JMIR Aging最新文献

Background: Many members of Black American communities, faced with the high prevalence of Alzheimer disease and related dementias (ADRD) within their demographic, find themselves taking on the role of informal caregivers. Despite being the primary individuals responsible for the care of individuals with ADRD, these caregivers often lack sufficient knowledge about ADRD-related health literacy and feel ill-prepared for their caregiving responsibilities. Generative AI has become a new promising technological innovation in the health care domain, particularly for improving health literacy; however, some generative AI developments might lead to increased bias and potential harm toward Black American communities. Therefore, rigorous development of generative AI tools to support the Black American community is needed.

Objective: The goal of this study is to test Lola, a multimodal mobile app, which, by relying on generative AI, facilitates access to ADRD-related health information by enabling speech and text as inputs and providing auditory, textual, and visual outputs.

Methods: To test our mobile app, we used the cognitive walk-through methodology, and we recruited 15 informal ADRD caregivers who were older than 50 years and part of the Black American community living within the region. We asked them to perform 3 tasks on the mobile app (ie, searching for an article on brain health, searching for local events, and finally, searching for opportunities to participate in scientific research in their area), then we recorded their opinions and impressions. The main aspects to be evaluated were the mobile app's usability, accessibility, cultural relevance, and adoption.

Results: Our findings highlight the users' need for a system that enables interaction with different modalities, the need for a system that can provide personalized and culturally and contextually relevant information, and the role of community and physical spaces in increasing the use of Lola.

Conclusions: Our study shows that, when designing for Black American older adults, a multimodal interaction with the generative AI system can allow individuals to choose their own interaction way and style based upon their interaction preferences and external constraints. This flexibility of interaction modes can guarantee an inclusive and engaging generative AI experience.

Background: Loneliness is a significant issue among older Asian Americans, exacerbated by the COVID-19 pandemic. Older age, lower income, limited education, and immigrant status heighten loneliness risk. Information communication technologies (ICTs) have been associated with decreased loneliness among older adults. However, older Asian Americans are less likely to use ICTs, particularly if they are immigrants, have limited English proficiency, or are low income. The Technology Acceptance Model posits that perceived usefulness (PU), and perceived ease of use (PEOU) are key factors in predicting technology use.

Objective: This study aimed to examine associations between PU, PEOU, ICT use, and loneliness among low-income, older Asian Americans.

Methods: Cross-sectional survey data were gathered from predominately older Asian Americans in affordable senior housing (N=401). Using exploratory factor analysis and Horn parallel analysis, we examined 12 survey items to identify factors accounting for variance in ICT use. We deployed structural equation modeling to explore relationships among the latent factors and loneliness, adjusting for demographic and cognitive factors.

Results: Exploratory factor analysis and Horn parallel analysis revealed 3 factors that accounted for 56.48% (6.78/12) total variance. PEOU combined items from validated subscales of tech anxiety and comfort, accounting for a 28.44% (3.41/12) variance. ICT use combined years of technological experience, computer, tablet, and smartphone use frequency, accounting for 15.59% (1.87/12) variance. PU combined 2 items assessing the usefulness of technology for social connection and learning and accounted for a 12.44% (1.49/12) variance. The 3-factor structural equation modeling revealed reasonable fit indexes (χ²₁₃₃=345.132; P<.001, chi-square minimum (CMIN)/df = 2595, comparative fit index (CFI)=0.93, Tucker-Lewis Index (TLI)=0.88). PEOU was positively associated with PU (β=.15; P=.01); PEOU and PU were positive predictors of ICT use (PEOU β=.26, P<.001; PU β=.18, P=.01); and ICT use was negatively associated with loneliness (β=-.28, P<.001). Demographic and health covariates also significantly influenced PU, PEOU, ICT use, and loneliness. English proficiency and education positively predicted PEOU (r=0.25, P<.001; r=0.26, P<.001) and ICT use (β=1.66, P=.03; β=.21, P<.001), while subjective cognitive decline and Asian ethnicity were positively associated with loneliness (β=.31, P<.001; β=.25, P<.001).

