Gender Differences in the Relationship Between Short-Form Video Addiction and Adolescent Depression: The Mediating Role of Attentional Bias

Abstract

Previous studies have shown that short-form video addiction (SVA) is a significant predictor of adolescent depression. However, little is known about the mediating mechanisms. Guided by the Interaction of Person-Affect-Cognition-Execution model, this study aimed to investigate the relationship between SVA and depression among Chinese adolescents and to examine the mediating role of attentional bias toward positive information (API) and negative information (ANI), including potential gender differences. A total of 4750 Chinese adolescents (M_age = 16.01, SD = 0.76, 62.15 percent male) completed self-reported scales for SVA, API, ANI, and depression. The structural equation modeling results indicated that both API and ANI mediated the relationship between SVA and depression. Moreover, multigroup analyses revealed that the mediating effect of ANI was significantly stronger in female than in male adolescents. These findings enhance our understanding of the relationship between SVA and depression in adolescents and suggest that developing gender-specific interventions could mitigate the detrimental effects of SVA on depression.

Introduction

Adolescence is a developmental period vulnerable to internalizing psychological problems, particularly depression, which is characterized by depressed mood, sleep disturbance, and feelings of worthlessness.^1–4 According to two meta-analyses, the prevalence of depression among Chinese adolescents aged 12–15 and 15–18 years was 24⁵ and 28 percent,⁶ respectively, in the past decade. Additionally, adolescent depression commonly persists, recurs, and continues into adulthood,⁷ leading to many negative consequences, such as poor academic performance,⁸ substance abuse,⁹ self-injury,¹⁰ and even suicide.^11,12 Given the negative consequences of depression in adolescents, investigating its risk factors and underlying mechanisms may provide a new target for relevant intervention and prevention strategies.

The Internet has become deeply integrated into people’s daily lives in recent years, and Internet addiction has emerged as a significant risk factor for increased depression.¹³ Among many Internet activities, short-form video has developed rapidly and its appealing content, personalized recommendations, and immersive experience^14,15 increase the risk of adolescent addiction. Short-form video addiction (SVA) refers to the phenomenon in which individuals spend substantial time using short-form video despite experiencing negative social and psychological consequences.¹⁵ For instance, the average daily time spent by Chinese Internet users on short-form videos exceeds 2.5 hours,¹⁶ and 11.9 percent of underage users report spending more than 2 hours per day watching short-form videos on weekdays.¹⁷ This excessive usage could cause various unfortunate consequences, including distraction,¹⁸ low learning or work efficiency,¹⁹ interpersonal problems,²⁰ and emotional distress,²¹ which are similar to the features of other behavioral addictions.²² As a subset of Internet addiction,¹⁵ the association between SVA and adolescent depression has drawn enormous attention.^21,23 According to the displacement hypothesis,²⁴ uncontrollable digital technology use displaces time that could be spent on “real-life” activities, further impairing individuals’ social adaptation and potentially contributing to depression. Therefore, SVA may be an important risk factor for depression among adolescents.

Although both cross-sectional and longitudinal studies have shown a direct association between SVA and depression in adolescents, the underlying mechanisms linking SVA to adolescent depression remain unclear. For instance, Qu et al. revealed the cooccurrence of SVA and depression in a sample of 1163 Chinese adolescents through cross-lagged panel network analysis.²¹ Similarly, Zhu et al. examined the direct effects of SVA on depression in 1302 adolescents,²⁵ while Chao et al. found that addictive users of short-form video reported higher levels of depression compared with nonusers in a sample of 1346 adolescents.²³ Moreover, while a few studies have explored the potential mechanisms in the relationship between SVA and depression, these studies have predominantly focused on college students. For instance, Liang et al. investigated the mediating role of self-objectification between short-form video use and depression in 366 female undergraduates,²⁶ but the findings may not be generalizable to adolescents. Therefore, it is crucial for this study to examine the potential mechanisms through which SVA influences depression in adolescents.

Previous studies have shown that excessive digital activities can impact individuals’ cognitive processes (e.g., attention) and potentially lead to depression,^27,28 and thus attentional bias²⁹ may mediate between SVA and adolescent depression. Furthermore, since gender presents different levels of Internet media use,³⁰ attentional bias,³¹ and depression,³² its role be considered in the association between SVA, attentional bias, and depression.

Mediating role of attentional bias

Attentional bias refers to individuals’ attentional orientation toward specific stimuli in the environment,³³ which can be divided into attention to positive information (API) and attention to negative information (ANI).³⁴ The Interaction of Person-Affect-Cognition-Execution (I-PACE) model posits that specific Internet-use disorders may impact mental health (e.g., depression) through cognitive factors.²⁷ Based on this viewpoint, API and ANI are considered crucial cognitive factors³⁴ that might mediate the association between SVA and adolescent depression. Empirical studies have also indirectly demonstrated the mediating roles of API and ANI. Specifically, addiction to digital media could impair individuals’ attentional systems, including alertness and inhibition of positive and negative stimuli.^18,28 Compared with neutral cues, Internet addicts of all types are easily attracted to and have difficulty disengaging from negative stimuli, and thus show more ANI and have less API.^35,36 This difference in attentional bias may be related to the mood-congruent hypothesis, which suggests that individuals pay more attention to information that reflects their mood.³⁷ Since addicts regularly experience more negative emotions, they may be more sensitive to negative rather than positive information. As a subcategory of Internet addiction, SVA may decrease API and increase ANI. Furthermore, API and ANI may profoundly affect depression. According to the cognitive model of depression, ANI is the central cognitive factor in the development, maintenance, and recurrence of depression.^38–40 ANI can lead to repeated negative thoughts about the self, world, and future, resulting in depression.^41,42 However, API can help individuals perceive positive aspects of their environment. Previous research found that an increase in API brings more positive information into processing and memory, altering the original cognitive model and alleviating depression.^42,43

