While much has been written on the age-based digital divide, more understanding of the relative importance of factors affecting use of the internet is needed. This paper analyses nationally representative data from the English Longitudinal Study of Ageing (ELSA) to understand older adults’ frequency of using the internet and reasons for not using it more. We examine the extent to which health, lifestyle, and sociodemographic correlate with the pronounced age gradient in not using the internet. We find that the reasons why people in the 80+ cohort did not use the internet more are not qualitatively different from the reasons people aged 50–64 or 65--79 did not use it more, but do differ between rare and regular users. We also find that of the myriad factors that are potentially relevant, only cognitive ability, educational attainment, and employment status were robustly associated with the age gradient in internet use.
The accessibility of interactive technologies is often evaluated using checklists that are low-level, numerous, and platform-specific. Such checklists are typically used by accessibility experts, leaving everyday designers and developers with little support for assessing their own interfaces. To make accessibility evaluations easier to conduct, we devised a set of nine ``ability heuristics'' that prompt designers to engage with accessibility throughout the design process. We empirically evaluated these ability heuristics with 37 design students, comparing them to usability heuristics and WCAG. The ability heuristics emphasized the quality of accessibility features compared to the other methods, and surfaced issues that were more broadly dispersed across disability groups. Further, the students found the heuristics were as easy to use as the alternative methods. We argue that the heuristics help to move beyond binary notions of accessibility, pushing designers to consider the quality of features across diverse disabilities and the range of abilities within.
Research on technology for older people focuses on older people’s experiences–and understandably so. However, the phenomena of othering, or seeing this group as different and worse off, is a persistent problem. In this project, we turned inwards through an 8-month collaborative autoethnography to understand our own experiences with technology issues and supporting others in technology use. We found that each member of our mixed age group faced pervasive and burdensome technology issues and recognized that some of the burden is associated with the evolution of technology tools. Our work contributes an expanded understanding of aging as a sociotechnical process and identifies counternarratives to implicit assumptions we held as HCI researchers working with older people. Our research also shows how reflexive positional methods can surface often unexamined experiences with technology issues and aging among researchers.
Ableist language perpetuates harmful stereotypes and exclusion, yet its nuanced nature makes it difficult to recognize and address. Artificial intelligence could serve as a powerful ally in the fight against ableist language, offering tools that detect and suggest alternatives to biased terms. This two-part study investigates the potential of large language models (LLMs), specifically ChatGPT, to rectify ableist language and educate users about inclusive communication. We compared GPT-4o generations with crowdsourced annotations from trained disability community members, then invited disabled participants to evaluate both. Participants reported equal agreement with human and AI annotations but significantly preferred the AI, citing its narrative consistency and accessible style. At the same time, they valued the emotional depth and cultural grounding of human annotations. These findings highlight the promise and limits of LLMs in handling culturally sensitive content. Our contributions include a dataset of nuanced ableism annotations and design considerations for inclusive writing tools.
Research in social accessibility aims to improve the lives of disabled people across diverse abilities and experiences by assisting with communication, relationships, and ecosystems of access. We seek to understand this intersectional body of work through analyzing social accessibility research from 2011 to 2025. Through constructivist grounded theory analysis of 90 papers (curated from 605), we develop the Three Praxes Framework: three sites of practice — Artifact (constructive), Ecosystem (relational), and Epistemology (theoretical) — two cross-cutting stances toward change (Temporal Orientation and Stakeholder Focus) — and one reflexive cycle modeling how insights can flow between praxes. Our analysis reveals these praxes operate largely in isolation, risking that insights remain academic exercises while assistive technologies reinforce existing barriers. We call on the field to realize a cycle where disabled people's lived experiences shape material realities, material practice generates theoretical knowledge, and both transform ecosystems of access.
Camera-based facial-gesture interfaces offer hands-free access for people with motor impairments (PwM), yet most recognition models are trained on able-bodied data and implicitly assume normative motor control and proprioception. We conducted a mixed-methods empirical study of 37 above-neck gestures performed by 11 PwM and 11 non-impaired participants. Results reveal systematic mismatches between user intention and model recognition in the PwM group, stemming from diverse patterns of body perception and control and leading to allocative harms. These mismatches concentrated in low-amplitude, asymmetric, and directional gestures. Building on these findings, we introduce FairGesture, a diagnostic auditing method for quantifying and interpreting such mismatches. FairGesture combines (1) the Perception Gap metric, (2) trajectory-based motion analysis, and (3) an analysis of user sensorimotor feedback, exploring the reasons behind these mismatches. The work reframes accuracy in gesture recognition as a problem of sensorimotor alignment, advancing user-centred evaluation and inclusive model design.
Sedentary behavior is a critical health risk for older adults. Although digital interventions are widely available, they primarily rely on screen-based notifications that can feel clinical or cognitively demanding, and are thus often ignored over time. This paper presents a three-phase Research through Design methodology to explore data physicalization approaches that ambiently represent sedentary data patterns using decor artifacts in older adults’ homes. These artifacts transformed abstract data into aesthetic, evolving forms that became part of the domestic landscape. Our research revealed how these physicalizations fostered self-reflection, family conversations, and encouraged active lifestyles. We demonstrate how qualities like aesthetic ambiguity and slow revelation can empower older adults, fostering a reflective relationship with their well-being. Ultimately, we argue that creating data physicalizations for older adults necessitates a shift from merely informing users to enabling them to live with and through their data.