Context: Mobile health (mHealth) applications are widely used for chronic disease management, but usability and accessibility challenges persist due to the diverse needs of users. Adaptive User Interfaces (AUIs) offer a promising approach to personalizing interactions and improving user experience. However, their adoption remains limited, partly due to a lack of understanding of how users perceive and evaluate different adaptation strategies. Addressing this gap is crucial for advancing user-centered design and requirements engineering in software systems for health contexts. Objective: This study identifies key factors influencing user preferences and trade-offs in mHealth adaptation design. Method: A Discrete Choice Experiment (DCE) was conducted with 186 participants living with chronic conditions who regularly use mHealth applications. Each participant completed a series of choice tasks, selecting their preferred adaptation designs from scenarios composed of six attributes with varying levels. A mixed logit model was applied to examine preference heterogeneity. Subgroup analyses were also conducted to explore variations in preferences across age, gender, health condition, and coping mechanism. Results: Participants preferred adaptation designs that preserved usability, offered controllability, introduced changes infrequently, and applied small-scale modifications. Conversely, adaptations affecting frequently used functions and those involving caregiver input were generally viewed less favorably. These findings highlight key trade-offs that influence user acceptance of adaptive mHealth interfaces. Conclusion: This study employs a data-driven approach to quantify user preferences, identify key trade-offs, and reveal variations across demographic and behavioral subgroups through preference heterogeneity modeling. These insights provide actionable guidance for designing more user-centered adaptive interfaces and contribute to advancing requirements prioritization practices in software engineering—particularly in the context of health technologies.

Mobile Health (mHealth) applications have demonstrated considerable potential in supporting chronic disease self-management; however, they remain underutilized due to low engagement, limited accessibility, and poor long-term adherence. These issues are particularly prominent among users with chronic disease, whose needs and capabilities vary widely. To address this, Adaptive User Interfaces (AUIs) offer a dynamic solution by tailoring interface features to users’ preferences, health status, and contexts. This article presents a two-stage study to develop and validate actionable AUI design guidelines for mHealth applications. In stage one , an AUI prototype was evaluated through focus groups, interviews, and a standalone survey, revealing key user challenges and preferences. These insights informed the creation of an initial set of guidelines. In stage two , the guidelines were refined based on feedback from 20 end users and evaluated by 43 software practitioners through two surveys. This process resulted in nine finalized guidelines. To assess real-world relevance, a case study of four mHealth applications was conducted, with findings supported by user reviews highlighting the utility of the guidelines in identifying critical adaptation issues. This study offers actionable, evidence-based guidelines that help software practitioners design AUI in mHealth to better support individuals managing chronic diseases.

Abstract Most software applications contain graphics such as charts, diagrams and maps. Currently, these graphics are designed with a “one size fits all" approach and do not cater to the needs of people with disabilities. Therefore, when using software with graphics, a colour-impaired user may struggle to interpret graphics with certain colours, and a person with dyslexia may struggle to read the text labels in the graphic. Our research addresses this issue by developing a framework that generates adaptive and accessible information graphics for multiple disabilities. Uniquely, the approach also serves people with multiple simultaneous disabilities. To achieve these, we used a case study of public space floorplans presented via a web tool and worked with four disability groups: people with low vision, colour blindness, dyslexia and mobility impairment. Our research involved gathering requirements from 3 accessibility experts and 80 participants with disabilities, developing a system to generate adaptive graphics that address the identified requirements, and conducting an evaluation with a total of 99 participants with disabilities. The evaluation showed that users found our solution easy to use and suitable for most of their requirements. The study also provides recommendations for front-end developers on engineering accessible graphics for their software and discusses the implications of our work on society from the perspective of public space owners and end users.

Growth of the older adult population has led to an increasing interest in technology-supported aged care. However the area has some challenges such as lack of care givers and limitations in understanding the emotional, social, physical, and mental well-being needs of older adults. Furthermore, there is a gap in the understanding between younger developers and ageing people of their requirements from digital systems. Digital health can play an important role supporting older adults’ well-being, emotional requirements, and social needs. We carried out a systematic review of the literature on RE for older adult digital health software. This was necessary to show the representatives of the current stage of understanding the needs of older adults in aged care digital health. Using established guidelines we developed a protocol, followed by the systematic search of eight databases. This resulted in 69 primary studies of high relevance, which were subsequently subjected to data extraction, synthesis, and reporting. This systematic literature review highlights key RE processes used in digital health software for older people. It explored the key features developed for many digital solutions, utilization of technology for older user well-being and care, and the evaluations of proposed solutions. The review also identified key limitations found in existing primary studies that inspire future research opportunities. Our results indicate that requirements gathering and understanding have a significant variation between different studies. The differences are in the quality, depth, and techniques adopted for requirement gathering and this reason for these differences is largely due to uneven adoption of RE methods.

