Validation of the Short Video Addiction Scale: A Psychometric Study Among Chinese Adolescents

Validation of the Short Video Addiction Scale: A Psychometric Study Among Chinese Adolescents | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validation of the Short Video Addiction Scale: A Psychometric Study Among Chinese Adolescents Zhihan Jiang, Tiejun Kang, Yixiao Chen, Weiping Chen, Heng Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6259796/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rise of short video platforms such as TikTok and Instagram Reels has led to increasing concerns about addiction, particularly among adolescents. This study introduces the Short Video Addiction Scale (SVAS), a concise, reliable tool designed to assess addictive behaviors specific to short video use. The SVAS, adapted from the Bergen Facebook Addiction Scale (BFAS), was validated with a sample of 2,959 Chinese adolescents. Results show strong internal consistency (Cronbach’s α = 0.884) and good structural validity, with a single-factor model explaining 63.9% of the variance. The scale correlates highly with the Mobile Phone Addiction Index (r = 0.768) and depressive symptoms (r = 0.497). The SVAS demonstrates excellent diagnostic accuracy (AUC = 0.875), making it a promising tool for early identification and intervention of short video addiction. Short Video Addiction Reliability Validity Short Video Addiction Scale Chinese Adolescents Figures Figure 1 Figure 2 Figure 3 Introduction The rapid rise of short video platforms such as TikTok, YouTube Shorts, and Instagram Reels has revolutionized the social media landscape in recent years. With their concise content, immediate appeal, highly accurate recommendation algorithms, and enhanced interactivity, these platforms have rapidly gained popularity, particularly among teenagers and young adults. Through features like "infinite scrolling" and real-time feedback (e.g., likes, comments, and shares), short video platforms expose users to vast amounts of content in a short period, often leading to increased usage time(Qi & Li, 2020a ). This high-frequency engagement mirrors broader social media usage patterns. Social media platforms generally refer to third-party internet-based platforms that focus on social interactions, user-generated content, and community-driven content sharing. These platforms, including Facebook, Instagram, and TikTok, exclude content obtained from third-party licenses(Pellegrino et al., 2022 ). As of 2024, approximately 60% of the global population is active on social media, with Facebook, YouTube, and Instagram as the most popular( We Are Social, & DataReportal, & Meltwater. (April 24, 2024). Most popular social networks worldwide as of April 2024, by number of monthly active users (in millions) [Graph]. In Statista. https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/ ) , while TikTok has seen the highest user growth in recent years ( Statista. (April 5, 2023). Number of TikTok users worldwide from 2018 to 2029 (in millions) [Graph]. In Statista. https://www.statista.com/forecasts/1142687/tiktok-users-worldwide ). At the individual level, users spend over 2.2 hours daily on various platforms( We Are Social, & DataReportal, & Hootsuite. (February 22, 2024). Daily time spent on social networking by internet users worldwide from 2012 to 2024 (in minutes) [Graph]. In Statista. Retrieved January 20, 2025, from https://www.statista.com/statistics/433871/daily-social-media-usage-worldwide/ ). While social media offers convenient virtual connections and real-time information, this high engagement often leads to addictive behaviors. However, the term "addiction" requires careful consideration, as it introduces certain challenges in research. First, social media addiction (SMA) is increasingly common but remains unrecognized in the ICD-11 and DSM-V diagnostic systems. More research is necessary to clarify the pathological mechanisms and diagnostic criteria. Second, SMA is often viewed as a subset of Internet Addiction (IA), but the boundary between the two remains unclear(Kuss & Griffiths, 2011 ). IA is a broader concept encompassing compulsive online activities such as gaming, shopping, and social interaction(Kuss & Griffiths, 2011 ). European experts have proposed replacing "Internet addiction" with "Problematic Internet Use" (PUI), which avoids overpathologizing high-frequency internet use and offers a more flexible framework for research(Fineberg et al., 2022 ). However, SMA specifically emphasizes dependency on virtual social interactions and real-time feedback, especially on short video platforms. These platforms' rapid content cycles and real-time engagement can foster compulsive use, making SMA distinct from general IA or PUI. Therefore, categorizing SMA simply as part of IA or PUI may obscure its unique psychological mechanisms(Montag et al., 2015 ). Moreover, SMA is not a monolithic concept but encompasses various behaviors across platforms like Facebook, TikTok, Instagram, and Twitter. While all fall under the social media umbrella, each platform has unique features, including content types, user interaction patterns, and recommendation algorithms. Thus, SMA research must account for platform diversity, as addiction behaviors cannot be generalized across all platforms. For instance, Facebook addiction is more closely tied to social needs and identity maintenance (Alhabash & Ma, 2017 ), whereas Instagram and Twitter focus on continuous consumption of images, short texts, or information, along with interactive feedback (Alhabash & Ma, 2017 ; Kircaburun & Griffiths, 2018 ). Short video platforms, as emerging social media, present heightened addiction risks. Unlike traditional platforms such as Facebook and Twitter, these platforms share common addictive behaviors, including six core features: significance, emotional regulation, tolerance, withdrawal, conflict, and relapse(Le Foll et al., 2022 ). However, they also have unique characteristics. The brief content and highly personalized recommendation algorithms of short video platforms make it easier for users to lose control over their time usage(Yang et al., 2024 ). The dependence on instant gratification, combined with the immersive visual and auditory stimulation, significantly increases the likelihood of addiction, particularly among adolescent users. These users often experience withdrawal symptoms, such as anxiety, low mood, and strong urges to continue using the platform, when they attempt to stop(Guo & Chai, 2024 ). Consequently, while short video platforms amplify the risk of social media addiction, they also demand more targeted and nuanced research into behavioral addiction. However, research in this field faces a significant bottleneck: the absence of targeted, cross-culturally applicable assessment tools. This limitation complicates the standardization of research and hinders cross-field and cross-cultural comparisons of short video addiction. Research on short video addiction assessment tools can benefit from established methodologies in the Facebook and social media domains. In 2012, Andreassen et al. developed the Bergen Facebook Addiction Scale (BFAS), a pioneering instrument for evaluating Facebook addiction(Andreassen et al., 2012 ). Initially comprising 18 items—three for each of the six core addiction components—the scale was refined by retaining the most highly correlated item from each component, resulting in a concise six-item measure(Andreassen et al., 2012 ). This streamlined design reduced respondent burden and enhanced the scale's usability and cross-cultural applicability. Structural validation through factor analysis confirmed a robust single-factor model (RMSEA = 0.05, CFI = 0.99), with high internal consistency (Cronbach's α = 0.83) and a 3-week test-retest reliability coefficient of 0.82, indicating stability across different times and groups(Andreassen et al., 2012 ). As social media platforms diversified, the Bergen Social Media Addiction Scale (BSMAS) was developed by generalizing "Facebook" to "social media," thereby encompassing platforms like Instagram, YouTube, and Twitter(Andreassen et al., 2017 ). Currently, BSMAS is the most widely used tool for assessing social media addiction(Varona et al., 2022 ). It has been translated and psychometrically validated across various countries and populations, demonstrating good reliability and validity(Brailovskaia & Margraf, 2022 ; Varona et al., 2022 ; Yam et al., 2019 ). However, some researchers caution that BSMAS, based on BFAS, may focus on specific platform characteristics, and results obtained using BSMAS should be interpreted with care(Brailovskaia & Margraf, 2022 ). Overall, the successful development of BFAS and BSMAS provides valuable insights for creating tools to assess addiction behaviors on other specific platforms, such as short video platforms. In China, there is a scarcity of tools for assessing short video addiction, and the lack of localized scales may hinder research in this area. Existing scales, such as the University Student Short Video Addiction Scale developed by Luo Guangfu (Guangfu, 2022 ), include 21 items across five dimensions—excessiveness, daily life interference, emotional experience, interactivity, and audiovisual stimulation—but their reliability and validity have not been fully verified. Similarly, Bai Ziyu's (Ziyu, 2024 ) University Student Short Video Addiction Scale, comprising 56 items, has undergone reliability and validity testing but only for a specific group of college students. Additionally, these scales' length may increase respondent burden, leading to lower completion rates and affecting data quality, thus limiting their application in broader populations. To address these issues, the researchers developed the Short Video Addiction Scale (SVAS)(Zhang et al., 2023 ), drawing inspiration from the concise and efficient design of the BFAS. Short video addiction primarily involves compulsive behavior and immersion in content consumption rather than social interaction(Chao et al., 2023 ). Users often become dependent on short, high-frequency content and experience time loss due to algorithmic recommendations(Qi & Li, 2020b ; Yang et al., 2024 ). Therefore, the SVAS items focus more on the audiovisual stimulation of content and the immersive experience of users (e.g., " When I use short-video apps, I often forget the time." and " When using short video apps, I am almost entirely devoted to it."), which is not emphasized in BFAS. Secondly, the engaging nature of short videos can lead users to extend their usage time, even affecting sleep patterns(Jiang & Yoo, 2024 ; Schrempft et al., 2024 ). This aligns with findings that excessive Facebook usage may impact sleep time(Andreassen et al., 2012 ). Therefore, the SVAS includes items assessing the impact on sleep (e.g., " Playing short video apps affects my sleep schedule.") to capture sleep disturbances caused by short video addiction. In conclusion, this study aims to evaluate the reliability and validity of the SVAS, adapted from BFAS, among Chinese adolescents, providing a reliable and rapid early screening tool for short video addiction for clinicians and researchers. Methods Sampling strategy and subjects This study involved 3,291 adolescents from grades 7, 8, 10, and 11 across eight middle schools in northwest China. Data were collected via questionnaires, with two trained researchers present to provide necessary explanations and guidance to participants, ensuring accurate and reliable responses. To maintain confidentiality, each participant completed the questionnaire independently. After excluding 332 incomplete questionnaires and those with contradictory responses, 2,959 valid responses were obtained. Measures Adaptation and translation of the Short Video Addiction Scale (SVAS) To ensure the SVAS accurately reflects the unique usage patterns of short video platforms—such as content consumption behavior, audiovisual immersion, and sleep disruption—we adapted the six items from Zhang et al.