Based on the above analyses, SVA may influence depression by decreasing the API and increasing the ANI levels. Relevant research has indicated that the lack of API plays a more important role than the presence of ANI in the development of prospective depression among adolescents.⁴⁴ However, previous studies have primarily focused on examining the mediating role of ANI between various types of Internet addiction and depression, often neglecting the impact of API in these relationships. Therefore, this study employed a parallel mediation model to examine the mediating role of API and ANI in the relationship between SVA and adolescent depression.

The potential gender differences

Based on previous studies, it is essential to explore potential gender differences in the associations among major variables. First, studies examining gender differences in the SVA have yielded inconsistent findings. Prior evidence suggests that women are more likely to use social media than men,^45,46 and short-form video social media tends to have more negative aspects for women.⁴⁷ However, other studies have shown the opposite results,⁴⁸ including men being more vulnerable to SVA than women.⁴⁹ Second, women and men also differ in depression. For instance, the prevalence of depression is significantly higher in girls than boys.³² Additionally, girls experienced increases in depression in early adolescence, while boys tend to develop higher levels of depression in late adolescence.⁵⁰ Third, gender differences are evident in the relationship between social media use and depression, with the association being significantly stronger for adolescent girls.⁵¹ Apparently, gender differences are critical when examining the impact of adolescents’ short-form video social media use on their depression. Fourth, significant gender differences were observed in attentional bias. In general, men tend to API and women tend to ANI.^52–54 API has a protective effect on the psychological well-being of men and alleviates negative emotions,³¹ whereas ANI is more likely to increase depression in women.⁵⁵ Given these gender differences, we infer that some gender differences may also be observed in the mediating effect of API and ANI on the relationship between SVA and adolescent depression.

The current study

The main purpose of this study was to examine the mediating role of API and ANI in the relationship between SVA and adolescent depression and its differences by gender. Based on the above theoretical and empirical studies, we propose the following two hypotheses H1:

API and ANI mediate the association between SVA and adolescent depression (see Figure 1 for the mediation model).

H2:

The mediating effects of API and ANI on the relationship between SVA and depression differ by gender.

FIG. 1.

Parallel mediation model of API and ANI between SVA and depression. ANI, attention to negative information; API, attention to positive information; SVA, short-form video addiction.

Materials and Methods

Participants

A total of 5214 adolescent students were recruited from two high schools in Chengdu and Chongqing, China, between December 2023 and January 2024. After excluding regular responses or many missing answers, 4750 valid questionnaires remained (62.15 percent male; M_age = 16.01, SD = 0.76, ranging from 14 to 18 years), and more sample characteristics are presented in Table 1. Participants voluntarily took approximately 15–20 minutes to complete our questionnaires during a class session, and we obtained their informed consent before data collection. This study was approved by the corresponding author’s ethics committee (H24132).

Table 1.

Sample Characteristics (N = 4750)

	N	Percent	M (SD)
Gender (male)	2952	62.15
Age			16.01 (0.76)
Number of siblings
0	1503	31.64
≥1	3247	68.36
Living place
City	2742	57.73
County town	977	20.57
Rural area	1031	21.71
Father’s education (Mother’s education)
Elementary school and below	729 (993)	15.35 (20.91)
Middle school	2258 (2064)	47.54 (43.45)
High school	1320 (1236)	27.79 (26.02)
College	273 (290)	5.75 (6.11)
Bachelor’s degree	154 (161)	3.24 (3.39)
Graduate school	16 (6)	0.34 (0.13)
Subjective socioeconomic status			4.78 (1.63)

The subjective socioeconomic status was measured by a 10-rung ladder described as follows: Think of this ladder as representing where your family stands in our society, with 1 representing lower class and 10 representing upper class.⁵⁶ Participants were asked to choose the rung that best represents their family class.

SD, standard deviation.

Measures

Short-form video addiction

Six items developed by Choi et al.⁵⁷ and revised by Wang et al.⁵⁸ were used to measure SVA (e.g., “I feel anxious if I cannot access to this short-form video app”). Each item was scored on a 5-point Likert scale (1= completely disagree, 5= completely agree), and this scale has been validated in Chinese adolescents.¹⁴ In this study, Cronbach’s alpha was 0.880, and the confirmatory factor analysis (CFA) indicated that the scale had an acceptable model fit (χ²/df = 17.317, comparative fit index [CFI] = 0.990, Tucker–Lewis index [TLI] = 0.982, standardized root mean square residual [SRMR] = 0.016, root mean square error of approximation [RMSEA] = 0.059).

