Patient portals are a convenient way for patients to communicate with healthcare providers and manage care. Active portal engagement is associated with enhanced knowledge, increased self-efficacy, and improved symptom management. However, gaps in portal use persist, particularly among older adults. We conducted a random-effects meta-analysis of studies published between 2005 and 2025, with publication year as a moderator. From 1,487 unique records, 18 studies were included in the meta-analysis. We found that an estimated 55% of older adults have used a patient portal at least once, and 49% were continuing users, although these rates varied widely across studies. Publication year was a significant moderator for ever having used a patient portal, suggesting increasing uptake over time, but was not significantly associated with continued use. These findings suggest that improving onboarding alone may not be sufficient; ongoing tech support is needed to sustain long-term patient portal engagement among older adults.
Recommendation systems for nutrition management have become increasingly popular. Current solutions focus on optimizing for users taste preferences, often neglecting practical factors including personal priorities, existing plans, and situational constraints. We propose similarity-based meal recommendation - a novel approach that considers users existing meal plans and recommends similar meals that also align with their nutrition goals. This approach is designed to support meal decisions in on-the-spot, particularly when individuals are in constrained settings with limited options for adjustment (e.g. a cafeteria). We conducted a feasibility study with 15 participants to evaluate the utility of similarity-based recommendations in constrained settings and refine the system design. Findings show (1) a novel approach for similarity-based meal recommendations, (2) insights into the dimensions of similarity influencing actionability across diverse, and (3) opportunities to enhance user control over the system's design. The study contributes to the advancement of human-centered AI technologies in nutrition.
Dementia is a global public health concern, with approximately 55 million individuals worldwide currently living with it. Individual cognitive stimulation therapy (iCST) has been shown to improve quality of life for persons living with dementia (PLwDs). However, providing iCST at scale remains a serious challenge, specifically as it can add considerable burden on care partners. This project focuses on a voice assistant (VA) to support care partners in delivering iCST. We developed a VA prototype and conducted a qualitative study with care partners (N=5). Our preliminary findings show that using a VA to deliver iCST is feasible and acceptable. We have also identified design requirements for the VA to effectively provide iCST, including need for personalization reflecting dementia severity and individual interests, collaboration between care partners and PLwDs, and accessible interactions to minimize frustration and distress. These findings can inform the future design of inclusive and accessible VAs.
Video games are becoming increasingly accessible, but there are still scant resources for gamers with disabilities to reliably discover and evaluate which games are accessible to them. To remedy this, I designed OURCADE: a social simulation role-playing game that utilizes player engagement to produce a wiki-based resource for game accessibility information. This design leverages existing insights around game accessibility, citizen science, and video game communities, while also exploring new questions around the potential of social video games as a medium for answering complex game accessibility questions.
MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being. We argue that integrating behavioral sensing in LLMs will likely lead to a new frontier in AI. In this Late-Breaking Work paper, we discuss the MindScape contextual journal App design that uses LLMs and behavioral sensing to generate contextual and personalized journaling prompts crafted to encourage self-reflection and emotional development. We also discuss the MindScape study of college students based on a preliminary user study and our upcoming study to assess the effectiveness of contextual AI journaling in promoting better well-being on college campuses. MindScape represents a new application class that embeds behavioral intelligence in AI.
Research at the intersection of human-computer interaction (HCI) and health is increasingly done by collaborative cross-disciplinary teams. The need for cross-disciplinary teams arises from the interdisciplinary nature of the work itself-with the need for expertise in a health discipline, experimental design, statistics, and computer science, in addition to HCI. This work can also increase innovation, transfer of knowledge across fields, and have a higher impact on communities. To succeed at a collaborative project, researchers must effectively form and maintain a team that has the right expertise, integrate research perspectives and work practices, align individual and team goals, and secure funding to support the research. However, successfully operating as a team has been challenging for HCI researchers, and can be limited due to a lack of training, shared vocabularies, lack of institutional incentives, support from funding agencies, and more; which significantly inhibits their impact. This workshop aims to draw on the wealth of individual experiences in health project team collaboration across the CHI community and beyond. By bringing together different stakeholders involved in HCI health research, together, we will identify needs experienced during interdisciplinary HCI and health collaborations. We will identify existing practices and success stories for supporting team collaboration and increasing HCI capacity in health research. We aim for participants to leave our workshop with a toolbox of methods to tackle future team challenges, a community of peers who can strive for more effective teamwork, and feeling positioned to make the health impact they wish to see through their work.
