8,38,67. Numerous empirical studies have proved that depression symptoms in adolescents are influenced by many factors including genetics, personality traits, family and peer relationships as well as lifestyle-related behaviors like screen time^4,24,44,51 .

Screen time refers to the total time that an individual spends on any electronic screen in a sedentary state during a specific period of time. It once consisted only of television viewing and computer gaming, but screen-based technology is rapidly diversifying and gaining popularity. Screen time now covers a broader range of content and devices, including mobile phones, computers, and tablets. Time spent on electronic devices has increased markedly around the world. According to a 2016 population-based study, U.S. adolescents aged 14–17 years spend about 4.59 hours a day for recreational screen time ⁵⁸. In China, adolescents spend an average of 2.83 hours of screen time a day, with 23.9% of adolescents spending more than 4 screen hours a day³⁹. Extensive exposure to electronic devices has been suggested as a significant risk factor for depression symptoms among adolescents⁷. However, research on this topic has been inconclusive. For example, some studies showed that greater screen time was associated with a higher risk of depression symptoms ^4,26, whereas other studies found an inverse relationship or no association^30,40. More research is needed to clarify the association between screen time and adolescent depression symptoms especially because screen-based technologies are on the rise.

Some research has indicated that screen time alone may not be sufficient to predict adolescent depression symptoms⁴¹. We hypothesize that mediators play an important role in the association between screen time and adolescent depression symptoms. Self-esteem and resilience are two such likely mediators. Low self-esteem and depression symptoms have been shown to be associated⁵⁴. Resilience can be defined as the capacity to adapt successfully and remain healthy following exposure to stress and adversity ⁵². It has been shown as a protective factor against the development of psychiatric conditions such as depression ^46,53. A mental health survey among Chinese high school students found that adolescents with greater resilience had milder depression symptoms ³³. Interventions targeting resilience in recent literature have shown promise in alleviating depression symptoms in young adults⁶⁶ and adolescent⁹. Meanwhile, self-esteem and resilience have both been found to be negatively influenced by excessive screen time ^57,34. Based on these findings, we preliminarily constructed the relationship network between adolescents depression symptoms, screen time, self-esteem and resilience (Fig. 1).

This study aims to clarify the associations between screen time and depression symptoms among Chinese adolescents, and to examine the roles of self-esteem and resilience in these associations. We hypothesized that longer screen time is associated with higher risks of adolescents depression symptoms, and self-esteem and resilience mediate this association.

Participants and Ethics

A total of 1624 students aged 9 to 16 years (Mean = 12.2, SD = 1.1) were recruited by stratified cluster sampling from five middle schools and two primary schools in Shanghai, China. In order to maximize diversity, different types of schools were recruited, including public and private schools, schools with only migrant students, and schools with mixed urban and migrant students. Participants who met the following criteria were excluded: (a) students who had been at the current school for less than six months; (b) students who met the diagnostic criteria of psychiatric disorders; (c) students or their parents refused to participate in this study.

Questionnaires were taken to the classroom by the head teachers and explained to the participating students. They were completed during self-study time and collected one hour later. Prior to the survey, the informed consent forms were signed by parents if they agreed their children to attend to the survey. The content and procedure of this study were approved by the medical ethics committee of Fudan University (IRB#2019-11-0788). The corresponding author confirms that this study was performed in accordance with the approved social experiments guidelines and regulations.

Measures

Depression symptoms

The Center for Epidemiological Studies Depression Scale (CES-D) has been widely applied in studies on screening for depression symptoms while showing good validity in various populations. However, its original version predated the third revision of DSM-III (APA, 1987). There are temporal limitations to the CES-D regarding criteria and definitions of major depression symptoms in DSM-IV and DSM-V. Eaton et al ¹⁴ revised the CES-D to meet the criteria in DSM-IV (APA, 1994) for symptoms of depression. The Center for Epidemiologic Studies Depression Scale-Revised (CESD-R) removed eight CES-D items considered no longer relevant to the current definition of depression symptoms, while adding ten items suggested by Zimmerman and Coryell ⁶⁹. Two original items in the CES-D were simplified, so the current CESD-R still consists of 20 items but the response scale was changed to a 5-point Likert-type scale rated from 0 (rarely or none of the time -\(<\)1 day) to 4 (most or all of the time -5 to7 days). The total score ranges from 0 to 80, with a cutoff of 16 or above indicating positive screening for depression symptoms, and higher scores indicating more depression symptoms. The Cronbach \(\alpha\) in the current study was 0.93.

