Related papers: The Digital Divide in Geriatric Care: Why Usability, Not Access, is the Real Problem

The Digital Divide in Geriatric Care: Why Usability, Not Access, is the Real Problem

URL: http://arxiv.org/abs/2601.17012v1
Date: Wed, 14 Jan 2026 18:31:15 GMT
Title: The Digital Divide in Geriatric Care: Why Usability, Not Access, is the Real Problem
Authors: Christine Ine,
Abstract summary: The rapid increase in the world's aging population to 16% by the year 2050 spurs the need for the application of digital health solutions.<n>This study contends that user experience (UX) poor design is the primary adoption barrier, rather than access.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The rapid increase in the world's aging population to 16% by the year 2050 spurs the need for the application of digital health solutions to enhance older individuals' independence, accessibility, and well-being. While digital health technologies such as telemedicine, wearables, and mobile health applications can transform geriatric care, their adoption among older individuals is not evenly distributed. This study redefines the "digital divide" among older health care as a usability divide, contends that user experience (UX) poor design is the primary adoption barrier, rather than access. Drawing on interdisciplinary studies and design paradigms, the research identifies the main challenges: visual, cognitive, and motor impairment; complicated interfaces; and lack of co-creation with older adults, and outlines how participatory, user-focused, and inclusive notions of design can transcend them. Findings reveal that older persons easily embrace those technologies that are intuitive, accessible, and socially embedded as they promote autonomy, confidence, and equity in health. The study identifies the effects of the design attributes of high-contrast screens, lower interaction flow, multimodal feedback, and caregiver integration as having strong influences on usability outcomes. In addition, it critiques the current accessibility guidelines as being technically oriented rather than experiential and demands an ethical, empathetic understanding of design grounded in human-centered usability rather than technical accessibility in itself.

Related papers

A Comprehensive Review of Datasets for Clinical Mental Health AI Systems [55.67299586253951]
We present the first comprehensive survey of clinical mental health datasets relevant to the training and development of AI-powered clinical assistants.<n>Our survey identifies critical gaps such as a lack of longitudinal data, limited cultural and linguistic representation, inconsistent collection and annotation standards, and a lack of modalities in synthetic data.
arXiv Detail & Related papers (2025-08-13T13:42:35Z)
On Demographic Transformation: Why We Need to Think Beyond Silos [0.0]
We examine the role of artificial intelligence (AI) and robotics in transforming geriatric care.<n>These technologies offer powerful tools for personalizing treatment, enhancing diagnostics, and enabling remote monitoring.<n>We conclude by advocating for a transdisciplinary framework that unites policymakers, healthcare professionals, engineers, ethicists, and community stakeholders.
arXiv Detail & Related papers (2025-07-03T19:12:01Z)
Deep Learning-Based Facial Expression Recognition for the Elderly: A Systematic Review [0.5242869847419834]
The rapid aging of the global population has highlighted the need for technologies to support elderly.<n>Facial expression recognition (FER) systems offer a non-invasive means of monitoring emotional states.<n>This study presents a systematic review of deep learning-based FER systems, focusing on their applications for the elderly population.
arXiv Detail & Related papers (2025-02-04T11:05:24Z)
Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [58.61680631581921]
Mental health disorders create profound personal and societal burdens, yet conventional diagnostics are resource-intensive and limit accessibility.<n>This paper examines these challenges and proposes solutions, including anonymization, synthetic data, and privacy-preserving training.<n>It aims to advance reliable, privacy-aware AI tools that support clinical decision-making and improve mental health outcomes.
arXiv Detail & Related papers (2025-02-01T15:10:02Z)
Investigating an Intelligent System to Monitor \& Explain Abnormal Activity Patterns of Older Adults [52.40826527071519]
Despite the growing potential of older adult care technologies, the adoption of these technologies remains challenging.<n>This work conducted a focus-group session with family caregivers to scope designs of the older adult care technology.<n>We developed a high-fidelity prototype and conducted its qualitative study with professional caregivers and older adults.
arXiv Detail & Related papers (2025-01-30T03:21:14Z)
Requirements Engineering for Older Adult Digital Health Software: A Systematic Literature Review [2.7992435001846827]
Growth of the older adult population has led to an increasing interest in technology-supported aged care. Digital health can be important in supporting older adults wellbeing, emotional requirements, and social needs. There is a gap in the understanding between developers and ageing people of their requirements.
arXiv Detail & Related papers (2024-11-06T04:35:39Z)
A Survey of Accessible Explainable Artificial Intelligence Research [0.0]
This paper presents a systematic literature review of the research on the accessibility of Explainable Artificial Intelligence (XAI) Our methodology includes searching several academic databases with search terms to capture intersections between XAI and accessibility. We stress the importance of including the disability community in XAI development to promote digital inclusion and accessibility.
arXiv Detail & Related papers (2024-07-02T21:09:46Z)
Towards Privacy-Aware and Personalised Assistive Robots: A User-Centred Approach [55.5769013369398]
This research pioneers user-centric, privacy-aware technologies such as Federated Learning (FL) FL enables collaborative learning without sharing sensitive data, addressing privacy and scalability issues. This work includes developing solutions for smart wheelchair assistance, enhancing user independence and well-being.
arXiv Detail & Related papers (2024-05-23T13:14:08Z)
Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence [1.2330982742485441]
The experience of older adults' interaction with the Ehealth interface is collected through interviews and transformed into operatable databases. The study identifies important design factors, such as intuitive visualization and straightforward explanations, that are critical for creating efficient Human Computer Interaction (HCI) tools among older users.
arXiv Detail & Related papers (2024-02-01T16:39:02Z)
A Survey on Computer Vision based Human Analysis in the COVID-19 Era [58.79053747159797]
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication
arXiv Detail & Related papers (2022-11-07T17:20:39Z)
What Do End-Users Really Want? Investigation of Human-Centered XAI for Mobile Health Apps [69.53730499849023]
We present a user-centered persona concept to evaluate explainable AI (XAI) Results show that users' demographics and personality, as well as the type of explanation, impact explanation preferences. Our insights bring an interactive, human-centered XAI closer to practical application.
arXiv Detail & Related papers (2022-10-07T12:51:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.