Due to the ageing of the world’s population, age-friendly cities are emerging to promote active ageing by optimising opportunities for health, participation and safety, in order to improve the quality of life of older people. Despite initiatives in different countries, there is a lack of empirical research exploring their impact on older people. The objective of this study is to evaluate an age-friendly city by analysing its relationship with life satisfaction, taking into account the age cohort variables of the elderly and whether they live alone or with someone else. A two-stage study, in which 66 subjects participated in the qualitative analysis (focus groups) in Stage I and 203 of the quantitative analysis (survey methodology) or Stage II. Despite the differences found in the different groups of elderly persons, for all of them the domains of outdoor spaces and buildings, and community support and health services, are significantly related to life satisfaction, with the latter showing itself to be a predictor of such satisfaction. It is important to take into account the different groups of elderly persons, so as to be able to establish suitable specific actions. This study aims to make a contribution to the development of public policies that influence the life satisfaction of the elderly.

This paper presents our experience on a real case of applying an indoor localization system for monitoring older adults in their own homes. Since the system is designed to be used by real users, there are many situations that cannot be controlled by system developers and can be a source of errors. This paper presents some of the problems that arise when real non-expert users use localization systems and discusses some strategies to deal with such situations. Two technologies were tested to provide indoor localization: Wi-Fi and Bluetooth Low Energy. The results shown in the paper suggest that the Bluetooth Low Energy based one is preferable in the proposed task.

This study examined the use of verbalisms by 62 children aged 7–14 who were totally blind from birth and 64 sighted children. It found that a child's degree of sight and gender did not affect the frequency with which verbalisms were used; only age had a significant positive effect. The study shows that language is a flexible structure that is used correctly by children who are congenitally blind as they adapt to the language used by those around them.

Background and Objectives: Artificial Intelligence (AI) is undergoing a paradigm shift in its application to healthcare, particularly in the context of ageing-related care, with significant implications for disease prevention, diagnosis, and treatment. The integration of advanced machine learning, deep neural networks, and natural language processing has enabled AI to analyze datasets with remarkable accuracy, surpassing the performance of traditional methods. AI-driven approaches have the potential to facilitate early disease detection, predict progression, and personalize treatments, optimizing healthcare resources. Furthermore, AI is contributing to the development of new treatments and supporting public health strategies. The objective is to assess the effectiveness of AI-based interventions for loneliness. Research Design and Methods: This article presents a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-analyses methodology. Databases searched were PubMed, Web of Science, Scopus and PsycInfo. A total of 19 articles were identified fulfilling the inclusion criteria, and using validated tools to assess loneliness. Studies were summarized indicating: country, sample, design of the study, measures of loneliness, the AI-based technology main findings and implications. Results: Findings highlight AI’s potential to enhance social well-being among older adults. From a policy perspective, AI-driven analytics enable targeted interventions by identifying trends in age-related health and social issues from the psychology of social intervention. The adoption of AI in ageing policies promotes efficient, inclusive frameworks that support healthy ageing and reduce pressure on traditional healthcare systems. Discussion and Implications: This study contributes to the growing evidence supporting AI’s role in addressing loneliness and improving overall quality of life in ageing populations.

Schools are a fundamental context for processes of promotion and generation of resilience, since they focus not only on aspects of academic development, but also of personal and social development. Resilience in the teacher has a dual function. On the one hand, as resilient professionals, teachers can foster the development of resilience in their students; on the other hand, teaching resilience facilitates their own personal and professional well-being. Despite research highlighting the benefits of resilience in education, there is scarce research focused on assessing resilience in teachers. Thus, the aim of this paper is to analyze the psychometric properties of the Spanish version of Connor and Davidson’s 10-item resilience scale (CD-RISC 10©) in a sample of 290 teachers. A confirmatory factorial analysis (CFA) is performed, which shows that the 10 items on the CD-RISC 10© Resilience Scale form a one-dimensional structure, with high reliability, McDonald’s Omega coefficient (ω = 0.80) and Cronbach’s alpha coefficient (α = 0.87). The results obtained in this work support the use of the scale for the assessment of resilience in teachers of Infant, Primary and Special Education, which is considered very useful, not only to know their degree of resilience, but also to assess the effectiveness of training programs.