Mitigating Identity-Related Anxiety through Humor and Immersive Storytelling with 360-Degree Video in Virtual Reality: A Study on Microaggressions' Mental Health Effects.

Background: Microaggressions are subtle slights that can cause significant psychological distress among marginalized groups. Few studies have explored interventions that might mitigate these effects. Objective: This study aimed to investigate if and how humor-infused immersive storytelling via virtual reality (VR) could reduce identity-related psychological distress caused by microaggressions. Methods: Using a community-based participatory research approach, we developed a 7-min 360-degree VR film depicting scenarios of microaggressions across various identities. Forty-six college students participated in a controlled study where they were exposed to this immersive VR experience. We measured identity-related psychological anxiety, character identification, perceived humor, and perceived psychological presence. Results: The findings demonstrated a significant anxiety reduction following the VR intervention, supporting the efficacy of humor-infused storytelling in alleviating the impact of microaggressions. Character identification significantly predicted anxiety reduction, while perceived humor and psychological presence did not directly influence anxiety reduction but indirectly contributed through enhanced character identification. Conclusions: Humor-infused immersive storytelling, facilitated by VR, effectively reduces identity-related psychological distress primarily through character identification. The structural equation modeling results emphasize the importance of integrating humor and psychological presence to enhance character connection, advocating for a balanced approach that combines traditional narrative elements with technological innovations in health interventions aimed at combating the adverse psychological effects of microaggressions.

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.

Reducing Loneliness and Improving Social Support among Older Adults through Different Modalities of Personal Voice Assistants.

This study examines the potential of AI-powered personal voice assistants (PVAs) in reducing loneliness and increasing social support among older adults. With the aging population rapidly expanding, innovative solutions are essential. Prior research has indicated the effectiveness of various interactive communication technologies (ICTs) in mitigating loneliness, but studies focusing on PVAs, particularly considering their modality (audio vs. video), are limited. This research aims to fill this gap by evaluating how voice assistants, in both audio and video formats, influence perceived loneliness and social support. This study examined the impact of voice assistant technology (VAT) interventions, both audio-based (A-VAT) and video-based (V-VAT), on perceived loneliness and social support among 34 older adults living alone. Over three months, participants engaged with Amazon Alexa™ PVA through daily routines for at least 30 min. Using a hybrid natural language processing framework, interactions were analyzed. The results showed reductions in loneliness (Z = -2.99, p < 0.01; pre-study loneliness mean = 1.85, SD = 0.61; post-study loneliness mean = 1.65, SD = 0.57), increases in social support post intervention (Z = -2.23, p < 0.05; pre-study social support mean = 5.44, SD = 1.05; post-study loneliness mean = 5.65, SD = 1.20), and a correlation between increased social support and loneliness reduction when the two conditions are combined (ρ = -0.39, p < 0.05). In addition, V-VAT was more effective than A-VAT in reducing loneliness (U = 85.50, p < 0.05) and increasing social support (U = 95, p < 0.05). However, no significant correlation between changes in perceived social support and changes in perceived loneliness was observed in either intervention condition (V-VAT condition: ρ = -0.24, p = 0.37; A-VAT condition: ρ = -0.46, p = 0.06). This study's findings could significantly contribute to developing targeted interventions for improving the well-being of aging adults, addressing a critical global issue.

The Impact of Interaction Time and Verbal Engagement with Personal Voice Assistants on Alleviating Loneliness among Older Adults: An Exploratory Study.

BACKGROUND: Loneliness in older adults is a critical issue that negatively affects their well-being. The potential of personal voice assistant (PVA) devices like Amazon's Alexa Echo in reducing loneliness is an emerging area of interest, but it remains under-researched. OBJECTIVE: this study aims to investigate the effect of interaction time and verbal engagement with PVA devices on reducing loneliness among older adults living alone. METHOD: In this experiment, individuals aged 75 and older (n = 15), living alone, were provided with Amazon Alexa Echo devices. They were instructed to interact with the device at least five times a day for a duration of four weeks. The study measured participants' loneliness levels using the UCLA loneliness scale both before and after the study. Additionally, the interaction time and verbal engagement with the device were measured by the total time of use and the total number of intentional commands spoken to Alexa during the four-week period. RESULTS: The findings revealed that the total time spent interacting with Alexa was a significant predictor of loneliness reduction. A mediation analysis indicated an indirect effect, showing that the number of intentional commands spoken to Alexa contributed to loneliness reduction indirectly by increasing the total time spent with the device (verbal engagement → interaction time → loneliness reduction). CONCLUSIONS: This study suggests that the key to reducing loneliness among older adults through PVA devices is not just initiating verbal interaction, but the overall time devoted to these interactions. While speaking to Alexa is a starting point, it is the duration of engagement that primarily drives loneliness alleviation.

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.

The Knowledge Gap Versus the Belief Gap and Abstinence-Only Sex Education.

The knowledge gap hypothesis predicts widening disparities in knowledge of heavily publicized public affairs issues among socioeconomic status groups. The belief gap hypothesis extends the knowledge gap hypothesis to account for knowledge and beliefs about politically contested issues based on empirically verifiable information. This analysis of 3 national surveys shows belief gaps developed between liberals and conservatives regarding abstinence-only sex education; socioeconomic status-based knowledge gaps did not widen. The findings partially support both belief gap and knowledge gap hypotheses. In addition, the unique contributions of exposure to Fox News, CNN, and MSNBC in this process were investigated. Only exposure to Fox News was linked to beliefs about abstinence-only sex education directly and indirectly through the cultivation of conservative ideology.

Persuading people to eat less junk food: a cognitive resource match between attitudinal ambivalence and health message framing.

This study investigated the interactive effects of attitudinal ambivalence and health message framing on persuading people to eat less junk food. Within the heuristic-systematic model of information processing, an attitudinal ambivalence (ambivalent or univalent toward eating junk food) by health message framing (advantage- or disadvantage-framed appeals) between-subjects experiment was conducted to explore a cognitive resource-matching effect and the underlying mediation processes. Ambivalent individuals reported a higher level of cognitive elaboration than univalent individuals did. The disadvantage frame engendered more extensive cognitive elaboration than the advantage frame did. Ambivalent individuals were more persuaded by the disadvantage frame and, for them, cognitive elaboration mediated the persuasion process via the systematic route. Univalent individuals were equally persuaded by the advantage frame and the disadvantage frame and, for them, neither the perceived frame valence nor cognitive elaboration mediated persuasion. Discussion of the null results among the univalent group leads to a response-reinforcement explanation. Theoretical and practical implications are discussed.