Innovation in Aging最新文献

Background and objectives: Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality among older adults. While clinical risk factors are well documented, less is known about how perceived neighborhood environments interact with individual coping resources to influence CVD risk. Informed by the Stress Process Model and the Transactional Model of Stress and Coping, this study examines the associations between perceived neighborhood social cohesion and physical disorder and CVD among U.S. older adults, and whether cognitive activity moderates these associations.

Research design and methods: Pooled data were drawn from the 2016 and 2018 waves of the Health and Retirement Study, including 6,249 adults aged 65 and older who completed the Leave-Behind Questionnaire. Perceived neighborhood social cohesion and physical disorder were measured using validated multi-item scales. Cognitive activity was assessed based on participation in five cognitively stimulating behaviors (e.g., reading, writing, playing word games). Survey-weighted logistic regressions were conducted to estimate associations between neighborhood characteristics and CVD, including interaction terms with cognitive activity.

Results: Higher levels of social cohesion were associated with lower odds of CVD. Neither physical disorder nor cognitive activity was independently associated with CVD. However, cognitive activity moderated both neighborhood associations: the positive association between physical disorder and CVD was attenuated at higher levels of cognitive activity, whereas the protective association between social cohesion and CVD was weaker among individuals with greater cognitive activity.

Discussion and implications: Cognitive activity may buffer cardiovascular risk in physically disordered neighborhoods, while its benefits may be less apparent in socially cohesive settings. These findings suggest that cognitive engagement and neighborhood perceptions jointly shape cardiovascular risk and underscore the importance of integrated, multilevel interventions that promote both individual-level cognitive resources and neighborhood-level supports in aging populations.

Background and objectives: This study examined the association of self-perceptions of aging (SPA) and memory trajectories and the mediating role of social connections in their association.

Research design and methods: Data from 4,808 adults aged 65+ were drawn from the Health and Retirement Study (2008-2018). Latent growth curve modeling was used to examine the effects of positive and negative SPA on the intercept and slope of memory trajectories across 3 time points over 8 years. Direct and indirect effects of SPA on memory trajectories through social disconnectedness and loneliness were estimated.

Results: Both positive and negative SPA had indirect effects on the intercept of memory trajectories through loneliness but not social disconnectedness. Specifically, higher positive and negative SPA at Time 1 were linked to lower and greater loneliness at Time 2, respectively. T2 loneliness, in turn, was negatively associated with memory at Time 3. Additionally, negative SPA at baseline had direct effects on memory scores at Time 3.

Discussion and implications: Findings suggest that loneliness is a pathway via which positive and negative SPA influence memory. Interventions to reduce loneliness could be a way to mitigate the effects of SPA on memory in later life. The direct and indirect effects of negative SPA on memory suggest that negative aging stereotypes are powerful and have long-lasting impacts on cognitive function. Demystifying and reframing aging can produce positive cognitive benefits at the population and individual levels.

Background and objectives: While cross-sectional associations between any pain and sleep problems have been established, longitudinal studies examining the temporal relationship between back pain and multidimensional sleep health remain limited. We evaluated whether the association between back pain and sleep problems was bidirectional in older men aged 65 years and above.

Research design and methods: Data came from the Osteoporotic Fractures in Men Study with a sample of 1,055 older men who completed 2 clinical sleep visits. A composite sleep problems score was created using self-report and actigraphy data reflecting irregularity, dissatisfaction, lack of daytime alertness, suboptimal timing, inefficiency, and suboptimal duration. Participants were queried by mail about back pain every 4 months, and we calculated the prevalence of any, frequent, severe, and activity-limiting back pain around their 2 sleep visits. Cross-lagged panel models estimated bidirectional associations between sleep problems and subsequent back pain, and vice versa, over 6 years.

Results: Multivariable-adjusted results showed that having any back pain, frequent back pain, severe back pain, and activity-limiting back pain predicted 12%-25% greater sleep problems 6 years later (Exp(β) = 1.12; 95% confidence interval [CI] = 1.03-1.21 to Exp(β) = 1.25; 95% CI = 1.05-1.48), but sleep problems did not predict subsequent back pain.

Discussion and implications: This study highlights the long-term temporal directionality of the association between back pain and sleep problems in older men. Back pain preceded more sleep problems, but an inverse association was not observed. Our findings suggest that interventions targeting back pain may help decrease sleep problems in older men and warrant further investigation into potential mechanisms.

