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.

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 todays 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.

UNLABELLED: 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.

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.

Voice-based Intelligent Virtual Assistants (IVAs) promise to improve healthcare management and Quality of Life (QOL) by introducing the paradigm of hands-free and eye-free interactions. However, there has been little understanding regarding the challenges for designing such systems for older adults, especially when it comes to healthcare related tasks. To tackle this, we consider the processes of care delivery and QOL enhancements for older adults as a collaborative task between patients and providers. By interviewing 16 older adults living independently or semi-independently and 5 providers, we identified 12 barriers that older adults might encounter during daily routine and while managing health. We ultimately highlighted key design challenges and opportunities that might be introduced when integrating voice-based IVAs into the life of older adults. Our work will benefit practitioners who study and attempt to create full-fledged IVA-powered smart devices to deliver better care and support an increased QOL for aging populations.

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.

Background/objectives

Use of cannabis is increasing in a variety of populations in the United States; however, few investigations about how and for what reasons cannabis is used in older populations exist.

Design

Anonymous survey.

Setting

Geriatrics clinic.

Participants

A total of 568 adults 65 years and older.

Intervention

Not applicable.

Measurements

Survey assessing characteristics of cannabis use.

Results

Approximately 15% (N = 83) of survey responders reported using cannabis within the past 3 years. Half (53%) reported using cannabis regularly on a daily or weekly basis, and reported using cannabidiol-only products (46%). The majority (78%) used cannabis for medical purposes only, with the most common targeted conditions/symptoms being pain/arthritis (73%), sleep disturbance (29%), anxiety (24%), and depression (17%). Just over three-quarters reported cannabis "somewhat" or "extremely" helpful in managing one of these conditions, with few adverse effects. Just over half obtained cannabis via a dispensary, and lotions (35%), tinctures (35%), and smoking (30%) were the most common administration forms. Most indicated family members (94%) knew about their cannabis use, about half reported their friends knew, and 41% reported their healthcare provider knowing. Sixty-one percent used cannabis for the first time as older adults (aged ≥61 years), and these users overall engaged in less risky use patterns (e.g., more likely to use for medical purposes, less likely to consume via smoking).

Conclusion

Most older adults in the sample initiated cannabis use after the age of 60 years and used it primarily for medical purposes to treat pain, sleep disturbance, anxiety, and/or depression. Cannabis use by older adults is likely to increase due to medical need, favorable legalization, and attitudes.

Cannabis use is growing among older adults to manage medical concerns including poor sleep. In this study, we characterized how patients seen at a geriatrics clinic use cannabis to address sleep disturbance. Specifically, we conducted an anonymous survey of 568 adults, including 83 who reported cannabis use within the past 3 years, to inquire about such use. We compared cannabis use characteristics between those using it for sleep disturbance versus all other conditions. We considered a p-value <.10 to be statistically significant. Among the cannabis users in our sample, 29% reported using cannabis for sleep disturbance (N = 24). They were more likely than other users to be female (p = .07), consume cannabis more frequently (p = .01), use products containing tetrahydrocannabinol (THC) (vs. cannabidiol [CBD]-only; p < .01), and use cannabis to target more symptoms (p < .01). As cannabis use continues to grow in older populations, it is essential to delineate better how cannabis may be used safely and effectively to improve older adults' sleep health.