Understanding Barriers and Design Opportunities to Improve Healthcare and QOL for Older Adults through Voice Assistants

Testing 3 Modalities (Voice Assistant, Chatbot, and Mobile App) to Assist Older African American and Black Adults in Seeking Information on Alzheimer Disease and Related Dementias: Wizard of Oz Usability Study

BACKGROUND

Older African American and Black adults are twice as likely to develop Alzheimer disease and related dementias (ADRD) and have the lowest level of ADRD health literacy compared to any other ethnic group in the United States. Low health literacy concerning ADRD negatively impacts African American and Black people in accessing adequate health care.

OBJECTIVE

This study explored how 3 technological modalities-voice assistants, chatbots, and mobile apps-can assist older African American and Black adults in accessing ADRD information to improve ADRD health literacy. By testing each modality independently, the focus could be kept on understanding the unique needs and challenges of this population concerning the use of each modality when accessing ADRD-related information.

METHODS

Using the Wizard of Oz usability testing method, we assessed the 3 modalities with a sample of 15 older African American and Black adults aged >55 years. The 15 participants were asked to interact with the 3 modalities to search for information on local events happening in their geographical area and search for ADRD-related health information.

RESULTS

Our findings revealed that, across the 3 modalities, the content should avoid convoluted and complex language and give the possibility to save, store, and share it to be fully accessible by this population. In addition, content should come from credible sources, including information tailored to the participants' cultural values, as it has to be culturally relevant for African American and Black communities. Finally, the interaction with the tool must be time efficient, and it should be adapted to the user's needs to foster a sense of control and representation.

CONCLUSIONS

We conclude that, when designing ADRD-related interventions for African American and Black older adults, it proves to be crucial to tailor the content provided by the technology to the community's values and construct an interaction with the technology that is built on African American and Black communities' needs and demands.

Using Voice-Activated Technologies to Enhance Well-Being of Older Adults in Long-Term Care Homes

Background and Objectives

Information communication technologies (ICTs) can enhance older adults' health and well-being. Most research on the use of voice-activated ICTs by older adults has focused on the experiences of individuals living in the community, excluding those who live in long-term care homes. Given evidence of the potential benefits of such technologies to mitigate social isolation and loneliness, more research is needed about their impacts in long-term care home settings. With this in mind, we evaluated impacts and engagement of older adults with voice- and touchscreen-activated ICTs in one long-term care home in Canada.

Research Design and Methods

Interviews were conducted with older adults who were provided with a Google Nest Hub Max and with staff as part of a larger implementation study. Participants completed semistructured interviews before the technology was implemented, and again at 6 and 12 months. The interviews were recorded, transcribed, and analyzed using thematic analysis techniques.

Results

We found that residents primarily used the technologies to engage in self-directed digital leisure and to engage with others both in and outside the home, and that this in turn enhanced their comfort, pleasure, and social connectedness. We also identified ongoing barriers to their engagement with the technology, including both personal and structural factors.

Discussion and Implications

Our findings suggest that implementation of voice-activated ICTs can bring added value to broader efforts to improve well-being and quality of life in long-term care by enhancing choice, self-determination, and meaningful relationships.

Urinary concerns among older adults: a qualitative analysis in the context of healthy aging

BACKGROUND

Urinary concerns increase with age impacting health and quality of life. The aims of this study were to describe: (1) urinary concerns as an age-related change (ARC); (2) the challenges of urinary concerns; (3) adaptation strategies used to manage urinary concerns; and (4) the value of engaging with aging (EWA) as a framework to promote self-management of urinary concerns.

METHODS

Data was used from semi-structured interviews with 29 older adults (mean age 77 years). An iterative coding process was used. A codebook was developed based on a-priori themes derived from the EWA framework, our previous publication, and a line-by-line coding of one of the transcripts. As the analysis progressed, additional codes emerged, enriching the codebook.

RESULTS

Six themes emerged: (1) the participants' experiences; (2) responses to urinary concerns, (3) adaptation and management strategies; (4) knowledge and understanding of urinary concerns; (5) available capacities and resources; and (6) the impact of the COVID-19 pandemic on urinary concerns. Participants tended to address their urinary concerns by adjusting routines, medication schedules, or diet patterns. They tried to secure restroom locations or use tools or reminders to resolve their urinary concerns. COVID-19 led to increased inconvenience for older adults to engage in outdoor activities due to the closure of public restrooms.

CONCLUSIONS

Our in-depth qualitative analysis found that participants developed personalized adjustments to address their needs and abilities to their urinary concerns. These findings offer insights into the individual aging experience, which will further enhance our understanding and advancement of person-centered care.

