Sensor-based technologies for healthy eating have gained attention in human-computer interaction research. With the prevalence of diet-related health issues such as obesity and diabetes, innovative interventions providing personalized dietary feedback are increasingly needed. Sensor-based technologies offer real-time monitoring of dietary habits. We reviewed 28 articles, published between 2014 and 2024, to uncover the state-of-the-art and research gaps. Sixteen articles (57.1%) reported on effectiveness: seven measured by machine learning model performance and nine through user study evaluation. We identified several eating behaviors that were supported: adequate food intake, meal size and quality, eating posture, swallowing sounds and difficulties, eating styles, and pace. These behaviors were associated with conditions such as diabetes, post-stroke issues, cardiovascular diseases, obesity, and binge eating. Limitations included little to no interventions for life-threatening conditions. Our findings present opportunities to develop tailored sensor-based interventions for various diet-related issues.

The emergence of large language models (LLMs) is transforming various health-related domains, including approaches to obesity management. Obesity remains one of the world’s leading health issues, prompting the research community to develop various weight-loss applications focused on physical activity, dietary planning, and related interventions. In this study, we explore the capability of the LLM ChatGPT for personalized dietary planning. We conducted two case studies: Case Study 1 examined self-supervised recipe generation using ChatGPT alone, while Case Study 2 investigated a self-supervised approach combining National Institute of Health standards with ChatGPT recipe recommendations. We also performed a user study to evaluate recipe recommendations from ChatGPT. Our results show that ChatGPT recommends appropriate recipes based on comparisons with the United States Department of Agriculture’s (USDA) recipe calculator. We found no significant difference between ChatGPT-generated recipe recommendation calories and USDA standards for either Case Study 1 (p = 0.8530) or Case Study 2 (p = 0.0687). In addition, we found significant weight loss in participants following these recipes in both Case Study 1 (p < 0.00001) and Case Study 2 (p = 0.0014). Furthermore, the user study with potential weight-loss participants revealed varying levels of satisfaction (p = 0.001) and identified themes related to meal preferences, effective prompt generation, and mixed concerns regarding privacy, trust, user consent, and data storage. We conclude by discussing additional findings from our case and user studies, and present opportunities, challenges, and design and ethical considerations for the research community.

Persuasive games, designed and tailored to change users’ behavior, have enhanced user experience across various domains. However, their role in emotion regulation remains understudied, despite the increasingly immersive environments they create that trigger emotional responses. Sensors have emerged as key artificial intelligence (AI) tools in persuasive games supporting emotion regulation compared to other AI technologies, such as chatbots. Yet the effectiveness, challenges, and applications of these sensors lack comprehensive exploration. Our scoping review analyzed 32 articles published from 2013 to 2023 on persuasive games using AI-based sensing technologies for emotion regulation. Results from 27 articles (84.8%) reporting the effectiveness of sensors revealed mostly large to moderate effects (d ≥ 0.8 ≤ 0.4). Common challenges with integrated sensors (camera systems, facial recognition, smartwatches, and altimetric pressure sensors) included emotion detection accuracy and ethical concerns. We provide design recommendations for developing ethically sound and effective persuasive games for emotion regulation in the future.

In recent years, there has been growing research interest in aligning technological designs with users’ lived experiences. The goal of this work is to review existing work on the current state-of-the-art on incorporating individual and community values and beliefs into various kinds of interactive computerized technologies, emphasizing socio-cultural sensitivity. After screening 235 records, 45 papers were included in this review. Our research reveals that researchers are at the forefront of developing advanced socio-cultural digital tools and interactive educational platforms. They frequently employ techniques like collaborative dialogue facilitation, personalized linguistic support, and the integration of culturally significant design principles, such as cultural narratives and symbols. This commitment to technology’s transformative potential extends beyond education, making its mark in healthcare, social networks, finance, and other domains. We conclude by providing an overview of the questions that other researchers can investigate in the future for designing technologies that are socio-culturally sensitive. Future studies would benefit from a wider use of theories to account for the complexity of human behavior while designing socio-culturally sensitive technologies.

Abstract Modern visualization software and programming libraries have made data visualization construction easier for everyone. However, the extent of accessibility design they support for blind and low‐vision people is relatively unknown. It is also unclear how they can improve chart content accessibility beyond conventional alternative text and data tables. To address these issues, we examined the current accessibility features in popular visualization tools, revealing limited support for the standard accessibility methods and scarce support for chart content exploration. Next, we investigate two promising accessibility approaches that provide off‐the‐shelf solutions for chart content accessibility: structured navigation and conversational interaction. We present a comparative evaluation study and discuss what to consider when incorporating them into visualization tools.

Distance has become inevitable in this modern society. Families are separated temporarily or permanently depending on different contexts as migration, divorce or death. Hence, it becomes a great challenge on parents and children separated through long distance. However, parents struggle to keep intimacy through technology in order to create memorable impacts in the lives of their children. Children have a strong sense of recalling events which contributes to bonding and intimacy after such distant relationships. This paper reflects on the researcher's experience of distant separation to present design opportunities as work in progress for using life logging technologies within this context.