(Zhang et al., 2023 ) that were revised based on the BFAS to assess excessive short video usage, and made corresponding adjustments. A bilingual translator, well-versed in psychological terminology and familiar with short video culture, translated the items into Chinese. In this process, items were adjusted to better align with short video usage behaviors. According to the International Test Commission (ITC)(Merenda, 2006 ) "ITC-Test Adaptation Guidelines" (2000), the independent translation process was initially completed by three native Chinese speakers who are fluent in both written and spoken English (a psychiatrist, a psychologist, and an educator). One translator participated regularly in project meetings to discuss and reach a consensus on the preliminary version. Subsequently, back-translation into English was performed. The translated and back-translated scale was then reviewed by experts in psychology and media research to ensure its suitability for Chinese adolescents. Additionally, a pretest was conducted with a small group of adolescents to assess the scale's comprehensibility and usability, leading to necessary adjustments based on their feedback. This comprehensive process ensured that the SVAS was linguistically and culturally appropriate for Chinese adolescents, while maintaining the theoretical framework and evaluation methods consistent with the BFAS. The final modified items are: (1) " When I use short-video apps, I often forget the time. " (2) " I use short video apps every day when I have free time." (3) " When using short video apps, I feel that my body and mind can completely relax. " (4) " Playing short video apps affects my sleep schedule." (5) " I try to reduce my time spent on short video apps, but I haven't succeeded" and (6) " I think about how I could free more time to spend on short video apps.". All Items are scored on the following scale: 0:Never, 1: Rarely, 2: Sometimes, 3:Often, 4:Always. Mobile Phone Addiction Index (MPAI) The MPAI is primarily used to diagnose mobile phone addiction among adolescents and college students. The scale uses a 5-point scoring system, where 1 means "never," and 5 means "always," consisting of 17 items across four dimensions: withdrawal, loss of control, inefficiency, and escapism. Withdrawal refers to the emotional responses individuals experience when they cannot use their mobile phones normally. Loss of control refers to individuals being unable to control the amount of time they spend on their phones. Inefficiency refers to reduced academic or work efficiency due to excessive mobile phone use. Escapism refers to using mobile phones to escape from the real world, with users immersing themselves in the mobile network world. Higher scores indicate a higher degree of mobile phone addiction, with total scores ranging from 43–51 indicating mild addiction, 52–68 indicating moderate addiction, and 69 or above indicating severe addiction. The Chinese version of the MPAI has shown satisfactory reliability and validity among students. (Hai et al., 2014 ) Patient Health Questionnaire (PHQ-9) The PHQ-9 is a widely used tool for assessing depressive symptoms. The PHQ-9 scale contains 9 items, each representing an aspect of depressive symptoms. Scoring is done using a 4-point system: 0 means "not at all (less than 1 day)," 1 means "several days (1–2 days)," 2 means "more than half the days (3–4 days)," and 3 means "nearly every day." The higher the score, the more severe the depressive symptoms. The PHQ-9 scale has good reliability and validity and performs well in diagnosing depression and assessing symptom severity across various populations with different genders, ages, occupations, and education levels(Patel et al., 2019 ). Studies have shown that when the total PHQ-9 score is ≥ 10, it has the highest combined sensitivity (88%) and specificity (85%)(Levis et al., 2019 ). Statistics Data analysis was conducted using SPSS 27.0 and AMOS 26.0. Descriptive statistics summarized demographic characteristics, with t-tests applied to binary variables (e.g., gender, grade) and F-tests for multiple categorical variables. The internal consistency of the Short Video Addiction Scale (SVAS) was assessed using Cronbach's α coefficient and split-half reliability. Factor analysis suitability was evaluated with the Kaiser-Meyer-Olkin (KMO) measure and Bartlett's test of sphericity. Exploratory factor analysis (EFA) examined the scale's structural validity, employing Kaiser’s criterion to extract common factors and calculate variance, along with factor loadings to assess item-factor associations. Confirmatory factor analysis (CFA) tested the hypothesized factor structure, with model fit evaluated using the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Normed Fit Index (NFI), Incremental Fit Index (IFI), and Root Mean Square Error of Approximation (RMSEA). CFI, NFI, IFI, and TLI values ≥ 0.90 indicate good model fit(Hu & Bentler, 1999 ), and RMSEA values ≤ 0.05 indicate good model fit(Schreiber et al., 2006 ). Due to non-normally distributed scale data, Spearman's correlation coefficient analyzed convergent validity with the Mobile Phone Addiction Index (MPAI) and Patient Health Questionnaire-9 (PHQ-9). Spearman's correlation coefficients between the SVAS total score and the other two scales, as well as scores of each symptom group, were calculated to examine convergent validity between short video addiction, mobile phone addiction, and depressive symptoms at different levels. Participants were categorized into addiction and non-addiction groups based on the MPAI (cutoff value of 43), and the diagnostic accuracy of the SVAS was evaluated using the area under the ROC curve (AUC) to assess the scale's ability to distinguish between addiction and non-addiction. Ethics The study procedures were carried out in accordance with the Declaration of Helsinki. The study received approval from the Ethics Committee of the College of Psychology, Northwest Normal University, Gansu Province, China (approval number: 20240889). All subjects were informed about the study and all provided informed consent. (Parental consent was sought for those younger than 18 years of age.) All data were kept confidential. Results Descriptive statistics The study sample comprised 2,959 participants, yielding a response rate of 89.9%. The gender distribution was balanced, with approximately equal numbers of male and female participants. Participants' ages ranged from 13 to 22 years, with a mean age of 16.4 years (SD = 1.60). The average scores were 8.9 (SD = 6.0) on the SVAS, 36.7 (SD = 15.8) on the MPAI, and 5.6 (SD = 6.1) on the PHQ-9. Females had higher average scores on the SVAS, MPAI, and PHQ-9 compared to males. Further details are provided in the figures. (Table. 1). Item Analysis The discriminant validity of the SVAS was assessed by comparing item scores between low- and high-addiction groups (Thorndike, 1995 ). Participants were categorized into subgroups based on total SVAS scores (low group: n = 744; high group: n = 624). Independent samples t-tests revealed statistically significant differences (p < 0.001) across all items (Table. 2). All items correlated significantly with the total score (r = 0.680–0.790, p < 0.001), with SVAS2 exhibiting the highest correlation (r = 0.790), followed by SVAS3 (r = 0.728). The overall Cronbach’s α for the scale was 0.884, indicating excellent reliability. Removal of any single item resulted in minimal changes to α values (range: 0.849–0.883), suggesting that all items contribute meaningfully to the scale’s consistency (Table. 3). Reliability and validity analysis Internal consistency reliability In this study, we evaluated the internal consistency of the SVAS using Cronbach's α and split-half reliability. The overall Cronbach's α was 0.884, and the split-half reliability was 0.770, indicating satisfactory reliability. The α values for the MPAI and the PHQ-9 were 0.849 and 0.933, respectively, also reflecting acceptable reliability levels. Exploratory factor analysis (EFA) In the EFA, the KMO value for the SVAS in this study was 0.858, and Bartlett's test of sphericity was significant (P < 0.001), indicating that exploratory factor analysis was feasible for the sample. The Scree Test showed that the curve flattened significantly after two inflection points (Fig. 1 ). Given that the SVAS scale is designed based on the single-dimensional theory of BFAS, and according to Kaiser’s criterion, we did not consider factors with eigenvalues less than 1. Therefore, the focus was on extracting a single factor. The EFA results showed that one factor with an eigenvalue greater than 1 was extracted, which accounted for 63.9% of the variance. The factor loadings ranged from 0.627 to 0.899 (Table. 4), indicating that the SVAS has good structural validity. Confirmatory factor analysis (CFA) Based on the EFA results, the CFA was conducted, which showed that in the single-dimensional factor structure, all standardized loadings for the six indicators (χ²/df = 3.871, P > 0.05) were above 0.50 (range = 0.63 to 0.90; Fig. 2 ). The model fit was satisfactory (RMSEA = 0.031, CFI = 0.999, GFI = 0.998, TLI = 0.996, RMR = 0.014). (Table. 