Although clustering techniques are commonly used in bibliometric analysis to identify research themes, few studies systematically assign these themes back to individual articles. This gap limits the interpretability of findings and hinders granular, article-level longitudinal analysis. This study introduces the Theme Assignment Algorithm for Articles (TAAA), a data-driven framework designed to map clustered themes to individual publications. We demonstrate its utility by identifying dominant research patterns and thematic shifts within JMIR Aging. TAAA was applied to 434 JMIR Aging articles published between 2020 and 2025. Keywords were harvested from 3 sources: Web of Science Core Collection (WoSCC) Keywords Plus, author-provided keywords, and abstract-derived terms. These were grouped into thematic clusters using the "following leader clustering algorithm". The TAAA, implemented via R and a web-based application, determined each article's primary theme using a statistical model to create a discrete article-level variable. Core themes were identified via h-index computation. Analytical visualization included Kano and Sankey diagrams, alongside volcano plots and heatmaps. The framework's robustness was further tested by applying a "differentially expressed genes" analogy to map "unknown" core metadata across "known" pre/post publication stages using Cohen kappa as the extent of mapping power. Analysis across the 3 keyword sources yielded 9, 7, and 9 core themes, respectively. The most prominent themes identified were HEALTH (39.4%), OLDER ADULTS (43.1%), and DEMENTIA (25.3%). Notably, DEMENTIA emerged as a consistent core theme across all sources and visual layers, validating the TAAA's ability to capture cross-source thematic coherence. The adaptation of dual heatmaps demonstrated the algorithm's capacity for comparative bibliometric mapping in JMIR Aging. A mapping precision of 0.33 provided quantitative evidence of a 2-stage publication pattern, though the separation between stages was not strictly confined to predefined time intervals. The TAAA framework provides a replicable, scalable, and interpretable method for article-level thematic assignment. Its ability to uncover consistent research patterns-specifically the dominance of dementia-related studies in JMIR Aging-demonstrates its value for bibliometricians and its potential adaptability to other domains, such as bioinformatics-inspired meta-analyses.
Survivors of hematopoietic stem cell transplantation (HSCT) in childhood face a high risk of metabolic and cardiovascular disease as well as accelerated aging. Estimates of the prevalence of these severe late effects have varied widely because they have been based on small cohorts or mixed populations of patients that received transplantations for both malignant and nonmalignant diseases, and the co-occurrence of these late effects was not assessed. Therefore, the true burden of these complications in survivors of hematological malignancies remains unclear. Moreover, the role of potentially modifiable risk factors such as health behaviors and inflammation has not yet been determined. This study aims to determine the prevalence of metabolic syndrome (MetS), endothelial dysfunction (ED), and accelerated aging and their risk factors, including treatment-related factors, inflammation, and health behaviors, in a large representative cohort of Dutch survivors of HSCT in childhood. Additionally, the study will examine the co-occurrence of these late effects. This cross-sectional cohort study will combine 2 cohorts of survivors of HSCT in childhood for a hematological malignancy. The first cohort (cohort 1; n=102) consists of survivors who received transplantations before 2002 and were participants of the Dutch Childhood Cancer Survivor Study LATER 2 cohort, a nationwide cohort study focusing on late effects among long-term childhood cancer survivors. The second cohort will include survivors who received transplantations between 2002 and 2021 (cohort 2; projected n=120) and visited the late effects (LATER) outpatient clinic of the Princess Máxima Center between 2024 and 2026. Key outcomes will be the prevalence of MetS (≥3 of 5 clinical criteria), accelerated aging (3 of 5 biological and clinical criteria), and ED (assessed by endothelial peripheral arterial tonometry) and their co-occurrence. A broad range of potential and modifiable risk factors will be investigated, including treatment-related factors; transplant complications (eg, graft-versus-host disease); and health behaviors, including physical activity, dietary intake and status assessed with nutritional biomarkers, substance use, sun exposure, and relaxation. Patient recruitment started in January 2024 and is estimated to last until June 2026. As of September 2025, a total of 77 participants have been included in the study. This study will provide insight into the prevalence of MetS, ED, and accelerated aging, as well as potentially modifiable risk factors, including those that have not been previously examined, among survivors of HSCT for hematological malignancies in childhood. The findings will inform surveillance guidelines and support the development of health behavioral and anti-inflammatory interventions to mitigate the risk of these severe late effects. DERR1-10.2196/77429.
