AI Technology panic-is AI Dependence Bad for Mental Health? A Cross-Lagged Panel Model and the Mediating Roles of Motivations for AI Use Among Adolescents.

Background: The emergence of new technologies, such as artificial intelligence (AI), may manifest as technology panic in some people, including adolescents who may be particularly vulnerable to new technologies (the use of AI can lead to AI dependence, which can threaten mental health). While the relationship between AI dependence and mental health is a growing topic, the few existing studies are mainly cross-sectional and use qualitative approaches, failing to find a longitudinal relationship between them. Based on the framework of technology dependence, this study aimed to determine the prevalence of experiencing AI dependence, to examine the cross-lagged effects between mental health problems (anxiety/depression) and AI dependence and to explore the mediating role of AI use motivations. Methods: A two-wave cohort program with 3843 adolescents (Male = 1848, Mage = 13.21 ± 2.55) was used with a cross-lagged panel model and a half-longitudinal mediation model. Results: 17.14% of the adolescents experienced AI dependence at T1, and 24.19% experienced dependence at T2. Only mental health problems positively predicted subsequent AI dependence, not vice versa. For AI use motivation, escape motivation and social motivation mediated the relationship between mental health problems and AI dependence whereas entertainment motivation and instrumental motivation did not. Discussion: Excessive panic about AI dependence is currently unnecessary, and AI has promising applications in alleviating emotional problems in adolescents. Innovation in AI is rapid, and more research is needed to confirm and evaluate the impact of AI use on adolescents' mental health and the implications and future directions are discussed.

Beyond Screen Time: The Different Longitudinal Relations between Adolescents' Smartphone Use Content and Their Mental Health.

Purpose: Previous studies focusing on the relationship between adolescents' screen time and mental health have uncovered contradictory results. By focusing on smartphone use content (SUC), this study uses specification curve analysis to explore the different effects of SUCs on mental health-based on longitudinal data. Methods: A total of 2552 adolescents were surveyed in the first (July 2020) and second year (April 2021). A total of 2049 eligible participants (average age = 14.39 ± 2.27, female = 1062) are included in the analysis. Participants reported 20 types of content used by them during smartphone use and their mental health (depression, anxiety, and somatization). Specification curve analysis was used to examine the longitudinal relationship between SUCs and their mental health. Results: Smartphone use for listening to music (median ß = 0.18, p < 0.001, NSRPD = 25/27, p < 0.05), chatting online (median ß = 0.15, p < 0.001, NSRPD = 24/27, p < 0.05), watching TV (median ß = 0.14, p < 0.001, NSRPD = 24/27, p < 0.05), and playing games (median ß = 0.09, p < 0.001, NSRPD = 19/27, p < 0.05) produce high to medium negative effects on subsequent mental health. Only using smartphones for online courses exerts no effect on their subsequent mental health (median ß = 0.01, p > 0.05, NSRPD = 0/27, p > 0.05). The left 15 types of smartphone content showed unstable effects on future mental health. Depending on the types of content used, these effects ranged from high, medium, and small to none. The relatively descending order of effect on mental health is listening to music, chatting online, watching TV, playing games, and types of content (e.g., browsing social media, making payments, reading online novels) with high but unstable effects, types of content with medium (e.g., browsing news and posting/sharing) but unstable effects, types of content (e.g., using the camera, obtaining life information, and making calls) with small but unstable effects, such as finishing homework and taking online courses. Conclusions: This study enlightens researchers and policymakers to update their understanding of adolescents' technology use, especially to adopt a differentiated attitude towards different media use content. As nutritionists often do, a "nutritionally balanced" digital diet for young people should be recommended to the public, rather than just suggesting limits on the amount of time they can spend using digital media.

Does parental media mediation make a difference for adolescents? Evidence from an empirical cohort study of parent-adolescent dyads.

Background and aims: Adolescents, who are undergoing brain changes, are vulnerable to many online risks in their use or overuse of digital technology. Parental media mediation (a set of practices parents use to guide children's media use and to reduce potential negative consequences of children from media) is considered an important way to help regulate and reduce adolescents' use or problematic use of digital media and protect them from online risks. However, previous studies have shown controversial results. These controversial results reflect a reproducibility crisis in psychological science due to selective reporting, selective analysis, and inadequate description of the conditions necessary to obtain results. Methods: To address this issue and reveal the authentic effect of parental media mediation strategies, this study presented the results of a specification curve analysis of 1176 combinations indicating the longitudinal effect of parental media mediation on adolescent smartphone use or problematic use. A total of 2154 parent-adolescent dyads (adolescents' ages ranged from 9 to 18, the average age was 12.13 ± 2.20, and 817 of the adolescents were male) participated in two waves of measurements. Results: The results showed that of the 12 parental media mediations, joint parental use for learning had the greatest effect in reducing future smartphone use or problematic use among adolescents. Overall, none of the parental media mediations had a substantial effect in reducing future smartphone use or problematic use among adolescents. Discussion and conclusions: The ineffectiveness of parental media mediation poses a challenge for researchers, the public, and policy-makers. More exploration is needed in the search of effective parental media mediations for adolescents.