Attentional bias

The Attention to Positive and Negative Information Scale developed by Noguchi et al.³⁴ and revised by Dai et al.⁵⁹ was used. This scale includes 22 items and divided into two subscales: (a) API (e.g., “I pay attention to positive characteristics of myself”) and (b) ANI (e.g., “I don’t forget when others do things that hurt me”). Each item was scored on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree), and has been validated in Chinese adolescents.²⁹ In this study, Cronbach’s alpha for the two subscales was 0.919 and 0.818, respectively. CFA indicated that each of the two subscales had acceptable model fit (χ²/df = 19.001, CFI = 0.968, TLI = 0.960, SRMR = 0.028, RMSEA = 0.062; χ²/df = 28.170, CFI = 0.952, TLI = 0.909, SRMR = 0.040, RMSEA = 0.076).

Depression

The Center for Epidemiological Studies Depression Scale developed by Radloff⁶⁰ was used to measure adolescent depression during the previous week. This scale consists of 20 items (e.g., “I felt depressed”) measured on a 4-point Likert scale (1 = <1 day, 4 = 5–7 days) and has been validated in Chinese adolescents.¹³ In this study, Cronbach’s alpha was 0.906, and CFA indicated that the scale had an acceptable model fit (χ²/df = 27.089, CFI = 0.915, TLI = 0.901, SRMR = 0.044, RMSEA = 0.074).

Data analysis

First, we used Statistical Product and Service Solutions (SPSS) (version 26.0) to calculate descriptive statistics and Pearson’s correlations. Second, we conducted a structural equation model in Mplus 8.3 to test the mediating role of API and ANI in the relationship between SVA and depression among adolescents. Gender was included as a covariate, as it was found to be associated with our core variables.^13,55,61 We used multiple goodness-of-fit indexes to evaluate model fit, including the χ² statistic, CFI, TLI, RMSEA, and SRMR. CFI and TLI >0.90 and RMSEA and SRMR ＜0.08 were considered to indicate an acceptable model fit.⁶² Finally, we conducted multigroup analyses with gender as the grouping variable. If the χ² comparison of the unconstrained and constrained models was significant, we further tested whether the mediating effects of API and ANI differed by gender.

Results

Common method bias

The Harman single-factor test was performed to test for common method bias, which showed six factors with characteristic roots >1, and the variance explained by the first common factor was 22.89 percent, indicating a nonsignificant common method bias in our study.⁶³

Descriptive statistics

Table 2 presents the means, standard deviations, and correlations among the variables. Depression was significantly correlated with SVA, API, and ANI (0.189 < |r| < 0.420).

Table 2.

Descriptive Statistics and Correlations Between Variables (N = 4750)

Variables	M	SD	1	2	3	4
1. SVA	2.28	0.85	1
2. API	3.61	0.73	−0.189^***	1
3. ANI	3.12	0.68	0.218^***	0.412^***	1
4. Depression	0.68	0.50	0.420^***	−0.315^***	0.332^***	1

***

p < 0.001.

ANI, attention to negative information; API, attention to positive information; SVA, short-form video addiction.

Testing the mediating effect of API and ANI

Structural equation modeling was used to examine the association between SVA, API, ANI, and depression, and the model showed a good fit: χ²/df = 27.365, p < 0.001, RMSEA = 0.075, CFI = 0.933, TLI = 0.920, and SRMR = 0.073. The χ² test was significant; however, as noted by Barrett,⁶⁴ the χ² test tends to become less reliable with large sample sizes, often indicating poor fit even when the model is acceptable. Given the large sample size in this study, we did not rely on the χ² test for evaluating model fit. The results of path analysis indicate that SVA was positively associated with depression (β = 0.179, p ＜ 0.001), negatively associated with API (β = −0.203, p ＜ 0.001), and positively associated with ANI (β = 0.248, p ＜ 0.001). API was negatively associated with depression (β = −0.415, p ＜ 0.001), and ANI was positively associated with depression (β = 0.565, p ＜ 0.001). Furthermore, API partially mediated the association between SVA and depression, with an effect value of 0.084 and 95 percent bootstrap confidence interval (CI) = (0.070, 0.098), while ANI partially mediated the association between SVA and depression, with an effect value of 0.140 and 95 percent bootstrap CI = (0.123, 0.159). Finally, the model explained 4.1percent of the variance of API (R² = 0.041), 6.2 percent of the variance of ANI (R² = 0.062), and 40 percent of the variance of depression (R² = 0.400).

Gender difference test of the mediating effect

A multigroup analysis was conducted to examine whether the mediating effects of API and ANI differed by gender. The unconstrained model showed that the data fit the model well (χ²/df = 14.949, p < 0.001, RMSEA = 0.077, CFI = 0.934, TLI = 0.924, SRMR = 0.076). After constraining all parameters in the path analysis to be equal in both genders, the overall model fit changed significantly (Δχ²/df = 36.072, p < 0.001), indicating significant gender differences in the mediating effect models of SVA, API, ANI, and depression. Figure 2 shows the path diagram for the different gender models, where each path coefficient is significant (p < 0.001).

FIG. 2.

Multigroup analysis model for gender differences in SVA and depression. The parameters outside and inside the parentheses indicate male and female adolescents, respectively. ***p < 0.001.