Self-esteem

Self-esteem was measured by the Rosenberg Self-esteem Scale (RSES), which consists of 10 items designed to estimate one’s global self-esteem. Each item was scored on a 4-point Likert scale, ranging from 1 (not true for me at all) to 4 (totally true for me). Reversing the scores of negatively worded questions, the total score was summed to assess one’s overall self-esteem and higher scores were associated with higher levels of self-esteem. The Chinese version of this scale showed great reliability and validity in the Chinese context¹⁷. The Cronbach \(\alpha\) in the current study was 0.81.

Resilience

The Connor-Davidson Resilience Scale (CD-RISC) was compiled by Connor and Davidson as a tool to assess one’s resilience¹⁰. The Chinese version was Sinicized and revised by Yu and Zhang⁶⁵. It consists of three dimensions with 25 items on a 5-point Likert scale, rated from 0 (Never) to 5 (almost all the time). The higher scores means higher levels of resilience and the scale has been proved to be reliable and valid in both the general and clinical population¹⁰. In the current study, the Cronbach \(\alpha\) was 0.92.

Screen time

The screen time scale was partially based on Youth Risk Behavior Survey (YRBS) questionnaire⁶. In our study, items setting references to the method of Tong⁵⁶.The students were asked “How much time did you spend using the following electric devices after school?” with the choices of “(1) smart phone, (2) computer, (3) tablets, (4) television, (5)video games”. The response options included: none or less than 30 minutes /day, 30–60 minutes /day, 1–2 hours /day, 2–3 hours /day and more than 3 hours per day. Considering students are more likely to watch television or play video games on weekends and use smart phones during the school day, each item was analyzed separately for weekends vs. weekdays.

Statistical analysis

Data analysis was performed using SPSS 26.0 and Amos 26.0 and p\(<\).05 was considered statistically significant. Frequency and percentages were used to describe the demographic variables as well as the usage of different electronic devices on study days and weekends. The Chi-square test was used to investigate differences in the detection rates of depression symptoms under different demographic variables, and for cells with expected values \(<\)5, Fisher test was used as recommended by Ruxton and Neuhäuser ⁵⁰ to determine whether two variables were independent or not. Hierarchical multiple regression was performed to test the association between depression symptoms and screen time, self-esteem as well as resilience, in order to analyze influencing factors of depression symptoms. The relevant demographic variables were adjusted in the initial model 1. Finally, the structural equation model (SEM) was applied to examine the paths of influence among variables as well as the mediating effect of self-esteem and resilience in the relationship between screen time and depression symptoms. The goodness-of-fit of estimated models can be judged by several criteria, including chi-square to degrees of freedom ratio (χ²/df), Comparative Fit Index (CFI), Tucher-Lewis index (TLI), Root Mean Square Error of Approximation (RMSEA) and Standardized Root Mean Square Residual (SRMR). If the following indices meet the corresponding conditions: χ²/df\(\le\) 3, CFI \(\ge\) 0.9, TLI\(\ge\) 0.9, RMSEA \(\le\) 0.08 and SRMR\(\le\) 0.05, the model can be regarded to have good fit ^28,61.

Table 1 indicated the demographic characteristics of participants and prevalence of depression symptoms. Results showed that 358 (22.04%) adolescents had depression symptoms, with higher percentage of girls (196, 54.75%) reporting them than boys (n = 162, 45.25%). The detection rate of depression symptoms in girls was significantly higher than that in boys (χ² = 8.15, p < .001). There were significant increases in the rates of depression symptoms with increasing age (χ² = 9.01, p < .05). Meanwhile, the detection rate of depression symptoms in adolescents living in single-parent families was higher than in those living in nuclear and extended families (χ² = 15.56, p < .001). There was no significant difference in the prevalence of depression symptoms by number of siblings, family income, parental education (p>.05), paternal age (χ² = 5.18, Fisher p>.05) or maternal age (χ² = 3.92, Fisher p>.05).

