JMIR Aging最新文献

[This corrects the article DOI: 10.2196/77140.].

Background: Little is known about how surrogates make end-of-life care choices for patients who lack the ability to make decisions for themselves.

Objective: The study aims (1) to identify key themes that emerged from participants' free-text responses to a large nationally representative vignette survey about surrogate decision-making in end-of-life care and (2) to determine if an advanced artificial intelligence (AI) chatbot could assist us in accurately and efficiently performing qualitative analyses.

Methods: Our dataset included 3931 free-text responses from a nationally representative survey of 6109 individuals. In this qualitative study, we first familiarized ourselves with the free-text responses and hand-coded the first 200 responses until we reached saturation. We then created a codebook, initial themes, subthemes, and illustrative quotes. Subsequently, we prompted ChatGPT-4o to analyze the entire dataset of 3931 responses and identify frequent keywords and generate themes and quotable quotes. We validated responses by comparing the AI's keyword counts to qualitative software (NVivo, Lumivero) counts and cross-validating AI-generated quotes with the original transcripts.

Results: We identified several key themes: surrogates more often chose comfort care for care recipients with dementia, particularly at advanced stages. They also strongly weighed the patients' perceived quality of life and functional status. Many reported making surrogate decisions based on their own lived experiences or values, rather than making decisions aligned with the patients' previously stated wishes. There was no significant difference between the AI and qualitative software's keyword counts. The most frequent keywords included "life" (2051/81,713, 2.51%), "quality" (903/81,713, 1.11%), and dementia (507/81,713, 0.62%). Overall, AI-generated themes closely aligned with aforementioned human-generated themes. Manual coding of the first 200 free-text responses required 4 hours, including codebook development. In contrast, ChatGPT-4o generated themes in <10 seconds using the predefined codebook. However, dataset preparation, output verification, iterative prompting, debugging, and validation required several weeks.

Conclusions: Surrogates often base end-of-life decisions on dementia stage, perceived quality of life, and their own lived experiences, rather than patient preferences. Using an AI chatbot to perform qualitative analysis on free-text responses may help extend the work of qualitatively trained investigators, especially for large datasets such as free-text responses to large surveys.

Background: Pandemics, such as COVID-19, and climate change-related catastrophic weather events are increasing, impacting social connectedness within communities by disrupting social cohesion, increasing loneliness, and affecting mental health and social well-being. Digital technology, in addition to being used for communication, education, and business transactions, also plays a vital role in maintaining a country's health and well-being, as well as sustaining economic growth.

Objective: This study aimed to explore the experiences of Māori kaumātua in using digital technology to meet their health needs within Ngāti Kahungunu, North Island, New Zealand, during the COVID-19 pandemic and Cyclone Gabrielle.

Methods: This qualitative study employed the Kaupapa Māori methodology to understand the challenges, resilience, and approaches used by Māori to maintain connectedness and access essential services. An inductive approach to thematic analysis, as recommended by Braun and Clarke, was used to ensure a thorough and robust data analysis. The user characteristic was assessed on a semantic level using the information provided in the narrative text.

Results: The findings highlight the role of digital technology in disaster management and underscore the urgent need to address digital disparities in support of vulnerable populations. In this study, 14 individuals were interviewed, comprising 71% (n=10) women and 29% (n=4) men. These participants fell into different age groups, with 9 participants being 65 years or older (older adults). Of the total participants, 43% (n=6) were limited users, 43% (n=6) comprised confident users, and the rest (n=2; 14%) were normal users. A total of 6 themes emerged from the interview data: social connectedness and resilience, digital literacy and access to information, barriers to telecommunications and digital technology, cultural appropriateness and psychological barriers, perceived threats of feeling insecure, and impact on mental health and emotional well-being.

Conclusions: Vulnerable situations such as pandemics and extreme weather events can have tremendous effects on the lives of Indigenous people who live remotely. The study also focused on the actions that should be taken to mitigate these challenges and overcome difficult circumstances, such as the pandemic and the cyclone. The recommendations include a better health care system and improved coordination among care providers, user-friendly digital solutions, ensuring local funding and community services, establishing training processes for basic digital skills, and fostering leadership and partnerships with Indigenous New Zealanders.

Background: With the rise of digital technology, infrastructure development has become vital for social welfare and public health. However, evidence on its effects on depressive symptoms among middle-aged and older adults remains limited.

Objective: This study evaluates the impact of digital infrastructure development on depressive symptoms among middle-aged and older adults, focusing on underlying mechanisms, heterogeneous effects, and health inequalities.

Methods: We use longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), 2011-2020 (N=56,211). Exploiting the quasi-natural experiment of the "Broadband China" pilot policy, we apply a difference-in-differences approach to estimate the effect on depressive symptoms. Mediation analysis follows the Baron-Kenny 3-step procedure, with bootstrap tests (95% CI) for robustness, and causal interpretation relies on standard assumptions for observational data. Subgroup analyses explore heterogeneity across age, education, and sex groups.

Results: Our findings indicate that the "Broadband China" pilot significantly reduces depressive symptoms among middle-aged and older adults (-0.33, P<.01). The positive effect is primarily mediated through strengthened social networks, including increased family connection, close social interactions, and greater social participation. Heterogeneity analysis shows that the benefits for depression reduction are more pronounced among women (-0.38, P<.01), middle-aged adults (-0.41, P<.01), and those with lower levels of education (-0.33, P<.01). Moreover, the results suggest that digital infrastructure plays a compensatory role in mitigating health disparities, thereby reducing inequalities in depression outcomes (-0.01, P<.01).