Concerns regarding the impacts of stereotyped, deficit-based, and problem-oriented approaches to older adult users have propelled HCI to explore new understandings and ways of approaching aging as a subject in recent years. Meanwhile, older adults’ relationships with digital technologies are also evolving, driven both by technological advancements and the destabilizing experience of the global pandemic. Now is an important time to take stock of these changes and their significance to the field of HCI and Aging. This workshop attends, therefore, to the need for collective reflection on where the field is now, how we got here, and where it is heading. In addition to highlighting emerging areas requiring research attention, the workshop will produce a snapshot in time to compare with several years hence as the field continues to evolve. The second part of the workshop responds to the need for a clear alternative to deficit based approaches to designing technologies for older adult users. We will pool the collective wisdom of the HCI and Aging community to generate a set of principles to guide research and development toward maximization of benefit and minimization of harm to older adult users/stakeholders.
Human papillomavirus (HPV) vaccinations are lower than expected. To protect the onset of head and neck cancers, innovative strategies to improve the rates are needed. Artificial intelligence may offer some solutions, specifically conversational agents to perform counseling methods. We present our efforts in developing a dialogue model for automating motivational interviewing (MI) to encourage HPV vaccination. We developed a formalized dialogue model for MI using an existing ontology-based framework to manifest a computable representation using OWL2. New utterance classifications were identified along with the ontology that encodes the dialogue model. Our work is available on GitHub under the GPL v.3. We discuss how an ontology-based model of MI can help standardize/formalize MI counseling for HPV vaccine uptake. Our future steps will involve assessing MI fidelity of the ontology model, operationalization, and testing the dialogue model in a simulation with live participants.
With advances in AI, computer vision, and interface understanding, there is the potential to offload much of the work currently spent by companies’ developers in making products accessible. There is also the potential to move our major accessibility approach from an ‘inclusively-designed-products-plus-AT focus to a ‘universal-interface-transformer focus. This would be a major reversal of approach and have significant ramifications for legislation, regulation, and the established large-scale accessibility industries that have grown up around them. Such a disruption would require concrete evidence that such a change would, in fact, be better for people with disabilities. It would also require a path from the former to the latter. This paper presents the case for such a shift, some of the benefits and ramifications, and the developments necessary to make the shift. It also outlines a hybrid approach between inclusive design and bespoke custom interfaces.
Human computer interaction (HCI) and implementation science (IS) each have been applied to improve the adoption and delivery of innovative health interventions, and the two fields have complementary goals, foci, and methods. While the IS community increasingly draws on methods from HCI, there are many unrealized opportunities for HCI to draw from IS and to catalyze bidirectional collaborations. This workshop will explore similarities and differences between fields, with a goal of articulating a research agenda at their intersection.
How well a caption fits an image can be difficult to assess due to the subjective nature of caption quality. What is a good caption? We investigate this problem by focusing on image-caption ratings and by generating high quality datasets from human feedback with gamification. We validate the datasets by showing a higher level of inter-rater agreement, and by using them to train custom machine learning models to predict new ratings. Our approach outperforms previous metrics – the resulting datasets are more easily learned and are of higher quality than other currently available datasets for image-caption rating.
Wearable devices have long held the potential to provide real-time objective measures of behavior. However, due to challenges in real-world deployment, these systems are rarely tested rigorously in free-living settings. To reduce this challenge for future researchers, in this paper, we describe our experience developing several generations of a multi-sensor, neck-worn eating-detection system that has been tested with 130 participants across multiple studies in both laboratory and free-living settings. We describe the challenges faced in the development and deployment of the system by (1) presenting example deployment details captured either by the sensing system or the ground truth collector and (2) using structured interviews and surveys with developers and stakeholders of the system, collecting qualitative data on their experience. We performed thematic analysis and provided detailed lessons learned explaining factors that impact the experience of building and deploying such a wearable in a free-living setting, reducing challenges for future researchers. We believe that our experience will help future researchers develop successful mobile health (mHealth) systems that translate into reliable free-living deployments.
The near-live simulation method has advantages over other methods when testing technologies for supporting team-based work because testing can be conducted without requiring an entire team to assemble. In this case study, we describe our experiences in conducting 14 remote near-live simulation sessions to evaluate a digital checklist application used in pediatric trauma resuscitation. The remote near-live simulation sessions were complex to design and conduct because participants did not have direct access to a device that could run the checklist application and the digital checklist needed to integrate with the vital sign monitor used during resuscitations. We describe how we designed the environment for running the near-live simulations and discuss the lessons learned from conducting these simulations. We highlight three factors that need to be considered when using the near-live simulation approach: (1) filming or selecting the video of the simulated event, (2) providing participants with access to the system that is being evaluated, and (3) integrating the system being tested with other systems in the simulation scenario.