Developing user-centred applications that address diverse user needs requires rigorous user research. This is time, effort and cost-consuming. With the recent rise of generative AI techniques based on Large Language Models (LLMs), there is a possibility that these powerful tools can be used to develop adaptive interfaces. This paper presents a novel approach to develop user personas and adaptive interface candidates for a specific domain using ChatGPT. We develop user personas and adaptive interfaces using both ChatGPT and a traditional manual process and compare these outcomes. To obtain data for the personas we collected data from 37 survey participants and 4 interviews in collaboration with a not-for-profit organisation. The comparison of ChatGPT generated content and manual content indicates promising results that encourage using LLMs in the adaptive interfaces design process.

Human-Centered e-Health Development: An Accessible Visual Modeling Tool

This paper shares insights from our first-hand experience with key recruitment challenges encountered in software engineering research, focusing on two distinct participant groups: end-users and software practitioners. By conducting a reflective analysis, we emphasise the particular challenges we faced when engaging these groups during empirical study recruitment phases. Significant challenges we faced in recruiting end-users include ensuring authenticity, maintaining engagement, achieving demographic diversity, and addressing privacy concerns. Conversely, we faced different challenges when recruiting software practitioners, including sourcing the right expertise, utilising online recruiting platforms, navigating time constraints, aligning incentives, obtaining a representative sample, and coordinating with remote and distributed teams. By detailing the strategies we employed to address these challenges, this paper contributes practical knowledge to enhance the efficacy and inclusiveness of research practices, ultimately fostering more robust software engineering research outcomes.

As software systems become more complex, some users find it challenging to use these tools efficiently, leading to frustration and decreased productivity. We tackle the shortcomings of conventional user support mechanisms in software and aim to create and assess a user support system that integrates Multimodal Large Language Models (MLLMs) for producing support messages. Our system initially segments the user interface to serve as a reference for selection and requests users to specify their preferences for support messages. Following this, the system creates personalised user support messages for each individual. We propose that user support systems enhanced with MLLMs can provide more efficient and bespoke assistance compared to conventional methods.

Abstract The COVID-19 pandemic changed the way we live, work and the way we conduct research. With the restrictions of lockdowns and social distancing, various impacts were experienced by many software engineering researchers, especially whose studies depend on human participants. We conducted a mixed methods study to understand the extent of this impact. Through a detailed survey with 89 software engineering researchers working with human participants around the world and a further nine follow-up interviews, we identified the key challenges faced, the adaptations made, and the surprising fringe benefits of conducting research involving human participants during the pandemic. Our findings also revealed that in retrospect, many researchers did not wish to revert to the old ways of conducting human-orienfted research. Based on our analysis and insights, we share recommendations on how to conduct remote studies with human participants effectively in an increasingly hybrid world when face-to-face engagement is not possible or where remote participation is preferred.

Abstract eHealth technologies have been increasingly used to foster proactive self-management skills for patients with chronic diseases. However, it is challenging to provide each user with their desired support due to the dynamic and diverse nature of the chronic disease and its impact on users. Many such eHealth applications support aspects of “adaptive user interfaces”—interfaces that change or can be changed to accommodate the user and usage context differences. To identify the state of the art in adaptive user interfaces in the field of chronic diseases, we systematically located and analysed 48 key studies in the literature with the aim of categorising the key approaches used to date and identifying limitations, gaps, and trends in research. Our data synthesis is based on the data sources used for interface adaptation, the data collection techniques used to extract the data, the adaptive mechanisms used to process the data, and the adaptive elements generated at the interface. The findings of this review will aid researchers and developers in understanding where adaptive user interface approaches can be applied and necessary considerations for employing adaptive user interfaces to different chronic disease-related eHealth applications.

Children and elderly users typically interact very differently with the same software interfaces. These should be taken into consideration while designing and developing software that will be used by these users. In order to better understand the characteristics of children and elderly users of software, we systematically reviewed the relevant literature. We identified different facets of children and the elderly that are relevant to their interaction with the software. The facets for children are- Special needs children, Interaction with technology and General characteristics. Facets for elderly users are- Technical proficiency, Interaction with technology, Technology expectations and General characteristics. We developed a tool with different descriptions of those facets collected from our review. We collected feedback on the tool from experts and refined the tool accordingly. The personas generated using the tool can be used as is or can be extended by adding more context-specific information to the personas. The tool will help designers and developers of software interfaces to better understand the requirements of these user groups and to incorporate their specific needs during design and development. This will result in reducing biases arising from age-related facets and help to create more inclusive software.

