Obesity is a global health challenge. According to the World Health Organization (WHO), between 1990 and 2022, adult obesity more than doubled. Weight management interventions (WMIs) support individuals in achieving and maintaining a healthy weight through dietary guidance, physical activity promotion and behavioural counselling. However, traditional WMIs often have limited accessibility. Digital WMIs or DWMIs are delivered via websites or smartphone applications and provide scalable and cost-effective alternatives. However, user needs for digital services and their prevalence in the existing commercial solutions remain underexplored. Hence, our study systematically identified 26 commercial DWMIs to identify their features, services, and data collection practices. Additionally, we performed a user needs analysis by recruiting 207 individuals involved in a real-life WMI. Our findings indicated that DWMIs integrated self-monitoring, goal setting, and behaviour change strategies, yet lack social support, virtual reality applications and adaptive personalisation. WMI clients prefer smartphone Apps and fitness trackers for tracking weight management progress and have varying levels of comfort in using digital resources. The presented results serve as recommendations for future directions in the design and implementation of services for DWMIs.

: The growing demand for mental health services has led to the rapid emergence of digital mental health applications. Simultaneously, recent advances in artificial intelligence (AI) have enabled the development of high-performance chatbots powered by large language models (LLMs). Our paper explored the potential of an LLM-based mental health counselling chatbot for university students. We developed the chatbot using the QLoRA fine-tuning approach of a lightweight open-source LLM on curated counselling transcript data. We evaluated the system in a user study, with 30 university students, and found that a single chatbot counselling session reduced negative emotions significantly. Overall, our results suggested that LLM-based counselling chatbots present promising opportunities for AI-driven mental health interventions.

We introduce ManifoldMind, a probabilistic geometric recommender system for exploratory reasoning over semantic hierarchies in hyperbolic space. Unlike prior methods with fixed curvature and rigid embeddings, ManifoldMind represents users, items, and tags as adaptive-curvature probabilistic spheres, enabling personalised uncertainty modeling and geometry-aware semantic exploration. A curvature-aware semantic kernel supports soft, multi-hop inference, allowing the model to explore diverse conceptual paths instead of overfitting to shallow or direct interactions. Experiments on four public benchmarks show superior NDCG, calibration, and diversity compared to strong baselines. ManifoldMind produces explicit reasoning traces, enabling transparent, trustworthy, and exploration-driven recommendations in sparse or abstract domains.

: The effect of emotions and personalisation on continuance use intentions in online health services is underexplored. Accordingly, we propose a research model for examining the impact of emotion- and personalisation-based factors on cancer website reuse intentions. We conducted a study using a real-world NGO cancer-support website, which was evaluated by 98 participants via an online questionnaire. Model relations were estimated using the PLS-SEM method. Our findings indicated that pre-use emotions did not significantly influence perceived personalisation. However, satisfaction with personalisation, and perceived usefulness mediated by satisfaction, increased reuse intentions. In addition, post-use positive emotions potentially influenced reuse intentions. Our paper, therefore, illustrates the applicability of theory regarding continuance use intentions to cancer-support websites and highlights the importance of personalisation for these purposes.

This paper addresses and evaluates approaches to incorporating personality data into a recommender system. Automatic personality recognition is enabled by the LIWC dictionary. Personality-aware pre-filtering techniques are developed and discussed, with the introduced non-targeted stratified personality sampling performing the best. A novel personality-aware model, FFM-SVD, is proposed and shown to outperform alternative models in prediction accuracy.

Deafblind people have both hearing and visual impairments, which makes communication with other people often dependent on expensive technologies e.g., Braille displays, or on caregivers acting as interpreters. This paper presents Morse I/O (MIO), a vibrotactile interface for Android, evaluated through experiments and interviews with deafblind participants. MIO was shown to enable consistent text entry and recognition after only a few hours of practice. The participants were willing to continue using the interface, although there were perceived difficulties in learning to use it. Overall, MIO is a cost-effective, portable interface for deafblind people without access to Braille displays or similar.

Purpose This paper aims to address the user perspective about usability, security and use of five authentication schemes (text and graphical passwords, biometrics and hardware tokens) from a population not covered previously in the literature. Additionally, this paper explores the criteria users apply in creating their text passwords. Design/methodology/approach An online survey study was performed in spring 2019 with university students in Mexico and Bosnia and Herzegovina. A total of 197 responses were collected. Findings Fingerprint-based authentication was most frequently perceived as usable and secure. However, text passwords were the predominantly used method for unlocking computer devices. The participants preferred to apply personal criteria for creating text passwords, which, interestingly, coincided with the general password guidelines, e.g. length, combining letters and special characters. Originality/value Research on young adults’ perceptions of different authentication methods is driven by the increasing frequency and sophistication of security breaches, as well as their significant consequences. This study provided insight into the commonly used authentication methods among youth from two geographic locations, which have not been accounted for previously.

Purpose This paper aims to examine the state of the art in electronic records management (ERM) with the goal of identifying the prevailing research topics, gaps and issues in the field. Design/methodology/approach First, a wide search was performed on academic research databases, limited to the period between 2008–2018. Second, the search results were reviewed for relevance and duplicates. Finally, the study sources were checked against the list of journals and conferences ranked by computing research and education and JourQual. The final sample of 55 selected studies was analyzed in depth. Findings ERM has lost some research momentum due to being deeply embedded in affiliate information systems areas and the changing records management landscape. Additionally, the requirement models specified by Governmental/National Archives might have constrained technology innovation in ERM. A lack of application was identified for the social media research area. Research limitations/implications Limitations were encountered in available search tool functionality and keyword confusion leading to inflated search results. While effort has been made to obtain optimal search results, some relevant articles may have been omitted. Originality/value The last ERM state-of-the-art review was in 1997. A lot has changed since then. This paper will help researchers understand the current state of ERM research, its understudied areas and identify gaps for future studies.