Gerontologist最新文献

Background and objectives: We explored the use of machine learning models for predicting safety accident subtypes among individuals with dementia using in-home sensors and to identify key predictors.

Research design and methods: An observational study was conducted using 966 days of in-home sensor data, sleep data from wearable Actiwatch devices, caregiver-completed structured safety accident diary data, and individual data collected in South Korea. Five machine learning classification models were developed to predict physical injury, nighttime behaviors/wandering, and risky behaviors. Model performance was compared, and the most important predictive features were extracted.

Results: The Gradient Boosting Machine showed the best performance in predicting physical injury and nighttime behaviors, while CatBoost performed best for risky behaviors. Activity patterns recorded using in-home sensors emerged as essential features for predicting different safety accident subgroups, particularly for nighttime behaviors and wandering.

Discussion and implications: These findings highlight the potential of these technologies to identify high-risk individuals with dementia. Further research is recommended to integrate these methods for daily safety monitoring of this population.

Background and objectives: Compared with people in any other age group, older adults worldwide have more suicidal ideation; however, their suicidal ideation is easily overlooked. Social support is currently one of the most widely researched social resources for preventing suicide. Currently, the impact of social support on suicidal ideation in this population is still uncertain. This study aimed to explore the impact of social support on suicidal ideation in older adults.

Research design and methods: A comprehensive literature search was conducted across multiple databases, including the Web of Science, PubMed, EMBASE, CINAHL, Cochrane Library, and Scopus databases. Two independent researchers selected the studies, extracted the data, and evaluated their quality. Statistical analysis was performed using STATA version 18.0.

Results: This review identified 23 studies, and 14 eligible studies were included in the meta-analyses. High social support was significantly associated with a low risk of suicidal ideation (OR: 0.75; 95% CI: 0.67 to 0.84; I 2 = 93.1%; p < .001). Social support is more effective in reducing the risk of SI in women than in men. Social support from families is most effective at reducing suicidal ideation in older adults, and social support can also influence suicidal ideation through mediating factors.

Discussion and implications: Social support is significantly correlated with suicidal ideation in older adults. Medical staff or researchers can prioritize social support from families and develop targeted interventions to more effectively reduce the risk of suicidal ideation in older adults.

Background and objectives: Sleep quality is a critical component of health and recovery for hospitalized older adults, yet current monitoring practices often lack the precision and detail required for effective intervention. This qualitative study aimed to evaluate the feasibility and acceptance of implementing the artificial intelligence-powered Sleepsense bed sensor for sleep monitoring in a geriatric inpatient hospital setting.

Research design and methods: This qualitative study involved interviews with 22 patients and focus groups with 33 interdisciplinary staff members. Data were analyzed using an interpretive description approach, guided by the technology acceptance model and unified theory of acceptance and use of technology, to explore the feasibility and acceptance of Sleepsense bed sensors in a geriatric inpatient setting.

Results: Key findings from thematic analysis emerged in three main themes representing the feasibility and acceptance of Sleepsense bed sensors among hospitalized older adults: user acceptance, integration with somnolog into clinical practice, and implementation barriers and practical challenges. Staff reported high acceptance of Sleepsense technology due to its nonintrusiveness and ability to reduce disruptive nighttime checks. However, challenges such as the need for consent, data interpretation, and occasional inaccuracies were also identified. Integrating Sleepsense with existing care practices was recommended to enhance patient care while maintaining staff confidence.

Discussion and implications: This study underscores the potential of advanced sleep monitoring technologies in subacute care settings and highlights the importance of addressing implementation barriers for effective adoption.

Background and objectives: Racial and ethnic disparities in healthcare use among older adults persist in the United States and remain a pressing equity issue. While prior studies have documented these disparities, a few have examined how they change over time across different care domains. This article aims to fill this gap by investigating how disparities in preventive, treatment, and long-term care use have changed over the past two decades.

