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Abstract Background The Arab Teens Lifestyle (ATLS) questionnaire was an initiative to assess the lifestyle habits influencing obesity rates in the Middle East and North Africa (MENA) region, including physical activity (PA) patterns, sedentary and eating behaviours. Since its implementation, the ATLS questionnaire has been used in several studies among different age groups and populations. This instrument has not previously been administered among the university students of the Eastern Province of Saudi Arabia, where the obesity rates are the highest in the country. This research was the first that aimed to identify lifestyle habits influencing the rates of obesity among 18-25-year-old university students in the Eastern Province of the Kingdom of Saudi Arabia (KSA) using the ATLS questionnaire. Methods Quantitative cross-sectional research among n = 426 students of the Eastern Province of Saudi Arabia using the ATLS questionnaire. Results Out of n = 426 participants, n = 200 (47%) were categorised (using body mass index) as normal weight; n = 113 (26.5%) were overweight, and n = 73 (17.1%) were obese. The findings showed that most of the nutritional, PA, and sedentary behavioural factors (e.g., screen time) in the questionnaire were not associated with obesity status amongst the participants. In the obese group, more of the males that consumed fruits, French fries, cakes, sweets and doughnuts more than three times per week were likely to be obese, which was not the case for females. Conclusion The reported lifestyles of the students could potentially lead to long-term negative health effects, which is of concern given the rising rates of overweight, obesity, and obesity-related non-communicable diseases (NCDs) among the Kingdom’s adult and ageing population. Further studies are recommended to explore the knowledge, attitudes, and perceptions of Saudi students in the Eastern Province in relation to PA, sedentary behaviours, and dietary habits, along with their views on how these can be improved.

This paper provides rigorous empirical evidence on the causal effects that upgrading slum dwellings has on the living conditions of the extremely poor. In particular, we study the impact of providing better houses in situ to slum dwellers in El Salvador, Mexico and Uruguay. We experimentally evaluate the impact of a housing project run by the NGO TECHO, a youth-led program which provides basic pre-fabricated houses to members of extremely poor population groups in Latin America. The main objective of the program is to improve household well-being. Our findings show that better houses have a positive effect on overall housing conditions and general well-being: the members of treated households are happier with their quality of life. In two countries, we also document significant improvements in children’s health; in El Salvador, slum dwellers also feel that they are safer than before. There are no statistically significant effects on the possession of durable goods or in terms of labor outcomes. Our results are unusually robust in terms of both internal and external validity because they are derived from experiments in three different Latin American countries.

BackgroundAs breast cancer survival rates improve and structural health resources are increasingly being stretched, health providers require people living with and beyond breast cancer (LwBBC) to self-manage aspects of their care. ObjectiveThis study aimed to explore how women use and experience social media to self-manage their psychosocial needs and support self-management across the breast cancer continuum. MethodsThe experiences of 21 women (age range 27-64 years) were explored using an in-depth qualitative approach. The women varied in the duration of their experiences of LwBBC, which facilitated insights into how they evolve and change their self-management strategies over time. Semistructured interviews were analyzed inductively using a thematic analysis, a polytextual analysis, and voice-centered relational methods. ResultsThe use of multiple social media platforms, such as YouTube, Facebook, WhatsApp, and Twitter, enabled women to self-manage aspects of their care by satisfying needs for timely, relevant, and appropriate support, by navigating identities disrupted by diagnosis and treatment and by allowing them to (re)gain a sense of control. Women described extending their everyday use of multiple platforms to self-manage their care. However, women experienced social media as both empowering and dislocating, as their engagement was impacted by their everyday experiences of LwBBC. ConclusionsHealth care professionals (HCPs) need to be more aware, and open to the possibilities, of women using multiple social media resources as self-management tools. It is important for HCPs to initiate value-free discussions and create the space necessary for women to share how social media resources support a tailored and timely self-managed approach to their unique psychosocial needs.

This article focuses on training work carried out in artificial intelligence (AI) at the National Center for Supercomputing Applications at the University of Illinois Urbana-Champaign via a research experience for undergraduates program named FoDOMMaT. It also describes why we are interested in AI, and concludes by discussing what we've learned from running this program and its predecessor over six years. [ABSTRACT FROM AUTHOR]

This article demonstrates how scientists and engineers can use modern artificial intelligence (AI) tools such as ChatGPT and GitHub Copilot to learn computer programming skills that are relevant to their jobs. It begins by summarizing common ways that AI tools can already help people learn programming in general and then presents six new opportunities catered to the needs of scientists and engineers: 1) create customized programming tutorials for one's own domain of work, 2) learn complex data visualization libraries, 3) learn to refactor exploratory code into more maintainable software, 4) learn about inherited legacy code, 5) learn new programming languages on demand within the context of one's workflow, and 6) question the assumptions that one's scientific code is making. Taken together, these opportunities point toward a future where AI can help scientists and engineers learn programming on demand within the context of their existing real-world workflows. [ABSTRACT FROM AUTHOR]