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While the relationship between internet usage and depression, and the mediating role of social participation in this association, has garnered significant attention, the COVID-19 pandemic has disrupted traditional forms of social participation. The extent to which this disruption has altered the association remains underexplored. This study aims to examine the impact of internet usage on depressive symptoms among middle-aged and older adults and to assess how the mediating role of social participation differs before and during the COVID-19 pandemic. Data for this study were sourced from the fourth wave (2018: 15,326 observations) and the fifth wave (2020: 15,758 observations) of the China Health and Retirement Longitudinal Study. A 2-way fixed-effects model incorporating an instrumental variable was employed to investigate the relationship between internet usage and depressive symptoms among middle-aged and older adults. Furthermore, a mediation effect model was applied following propensity score matching to assess changes in the mediating role of social participation on the relationship between internet usage and depressive symptoms before and during the pandemic, as well as to explore changes in 3 internet usage functions. Subgroup analyses based on age and urban-rural residence were performed to assess heterogeneity in this association. The average Center for Epidemiological Studies Depression Scale (CESD-10) score among middle-aged and older Chinese people increased from 1.94 in 2018 to 1.98 in 2020. Internet usage was negatively associated with depressive symptoms (β=-.041; P<.01), with social participation serving as a partial mediator. The proportion of the mediating effect of social participation on the relationship between distinct internet usage functions and depression shifted during the pandemic: the social function declined from 12.55% in 2018 to 9.30% in 2020, while the informational and recreational function increased from 7.53% and 11.29% in 2018 to 8.85% and 16.37% in 2020, respectively. Both the total and indirect effects were statistically significant across subgroups, with a higher mediation proportion observed among rural residents and women than among urban residents and men. Internet usage was negatively associated with depressive symptoms among middle-aged and older adults. Despite a noticeable increase in depression scores in 2020 compared with 2018, the mediating effect of social participation diminished, while the direct effect of internet usage became more pronounced during the pandemic. These findings underscore the need to prioritize mental health recovery in this population, promote diverse forms of social participation, and leverage the internet's potential to enhance social participation and mental well-being.

Informal caregivers of people with dementia frequently experience substantial psychological burden, including elevated stress and depressive symptoms. eHealth interventions have emerged as a scalable solution to support caregivers. However, their effectiveness and the influence of intervention characteristics remain unclear. This study aimed to evaluate the effectiveness of eHealth interventions in reducing caregiver burden and depressive symptoms among informal caregivers of people with dementia, and to explore potential moderators of intervention effectiveness, including delivery modality, human support, and intervention duration. This systematic review and meta-analysis was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 and PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Search extension) guidelines. Eight electronic databases and clinical trial registries were searched from inception to March 10, 2026. Eligible studies were randomized controlled trials involving informal caregivers of people with dementia receiving interactive eHealth interventions compared with usual care or inactive controls. Study selection, data extraction, and risk-of-bias assessment were conducted independently by 2 reviewers (ML and JXR). Meta-analyses were conducted using a random-effects model with the Hartung-Knapp-Sidik-Jonkman adjustment. Heterogeneity was assessed using the I2 statistic, and 95% prediction intervals (PIs) were calculated for the primary analyses. The certainty of evidence was evaluated using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) criteria. Thirty-five trials (N=3388) were included. eHealth interventions were associated with a statistically significant reduction in caregiver burden (k=35, standardized mean difference [SMD] -0.26, 95% CI -0.42 to -0.10; P=.002; τ2=0.16, τ=0.40; 95% PI -1.10 to 0.58) and a modest reduction in depressive symptoms (k=23, SMD -0.27, 95% CI -0.53 to -0.01; P=.04; τ2=0.31, τ=0.55; 95% PI -1.45 to 0.91). While average effects were statistically significant, wide PIs indicate substantial between-study heterogeneity and variability in real-world effectiveness. Subgroup analyses suggested that short-term (≤8 weeks) intervention was associated with borderline significantly stronger effects for caregiver burden, while human-supported and mobile-based interventions demonstrated larger point estimates with substantial heterogeneity. Meta-regression indicated that caregiver age was a potential moderator (P=.05) in univariate analysis, with decreasing effectiveness observed in older populations. Sensitivity analysis of 27 high-quality studies confirmed robust effects (SMD -0.31, P=.002). No significant small-study effects were detected (Egger test: P>.35). The overall certainty of evidence was moderate. eHealth interventions provide modest but variable benefits in reducing burden and depressive symptoms among informal caregivers of people with dementia. This review is timely, given the rapidly expanding digital dementia care and extends prior evidence using a more conservative Hartung-Knapp-Sidik-Jonkman framework with PIs, showing that effects vary from minimal to substantial. While average effects support effectiveness, wide PIs and moderate certainty evidence indicate that outcomes are context-dependent and influenced by heterogeneity and methodological limitations. Hybrid digital-human models should be prioritized to enhance consistency and real-world impact.