Background and objectives: To better support aging in place, we first must understand the needs of the older adult population. We conducted a systematic review to understand the needs of older adults in the home.

Research design and methods: We queried the PubMed, CINAHL, and ProQuest databases to identify literature related to needs assessments of older adults in the home. Records were included if: (1) the population focused on older adults (aged 65 years and older); (2) a needs assessment was conducted; (3) the older adult population was aging in place and not in a long-term care facility; (4) English language publication; (5) published since 2013; and (6) pertaining solely to older adult caregivers' needs. The needs identified in each article were extracted and categorized based on emergent themes.

Results: A total of 1,963 records were identified. After removing duplicate records and those not meeting the inclusion criteria, 65 articles were included in the final analysis. Six need-related theme domains were identified: health management needs; social needs; homecare and practical needs; information needs; technology needs; and healthcare system needs.

Discussion and implications: Through the systematic review, we identified a wide range of unmet needs for older adults aging in the home. The unmet needs of older adults are multifaceted and provide ideal targets for the development of novel technological solutions. In particular, recent advances in artificial intelligence (AI), especially generative AI such as large language models (LLMs), surface the potential for technology to address unmet needs across multiple domains. We discuss the potential for AI to lower barriers to technology uptake for older adults and create novel solutions to each of the need domains identified. Ultimately, AI-enabled solutions may increase independence for older adults and potentially increase the ability to age in place.

Background and objectives: Previous evidence showed that frailty in older age and precarious housing characteristics, respectively, contribute to poorer heat-related health outcomes. It is not known whether these conditions would interact with each other to produce a larger impact on older people's health.

Research design and methods: A cross-sectional questionnaire survey was conducted in May-July 2024 in a sample of older people aged 60 or over in Hong Kong, a city on the Southern coast of China. Frailty was measured using the Fried phenotype (five items). Housing characteristics were measured by whether it is a small flat (<100 square feet per person), living alone, inadequate housing (subdivided units or other forms), or whether the respondent had an air conditioner at home. Heat-related health outcomes were self-rated health, thermal comfort at home, and any heat-related signs/symptoms during summertime. Multivariable Poisson regression with robust standard error was used. Relative risk due to interaction was used to characterize additive interaction between housing characteristics and frailty.

Results: Among the 1,393 respondents who completed the questionnaire, about 60% reported being frail. Those who were frail were less likely to report thermally comfortable at home (RR: 0.75; 95% CI: 0.66, 0.87) and had a higher chance of reporting any heat-related signs/symptoms (RR: 1.28; 95% CI: 1.18, 1.38). We found evidence of additive interaction between frailty and living alone, in which, only for those who were robust, living alone is related to a lower risk of heat-related signs/symptoms.

Discussion and implications: Targeted interventions to improve the well-being of community-dwelling older adults during periods of extreme heat should be designed especially for those who are frail.

Background and objectives: Indicators of midlife health decline are needed to determine people's ability to engage in meaningful activities as they age and to implement early interventions to improve long-term health trajectories. Existing measures of physiological aging are unidimensional, expensive, difficult to collect, or focused on advanced disability, making it challenging to identify exposures that contribute to accelerate aging beginning in midlife.

Research design and methods: Using the Health and Retirement Study (HRS), we developed the Index of Aging in Midlife and Beyond (IAM+), a 10-item scale that accounts for a range of abilities and domains of health using survey items including multimorbidity, functional capacity, and self-reported cognitive, mental, physical, and sensory health. We evaluated overall reliability and variability. Then, we tested the association between physically demanding jobs in ages 51-56 with trajectories of IAM+ scores in midlife and old age. Finally, we considered predictive validity by examining associations between midlife IAM+ scores and later life activity and health outcomes.

Results: The IAM+ had acceptable reliability (alpha = 0.739-0.773). Distributions showed substantial variability. Physically demanding jobs were associated with higher IAM+ scores and accelerated increases in scores in midlife; differences in level, but not slopes, were sustained after retirement age. Higher IAM+ scores in midlife predicted reduced engagement in activities 10 years later, including early labor force exits, and predicted allostatic load, frailty, and mortality 20 years later.

Discussion and implications: The IAM+ is an acceptable and accessible new measure for evaluating health in midlife, with many applications for promoting active and productive engagement through later life.