Descriptive analysis was conducted on different types of screen time. As shown in Table 2, fewer (5.9%) adolescents used smart phones for more than 2 hours on weekdays than on weekends (18.7%). Fewer (1.6%) adolescents used computers for more than 2 hours on weekdays than on weekends (5.1%). The number of students who used tablets, television, and video games for more than 2 hours on weekdays were respectively 27 (2.3%), 40 (2.5%) and 14 (0.8%). On weekends the number increased respectively to 100 (6.2%), 118 (7.3%) and 39 (2.4%). Finally, adolescents used smart phone more than any other types of devices.

Table 1

Prevalence of depression symptoms by participant demographic characteristics
	n (%)	Students with depression symptoms	χ²
Gender
Boys	843 (51.9)	162 (19.2)	8.15^***
Girls	781 (48.1)	196 (25.1)	8.15^***
Grade
Fourth	80 (4.9)	12 (15.0)	9.01^*
Fifth	75 (4.6)	14 (18.7)
Sixth	519 (32.0)	99 (19.1)
Seventh	568 (35.0)	137 (24.1)
Eighth	382 (23.5)	96 (25.1)
One-child family
Yes	829 (51.0)	178 (21.5)	0.32
No	795 (49.0)	180 (22.6)	0.32
Family type
Nuclear family	952 (58.6)	202 (21.2)	15.56^***
Extended family	585 (36.0)	122 (20.9)
Single-parent family	87 (5.4)	34 (39.1)
Father's education
Middle school and below	481 (29.6)	101 (21.0)	7.17
High school	512 (31.5)	133 (26.0)
Junior college	351 (21.6)	71 (20.2)
Bachelor or above	280 (17.2)	53 (18.9)
Mother's education
Middle school and below	565 (34.8)	131 (23.2)	6.97
High school	424 (26.1)	107 (25.2)
Junior college	391 (24.1)	77 (19.7)
Bachelor or above	244 (15.1)	43 (17.6)
Father's age
<30	12 (0.7)	1 (8.3)	4.79
30–40	803 (49.4)	175 (21.8)
41–50	723 (44.5)	156 (21.6)
>50	86 (5.3)	26 (30.2)
Mother's age
<30	28 (1.7)	5 (17.9)	3.92
30–40	1016 (62.6)	225 (22.1)
41–50	551 (33.9)	121 (22.0)
>50	29 (1.8)	7 (24.1)
Family annual income (USD )
<7000	139 (8.6)	34 (26.2)	4.17
7000–14000	360 (22.3)	89 (24.3)
14000–21000	337 (20.8)	66 (19.6)
21000–28000	335 (20.6)	68 (20.2)
28000–43000	263 (16.2)	59 (22.7)
>43000	190 (11.7)	42 (21.5)
Notes: * p < .05, *** p < .001.

Table 2

Exposure levels of screen time by time of the week and by type of device
	Weekday n(%)					Weekend n(%)
Variables	None or ≤ 30mins	30–60 mins	1–2 hours	2–3 hours	≥ 3 hours	None or ≤30mins	30–60 mins	1–2 hours	2–3 hours	≥ 3 hours
Smart phone	957 (58.9)	431 (26.5)	140 (8.6)	37 (2.3)	59 (3.6)	506 (31.2)	502 (30.9)	312 (19.2)	125 (7.7)	179 (11.0)
Computer	1461 (90.0)	104 (6.4)	33 (2.0)	13 (0.8)	13 (0.8)	1242 (76.5)	214 (13.2)	85 (5.2)	39 (2.4)	44 (2.7)
Tablet	1355 (83.4)	190 (11.7)	42 (2.6)	18 (1.1)	19 (1.2)	1151 (70.9)	265 (16.3)	108 (6.7)	53 (3.3)	47 (2.9)
Television	1205 (74.2)	308 (19.0)	71 (4.4)	18 (1.1)	22 (1.4)	844 (52.0)	450 (27.7)	212 (13.1)	66 (4.1)	52 (3.2)
Video games	1562 (96.2)	36 (2.2)	12 (0.7)	4 (0.2)	10 (0.6)	1498 (92.2)	62 (3.8)	25 (1.5)	18 (1.1)	21 (1.3)
Notes: Screen time exposure units by per day.