The utility of aging metrics that incorporate cognitive and physical function is not fully understood. We aim to compare the predictive capacities of 3 distinct aging metrics-motoric cognitive risk syndrome (MCR), physio-cognitive decline syndrome (PCDS), and cognitive frailty (CF)-for incident dementia and all-cause mortality among community-dwelling older adults. We used longitudinal data from waves 10-15 of the Health and Retirement Study. Cox proportional hazards regression analysis was employed to evaluate the effects of MCR, PCDS, and CF on incident all-cause dementia and mortality, controlling for socioeconomic and lifestyle factors, as well as medical comorbidities. Discrimination analysis was conducted to assess and compare the predictive accuracy of the 3 aging metrics. A total of 2367 older individuals aged 65 years and older, with no baseline prevalence of dementia or disability, were ultimately included. The prevalence rates of MCR, PCDS, and CF were 5.4%, 6.3%, and 1.3%, respectively. Over a decade-long follow-up period, 341 cases of dementia and 573 deaths were recorded. All 3 metrics were predictive of incident all-cause dementia and mortality when adjusting for multiple confounders, with variations in the strength of their associations (incident dementia: MCR odds ratio [OR] 1.90, 95% CI 1.30-2.78; CF 5.06, 95% CI 2.87-8.92; PCDS 3.35, 95% CI 2.44-4.58; mortality: MCR 1.60, 95% CI 1.17-2.19; CF 3.26, 95% CI 1.99-5.33; and PCDS 1.58, 95% CI 1.17-2.13). The C-index indicated that PCDS and MCR had the highest discriminatory accuracy for all-cause dementia and mortality, respectively. Despite the inherent differences among the aging metrics that integrate cognitive and physical functions, they consistently identified risks of dementia and mortality. This underscores the importance of implementing targeted preventive strategies and intervention programs based on these metrics to enhance the overall quality of life and reduce premature deaths in aging populations.

Sleep efficiency is often used as a measure of sleep quality. Getting sufficiently high-quality sleep has been associated with better cognitive function among older adults; however, the relationship between day-to-day sleep quality variability and cognition has not been well-established. We aimed to determine the relationship between day-to-day sleep efficiency variability and cognitive function among older adults, using accelerometer data and 3 cognitive tests. We included older adults aged >65 years with at least 5 days of accelerometer wear time from the National Health and Nutrition Examination Survey (NHANES) who completed the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer's Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Sleep efficiency was derived using a data-driven machine learning algorithm. We examined associations between sleep efficiency variability and scores on each cognitive test adjusted for age, sex, education, household income, marital status, depressive symptoms, diabetes, smoking habits, alcohol consumption, arthritis, heart disease, prior heart attack, prior stroke, activities of daily living, and instrumental activities of daily living. Associations between average sleep efficiency and each cognitive test score were further examined for comparison purposes. A total of 1074 older adults from the NHANES were included in this study. Older adults with low average sleep efficiency exhibited higher levels of sleep efficiency variability (Pearson r=-0.63). After adjusting for confounding factors, greater average sleep efficiency was associated with higher scores on the DSST (per 10% increase, β=2.25, 95% CI 0.61 to 3.90) and AFT (per 10% increase, β=.91, 95% CI 0.27 to 1.56). Greater sleep efficiency variability was univariably associated with worse cognitive function based on the DSST (per 10% increase, β=-3.34, 95% CI -5.33 to -1.34), CERAD-WL (per 10% increase, β=-1.00, 95% CI -1.79 to -0.21), and AFT (per 10% increase, β=-1.02, 95% CI -1.68 to -0.36). In fully adjusted models, greater sleep efficiency variability remained associated with lower DSST (per 10% increase, β=-2.01, 95% CI -3.62 to -0.40) and AFT (per 10% increase, β=-.84, 95% CI -1.47 to -0.21) scores but not CERAD-WL (per 10% increase, β=-.65, 95% CI -1.39 to 0.08) scores. Targeting consistency in sleep quality may be useful for interventions seeking to preserve cognitive function among older adults.