5). Discriminant validity The Spearman’s correlation coefficients between the SVAS and the MPAI and PHQ-9 were 0.768 and 0.497, respectively. The SVAS score showed a positive correlation with both the MPAI and PHQ-9 scores. We also explored the relationships between the four subscales of the MPAI—withdrawal, loss of control, inefficiency, and escapism—and the SVAS. The results showed that the SVAS had the strongest correlation with the loss of control subscale of the MPAI, with a Spearman’s correlation coefficient of 0.782; the weakest correlation was with the avoidance subscale, with a Spearman’s correlation coefficient of 0.589 (Table. 6). ROC Analysis The AUC for the SVAS was 0.875. The optimal cutoff value was 9 points, with sensitivity and specificity values provided. This result indicates that the SVAS provides a significant level of discrimination (95% CI: 0.863–0.888; Fig. 3 ) Discussion This study aimed to evaluate the psychometric properties of the Chinese version of the SVAS, adapted from the BFAS. The results demonstrated that the Chinese SVAS exhibits strong reliability, validity, and discriminant ability. Factor analysis confirmed that the scale adheres to a unidimensional structure, aligning with the original BFAS model. In our study, the unidimensional factor structure was supported by CFA, showing adequate fit indices for the single-factor model within our sample (RMSEA = 0.031, CFI = 0.999, TLI = 0.996). This result aligns with the original author's unidimensional structure for the scale(Andreassen et al., 2012 ). The applicability of this unidimensional structure may stem from the high cohesiveness of short video addiction behavior, where addicts exhibit a highly consistent compulsive usage pattern at both the psychological and behavioral levels(Griffiths, 2005 ). Such uniform behavior patterns likely facilitate the single-factor model in capturing the core characteristics of this addiction. Furthermore, we also noted the stability and strength of the factor loadings. The factor loadings for the SVAS ranged from 0.627 to 0.899, significantly higher than the recommended minimum threshold, indicating that the scale items effectively reflect the underlying factor. The high Cronbach’s α coefficient indicates that both the Chinese version of SVAS and its individual items exhibit good reliability. This is consistent with the values reported in previous similar studies (ranging from 0.876 to 0.960)(Zheng. et al., 2022). Moreover, the high split-half reliability further strengthens the reliability of the scale. For further validation, we used the MPAI and the PHQ-9 as reference tools. Although the specific content measured by these two scales differs from that of the SVAS, they provide valuable information regarding mobile phone addiction and mental health status, which indirectly reflect the potential status of short video addiction. Specifically, the MPAI, by assessing an individual's dependence on mobile phones, can indirectly reflect dependence on short video applications on mobile phones. A higher score on the MPAI suggests a stronger dependence on mobile phones, which may increase the risk of short video addiction. The PHQ-9, mainly used to assess depressive symptoms, is relevant as depressive states may be associated with excessive use of short videos as a form of escapism. By analyzing the relationship between the PHQ-9 scores and the short video addiction scale scores, we can indirectly infer the impact of short video addiction on an individual's mental health. The Spearman's correlation coefficient between the SVAS and MPAI was 0.768, indicating a high correlation, which aligns with our expectations. While mobile phone addiction and short video addiction are conceptually different, there is an overlap in the general features of addictive behaviors, and the viewing and sharing of short videos are heavily dependent on mobile phones. Thus, the strong correlation between the SVAS and MPAI not only validates the effectiveness of the SVAS but also supports the theory that short video addiction is a specific form of mobile phone addiction. The Spearman’s correlation between the SVAS and PHQ-9 was moderate (r = 0.497), suggesting that short video addiction may be associated with depressive symptoms in adolescents. Previous studies have indicated a positive correlation between short video addiction and depression, with adolescents who spend more time on short videos potentially experiencing more severe depressive symptoms. The negative emotional experiences arising from the time spent on short video viewing, such as declining academic performance, poor family relationships, insomnia, and reduced social activities, may contribute to the development of negative emotional experience(Chao et al., 2023 ). Adolescents may continue to indulge in short videos to escape from reality and alleviate their distress, ultimately forming a vicious cycle. The convergent validity analysis in this study provides additional evidence for this view, as adolescents with higher SVAS scores also tended to score higher on the PHQ-9, suggesting that they may face a dual risk of both short video addiction and depressive symptoms. Additionally, we explored the relationship between the four subscales of the MPAI—withdrawal, loss of control, inefficiency, and escapism—and the SVAS. The results showed that the SVAS had the strongest correlation with the loss of control subscale of the MPAI (r = 0.782), and the weakest correlation with the avoidance subscale (r = 0.589). This finding may reflect the differences in the manifestations of short video addiction and mobile phone addiction, suggesting that short video addiction is more closely associated with the inability to control usage time and impulsive behaviors, whereas the escapism characteristic of mobile phone addiction may be less pronounced in short video addiction. The SVAS also offers a fresh perspective on understanding the neuropsychological mechanisms underlying short video addiction. Recent research has identified various factors that contribute to social media addiction, including personality traits, psychological motivations, decision-making processes, and neurobiological foundations(Balakrishnan & Griffiths, 2017 ; Becker & Murphy, 1988 ; Casale & Fioravanti, 2018 ; Robinson & Berridge, 2008 ). For example, individuals with certain personality traits, such as narcissism, high neuroticism, or extroversion, are more prone to addictive behaviors. The SVAS can identify individuals who are more susceptible to short video addiction. Furthermore, several internet usage models have been highlighted in retrospective studies. David's Cognitive Behavioral Model (CBM) emphasizes the impact of maladaptive cognitive patterns and mental health issues, such as depression and anxiety, on pathological internet use(Davis, 2001 ). This model suggests that short video addiction is closely tied to an individual's mental health, underscoring the importance of assessing psychological health—particularly emotional regulation and self-control—when using the SVAS. The Social Skill Model (SSM), developed from CBM, emphasizes the role of social skill deficits and social anxiety in problematic internet use, especially regarding the preference for online social interaction (POSI). The SVAS can assess whether individuals with social anxiety or lower social skills are more likely to develop addictive short video usage behaviors. The Interactive Processes of Addiction Cognitive-Emotional Execution(I-PACE) model considers a broader range of factors, including personality, psychopathology, cognitive biases, emotional regulation failures, and neurobiological influences(Varona et al., 2022 ). By integrating the multidimensional factors from the I-PACE model into the SVAS, we gain deeper insights into the psychological mechanisms of short video addiction, while contributing to the theoretical advancement in internet addiction research. Strengths and Limitations The strengths of this study include: (1) Focus on adolescents: Adolescents are a critical and sensitive group regarding short video usage, as their mental health is particularly vulnerable to the impact of short video addiction. This study specifically focuses on adapting and validating a short video addiction assessment tool for adolescents, addressing a significant gap in the research field. (2) Large sample size and high response rate: The study included 3,291 adolescents, with a final valid questionnaire return rate of 89.9% (n = 2,959). The large sample size and high response rate ensure that the results possess high statistical power and reliability, making the findings not only applicable to the adolescent group involved in this study but also more likely to be generalized to other similar adolescent populations. However, the limitations of this study lie in its cross-sectional design, which prevents the evaluation of test-retest reliability. Although translation-back-translation and expert review methods were employed to ensure the cultural adaptation of the scale, cultural differences may still pose certain limitations. Conclusion The results of this study show that the adapted and back-translated SVAS exhibits strong reliability and validity in the Chinese sample. The SVAS, with its concise and user-friendly items, holds promise as a comprehensive assessment tool. It can be employed by professionals for quick evaluations in clinical settings and also serve as a self-assessment tool, helping individuals across age groups identify their risk of short video addiction. This dual functionality makes the scale valuable for the prevention, intervention, and treatment of short video addiction. Future research should involve larger, more diverse samples to gather additional practical insights into the use of the Chinese SVAS. Furthermore, since the degree of short video addiction in patients typically fluctuates over time and with different levels of intervention, future studies should include longitudinal follow-up to examine these dynamics. Declarations Ethics approval and consent to participate The study procedures were carried out in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of the College of Psychology, Northwest Normal University, Gansu Province, China (Approval Number: 20240889 ). Written informed consent was obtained from all participants. For participants under 18 years of age, parental consent was additionally sought. Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are not publicly available due to institutional restrictions on participant confidentiality but may be made available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Program for Postdoctoral Research Supported by the State of China (Grant Number: GZC20231941 ). The funding body played no role in the design of the study, data collection, analysis, interpretation, or manuscript preparation. Authors' contributions Zhihan Jiang : Conceptualization, Methodology, Formal Analysis, Writing – Original Draft. Tiejun Kang : Data Collection, Methodology, Writing – Review & Editing. Yixiao Chen : Conceptualization, Methodology, Writing – Original Draft. Weiping Chen : Data Collection, Methodology. Heng Wu : Investigation, Supervision, Writing – Review & Editing. All authors read and approved the final manuscript. Acknowledgements The authors sincerely thank the participating schools, adolescents, and their families for their cooperation. We also acknowledge the contributions of bilingual translators and pretest participants in refining the SVAS. Special thanks to Dr. Zhang for sharing preliminary scale items adapted from the BFAS. References Alhabash, S., & Ma, M. (2017). A Tale of Four Platforms: Motivations and Uses of Facebook, Twitter, Instagram, and Snapchat Among College Students? Social Media + Society , 3 , 205630511769154. https://doi.org/10.1177/2056305117691544 Andreassen, C. S., Pallesen, S., & Griffiths, M. D. (2017). The relationship between addictive use of social media, narcissism, and self-esteem: Findings from a large national survey. 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Compr Psychiatry , 118 , 152346. https://doi.org/10.1016/j.comppsych.2022.152346 Griffiths, M. (2005). A “Components” Model of Addiction within a Biopsychosocial Framework. 10 , 191-197. https://doi.org/10.1080/14659890500114359 Guangfu, L. (2022). Development and application of short video addiction scale for college students [Master Degree, Yangtze University]. https://link.cnki.net/doi/10.26981/d.cnki.gjhsc.2022.000146 Guo, J., & Chai, R. (2024). Adolescent short video addiction in China: unveiling key growth stages and driving factors behind behavioral patterns. Front Psychol , 15 , 1509636. https://doi.org/10.3389/fpsyg.2024.1509636 Hai, H., Lu-ying, N., Chun-yan, Z., & He-ming, W. (2014). Reliability and Validity of Mobile Phone Addiction Index for Chinese College Students. Chinese journal of clinical psychology , 835-838. Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal , 6 (1), 1-55. https://doi.org/10.1080/10705519909540118 Jiang, L., & Yoo, Y. (2024). Adolescents' short-form video addiction and sleep quality: the mediating role of social anxiety. BMC Psychol , 12 (1), 369. https://doi.org/10.1186/s40359-024-01865-9 Kircaburun, K., & Griffiths, M. D. (2018). Instagram addiction and the Big Five of personality: The mediating role of self-liking. J Behav Addict , 7 (1), 158-170. https://doi.org/10.1556/2006.7.2018.15 Kuss, D. J., & Griffiths, M. D. (2011). Online social networking and addiction--a review of the psychological literature. Int J Environ Res Public Health , 8 (9), 3528-3552. https://doi.org/10.3390/ijerph8093528 Le Foll, B., Piper, M. E., Fowler, C. D., Tonstad, S., Bierut, L., Lu, L., Jha, P., & Hall, W. D. (2022). Tobacco and nicotine use. Nature Reviews Disease Primers , 8 (1), 19. https://doi.org/10.1038/s41572-022-00346-w Levis, B., Benedetti, A., & Thombs, B. D. (2019). Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis. Bmj , 365 , l1476. https://doi.org/10.1136/bmj.l1476 Merenda, P. F. (2006). An overview of adapting educational and psychological assessment instruments: past and present. Psychol Rep , 99 (2), 307-314. https://doi.org/10.2466/pr0.99.2.307-314 Montag, C., Bey, K., Sha, P., Li, M., Chen, Y. F., Liu, W. Y., Zhu, Y. K., Li, C. B., Markett, S., Keiper, J., & Reuter, M. (2015). Is it meaningful to distinguish between generalized and specific Internet addiction? Evidence from a cross-cultural study from Germany, Sweden, Taiwan and China. Asia Pac Psychiatry , 7 (1), 20-26. https://doi.org/10.1111/appy.12122 Patel, J. S., Oh, Y., Rand, K. L., Wu, W., Cyders, M. A., Kroenke, K., & Stewart, J. C. (2019). Measurement invariance of the patient health questionnaire-9 (PHQ-9) depression screener in U.S. adults across sex, race/ethnicity, and education level: NHANES 2005-2016. Depress Anxiety , 36 (9), 813-823. https://doi.org/10.1002/da.22940 Pellegrino, A., Stasi, A., & Bhatiasevi, V. (2022). Research trends in social media addiction and problematic social media use: A bibliometric analysis. Front Psychiatry , 13 , 1017506. https://doi.org/10.3389/fpsyt.2022.1017506 Qi, W., & Li, D. (2020a). A User Experience Study on Short Video Social Apps Based on Content Recommendation Algorithm of Artificial Intelligence. Int. J. Pattern Recognit. Artif. Intell. , 35 , 2159008:2159001-2159008:2159013. Qi, W., & Li, D. (2020b). A User Experience Study on Short Video Social Apps Based on Content Recommendation Algorithm of Artificial Intelligence. International Journal of Pattern Recognition and Artificial Intelligence , 35 , 2159008. https://doi.org/10.1142/S0218001421590084 Robinson, T. E., & Berridge, K. C. (2008). Review. The incentive sensitization theory of addiction: some current issues. Philos Trans R Soc Lond B Biol Sci , 363 (1507), 3137-3146. https://doi.org/10.1098/rstb.2008.0093 Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review. The Journal of Educational Research , 99 (6), 323-338. https://doi.org/10.3200/JOER.99.6.323-338 Schrempft, S., Baysson, H., Chessa, A., Lorthe, E., Zaballa, M. E., Stringhini, S., Guessous, I., & Nehme, M. (2024). Associations between bedtime media use and sleep outcomes in an adult population-based cohort. Sleep Med , 121 , 226-235. https://doi.org/10.1016/j.sleep.2024.06.029 Statista. (April 5, 2023). Number of TikTok users worldwide from 2018 to 2029 (in millions) [Graph]. In Statista. https://www.statista.com/forecasts/1142687/tiktok-users-worldwide . Thorndike, R. M. (1995). Book Review : Psychometric Theory (3rd ed.) by Jum Nunnally and Ira Bernstein New York: McGraw-Hill, 1994, xxiv + 752 pp. Applied Psychological Measurement , 19 (3), 303-305. Varona, M. N., Muela, A., & Machimbarrena, J. M. (2022). Problematic use or addiction? A scoping review on conceptual and operational definitions of negative social networking sites use in adolescents. Addict Behav , 134 , 107400. https://doi.org/10.1016/j.addbeh.2022.107400 We Are Social, & DataReportal, & Hootsuite. (February 22, 2024). Daily time spent on social networking by internet users worldwide from 2012 to 2024 (in minutes) [Graph]. In Statista. Retrieved January 20, 2025, from https://www.statista.com/statistics/433871/daily-social-media-usage-worldwide/ . We Are Social, & DataReportal, & Meltwater. (April 24, 2024). Most popular social networks worldwide as of April 2024, by number of monthly active users (in millions) [Graph]. In Statista. https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/ . Yam, C. W., Pakpour, A. H., Griffiths, M. D., Yau, W. Y., Lo, C. M., Ng, J. M. T., Lin, C. Y., & Leung, H. (2019). Psychometric Testing of Three Chinese Online-Related Addictive Behavior Instruments among Hong Kong University Students. Psychiatr Q , 90 (1), 117-128. https://doi.org/10.1007/s11126-018-9610-7 Yang, Y., Liu, R.-D., Ding, Y., Lin, J., Ding, Z., & Yang, X. (2024). Time distortion for short-form video users. Computers in Human Behavior , 151 , 108009. https://doi.org/https://doi.org/10.1016/j.chb.2023.108009 Zhang, N., Hazarika, B., Chen, K., & Shi, Y. (2023). A cross-national study on the excessive use of short-video applications among college students. Computers in Human Behavior , 145 , 107752. https://doi.org/https://doi.org/10.1016/j.chb.2023.107752 Zheng., M., Yongzhi., J., Tonglin., J., & Chaoqun., W. (2022). Preliminary development of problematic short video media use scale for university students. Chinese Journal of Behavioral Medicine and Brain Science , 31 (5), 462-468. https://doi.org/10.3760/cma.j.cn371468-20220117-00024 Ziyu, B. (2024). Compilation and Preliminary Application of Short Video Addiction Scale for College Students [Master degree, Chengde Medical University]. https://link.cnki.net/doi/10.27691/d.cnki.gcdyx.2024.000009 Tables Tables 1 to 6 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.docx APPENDIXA.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Kang","email":"","orcid":"","institution":"Department of Psychosomatic Medicine, Shanghai Tongji Hospital, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tiejun","middleName":"","lastName":"Kang","suffix":""},{"id":447851832,"identity":"4817c247-af36-4ee2-8abf-2cd2a15eb810","order_by":2,"name":"Yixiao Chen","email":"","orcid":"","institution":"Department of Psychosomatic Medicine, Shanghai Tongji Hospital, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yixiao","middleName":"","lastName":"Chen","suffix":""},{"id":447851833,"identity":"30759348-bc42-4b84-bd28-f991e9b5a942","order_by":3,"name":"Weiping Chen","email":"","orcid":"","institution":"Northwest Normal University","correspondingAuthor":false,"prefix":"","firstName":"Weiping","middleName":"","lastName":"Chen","suffix":""},{"id":447851834,"identity":"8a971389-9f9a-42b1-8c2a-4f931c6487eb","order_by":4,"name":"Heng Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACAwhlA+EkMDAwNhCpJU2CZC2HJWAChLWYs/cefs3bdr6OX7r9QsEDBhvZDQeYnz3Ap8Wy51yaNW/bbQnJOWcKgA5LM95wgM3cAK/DbuSYGecCtQAZCUAthxM3HOBhk8Cr5f4bkJZzMC3/idByg8f4cW7bAaCW9ANALQeI0HImx4z5z7lkyZkzcoCBbJBsPPMwmxl+LcfPGH+cUWbHzy+R/szwR4WdbN/x5md4tQABzBk8ZgbgaGImoB6k5AOEZn/8gLDiUTAKRsEoGIkAAGG/SrfQklmWAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Psychosomatic Medicine, Shanghai Tongji Hospital, Tongji University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Heng","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-03-19 09:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6259796/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6259796/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82136610,"identity":"f53feb31-4f18-49a0-9165-c2e4b60d9a1a","added_by":"auto","created_at":"2025-05-07 06:13:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51360,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot of exploratory factor analysis for the Short Video Addiction Scale (SVAS).