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.

Background: The effect of physical activity and exercise on hunger and satiety has been well-studied in younger adults, but the influence of aging is less understood. While some evidence suggests that acute bouts of exercise induce a compensatory eating drive, long-term activity may improve satiety sensitivity. The objective of this study was to investigate the effects of exercise on appetite in older adults.Methods: We systematically reviewed available literature investigating the effect of exercise on appetite in older adults adults (CRD42020208953). PubMed, PsycINFO, Academic Search Complete, the Sports Medicine & Education Index, and Web of Science, were searched for peer-reviewed articles published in English with no date restriction. Included studies implemented a primary exercise or physical activity intervention with a control group, on a generally healthy population ≥60 years of age. Selected studies included at least one appetite outcome. Risk of bias was assessed using the 11-point Physiotherapy Evidence Database (PEDro) tool. Standardized mean difference summary statistics (Hedge's g effect sizes) and 95% confidence intervals were reported.Results: We identified 15 reports (13 studies) which met all inclusion criteria (5 resistance training, 3 aerobic, 6 mixed modalities). Studies included 443 participants (Age = 68.9 ± 5.2, 82.3% female) and had generally “good” bias scores (PEDro = 6.4 ± 0.88). Random effects meta-analyses revealed that the exercising group showed statistically significant reductions in glucose [SMD = −0.34 (95% CI: −0.67, −0.02), p < 0.05, PEDro =6.4 ± 0.45] and leptin [SMD = −0.92 (95% CI: −1.28, −0.57), p < 0.00001, PEDro = 6.2 ± 0.75].Discussion: This systematic review revealed that exercise and physical activity may modulate resting hunger and satiety in older adults. Decreases in fasting leptin and glucose hormones suggest that exercise promotes satiety sensitivity in adults aged 60+. This review highlights that engaging in exercise and activity programs may provide a meaningful avenue for improving chronic and functional disease burden in later life by promoting appetite control and balanced energy intake. Recommendations for future research include investigations of appetite in response to varied exercise modalities within more diverse and representative samples of older adults.

Background: Technologies that serve as assistants are growing more popular for entertainment and aiding in daily tasks. Artificial intelligence (AI) in these technologies could also be helpful to deliver interventions that assist older adults with symptoms or self-management. Personality traits may play a role in how older adults engage with AI technologies. To ensure the best intervention delivery, we must understand older adults’ engagement with and usability of AI-driven technologies. Objective: This study aimed to describe how older adults engaged with routines facilitated by a conversational AI assistant. Methods: A randomized pilot trial was conducted for 12-weeks in adults aged 60 years or older, self-reported living alone, and having chronic musculoskeletal pain. Participants (N=50) were randomly assigned to 1 of 2 intervention groups (standard vs enhanced) to engage with routines delivered by the AI assistant Alexa (Amazon). Participants were encouraged to interact with prescribed routines twice daily (morning and evening) and as needed. Data were collected and analyzed on routine engagement characteristics and perceived usability of the AI assistant. An analysis of the participants’ personality traits was conducted to describe how personality may impact engagement and usability of AI technologies as interventions. Results: The participants had a mean age of 79 years, with moderate to high levels of comfort and trust in technology, and were predominately White (48/50, 96%) and women (44/50, 88%). In both intervention groups, morning routines (n=62, 74%) were initiated more frequently than evening routines (n=52, 62%; z=−2.81, P=.005). Older adult participants in the enhanced group self-reported routine usability as good (mean 74.50, SD 11.90), and those in the standard group reported lower but acceptable usability scores (mean 66.29, SD 6.94). Higher extraversion personality trait scores predicted higher rates of routine initiation throughout the whole day and morning in both groups (standard day: B=0.47, P=.004; enhanced day: B=0.44, P=.045; standard morning: B=0.50, P=.03; enhanced morning: B=0.53, P=.02). Higher agreeableness (standard: B=0.50, P=.02; enhanced B=0.46, P=.002) and higher conscientiousness (standard: B=0.33, P=.04; enhanced: B=0.38, P=.006) personality trait scores predicted better usability scores in both groups. Conclusions: he prescribed interactive routines delivered by an AI assistant were feasible to use as interventions with older adults. Engagement and usability by older adults may be influenced by personality traits such as extraversion, agreeableness, and conscientiousness. While integrating AI-driven interventions into health care, it is important to consider these factors to promote positive outcomes.

The timing, amount, and quality of sleep are critical for an individual’s health and quality of life. This paper provides a focused narrative review of the existing literature around multidimensional environments and sleep health for aging adults. Five electronic databases, Scopus, Web of Science, PubMed/Medline; EBSCOhost, PsycINFO (ProQuest), and Google Scholar yielded 54,502 total records. After removing duplicates, non-peer reviewed academic articles, and nonrelevant articles, 70 were included for review. We were able to categorize environmental factors into housing security, home environment, and neighborhood environment, and, within each environmental category, specific elements/aspects are discussed. This paper provides a comprehensive map connecting identified levels of influence (individual, home/house, and neighborhood-level) in which subfactors are listed under each level of influence/category with the related literature list. Our review highlights that multidimensional environmental factors can affect aging adults’ sleep health and eventually their physical, mental, and cognitive health and that sleep disparities exist in racial minorities in socioeconomically disadvantaged communities in which cumulative environmental stressors coexist. Based on this focused narrative review on the multidimensional sleep environments for aging adults, knowledge gaps are identified, and future research directions are suggested.

Caring for a person living with dementia (PLWD) can be challenging, making support services like respite important. Of the types of respite, research on paid professional in-home care specific to family caregivers of PLWD is limited. This study aimed to identify characteristics of dementia caregivers using paid in-home respite. A self-administered online survey (N = 98) examined use of in-home respite. Measures assessed depressive symptoms, burden, and self-rated health. Analyses included descriptive statistics and qualitative coding. Most respondents utilized weekly schedules of in-home respite with a moderate correlation between dependency and hours of respite. Caregivers’ self-reported average health, depressive symptoms, and mild-to-moderate burden. The majority of users had lowered perceived stress, were satisfied with services, and indicated the importance of in-home respite during a pandemic. Future research should assess changes in use of paid in-home respite services and overall landscape of respite options for dementia caregivers.