Mobile health (mHealth) technologies are increasingly promoted as tools for chronic disease management and healthy aging, yet adoption remains persistently uneven across demographic groups. Japan, where 29.1% of the population is 65 years or older-the highest proportion globally-exemplifies the challenges of mHealth promotion in super-aging societies. Despite high smartphone penetration (90.1%) and active national digital transformation initiatives, only 21.6% of Japanese adults report regular mHealth app use, with marked disparities by age and sex. This study examined determinants of mHealth acceptance by extending the unified theory of acceptance and use of technology to incorporate eHealth literacy, self-efficacy, perceived risk, distrust, and health-related factors (health status and health interest). Age- and sex-specific differences in acceptance mechanisms were also investigated using multigroup structural equation modeling (SEM). We conducted a cross-sectional online survey in November 2023 with 960 Japanese adults sampled across 7 age strata (aged 18-27 years to aged ≥78 years). SEM tested hypothesized relationships among 9 constructs. Health status and health interest were included as observed covariates. Multigroup SEM with configural, metric, and structural invariance testing examined age- and sex-specific differences, and binary logistic regression identified predictors of current mHealth app use. The structural model demonstrated good fit (χ2/df=2.06; comparative fit index 0.953; Tucker-Lewis index 0.945; root mean square error of approximation 0.047) and explained 71.6% of the variance in behavioral intention. Effort expectancy (β=0.404), facilitating conditions (β=0.349), and performance expectancy (β=0.188) were the primary proximal predictors of behavioral intention. Social influence exerted strong upstream effects on effort expectancy (β=0.811), eHealth literacy (β=0.507), and self-efficacy (β=0.422). Health interest positively influenced performance expectancy (β=0.133), whereas neither health interest nor health status showed a significant direct effect on distrust. Distrust did not directly predict behavioral intention in the overall sample. Multigroup analyses identified 5 significant age differences and 5 sex differences. eHealth literacy increased distrust among young adults but reduced perceived risk among middle-aged and older adults. Self-efficacy negatively predicted performance expectancy among young adults yet positively predicted it among middle-aged and older adults. Distrust significantly reduced behavioral intention only among middle-aged adults. mHealth acceptance in Japan's aging society is characterized by stable proximal determinants of behavioral intention alongside heterogeneous upstream belief formation processes that vary systematically by age and sex. Health interest, rather than health status, emerged as the key contextual driver of perceived usefulness. At the theoretical level, this study clarifies how eHealth literacy, self-efficacy, and distrust function as age- and sex-contingent antecedents within an extended unified theory of acceptance and use of technology framework. At the practical level, these findings highlight the need for trust-centered, demographically tailored, and literacy-sensitive strategies to promote equitable mHealth adoption in rapidly aging societies.
Telehealth was essential for maintaining care continuity during the COVID-19 pandemic, leading to its rapid adoption across the United States. Telehealth has been heralded as a strategy for improving health care access and reducing health disparities, especially for community-dwelling older adults who face significant barriers to in-person care. However, data on telehealth use among socially and financially vulnerable older adults are limited, and little is known about characteristics associated with telehealth use in this population. Guided by the Systems Engineering Initiative for Patient Safety (SEIPS) 3.0 framework, this study examined factors associated with postpandemic telehealth use among older adults living at home and receiving publicly funded home- and community-based services (HCBS), considering HCBS receipt as an indicator of social and financial vulnerability. This cross-sectional study included older adults aged 65 years or older living at home with available telehealth use data who participated in the 2021-2022 survey wave of the National Core Indicators-Aging and Disabilities Adult Consumer Survey. We used complete-case multivariable logistic regression, adjusting for sociodemographic and health-related factors with state-level random intercepts, to examine associations between telehealth use and covariates of interest (age, sex, race/ethnicity, zip code, rural-urban commuting area code, internet access, self-perceived overall health, medical transportation access, living alone, number of known non-Alzheimer disease and related dementias [ADRD] diagnoses, known ADRD diagnosis, and HCBS program/payer type). Based on the regression results, we estimated bivariate associations between internet access and key sociodemographic variables (age, sex, race/ethnicity, and zip code rural-urban commuting area) using the Pearson chi-square test. Findings were organized and interpreted through the SEIPS 3.0 framework. Of the 3680 participants, 1467 (40%) were telehealth users and 2213 (60%) were nonusers. Significantly lower odds of telehealth were observed for older adults in older age groups, males, Black individuals, those living in nonmetropolitan areas, and recipients of Older Americans Act services (odds ratios [OR] between 0.66 and 0.80). Individuals with more than one known non-ADRD diagnosis (OR 1.49, 95% CI 1.02-2.17) and those with an ADRD diagnosis (OR 1.33, 95% CI 1.07-1.66) had higher odds of telehealth use. Internet access was strongly associated with telehealth use (OR 2.51, 95% CI 2.15-2.92). Follow-up bivariate analyses between internet access and sociodemographic characteristics revealed that those of younger age, females, and White individuals had higher levels of internet access. Differences in telehealth use among older HCBS recipients are associated with multiple individual, technological, and organizational factors. Interpreted through the SEIPS 3.0 framework, these findings underscore the importance of viewing telehealth use as the outcome of multiple features of the health care system. Future research should clarify the mechanisms driving variation in telehealth use to identify and address barriers to telehealth adoption among vulnerable older adults.
Consumer wearables provide users with a wealth of data, including an estimation of their "biological age." In this News and Perspectives article, JMIR Correspondent Jenna Congdon reports on the accuracy and utility of age prediction by wearables.