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6259796/v1/f69dbda18f461d690d24b25b.png"},{"id":82136614,"identity":"d9179f93-e320-4d3b-8c9c-7c7da2a9ac93","added_by":"auto","created_at":"2025-05-07 06:13:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203260,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized Single-Factor Structural Model for the Short Video Addiction Scale (SVAS) (N=2,959)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6259796/v1/ab16e9f0c4caf81df3effd43.png"},{"id":82136613,"identity":"6448cbf5-afe7-4408-b651-92b9ad1c80a4","added_by":"auto","created_at":"2025-05-07 06:13:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29575,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) Curve Demonstrating the Diagnostic Accuracy of the Short Video Addiction Scale (SVAS) Among Chinese Adolescents (AUC = 0.875)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6259796/v1/49f278c4715d3485087ceb15.png"},{"id":85423893,"identity":"9e3c3054-d529-4fa2-8a49-b976bd84081c","added_by":"auto","created_at":"2025-06-25 16:16:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":991354,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6259796/v1/f557236c-19e1-4af7-8c54-ca0a4f75a3e7.pdf"},{"id":82140243,"identity":"b40a280c-4d35-4c18-80d9-bf07cbf1a5d1","added_by":"auto","created_at":"2025-05-07 06:29:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":976526,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6259796/v1/65ec2b524f8cda95b219b42c.docx"},{"id":82136612,"identity":"836e1287-169b-4ffe-99e6-1608d0849166","added_by":"auto","created_at":"2025-05-07 06:13:58","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18076,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIXA.docx","url":"https://assets-eu.researchsquare.com/files/rs-6259796/v1/870d1fbf33d804a66df94bba.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validation of the Short Video Addiction Scale: A Psychometric Study Among Chinese Adolescents","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rapid rise of short video platforms such as TikTok, YouTube Shorts, and Instagram Reels has revolutionized the social media landscape in recent years. With their concise content, immediate appeal, highly accurate recommendation algorithms, and enhanced interactivity, these platforms have rapidly gained popularity, particularly among teenagers and young adults. Through features like \"infinite scrolling\" and real-time feedback (e.g., likes, comments, and shares), short video platforms expose users to vast amounts of content in a short period, often leading to increased usage time(Qi \u0026amp; Li, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). This high-frequency engagement mirrors broader social media usage patterns. Social media platforms generally refer to third-party internet-based platforms that focus on social interactions, user-generated content, and community-driven content sharing. These platforms, including Facebook, Instagram, and TikTok, exclude content obtained from third-party licenses(Pellegrino et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As of 2024, approximately 60% of the global population is active on social media, with Facebook, YouTube, and Instagram as the most popular(\u003cem\u003eWe Are Social, \u0026amp; DataReportal, \u0026amp; Meltwater. (April 24, 2024). Most popular social networks worldwide as of April 2024, by number of monthly active users (in millions) [Graph]. In Statista.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/\u003c/span\u003e\u003cspan address=\"https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e, while TikTok has seen the highest user growth in recent years (\u003cem\u003eStatista. (April 5, 2023). Number of TikTok users worldwide from 2018 to 2029 (in millions) [Graph]. In Statista.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.statista.com/forecasts/1142687/tiktok-users-worldwide\u003c/span\u003e\u003cspan address=\"https://www.statista.com/forecasts/1142687/tiktok-users-worldwide\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e At the individual level, users spend over 2.2 hours daily on various platforms(\u003cem\u003eWe Are Social, \u0026amp; DataReportal, \u0026amp; Hootsuite. (February 22, 2024). Daily time spent on social networking by internet users worldwide from 2012 to 2024 (in minutes) [Graph]. In Statista. Retrieved January 20, 2025, from\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.statista.com/statistics/433871/daily-social-media-usage-worldwide/\u003c/span\u003e\u003cspan address=\"https://www.statista.com/statistics/433871/daily-social-media-usage-worldwide/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e While social media offers convenient virtual connections and real-time information, this high engagement often leads to addictive behaviors.\u003c/p\u003e \u003cp\u003eHowever, the term \"addiction\" requires careful consideration, as it introduces certain challenges in research. First, social media addiction (SMA) is increasingly common but remains unrecognized in the ICD-11 and DSM-V diagnostic systems. More research is necessary to clarify the pathological mechanisms and diagnostic criteria. Second, SMA is often viewed as a subset of Internet Addiction (IA), but the boundary between the two remains unclear(Kuss \u0026amp; Griffiths, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). IA is a broader concept encompassing compulsive online activities such as gaming, shopping, and social interaction(Kuss \u0026amp; Griffiths, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). European experts have proposed replacing \"Internet addiction\" with \"Problematic Internet Use\" (PUI), which avoids overpathologizing high-frequency internet use and offers a more flexible framework for research(Fineberg et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, SMA specifically emphasizes dependency on virtual social interactions and real-time feedback, especially on short video platforms. These platforms' rapid content cycles and real-time engagement can foster compulsive use, making SMA distinct from general IA or PUI. Therefore, categorizing SMA simply as part of IA or PUI may obscure its unique psychological mechanisms(Montag et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, SMA is not a monolithic concept but encompasses various behaviors across platforms like Facebook, TikTok, Instagram, and Twitter. While all fall under the social media umbrella, each platform has unique features, including content types, user interaction patterns, and recommendation algorithms. Thus, SMA research must account for platform diversity, as addiction behaviors cannot be generalized across all platforms. For instance, Facebook addiction is more closely tied to social needs and identity maintenance (Alhabash \u0026amp; Ma, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), whereas Instagram and Twitter focus on continuous consumption of images, short texts, or information, along with interactive feedback (Alhabash \u0026amp; Ma, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kircaburun \u0026amp; Griffiths, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eShort video platforms, as emerging social media, present heightened addiction risks. Unlike traditional platforms such as Facebook and Twitter, these platforms share common addictive behaviors, including six core features: significance, emotional regulation, tolerance, withdrawal, conflict, and relapse(Le Foll et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, they also have unique characteristics. The brief content and highly personalized recommendation algorithms of short video platforms make it easier for users to lose control over their time usage(Yang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The dependence on instant gratification, combined with the immersive visual and auditory stimulation, significantly increases the likelihood of addiction, particularly among adolescent users. These users often experience withdrawal symptoms, such as anxiety, low mood, and strong urges to continue using the platform, when they attempt to stop(Guo \u0026amp; Chai, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, while short video platforms amplify the risk of social media addiction, they also demand more targeted and nuanced research into behavioral addiction. However, research in this field faces a significant bottleneck: the absence of targeted, cross-culturally applicable assessment tools. This limitation complicates the standardization of research and hinders cross-field and cross-cultural comparisons of short video addiction.\u003c/p\u003e \u003cp\u003eResearch on short video addiction assessment tools can benefit from established methodologies in the Facebook and social media domains. In 2012, Andreassen et al. developed the Bergen Facebook Addiction Scale (BFAS), a pioneering instrument for evaluating Facebook addiction(Andreassen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Initially comprising 18 items\u0026mdash;three for each of the six core addiction components\u0026mdash;the scale was refined by retaining the most highly correlated item from each component, resulting in a concise six-item measure(Andreassen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This streamlined design reduced respondent burden and enhanced the scale's usability and cross-cultural applicability. Structural validation through factor analysis confirmed a robust single-factor model (RMSEA\u0026thinsp;=\u0026thinsp;0.05, CFI\u0026thinsp;=\u0026thinsp;0.99), with high internal consistency (Cronbach's α\u0026thinsp;=\u0026thinsp;0.83) and a 3-week test-retest reliability coefficient of 0.82, indicating stability across different times and groups(Andreassen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). As social media platforms diversified, the Bergen Social Media Addiction Scale (BSMAS) was developed by generalizing \"Facebook\" to \"social media,\" thereby encompassing platforms like Instagram, YouTube, and Twitter(Andreassen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Currently, BSMAS is the most widely used tool for assessing social media addiction(Varona et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It has been translated and psychometrically validated across various countries and populations, demonstrating good reliability and validity(Brailovskaia \u0026amp; Margraf, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Varona et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yam et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, some researchers caution that BSMAS, based on BFAS, may focus on specific platform characteristics, and results obtained using BSMAS should be interpreted with care(Brailovskaia \u0026amp; Margraf, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Overall, the successful development of BFAS and BSMAS provides valuable insights for creating tools to assess addiction behaviors on other specific platforms, such as short video platforms.