The bootstrap method was used to examine the indirect effects for different genders. As shown in Table 3, the mediating effects of API and ANI were significant in both male and female adolescents. Regarding the mediating effect of API, the effect values for male and female adolescents were 0.097 and 0.063, respectively, and the test of variance indicated no significant gender differences [Wald (1) = 2.023, p > 0.05]. Regarding the mediating effect of ANI, the effect values for male and female adolescents were 0.119 and 0.165, respectively, and the test of variance indicated a significantly stronger effect for female adolescents [Wald (1) = 10.359, p < 0.01]. In addition, the male adolescent model explained 5.4 percent of the variance of API (R² = 0.054), 4.5 percent of the variance of ANI (R² = 0.045), and 35.4 percent of the variance of depression (R² = 0.354). The female adolescent model explained 2.3 percent of the variance of API (R² = 0.023), 8.8 percent of the variance of ANI (R² = 0.088), and 45.4 percent of the variance of depression (R² = 0.454).

Table 3.

Specific Effects for Each Pathway in the Male and Female Model

	Male group			Female group
	β	SE	95 percent CI	β	SE	95percent CI
SVA→Depression	0.177^***	0.025	(0.135, 0.218)	0.198^***	0.027	(0.154, 0.243)
SVA→API→Depression	0.097^***	0.012	(0.079, 0.117)	0.063^***	0.013	(0.042, 0.084)
SVA→ANI→Depression	0.119^***	0.014	(0.096, 0.144)	0.165^***	0.017	(0.138, 0.192)

***

p < 0.001.

CI, confidence interval; SE, standard error.

Discussion

Previous studies have shown that SVA can profoundly affect adolescent depression.^23,65 However, the underlying mediating mechanisms remain largely unknown. Based on the theoretical and empirical findings for SVA, attentional bias, and depression, we examined the mediating role of API and ANI between SVA and depression and its differences by gender. The results showed that API and ANI mediated the association between SVA and depression. Furthermore, the mediating effect of ANI was significantly stronger in female than in male adolescents, whereas the mediating effect of API showed no differences between the genders.

This study found that the association between SVA and depression was mediated by API and ANI, supporting H1. Our study supports the I-PACE model,²⁷ according to which specific Internet use disorders reinforce cognitive factors (e.g., attentional bias), and thus affect mental health (e.g., depression). Our finding is also consistent with the studies supporting addiction to digital media could impact individuals’ attentional bias, which in turn correlated with depression.^18,28,29 Frequent use of digital media can affect the normal development of adolescents’ attention.⁶⁶ Short-form video platforms are filled with fragmented information,¹⁸ and excessive browsing may affect adolescents’ alertness and inhibition to stimuli, leading to attentional bias.³⁵ In this study, SVA was negatively associated with API and positively associated with ANI, which can be explained by the fact that individuals are more likely to be attracted to negative information while using digital media.⁶⁷ Previous research has shown that Internet addiction can bring negative experiences and emotions to adolescents.^68,69 SVA may also induce a negative state for adolescents, which can affect their perception of information, causing them to process negative information more effectively as it is consistent with their negative state and to ignore positive information. Moreover, API and ANI are closely related to adolescent depression. The cognitive model of depression^38–40 posits that ANI is a central risk factor for depression. What we attend to (either positively or negatively) can affect our life experiences. API can help adolescents to feel positive emotions and actively cope with adversity instead of perceiving it as threatening,^70,71 which alleviates their depression. Conversely, ANI can cause adolescents to constantly ruminate on self-relevant negative information, leading to repeated experiences of sadness.^42,55 Previous research has found that ANI may contribute to emotion regulation difficulties associated with depression by reducing the intensity and duration of positive emotional experiences.⁷² Therefore, adolescents with high SVA tend to generate and accumulate negative emotions due to the increase in ANI and a decrease in API when faced with stressful events in daily life. They may also have difficulty obtaining pleasurable experiences to regulate their negative emotions, thus increasing the risk of depression over time.

More importantly, we found gender differences only in the mediating effect of ANI between SVA and depression, with the mediating effect of ANI was significantly stronger in female than male adolescents, which partially supports H2. Such gender differences may be attributed to the different cognitive styles of females and males. In general, females exhibit a cognitive style characterized by negative self-evaluation, which may bias the attentional information, making them more vulnerable to negative information and thus increasing the risk of depression.⁷³ Short-form video platforms consist of other people’s carefully curated “ideal” lives, and female adolescents negatively compare themselves with others and focus more on the information that threatens their self-worth.²⁶ In contrast, male adolescents seem to have more effortful control in terms of stronger avoidance of negative information,³³ which may lead to a smaller mediating effect of ANI between SVA and depression than female adolescents. Notably, the present study found no significant gender differences in the mediating effects of API. Previous studies have shown that males have a “protective attentional bias effect,”^31,33 which compensates for the adverse effects of negative information with more API. However, some research also supports the idea that females have stronger self-defense mechanisms in negative states and self-regulate by focusing on positive information.⁷⁴ Although females are more alert to negative information, they may also unconsciously self-protect through positive information. As a result, ANI may have a stronger association with SVA and depression in female adolescents, whereas API may be equally important in explaining the association between SVA and depression in both male and female adolescents.