You have interrupted me again!: making voice assistants more dementia-friendly with incremental clarification

In spontaneous conversation, speakers seldom have a full plan of what they are going to say in advance: they need to conceptualise and plan incrementally as they articulate each word in turn. This often leads to long pauses mid-utterance. Listeners either wait out the pause, offer a possible completion, or respond with an incremental clarification request (iCR), intended to recover the rest of the truncated turn. The ability to generate iCRs in response to pauses is therefore important in building natural and robust everyday voice assistants (EVA) such as Amazon Alexa. This becomes crucial with people with dementia (PwDs) as a target user group since they are known to pause longer and more frequently, with current state-of-the-art EVAs interrupting them prematurely, leading to frustration and breakdown of the interaction. In this article, we first use two existing corpora of truncated utterances to establish the generation of clarification requests as an effective strategy for recovering from interruptions. We then proceed to report on, analyse, and release SLUICE-CR: a new corpus of 3,000 crowdsourced, human-produced iCRs, the first of its kind. We use this corpus to probe the incremental processing capability of a number of state-of-the-art large language models (LLMs) by evaluating (1) the quality of the model's generated iCRs in response to incomplete questions and (2) the ability of the said LLMs to respond correctly after the users response to the generated iCR. For (1), our experiments show that the ability to generate contextually appropriate iCRs only emerges at larger LLM sizes and only when prompted with example iCRs from our corpus. For (2), our results are in line with (1), that is, that larger LLMs interpret incremental clarificational exchanges more effectively. Overall, our results indicate that autoregressive language models (LMs) are, in principle, able to both understand and generate language incrementally and that LLMs can be configured to handle speech phenomena more commonly produced by PwDs, mitigating frustration with today's EVAs by improving their accessibility.

Public opinion on types of voice systems for older adults

Public opinion may influence the adoption of technologies for older adults, yet studies on different contexts of technology for older adults is limited. In an online YouGov survey (N = 500) with text-and-image vignettes, participants gave more positive ratings of social acceptability, trust, and perceived impact on eldercare when the voice assistant ("VA" system) shown in the vignette performed a functional task (medication adherence) versus when it performed a social task (companionship). The VA received more positive sentiment comments when it appeared to use a machine learning (ML)-based dialogue system compared to when it appeared to be using a rule-based dialogue system. These results may assist designers and stakeholders select what type of voice system to develop or use with older adults.

Screen or No Screen? Lessons Learnt from a Real-World Deployment Study of Using Voice Assistants With and Without Touchscreen for Older Adults

While voice user interfaces offer increased accessibility due to hands-free and eyes-free interactions, older adults often have challenges such as constructing structured requests and perceiving how such devices operate. Voice-first user interfaces have the potential to address these challenges by enabling multimodal interactions. Standalone voice + touchscreen Voice Assistants (VAs), such as Echo Show, are specific types of devices that adopt such interfaces and are gaining popularity. However, the affordances of the additional touchscreen for older adults are unknown. Through a 40-day real-world deployment with older adults living independently, we present a within-subjects study (N = 16; age M = 82.5, SD = 7.77, min. = 70, max. = 97) to understand how a built-in touchscreen might benefit older adults during device setup, conducting self-report diary survey, and general uses. We found that while participants appreciated the visual outputs, they still preferred to respond via speech instead of touch. We identified six design implications that can inform future innovations of senior-friendly VAs for managing healthcare and improving quality of life.

Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models to Map Voice Responses in SDoH Survey

Social Determinants of Health (SDoH) have been shown to have profound impacts on health-related outcomes, yet this data suffers from high rates of missingness in electronic health records (EHR). Moreover, limited English proficiency in the United States can be a barrier to communication with health care providers. In this study, we have designed a multilingual conversational agent capable of conducting SDoH surveys for use in healthcare environments. The agent asks questions in the patient's native language, translates responses into English, and subsequently maps these responses via a large language model (LLM) to structured options in a SDoH survey. This tool can be extended to a variety of survey instruments in either hospital or home settings, enabling the extraction of structured insights from free-text answers. The proposed approach heralds a shift towards more inclusive and insightful data collection, marking a significant stride in SDoH data enrichment for optimizing health outcome predictions and interventions.

