Bored to death: Artificial Intelligence research reveals the role of boredom in suicide behavior.

Background: Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited. Objective: This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors. Methods: The primary dataset included 228,052 Facebook postings by 1,006 users who completed the gold-standard Columbia Suicide Severity Rating Scale. This dataset was analyzed using a bottom-up research pipeline without a-priory hypotheses and its findings were validated using a top-down analysis of a new dataset. This secondary dataset included responses by 1,062 participants to the same suicide scale as well as to well-validated scales measuring depression and boredom. Results: An almost fully automated, AI-guided research pipeline resulted in four Facebook topics that predicted the risk of suicide, of which the strongest predictor was boredom. A comprehensive literature review using APA PsycInfo revealed that boredom is rarely perceived as a unique risk factor of suicide. A complementing top-down path analysis of the secondary dataset uncovered an indirect relationship between boredom and suicide, which was mediated by depression. An equivalent mediated relationship was observed in the primary Facebook dataset as well. However, here, a direct relationship between boredom and suicide risk was also observed. Conclusion: Integrating AI methods allowed the discovery of an under-researched risk factor of suicide. The study signals boredom as a maladaptive 'ingredient' that might trigger suicide behaviors, regardless of depression. Further studies are recommended to direct clinicians' attention to this burdening, and sometimes existential experience.

Effective parental strategies against problematic smartphone use among adolescents: A 6-month prospective study.

Problematic Smartphone Use (PSU) among adolescents is growing problem worldwide and multiple studies investigated its associated parental risk and protective factors. The available studies, however, typically do not address the multidimensional nature of PSU. They also rely heavily on cross-sectional designs with a small number of potentially contributing variables. This 6-month prospective study examines the relationships between a large range of parental factors with the three known dimensions of PSU: social environment pressure, emotional gain, and addiction-like behaviors. Specifically the study examines whether, and to what extent, the various dimensions of current and future PSU are related to parental support giving, general quality of communication, specific communication about smartphone use, and the four common mediation strategies. The sample comprised 1187 triads of mothers, fathers, and adolescents. The data-analysis pipeline consisted of three consecutive phases: (1) analyses of parental factors at baseline, (2) analyses of parental factors change over 6-months, and (3) interaction analyses between parental factors and the time-period of the study. The pipeline elicited three factors that moderated the change in PSU over time: Communication about smartphones with mothers, parental support giving, and parental co-use. The implications of these findings are discussed in the context of the existing literature and the familial, microsystem theoretical framework. Altogether, this prospective study scrutinized key parental factors and strategies that could be utilized by parents for coping with one of the most difficult parenting challenges of the digital era. Further research may build upon these findings to develop designated interventions to reduce PSU.

Screen Time and Autism Spectrum Disorder: A Systematic Review and Meta-Analysis.

Importance: Contemporary studies raise concerns regarding the implications of excessive screen time on the development of autism spectrum disorder (ASD). However, the existing literature consists of mixed and unquantified findings. Objective: To conduct a systematic review and meta-analyis of the association between screen time and ASD. Data Sources: A search was conducted in the PubMed, PsycNET, and ProQuest Dissertation & Theses Global databases for studies published up to May 1, 2023. Study Selection: The search was conducted independently by 2 authors. Included studies comprised empirical, peer-reviewed articles or dissertations published in English with statistics from which relevant effect sizes could be calculated. Discrepancies were resolved by consensus. Data Extraction and Synthesis: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. Two authors independently coded all titles and abstracts, reviewed full-text articles against the inclusion and exclusion criteria, and resolved all discrepancies by consensus. Effect sizes were transformed into log odds ratios (ORs) and analyzed using a random-effects meta-analysis and mixed-effects meta-regression. Study quality was assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. Publication bias was tested via the Egger z test for funnel plot asymmetry. Data analysis was performed in June 2023. Main Outcomes and Measures: The 2 main variables of interest in this study were screen time and ASD. Screen time was defined as hours of screen use per day or per week, and ASD was defined as an ASD clinical diagnosis (yes or no) or ASD symptoms. The meta-regression considered screen type (ie, general use of screens, television, video games, computers, smartphones, and social media), age group (children vs adults or heterogenous age groups), and type of ASD measure (clinical diagnosis vs ASD symptoms). Results: Of the 4682 records identified, 46 studies with a total of 562 131 participants met the inclusion criteria. The studies were observational (5 were longitudinal and 41 were cross-sectional) and included 66 relevant effect sizes. The meta-analysis resulted in a positive summary effect size (log OR, 0.54 [95% CI, 0.34 to 0.74]). A trim-and-fill correction for a significant publication bias (Egger z = 2.15; P = .03) resulted in a substantially decreased and nonsignificant effect size (log OR, 0.22 [95% CI, -0.004 to 0.44]). The meta-regression results suggested that the positive summary effect size was only significant in studies targeting general screen use (ß [SE] = 0.73 [0.34]; t58 = 2.10; P = .03). This effect size was most dominant in studies of children (log OR, 0.98 [95% CI, 0.66 to 1.29]). Interestingly, a negative summary effect size was observed in studies investigating associations between social media and ASD (log OR, -1.24 [95% CI, -1.51 to -0.96]). Conclusions and Relevance: The findings of this systematic review and meta-analysis suggest that the proclaimed association between screen use and ASD is not sufficiently supported in the existing literature. Although excessive screen use may pose developmental risks, the mixed findings, the small effect sizes (especially when considering the observed publication bias), and the correlational nature of the available research require further scientific investigation. These findings also do not rule out the complementary hypothesis that children with ASD may prioritize screen activities to avoid social challenges.

