Journals of Gerontology Series B-Psychological Sciences and Social Sciences最新文献

Objectives: Understanding older adults' health beliefs regarding falls is important for the design of participant-centric programs. Although there is evidence on older adults' perceptions toward falls and falls prevention, there is insufficient understanding of older adults' adaptive responses to cope with falls. In addition, these perceptions are understudied within the context of Asia for the development of programs in Asia.

Methods: In-depth interviews were conducted with 30 community-dwelling older adults (aged 60 and older) in Singapore. The Health Belief Model, adapted to falls and falls prevention, helped guide interviews.

Results: Our findings uncovered a discordance between perceived susceptibility to falls and actual physical ability, influencing the actions toward falls prevention. Adaptive responses to falls were encapsulated as the concept "being careful" in this context. This manifested in two groups: those who monitor their physical health due to perceived high risk and those who only manage external hazards due to perceived low risk. In addition, we examined cues to actions: caregiver and clinician support, managing chronic pain, and prioritizing social roles; and discussed their implications for future program development. The findings also highlighted the importance of engaging family and caregivers in falls prevention efforts, a strategy that resonates deeply across Asian sociocultural contexts.

Discussion: Falls prevention practices should integrate tailored education and behavioral strategies based on older adults' perceived susceptibility to falls. In Asian contexts and other cultures with a strong focus on family, caregiver-supported interventions in addition to personalized interventions should also be optimized to encourage participation and sustain adherence.

Objectives: Previous studies have shown that Asian Americans have lower disability and mortality rates than other racial/ethnic groups, indicating a more favorable health profile. This phenomenon is often attributed to the large proportion of Asians being foreign-born and positively selected. However, the health status of U.S.-born older Asians and its trend over time remain unclear.

Methods: We used data from the American Community Survey to describe changes in age-adjusted disability prevalence among native-born older Asians relative to other racial/ethnic groups since 2005.

Results: Although U.S.-born Asians aged 50 and older had lower disability prevalence than other racial/ethnic groups in 2005-09, their prevalence stagnated over time, while other groups experienced reductions. Consequently, the health advantage of U.S.-born older Asians diminished between 2005 and 2022. A key explanation for this phenomenon is a relative decline in socioeconomic status (SES) among older Asians compared to Whites over time. Asians experienced stagnation in high school attainment and a clear decline in the proportion of the population above the bottom income quintile, while Whites (and most others) experienced improvement in both SES measures. Furthermore, U.S.-born older Asians with low SES experienced an increase in disability, a trend not observed in any other racial or nativity group. We found suggestive evidence that declining community and family support among native-born older Asians may have also eroded their health advantage.

Discussion: The "model minority" stereotype increasingly misrepresents the well-being of U.S.-born older Asians, a population that requires further research attention.

Objectives: An estimated 17% of U.S. adults ages 55+ are childless, a fraction that has increased across recent cohorts. Most studies find no or negligible mental health consequences of childlessness for older adults, yet studies typically compare broad categories of childless persons and parents, neglecting potentially important sources of heterogeneity. We evaluate associations between parental status (childless, biological children, stepchildren only, no living children) and 3 dimensions of mental health (depressive symptoms, and social and emotional loneliness) and how these patterns differ by marital status and gender.

Methods: Data are from the pooled 2016 and 2018 waves of the Health and Retirement Study (N = 19,354). We estimated ordinary least squares regression models and tested 2- and 3-way interaction terms to evaluate the association between parental status and mental health, and the extent to which these associations are moderated by marital status and gender. Multivariable analyses are adjusted for sociodemographic, social integration, and health covariates.

Results: Parental status is not a significant predictor of depressive symptoms in fully adjusted models, and patterns do not differ by marital status and gender. However, men with step-children or biological children report significantly less emotional loneliness relative to childless men, and relative to their female counterparts. Women who have lost all children to death have significantly more emotional loneliness than both their male counterparts and childless women.

Discussion: Parental statuses have negligible effects on older adults' mental health; policies and practices to mitigate social isolation should enhance nonfamilial ties and community engagement.