In conclusion, our study is the first to examine the mediating effects of both API and ANI on the relationship between SVA and adolescent depression, as well as their gender differences. This study has several practical implications. First, considering that adolescents may be exposed to an elevated risk of depression due to SVA, the government and relevant departments should further guide and control the healthy development of short-form video applications, and families should attend to and supervise adolescents’ use of videos. Schools can carry out media education to guide students in the appropriate use of short-form videos, thereby reducing the negative effects of excessive use. Second, since this study found that API and ANI play an important mediating role between SVA and depression, interventions targeting attentional bias could be beneficial in alleviating adolescent depression. Specifically, attentional bias modification training (ABMT) techniques—such as the cue-target task, goal-directed attention training, and attention training—have demonstrated effectiveness in improving emotional regulation.^43,75 Educators could implement computer-based ABMT to help adolescents focus more on positive stimuli and better regulate their emotions, potentially reducing the impact of SVA on depression. Finally, the results on gender differences indicated that reducing ANI in female adolescents was more likely to alleviate depression compared with male adolescents. Therefore, educators should prioritize strategies to regulate attentional bias in females, such as organizing group counseling sessions and workshops aimed at reducing ANI related to daily life. Meanwhile, it is important to provide male adolescents with positive reinforcement and encouragement to strengthen their API, which could also help mitigate depression.

Nevertheless, there are limitations to this study that should be acknowledged along with considerations for future research. First, a cross-sectional approach was used in this study, which only demonstrated the correlations between variables. Given that adolescents with greater depression may use more short-form videos, a reverse association cannot be ruled out. Future research should utilize longitudinal designs and experimental methods to verify causal relationships. Second, all variables were measured by adolescents’ self-report, which may bias the responses by some factors such as social desirability. To mitigate potential common method bias, future research should adopt more comprehensive data collection approach, including collecting data from multiple reporters (e.g., parents, peers, and teachers) and considering objective indicators (such as the use time of short-form video). Finally, we did not distinguish between the motivation and type of SVA. Future studies should consider whether different uses of short-form videos have varying effects on adolescents’ attentional bias and depression.

Footnotes

Acknowledgments

The authors thank the students who participated and also thank every teacher for their hard work in collecting data.

Ethical Approval

This research received ethical approval from the Ethics Committee of Faculty of Psychology, Southwest University (IRB protocol number: H24132). All participants provided informed consent and they were assured that the data collected would be used solely for research.

Data Availability Statement

The authors do not have permission to share data.

Authors’ Contributions

Y.W.: Conceptualization, methodology, investigation, data curation, formal analysis, and writing—original draft. Y.B.: Visualization, writing—review and editing, and writing—original draft. X.L.: Methodology and formal analysis. W.X.: Investigation, data curation, and writing—original draft. Y.L.: Writing—review and editing, supervision, project administration, resources, and funding acquisition.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This work was supported by the project of Humanities and Social Science Fund of Ministry of Education of China [grant numbers 23XJA190002].

References

McLaughlin

, King

. Developmental trajectories of anxiety and depression in early adolescence. J Abnorm Child Psychol, 2015; 43(2):311–323; doi: 10.1007/s10802-014-9898-1

Merikangas

, He

J-P

, Burstein

, et al. Lifetime prevalence of mental disorders in US adolescents: Results from the National Comorbidity Survey Replication–Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry, 2010; 49(10):980–989; doi: 10.1016/j.jaac.2010.05.017

Shorey

, Ng

, Wong

. Global prevalence of depression and elevated depressive symptoms among adolescents: A systematic review and meta‐analysis. Br J Clin Psychol, 2022; 61(2):287–305; doi: 10.1111/bjc.12333

Tang

, Tang

, Ren

, et al. Prevalence of depressive symptoms among adolescents in secondary school in mainland China: A systematic review and meta-analysis. J Affect Disord, 2019; 245:498–507; doi: 10.1016/j.jad.2018.11.043

Zhang

, Jin

, Yu

. Prevalence of mental health problems among junior high school students in Chinese mainland from 2010 to 2020: A meta analysis. Adv Psychol Sci, 2022; 30(5):965–977; doi: 10.3969/j.issn.1671-3710.2022.5.xlxdt202205002

, Zhang

, Yu

. Prevalence of mental health problems among senior high school students in mainland of China from 2010 to 2020: A meta-analysis. Adv Psychol Sci, 2022; 30(5):978–990; doi: 10.3969/j.issn.1671-3710.2022.5.xlxdt202205003

Ellis

, Seal

, Simmons

, et al. Longitudinal trajectories of depression symptoms in adolescence: Psychosocial risk factors and outcomes. Child Psychiatry Hum Dev, 2017; 48(4):554–571; doi: 10.1007/s10578-016-0682-z

Liu

, Bullock

, Coplan

, et al. Developmental cascade models linking peer victimization, depression, and academic achievement in Chinese children. Br J Dev Psychol, 2018; 36(1):47–63; doi: 10.1111/bjdp.12212

Mason

, Mennis

, Russell

, et al. Adolescent depression and substance use: The protective role of prosocial peer behavior. J Abnorm Child Psychol, 2019; 47(6):1065–1074; doi: 10.1007/s10802-018-0501-z

10.