Access to geriatric mental health (GMH) care is limited in rural areas. To meet this need, the Veterans Health Administration provides specialty tele-GMH care for aging rural veterans via regional telehealth hubs. This study aims to create a roadmap describing key phases and determinants underlying the implementation and sustainment of tele-GMH services as part of a qualitative longitudinal evaluation of tele-GMH teams. Semistructured interviews were conducted with clinicians from all 8 tele-GMH teams (n=25) at 3 time points across a 3-year period (October 2021-September 2024). Interview (n=46) data were summarized into key domains using a templated rapid qualitative approach, guided by the Consolidated Framework for Implementation Research (CFIR) 2.0. Further thematic analysis and team discussion elucidated the findings. We identified key activities and determinants of success in three phases: (1) preimplementation (engaging leaders, securing funding/hiring, and defining services); (2) implementation scale-up and expansion (advertising, addressing challenges, seeking feedback, refining, and growth); and (3) sustainment (maintenance). Activities within each phase were cyclical and iterative (ie, nonlinear). Barriers to implementation included unfamiliarity with local aging resources; facilitators included tailoring strategies and engaging referring clinicians. Similar processes emerged across regions in the development and sustainment of tele-GMH services, allowing for the creation of a unified roadmap. Limitations including sampling bias are discussed. Further work could apply and evaluate the utility of the roadmap to guide creation of tele-GMH services in new regions to enhance access to specialty care for aging rural veterans.
Maintaining cognitive efficiency and independence is a central goal of healthy aging. Socially assistive robots (SARs) are increasingly proposed as scalable digital health solutions to support daily activities in older adults and to facilitate aging-in-place. However, concerns remain regarding whether robot-mediated assistance reduces or inadvertently increases cognitive load, potentially undermining usability, user acceptance, and long-term real-world adoption, particularly in aging populations. This study aimed to examine how robot-assisted (human-robot interaction [HRI]) and human-assisted (human-human interaction [HHI]) support influences cognitive load during task performance in younger and older adults. A multimodal assessment framework integrating behavioral, subjective, and physiological measures was used to identify age-related differences in cognitive effort and stress associated with different forms of assistance. A total of 60 healthy adults (30 younger adults: mean age 34.8, SD 10.1 years; and 30 older adults: mean age 72.3, SD 5.5 years) completed a modified Trail Making Test under 7 within-subject conditions: independent performance (baseline), 3 robot-assisted conditions, and 3 human-assisted conditions, each corresponding to low, medium, and high cognitive load levels. Performance accuracy and completion time were recorded as behavioral indicators. Perceived cognitive load was assessed using the National Aeronautics and Space Administration Task Load Index, and physiological stress was evaluated via pre- and postcondition salivary cortisol concentrations. Linear mixed-effects models were applied to examine main effects and interactions of age group, assistance type, cognitive load level, and time. Significant interactions between age group and assistance type were observed for accuracy (F1, 404.53=6.50; P=.01) and perceived cognitive load (F1, 403.45=4.58; P=.03). Older adults demonstrated lower accuracy and higher perceived cognitive load during robot-assisted conditions compared with human-assisted conditions, whereas no such differences were observed in younger adults. Across age groups, human assistance improved performance at low and medium cognitive load levels. Physiological analysis revealed a significant age×assistance× time interaction (F1, 156=5.16; P=.02), with older adults showing increased posttask cortisol concentrations during robot-assisted interaction, indicating higher physiological stress. While both human and robotic assistance enhanced task performance relative to independent completion, the type of support critically shaped cognitive load responses in older adults. Robot-assisted interaction was associated with increased behavioral errors, higher perceived workload, and elevated physiological stress, suggesting that current SAR implementations may impose additional extraneous cognitive load in older users. These findings highlight the importance of designing adaptive, age-sensitive digital assistive systems that minimize cognitive burden through simplified interaction, responsive pacing, and multimodal support. Multimodal cognitive load assessment provides a valuable framework for optimizing the usability and effectiveness of assistive digital health technologies for aging populations.
The issue of population aging has emerged as a critical global challenge, driving the imperative for effective self-care and scalable health management solutions for older adults. Against the backdrop of the accelerating application of generative artificial intelligence (GenAI) in health care, a systematic evaluation is necessary to investigate how multimodal GenAI can support older adults in maintaining health and managing well-being. This study aimed to systematically evaluate the role, application contexts, empirical impacts, and developmental potential of diverse GenAI tools across critical geriatric health domains. A comprehensive search was executed across 11 major databases, including Web of Science, Scopus, PubMed, Medline, CINAHL, Cochrane, ACM Digital Library, IEEE Xplore, ScienceDirect, APA PsycInfo, and Google Scholar, with search transparency adhering to the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) extension. A total of 28 studies met the inclusion criteria. Of the total, 82% (n=23) of the included publications were released within the last 2 years (2024-2025). Analysis of technology revealed that over half (n=14) of the applications were based on text-driven conversational agents, while multimodal systems, leveraging generated audio, images, and sensor data, are rapidly emerging. GenAI applications were validated to support cognitive function maintenance, mental health, and chronic condition management through personalized content generation and multimodal interaction. However, current validation is primarily limited to cognitively normal, low-risk older adult populations. Persistent technical challenges include overreliance on text-based interaction, barriers in voice recognition accuracy, and suboptimal user interface adaptability. Preliminary evidence suggests a promising role for GenAI in enhancing older adults' health self-management through highly personalized and multimodal interventions, particularly in cognitive and mental health support. To realize this potential and ensure equitable access, future efforts must prioritize strengthening interdisciplinary collaboration to integrate wearable technologies and edge computing, alongside establishing robust ethical frameworks to address data privacy, algorithmic bias, and the digital divide, which will be critical to building a safe, equitable, and effective environment for active aging.