\u003c/p\u003e \u003cp\u003eIn China, there is a scarcity of tools for assessing short video addiction, and the lack of localized scales may hinder research in this area. Existing scales, such as the University Student Short Video Addiction Scale developed by Luo Guangfu (Guangfu, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), include 21 items across five dimensions\u0026mdash;excessiveness, daily life interference, emotional experience, interactivity, and audiovisual stimulation\u0026mdash;but their reliability and validity have not been fully verified. Similarly, Bai Ziyu's (Ziyu, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) University Student Short Video Addiction Scale, comprising 56 items, has undergone reliability and validity testing but only for a specific group of college students. Additionally, these scales' length may increase respondent burden, leading to lower completion rates and affecting data quality, thus limiting their application in broader populations. To address these issues, the researchers developed the Short Video Addiction Scale (SVAS)(Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), drawing inspiration from the concise and efficient design of the BFAS. Short video addiction primarily involves compulsive behavior and immersion in content consumption rather than social interaction(Chao et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Users often become dependent on short, high-frequency content and experience time loss due to algorithmic recommendations(Qi \u0026amp; Li, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, the SVAS items focus more on the audiovisual stimulation of content and the immersive experience of users (e.g., \" When I use short-video apps, I often forget the time.\" and \" When using short video apps, I am almost entirely devoted to it.\"), which is not emphasized in BFAS. Secondly, the engaging nature of short videos can lead users to extend their usage time, even affecting sleep patterns(Jiang \u0026amp; Yoo, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Schrempft et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This aligns with findings that excessive Facebook usage may impact sleep time(Andreassen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, the SVAS includes items assessing the impact on sleep (e.g., \" Playing short video apps affects my sleep schedule.\") to capture sleep disturbances caused by short video addiction.\u003c/p\u003e \u003cp\u003eIn conclusion, this study aims to evaluate the reliability and validity of the SVAS, adapted from BFAS, among Chinese adolescents, providing a reliable and rapid early screening tool for short video addiction for clinicians and researchers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSampling strategy and subjects\u003c/h2\u003e \u003cp\u003eThis study involved 3,291 adolescents from grades 7, 8, 10, and 11 across eight middle schools in northwest China. Data were collected via questionnaires, with two trained researchers present to provide necessary explanations and guidance to participants, ensuring accurate and reliable responses. To maintain confidentiality, each participant completed the questionnaire independently. After excluding 332 incomplete questionnaires and those with contradictory responses, 2,959 valid responses were obtained.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAdaptation and translation of the Short Video Addiction Scale (SVAS)\u003c/h2\u003e \u003cp\u003eTo ensure the SVAS accurately reflects the unique usage patterns of short video platforms\u0026mdash;such as content consumption behavior, audiovisual immersion, and sleep disruption\u0026mdash;we adapted the six items from Zhang et al.(Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) that were revised based on the BFAS to assess excessive short video usage, and made corresponding adjustments. A bilingual translator, well-versed in psychological terminology and familiar with short video culture, translated the items into Chinese. In this process, items were adjusted to better align with short video usage behaviors. According to the International Test Commission (ITC)(Merenda, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) \"ITC-Test Adaptation Guidelines\" (2000), the independent translation process was initially completed by three native Chinese speakers who are fluent in both written and spoken English (a psychiatrist, a psychologist, and an educator). One translator participated regularly in project meetings to discuss and reach a consensus on the preliminary version. Subsequently, back-translation into English was performed. The translated and back-translated scale was then reviewed by experts in psychology and media research to ensure its suitability for Chinese adolescents. Additionally, a pretest was conducted with a small group of adolescents to assess the scale's comprehensibility and usability, leading to necessary adjustments based on their feedback. This comprehensive process ensured that the SVAS was linguistically and culturally appropriate for Chinese adolescents, while maintaining the theoretical framework and evaluation methods consistent with the BFAS. The final modified items are: (1) \" When I use short-video apps, I often forget the time. \" (2) \" I use short video apps every day when I have free time.\" (3) \" When using short video apps, I feel that my body and mind can completely relax. \" (4) \" Playing short video apps affects my sleep schedule.\" (5) \" I try to reduce my time spent on short video apps, but I haven't succeeded\" and (6) \" I think about how I could free more time to spend on short video apps.\". All Items are scored on the following scale: 0:Never, 1: Rarely, 2: Sometimes, 3:Often, 4:Always.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMobile Phone Addiction Index (MPAI)\u003c/h3\u003e\n\u003cp\u003eThe MPAI is primarily used to diagnose mobile phone addiction among adolescents and college students. The scale uses a 5-point scoring system, where 1 means \"never,\" and 5 means \"always,\" consisting of 17 items across four dimensions: withdrawal, loss of control, inefficiency, and escapism. Withdrawal refers to the emotional responses individuals experience when they cannot use their mobile phones normally. Loss of control refers to individuals being unable to control the amount of time they spend on their phones. Inefficiency refers to reduced academic or work efficiency due to excessive mobile phone use. Escapism refers to using mobile phones to escape from the real world, with users immersing themselves in the mobile network world. Higher scores indicate a higher degree of mobile phone addiction, with total scores ranging from 43\u0026ndash;51 indicating mild addiction, 52\u0026ndash;68 indicating moderate addiction, and 69 or above indicating severe addiction. The Chinese version of the MPAI has shown satisfactory reliability and validity among students. (Hai et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e\n\u003ch3\u003ePatient Health Questionnaire (PHQ-9)\u003c/h3\u003e\n\u003cp\u003eThe PHQ-9 is a widely used tool for assessing depressive symptoms. The PHQ-9 scale contains 9 items, each representing an aspect of depressive symptoms. Scoring is done using a 4-point system: 0 means \"not at all (less than 1 day),\" 1 means \"several days (1\u0026ndash;2 days),\" 2 means \"more than half the days (3\u0026ndash;4 days),\" and 3 means \"nearly every day.\" The higher the score, the more severe the depressive symptoms. The PHQ-9 scale has good reliability and validity and performs well in diagnosing depression and assessing symptom severity across various populations with different genders, ages, occupations, and education levels(Patel et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Studies have shown that when the total PHQ-9 score is \u0026ge;\u0026thinsp;10, it has the highest combined sensitivity (88%) and specificity (85%)(Levis et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eData analysis was conducted using SPSS 27.0 and AMOS 26.0. Descriptive statistics summarized demographic characteristics, with t-tests applied to binary variables (e.g., gender, grade) and F-tests for multiple categorical variables. The internal consistency of the Short Video Addiction Scale (SVAS) was assessed using Cronbach's α coefficient and split-half reliability. Factor analysis suitability was evaluated with the Kaiser-Meyer-Olkin (KMO) measure and Bartlett's test of sphericity. Exploratory factor analysis (EFA) examined the scale's structural validity, employing Kaiser\u0026rsquo;s criterion to extract common factors and calculate variance, along with factor loadings to assess item-factor associations. Confirmatory factor analysis (CFA) tested the hypothesized factor structure, with model fit evaluated using the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Normed Fit Index (NFI), Incremental Fit Index (IFI), and Root Mean Square Error of Approximation (RMSEA). CFI, NFI, IFI, and TLI values\u0026thinsp;\u0026ge;\u0026thinsp;0.90 indicate good model fit(Hu \u0026amp; Bentler, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), and RMSEA values\u0026thinsp;\u0026le;\u0026thinsp;0.05 indicate good model fit(Schreiber et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Due to non-normally distributed scale data, Spearman's correlation coefficient analyzed convergent validity with the Mobile Phone Addiction Index (MPAI) and Patient Health Questionnaire-9 (PHQ-9). Spearman's correlation coefficients between the SVAS total score and the other two scales, as well as scores of each symptom group, were calculated to examine convergent validity between short video addiction, mobile phone addiction, and depressive symptoms at different levels. Participants were categorized into addiction and non-addiction groups based on the MPAI (cutoff value of 43), and the diagnostic accuracy of the SVAS was evaluated using the area under the ROC curve (AUC) to assess the scale's ability to distinguish between addiction and non-addiction.