Conclusions: Digital infrastructure reduces depressive symptoms among aging populations mainly by strengthening social networks. Embedding infrastructure into long-term strategies, enhancing digital literacy, and integrating digital health services are key to promoting healthy aging and reducing inequalities.

Background: Most studies on interventions using social robots to reduce loneliness have been conducted in facilities in Western countries.

Objective: This study evaluated the effectiveness of digital social robot interventions in reducing loneliness among community-dwelling older Japanese adults using a randomized controlled trial and qualitative analysis.

Methods: Individuals aged ≥65 years who lived alone in Tokyo and neighboring areas and experienced loneliness were recruited. In total, 73 eligible participants were randomly assigned to either an intervention or a control group. The 4-week intervention involved a humanoid social communication robot (BOCCO emo), which facilitated conversations with human operators and family members and reminded participants of daily tasks. The primary outcome was loneliness, with mental health (psychological well-being, depression, and self-rated health), the frequency of laughter in daily life, health competence, and interpersonal relationships (social network and generalized trust) as secondary outcomes. Participants were evaluated at baseline and follow-up using a self-administered questionnaire. In the follow-up survey, participants in the intervention group provided open-ended responses regarding their experiences using the social robot.

Results: In total, 68 participants completed both the baseline and follow-up surveys (34 in each group). The average age of the participants was 82.3 (SD 6.5) years, and 64 (N=68, 94%) participants were women. A linear mixed-effects model with a random intercept indicated that loneliness decreased more in the intervention group than in the control group (difference-in-difference -3.1, 95% CI -5.9 to -0.4). Psychological well-being also improved in the intervention group (difference-in-difference 1.9, 95% CI 0.1 to 3.7). We identified 4 categories through content analysis: emotional support and psychological connection, lifestyle assistance, enrichment of social interaction, and cognitive and mental stimulation.

Conclusions: Social robots can reduce loneliness among community-dwelling older adults in non-Western societies. Information and communication technology appears to be an effective approach for alleviating loneliness and enhancing well-being among older adults in community settings.

Background: People with dementia commonly display behavioral and psychological symptoms, which have multiple negative consequences. Artificial intelligence-based technologies (AITs) have the potential to support earlier detection of the behavioral and psychological symptoms of dementia (BPSD). The recent surge of interest in this topic underscores the need to comprehensively examine the existing evidence.

Objective: This scoping review aimed to identify and summarize the types and uses of AITs currently used for the early detection of BPSD among people diagnosed with the disease. We also examined which health care professionals were involved, nursing involvement and experience, the care settings in which these technologies are used, and the characteristics of the BPSD that were assessed.

Methods: Our scoping review was conducted in accordance with the Joanna Briggs Institute manual for scoping reviews. Searches were conducted in March 2025 in the following bibliographic databases: MEDLINE ALL Ovid, Embase, APA PsycINFO Ovid, CINAHL EBSCO, Web of Science Core Collection, the Cochrane Library Wiley, and ProQuest Dissertations and Theses A&I. Additional searches were performed using citation tracking strategies and by consulting the Association for Computing Machinery Digital Library. Eligible studies included primary research involving people with dementia and examining the use of AITs for the detection of BPSD in real-world care settings.

Results: After screening 3670 articles for eligibility, the review includes 12 studies. The studies retained were conducted between 2012 and 2025 in 5 countries and encompassed a range of care settings. The AITs used were predominantly based on classic machine learning approaches and used information from environmental sensors, wearable devices, and data recording systems. These studies primarily assessed behavioral and physiological parameters and focused specifically on symptoms, such as agitation and aggression. None of the retained studies explored nurses' roles or their specific skills in using these technologies.

Conclusions: The use of AITs for managing BPSD represents an emerging field of research offering novel opportunities to enhance their detection in various health care contexts. We recommended that nurses be actively engaged in developing and assessing these technologies. Future research should prioritize investigations into how effective AITs are across diverse populations, whether they can have a long-term impact on managing BPSD, and whether they can improve the quality of life of patients and caregivers.

Background: The development and introduction of an artificial intelligence (AI)-based clinical decision support system (CDSS) in surgical departments as part of the "Supporting Surgery with Geriatric Co-management and AI" project addresses the challenges of an increasingly aging population. The system enables digital comanagement of older patients by providing evidence-based evaluations of their health status, along with corresponding medical recommendations, with the aim of improving their perioperative care.

Objective: The use of an AI-based CDSS in patient care raises ethical challenges. Gathering the opinions, expectations, and concerns of older adults (as potential patients) regarding the CDSS enables the identification of ethical opportunities, concerns, and limitations associated with implementing such a system in hospitals.

Methods: We conducted 5 focus groups with participants aged 65 years or older. The transcripts were evaluated using qualitative content analysis and ethically analyzed. Categories were inductively generated, followed by a thematic classification of participants' statements. We found that technical understanding did not influence the older adults' opinions.

Results: Ethical opportunities and concerns were identified. On the one hand, diagnosis and treatment could be accelerated, the patient-AI-physician interaction could enhance medical treatment, and the coordination of hospital processes could be improved. On the other hand, the quality of the CDSS depends on an adequate data foundation and robust cybersecurity. Potential risks included habituation effects, loss of a second medical opinion, and illness severity influencing patients' attitude toward medical recommendations. The risk of overdiagnosis and overtreatment was discussed controversially, and treatment options could be influenced by interests and finances. Additional concerns included challenges with time savings, potential declines in medical skills, and effects on the length of hospital stay.

Conclusions: To address the ethical challenges, we recommend allocating sufficient time for use of the CDSS and emphasizing individualized review of the CDSS results. Furthermore, we suggest limiting private financial sponsorship.

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