Sedentary behavior among older adults is a major public health concern, contributing to the increased risk of chronic diseases and functional decline. With aging populations worldwide, prolonged sitting time (averaging up to 13 h/d in older adults) has been independently associated with cardiovascular disease, metabolic disorders, cognitive decline, and all-cause mortality. Mobile health (mHealth) interventions offer a promising approach to address this issue. However, there remains a lack of evidence-based, systematically developed mHealth programs specifically targeting sedentary behavior in older populations. This study aimed to develop an mHealth intervention program for reducing sedentary behavior in older adults using the Delphi consensus method. Guided by the Behavior Change Wheel framework, a preliminary mHealth intervention was developed using a combination of qualitative and quantitative methods, including a comprehensive literature review, clinical guidelines analysis, qualitative interviews, and a cross-sectional survey. The intervention was then refined through 2 rounds of Delphi surveys with 16 multidisciplinary experts in geriatric care, behavioral science, and health promotion. Consensus criteria were predefined as mean importance score >3.5 and coefficient of variation ≤0.25 on a 5-point Likert scale. Both Delphi rounds achieved 100% response rates, with high expert authority coefficients (Cr=0.900 for Round 1 and Cr=0.907 for Round 2). The Kendall coordination coefficients (Kendall W) were 0.151 (P<.001) and 0.214 (P=.001) for the 2 rounds, respectively. Following 2 rounds of expert consultation, a total of 27 intervention items were finalized, comprising 3 core components addressing capability (eg, knowledge provision and behavioral skills training), opportunity (eg, social support and environmental restructuring), and motivation (eg, goal-setting, feedback, and incentives) factors influencing sedentary behavior. This study developed a theoretical framework-based, consensus-driven mHealth intervention program for reducing sedentary behavior in older adults. The intervention uniquely integrates the Behavior Change Wheel framework with expert validation, offering a comprehensive approach that simultaneously targets capability, opportunity, and motivation. The findings provide a structured foundation for future feasibility testing and effectiveness evaluation of mHealth interventions in aging populations. Future researchers should translate the developed mHealth intervention into an adaptive mHealth platform, followed by pilot testing and large-scale randomized controlled trials to evaluate its feasibility and effectiveness in real-world settings.
Sedentary behavior is associated with negative health outcomes. High levels of sedentary behavior are common among Alzheimer disease and related dementias (ADRD) caregivers already at risk of other adverse health effects, yet few interventions target sedentary behavior within this population. There is a need for trials intended to reduce time spent sedentary, which may be achievable by increasing the frequency of disruptions to sedentary time. Remotely delivered behavior change techniques (BCTs) may be effective for disrupting sedentary behavior in this population through short bursts of walking, although it is unclear how BCTs promote this behavior and potentially act via the hypothesized mechanism of behavioral automaticity. The goal of the trial is to examine whether a significant proportion of ADRD caregivers (≥50%) receiving an SMS text message-delivered BCT intervention form a habit to engage in hourly walking 4 times per day, with the broader objective of disrupting sedentary time in this population. This trial is a 12-week, decentralized, single-arm, National Institutes of Health Stage II behavioral trial. The trial will deliver a personalized, multicomponent BCT intervention to disrupt time spent sedentary by encouraging forming a habit of hourly walking among caregivers of persons with ADRD via the key mechanism of behavior change behavioral automaticity. The intervention includes 4 daily SMS text message-delivered BCT components previously used in interventions to disrupt sedentary behavior-Goal setting, Action planning, Prompts/cues, and Self-monitoring. Formation of an hourly walking habit is the primary outcome and will be defined as walking an additional 250 steps or more per hour for the same 4 consecutive hours as set up in a personalized walking plan on 7 consecutive days. Secondary outcomes include evaluating associations between habit formation and behavioral automaticity, and between longitudinal behavioral automaticity and habitual hourly walking over time. Additionally, heterogeneity of treatment effects will be evaluated. Exploratory analyses will examine potential moderating variables that may influence the intervention effect. The trial uses digital enrollment strategies, SMS text message intervention delivery, passive data collection via Fitbit (Google) devices, and online survey assessments to collect data remotely. This study was funded by the National Institute on Aging in June 2024. Recruitment and data collection began in March 2025. As of August 2025, 40% (n=40) of the planned sample has been enrolled. Data collection is expected to be complete by June 2026. Data analysis and publication of results are expected by Fall and Winter 2026, respectively. Results will have the potential to advance knowledge about the effectiveness of BCTs to form a habit of hourly walking and may provide opportunities for future public health impact to promote physical activity in caregivers of those living with ADRD.