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics\u003c/h3\u003e\n\u003cp\u003e The study procedures were carried out in accordance with the Declaration of Helsinki. The study received approval from the Ethics Committee of the College of Psychology, Northwest Normal University, Gansu Province, China (approval number: 20240889). All subjects were informed about the study and all provided informed consent. (Parental consent was sought for those younger than 18 years of age.) All data were kept confidential.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eDescriptive statistics\u003c/h2\u003e\n \u003cp\u003eThe study sample comprised 2,959 participants, yielding a response rate of 89.9%. The gender distribution was balanced, with approximately equal numbers of male and female participants. Participants\u0026apos; ages ranged from 13 to 22 years, with a mean age of 16.4 years (SD\u0026thinsp;=\u0026thinsp;1.60). The average scores were 8.9 (SD\u0026thinsp;=\u0026thinsp;6.0) on the SVAS, 36.7 (SD\u0026thinsp;=\u0026thinsp;15.8) on the MPAI, and 5.6 (SD\u0026thinsp;=\u0026thinsp;6.1) on the PHQ-9. Females had higher average scores on the SVAS, MPAI, and PHQ-9 compared to males. Further details are provided in the figures. (Table. 1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eItem Analysis\u003c/h2\u003e\n \u003cp\u003eThe discriminant validity of the SVAS was assessed by comparing item scores between low- and high-addiction groups (Thorndike, \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e). Participants were categorized into subgroups based on total SVAS scores (low group: n\u0026thinsp;=\u0026thinsp;744; high group: n\u0026thinsp;=\u0026thinsp;624). Independent samples t-tests revealed statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) across all items (Table. 2).\u003c/p\u003e\n \u003cp\u003eAll items correlated significantly with the total score (r\u0026thinsp;=\u0026thinsp;0.680\u0026ndash;0.790, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with SVAS2 exhibiting the highest correlation (r\u0026thinsp;=\u0026thinsp;0.790), followed by SVAS3 (r\u0026thinsp;=\u0026thinsp;0.728). The overall Cronbach\u0026rsquo;s \u0026alpha; for the scale was 0.884, indicating excellent reliability. Removal of any single item resulted in minimal changes to \u0026alpha; values (range: 0.849\u0026ndash;0.883), suggesting that all items contribute meaningfully to the scale\u0026rsquo;s consistency (Table. 3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eReliability and validity analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003eInternal consistency reliability\u003c/h2\u003e\n \u003cp\u003eIn this study, we evaluated the internal consistency of the SVAS using Cronbach\u0026apos;s \u0026alpha; and split-half reliability. The overall Cronbach\u0026apos;s \u0026alpha; was 0.884, and the split-half reliability was 0.770, indicating satisfactory reliability. The \u0026alpha; values for the MPAI and the PHQ-9 were 0.849 and 0.933, respectively, also reflecting acceptable reliability levels.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eExploratory factor analysis (EFA)\u003c/h2\u003e\n \u003cp\u003eIn the EFA, the KMO value for the SVAS in this study was 0.858, and Bartlett\u0026apos;s test of sphericity was significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that exploratory factor analysis was feasible for the sample. The Scree Test showed that the curve flattened significantly after two inflection points (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Given that the SVAS scale is designed based on the single-dimensional theory of BFAS, and according to Kaiser\u0026rsquo;s criterion, we did not consider factors with eigenvalues less than 1. Therefore, the focus was on extracting a single factor. The EFA results showed that one factor with an eigenvalue greater than 1 was extracted, which accounted for 63.9% of the variance. The factor loadings ranged from 0.627 to 0.899 (Table. 4), indicating that the SVAS has good structural validity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eConfirmatory factor analysis (CFA)\u003c/h2\u003e\n \u003cp\u003eBased on the EFA results, the CFA was conducted, which showed that in the single-dimensional factor structure, all standardized loadings for the six indicators (\u0026chi;\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.871, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were above 0.50 (range\u0026thinsp;=\u0026thinsp;0.63 to 0.90; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The model fit was satisfactory (RMSEA\u0026thinsp;=\u0026thinsp;0.031, CFI\u0026thinsp;=\u0026thinsp;0.999, GFI\u0026thinsp;=\u0026thinsp;0.998, TLI\u0026thinsp;=\u0026thinsp;0.996, RMR\u0026thinsp;=\u0026thinsp;0.014). (Table. 5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eDiscriminant validity\u003c/h2\u003e\n \u003cp\u003eThe Spearman\u0026rsquo;s correlation coefficients between the SVAS and the MPAI and PHQ-9 were 0.768 and 0.497, respectively. The SVAS score showed a positive correlation with both the MPAI and PHQ-9 scores. We also explored the relationships between the four subscales of the MPAI\u0026mdash;withdrawal, loss of control, inefficiency, and escapism\u0026mdash;and the SVAS. The results showed that the SVAS had the strongest correlation with the loss of control subscale of the MPAI, with a Spearman\u0026rsquo;s correlation coefficient of 0.782; the weakest correlation was with the avoidance subscale, with a Spearman\u0026rsquo;s correlation coefficient of 0.589 (Table. 6).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eROC Analysis\u003c/h2\u003e\n \u003cp\u003eThe AUC for the SVAS was 0.875. The optimal cutoff value was 9 points, with sensitivity and specificity values provided. This result indicates that the SVAS provides a significant level of discrimination (95% CI: 0.863\u0026ndash;0.888; Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to evaluate the psychometric properties of the Chinese version of the SVAS, adapted from the BFAS. The results demonstrated that the Chinese SVAS exhibits strong reliability, validity, and discriminant ability. Factor analysis confirmed that the scale adheres to a unidimensional structure, aligning with the original BFAS model.\u003c/p\u003e \u003cp\u003eIn our study, the unidimensional factor structure was supported by CFA, showing adequate fit indices for the single-factor model within our sample (RMSEA\u0026thinsp;=\u0026thinsp;0.031, CFI\u0026thinsp;=\u0026thinsp;0.999, TLI\u0026thinsp;=\u0026thinsp;0.996). This result aligns with the original author's unidimensional structure for the scale(Andreassen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The applicability of this unidimensional structure may stem from the high cohesiveness of short video addiction behavior, where addicts exhibit a highly consistent compulsive usage pattern at both the psychological and behavioral levels(Griffiths, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Such uniform behavior patterns likely facilitate the single-factor model in capturing the core characteristics of this addiction.\u003c/p\u003e \u003cp\u003eFurthermore, we also noted the stability and strength of the factor loadings. The factor loadings for the SVAS ranged from 0.627 to 0.899, significantly higher than the recommended minimum threshold, indicating that the scale items effectively reflect the underlying factor.\u003c/p\u003e \u003cp\u003eThe high Cronbach\u0026rsquo;s α coefficient indicates that both the Chinese version of SVAS and its individual items exhibit good reliability. This is consistent with the values reported in previous similar studies (ranging from 0.876 to 0.960)(Zheng. et al., 2022). Moreover, the high split-half reliability further strengthens the reliability of the scale.\u003c/p\u003e \u003cp\u003eFor further validation, we used the MPAI and the PHQ-9 as reference tools. Although the specific content measured by these two scales differs from that of the SVAS, they provide valuable information regarding mobile phone addiction and mental health status, which indirectly reflect the potential status of short video addiction. Specifically, the MPAI, by assessing an individual's dependence on mobile phones, can indirectly reflect dependence on short video applications on mobile phones. A higher score on the MPAI suggests a stronger dependence on mobile phones, which may increase the risk of short video addiction. The PHQ-9, mainly used to assess depressive symptoms, is relevant as depressive states may be associated with excessive use of short videos as a form of escapism. By analyzing the relationship between the PHQ-9 scores and the short video addiction scale scores, we can indirectly infer the impact of short video addiction on an individual's mental health.\u003c/p\u003e \u003cp\u003eThe Spearman's correlation coefficient between the SVAS and MPAI was 0.768, indicating a high correlation, which aligns with our expectations. While mobile phone addiction and short video addiction are conceptually different, there is an overlap in the general features of addictive behaviors, and the viewing and sharing of short videos are heavily dependent on mobile phones. Thus, the strong correlation between the SVAS and MPAI not only validates the effectiveness of the SVAS but also supports the theory that short video addiction is a specific form of mobile phone addiction. The Spearman\u0026rsquo;s correlation between the SVAS and PHQ-9 was moderate (r\u0026thinsp;=\u0026thinsp;0.497), suggesting that short video addiction may be associated with depressive symptoms in adolescents. Previous studies have indicated a positive correlation between short video addiction and depression, with adolescents who spend more time on short videos potentially experiencing more severe depressive symptoms. The negative emotional experiences arising from the time spent on short video viewing, such as declining academic performance, poor family relationships, insomnia, and reduced social activities, may contribute to the development of negative emotional experience(Chao et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Adolescents may continue to indulge in short videos to escape from reality and alleviate their distress, ultimately forming a vicious cycle. The convergent validity analysis in this study provides additional evidence for this view, as adolescents with higher SVAS scores also tended to score higher on the PHQ-9, suggesting that they may face a dual risk of both short video addiction and depressive symptoms.