New trends in developmental coordination disorder: Multivariate, multidimensional and multimodal.

Developmental coordination disorder (DCD) is a motor development disorder that affects an individual's growth and development, and may persist throughout life. It is not caused by intellectual or physical disability. Studies have suggested DCD often occurs in childhood, resulting in a series of abnormal manifestations that hinder children's normal development; cohort studies suggest a higher incidence in boys than in girls. Early diagnosis and appropriate interventions can help relieve symptoms. Unfortunately, the relevant research still needs to be further developed. In this paper, we first start from the definition of DCD, systematically investigate the relevant research papers in the past decades and summarize the current research hotspots and research trends in this field. After summarizing, it is found that this research field has attracted more researchers to join, the number of papers published has increased year by year and has become a hot spot in multidisciplinary research, such as education, psychology, sports rehabilitation, neurobiology, and neuroimaging. The continuous development of the correlation between perinatal factors and DCD, various omics studies, and neuroimaging methods also brings new perspectives and working targets to DCD research. DCD-related research will continue to deepen along the research direction of multivariate, multidimensional, and multimodal.

Do the core symptoms play key roles in the development of problematic smartphone use symptoms.

Aims: Previous research determined the core symptoms (loss of control and being caught in the loop) of problematic smartphone use (PSU), which are of great importance to understand the structure and potential intervention targets of PSU. However, the cross-sectional design fails to reveal causality between symptoms and usually conflates the between- and within-subjects effects of PSU symptoms. This study aims to determine whether the core symptoms of PSU, indeed, dominate the future development of PSU symptoms from longitudinal between- and within-subjects levels. Materials and methods: In this study, 2191 adolescents were surveyed for 3 years for PSU symptoms. A cross-lagged panel model (CLPM) was used to explore longitudinal between-subjects causal relationships between symptoms, and a graphic vector autoregressive model (GVAR) was used to separate the between- and within-subjects effects and detect the longitudinal effect at the within-subject level. Results: The results of CLPM indicated that the core symptoms (both loss of control and being caught in the loop) of PSU, indeed, dominate the future development of PSU symptoms at a longitudinal between-subjects level. From T1 to T2, the cross-lagged model showed that both the loss of control (out-prediction = 0.042) and being caught in the loop (out-prediction = 0.053) at T1 have the highest out-prediction over other symptoms at T2. From T2 to T3, the loss of control (out-prediction = 0.027) and being caught in the loop (out-prediction = 0.037) at T2 also have the highest out-prediction over other symptoms of PSU at T3. While, after separating the between- and within-subjects effects, only being caught in the loop at T1 played a key role in promoting the development of other PSU symptoms at T3 at the within-subjects level. The contemporaneous network showed intensive connection, while the cross-sectional between-subjects network is very sparse. Conclusion: These findings not only confirm and extend the key roles of core symptoms in the dynamic aspect of PSU symptoms and PSU itself but also suggest that interventions should consider the core symptoms of PSU, individual- and group-level effects and that individualized intervention programs are needed in future.

Beyond Screen Time: Exploring the Associations between Types of Smartphone Use Content and Adolescents' Social Relationships.

The past two decades have witnessed controversy over whether the use of digital technology has damaged or enhanced adolescents' social relationships, which influences their development. In this study, we addressed this debate by specifying the effect of different types of smartphone use content on social relationships, rather than simply relying on screen time spent on digital media. To avoid selective analysis and report of different variables, we used specification curve analysis (SCA) in a large dataset (N = 46,018) to explore the correlations between 20 types of smartphone use content and adolescents' social relationships (parent-child, peer, and teacher-student). The types of smartphone use content were measured by the revised version of Mobile Phone Use Pattern Scale, the Parent-Child Relationship Scale, the Peer Relationship Scale, and the Teacher-Student Relationship Scale assessed three different social relationships, respectively. Of the 20 types of smartphone use content, only playing games (negatively explaining 1% of the variation), taking online courses (positively explaining 1.6% of the variation), using search engines (positively explaining 1.2% of the variation), using a dictionary (positively explaining 1.3% of the variation), and obtaining life information (positively explaining 1.5% of the variation) showed a significant effect size. The association between smartphone use and adolescents' social relationships depends on the various types of content with which adolescents engage during smartphone use. The various effects of different types of smartphone use content deserve the attention of both the public and policy-makers.

Trajectory of problematic smartphone use among adolescents aged 10-18 years: The roles of childhood family environment and concurrent parent-child relationships.

