Background: Research has revealed potential links between specific dietary habits and accelerated aging. However, most studies focus only on singular diets or lack ethnic diversity.
Objective: This study aimed to investigate the associations between 5 dietary indices and the risk of accelerated aging and develop an interpretable machine learning (ML) model for accelerated aging prediction.
Methods: We explored associations between dietary indices and the risk of accelerated aging using data from the US National Health and Nutrition Examination Survey (NHANES) and the UK Biobank. A weighted linear regression analysis was used to determine whether accelerated aging was linked to dietary habits, and the covariates were gradually adjusted to ensure that the association was stable. Nonlinear correlations were explored using restricted cubic spline curves. In addition, multiple ML algorithms were used to build predictive models of accelerated aging risk.
Results: Except for the Dietary Inflammation Index (β=0.35, 95% CI 0.23-0.74), the other 4 dietary indices (Alternative Healthy Eating Index, Alternative Mediterranean Diet, Healthy Eating Index-2020, and Dietary Approaches to Stop Hypertension) were negatively associated with the risk of accelerated aging in NHANES participants. Similar results were observed in UK Biobank participants. Nine ML algorithms were used to develop risk prediction models, among which the gradient boosting decision tree model showed the best overall performance. A web-based prediction platform was developed and made publicly available.
Conclusions: Significant associations between accelerated aging and dietary indices were observed. High compliance with the Dietary Inflammation Index had a promoting effect on accelerated aging, while high compliance with the Alternative Healthy Eating Index, Alternative Mediterranean Diet, Healthy Eating Index-2020, and Dietary Approaches to Stop Hypertension showed varying degrees of protection against accelerated aging.
Background: Falls are a major cause of disability among older adults, and early identification of functional decline is essential for prevention. Artificial intelligence (AI) systems may enhance mobility screening by providing objective, real-time feedback.
Objective: This study aimed to evaluate whether AI-assisted dynamic postural control screening combined with adaptive training improves functional mobility outcomes in older adult populations.
Methods: A quasi-experimental study was conducted with 2005 older adults recruited from community centers and health care institutions in Keelung, Taiwan. Participants were assigned to either an experimental group (n=1451), which underwent AI-assisted screening with adaptive exercise prescriptions, or a control group (n=554), which completed follow-ups through regular physical assessments with standard care without AI-tailored training. The AI system integrated skeletal tracking with the Short Physical Performance Battery to assess balance, gait speed (4-m walk), and sit-to-stand performance. Independent-samples 2-tailed t tests and repeated-measures ANOVA were applied, and effect sizes (Cohen d and η²) with 95% CIs were reported.
Results: The experimental group demonstrated significantly greater improvements compared with the control group in Short Physical Performance Battery scores (Δ=0.8 vs 0.3; t2003=3.41; P=.001; Cohen d=0.45, 95% CI 0.18-0.72), gait speed (Δ=15 cm/s vs 5 cm/s; t2003=4.85; P<.001; Cohen d=0.62, 95% CI 0.35-0.88), and sit-to-stand time (Δ=-1.4 s vs -0.6 s; t2003=3.12; P=.002; Cohen d=0.39, 95% CI 0.12-0.65). Here "Δ" refers to the change score, calculated as post-intervention minus baseline (ie, the amount of improvement during the study period). Participation rate was strongly associated with outcomes, with 1-way ANOVA showing significant group differences (F2,1448=8.74-12.21; P<.001; η²=0.07-0.10).
Conclusions: AI-assisted dynamic postural control screening combined with adaptive training substantially improved functional performance in mobility, balance, and gait among older adults. While fall incidence was not directly measured, these functional gains may have implications for fall risk reduction. Future longitudinal studies with extended follow-up (12-24 mo) and prospective fall incidence tracking across diverse populations are required to validate whether these improvements translate into actual reductions in fall risk.
Background: Loneliness has emerged as a global public health issue, with recent data indicating that 27.6% of adults aged 65 to 80 report feelings of loneliness despite the postpandemic resumption of social activities. Older caregivers face unique challenges that may exacerbate feelings of loneliness due to the demanding nature of caregiving responsibilities. While internet use has been suggested as a potential intervention to reduce loneliness, its moderating effect on the relationship between caregiving-related health effects and loneliness remains understudied.
