Gerontologist最新文献

Background and objectives: Taiwan and Finland represent contrasting welfare and cultural contexts-familialist-market and universalist models-yet both face demographic shifts, changing family formations, and evolving housing needs in later life. This study explores how older adults in these contexts navigate decisions to age in place or relocate, using Clapham's (2005) housing pathways framework to analyze the roles of personal control, identity, social support, and inequality.

Research design and methods: This qualitative study draws on in-depth, semi-structured interviews with 42 participants in Taiwan (aged 65-93) and 40 in Finland (aged 57-92). Data were analyzed abductively through thematic analysis.

Results: In Taiwan, personal control was relational, rooted in intergenerational reciprocity, ancestral property ties, and gendered caregiving norms; in Finland, it was individual, supported by anticipatory planning and accessible housing. Taiwanese identity was place-based, while Finnish identity was community-built, often after relocation. Social support in Taiwan centered on kin and neighborhood networks, with migrant care workers supplementing gaps; in Finland, communal housing fostered peer reciprocity alongside formal services and adult children. Inequalities were shaped by financial constraints and limited public provision in Taiwan, and by residual access gaps in Finland.

Discussion and implications: Housing decisions were not purely individual choices but negotiated within structural, cultural, and relational contexts. Policies should move beyond promoting aging in place as default, instead enabling it as an empowered choice. Expanding affordable, accessible housing and community services in Taiwan and sustaining inclusive, service-integrated housing in Finland are key to supporting diverse later-life housing pathways.

Background and objectives: Prior research with older lesbian, gay, bisexual, transgender, queer, and intersex (LGBTQI+) populations indicates a need for greater documentation of palliative care clinicians' perspectives on communication and clinician knowledge about these patients' needs. LGBTQI+ patients report barriers to their disclosure of sexual orientation and gender identity data, dismissal of chosen care partners, and personal experiences of discrimination in palliative care.

Research design and methods: Queer gerontology was applied in this qualitative descriptive study. One-on-one interviews were transcribed, coded, and analyzed using reflexive thematic analysis. Twenty clinicians and associated health care professionals with palliative care training who currently or within the past 5 years had provided care and/or treatment for older LGBTQI+ patients living with serious illness within Colorado participated in this study.

Results: Three main themes emerged: (1) limited visibility of LGBTQI+ patients, (2) asking about sexual orientation and gender identity, and (3) acknowledging limitations around the current state of care for LGBTQI+ patients and care partners.

Discussion and implications: Clinical environments must be inclusive and welcoming to promote a safe space for lesbian, gay, bisexual, transgender, queer, and intersex patients and care partners. Avoiding assumptions around these patients having the same or similar experiences to cisgender, heterosexual patients is critical to making meaningful connections. There is a significant need for lesbian, gay, bisexual, transgender, queer, and intersex-specific culturally relevant training. We recommend improvements in communication, inclusivity, and training/education. Findings indicate several areas to improve trust, equity, and inclusivity.

Background and objectives: Care transitions for people living with dementia are critical periods requiring coordinated, person-centered support. Effective transitions can reduce caregiver burden, prevent adverse outcomes, and improve care quality. However, the barriers, facilitators, and lived experiences during transitions remain poorly understood. This systematic review synthesizes evidence on these factors from the perspectives of people with dementia and their informal caregivers.

Research design and methods: A comprehensive search across MEDLINE, CINAHL, PsycINFO, ProQuest, and Web of Science identified 67 eligible English-language studies published from 2018 to 2023. Quality appraisal used Joanna Briggs Institute tools. The protocol was registered on PROSPERO: CRD42023452669.

Results: Four themes captured the barriers, facilitators, and experiences shaping care transitions for people with dementia and their caregivers. Systemic influences included fragmented governance, funding and policy inconsistencies, and structural challenges in care coordination and delivery, mitigated by proactive planning and integrated care. Health and social care workforce factors highlighted gaps in dementia training, staffing, and communication, with empathetic, informed staff improving transitions. Emotions and decision making reflected caregiver burden, uncertainty, and advocacy, eased by early guidance and peer support. Cultural, social and situational influences showed how values, socioeconomic status, and rurality affected transition choices, underscoring the need for culturally sensitive, person-centered support.