Objectives: The aim of this work is to use a machine learning framework to develop simple risk scores for predicting β-amyloid (Aβ) and tau positivity among individuals with mild cognitive impairment (MCI).

Methods: Data for 657 individuals with MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set were used. A modified version of AutoScore, a machine learning-based software tool, was used to develop risk scores based on hierarchical combinations of predictor categories, including demographics, neuropsychological assessments, APOE4 status, and imaging biomarkers.

Results: The highest area under the receiver operating characteristic curve (AUC) for predicting Aβ positivity was 0.79, which was achieved by 2 separate models with predictors of age, Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-cog), APOE4 status, and either Trail Making Test Part B (TMT-B) or white matter hyperintensity. The best-performing model for tau positivity had an AUC of 0.91 using age, ADAS-13, and TMT-B scores, APOE4 information, abnormal hippocampal volume, and amyloid status as predictors.

Discussion: Simple integer-based risk scores using available data could be used for predicting Aβ and tau positivity in individuals with MCI. Models have the potential to improve clinical trials through improved screening of individuals.

Objectives: Cognitive aging entails diverse patterns of cognitive profiles, brain imaging, and biomarkers. Yet, few studies have explored the performance of multiple clustering algorithms on a single data set. Here, we employ data-driven methods to analyze neuropsychological performance in older individuals with normal cognition (NC) and mild cognitive impairment (MCI).

Methods: A total of 311 older adults without dementia completed a comprehensive assessment, consisting of 17 cognitive tests and a memory complaint questionnaire. We utilized 2 clustering algorithms: nonnegative matrix factorization (NMF) and model-based clustering (MBC). Cluster characteristics were examined in demographic, clinical, and brain morphometric data.

Results: Both NMF and MBC uncovered two- and three-cluster solutions, with satisfactory data fit. The two-cluster profiles encompassed a cognitively intact (CI) group and a cognitively suboptimal (CS) group, distinguished by cognitive performance. The 3-cluster solutions included CI-memory proficient, CI-nonmemory proficient, and CS groups. Remarkably, patterns of cognitive heterogeneity and their association with demographic and neuroimaging variables were highly comparable across NMF and MBC. Phenotypic homogeneity improved after identifying participants with consistent and mismatched memberships from the 2 algorithms.

Discussion: The results indicate that 2 distinct data-driven algorithms, with different heuristics, generated comparable patterns regarding cognitive heterogeneity within NC and MCI. These findings may inform future subtyping studies in cognitive aging, where replication of stratifications found across different methods is strongly recommended.

Objectives: Previous research on social activity engagement in later life predominantly employed an individual-focused approach, restricting our understanding of how engagement in social activities as a couple can influence both relational and individual outcomes. This study examines the relationship between couples' combination of social engagement and husbands' and wives' marital satisfaction, and subsequently, their depressive symptoms.

Methods: Three waves of data on a sample of 3,889 couples from the China Health and Retirement Longitudinal Study were used. We tested 3 operationalizations of couples' combination of social engagement-profile-based similarity (i.e., how similar a husband and wife are in their engagement in specific activities), difference score-based similarity (i.e., the absolute difference between a husband and a wife), and a couple's overall engagement level (i.e., the average of a couple's engagement scores)-to ascertain their associations with marital satisfaction and depressive symptoms. We also investigated how these associations differed between rural and urban couples.

Results: Our results reveal that-in urban but not rural areas-a couple's higher overall engagement level positively influences both partners' relational and individual well-being, and these associations vary by gender.

Discussion: Couples' overall level of activity engagement during midlife and older adulthood positively influences both partners' well-being. Promoting social engagement within couples presents a promising intervention strategy to disrupt the well-documented reciprocal link between social engagement and depressive symptoms.

Objectives: Cognitive change is a complex phenomenon encompassing both retest-related performance gains and potential cognitive decline. Disentangling these dynamics is necessary for effective tracking of subtle cognitive change and risk factors for Alzheimer's Disease and Related Dementias (ADRD).