Lei

, Xiong

, Rao

, et al. Relationships among self-esteem, depression and self-injury in adolescents: A longitudinal study. Front Public Health, 2024; 12:1406283; doi: 10.3389/fpubh.2024.1406283

11.

Lee

, Kim

, Hyun

, et al. Depressive symptoms and neuroticism mediate the association between traumatic events and suicidality—A latent class mediation analysis of UK Biobank Database. J Affect Disord, 2024; 356:13–21; doi: 10.1016/j.jad.2024.03.171

12.

Wiebenga

JXM

, Dickhoff

, Merelle

SYM

, et al. Prevalence, course, and determinants of suicide ideation and attempts in patients with a depressive and/or anxiety disorder: A review of NESDA findings. J Affect Disord, 2021; 283:267–277; doi: 10.1016/j.jad.2021.01.053

13.

Zhao

, Huang

, Li

. Does adolescents’ Internet addiction trigger depressive symptoms and aggressive behavior, or vice versa? The moderating roles of peer relationships and gender. Comput Hum Behav, 2022; 129:107143; doi: 10.1016/j.chb.2021.107143

14.

, Wang

, Zhu

, et al. Parental neglect and short-form video application addiction in Chinese adolescents: The mediating role of alexithymia and the moderating role of refusal self-efficacy. Child Abuse Negl, 2023; 143:106345; doi: 10.1016/j.chiabu.2023.106345

15.

Zhang

, Wu

, Liu

. Exploring short-form video application addiction: Socio-technical and attachment perspectives. Telemat Inf, 2019; 42:101243; doi: 10.1016/j.tele.2019.101243

16.

China Netcasting Services Association. Research report on the development of internet audiovisual in China 2024. Chengdu, Sichuan; 2024. Available from: http://www.cnsa.cn/art/2024/3/28/art_1977_43660.html [Last accessed: November 29, 2024].

17.

The Communist Youth League of China, China Internet Network Information Center. The 5th National Survey on Internet Use by Minors. China; 2023. Available from: https://qnzz.youth.cn/qckc/202312/P020231223672191910610.pdf [Last accessed: November 29, 2024].

18.

Chen

, Li

, Guo

, et al. The effect of short-form video addiction on users’ attention. Behav Inf Technol, 2023; 42(16):2893–2910; doi: 10.1080/0144929X.2022.2151512

19.

Zhang

, Hazarika

, Chen

, et al. A cross-national study on the excessive use of short-video applications among college students. Comput Hum Behav, 2023; 145:107752; doi: 10.1016/j.chb.2023.107752

20.

, Jiang

, Xu

, et al. Drivers and consequences of Short-Form Video (SFV) addiction amongst adolescents in China: Stress-Coping theory perspective. Int J Environ Res Public Health, 2022; 19(21):14173; doi: 10.3390/ijerph192114173

21.

, Liu

, Jia

, et al. The longitudinal relationships between short video addiction and depressive symptoms: A cross-lagged panel network analysis. Comput Hum Behav, 2024; 152:108059; doi: 10.1016/j.chb.2023.108059

22.

Van Rooij

, Prause

. A critical review of “Internet addiction” criteria with suggestions for the future. J Behav Addict, 2014; 3(4):203–213; doi: 10.1556/JBA.3.2014.4.1

23.

Chao

, Lei

, He

, et al. TikTok use and psychosocial factors among adolescents: Comparisons of non-users, moderate users, and addictive users. Psychiatry Res, 2023; 325:115247; doi: 10.1016/j.psychres.2023.115247

24.

Kraut

, Patterson

, Lundmark

, et al. Internet paradox: A social technology that reduces social involvement and psychological well-being? Am Psychol, 1998; 53(9):1017–1031; doi: 10.1037/0003-066x.53.9.1017

25.

Zhu

, Jiang

, Lei

, et al. The relationship between short-form video use and depression among Chinese adolescents: Examining the mediating roles of need gratification and short-form video addiction. Heliyon, 2024; 10(9):e30346; doi: 10.1016/j.heliyon.2024.e30346

26.

Liang

, Guo

, Zhao

. The impact of short-video use on depression of female undergraduate: A chain mediating model of self objectification and body satisfaction. J Psychol Sci, 2020; 43(05):1220–1226; doi: 10.16719/j.cnki.1671-6981.20200527

27.

Brand

, Young

, Laier

, et al. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: An Interaction of Person-Affect-Cognition-Execution (I-PACE) model. Neurosci Biobehav Rev, 2016; 71:252–266; doi: 10.1016/j.neubiorev.2016.08.033

28.

Sbarra

, Briskin

, Slatcher

. Smartphones and close relationships: The case for an evolutionary mismatch. Perspect Psychol Sci, 2019; 14(4):596–618; doi: 10.1177/1745691619826535

29.

Xiao

, Peng

, Liao

. Exploring the associations between social media addiction and depression: Attentional bias as a mediator and socio-emotional competence as a moderator. Int J Environ Res Public Health, 2022; 19(20):13496; doi: 10.3390/ijerph192013496

30.

