To summarise existing systematic reviews which assessed the effects of physical exercise on activities of daily living, walking, balance and visual processing in people with dementia or mild cognit...

Technology is widely promoted as a solution to greater independence and better health for the rapidly growing UK older population. If this is to be realised, we need to understand barriers and facilitators to uptake and investigate who wants this technology and who does not express an interest in use. This analysis is based on data from a population-based cohort study, the Cognitive Function and Ageing Study (CFAS)-II, which focused on brain health in older people and included questions about access to- and interest in- internet technologies. The factors affecting willingness to use technologies that support memory and ADL were identified using binary logistic regression analysis. 541 people aged 75 years and older from Cambridgeshire, Nottingham and Newcastle responded. Older adults were more willing to use technologies directed towards improving memory (65%) than towards ADL supportive technologies (38%). Regression analysis showed that an older age (OR = 0.64, 95% CI = 0.34–0.98), female gender (OR = 0.64, 95% CI = 0.42–0.99), no access to technology including laptops and tablets (OR = 0.48, 95% CI = 0.32–0.72), and self-reported physically less slowing down (but no objective health indicators) (OR = 0.57, 95% CI = 0.36–0.88) were strongly associated with UK older adults’ lesser willingness to use memory assistive technologies while not having access to laptops and tablets (OR = 0.57, 95% CI = 0.39–0.84) was associated with willingness to use ADL supportive technologies. Older people, females and those with less access to technologies should be considered as target groups by healthcare providers, policymakers, and technology producers to promote technology and support healthy and independent ageing.

The effectiveness of exergames on fall risk and related physical and cognitive function in older adults is still unclear, with conflicting findings. The discrepancy in these results could be due to the different components and task-specific demands of individual exergame interventions. This open-label quasi-randomized study aimed to compare the efficacy of 2 different home-based dual-task exergame treatments on cognition, mobility, and balance in older people. Fifty older adults (65–85 years of age) were allocated to one of two 8-week exergame interventions: Cognitive-Intensive Exergame Training (CIT) or Physical-Intensive Exergame Training (PIT). Cognitive functions, balance, and mobility were assessed at baseline and after 8 weeks. Group × time interaction was measured by repeated-measure ANOVA, and both intention-to-treat (ITT) and per-protocol (PP) analyses were performed to assess the effectiveness of exergame interventions. ITT analyses showed that improvement in visual processing speed and visuospatial working memory was greater in the CIT group, with a medium effect size (p = .04; η² = 0.09 and p = .01; η² = 0.12). The improvement in verbal memory and attention was significant within both groups (p < .05), but this improvement was not different between the groups (p > .05). A significant improvement in balance was also observed in the PIT group, with a medium effect size (p = .04; η² = 0.09). Although mobility improved significantly in both groups (p < .01), there was no significant difference between groups (p = .08). These results were largely supported by the PP analysis. Dual-task exergame training can improve mobility and cognition in older adults. However, the different cognitive and physical demands of these interventions may have varying impacts on fall risk and related physical or cognitive functions. Therefore, a training program that includes both cognitive and physical domains with appropriate intensity is essential for the development of tailored exergame interventions to reduce fall risk in older adults.

Visual processing deficits have frequently been reported when studied in individuals with dementia, which suggests their potential utility in supporting dementia screening. The study uses EPIC-Norfolk Prospective Population Cohort Study data (n = 8623) to investigate the role of visual processing speed assessed by the Visual Sensitivity Test (VST) in identifying the risk of future dementia using Cox regression analyses. Individuals with lower scores on the simple and complex VST had a higher probability of a future dementia diagnosis HR1.39 (95% CI 1.12, 1.67, P < 0.01) and HR 1.56 (95% CI 1.27, 1.90, P < 0.01), respectively. Although other more commonly used cognitive dementia screening tests were better predictors of future dementia risk (HR 3.45 for HVLT and HR 2.66, for SF-EMSE), the complex VST showed greater sensitivity to variables frequently associated with dementia risk. Reduced complex visual processing speed is significantly associated with a high likelihood of a future dementia diagnosis and risk/protective factors in this cohort. Combining visual processing tests with other neuropsychological tests could improve the identification of future dementia risk.

Introduction: The use of digital assistive technologies by and for people living with dementia is promising for supporting social health and advocated as a partial solution to growing prevalence worldwide. A state-of-the-art position paper published in 2017 identified challenges regarding digital assistive technologies, around five themes: development, usability, (cost-)effectiveness, implementation and ethics. This systematic review summarizes progress on the challenges found in 2017, and persisting or emerging challenges. Methods: A systematic review of systematic reviews was conducted, focused on studies published after 2016. The inclusion criteria required that the target group included, at least in part, people with dementia living in the community and that the technologies aimed to support social health. For the five themes, literature searches were conducted in Medline, CINAHL, PsycINFO, and Embase databases. Results: A total of 112 reviews were included, covering various applications such as smart homes, care robots, exergaming and everyday technologies. No applications of artificial intelligence were included. The challenges included personalization of applications (development); limited use of standardized methods (usability); insufficient quantity and quality of randomized controlled trials (cost-effectiveness); overly high expectations of assistive technologies (implementation); and the need for more equitable access to technologies (ethics). Conclusion: Much research has been conducted since the 2017 state of the art position paper. While some challenges identified at that time remain relevant, others have been addressed, and new challenges have emerged. Future research should prioritize emerging artificial intelligence applications; the development of integrated assistive technologies; evaluation using robust methods and meaningful outcomes; and the promotion of more accessible and inclusive technologies.

INTRODUCTION: Alzheimer's disease is characterized by amyloid‐β (Aβ) and tau accumulation. Identifying individuals with rapid proteinopathy progression is crucial for timely intervention and trial enrichment. METHODS: We analyzed longitudinal data from 456 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, including 375 with digital cognitive test scores. Amyloid and tau accumulation rates were estimated from positron emission tomography (PET) imaging using linear mixed‐effects models. Participants were classified as fast or slow accumulators via Gaussian modeling. Predictors of accumulation and clinical conversion were assessed with logistic and Cox regression models, incorporating demographics, cognitive measures and plasma biomarkers. RESULTS: Plasma p‐tau217 and Aβ42/Aβ40 predicted rapid accumulation and conversion, with p‐tau217 the strongest marker (odds ratio [OR] up to 6.6). Baseline digital cognitive measures contributed significantly to the prediction, achieving comparable or superior predictive accuracy to traditional cognitive tests (area under the curve [AUC] up to 0.92; C‐index 0.82). DISCUSSION: Plasma p‐tau217 and Aβ42/Aβ40 emerged as robust predictors of the progression of disease pathology, supported by cognitive measures.