Social Media Images Can Predict Suicide Risk Using Interpretable Large Language-Vision Models.

Background: Suicide, a leading cause of death and a major public health concern, became an even more pressing matter since the emergence of social media two decades ago and, more recently, following the hardships that characterized the COVID-19 crisis. Contemporary studies therefore aim to predict signs of suicide risk from social media using highly advanced artificial intelligence (AI) methods. Indeed, these new AI-based studies managed to break a longstanding prediction ceiling in suicidology; however, they still have principal limitations that prevent their implementation in real-life settings. These include "black box" methodologies, inadequate outcome measures, and scarce research on non-verbal inputs, such as images (despite their popularity today).Objective: This study aims to address these limitations and present an interpretable prediction model of clinically valid suicide risk from images.Methods: The data were extracted from a larger dataset from May through June 2018 that was used to predict suicide risk from textual postings. Specifically, the extracted data included a total of 177,220 images that were uploaded by 841 Facebook users who completed a gold-standard suicide scale. The images were represented with CLIP (Contrastive Language-Image Pre-training), a state-of-the-art deep-learning algorithm, which was utilized, unconventionally, to extract predefined interpretable features (eg, "photo of sad people") that served as inputs to a simple logistic regression model.Results: The results of this hybrid model that integrated theory-driven features with bottom-up methods indicated high prediction performance that surpassed common deep learning algorithms (area under the receiver operating characteristic curve [AUC] = 0.720, Cohen d = 0.82). Further analyses supported a theory-driven hypothesis that at-risk users would have images with increased negative emotions and decreased belongingness.Conclusions: This study provides a first proof that publicly available images can be leveraged to predict validated suicide risk. It also provides simple and flexible strategies that could enhance the development of real-life monitoring tools for suicide.

Mothers' Perceptions of Children's Screen Use During the COVID-19 Lockdown in Israel.

The contemporary parenting challenge of regulating children's screen time became even more difficult during the coronavirus pandemic (COVID-19). The current research addresses the characteristics of this challenge and explores mothers' perceptions regarding their children's screen use, through two consecutive studies. Study 1 included 299 mothers of elementary school children, who were asked to complete questionnaires regarding their children's screen habits. Mothers were also asked about their own attitudes towards screens, as parents, and about their personal feelings of frustration and guilt. Study 2 replicated this procedure among a new sample of 283 mothers who also completed validated scales assessing their sense of parental competence and authority style. Retrospective reports of mothers indicated that, during the lockdown, entertainment use of screens increased by 73% among 4th-6th graders and by 108% among 1st-3rd graders. Educational use increased by 86% in both age groups. Mothers' guilt increased as well and was predicted by children's entertainment use (but not educational use), after accounting for demographic variables and mothers' attitudes. Other factors, such as parenting style and having at-least one child with a diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD), were associated with entertainment use (regardless of the COVID-19 lockdown). Factors that were found to moderate the lockdown effect were mothers' attitudes towards screens and parental confidence. The findings are discussed in the context of parents' efforts to regulate their children's screen use.

OMG, R U OK? Therapeutic Relationships between Caregivers and Youth at Risk on Social Media.