Nayak

. Relationship among smartphone usage, addiction, academic performance and the moderating role of gender: A study of higher education students in India. Comput Educ, 2018; 123:164–173; doi: 10.1016/j.compedu.2018.05.007

31.

, Dong

. Children’s perception of interparental conflict and anxiety: The mediating effect of attention to positive and negative information and the moderating effect of gender. Psychol Dev Educ, 2023; 39(04):488–496; doi: 10.16187/j.cnki.issn1001-4918.2023.04.04

32.

, Zhang

, Chen

, et al. Report on National Mental Health Development in China (2021–2022). Social Sciences Academic Press: Beijing, China; 2023.

33.

Pintzinger

, Pfabigan

, Tran

, et al. Attentional biases in healthy adults: Exploring the impact of temperament and gender. J Behav Ther Exp Psychiatry, 2016; 52:29–37; doi: 10.1016/j.jbtep.2016.02.003

34.

Noguchi

, Gohm

, Dalsky

. Cognitive tendencies of focusing on positive and negative information. J Res Pers, 2006; 40(6):891–910; doi: 10.1016/j.jrp.2005.09.008

35.

, Guo

, Jou

, et al. Investigating the attentional bias and information processing mechanism of mobile phone addicts towards emotional information. Comput Hum Behav, 2020; 110:106378; doi: 10.1016/j.chb.2020.106378

36.

Ünal-Aydın

, Balıkçı

, Sönmez

, et al. Associations between emotion recognition and social networking site addiction. Psychiatry Res, 2020; 284:112673; doi: 10.1016/j.psychres.2019.112673

37.

Becker

, Leinenger

. Attentional selection is biased toward mood-congruent stimuli. Emotion, 2011; 11(5):1248–1254; doi: 10.1037/a0023524

38.

Beck

. The evolution of the cognitive model of depression and its neurobiological correlates. Am J Psychiatry, 2008; 165(8):969–977; doi: 10.1176/appi.ajp.2008.08050721

39.

Beck

, Alford

. Depression: Causes and Treatment. University of Pennsylvania Press: Philadelphia, Pennsylvania, USA; 2009.

40.

Kovacs

, Beck

. Maladaptive cognitive structures in depression. Am J Psychiatry, 1978; 135(5):525–533; doi: 10.1176/ajp.135.5.525

41.

Hou

, Zhu

, Fang

. Mobile phone addiction and depression: Multiple mediating effects of social anxiety and attentional bias to negative emotional information. Acta Psychol Sin, 2021; 53(4):362–373; doi: 10.3724/SP.J.1041.2021.00362

42.

, Yu

, Xie

, et al. Positive affect promotes well-being and alleviates depression: The mediating effect of attentional bias. Psychiatry Res, 2015; 228(3):482–487; doi: 10.1016/j.psychres.2015.06.011

43.

Krejtz

, Holas

, Rusanowska

, et al. Positive online attentional training as a means of modifying attentional and interpretational biases among the clinically depressed: An experimental study using eye tracking. J Clin Psychol, 2018; 74(9):1594–1606; doi: 10.1002/jclp.22617

44.

, Dai

DWT

, Mak

, et al. Cognitive factors associated with depression and anxiety in adolescents: A two-year longitudinal study. Int J Clin Health Psychol, 2018; 18(3):227–234; doi: 10.1016/j.ijchp.2018.04.001

45.

Chen

, Liu

, Ding

, et al. Gender differences in factors associated with smartphone addiction: A cross-sectional study among medical college students. BMC Psychiatry, 2017; 17(1):341–349; doi: 10.1186/s12888-017-1503-z

46.

, Han

, Yu

, et al. Do men become addicted to internet gaming and women to social media? A meta-analysis examining gender-related differences in specific internet addiction. Comput Hum Behav, 2020; 113:106480; doi: 10.1016/j.chb.2020.106480

47.

, Jiang

, Wang

, et al. Relationship between short-form video social media addiction and sleep disturbance of college students: The mediating role of nighttime social media use and the moderating role of gender. Chinese J Clin Psychol, 2021; 29(01):46–50; doi: 10.16128/j.cnki.1005-3611.2021.01.009

48.

Ryan

, Chester

, Reece

, et al. The uses and abuses of Facebook: A review of Facebook addiction. J Behav Addict, 2014; 3(3):133–148; doi: 10.1556/jba.3.2014.016

49.

Wang

, Zhao

, Zhang

, et al. Life history strategies and problematic use of short-form video applications. Evol Psychol Sci, 2021; 7(1):39–44; doi: 10.1007/s40806-020-00255-9

50.

Salk

, Petersen

, Abramson

, et al. The contemporary face of gender differences and similarities in depression throughout adolescence: Development and chronicity. J Affect Disord, 2016; 205:28–35; doi: 10.1016/j.jad.2016.03.071

51.

Kelly

, Zilanawala

, Booker

, et al. Social media use and adolescent mental health: Findings from the UK Millennium Cohort Study. EClinicalMedicine, 2018; 6:59–68; doi: 10.1016/j.eclinm.2018.12.005

52.

Pan

, Zhou

, Liu

, et al. Study on attentional bias of emotional faces in emotion induced situations. Psychol Tech Appl, 2018; 6(10):600–605; doi: 10.16842/j.cnki.issn2095-5588.2018.10.002

53.