\u003c/p\u003e \u003cp\u003eAdditionally, we explored the relationship between the four subscales of the MPAI\u0026mdash;withdrawal, loss of control, inefficiency, and escapism\u0026mdash;and the SVAS. The results showed that the SVAS had the strongest correlation with the loss of control subscale of the MPAI (r\u0026thinsp;=\u0026thinsp;0.782), and the weakest correlation with the avoidance subscale (r\u0026thinsp;=\u0026thinsp;0.589). This finding may reflect the differences in the manifestations of short video addiction and mobile phone addiction, suggesting that short video addiction is more closely associated with the inability to control usage time and impulsive behaviors, whereas the escapism characteristic of mobile phone addiction may be less pronounced in short video addiction.\u003c/p\u003e \u003cp\u003eThe SVAS also offers a fresh perspective on understanding the neuropsychological mechanisms underlying short video addiction. Recent research has identified various factors that contribute to social media addiction, including personality traits, psychological motivations, decision-making processes, and neurobiological foundations(Balakrishnan \u0026amp; Griffiths, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Becker \u0026amp; Murphy, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Casale \u0026amp; Fioravanti, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Robinson \u0026amp; Berridge, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). For example, individuals with certain personality traits, such as narcissism, high neuroticism, or extroversion, are more prone to addictive behaviors. The SVAS can identify individuals who are more susceptible to short video addiction.\u003c/p\u003e \u003cp\u003eFurthermore, several internet usage models have been highlighted in retrospective studies. David's Cognitive Behavioral Model (CBM) emphasizes the impact of maladaptive cognitive patterns and mental health issues, such as depression and anxiety, on pathological internet use(Davis, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This model suggests that short video addiction is closely tied to an individual's mental health, underscoring the importance of assessing psychological health\u0026mdash;particularly emotional regulation and self-control\u0026mdash;when using the SVAS. The Social Skill Model (SSM), developed from CBM, emphasizes the role of social skill deficits and social anxiety in problematic internet use, especially regarding the preference for online social interaction (POSI). The SVAS can assess whether individuals with social anxiety or lower social skills are more likely to develop addictive short video usage behaviors. The Interactive Processes of Addiction Cognitive-Emotional Execution(I-PACE) model considers a broader range of factors, including personality, psychopathology, cognitive biases, emotional regulation failures, and neurobiological influences(Varona et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By integrating the multidimensional factors from the I-PACE model into the SVAS, we gain deeper insights into the psychological mechanisms of short video addiction, while contributing to the theoretical advancement in internet addiction research.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThe strengths of this study include: (1) Focus on adolescents: Adolescents are a critical and sensitive group regarding short video usage, as their mental health is particularly vulnerable to the impact of short video addiction. This study specifically focuses on adapting and validating a short video addiction assessment tool for adolescents, addressing a significant gap in the research field. (2) Large sample size and high response rate: The study included 3,291 adolescents, with a final valid questionnaire return rate of 89.9% (n\u0026thinsp;=\u0026thinsp;2,959). The large sample size and high response rate ensure that the results possess high statistical power and reliability, making the findings not only applicable to the adolescent group involved in this study but also more likely to be generalized to other similar adolescent populations.\u003c/p\u003e \u003cp\u003eHowever, the limitations of this study lie in its cross-sectional design, which prevents the evaluation of test-retest reliability. Although translation-back-translation and expert review methods were employed to ensure the cultural adaptation of the scale, cultural differences may still pose certain limitations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of this study show that the adapted and back-translated SVAS exhibits strong reliability and validity in the Chinese sample. The SVAS, with its concise and user-friendly items, holds promise as a comprehensive assessment tool. It can be employed by professionals for quick evaluations in clinical settings and also serve as a self-assessment tool, helping individuals across age groups identify their risk of short video addiction. This dual functionality makes the scale valuable for the prevention, intervention, and treatment of short video addiction. Future research should involve larger, more diverse samples to gather additional practical insights into the use of the Chinese SVAS. Furthermore, since the degree of short video addiction in patients typically fluctuates over time and with different levels of intervention, future studies should include longitudinal follow-up to examine these dynamics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study procedures were carried out in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of the College of Psychology, Northwest Normal University, Gansu Province, China (Approval Number: \u003cstrong\u003e20240889\u003c/strong\u003e). Written informed consent was obtained from all participants. For participants under 18 years of age, parental consent was additionally sought.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to institutional restrictions on participant confidentiality but may be made available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the \u003cstrong\u003eProgram for Postdoctoral Research Supported by the State of China\u003c/strong\u003e (Grant Number: \u003cstrong\u003eGZC20231941\u003c/strong\u003e). The funding body played no role in the design of the study, data collection, analysis, interpretation, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZhihan Jiang\u003c/strong\u003e: Conceptualization, Methodology, Formal Analysis, Writing \u0026ndash; Original Draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTiejun Kang\u003c/strong\u003e: Data Collection, Methodology, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYixiao Chen\u003c/strong\u003e: Conceptualization, Methodology, Writing \u0026ndash; Original Draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeiping Chen\u003c/strong\u003e: Data Collection, Methodology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeng Wu\u003c/strong\u003e: Investigation, Supervision, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank the participating schools, adolescents, and their families for their cooperation. We also acknowledge the contributions of bilingual translators and pretest participants in refining the SVAS. Special thanks to Dr. Zhang for sharing preliminary scale items adapted from the BFAS.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlhabash, S., \u0026amp; Ma, M. (2017). A Tale of Four Platforms: Motivations and Uses of Facebook, Twitter, Instagram, and Snapchat Among College Students? \u003cem\u003eSocial Media + Society\u003c/em\u003e,\u003cem\u003e 3\u003c/em\u003e, 205630511769154. https://doi.org/10.1177/2056305117691544\u003c/li\u003e\n\u003cli\u003eAndreassen, C. S., Pallesen, S., \u0026amp; Griffiths, M. D. (2017). The relationship between addictive use of social media, narcissism, and self-esteem: Findings from a large national survey. \u003cem\u003eAddict Behav\u003c/em\u003e,\u003cem\u003e 64\u003c/em\u003e, 287-293. https://doi.org/10.1016/j.addbeh.2016.03.006\u003c/li\u003e\n\u003cli\u003eAndreassen, C. 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(2024). \u003cem\u003eCompilation and Preliminary Application of Short Video \u003c/em\u003e\u003cem\u003eAddiction Scale for College Students\u003c/em\u003e [Master degree, Chengde Medical University]. https://link.cnki.net/doi/10.27691/d.cnki.gcdyx.2024.000009\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 6 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Short Video Addiction, Reliability, Validity, Short Video Addiction Scale, Chinese Adolescents","lastPublishedDoi":"10.21203/rs.3.rs-6259796/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6259796/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rise of short video platforms such as TikTok and Instagram Reels has led to increasing concerns about addiction, particularly among adolescents. This study introduces the Short Video Addiction Scale (SVAS), a concise, reliable tool designed to assess addictive behaviors specific to short video use. The SVAS, adapted from the Bergen Facebook Addiction Scale (BFAS), was validated with a sample of 2,959 Chinese adolescents. Results show strong internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.884) and good structural validity, with a single-factor model explaining 63.9% of the variance. The scale correlates highly with the Mobile Phone Addiction Index (r\u0026thinsp;=\u0026thinsp;0.768) and depressive symptoms (r\u0026thinsp;=\u0026thinsp;0.497). The SVAS demonstrates excellent diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.875), making it a promising tool for early identification and intervention of short video addiction.\u003c/p\u003e","manuscriptTitle":"Validation of the Short Video Addiction Scale: A Psychometric Study Among Chinese Adolescents","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 06:13:54","doi":"10.21203/rs.3.rs-6259796/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8291059a-0841-4b41-b0a0-a6e341943eeb","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-25T16:08:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 06:13:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6259796","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6259796","identity":"rs-6259796","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}