Disturbed sleep patterns are common among older adults and may contribute to cognitive and physical declines. However, evidence for the relationship between sleep duration and cognitive frailty, a concept combining physical frailty and cognitive impairment in older adults is lacking. The objective of our study was to examine the associations of sleep duration and its changes with cognitive frailty. We analyzed data from the 2008-2018 waves of the Chinese Longitudinal Healthy Longevity Survey. Cognitive frailty was rendered based on the modified Fried frailty phenotype and Mini-Mental State Examination. Sleep duration was categorized as short (<6 h), moderate (6-9 h), and long (>9 h). We examined the association of sleep duration with cognitive frailty status at baseline using logistic regressions and with future incidence of cognitive frailty using Cox proportional hazards models. Restricted cubic splines were employed to explore potential non-linear associations. Among 11,303 participants, 1,298 (11.5%) had cognitive frailty at baseline. Compared to participants who had moderate sleep duration, the odds of having cognitive frailty were higher in those with long sleep duration (odds ratio [OR] =1.71, 95% confidence interval [CI] =1.48-1.97, p<0.001). A J-shaped association between sleep duration and cognitive frailty was also observed (p<0.001). Additionally, during a median follow-up of 6.7 years among 5,201 participants who were not cognitively frail at baseline, 521 (10.0%) developed cognitive frailty. A higher risk of cognitive frailty was observed in participants with long sleep duration (hazard ratio [HR] =1.32, 95% CI =1.07-1.62, p=0.008). Long sleep duration was associated with cognitive frailly in older Chinese adults. These findings provide insights into the relationship between sleep duration and cognitive frailty, with potential implications for public health policies and clinical practice.

Literature has shown an increase in research relating to autism and aging, and more specifically, autism and healthy aging. Within the literature, there is a clear lack of knowledge and understanding of the impacts of dementia and autism as co-occurring experiences. More specifically, there is a lack of clinical knowledge about the ways in which the cognitive profiles of autism and dementia do and do not overlap, and how this overlap might affect dementia assessments for this population. This is likely to result in challenges with diagnosis and may lead to misdiagnosis or a lack of diagnosis for the autistic population. This paper reports on a narrative literature review that addresses the lack of understanding of potential similarities and differences in the cognitive profiles of autistic people and patients with dementia. It aims to identify the cognitive functions that are sensitive only to dementia and are less likely to be sensitive to autism. This will allow for a deeper understanding of how to grant sensitivity and specificity in the assessment of dementia within the autistic population. The narrative literature review relies on existing guidelines (eg, PRISMA [Preferred Reporting Items for Systematic Reviews and Meta-Analyses]) to inform a newly developed tool, namely the Quality of Evidence Tool (QoET), used to assess publications identified in relevant databases (eg, PubMed, PsycINFO, and others) that report on cognitive functions in autism. Four main cognitive domains were chosen: memory, executive function, attention, and language, as these are the most commonly impaired in people with dementia due to Alzheimer disease and are known to be affected by autism. The QoET was used to select publications that informed us of cognitive functions that would meet our aim, that is, functions affected by dementia and not by autism. This narrative review will identify cognitive domains and functions affected by dementia, not autism, and the assessment tools that yield these findings. It is expected that the rigorous methodological approach devised will shed light on how existing cognitive assessments should be used to increase the specificity of dementia risk detection among autistic individuals, thereby addressing early concerns raised by individuals or health care practitioners. It is expected that the review will be ready for publication by December 2026. As of April 2026, memory and executive function have been drafted. This narrative review does not seek to give a definitive answer on the cognitive domain, function, and outcome measures or tests used in dementia detection, which can help detect risk in autistic individuals. Instead, it aims to provide a starting point, to pragmatically and efficiently explore and synthesize the broad evidence base, to inform future work and potentially highlight topics (eg, cognitive functions of interest) on which future systematic reviews could specifically focus.