Research design and methods: Using 11 waves (1996-2016) of the Health and Retirement Study, a nationally representative longitudinal survey of U.S. older adults (N = 23,724), this study examines trends in healthcare use by race/ethnicity. Healthcare use was categorized as preventive care (physician visits and dental care), treatment care (hospital stays and outpatient surgeries), and long-term care (home health care and nursing home care). Mixed-effects logistic regression models assessed racial/ethnic differences over time.

Results: Non-Hispanic Black and Hispanic older adults were less likely to use preventive and treatment care than their non-Hispanic White counterparts, with disparities widening notably after 2008. In contrast, long-term care use increased among Non-Hispanic Black older adults but declined among Hispanic older adults over time. Significant interaction terms confirmed that disparities shifted over time and differed by care type.

Discussion and implications: Findings indicate that national healthcare reforms have not equally benefited all racial and ethnic groups. Persistent and growing disparities highlight the need for targeted, culturally responsive policies and equity-focused monitoring systems to ensure fair access to healthcare for an increasingly diverse aging population.

Background and objectives: CONFIDENCE is a culturally tailored intervention to reduce caregiver financial strain, which disproportionately impacts Latino family caregivers to persons living with dementia. CONFIDENCE demonstrated preliminary efficacy at lowering financial strain in a feasibility study, yet attendance in group videoconference sessions was low (62%). CONFIDENCE was revised to integrate the NeuViCareTM application (app) that leveraged an interactive artificial intelligence (AI) assistant and text message reminders to engage participants between sessions. This study examined whether app registration was associated with attendance, as well as overall utilization of app features.

Research design and methods: CONFIDENCE was delivered in a community-based organization, where registration for the NeuViCareTM app was offered at no cost to complement the 4-week facilitated group-based intervention. Investigators tracked caregiver attendance in sessions, app registration, and uptake of app features. Bivariate statistics were used to assess the relationship between app registration and attendance.

Results: Fifty-five (55) caregivers completed the CONFIDENCE intervention, among whom 50.9% (28) registered for the NeuViCareTM app. App users attended an average of 2.9 (72%) sessions, compared to 2.0 (50%) of non-users (p = .002). Interactive features of the app (e.g., messaging an AI digital assistant) were infrequently used by participants.

Discussion and implications: Findings support further investigation into technology-enhanced psychoeducation for family caregivers of persons living with dementia to promote intervention adherence, particularly Latino caregivers who face greater caregiving burden and limited access to supportive resources despite higher dementia prevalence. These findings contribute to the emerging literature on technology adoption, including interactive AI, in service contexts.

Background and objectives: Many studies on informal caregiving experiences recruited samples from clinical settings or preexisting datasets, resulting in suspected selection biases. There is also a limited understanding of how culture shapes the perceptions of positive caregiving beyond the Asian context. In Nigeria, existing studies on older adults' care primarily focus on young and middle-aged caregivers. This study fills existing gaps by investigating the experiences of informal caregiving among community-dwelling older carers in Nigeria.

Research design and methods: Thirty in-depth interviews were conducted with community-dwelling older informal caregivers aged 54-88 years against the backdrop of a life expectancy of 53 years in Nigeria. Van Manen's hermeneutic phenomenological design guided this study. We managed the qualitative data with QSR NVivo 12 software.

Results: Our findings include themes of financial constraint, poor health, a crowded-out personal life, a mismatch between care recipients' expectations and caregivers' capacity, blessings from God, benefits elicited by reciprocity, and individuals' perception of the intrinsic worth of caregiving.

Discussion and implications: Our findings highlight both the challenging and rewarding aspects of informal caregiving. In the absence of formal support systems, the demands of caregiving may have a more pronounced impact on caregivers in Nigeria. Moreover, the nuances in our participants' experience of positive caregiving outcomes are shaped by their adherence to Afrocentric cultural norms. Our findings highlight the need to develop tailored support programs and a deeper level of work to fortify cultural norms that promote the wellbeing of older people in the family in Nigeria.

