A Framework for Design of Conversational Agents to Support Health Self-Care for Older Adults

Smart Speaker and ICT Use in Relationship With Social Connectedness During the Pandemic: Loneliness and Social Isolation Found in Older Adults in Low-Income Housing

BACKGROUND AND OBJECTIVES

Social well-being of older adults living in low-income housing was disproportionately affected by the coronavirus disease 2019 pandemic. We explored low-income residents' experiences of social isolation and loneliness and strategies to remain socially connected during the pandemic.

RESEARCH DESIGN AND METHODS

As part of a larger, 3-phase user-centered design study, we conducted a qualitative study using focus groups to gain insights into social isolation experiences and the role of information and communication technologies (ICTs), including smart speakers, in social connectedness (N = 25, 76% African American). We also collected survey data to describe social isolation, loneliness, and current ICT use in the sample. Participants included both smart speaker users and nonusers.

RESULTS

Experiences of social isolation and loneliness varied by participants' sociodemographic characteristics and previous experiences with smart speakers. Qualitative analysis showed participants demonstrated coping strategies developed during the pandemic to adapt to new norms of connecting with others, including technology-enabled social interactions. Participants expressed a strong desire to build community together in their facilities and highlighted the potential role of smart speakers in making meaningful social connections, encompassing safety checks to have a means for emergencies, and providing a virtual companion. Access, digital literacy, training, security, and privacy issues were discussed as factors affecting their adoption of new ICT for enhanced social connectedness.

DISCUSSION AND IMPLICATIONS

This study highlights the importance of understanding the unique social isolation experience, demographics, and social determinants of health of low-income residents to develop ICT-based interventions for social connectedness.

Supporting mental health self-care discovery through a chatbot

Good mental health is imperative for one's wellbeing. While clinical mental disorder treatments exist, self-care is an essential aspect of mental health. This paper explores the use and perceived trust of conversational agents, chatbots, in the context of crowdsourced self-care through a between-subjects study (N = 80). One group used a standalone system with a conventional web interface to discover self-care methods. The other group used the same system wrapped in a chatbot interface, facilitating utterances and turn-taking between the user and a chatbot. We identify the security and integrity of the systems as critical factors that affect users' trust. The chatbot interface scored lower on both these factors, and we contemplate the potential underlying reasons for this. We complement the quantitative data with qualitative analysis and synthesize our findings to identify suggestions for using chatbots in mental health contexts.

Benefits of, Barriers to, and Needs for an Artificial Intelligence-Powered Medication Information Voice Chatbot for Older Adults: Interview Study With Geriatrics Experts

BACKGROUND

One of the most complicated medical needs of older adults is managing their complex medication regimens. However, the use of technology to aid older adults in this endeavor is impeded by the fact that their technological capabilities are lower than those of much of the rest of the population. What is needed to help manage medications is a technology that seamlessly integrates within their comfort levels, such as artificial intelligence agents.

OBJECTIVE

This study aimed to assess the benefits, barriers, and information needs that can be provided by an artificial intelligence-powered medication information voice chatbot for older adults.

METHODS

A total of 8 semistructured interviews were conducted with geriatrics experts. All interviews were audio-recorded and transcribed. Each interview was coded by 2 investigators (2 among ML, PR, METR, and KR) using a semiopen coding method for qualitative analysis, and reconciliation was performed by a third investigator. All codes were organized into the benefit/nonbenefit, barrier/nonbarrier, and need categories. Iterative recoding and member checking were performed until convergence was reached for all interviews.

RESULTS

The greatest benefits of a medication information voice-based chatbot would be helping to overcome the vision and dexterity hurdles experienced by most older adults, as it uses voice-based technology. It also helps to increase older adults' medication knowledge and adherence and supports their overall health. The main barriers were technology familiarity and cost, especially in lower socioeconomic older adults, as well as security and privacy concerns. It was noted however that technology familiarity was not an insurmountable barrier for older adults aged 65 to 75 years, who mostly owned smartphones, whereas older adults aged >75 years may have never been major users of technology in the first place. The most important needs were to be usable, to help patients with reminders, and to provide information on medication side effects and use instructions.

CONCLUSIONS

Our needs analysis results derived from expert interviews clarify that a voice-based chatbot could be beneficial in improving adherence and overall health if it is built to serve the many medication information needs of older adults, such as reminders and instructions. However, the chatbot must be usable and affordable for its widespread use.