Conclusions: This study suggests that targeted interventions enhancing PU or PEOU could increase ICT acceptance and reduce loneliness among low-income Asian Americans. Findings also underscore the importance of considering limited English proficiency and subjective cognitive decline when designing interventions and in future research.

Background: Providing ongoing support to the increasing number of caregivers as their needs change in the long-term course of dementia is a severe challenge to any health care system. Conversational artificial intelligence (AI) operating 24/7 may help to tackle this problem.

Objective: This study describes the development of a generative AI chatbot-the PDC30 Chatbot-and evaluates its acceptability in a mixed methods study.

Methods: The PDC30 Chatbot was developed using the GPT-4o large language model, with a personality agent to constrain its behavior to provide advice on dementia caregiving based on the Positive Dementia Caregiving in 30 Days Guidebook-a laypeople's resource based on a validated training manual for dementia caregivers. The PDC30 Chatbot's responses to 21 common questions were compared with those of ChatGPT and another chatbot (called Chatbot-B) as standards of reference. Chatbot-B was constructed using PDC30 Chatbot's architecture but replaced the latter's knowledge base with a collection of authoritative sources, including the World Health Organization's iSupport, By Us For Us Guides, and 185 web pages or manuals by Alzheimer's Association, National Institute on Aging, and UK Alzheimer's Society. In the next phase, to assess the acceptability of the PDC30 Chatbot, 21 family caregivers used the PDC30 Chatbot for two weeks and provided ratings and comments on its acceptability.

Results: Among the three chatbots, ChatGPT's responses tended to be repetitive and not specific enough. PDC30 Chatbot and Chatbot-B, by virtue of their design, produced highly context-sensitive advice, with the former performing slightly better when the questions conveyed significant psychological distress on the part of the caregiver. In the acceptability study, caregivers found the PDC30 Chatbot highly user-friendly, and its responses quite helpful and easy to understand. They were rather satisfied with it and would strongly recommend it to other caregivers. During the 2-week trial period, the majority used the chatbot more than once per day. Thematic analysis of their written feedback revealed three major themes: helpfulness, accessibility, and improved attitude toward AI.

Conclusions: The PDC30 Chatbot provides quality responses to caregiver questions, which are well-received by caregivers. Conversational AI is a viable approach to improve the support of caregivers.

Background: Smartphone apps can be used to monitor chronic conditions and offer opportunities for self-assessment conveniently at home. However, few digital studies include older adults.

Objective: We aim to describe a new electronic cohort of older adults embedded in the Framingham Heart Study including baseline smartphone survey return rates and survey completion rates by smartphone type (iPhone [Apple Inc] and Android [Google LLC] users). We also aim to report survey results for selected baseline surveys and participant experience with this study's app.

Methods: Framingham Heart Study Offspring and Omni (multiethnic cohort) participants who owned a smartphone were invited to download this study's app that contained a range of survey types to report on different aspects of health including self-reported measures from the Patient-Reported Outcomes Measurement Information System (PROMIS). iPhone users also completed 4 tasks including 2 cognitive and 2 physical function testing tasks. Baseline survey return and completion rates were calculated for 12 surveys and compared between iPhone and Android users. We calculated standardized scores for the PROMIS surveys. The Mobile App Rating Scale (MARS) was deployed 30 days after enrollment to obtain participant feedback on app functionality and aesthetics.

Results: We enrolled 611 smartphone users (average age 73.6, SD 6.3 y; n=346, 56.6% women; n=88, 14.4% Omni participants; 478, 78.2% iPhone users) and 596 (97.5%) returned at least 1 baseline survey. iPhone users had higher app survey return rates than Android users for each survey (range 85.5% to 98.3% vs 73.8% to 95.2%, respectively), but survey completion rates did not differ in the 2 smartphone groups. The return rate for the 4 iPhone tasks ranged from 80.9% (380/470) for the gait task to 88.9% (418/470) for the Trail Making Test task. The Electronic Framingham Heart Study participants had better standardized t scores in 6 of 7 PROMIS surveys compared to the general population mean (t score=50) including higher cognitive function (n=55.6) and lower fatigue (n=45.5). Among 469 participants who returned the MARS survey, app functionality and aesthetics was rated high (total MARS score=8.6 on a 1-10 scale).

