Understanding older people's voice interactions with smart voice assistants: a new modified rule-based natural language processing model with human input.

The COVID-19 pandemic has expedited the integration of Smart Voice Assistants (SVA) among older people. The qualitative data derived from user commands on SVA is pivotal for elucidating the engagement patterns of older individuals with such systems. However, the sheer volume of user-generated voice interaction data presents a formidable challenge for manual coding. Compounding this issue, age-related cognitive decline and alterations in speech patterns further complicate the interpretation of older users' SVA voice interactions. Conventional dictionary-based textual analysis tools, which count word frequencies, are inadequate in capturing the evolving and communicative essence of these interactions that unfold over a series of dialogues and modify with time. To address these challenges, our study introduces a novel, modified rule-based Natural Language Processing (MR-NLP) model augmented with human input. This reproducible approach capitalizes on human-derived insights to establish a lexicon of critical keywords and to formulate rules for the iterative refinement of the NLP model. English speakers, aged 50 or older and residing alone, were enlisted to engage with Amazon Alexa™ via predefined daily routines for a minimum of 30 min daily spanning three months (N = 35, mean age = 77). We amassed time-stamped, textual data comprising participants' user commands and responses from Alexa™. Initially, a subset constituting 20% of the data (1,020 instances) underwent manual coding by human coder, predicated on keywords and commands. Separately, a rule-based Natural Language Processing (NLP) methodology was employed to code the identical subset. Discrepancies arising between human coder and the NLP model programmer were deliberated upon and reconciled to refine the rule-based NLP coding framework for the entire dataset. The modified rule-based NLP approach demonstrated notable enhancements in efficiency and scalability and reduced susceptibility to inadvertent errors in comparison to manual coding. Furthermore, human input was instrumental in augmenting the NLP model, yielding insights germane to the aging adult demographic, such as recurring speech patterns or ambiguities. By disseminating this innovative software solution to the scientific community, we endeavor to advance research and innovation in NLP model formulation, subsequently contributing to the understanding of older people's interactions with SVA and other AI-powered systems.

Exploring the Benefits, Barriers, and Facilitators of Physical Activity and Interest in Intergenerational Physical Activity Programming Within a Hispanic/Latino Community in the Midwest.

Hispanic/Latino individuals across the lifespan generally do not engage in enough physical activity. Intergenerational programming is an innovative solution that could improve opportunities for physical activity across the lifespan in a culturally relevant manner; however, few studies have explored perceptions of intergenerational physical activity programming among Hispanic/Latino communities. This pre-implementation study aimed to: (a) explore the perceived benefits, barriers, and facilitators of physical activity among an intergenerational sample of Hispanic/Latino community members and stakeholders and (b) assess interest in intergenerational physical activity programming. This qualitative study consisted of a total of eight focus groups (N = 45 participants): Hispanic/Latino youth (2 groups), their parents (2 groups), older Hispanic/Latino adults (3 groups), and community stakeholders who work with older adults (1 group). We used thematic analysis techniques integrating the Framework Method to compare and contrast perspectives between participant groups. We found that all groups identified physical, mental, and/or social benefits to physical activity. Primary barriers included limited physical accessibility, environmental considerations, and time constraints. Primary facilitators included physical accessibility, programming format, environmental supports, and social support. Overall, there was general interest in intergenerational physical activity programming across all groups. Practitioners are encouraged to: (a) be aware of how different age groups may view and respond to program context, fit, and communications; (b) reflect on their capacity for high-quality implementation, and (c) weigh the costs and benefits of various programming decisions. Findings can be used to design culturally, linguistically, and contextually relevant intergenerational physical activity programming and to promote health equity.

Reducing Loneliness Among Aging Adults: The Roles of Personal Voice Assistants and Anthropomorphic Interactions.