How do Older Adults Set Up Voice Assistants? Lessons Learned from a Deployment Experience for Older Adults to Set Up Standalone Voice Assistants

While standalone Voice Assistants (VAs) are promising to support older adults' daily routine and wellbeing management, onboarding and setting up these devices can be challenging. Although some older adults choose to seek assistance from technicians and adult children, easy set up processes that facilitate independent use are still critical, especially for those who do not have access to external resources. We aim to understand the older adults' experience while setting up commercially available voice-only and voice-first screen-based VAs. Rooted in participants observations and semi-structured interviews, we designed a within-subject study with 10 older adults using Amazon Echo Dot and Echo Show. We identified the values of the built-in touchscreen and the instruction documents, as well as the impact of form factors, and outline important directions to support older adult independence with VAs.

"Ask, 'When You Do This, How Much Pain Are You In?'": Content Preferences for a Conversational Pain Self-Management Software Application

The purpose of the current study was to examine older adults' preferences for conversational pain management content to incorporate in an interactive application (app) for pain self-management. Conversational statements and questions were written as a script to encourage evidence-based pain self-management behaviors. The content was converted from text to female chatbot speech and saved as four groups of MP3 files. A purposive sample of 22 older adults participated in a guided interaction through the MP3 files. One-on-one interviews were conducted to garner participants' conversational content preferences. Overall, participants want the conversational content to increase health care provider engagement in pain management communication. Older adults preferred the inclusion of conversational statements and questions for monitoring the multifaceted dimensions of pain, treatment accountability, guidance for alternative treatments, and undesirable effects from pain treatments. The design of mobile health apps must incorporate the needs and preferences of older adults. [Journal of Gerontological Nursing, 49(1), 11-17.].

Ascertaining Whether an Intelligent Voice Assistant Can Meet Older Adults' Health-Related Needs in the Context of a Geriatrics 5Ms Framework

The Geriatrics 5Ms: Medications, Mind, Mobility, what Matters most and Multicomplexity is a framework to address the complex needs of older adults. Intelligent Voice Assistants (IVAs) are increasingly popular and have potential to support health-related needs of older adults. We utilized previously collected qualitative data on older adults' views of how an IVA may address their health-related needs and ascertained their fit into the Geriatrics 5Ms framework. The codes describing health challenges and potential IVA solutions fit the framework: (1) Medications: difficulty remembering medications.

SOLUTION

reminders. (2) Mind: isolation, anxiety, memory loss.

SOLUTION

companionship, memory aids. (3) Mobility: barriers to exercise.

SOLUTION

incentives, exercise ideas. (4) Matters most: eating healthy foods.

SOLUTION

suggest and order nutritious foods, (5) Multicomplexity; managing multimorbidity.

SOLUTION

symptom tracking and communicating with health care professionals. Incorporating the 5Ms framework into IVA design can aid in addressing health care priorities of older adults.

Towards Visualization of Time-Series Ecological Momentary Assessment (EMA) Data on Standalone Voice-First Virtual Assistants

Population aging is an increasingly important consideration for health care in the 21th century, and continuing to have access and interact with digital health information is a key challenge for aging populations. Voice-based Intelligent Virtual Assistants (IVAs) are promising to improve the Quality of Life (QoL) of older adults, and coupled with Ecological Momentary Assessments (EMA) they can be effective to collect important health information from older adults, especially when it comes to repeated time-based events. However, this same EMA data is hard to access for the older adult: although the newest IVAs are equipped with a display, the effectiveness of visualizing time-series based EMA data on standalone IVAs has not been explored. To investigate the potential opportunities for visualizing time-series based EMA data on standalone IVAs, we designed a prototype system, where older adults are able to query and examine the time-series EMA data on Amazon Echo Show - a widely used commercially available standalone screen-based IVA. We conducted a preliminary semi-structured interview with a geriatrician and an older adult, and identified three findings that should be carefully considered when designing such visualizations.

Toward a Unified Metadata Schema for Ecological Momentary Assessment with Voice-First Virtual Assistants

Ecological momentary assessment (EMA) is used to evaluate subjects' behaviors and moods in their natural environments, yet collecting real-time and self-report data with EMA is challenging due to user burden. Integrating voice into EMA data collection platforms through today's intelligent virtual assistants (IVAs) is promising due to hands-free and eye-free nature. However, efficiently managing conversations and EMAs is non-trivial and time consuming due to the ambiguity of the voice input. We approach this problem by rethinking the data modeling of EMA questions and what is needed to deploy them on voice-first user interfaces. We propose a unified metadata schema that models EMA questions and the necessary attributes to effectively and efficiently integrate voice as a new EMA modality. Our schema allows user experience researchers to write simple rules that can be rendered at run-time, instead of having to edit the source code. We showcase an example EMA survey implemented with our schema, which can run on multiple voice-only and voice-first devices. We believe that our work will accelerate the iterative prototyping and design process of real-world voice-based EMA data collection platforms.