Software Engineering (SE) researchers need to interact with diverse communities such as disabled users, older adults, and people from various cultures and professions. This interactions have always been challenging that and were exacerbated by the COVID-19 pandemic which led to the adoption of new methods such as Zoom-based interviews, collaborative tools-based workshops and so on. These issues can be overcome by collective knowledge sharing. This paper presents a knowledge sharing framework for the SE research community to support information sharing about research frameworks, tools, and guidelines to be used when interacting with particular participant groups. We developed this framework using JavaScript (JS) and CSS and hosted the knowledge data as YAML files in the GitHub repository. The framework facilitates adding new content to the tool while consuming what is already there. By using GitHub and establishing a CI/CD pipeline to Vercel (the hosting service) we supported automatic integration of content after it has been approved by a moderator. We developed this framework using the iterative development approach and sought feedback from the SE research community in each iteration.

The rising prevalence of chronic diseases necessitates effective self-management strategies. mHealth interventions have shown promise in supporting self-management, but their under-utilization remains a challenge. Individuals with chronic diseases exhibit significant variations in their conditions, severity levels, and associated complications, highlighting the need for more tailored approaches. Adaptive User Interfaces (AUIs) can be used as a solution to address the diverse and dynamic needs of individuals with chronic diseases. We have created an AUI prototype based on existing literature, incorporating presentation, content, and behaviour adaptation. Our user study employs a mixed-method research approach to gather insights from users by interacting with our prototype. The future plans of the study aim to utilise insights obtained from the data analysis to automatically generate AUIs using a model-driven approach.

Defect reporting customarily exists in most applications and web sites to support issue reporting by end users and for developers to receive actionable feedback. However, the impact of "human-centric" issues - such as age, gender, language, culture, physical and mental challenges, and socio-economic status - is often overlooked in the development process and during product inception and defect reporting. Most defect reporting tools lack necessary human-centric features to enable a challenged user to adequately navigate and report defects i.e. do not take into account the human differences between end users that cause defects for them in their software and make reporting of such defects difficult for them. Most defect reporting tools also lack sufficient defect report structuring, reporting guidance, and do not emphasize the possible perceived severity of the defect to developers from end users who are very different to them. If users are unable to report defects due to usability issues, this makes those same defect reports difficult to understand by the developer. In this paper, we aim to improve human-centric defect reporting by employing cognitive walkthrough with diverse end user personas, develop a prototype of an improved defect reporting tool, and evaluate the prototype’s defect reporting process from end user and developer perspectives. Our findings offer a foundation for improved human-centric defect evaluation, reporting and fixing.

Most prior research into adaptable and adaptive user interfaces primarily focuses on facilitating consistent user experiences across devices and pays less attention to facilitating universal access for diverse end users. We address this shortcoming by developing adaptable user interface components for a category of diverse users, the vision impaired. This paper presents a framework that supports run time adaptation of web components to suit vision impaired users by using a set of adaptable, reusable widgets. We developed a prototype using these components and evaluated its effectiveness. Our results show that an such an adaptable user interface provides significant benefit in many key W3C accessibility areas, helping to make the web more accessible for diverse end users.

We are developing an application to automatically generate an accessible graphic from a floor plan image. Floor plans generally contain large regions with functionally different sub-areas. A problem faced by visually impaired users in exploring such accessible floor plans is understanding the boundaries of these sub-areas. We present an effective method to partition such open plan areas. Initially, we conducted a formative user study to understand how people partition open plan areas. Based on the findings of the study, we identified a general set of guidelines for partitioning open plans. These guidelines were used to generate a set of candidate lines for sub-areas. An obstacle avoiding shortest-path Voronoi diagram was used to determine boundaries for each sub-area. Candidate lines such as wall extensions were automatically generated to replace the identified boundaries. We selected the best replacement for each boundary by scoring candidate lines using a set of criteria such as line length. Finally the proposed method was tested on a standard floor plan corpus using three novel measures.

Better access to on-line information graphics is a pressing need for people who are blind or have severe vision impairment. We present a new model for accessible presentation of on-line information graphics and demonstrate its use for presenting floor plans. While floor plans are increasingly provided on-line, people who are blind are at best provided with only a high-level textual description. This makes it difficult for them to understand the spatial arrangement of the objects on the floor plan. Our new approach provides users with significantly better access to such plans. The users can automatically generate an accessible version of a floor plan from an on-line floor plan image quickly and independently by using a web service. This generates a simplified graphic showing the rooms, walls, doors and windows in the original floor plan as well as a textual overview. The accessible floor plan is presented on an iPad using audio feedback. As the users touch graphic elements on the screen, the element they are touching is described by speech and non-speech audio in order to help them navigate the graphic.