Objective: This study aims to investigate: (1) the association between caregiving-related health effects and loneliness among older informal caregivers; (2) the relationship between internet use frequency and loneliness; and (3) whether internet use moderates the association between caregiving-related health effects and loneliness.
Methods: We analyzed cross-sectional data from the 2019-2020 California Health Interview Survey, focusing on 3957 informal caregivers aged 65 and older. Loneliness was measured using a modified 3-item UCLA Loneliness Scale. Health effects of caregiving were assessed by self-reported physical or mental health problems due to caregiving responsibilities. Internet use frequency was measured on a 4-point scale. Multivariable linear regressions were used to test the study aims, adjusting for sociodemographic factors, health status, and caregiving-context characteristics.
Results: Among participants, 475 (12.0%) reported experiencing physical or mental health problems due to caregiving responsibilities. After adjusting for covariates, caregivers who experienced health problems related to caregiving reported higher levels of loneliness compared to those who did not (β=0.76, SE .07, P<.001). More frequent internet use was associated with a lower level of loneliness (β=-0.11, SE 0.03, P<.001). Additionally, internet use significantly moderated the relationship between caregiving-related health effects and loneliness (β=-.16, SE 0.07, P=.02), suggesting that the negative impact of caregiving-related health effects on loneliness was attenuated among caregivers who used the internet more frequently.
Conclusions: Caregiving-related health effects are associated with increased loneliness among older informal caregivers, but more frequent internet use may both directly reduce loneliness and buffer against the adverse impact of caregiving on loneliness. These findings align with recent research highlighting the potential of technology-based interventions to combat social disconnection among older adults. Health care providers and policy makers should consider implementing programs that enhance internet access among older caregivers as part of comprehensive strategies to address loneliness in this vulnerable population.
Background: Assistive technologies (ATs) are used increasingly in community settings to assist in the care of older adults. Despite a rapid increase in the capabilities and uptake of these technologies, gaps remain in understanding the main barriers to their usage.
Objective: This systematic review investigated the barriers and facilitators to the use of AT in the care of older adults.
Methods: Six electronic databases were searched from January 2011 to March 2024. Primary studies were included if they used qualitative methods reporting findings related to barriers or facilitators to the implementation of AT (eg, ambient and wearable sensors, alarms, telehealth or mobile health [mHealth]) for older adults (from the perspective of either carers or older adults) in community settings. All data were screened independently by two reviewers. Study quality was assessed using the Critical Appraisal Skills Program (CASP). Data from each included study were synthesized using thematic synthesis, before barriers were mapped against the domains of the Technology Acceptance Model (TAM).
Results: Ninety-five studies were included in the review. The number of studies published in the field of barriers to AT use has increased 3-fold post-COVID-19 in comparison to the previous decade. Ten barriers-privacy, cost, insufficient knowledge, fear of misuse, usability, poor functionality, perceived lack of need, stigma, and lack of human interaction-were identified, as well as three facilitators-awareness of health benefits, targeted training, and user-centered design. Persistent barriers relating to all domains of the TAM were identified, with the majority of these relating to the "behavioral intention to use" domain (cost, privacy, stigma, and fear of misuse). The majority of studies had a moderate/high risk of bias.
Conclusions: There remain distinct barriers to sustained usage of AT for the care of older adults, particularly concerning adoption as defined by the TAM. Further studies investigating the acceptability of ATs are needed to increase the understanding of optimization strategies.
Background: Digital inclusion has become increasingly important in promoting healthy aging, yet its association with mental health among older adults appears complex and heterogeneous. The role of cognitive function as a moderator and the underlying mechanisms remain understudied.
Objective: This study aims to examine cognitive function's moderating role in the relationship between digital inclusion and depression risk among older adults, and to investigate multiple pathways of association.
Methods: Using data from the 2020 wave of the China Health and Retirement Longitudinal Study, we analyzed 18,673 adults aged 60 years and above (mean age 68.4 y, SD 6.5; 50.8% male participants [n=9486], 49.2% female participants [n=9187]). We constructed interaction effect models to test the moderation hypothesis and employed path analysis with bootstrapped 95% confidence intervals (2000 iterations) to investigate multiple pathways through which digital inclusion is associated with depression.