The scores of depression symptoms were taken as the dependent variables, and the general demographic variables such as gender, resilience as well as self-esteem were used as predictors in this study in a hierarchical regression analysis⁶². As shown in Table 3, model 1, only gender and family annual income had possible prediction effects (β = 0.05, p < .05), and the overall explanation rate R² as control variables was 0.02. In model 2, screen time had a negative impact on the mental health of adolescents, with longer screen time associated with more adolescent depression symptoms (β = 0.11, p < .001). In addition, when screen time was added to the hierarchical model, the overall R² of the model increased significantly (R² = 0.05, ΔR² = 0.03). Finally, in model 3, resilience was negatively associated with depression symptoms of adolescents (β = -0.16, p < .001), so did self-esteem (β = -0.55, p < .001). Thus, adolescents with lower the resilience and self-esteem are more likely to have depression symptoms. With resilience and self-esteem being added to the hierarchical model, model 3 had a better explanation for depression symptoms than the first two models, with the overall explanation rate R² reaching 0.4, and the R²variation being 0.37.

Table 3

Hierarchical multiple regression models incorporating adolescent characteristics
Variables	Model 1		Model 2		Model 3
Variables	β	t	β	t	β	t
Gender	0.08	3.38^**	0.10	3.91^***	0.05	2.43^*
Grade	0.11	4.14^***	0.08	3.21^***	-0.01	-0.19
One-child family	0.03	0.90	0.03	1.14	0.02	0.72
Family type	0.06	2.26^*	0.50	1.91	0.01	0.30
Father’s education	-0.02	-0.57	-0.11	-0.32	0.01	0.21
Mother’s education	-0.08	-2.43^*	-0.74	-2.18^*	-0.02	-0.49
Family annual income	0.02	0.81	0.03	1.01	0.05	2.38^*
Total screen time			0.14	5.65^***	0.11	4.79^***
Resilience					-0.16	-4.55^***
Self-esteem					-0.55	-24.58^***
R²	0.02		0.05		0.40
ΔR²	0.02		0.03		0.37
Notes: p < .05, p < .01, **p < .001;β values were standardized coefficients.

The structural equation model was constructed with screen time as the independent variable, depression symptoms as the dependent variable, and self-esteem and resilience as mediating variables to examine the mediating effect of self-esteem and resilience. The indirect effects of screen time and depression symptoms were determined using the bias-corrected bootstrap analysis with 95% CI in 5000 samples. All variables were standardized before analysis. The goodness-of-fit of model as following: χ2/df = 3.27, CFI = 0.99, TLI = 0.99, RMSEA = 0.04, SRMR = 0.02. The indices met the criteria for good fit. Figure 1 shows that screen time negatively associated with self-esteem (β = -0.11, p < .001), and resilience (β = -0.13, p < .001). Self-esteem had positively associated with resilience (β = 0.51, p < .001). Screen time positively associated with depression symptoms (β = 0.10, p < .001). Finally, self-esteem and resilience (β = -0.11, p < .001) were negatively associated with depression symptoms (β = -0.59, p < .001).

Mediation effects were further examined in Table 4. The total indirect effect value was 0.17, the relative effect value was 43.25%, and the upper and lower limits of the Bootstrap did not include 0, which indicated that the total indirect effect value was significant, accounting for 43.25 in the total effect size. Self-esteem and resilience both had a significant mediating role. Specifically, the mediating effect was generated through three mediating chains. First, the simple mediating effect value of screen time → self-esteem → depression symptoms was 0.12, and the relative effect value was 33.63%. The Bootstrap did not include 0, indicating that this simple mediating effect was significant. Increasing screen time was associated with a decline in self-esteem and an increase in depression symptoms. Second, the simple mediating effect value of screen time-resilience-depression symptoms was 0.03, and the relative effect value was 6.46%. The Bootstrap also did not include 0, indicating that this simple mediating effect was significant. Increasing screen time was associated with a lower level of resilience and aggravated the depression symptoms. Finally, the chain mediated effect value of screen time-self-esteem-resilience-depression symptoms was 0.02, and the relative effect size was 3.15%. The Bootstrap did not include 0, indicating that this chain mediating effect was significant. The increase of screen time exposure first led to a decrease of self-esteem and resilience, and further caused the aggravation of depression symptoms. These results showed that self-esteem and resilience acted as mediating variables between screen time and depression symptoms, and there was a chain mediated effect.