Background and objectives: Problems with vision or hearing in midlife and old age can have a detrimental impact on individuals' autonomy in everyday life, social participation, and quality of life. However, little is known about historical trends in self-reported vision and hearing trajectories among middle-aged adults and how these differ across nations.

Research design and methods: The researchers used harmonized data (n = 96,896; data collection 1996-2020) on self-reported near and distant vision as well as hearing from nationally representative panel surveys across a total of 16 countries (United States, Mexico, South Korea, England, and countries in Continental, Mediterranean, and Nordic Europe) in order to compare historical change in middle-aged adults' sensory functioning trajectories. The researchers included sex, age, education, chronic conditions, and depressive symptoms as moderators of historical change.

Results: Longitudinal multilevel regression models revealed that at age 50 and across most birth year cohorts, U.S. individuals had poorer self-reported near and distant vision than individuals in England and Continental Europe. For the United States, there was no evidence of historical change in self-reported hearing and distant vision. Across most countries, including the United States, there was a historical decline in self-reported near vision. Women reported better hearing than men; this sex discrepancy narrowed across historical time. Education and depressive symptoms were more closely associated with self-reported sensory functioning in the United States than in most other regions.

Discussion and implications: The findings of this study suggest that there is little evidence for global historical improvements in self-reported sensory functioning.

Background and objectives: The Age-Friendly Health Systems model has emerged as a geriatric care model focusing on the 4Ms: what matters, mobility, mentation, and medications. Representation of the 4Ms in interdisciplinary communication could have implications for outcomes including avoidable nursing home-to-hospital transfers. The objective of this article was to explore the association between the 4Ms found in text messages and avoidable transfer of residents with and without Alzheimer's Disease and Related Dementias (ADRD).

Research design and methods: The researchers merged data from two primary datasets: (a) text messages (n = 30,066) between nursing home (NH) healthcare workers from an HIPAA-compliant messaging platform and (b) resident transfer data (n = 3,687) from NHs (n = 16) that participated in a 5-year (2016-2020) Centers for Medicare and Medicaid Services demonstration project. They used natural language processing to extract 4M terms from text messages and fit generalized linear mixed models to estimate avoidable NH-to-hospital transfer, controlling for resident characteristics, NH characteristics, and the 4Ms.

Results: Merged data contained 1,031 observations grouped by temporal proximity to the transfer date. Cardiopulmonary resuscitation status, late-stage ADRD, NH bed size, and location were associated with avoidable NH-to-hospital transfer of residents. Text messages containing terms representing mentation and mobility were also associated with avoidable NH-to-hospital transfers.

Discussion and implications: These results suggest that natural language processing can be used to identify components of age-friendly care in unstructured data. Association between resident level and nursing home factors and avoidable transfers should be considered as nursing homes implement strategies to reduce avoidable transfer of residents with ADRD.

Background and objectives: Few doctoral psychology trainees choose to specialize in geropsychology, despite the rising need for trained professionals in this area. Guided by the theory of planned behavior, this study aimed to identify modifiable factors that relate to trainees having stronger intentions to work with older adult clients.

Research design and methods: A sample of 188 doctoral trainees in clinical and counseling psychology programs in the United States completed survey measures online. Structural equation modeling was used to test hypothesized relations of ageist attitudes, aging-specific preparation, perceived competence, social approval, and attitudes about work with older adults, and intention to work with older adult clients.

Results: Perceiving greater social approval for working with older adults directly related to a stronger intention to do this work. Ageist attitudes indirectly influenced intention through perceived competence for and attitudes about work with older adults. Receiving more training in aging also indirectly related to intention through greater perceived competence for work with older adult clients.

Discussion and implications: These findings supported the theory of planned behavior as a conceptual framework for understanding psychology trainees' intentions to work with older adults and suggested ageist attitudes and aging-specific training as modifiable factors that influence trainees' interest in clinical work with older adults. Doctoral psychology training programs could encourage additional interest in clinical work with older adults by making an explicit statement of support for this work, by working to reduce their trainees' ageist biases, and by providing more aging-specific training and education.