Positive aging, a concept found in positive psychology, serves as the theoretical foundation for this study. To age positively, one must manage hidden or unrecognized challenges, show flexibility in behavior and thought, adopt a positive outlook on problems involving regression, and make decisions that promote one's well-being. This study examined the role of wisdom and life purpose in the mental well-being of middle-aged and older adults. More specifically, we tested 4 hypotheses: wisdom would exhibit a positive correlation with mental well-being, quality of life would exhibit a positive correlation with mental well-being, meaning and purpose would exhibit a positive correlation with mental well-being, and freedom would exhibit a positive correlation with mental well-being. The research used a multianalytical methodology combining covariance-based structural equation modeling and artificial neural network techniques to analyze data from 377 individuals aged 50 to 102 years. Results from the covariance-based structural equation modeling indicate that meaning and purpose, wisdom, and quality of life were significantly associated with the mental well-being, accounting for 71% of the explained variance. Additionally, the artificial network analysis yielded exact forecasts of mental well-being. The artificial network model achieved an accuracy of 82.1% and 73% on the training and test sets, respectively, for predicting mental well-being. Sensitivity analysis revealed that meaning and purpose were the most critical factors in explaining participants' mental well-being. These findings have prominent theoretical implications for social psychology researchers and practical consequences for authorities involved in the care of older adults, who can use the results to develop strategic plans and take necessary actions.

Interventions and care that can evoke positive emotions and reduce apathy or agitation are important for people with dementia. In recent years, socially assistive robots used for better dementia care have been found to be feasible. However, the immediate responses of people with dementia when they are given multiple sensory modalities from socially assistive robots have not yet been sufficiently elucidated. This study aimed to quantitatively examine the immediate emotional responses of people with dementia to stimuli presented by socially assistive robots using facial expression analysis in order to determine whether they elicited positive emotions. This pilot study adopted a single-arm interventional design. Socially assistive robots were presented to nursing home residents in a three-step procedure: (1) the robot was placed in front of participants (visual stimulus), (2) the robot was manipulated to produce sound (visual and auditory stimuli), and (3) participants held the robot in their hands (visual, auditory, and tactile stimuli). Expression intensity values for \"happy,\" \"sad,\" \"angry,\" \"surprised,\" \"scared,\" and \"disgusted\" were calculated continuously using facial expression analysis with FaceReader. Additionally, self-reported feelings were assessed using a 5-point Likert scale. In addition to the comparison between the subjective and objective emotional assessments, expression intensity values were compared across the aforementioned 3 stimuli patterns within each session. Finally, the expression intensity value for \"happy\" was compared between the different types of robots. A total of 29 participants (mean age 88.7, SD 6.2 years; n=27 female; Japanese version of Mini-Mental State Examination mean score 18.2, SD 5.1) were recruited. The expression intensity value for \"happy\" was the largest in both the subjective and objective assessments and increased significantly when all sensory modalities (visual, auditory, and tactile) were presented (median expression intensity 0.21, IQR 0.09-0.35) compared to the other 2 patterns (visual alone: median expression intensity 0.10, IQR 0.03-0.22; P<.001; visual and auditory: median expression intensity 0.10, IQR 0.04-0.23; P<.001). The comparison of different types of robots revealed a significant increase when all stimuli were presented by doll-type and animal-type robots, but not humanoid-type robots. By quantifying the emotional responses of people with dementia, this study highlighted that socially assistive robots may be more effective in eliciting positive emotions when multiple sensory stimuli, including tactile stimuli, are involved. More studies, including randomized controlled trials, are required to further explore the effectiveness of using socially assistive robots in dementia care.