Discussion and implications: Care transitions remain complex, shaped by systemic, workforce, emotional, and cultural factors. Addressing inequities and coordination gaps is critical for more integrated transitional care. Strengthening dementia-specific training, home-based care models, and culturally responsive communication may improve continuity, person-centeredness, and caregiver support.

Background and objectives: Older adults with dementia are at higher risk of hospitalized for urinary tract infections (UTIs), with worse health outcomes. Digital interventions, such as smartphone apps, wearable devices, and telehealth, hold promise for improving UTI detection, monitoring, and prevention. However, their effectiveness for people with dementia and their caregivers remains unclear. This review aims to identify: (1) The types of digital interventions and devices used for UTI management and prevention in people with dementia and their caregivers; and (2) The outcome variables and key findings of these interventions.

Research design and methods: A scoping review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews framework, searching PubMed, CINAHL, Embase, IEEE Xplore, and Web of Science for studies from 1998 to 2024. The review included quantitative, qualitative, and mixed-method studies that described digital interventions for UTI management in persons with dementia. Studies were excluded if they lacked detailed intervention descriptions or outcome reporting. Data were charted and summarized to address the study aims.

Results: Seven studies were included from 1,800 screened. Three digital interventions were evaluated: Technology Integrated Health Management (TIHM), a real-time locating system (RTLS), and a smart diaper system (SDS). The TIHM and RTLS showed high effectiveness in UTI detection, with sensitivities up to 91%, while the SDS had lower sensitivity.

Discussion and implications: The review highlights the potential of sensor-based technology and AI in early UTI detection. However, most interventions lack theoretical foundations and preventive strategies, suggesting a need for more comprehensive approaches involving caregivers and clinical guidelines.

Background and objectives: Social isolation affects one in four people and is associated with adverse health outcomes, yet accurate prediction models remain lacking. This study develops and validates an interpretable machine learning (ML) approach to predict social isolation and identify key predictors among middle-aged and older adults in China.

Research design and methods: Training data came from the China Health and Retirement Longitudinal Study. Baseline data from 2011 included 12,785, 12,323, and 11,590 participants for the 2-, 4-, and 7-year prediction models. External validation used the China Family Panel Studies 2010, 2012, and 2016. Five ML algorithms were used to construct prediction models with 283 candidate predictors. SHapley Additive exPlanations explained the feature importance. Classic logistic regression and restricted cubic spline (RCS) explored potential causal associations.

Results: In the development phase, the gradient boosting machine (GBM) performed best across 2-, 4-, and 7-year models (area under the receiver operating characteristic curve [AUC-ROC] = 0.767, 0.729, and 0.749). In the external validation, the GBM had AUC-ROC with 0.649 and 0.678 for the 2- and 7-year prediction models. Age, monthly nonfood consumption, and net primary residence value were consistently identified as the top predictors. Environmental exposures (greenness exposure, rainy days) and community environment (convenience stores, out-migrants) also emerged as important predictors. RCS analysis revealed nonlinear associations between these external factors and social isolation.

Discussion and implications: With multimodal data, the GBM outperformed existing models for identifying social isolation risk. Its interpretability highlights actionable and potentially reversible targets, especially at community and environmental levels.

This conceptual article advances a feminist gerontological perspective on the intellectual and emotional labor of older adults-particularly women. Building on theories of invisible labor, care ethics, and adult learning, this article argues that unpaid, informal knowledge work (e.g., caregiving, mentoring, volunteering, community education) constitutes a form of cognitive labor that is persistently under-acknowledged in aging and education discourse. The analysis highlights the gendered dimensions of later-life learning, contending that older women act as vital yet unrecognized intellectual actors in family and community life. Through a critical synthesis of literature across gerontology, feminist theory, and adult education, this article proposes a new framework for recognizing and valuing intellectual contributions of older learners beyond formal institutions. By documenting this perspective, this article challenges deficit narratives of aging, stimulates dialog about the ethical and political stakes of recognizing cognitive labor, and identifies directions for future research and policy to advance gerontological scholarship.