The perception of feeling lonely is an influential factor in determining quality of life among aging adults. As the US Census Bureau projects that the number of Americans ages 65 and older will double by 2060, reducing loneliness is imperative. Personal voice assistants (PVAs) such as Amazon's Echo offer the ease-of-use of voice control with a friendly, helpful artificial intelligence. This study aimed to understand the influence of a PVA on loneliness reduction among adults of advanced ages, i.e., 75+, and explore anthropomorphism as a potential underlying mechanism. Participants (N = 16) ages 75 or older used an Amazon Echo PVA for 8 weeks in an independent living facility in the Midwest. Surveys were used to collect information about perceived loneliness, and PVA interaction data was recorded and analyzed. Participants consistently exceeded the required daily interactions. As hypothesized, after the first 4 weeks of the intervention, aging adults reported significantly lower loneliness (baseline mean = 2.22, SD = 0.42; week 4 mean = 1.99, SD = 0.45, Z = -2.45, and p = 0.01). Four dominant anthropomorphic themes emerged after thematic analysis of the entire 8 weeks' PVA interaction data (Cohen's Kappa = 0.92): (1) greetings (user-initiated, friendly phrases); (2) comments/questions (user-initiated, second-person pronoun), (3) polite interactions (user-initiated, direct-name friendly requests), (4) reaction (user response to Alexa). Relational greetings predicted loneliness reductions in the first 4 weeks and baseline loneliness predicted relational greetings with the PVA during the entire 8 weeks, suggesting that anthropomorphization of PVAs may play a role in mitigating loneliness in aging adults.

Virtual Reality for Therapeutic Recreation in Dementia Hospice Care: A Feasibility Study.

OBJECTIVES: To explore acceptability, tolerability, and subjective experience of virtual reality (VR) as therapeutic recreation for hospice patients living with dementia (hPLWD). DESIGN: Descriptive study setting. COMMUNITY HOSPICE AGENCY PARTICIPANTS: A convenience sample of n = 25 hPLWD cared for by a local hospice agency. INTERVENTION: Participants viewed a beach scene using VR headset for ≤30 minutes. MEASUREMENTS: Tolerability was measured with Pain Assessment IN Advanced Dementia (PAINAD)scores at baseline, every 5 minutes during VR use, and 5 minutes after headset removal. Additionally, follow-up phone calls to caregivers several hours after the intervention were performed to inquire regarding any noted adverse effect after the intervention. The subjective experience was measured with qualitative semistructured interviews with the hPLWD regarding enjoyment for the VR. Descriptive statistics were performed on all collected data. RESULTS: The VR was stopped early in 2 of the participants due to a ≥2-point increase in PAINAD score. Baseline behavioral and psychological symptoms of dementia (BPSD) were reported to have worsened in 2 (8%) of the participants at follow-up. There was no significant difference between dementia type and usage time or dementia severity and usage time. Of the 25 participants, 14 (56%) reported enjoying VR and 12 (48%) would do it again. CONCLUSION: Virtual reality use was generally safe and enjoyable in hPLWD. Virtual reality can provide meaningful activity and enhance quality of life for hPLWD at the end of life. In the future, VR may be a useful intervention for BPSD in hPLWD.

Optic flow improves step width and length in older adults while performing dual task.

BACKGROUND: Dual-task paradigms are used to investigate gait and cognitive declines in older adults (OA). Optic-flow is a virtual reality environment where the scene flows past the subject while walking on a treadmill, mimicking real-life locomotion. AIMS: To investigate cost of environment (no optic-flow v. optic-flow) while completing single- and dual-task walking and dual-task costs (DTC; single- v. dual-task) in optic-flow and no optic-flow environments. METHODS: Twenty OA and seven younger adults (YA) walked on a self-paced treadmill in 3-min segments per task and both environments. Five task conditions included: no task, semantic fluency (category), phonemic fluency (letters), word reading, and serial-subtraction. RESULTS: OAs had a benefit of optic-flow compared to no optic-flow for step width (p = 0.015) and step length (p = 0.045) during letters compared to the YA. During letters, OA experienced improvement in step width DTC; whereas YA had a decrement in step width DTC from no optic-flow to optic-flow (p = 0.038). During serial-subtraction, OA had less step width DTC when compared to YA in both environments (p = 0.02). DISCUSSION: During letters, step width and step length improved in OA while walking in optic-flow. Also, step width DTC differed between the two groups. Sensory information from optic-flow appears to benefit OA. Letters relies more on verbal ability and word knowledge, which are preserved in aging. However, YA use a complex speech style during dual tasking, searching for complex words and an increased speed of speech. CONCLUSIONS: OA can benefit from optic-flow by improving spatial gait parameters, specifically, step width, during dual-task walking.