Syrjänen

, Wiens

. Gender moderates valence effects on the late positive potential to emotional distracters. Neurosci Lett, 2013; 551:89–93; doi: 10.1016/j.neulet.2013.07.018

54.

, Chen

, Jia

, et al. Cognitive bias by gender interaction on N170 response to emotional facial expressions in major and minor depression. Brain Topogr, 2016; 29(2):232–242; doi: 10.1007/s10548-015-0444-4

55.

Hou

, Lai

, Lin

, et al. Influence of neuroticism on depression among college students by gender: The mediating roles of negative cognitive bias and anhedonia. Curr Psychol, 2024; 43(7):5901–5913; doi: 10.1007/s12144-023-04793-w

56.

Adler

, Epel

, Castellazzo

, et al. Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy, White women. Health Psychol, 2000; 19(6):586–592; doi: 10.1037//0278-6133.19.6.586

57.

Choi

, Lim

. Effects of social and technology overload on psychological well-being in young South Korean adults: The mediatory role of social network service addiction. Comput Hum Behav, 2016; 61:245–254; doi: 10.1016/j.chb.2016.03.032

58.

Wang

, Wang

, Lei

. Longitudinal links among paternal and maternal harsh parenting, adolescent emotional dysregulation and short-form video addiction. Child Abuse Negl, 2023; 141:106236; doi: 10.1016/j.chiabu.2023.106236

59.

Dai

, Feng

, Xu

, et al. Validity and reliability of the Chinese attention to positive and negative inventory in college students. Chinese J Mental Health, 2015; 29(05):395–400; doi: 10.3969/j.issn.1000-6729.2015.05.017

60.

Radloff

. The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Meas, 1977; 1(3):385–401; doi: 10.1177/014662167700100306

61.

Park

, Lee

. Gender differences in smartphone addiction and depression among Korean adolescents: Focusing on the internal mechanisms of attention deficit and self-control. Comput Hum Behav, 2022; 136:107400; doi: 10.1016/j.chb.2022.107400

62.

, Bentler

. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Modeling, 1999; 6(1):1–55; doi: 10.1080/10705519909540118

63.

Zhou

, Long

. Statistical remedies for common method biases. Adv Psychol Sci, 2004(;06):942–950; doi: 10.3969/j.issn.1671-3710.2004.06.018

64.

Barrett

. Structural equation modelling: Adjudging model fit. Pers Individ Dif, 2007; 42(5):815–824; doi: 10.1016/j.paid.2006.09.018

65.

Sha

, Dong

. Research on adolescents regarding the indirect effect of depression, anxiety, and stress between TikTok use disorder and memory loss. Int J Environ Res Public Health, 2021; 18(16):8820; doi: 10.3390/ijerph18168820

66.

Firth

, Torous

, Stubbs

, et al. The “online brain”: How the Internet may be changing our cognition. World Psychiatry, 2019; 18(2):119–129; doi: 10.1002/wps.20617

67.

Bebbington

, MacLeod

, Ellison

, et al. The sky is falling: Evidence of a negativity bias in the social transmission of information. Evol Hum Behav, 2017; 38(1):92–101; doi: 10.1016/j.evolhumbehav.2016.07.004

68.

, Houmei

, Wang

, et al. Problematic internet use and cyberbullying perpetration among Chinese adolescents: The mediating and moderating roles of negative emotions. Int J Ment Health Addiction, 2023; 21(3):1515–1533; doi: 10.1007/s11469-021-00675-z

69.

Xie

, Yang

, Lei

. The relationship of internet gaming addiction and suicidal ideation among adolescents: The mediating role of negative emotion and the moderating role of hope. Int J Environ Res Public Health, 2023; 20(4):3375; doi: 10.3390/ijerph20043375

70.

Mak

, Ng

, Wong

. Resilience: Enhancing well-being through the positive cognitive triad. J Couns Psychol, 2011; 58(4):610–617; doi: 10.1037/a0025195

71.

Mauss

, Shallcross

, Troy

, et al. Don’t hide your happiness! Positive emotion dissociation, social connectedness, and psychological functioning. J Pers Soc Psychol, 2011; 100(4):738–748; doi: 10.1037/a0022410

72.

Everaert

, Bronstein

, Castro

, et al. When negative interpretations persist, positive emotions don’t! Inflexible negative interpretations encourage depression and social anxiety by dampening positive emotions. Behav Res Ther, 2020; 124:103510; doi: 10.1016/j.brat.2019.103510

73.

Nolen-Hoeksema

, Larson

, Grayson

. Explaining the gender difference in depressive symptoms. J Pers Soc Psychol, 1999; 77(5):1061–1072; doi: 10.1037/0022-3514.77.5.1061

74.

Zhu

, Zheng

. Effects of affective priming on attentional bias induced by short films. J Psychol Sci, 2009; 32(02):327–330; doi: 10.16719/j.cnki.1671-6981.2009.02.010

75.

Dai

, Hu

, Feng

. Attentional bias modification reduces clinical depression and enhances attention toward happiness. J Psychiatr Res, 2019; 109:145–155; doi: 10.1016/j.jpsychires.2018.11.024