Background: This is one of the few studies that examines adolescent Internet addiction (IA) among Middle Eastern population. The purpose of this study is to determine whether adolescents’ family and school environments play a role in their Internet Addiction.Methods: We conduced a survey that included 479 adolescents in Qatar. The survey collected demographic data, the Internet Addiction Diagnostic Questionnaire (IADQ), the Brief Family Relationship Scale (BFRS) and questions from the WHO Health Behavior in School-aged Children (HBSC) survey that assess school environment, academic performance, teacher support, and peer support of the adolescents. Factorial analysis, multiple regression, and logistic regression were used for statistical analysis.Results: Family environment and school environment were negative and significant predictors of adolescent Internet addiction. The prevalence rate was 29.64%.Conclusion: Results imply that interventions and digital parenting programs should not only target adolescents, but also include entities in the developmental environment of adolescents, i.e. their family and school.

Background: Family and school environment play a crucial role across the different developmental stages of adolescence. This paper investigates the potential mediating role of family and school environments in the relationship between the three psychosocial predictors of depression, anxiety, stress, and Internet addiction (IA). Specifically, it focuses on the two stages of early and middle adolescence. Methods: The study involved a survey of 407 adolescents from Qatar, comprising 250 early adolescents and 157 middle adolescents. Inclusion criteria for the study included adolescents between the ages of 10 to 17 years old, residents of Qatar and studying in a Qatar-based school. To assess the constructs of the three psychosocial predictors, IA, family environment, the study utilized the Depression, Stress, and Anxiety Scale (DASS), the Internet Addiction Diagnostic Questionnaire (IADQ), and the Brief Family Relationship Scale, respectively. School environment was measured using questions from the “Health Behavior in School-aged Children: WHO Collaborative Cross-National survey/study (HBSC) 2013–2014. The study applied standard mediation analysis between the DASS components and IA with family and school environment as the mediators. Results: Results from the mediation analysis reveal insights into the relationships between psychosocial predictors and IA. The findings indicate that family and school environments partially mediated the relationship with regards to depression, stress, and anxiety in early adolescents. In middle adolescents, family environment partially mediated the relationship with depression and stress and fully mediating the relationship with anxiety. Meanwhile, school environment only exhibited partial mediation in the relationship with anxiety in middle adolescence. Conclusions: These results highlight the crucial role parents and schools play in addressing problematic technology usage that develops as a response to depression, anxiety, and stress among adolescents. Moreover, the study reveals nuances in the mediating role of family and school environment in early and middle adolescence. This highlights the evolving nature of these influences across the different stages of development. Notably, this study contributes to the literature by moving beyond the conventional focus on the so-called WEIRD population, and offering valuable insights from a region that is underrepresented in current research.

This paper aims to objectively compare the use of mental health apps between the pre-COVID-19 and during COVID-19 periods and to study differences amongst the users of these apps based on age and gender. The study utilizes a dataset collected through a smartphone app that objectively records the users’ sessions. The dataset was analyzed to identify users of mental health apps (38 users of mental health apps pre-COVID-19 and 81 users during COVID-19) and to calculate the following usage metrics; the daily average use time, the average session time, the average number of launches, and the number of usage days. The mental health apps were classified into two categories: guidance-based and tracking-based apps. The results include the increased number of users of mental health apps during the COVID-19 period as compared to pre-COVID-19. Adults (aged 24 and above), compared to emerging adults (aged 15–24 years), were found to have a higher usage of overall mental health apps and guidance-based mental health apps. Furthermore, during the COVID-19 pandemic, males were found to be more likely to launch overall mental health apps and guidance-based mental health apps compared to females. The findings from this paper suggest that despite the increased usage of mental health apps amongst males and adults, user engagement with mental health apps remained minimal. This suggests the need for these apps to work towards improved user engagement and retention.

The study aims to identify typical interplay between the use of social media apps on smartphones and Problematic Internet Usage (PIU). Our study utilizes data from a smartphone app that objectively monitors user usage, including the apps used and the start and finish times of each app session. This study included 334 participants who declared a need to be aware of their smartphone usage and control it. Problematic Internet Usage (PIU) was measured using the Problematic Internet Use Questionnaire-Short Form-6 (PIUQ-SF6). The total PIU score can range from 6 to 30, with a score above 15 indicating that a person is at risk of PIU. Time spent on Social Media (SM) apps of Facebook, WhatsApp, and Instagram, and whether people used each of these apps were studied along with the total PIU score. K-Prototype clustering was utilized for the analysis. Four distinct clusters, typifying the relationship between social media use and PIU, were identified. All the individuals in Cluster 1 (Light SM Use Cluster; Cluster size = 270, 80.84% of total dataset) spent between 0 and 109.01 min on Instagram, between 0 and 69.84 min on Facebook, and between 0 and 86.42 min on WhatsApp and its median PIU score was 17. Those who were in cluster 2 (Highly Visual SM Cluster; Cluster size = 23, 6.89% of total dataset) all used Instagram, and each member spent between 110 and 307.63 min on Instagram daily. The cluster median PIU score and average daily usage of Instagram were respectively 20 and 159.66 min. Those who were in Cluster 3 (Conversational SM Cluster; Cluster size = 19, 5.69% of total dataset) all used WhatsApp, and spent between 76.68 and 225.22 min on WhatsApp daily. The cluster median PIU score and average time spent per day on WhatsApp were 20 and 132.65 min, respectively. Those who were in Cluster 4 (Social Networking Cluster; (Cluster size = 22, 6.59% of total dataset) all used Facebook, and each spent between 73.09 and 272.85 min daily on Facebook. The cluster median PIU score and average time spent per day on Facebook were 18 and 133.61 min respectively. The clusters indicate that those who use a particular social media app spend significantly less time on other social media apps. This indicates that problematic attachment to social media occurs primarily for one of three reasons: visual content and reels, conversations with peers, or surfing network content and news. This finding will help tailor interventions to fit each cluster, for example by strengthening interpersonal skills and resistance to peer pressure in the case of Cluster 3 and increasing impulse control in the case of Cluster 2.

This paper proposes the first large-scale and annotated Qatari sign language dataset for continuous sign language processing. This dataset focuses on phrases and sentences commonly used in healthcare settings and contains 6300 records of 900 sentences. The dataset collection process involves diverse participants, including both hearing-impaired individuals and sign interpreters, to capture variations in signing styles, speeds, and other linguistic nuances. The data collection setup integrates advanced technology, including true depth cameras, to comprehensively record signing movements from various angles. The collected dataset is rich in content, encompassing different signing variations and linguistic intricacies. The dataset is publicly available in IEEE Dataport. The paper also analyzes the data captured to understand the trends and patterns within the data. As the global population with hearing difficulties continues to grow, there is a pressing need for effective sign language recognition systems to bridge the communication gap between the deaf and non-deaf communities and the introduction of the JUMLA-QSL-22 dataset constitutes a significant stride toward addressing this imperative need.