Cognitive decline in aging populations underscores the need for early interventions in mild cognitive impairment (MCI), where pharmacological treatments show limited benefit. Eye-movement metrics serve as sensitive markers of cognitive deficits in MCI, and digital programs integrating these tasks offer scalable, data-driven training approaches. This study aimed to evaluate the effectiveness of a digital cognitive training program incorporating eye-movement tasks in individuals with MCI, and to determine whether eye-movement indicators can serve as objective markers of cognitive improvement. A total of 12 participants aged 60-85 years with MCI (Korean version of the Montreal Cognitive Assessment [K-MoCA] score of ≤22) completed baseline and postintervention assessments using the K-MoCA and Mini-Mental State Examination-Korean version (MMSE-K). Longitudinal changes in visuospatial attention and oculomotor performance were examined using Spearman correlations across sessions, and pre-post comparisons of eye-tracking metrics were conducted to assess training-related improvements. Cognitive scores improved significantly, with K-MoCA increasing by 1.5 points (from mean 20.3, SD 1.1 to mean 21.8, SD 1.7; P=.004; Cohen d=1.38) and MMSE-K by 1.3 points (from mean 21.9, SD 2.0 to mean 23.2, SD 2.2; P=.002; Cohen d=1.29). Fixation duration decreased (r=0.248; P=.003), and saccade velocity increased (r=0.258; P=.002), indicating enhanced visual processing efficiency and faster attentional shifts, whereas fixation count and saccade amplitude showed no consistent changes. In addition, saccade duration decreased by 21.72 ms, and saccade velocity increased by 114.54 °/s. Digital cognitive training yielded measurable gains in visuospatial attention and oculomotor efficiency in MCI, with optimized fixation and saccade patterns indicating enhanced attentional control and information processing. These findings support eye-movement metrics as sensitive indicators of cognitive change and highlight digital interventions as scalable, noninvasive tools for cognitive support in aging populations.
Frailty and pre-frailty are highly prevalent conditions among older adults and are associated with increased functional decline, fall risk, hospitalization, and mortality. Multicomponent supervised exercise programs have demonstrated efficacy in improving physical performance and mitigating frailty, particularly when adapted to older adults' functional capacity. However, evidence regarding Vivifrail-based interventions in frail and pre-frail older adults in Brazil remains limited. This study aims to evaluate the effects of a 12-week supervised multicomponent exercise program on functional capacity and fall risk among frail and pre-frail older adults. Additionally, the study intends to characterize participants according to frailty status, clinical-functional vulnerability, cognitive status, depressive symptoms, physical activity level, muscle mass, fear of falling, and sociodemographic and clinical characteristics at baseline. This study protocol describes a prospective, parallel-group, single-blind randomized controlled trial. Older adults aged 60 years and older regularly attending activities at the CONVIVER Community Center in Rio Verde, Goiás, Brazil, will be screened and randomized in a 1:1 ratio into either an intervention group or a control group. The intervention group will participate in a supervised multicomponent exercise program based on the Vivifrail model for 12 weeks, whereas the control group will participate in health education workshops focused on healthy aging. The primary outcomes will be functional capacity, assessed using the 6-Minute Walk Test, and fall risk/mobility performance, assessed using the Timed Up and Go Test. Baseline assessments will additionally include frailty status (Edmonton Frailty Scale), clinical-functional vulnerability (CFVI-20), cognitive status (Mini-Mental State Examination), depressive symptoms (Geriatric Depression Scale-15), physical activity level (International Physical Activity Questionnaire), calf circumference, fear of falling (Falls Efficacy Scale-International), and sociodemographic and clinical characteristics. Recruitment and baseline assessments are planned to occur between July and December 2026 at the CONVIVER Community Center. A total of 70 participants is expected to be enrolled and randomized into the intervention group (n=35) or the control group (n=35). At the time of manuscript submission, participant recruitment had not yet started, and no outcome data had been collected or analyzed. Final results are expected to be published in late 2027. This randomized controlled trial protocol describes a supervised multicomponent exercise intervention tailored to frail and pre-frail older adults in a Brazilian community setting. If effective, the intervention may represent a feasible, low-cost, and scalable strategy to improve functional capacity and reduce fall risk in vulnerable older populations while supporting evidence-based healthy aging initiatives. Brazilian Registry of Clinical Trials RBR-9zvtc5b; https://ensaiosclinicos.gov.br/rg/RBR-9zvtc5b.
Continuous advancements in voice artificial intelligence technologies aim to assist older adults and caregivers, potentially improving quality of life and reducing caregiving burdens. Although research has explored the potential of voice-enabled artificial intelligence (VAI) assistants, such as Alexa (Amazon.com, Inc) and Google Home, to support older adults' health in informal care settings, there remains a significant gap in understanding the ethical dimensions and values that may influence their future adoption by caregivers and care recipients. This research aims to explore older adult and informal family caregivers' perspectives of VAI assistants for supporting informal care, including the ethical dimensions and values that influence their decisions about future adoption for these purposes. This research uses participatory speculative design to explore older adults' and informal family caregivers' perspectives of how VAI might support informal care in the future, and the ethical concerns they have about adopting VAI technologies. We conducted 8 workshop sessions with older adults and caregivers (n=9) over four months. Each phase focused on one of three goals: (1) to understand existing experiences, (2) to envision future VAI technologies, and (3) to reflect on ethical values that shape acceptance. In workshops, we aimed to gain insights into their experiences and challenges in managing informal care tasks and how future implementation of VAI might support the caregiving process to address their needs and concerns while emphasizing the ethical dimensions they value. The findings suggest older adults and informal family caregivers see potential opportunities for VAIs to support informal aging care by automating daily health tasks to improve efficiency, enhancing mental health and well-being, and offering companionship. However, participants felt that VAI alone might not be sufficient to address the complex needs of informal care. Additionally, they raised several ethical concerns related to transparency, privacy, inclusiveness, trust, affordability, and autonomy, which they felt needed to be addressed to encourage adoption of VAI technologies for informal care in the future. Based on the findings, we offer insights and design implications for VAI systems that balance efficiency with ethical values to support diverse caregiving needs and potentially encourage future adoption in the informal care space.