Conclusions: We effectively engaged community-dwelling older adults to use a smartphone app designed to collect health information relevant to older adults. High app survey return rates and very high app survey completion rates were observed along with high participant rating of this study's app.

Background: As Alzheimer disease (AD) and AD-related dementias (ADRD) progress, individuals increasingly require assistance from unpaid, informal caregivers to support them in activities of daily living. These caregivers may experience high levels of financial, mental, and physical strain associated with providing care. CareVirtue is a web-based tool created to connect and support multiple individuals across a care network to coordinate care activities and share important information, thereby reducing care burden.

Objective: This study aims to use a computational informatics approach to thematically analyze open text written by AD/ADRD caregivers in the CareVirtue platform. We then explore relationships between identified themes and use patterns.

Methods: We analyzed journal posts (n=1555 posts; 170,212 words) generated by 51 unique users of the CareVirtue platform. Latent themes were identified using a neural network approach to topic modeling. We calculated a sentiment score for each post using the Valence Aware Dictionary and Sentiment Reasoner. We then examined relationships between identified topics; semantic sentiment; and use-related data, including post word count and self-reported mood.

Results: We identified 5 primary topics in users' journal posts, including descriptions of specific events, professional and medical care, routine daily activities, nighttime symptoms, and bathroom/toileting issues. This 5-topic model demonstrated adequate fit to the data, having the highest coherence score (0.41) among those tested. We observed group differences across these topics in both word count and semantic sentiment. Further, posts made in the evening were both longer and more semantically positive than other times of the day.

Conclusions: Users of the CareVirtue platform journaled about a variety of different topics, including generalized experiences and specific behavioral symptomology of AD/ADRD, suggesting a desire to record and share broadly across the care network. Posts were the most positive in the early evening when the tool was used habitually, rather than when writing about acute events or symptomology. We discuss the value of embedding informatics-based tools into digital interventions to facilitate real-time content delivery.

Background: There are numerous communication barriers between family caregivers and providers of people living with dementia, which can pose challenges to caregiving and clinical decision-making. To address these barriers, a new web and mobile-enabled app, called CareHeroes, was developed, which promotes the collection and secured sharing of clinical information between caregivers and providers. It also provides caregiver support and education.

Objective: The primary study objective was to examine whether dementia caregivers would use CareHeroes as an adjunct to care and gather psychosocial data from those who used the app.

Methods: This paper presents the implementation process used to integrate CareHeroes into clinical care at 2 memory clinics and preliminary outcome evaluation. Family caregivers receiving services at clinics were asked to use the app for a 12-month period to collect, track, and share clinical information with the care recipient's provider. They also used it to assess their own mental health symptoms. Psychosocial outcomes were assessed through telephone interviews and user data were collected by the app.

Results: A total of 21 caregivers enrolled in the pilot study across the 2 memory clinics. Usage data indicated that caregivers used many of the features in the CareHeroes app, though the chatbot was the most frequently used feature. Outcome data indicated that caregivers' depression was lower at 3-month follow-up (t11=2.03, P=.03).

Conclusions: Recruitment and retention of the pilot study were impacted by COVID-19 restrictions, and therefore more testing is needed with a larger sample to determine the potential impact of CareHeroes on caregivers' mental health. Despite this limitation, the pilot study demonstrated that integrating a new supportive app for caregivers as an adjunct to clinical dementia care is feasible. Implications for future technology intervention development, implementation planning, and testing for caregivers of people living with dementia are discussed.

Background: To diagnose Alzheimer disease (AD), individuals are classified according to the severity of their cognitive impairment. There are currently no specific causes or conditions for this disease.

Objective: The purpose of this systematic review and meta-analysis was to assess AD prevalence across different stages using machine learning (ML) approaches comprehensively.

Methods: The selection of papers was conducted in 3 phases, as per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelines: identification, screening, and final inclusion. The final analysis included 24 papers that met the criteria. The selection of ML approaches for AD diagnosis was rigorously based on their relevance to the investigation. The prevalence of patients with AD at 2, 3, 4, and 6 stages was illustrated through the use of forest plots.