Presentations at academic conferences are an important avenue for discussing scientific problems and disseminating discoveries. Despite efforts to modernize conference session formats in gerontology and other health research fields, session content is critiqued as being overly technical and sometimes disconnected from real-world problems and solutions. In this forum article, the researchers assert that partnering with people with lived experience can help gerontological researchers develop and share conference presentations that are enriched, accessible, and better aligned with important aging-related issues. This article provides guidelines for this type of partnership. The researchers review participatory research approaches and describe the current landscape of engagement in partnership with people with lived experiences to disseminate gerontological research at conferences. They then describe their own recent partnership with a person with lived experience when presenting a symposium at a Gerontological Society of America Annual Scientific Meeting. This experience is presented as a case study that helped us to develop the PARTNER Model for symposium development and delivery. An overview of this model is provided, connecting each tenet to the researchers' experiences in their recent partnership. The article concludes with recommendations for improvements that can support researchers to thoughtfully engage in these types of partnership for future conference presentations.

Background and objectives: Artificial Intelligence (AI) is undergoing a paradigm shift in its application to healthcare, particularly in the context of ageing-related care, with significant implications for disease prevention, diagnosis, and treatment. The integration of advanced machine learning, deep neural networks, and natural language processing has enabled AI to analyze datasets with remarkable accuracy, surpassing the performance of traditional methods. AI-driven approaches have the potential to facilitate early disease detection, predict progression, and personalize treatments, optimizing healthcare resources. Furthermore, AI is contributing to the development of new treatments and supporting public health strategies. The objective is to assess the effectiveness of AI-based interventions for loneliness.

Research design and methods: This article presents a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-analyses methodology. Databases searched were PubMed, Web of Science, Scopus and PsycInfo. A total of 19 articles were identified fulfilling the inclusion criteria, and using validated tools to assess loneliness. Studies were summarized indicating: country, sample, design of the study, measures of loneliness, the AI-based technology main findings and implications.

Results: Findings highlight AI's potential to enhance social well-being among older adults. From a policy perspective, AI-driven analytics enable targeted interventions by identifying trends in age-related health and social issues from the psychology of social intervention. The adoption of AI in ageing policies promotes efficient, inclusive frameworks that support healthy ageing and reduce pressure on traditional healthcare systems.

Discussion and implications: This study contributes to the growing evidence supporting AI's role in addressing loneliness and improving overall quality of life in ageing populations.

Background and objectives: While participatory arts are thought to benefit older adults' well-being, evidence has focused on dementia. This umbrella review synthesizes evidence from systematic reviews (SRs) and meta-analyses (MAs) on the impacts of participatory arts for older adults without dementia, and conceptually organizes findings using the proposed "Aesthetic-Engagement-Creativity" (AEC) framework.

Research design and methods: Following PRIOR guidelines, we searched MEDLINE, PubMed, PsycINFO, The Cochrane Library of Systematic Reviews and gray literature to September 2023, with an update in June 2025. Study characteristics and well-being outcomes were extracted. We used the AEC framework for conceptual categorization, assessed primary study overlap using the corrected covered area (CCA) and the methodological quality with the AMSTAR 2.

Results: Eighteen reviews were included, with minimal study overlap (CCA = 1.96%). However, the evidence base was weak; 12 reviews were of low or critically low quality. A synthesis of the six moderate-to-high quality reviews revealed that dance was the most studied modality, associated with improved physical well-being. The benefits for other well-being domains and art modalities were mixed, and significant heterogeneity in study designs and measures complicated comparisons. Our retrospective application of the AEC framework suggests that considering participants' aesthetic preferences and optimizing engagement and creativity levels may be important, but this link is speculative.

Discussion and implications: Participatory arts show potential for promoting well-being of older adults, but robust conclusions are constrained by the poor quality, underrepresented art modalities, and heterogeneity of the existing evidence. The AEC framework is offered as a conceptual tool requiring future empirical validation.

Recent advancements in artificial intelligence, particularly in speech technologies, hold significant potential for improving the health and wellbeing of older adults by offering non-invasive, accessible and scalable solutions to increase social engagement, assist with daily activities and diagnose disease. However, the development of AI models has been almost exclusively based on English-language datasets, marginalizing the over one billion older adults worldwide who speak non-English, low resource languages. This lack of linguistic inclusivity restricts their access to innovations, contributes to delayed diagnoses and reduced quality of care, and exacerbates existing healthcare inequities. Here we highlight the urgent need to curate speech datasets in low resource languages by prioritizing community agency, ensuring equitable distribution of benefits, and establishing sustainable pathways for long-term participation and empowerment, with the aim of advancing inclusive and equitable speech-based healthcare tools for older adults.