Health science researchers studying human behavior rely on wearable cameras to visually confirm behaviors in real-world settings. However, privacy concerns significantly impede their adoption. Lens orientation and activity-oriented cameras have potential in balancing the need to visually validate the wearers’ activities while reducing privacy concerns. To increase adoption and further alleviate privacy concerns while maintaining utility, generative stylizing approaches, like cartooning using generative adversarial networks (GANs), have recently shown promise. We investigate different cartoon-based obfuscation of activity-oriented footage through two studies. The first deploys crowdsourcing methods (n=60), while the second is experiential, where participants (n=49) don the device for an entire day and report concerns on their footage. Our findings support that cartoonization of activity-oriented data significantly reduces privacy concerns, particularly among bystanders in high privacy-concerning scenarios, while maintaining context verification (90% of participants). Through thematic analysis, we provide further insight for the community on best practices for cartoonization of activity-oriented videos.
There has been a growing interest in HCI to understand the specific technological needs of people with dementia and supporting them in self-managing daily activities. One of the most difficult challenges to address is supporting the fluctuating accessibility needs of people with dementia, which vary with the specific type of dementia and the progression of the condition. Researchers have identified auto-personalized interfaces, and more recently, Artificial Intelligence or AI-driven personalization as a potential solution to making commercial technology accessible in a scalable manner for users with fluctuating ability. However, there is a lack of understanding on the perceptions of people with dementia around AI as an aid to their everyday technology use and its role in their overall self-management systems, which include other non-AI technology, and human assistance. In this paper, we present future directions for the design of AI-based systems to personalize an interface for dementia-related changes in different types of memory, along with expectations for AI interactions with the user with dementia.
This study presents the evaluation of ability-based methods extended to keyboard generation for alternative communication in people with dexterity impairments due to motor disabilities. Our approach characterizes user-specific cursor control abilities from a multidirectional point-select task to configure letters on a virtual keyboard based on estimated time, distance, and direction of movement. These methods were evaluated in three individuals with motor disabilities against a generically optimized keyboard and the ubiquitous QWERTY keyboard. We highlight key observations relating to the heterogeneity of the manifestation of motor disabilities, perceived importance of communication technology, and quantitative improvements in communication performance when characterizing an individual's movement abilities to design personalized AAC interfaces.
The last several years have seen a strong growth of telerobotic technologies with promising implications for many areas of learning. HCI has contributed to these discussions, mainly with studies on user experiences and user interfaces of telepresence robots. However, only a few telerobot studies have addressed everyday use in real-world learning environments. In the post-COVID 19 world, sociotechnical uncertainties and unforeseen challenges to learning in hybrid learning environments constitute a unique frontier where robotic and immersive technologies can mediate learning experiences. The aim of this workshop is to set the stage for a new wave of HCI research that accounts for and begins to develop new insights, concepts, and methods for use of immersive and telerobotic technologies in real-world learning environments. Participants are invited to collaboratively define an HCI research agenda focused on robot-mediated learning in the wild, which will require examining end-user engagements and questioning underlying concepts regarding telerobots for learning.
Although clinical training in implicit bias is essential for healthcare equity, major gaps remain both for effective educational strategies and for tools to help identify implicit bias. To understand the perspectives of clinicians on the design of these needed strategies and tools, we conducted 21 semi-structured interviews with primary care clinicians about their perspectives and design recommendations for tools to improve patient-centered communication and to help mitigate implicit bias. Participants generated three types of solutions to improve communication and raise awareness of implicit bias: digital nudges, guided reflection, and data-driven feedback. Given the nuance of implicit bias communication feedback, these findings illustrate innovative design directions for communication training strategies that clinicians may find acceptable. Improving communication skills through individual feedback designed by clinicians for clinicians has the potential to improve healthcare equity.
During the COVID-19 pandemic, we had to transition our user-centered research and design activities in the emergency medical domain of trauma resuscitation from in-person settings to online environments. This transition required that we replicate the in-person interactions remotely while maintaining the critical social connection and the exchange of ideas with medical providers. In this paper, we describe how we designed and conducted four user-centered design activities from our homes: participatory design workshops, near-live simulation sessions, usability evaluation sessions, and interviews and design walkthroughs. We discuss the differences we observed in our interactions with participants in remote sessions, as well as the differences in the interactions among the research team members. From this experience, we draw several lessons and outline the best practices for remotely conducting user-centered design activities that have been traditionally held in person.
User-centered design is typically framed around meeting the preferences and needs of populations involved in the design process. However, when designing technology for people with disabilities, in particular dementia, there is also a moral imperative to ensure that human rights of this segment of the population are consciously integrated into the process and respectfully included in the product. We introduce a human rights-based user-centered design process which is informed by the United Nations Convention on the Rights of Persons with Disabilities (CRPD). We conducted two editions of a three-day-long design workshop during which undergraduate students and dementia advocates came together to design technology for people with dementia. This case study demonstrates our novel approach to user-centered design that centers human rights through different stages of the workshop and actively involves people with dementia in the design process.