Results: The prevalence rate for both cognitively normal (CN) and AD across 6 studies was 49.28% (95% CI 46.12%-52.45%; P=.32). The prevalence estimate for the 3 stages of cognitive impairment (CN, mild cognitive impairment, and AD) is 29.75% (95% CI 25.11%-34.84%, P<.001). Among 5 studies with 14,839 participants, the analysis of 4 stages (nondemented, moderately demented, mildly demented, and AD) found an overall prevalence of 13.13% (95% CI 3.75%-36.66%; P<.001). In addition, 4 studies involving 3819 participants estimated the prevalence of 6 stages (CN, significant memory concern, early mild cognitive impairment, mild cognitive impairment, late mild cognitive impairment, and AD), yielding a prevalence of 23.75% (95% CI 12.22%-41.12%; P<.001).

Conclusions: The significant heterogeneity observed across studies reveals that demographic and setting characteristics are responsible for the impact on AD prevalence estimates. This study shows how ML approaches can be used to describe AD prevalence across different stages, which provides valuable insights for future research.

Background: Web-based advance care planning (ACP) interventions offer a promising solution to improve ACP engagement, but none are specifically designed to meet the needs of patients with heart failure and their caregivers.

Objective: We aimed to develop and assess the usability and acceptability of a web-based ACP decision aid called "My Voice," which is tailored for patients with heart failure and their caregivers.

Methods: This study's team and advisory board codeveloped the content for both patient and caregiver modules in "My Voice." Using a mixed methods approach, we iteratively tested usability and acceptability, incorporating feedback from patients, caregivers, and health care professionals (HCPs).

Results: We interviewed 30 participants (11 patients, 9 caregivers, and 10 HCPs). Participants found the website easy to navigate, with simple and clear content facilitating communication of patients' values and goals. They also appreciated that it allowed them to revisit their care goals periodically. The average System Usability Scale score was 74 (SD 14.8; range: 42.5-95), indicating good usability. Over 80% (8/11) of patients and 87% (7/8) of caregivers rated the website's acceptability as good or excellent. Additionally, 70% (7/10) of HCPs strongly agreed or agreed with 11 of the 15 items testing the website's acceptability.

Conclusions: "My Voice" shows promise as a tool for patients with heart failure to initiate and revisit ACP conversations with HCPs and caregivers. We will evaluate its efficacy in improving patient and caregiver outcomes in a randomized controlled trial.

Background: Previous studies have explored how sensor technologies can assist in in the detection, recognition, and prevention of subjective loneliness. These studies have shown a correlation between physiological and behavioral sensor data and the experience of loneliness. However, little research has been conducted on the design requirements from the perspective of older people and stakeholders in technology development. The use of these technologies and infrastructural questions have been insufficiently addressed. Systems generally consist of sensors or software installed in smartphones or homes. However, no studies have attempted to use smart textiles, which are fabrics with integrated electronics.

Objective: This study aims to understand the design requirements for a smart textile loneliness monitoring system from the perspectives of older people and stakeholders.

Methods: We conducted co-design workshops with 5 users and 6 stakeholders to determine the design requirements for smart textile loneliness monitoring systems. We derived a preliminary product concept of the smart wearable and furniture system. Digital and physical models and a use case were evaluated in a focus group study with older people and stakeholders (n=7).

Results: The results provided insights for designing systems that use smart textiles to monitor loneliness in older people and widen their use. The findings informed the general system, wearables and furniture, materials, sensor positioning, washing, sensor synchronization devices, charging, intervention, and installation and maintenance requirements. This study provided the first insight from a human-centered perspective into smart textile loneliness monitoring systems for older people.

Conclusions: We recommend more research on the intervention that links to the monitored loneliness in a way that addresses different needs to ensure its usefulness and value to people. Future systems must also reflect on questions of identification of system users and the available infrastructure and life circumstances of people. We further found requirements that included user cooperation, compatibility with other worn medical devices, and long-term durability.