Branched-chain amino acids (BCAAs) are essential amino acids for protein metabolism. Preclinical research in mice suggested that BCAA intake relative to other amino acids, in the context of a high-carbohydrate diet, was associated with hyperphagia, obesity, and reduced lifespan. These effects were not attributed to BCAAs alone, nor did they manifest through canonical mechanistic target of rapamycin-insulin-like growth factor 1 pathways; rather, they resulted from indirect effects of other amino acids, notably tryptophan, on appetite. As population aging and obesity-related chronic diseases present significant public health challenges, understanding appetite regulation is critical. To date, no clinical trial has examined the effects of BCAAs on appetite regulation in older adults. On the basis of our preclinical results, we hypothesized that, compared to the control diet, a diet supplemented either with BCAA or with BCAAs and methionine would increase appetite and energy intake, whereas supplementation with BCAA and tryptophan would not increase appetite. We aimed to translate these preclinical findings to humans by examining the effects of BCAAs per se and in combination with tryptophan and methionine on appetite and other health measures in a cohort of older participants. This randomized controlled clinical trial recruited 110 adults (aged 65-80 y; BMI 20-35 kg/m2). Participants were randomly allocated to four 4-week intervention groups: (1) control (no supplementation), (2) BCAAs, (3) BCAAs+tryptophan, or (4) BCAAs+methionine. All participants received a controlled diet, with intervention groups additionally receiving amino acid supplements. The primary outcomes are appetite assessed via self-reports and fibroblast growth factor 21 levels (a marker of protein appetite), and energy intake quantified from dietary intake data. Secondary outcomes include body composition, cardiometabolic health, gut microbiota, blood biomarkers, sleep, and physical performance. Descriptive statistics will be used to summarize participant characteristics. Linear mixed models will assess intervention effects, with and without adjustment for relevant covariates. Diet-specific self-reported appetite and palatability scores will be analyzed using generalized additive mixed models. The trial was registered on April 12, 2021. Recruitment commenced in April 2022 and was completed in November 2025, with 308 individuals screened and 100 completing the study. Data analyses are planned for completion by December 2026, with results expected to be published in 2027. Data cleaning and analysis are currently in progress and are expected to be completed by December 2026, with trial results expected to be published in 2027. This study will clarify the effects of BCAAs, either alone or in combination with tryptophan or methionine, on appetite and related outcomes in an older population. The findings may inform nutritional strategies targeting appetite regulation and metabolic health to support healthy aging.
As populations age globally, accurate and feasible dietary assessment for older adults has become increasingly important. South Korea has already become an "aged society," with over 14.2% of its population being aged 65 years and older, and is projected to become one of the world's most rapidly super-aged societies by 2050, with more than 40% of its population in this age group. Similarly, the Asia-Pacific region is experiencing accelerated population aging, with 10 countries classified as "aging societies" (>7% aged ≥65 years), 5 as "aged societies" (>14%), and 11 as "super-aged societies" (>21%) in 2025. Despite the growing need for accurate dietary monitoring in this demographic, nutritional assessment remains challenging due to limitations of conventional methods, compounded by cognitive burden, functional decline, and low literacy. Although various technology-based solutions, including web-based, scanner-based, and mobile tools, have been introduced, challenges related to usability, accuracy, and cost remain unresolved. This study aims to evaluate the validity and usability of a speech-based dietary assessment tool as a potential alternative to a traditional pen-and-paper food diary among older adults. In a randomized crossover study, adults aged ≥65 years (n=18) completed 2 nonconsecutive 3-day dietary assessment periods using both a speech-recording (SR) method and a food diary (FD). Mean daily nutrient intakes were compared between methods, and agreement was examined using Bland-Altman plots. Usability was assessed after each method using the System Usability Scale (SUS). Mean daily intakes estimated by SR and FD were similar in magnitude across most nutrients. The paired mean difference in energy intake (FD-SR) was 38.38 (95% CI -176.63 to 253.40) kcal. Across nutrients, mean differences were generally small, and most 95% CIs included zero, indicating limited evidence of a large systematic difference at the group level. The correlation coefficient between SR and FD ranged from 0.178 to 0.907 depending on the nutrient assessed, though CIs were often wide (eg, energy intake; Pearson r=0.52, 95% CI 0.05-0.81). Bland-Altman analyses for energy and macronutrients show mean differences close to zero, although substantial individual-level variability was observed. Cholesterol demonstrated greater dispersion and possible proportional bias at higher intake levels. Mean SUS scores were 66.25 for FD and 72.78 for SR, with a paired mean difference (FD-SR) of -6.53 (95% CI -11.75 to -1.30), indicating higher usability ratings for the SR method on average. These findings suggest that in older adults, the speech recognition method-produced dietary intake estimates may be a feasible approach with broadly comparable nutrient intake levels to written food diaries and modestly higher usability ratings. Further work is needed in larger samples to compare approaches with increased precision.