The rapid aging of the global population presents complex challenges for health systems, families, and societies. Artificial intelligence (AI) technologies-from predictive analytics and conversational agents to robotic caregivers and remote monitoring-offer scalable solutions to support older adults throughout their life courses. This article examines the evolving landscape of AI-enabled care for aging populations, structured around the geriatric 4Ms: what Matters, Medication, Mentation, and Mobility. We synthesize current evidence on the application of AI in personalized medicine, cognitive support, fall detection, and chronic disease management while exploring the cultural, economic, and policy contexts that influence technology adoption. The market for age-related technology is expanding; however, disparities in access, digital literacy, and algorithmic bias risk exacerbating inequities. We identify persistent gaps in implementation, including the underrepresentation of diverse older adults in training data sets, limited longitudinal studies, and a lack of integration across diagnostic and therapeutic systems. We propose a future research agenda centered on five priorities: (1) establishing life-course AI research agendas, (2) promoting inclusive and participatory development processes, (3) advancing gerontological design principles, (4) expanding AI literacy across the aging services workforce, and (5) developing robust ethical and regulatory infrastructures. The article calls for interdisciplinary collaboration among gerontologists, engineers, policymakers, and ethicists to ensure that AI innovations are safe, equitable, and responsive to the lived experiences of older adults. Ultimately, we argue that AI must be implemented not as isolated tools but as components of comprehensive care ecosystems that support aging in place, reduce caregiver burden, and preserve human dignity.

Background and objectives: Elder abuse (EA) conceptualizations are evolving from conventional binary understandings toward a severity lens that more accurately captures the spectrum of victim experiences and complexity of EA intervention. Although momentum behind a focus on severity has grown, our understanding of EA severity risk factors is methodologically limited by studies using clinical convenience samples and/or cross-sectional designs. Informed by the Contextual Theory of Elder Abuse, this article sought to advance the state of science surrounding EA severity risk factors using data from a longitudinal, population-based design and examining factors from several levels of ecological influence.

Research design and methods: Using the Canadian Longitudinal Study on Aging, this study analyzed a sample of EA victims (n = 2,364) reporting past-year emotional/psychological, physical, and/or financial abuse, who completed baseline and follow-up waves of data collection. EA victimization was assessed using validated tools and behaviorally defined items describing specific mistreatment behaviors. Calculation of EA severity for each subtype combined dimensions of behavioral multiplicity (number of mistreatment behaviors) and frequency. Independent change variables were used to confirm the direction of change underlying potential risk factors prior to EA victimization. Multinomial logistic regression was used to identify factors associated with increased levels of EA severity.

Results: Across subtypes, the most consistent risk factors for heightened EA severity were perpetrator cohabitation and the older adult's experience of child maltreatment. Other risk factors were identified across physical, psycho-emotional, social, and sociocultural domains. Risk profiles varied across mistreatment subtypes.

Discussion and implications: This study represents the most methodologically rigorous examination of EA severity risk conducted to date. Findings will enhance our capacity to identify EA victims in particularly harmful scenarios and inform mechanistically driven interventions designed to reduce the magnitude of the problem, as well as practice decisions related to case prioritization, triaging, and risk assessment.

Background and objectives: Alzheimer's disease (AD) and AD-related dementias (ADRD) are expected to affect over 100 million people by 2050, placing a significant strain on public health systems. Social determinants of health (SDoH), which include factors such as socioeconomic conditions and environment, play a crucial role in AD risk. Despite growing evidence, the understanding of SDoH's impact on AD remains limited.

Research design and methods: This study leverages large language models and knowledge graphs (KGs) to extract AD-related SDoH knowledge from literature and electronic health records (EHR). We integrate this knowledge into biological research on AD through KG construction and graph deep learning, performing KG-link predictions validated by multimodal biological data from single-cell RNA-seq and proteomics.

Results: We generated an SDoH knowledge graph with around 92k triplets, integrating literature and EHR data. In various link prediction experiments, we observed higher accuracy when integrating SDoH into knowledge graphs. Additionally, exploratory predictions uncovered potential SDoH-gene interactions, many of which were validated through differential expression analysis using proteomics and RNA-seq data.

Discussion and implications: This novel KG-based analysis enhances link prediction in AD-related biomedical networks by integrating SDoH and biological knowledge. Our findings highlight the potential interaction between social determinants and biological factors in AD, offering insights into more personalized and socially aware healthcare interventions.