Table 4

Mediation analysis of the associations between screen time and depression symptoms by self-esteem and resilience
Path of Mediating Effects	Effect Value	Boot SE	95% CI	Relative Effects Value (%)
Total Indirect effect	0.17	0.05	0.08–0.26	43.25
Screen time → Self-esteem → Depression	0.12	0.04	0.05–0.21	33.63
Screen time → Resilience → Depression	0.03	0.01	0.01–0.05	6.46
Screen time → Self-esteem → Resilience → Depression	0.02	0.01	0.01–0.02	3.15

The study found that the overall prevalence rate of depression symptoms among Chinese adolescents was approximately 22%, which is consistent with some previous findings, but lower than the 27.4% detected by Zhao et al⁶⁷ and 28.4% detected by Liu ³⁵ in similar populations. More students used long screen time (≥ 2 hours) on the weekend than during school days. More students preferred mobile phones than any other devices. Average screen times spent per day in the current study were similar to those of previous publications²⁹.

The results also showed that there is a significant positive correlation between screen time and adolescent depression symptoms, which is consistent with the results of most previous studies ^26,60. There are several possible explanations. First, as screen time increases, adolescents spend less time socializing and higher tendency to be socially isolated⁵, leading to a higher risk of depression symptoms. Second, adolescents may be exposed to negative internet content such as cyber-bullying with increased screen time. Depression is closely related to cyber-bullying³¹. Third, adolescents who spend more screen time may have trouble maintaining healthy behaviors, such as sleeping and physical activities ^42,59. Poor sleep and decreased physical activities are associated with more severe depression symptoms^12,13. Furthermore, increased depression symptoms in adolescents may lead to more screen time, perpetuating vicious cycle ²⁷.

Our study also found significant negative associations between screen time, self-esteem and resilience. Adolescents with more screen time reported lower self-esteem and lower resilience. Some research suggests that low self-esteem is a vulnerability factor for depression ⁴⁴. People with low self-esteem tend to suppress positive emotions and feel undeserving of positive outcomes, which lead to the development of depression ^63,64.As for resilience, adolescents with high levels of resilience may have more psychological flexibility to face and alleviate depression symptoms and to recover from negative life events sooner²³. In addition, adolescents with high level of resilience may have high self-esteem and self-efficacy, positive attributional style and more social support ^10,43. These positive psychological traits may buffer the adverse effects of depression symptoms and bring about better psychological health.

Our study also found that self-esteem and resilience played mediating roles between screen time and depression symptoms. Adolescents with longer screen time experienced lower levels of self-esteem and resilience and are more likely to suffer from depression symptoms. A possible mechanism for the pathway would be that adolescents who spend longer time on electronic devices are more likely to suffer from other physical and psychological problems such as obesity, social isolation, cyber-bullying, etc. ^5,31. The feeling of sadness and emptiness associated with social isolation can lower one’s self-esteem and increase depression risks⁴⁹ because psychosocial functioning and self-esteem especially during adolescence depend on good peer relationships ²². Meanwhile, overweight and obese adolescents may experience more weight-based teasing and bullying, stigmatization, poor body image and low self-esteem. These cumulative adverse childhood experiences not only lowers self-esteem but also damages resilience¹⁹. Importantly, adolescents with low self-esteem and resilience are more likely to ruminate about past experiences³², avoid social interactions ²¹, and feel that they were undeserving of positive outcomes⁶⁴. Adolescents in this kind of negative cycles are more likely to develop depression symptoms⁴⁴.