Climate change has intensified the frequency and duration of extreme heat events worldwide, posing growing public health risks, particularly for older adults who are physiologically more susceptible to heat-related illnesses. Concurrently, alcohol consumption among older adults in the United States has risen significantly over the past 2 decades, increasing vulnerability to dehydration, cardiovascular strain, and cognitive impairment during heat exposure. Emerging evidence suggests that environmental stressors such as extreme heat may exacerbate maladaptive coping behaviors, including alcohol use; however, few studies have examined this association in aging populations. Moreover, little is known about how early-life experiences such as childhood adversity or positive parental relationships shape behavioral responses to environmental stressors later in life. This study examines the relationship between extreme heat and alcohol consumption among older Americans, emphasizing the moderating role of early-life experiences within a life course framework. Using data from individuals aged >50 years in the Health and Retirement Study (1996-2018), we analyzed the association between extreme heat exposure (>95 °F) and alcohol consumption, while examining whether early-life experiences such as parental substance abuse, law enforcement encounters, and relationships with fathers moderate this relationship. Extreme heat exposure was significantly associated with increased alcohol consumption (0.21% per additional extreme heat day, P<.001). A positive father-child relationship buffered this effect, while adverse early-life experiences, including law enforcement encounters (0.08%, P<.001) and parental substance abuse (0.05%, P<.001), exacerbated it. Given the link between extreme heat and alcohol use in older adults, further longitudinal research and targeted interventions are needed to mitigate associated health risks. Strengthening positive childhood experiences may offer long-term protective effects by fostering resilience and healthier coping mechanisms that persist into later life.
The growing aging population has increased the need for technologies that support informal caregivers in home-based older adult care. Digital twin (DT) systems offer promising capabilities; yet, their effectiveness depends on usability, an aspect still insufficiently evaluated among caregivers. This study aimed to assess the usability of an older adult care DT system using a dual-method evaluation that integrates subjective and objective behavioral performance. Fifty caregivers participated in a usability assessment combining the System Usability Scale (SUS) and detailed system activity log analytics. Log-based measures included task completion, time on task, errors, and abandonment rate. A composite user engagement score was computed and analyzed for correlation and predictive association with SUS ratings. Engagement clusters were also explored. Caregivers reported an excellent mean SUS score of 80.45. System logs showed a 94.08% task completion rate, 2.66% abandonment, and an average task duration of 89.16 seconds. User engagement score demonstrated significant correlations with SUS (r=0.626, ρ=0.552; P<.001) and significantly predicted usability in regression analysis (β=52.94, R²=0.392; P<.001). Engagement-based clustering identified high-, medium-, and low-tier user groups, each exhibiting distinct usability patterns. Integrating subjective usability ratings with objective behavioral metrics provides a rigorous and comprehensive approach to evaluating DT systems for older adult care. The findings highlight strong usability of the system and offer actionable insights for refining caregiver support technologies.
Everyday listening ability is essential for individual health and well-being. Age-related hearing loss (ARHL) is associated with reduced communication engagement, social isolation, loneliness, cognitive decline, and increased dementia risk. Interventions that simultaneously target auditory processing and cognitive function, particularly within engaging and ecologically valid contexts, may offer greater benefits than unimodal approaches. However, culturally adapted, web-based, gamified auditory-cognitive dual-task training (ACDT) tailored for older adults with ARHL remains underexplored. At the time of this writing, few auditory or auditory-cognitive training programs are available in Chinese languages, creating linguistic and cultural barriers for older adults. This study aimed to (1) assess the feasibility and acceptability of home-based ACDT among older Chinese adults with ARHL and (2) examine its preliminary effects on global cognition, hearing, social engagement, and loneliness. It was hypothesized that the intervention group would demonstrate greater improvements in global cognition, hearing, and social engagement than the control group. Sixty community-dwelling older adults with mild-to-moderate ARHL were randomized 1:1 to either the ACDT group or a waitlist control group in a single-blinded pilot randomized controlled trial. Demographic data and outcome measures were collected at baseline, week 6, and week 12. Postintervention interviews were conducted to assess the feasibility and acceptability of ACDT. A total of 60 participants were randomized (mean age 67.65, SD 4.78 years; 45/60, 75% male). ACDT demonstrated high feasibility and acceptability. The ACDT group showed significant improvements in focused attention (mean change=-0.15; P=.02; d=-0.46) and divided attention (mean change=-0.21; P=.002; d=-0.63). Significant cognitive improvements on the Hong Kong-Montreal Cognitive Assessment were identified in naming (r=0.37; P=.05) and visual cognition (r=0.44; P=.02) in the intervention group, while no significant improvements were found in the control group. Both groups reported significant decreases in emotional hearing handicap, with slightly greater improvement in the intervention group (r=0.39; P=.03) than in the control group (r=0.37; P=.04). Linear mixed model analysis revealed a small to moderate group effect (Cohen d=0.38) for 5-minute delayed recall on the Auditory Verbal Learning Test, with the fixed effects explaining 69% of the variance (marginal R²=0.69). A significant time×group interaction was observed for left-ear thresholds (P=.01). Qualitative analysis identified three key themes: (1) intervention coherence and participants' affective attitude toward ACDT; (2) perceived benefits in cognition, information acquisition, and self-awareness from ACDT; and (3) perceiving ACDT as less burdensome with enhanced self-efficacy. Future iterations should incorporate artificial intelligence-enhanced personalization. Large-scale randomized controlled trials involving diverse samples and active control conditions are needed to confirm sustained effects on auditory and cognitive health, dual-task listening-cognitive abilities, and real-world functioning.