The rising of social media has opened new opportunities for forming therapeutic relationships with youth at risk who have little faith in institutionalized interventions. The goal of this study is to examine whether and how youth care workers utilize social media communications for reaching out to detached adolescents and providing them emotional support. Qualitative in-depth interviews (N = 17) were conducted with counselors, social workers, and clinical psychologists who work with youth at risk. A thematic analysis of the interviews revealed three principal psychosocial usages of social media: (1) Reaching out and maintaining reciprocal and meaningful therapeutic relationships with youth at risk over time; (2) Identifying risks and emotional distress; and (3) "stepping in" and providing psychosocial assistance, when needed. These beneficial practices are made possible through the high accessibility and the sense of secured mediation that characterize social media communication and that complement the psychosocial needs of youth at risk. Alongside these advantages, the analysis yielded several significant challenges in social media therapeutic relationships, including privacy dilemmas and blurring of authority and boundaries. Given that social media communication is a relatively new phenomenon, the applied psychosocial practices are shaped through a process of trial and error, intuitive decisions, and peer learning. Although the main conclusion from this study supports the notion that the advantages of social media therapeutic relationships with youth at risk outweigh their problematic aspects, future research is recommended to establish clear guidelines for youth caregivers who wish to integrate the new media in their daily psychosocial work.

Deep neural networks detect suicide risk from textual facebook posts.

Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56-0.58). In this study, Artificial Neural Network (ANN) models were constructed to predict suicide risk from everyday language of social media users. The dataset included 83,292 postings authored by 1002 authenticated Facebook users, alongside valid psychosocial information about the users. Using Deep Contextualized Word Embeddings for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (0.621 ≤ AUC ≤ 0.629), the MTM produced significantly improved prediction accuracy (0.697 ≤ AUC ≤ 0.746), with substantially larger effect sizes (0.729 ≤ d ≤ 0.936). Subsequent content analyses suggested that predictions did not rely on explicit suicide-related themes, but on a range of text features. The findings suggest that machine learning based analyses of everyday social media activity can improve suicide risk predictions and contribute to the development of practical detection tools.

The Personal Autonomous Car: Personality and the Driverless Car.

Road traffic accidents, congestion and their ensuing issues are of international concern. A recent technological development to alleviate this situation is the autonomic car. A driverless vehicle will transport its passengers to their destinations. User experience would be enhanced by adapting the workings of the vehicle in line with the personality of its user. An autonomic car information system preference questionnaire was designed, focusing on different components of a futuristic information system. Participants comprised 155 students. The results demonstrated two factors: willingness to share information and need for control. A regression analysis on the automatic car preferences, personality (the Big 5), gender, and age showed that openness, consciousnesses, and age were related to different preferences. The results are assessed, followed by a discussion on personality in relation to the autonomic car.

In times of war, adolescents do not fall silent: Teacher-student social network communication in wartime.

Exposure to war is associated with psychological disturbances, but ongoing communication between adolescents and teachers may contribute to adolescents' resilience. This study examined the extent and nature of teacher-student communication on Social Network Sites (SNS) during the 2014 Israel-Gaza war. Israeli adolescents (N = 208, 13-18 yrs) completed information about SNS communication. A subset of these (N = 145) completed questionnaires on social rejection and distress sharing on SNS. More than a half (56%) of the respondents communicated with teachers via SNS. The main content category was 'emotional support'. Adolescents' perceived benefits from SNS communication with teachers were associated with distress sharing. Social rejection was negatively associated with emotional support and perceived benefits from SNS communication. We conclude that SNS communication between teachers and students may provide students with easy access to human connections and emotional support, which is likely to contribute to adolescents' resilience in times of war.

If I only knew why: the relationship between brooding, beliefs about rumination, and perceptions of treatments.

People who tend to engage in brooding, the maladaptive subtype of rumination, are at risk to develop depression. Brooders often endorse metacognitive beliefs that self-focused ruminative thinking is beneficial. In the current study, we examined whether brooding and positive beliefs about rumination are associated with perceptions of and preferences for treatments for depression. Participants (N=118) read descriptions of two different clusters of treatments for depression, Insight-Oriented (IO) treatments and Activation-Oriented (AO) treatments. They then rated treatment efficacy and credibility and completed self-report measures of rumination (including brooding and reflection subscales), beliefs about rumination, and depression. Brooding and metacognitive positive beliefs about rumination were associated with positive perceptions of IO (but not AO) treatments. Positive beliefs about rumination contributed to the prediction of perceptions of IO treatments (but not AO treatments) beyond the effect of brooding. We discuss the implications of these findings for individuals' decision-making processes regarding which type of treatment to seek.