In addition to the simple mediating roles of self-esteem and resilience, a chain mediating effect between screen time and depression symptoms was also found combining self-esteem and resilience. One possible explanation is that self-esteem could be seen as a part of the individual domain within the framework of resilience ^11,20. Resilience can be seen as the process, ability or result of successful adaptation to a challenging or threatening environment¹⁰. Adolescents with high self-esteem are more confident about their capacity to overcome adversity and the results of good adaptation, and this confidence enhances the development of resilience³⁷. In some studies, self-esteem fully mediates the association between resilience and depression symptoms ¹⁶. Our study confirms that self-esteem and resilience have independent and synergistic mediating roles in the association between screen time and adolescent depression symptoms.

Implications

Electronic devices, such as smart phone, computers and tablets have become a part of daily life for adolescents in modern society. These devices are important tools for learning and living. Parents and educators have the potential to moderate adolescent screen time based on their mental health needs and according to evidence-based guidelines. For adolescents with the low levels of self-esteem and resilience, screen time should be limited to prevent depression symptoms. For adolescents with over exposure to electronic devices, future intervention can target their self-esteem and resilience level through training, buffering the adverse effects of screen time on depression symptoms.

Limitations

First, a self-reported questionnaires were used to collect the information of screen time and electronic devices using, rather than accelerometers. Second, this was a cross-sectional study, which limited our ability to explore causality. A longitudinal study should be carried out in the future to examine the direction of association between screen time and depression symptoms among adolescents. Third, this study was conducted in Chinese primary and middle school students, which limits its generalizability to other populations.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Acknowledgments

This study received grant funding from the Stanford University Maternal and Child Health Research Institute (2021 CE award), the Stanford Rural Education Action Program and the Chinese National Social Science Fund (20BRK048). We thank the schools and students who participated and the volunteers who dedicated their valuable time and hard work to teach and to mentor migrant children.

The content and procedure of this study were approved by the medical ethics committee of Fudan University (IRB#2019-11-0788). The corresponding author confirms that this study was performed in accordance with the approved social experiments guidelines and regulations.

Author contributions

T. L. conceptualized and designed the study. T. L., X. S., Y. J., W. H., W. M. and A. C. participated in data collection, analysis, and interpretation. Y. J. and L. W. wrote the manuscript. X. S., S. M., and R. S. contributed to the review, editing and approval of the final manuscript.

Disclosure of Potential Conflicts of Interest

Dr. Singh has received research support from Stanford’s Maternal Child Health Research Institute and Stanford’s Department of Psychiatry and Behavioral Sciences, National Institute of Mental Health, National Institute of Aging, Patient Centered Outcomes Research Institute, Johnson and Johnson, and the Brain and Behavior Research Foundation. She is on the advisory board for Sunovion and Skyland Trail, is a consultant for Johnson and Johnson, Alkermes, Neumora, AbbVie, Karuna Therapeutics, Inc., and Boehringer-Ingelheim. She receives honoraria from the American Academy of Child and Adolescent Psychiatry, and royalties from American Psychiatric Association Publishing and Thrive Global. She has previously consulted for X, moonshot factory, Alphabet Inc. and Limbix Health. No other authors report any conflicts of interest.

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Competing interest reported. Dr. Singh has received research support from Stanford’s Maternal Child Health Research Institute and Stanford’s Department of Psychiatry and Behavioral Sciences, National Institute of Mental Health, National Institute of Aging, Patient Centered Outcomes Research Institute, Johnson and Johnson, and the Brain and Behavior Research Foundation. She is on the advisory board for Sunovion and Skyland Trail, is a consultant for Johnson and Johnson, Alkermes, Neumora, AbbVie, Karuna Therapeutics, Inc., and Boehringer-Ingelheim. She receives honoraria from the American Academy of Child and Adolescent Psychiatry, and royalties from American Psychiatric Association Publishing and Thrive Global. She has previously consulted for X, moonshot factory, Alphabet Inc. and Limbix Health. No other authors report any conflicts of interest.

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How self-esteem and resilience mediate the association between screen time and depression symptoms in Chinese adolescents

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Self-esteem

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Screen time

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