Method: We applied a computational cognitive model of learning and forgetting to data from Einstein Aging Study (EAS; n = 316). EAS participants completed multiple bursts of ultra-brief, high-frequency cognitive assessments on smartphones. Analyzing response time data from a measure of visual short-term working memory, the Color Shapes task, and from a measure of processing speed, the Symbol Search task, we extracted several key cognitive markers: short-term intraindividual variability in performance, within-burst retest learning and asymptotic (peak) performance, across-burst change in asymptote and forgetting of retest gains.

Results: Asymptotic performance was related to both mild cognitive impairment (MCI) and age, and there was evidence of asymptotic slowing over time. Long-term forgetting, learning rate, and within-person variability uniquely signified MCI, irrespective of age.

Discussion: Computational cognitive markers hold promise as sensitive and specific indicators of preclinical cognitive change, aiding risk identification and targeted interventions.

The National Institute on Aging (NIA) is at the forefront of leveraging advances in artificial intelligence (AI) to better understanding of aging and the diagnosis and treatment of Alzheimer's Disease (AD) and Alzheimer's disease-related dementias (ADRD). Recent NIA-supported projects have highlighted the transformative potential of AI, digital health, and computational approaches in improving the modeling, detection, and monitoring of changes in healthy aging and AD/ADRD. This perspective is forward looking, emphasizing key areas and efforts in AI-driven precision measurement in cognition, behavior, and psychological function.

Objectives: Existing family and caregiving studies have primarily focused on the mental health of either older adults or adult children. Less is known about the effect of intergenerational relations on the mental health of both generations. This study examined the association between intergenerational solidarity and mental health among older Chinese Americans and their adult children using a dyadic analysis, considering the gendered nature of these relationships.

Methods: This study included 214 father-child and 339 mother-child dyads. Intergenerational solidarity (emotional closeness, contact frequency, upward emotional support, upward financial support) and mental health (anxiety, depression, loneliness) were assessed in both generations. Actor-Partner Interdependence Models were used.

Results: Greater emotional closeness with their adult children reported by mothers was associated with better mental health in mothers, whereas children's reported emotional closeness with fathers, but not with mothers, was associated with better mental health in children. Daily contact reported by fathers and adult children showed a positive association with their respective mental health. Higher upward emotional support reported by fathers, mothers, and children was associated with mental health in each respective group. Higher upward financial support reported by fathers and mothers was linked to better mental health in each respective group.

Discussion: These findings enrich the intergenerational solidarity model by showing how intergenerational solidarity shapes well-being across generations in immigration contexts, varying by solidarity dimension and parental gender. The results suggest that targeted mental health interventions, such as fostering emotional support within immigrant families, may promote well-being for both generations.

Objectives: In an increasingly digital modern society, the loneliness of older adults is a pressing public health concern, and social media is considered a potential solution. Despite past reviews that have attempted to synthesize the association between social media usage (SMU) and loneliness in older adults, the precise connection between the 2 remains unclear. The purpose of this study was to quantify the direct relationship between SMU and loneliness in older adults.

Methods: As of August 2023, 3 databases (Web of Science, ProQuest, PubMed) were used for the literature search. A study was included if it measured the relationship between loneliness and SMU in older people over 50 years old. In total, the present study identified 29 effect sizes, representing data from 19 distinct research reports and over 24,877 participants.

Results: The meta-analysis applying a random model, shows a weak negative correlation between SMU and loneliness (r = -0.06). The region development moderated the relationship, and specifically, the negative correlation between SMU and loneliness increased to a medium size (r = -0.24) when the samples were in developing regions.

Discussion: SMU is negatively correlated with loneliness in old age, which suggests that promotion of SMU among older adults should be implemented, with attention to the inequality between regions and the privacy and availability concerns of older adults. Future research needs to make more efforts in terms of terminology consistency and potential moderators to obtain a more robust understanding of the association between SMU and the loneliness of older adults.