Background and aims: Adolescence is a period of high incidence of problematic smartphone use. Understanding the developmental trajectory of problematic smartphone use in adolescence and its influencing factors could guide the choice of timing for prevention and intervention. This study fitted the growth trajectory of problematic smartphone use among adolescents and examined its associations with the childhood family environment and concurrent parent-child relationships. Methods: Using a cohort sequential design, we investigated 2,548 Chinese adolescents and their parents three times in three years. Multiple group multiple cohort growth models were used to fit the growth trajectory. Results: The quadratic growth trajectory of problematic smartphone use in adolescents aged 10-18 years showed a clear increasing trend, with a possible decreasing trend in late adolescence or early adulthood. Early life socioeconomic status, childhood family unpredictability, and the concurrent parent-child relationship had unique impacts on the development of problematic smartphone use during adolescence. Discussion and conclusions: Early adolescence is a favorable time for problematic smartphone use prevention and intervention. A supportive family environment should be maintained throughout the different developmental stages of children and adolescents.

The Development of Teacher Burnout and the Effects of Resource Factors: A Latent Transition Perspective.

To better understand burnout and its development, researchers have shown an increasing interest in recent years in identifying different profiles of burnout and its development process. However, there have been few longitudinal studies on the profile and development of teacher burnout. This study used a person-centred approach to explore the profiles of teacher burnout, transition probabilities and the associations between these aspects and resource factors. Data were collected from 3743 primary school teachers in a two-wave longitudinal test over three years. The results showed that teacher burnout exhibited six relatively stable profiles across the whole study population and that the transition of individual profiles over time followed a certain probability. Psychological capital and professional identity were important resource factors in reducing the occurrence of teacher burnout and increasing transition probability toward burnout symptom alleviation over time, while positive coping played an important role in reducing the occurrence of teacher ineffectiveness. Therefore, the results indicated that the overall teacher burnout profile was stable, a discovery which has important implications for conducting group interventions to benefit more teachers, while the individual burnout profile exhibited a latent transition probability over time. Interventions employing different resource factors can be adopted to alleviate the symptoms of different burnout profiles.

Effect of Growth Mindset on Mental Health Two Years Later: The Role of Smartphone Use.

The negative association between the growth mindset and mental health problems suggests that prevention and intervention programs to improve mental health by targeting mindset may have potential clinical value. However, research on the longitudinal effect of mindset on adolescent mental health and its underlying mechanisms is lacking. Using a three-wave longitudinal design, we obtained data from a diverse sample of Chinese adolescents (n = 2543). Longitudinal multiple mediation models were constructed to examine the effects of the growth mindset on levels of anxiety and depression two years later. In addition, the mediating effects of smartphone use for entertainment and problematic smartphone use (PSU) were examined. After controlling for various covariates and the autoregressive effects of mental health problems, the growth mindset had significant negative effects on anxiety (ß = −0.053, p = 0.004) and depression (ß = −0.074, p < 0.001). Smartphone use had a significant mediating role in the effect of mindset on anxiety (ß = −0.016, p < 0.001) and depression (ß = −0.016, p < 0.001). The growth mindset has long-lasting positive effects on adolescent mental health. Smartphone use for entertainment and PSU mediate the effect of mindset on adolescent mental health.

Understanding juveniles' problematic smartphone use and related influencing factors: A network perspective.

BACKGROUND AND AIMS: To understand the interaction between problematic smartphone use (PSU) and related influencing factors (individual variables, family environment, and school environment) and to determine the most influential factors affecting the use of smartphones by juveniles to implement effective interventions in the future. METHODS: A total of 3,442 children and adolescents (3,248 actual participants (males = 1,638, average age = 12.27 ± 2.36)) were included in the study. This study measured juveniles' PSU and its influencing factors: individual variables (4 factors), family environments (13 factors), and school environments (5 factors). This study employed a network analysis approach for data assessment. RESULTS: This study found that there were several central influencing factors (such as self-control ability, loss of control, parent-child relationship, and peer attitudes towards smartphone use) and bridge factors (such as peer attitudes towards smartphone use, peer pressure for smartphone use, and fear of missing out). DISCUSSION AND CONCLUSIONS: Juveniles' PSU included several core symptoms and critical influencing factors. Intervention based on these factors may be effective, timely, and inexpensive.

A network analysis of problematic smartphone use symptoms in a student sample.

BACKGROUND AND AIMS: Previous research has established risk factors for problematic smartphone use (PSU), but few studies to date have explored the structure of PSU symptoms. This study capitalizes on network analysis to identify the core symptoms of PSU in a large sample of students. METHODS: This research investigated 26,950 grade 4 students (male = 13,271) and 11,687 grade 8 students (male = 5,739) using the smartphone addiction proneness scale (SAPS). The collected data were analyzed using a network analysis method, which can provide centrality indexes to determine the core symptoms of PSU. The two networks from the different groups were compared using a permutation test. RESULTS: The results indicated that the core symptoms of students' problematic smartphone use were the loss of control and continued excessive use across the two samples. DISCUSSION AND CONCLUSIONS: These findings suggest that loss of control is a key feature of problematic smartphone use. The results also provide some evidence relevant to previous research from the perspective of network analysis and some suggestions for future treatment or prevention of students' problematic smartphone use.