Results: Cognitive function significantly moderated the digital inclusion-depression relationship (β=-.002, P=.03). The association was not statistically significant at low cognitive function (β=-.137, P=.33) but strongly protective at high cognitive function (β=-.517, P<.001), revealing a "cognitive threshold effect." Path analysis identified 3 significant pathways: direct effects (66.7% of total effect), cognitive enhancement (8.3%), and social participation (8%). Importantly, higher digital inclusion was not found to be associated with increased depression risk at any cognitive function level.
Conclusions: Our findings suggest that older adults require adequate cognitive resources to derive mental health benefits from digital participation, though no harmful effects were observed at lower cognitive levels. This asymmetric pattern has important implications for designing cognitive-informed digital inclusion programs that integrate digital skills training with cognitive enhancement strategies for promoting mental health in aging populations.
Background: While the positive effects of digital technology on cognitive function are established, the specific impacts of different types of technology activities on distinct cognitive domains remain underexplored.
Objective: This study aimed to examine the associations between transitions into and out of various technology activities and trajectories of cognitive domains among community-dwelling older adults without dementia.
Methods: Data were drawn from 5566 community-dwelling older adults without dementia who participated in the National Health and Aging Trends Study from 2015 to 2022. Technology activities assessed included online shopping, banking, medication refills, social media use, and checking health conditions online. The cognitive domains measured were episodic memory, executive function, and orientation. Asymmetric effects models were used to analyze the associations between technology activity transitions and cognitive outcomes, adjusting for demographic, socioeconomic, and health-related covariates. Lagged models were applied for sensitivity analysis.
Results: In the asymmetric effects models, the onset of online shopping (β=.046, P=.02), medication refills (β=.073, P<.001), and social media use (β=.065, P=.01) was associated with improved episodic memory. The cessation of online shopping was associated with faster episodic memory decline (β=-.023, P=.047). In contrast, the cessation of online banking (β=-.078, P=.01) and social media use (β=-.066, P=.003) was associated with decreased episodic memory. The initiation of instrumental, social, and health-related technology activities was associated with slower cognitive decline in orientation. The lagged models further emphasized the effects of stopping online banking and starting online medication refills in relation to episodic memory, as well as the positive associations between online shopping and social media use and orientation. All significant effects were of small magnitude.
Conclusions: Combining findings from the main and sensitivity analyses, results suggest that interventions designed to support episodic memory in older adults should emphasize promoting the use of online medication refill services and sustaining engagement with online banking, particularly among those who have already established these habits. To support orientation, strategies should focus on facilitating adoption of online shopping and social media use, helping older adults become comfortable navigating these platforms. Future trials are needed to assess the clinical relevance of targeted interventions for specific cognitive domains, to promote the initiation and maintenance of digital activities to help mitigate domain-specific cognitive decline in aging populations.
Background: Hearing loss and depression are important health issues among the middle-aged and older population.
Objective: This study aimed to investigate the associations between hearing loss and depressive symptom trajectories in the Chinese middle-aged and older adult population.
Methods: The survey data of 2011, 2013, 2015, and 2018 waves collected in the China Health and Retirement Longitudinal Study were used for analysis. The latent growth mixture modeling approach was used to explore the trajectories of depressive symptoms. Hearing loss was identified through self-reporting, and depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression scale. The associations between hearing loss and depressive symptom trajectories were examined using logistic regression models.
Results: A total of 4768 participants without depressive symptoms at baseline were included for analysis. Among them, 4 depressive symptom trajectories, including "stable low symptoms" (n=3656, 76.68%), "slowly progressing symptoms" (n=503, 10.55%), "relieved symptoms after progression" (n=467, 9.79%), and "rapidly progressing symptoms" (n=142, 2.98%) were identified. Hearing loss was found to be significantly associated with the trajectory of "rapidly progressing symptoms."
Conclusions: The trajectories of depressive symptoms in middle-aged and older people have 4 types with distinct patterns. Hearing loss is associated with the progression of depressive symptoms, and its impact is more significant for males, affecting not only symptom severity but also progression speed. These findings indicate that the mental health status of middle-aged and older people with hearing loss requires careful consideration, and timely interventions should be provided.
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