US Latine and Hispanic communities face a 1.5 times greater risk of developing Alzheimer disease and related dementia (ADRD) with limited access to culturally and linguistically congruent primary prevention education. The COVID-19 pandemic exacerbated the digital divide, highlighting a need to focus on alternative digital methods for delivering brain health and ADRD primary prevention education. Social media emerged as a promising tool. The objective of this paper is two-fold. We first describe the development and pilot study of our social media-based Latine-Hispanic Digital Brain Health Program guided by evidence-based frameworks in ADRD. We then present the quantitative and qualitative results from the first 14 months of the program (October 2023-December 2024). We used human-centered design to develop the Digital Alzheimer Health Education Model, which was implemented via 3 social media platforms-Facebook, Instagram, and X (formerly known as Twitter). Our bilingual and bicultural team implemented the model by creating and disseminating tailored educational content in English and Spanish for the resulting Latine-Hispanic Digital Brain Health Program, emphasizing consistency and rapport, storytelling, cultural relevance, linguistic inclusivity, and visual representation. A mixed methods analysis (descriptive statistics and sentiment analysis) was conducted using social media data analytics and users' comments to guide program evaluation and refinement. From October 2023 to December 2024, we retained 857 followers across our social media platforms (Instagram: n=534; Facebook: n=124; and X: n=199). Growth in follows, consistent reach and engagement, and positive sentiment were observed on Facebook and Instagram. X was not included in the analysis due to data access limitations. The development and pilot study of the Latine-Hispanic Digital Brain Health Program have demonstrated potential in leveraging social media to disseminate brain health and ADRD prevention education to the US Latine and Hispanic communities in English and Spanish. Our preliminary findings demonstrate that culturally and linguistically congruent social media-based approaches hold potential to improve engagement with brain health and ADRD primary prevention education among US Latine and Hispanic populations.
Depressive symptoms are linked to nutritional vulnerability and functional decline in aging populations, but their relationships with nutritional risk and lower-extremity physical performance are often examined separately. Tree-based exploratory approaches may provide transparent subgroup characterization. This study aimed to examine the cross-sectional associations of depressive symptoms with nutritional risk and lower-extremity physical performance among community-dwelling middle- to older-aged adults and to compare a tree-based exploratory approach with regression-based methods for characterizing depression-related patterns within the study sample. This cross-sectional secondary analysis included 1010 community-dwelling adults aged ≥50 years recruited from 1 hospital and 3 community centers in northern Taiwan. Depressive symptoms were assessed using the Geriatric Depression Scale-Short Form (GDS-15). Nutritional status was measured using the Mini Nutritional Assessment-Short Form (MNA-SF), and physical performance was evaluated using the Short Physical Performance Battery (SPPB), gait speed, and the timed up and go (TUG) test. Sociodemographic, lifestyle, and clinical variables were included as covariates. Outcomes were dichotomized using established clinical cutoffs. χ² automatic interaction detection (CHAID) decision trees were used as a tree-based exploratory approach for subgroup characterization. Multivariable logistic regression and least absolute shrinkage and selection operator (LASSO) logistic regression models were used for comparison. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and classification accuracy. Of the 1010 participants, 143 (14.2%) screened positive for depressive symptoms (GDS-15 ≥5). Compared with participants without depressive symptoms, those with depressive symptoms had poorer nutritional status and lower-extremity physical performance, including a higher prevalence of nutritional risk (53/143, 37.1% vs 105/867, 12.1%; P<.001), SPPB impairment (53/143, 37.1% vs 118/867, 13.6%; P<.001), gait speed impairment (73/143, 51% vs 270/865, 31.2%; P<.001), and TUG impairment (25/143, 17.5% vs 55/864, 6.4%; P<.001). Across CHAID models, GDS-15 score was consistently selected as the primary splitting variable, while BMI, calf circumference, age, education level, and comorbidity severity provided additional hierarchical refinement of subgroup patterns. In comparative model analyses, LASSO logistic regression analysis had the highest classification performance for the MNA-SF, SPPB, and gait speed outcomes, whereas CHAID provided transparent, rule-based subgroup characterization with acceptable within-sample classification performance. Model performance for TUG was less consistent across approaches. In this community-based sample of adults aged 50 years and older, depressive symptom severity was associated with nutritional vulnerability and poorer lower-extremity physical performance. CHAID identified hierarchical subgroup patterns linking depressive symptoms, including subthreshold levels, with nutritional and functional vulnerability. Although LASSO logistic regression analysis had higher classification performance, CHAID offered transparent, rule-based subgroup characterization. These findings support the relevance of integrating depression screening with nutritional and functional assessment in prevention-oriented geriatric care, although further validation is needed.