A Wii Bit of Fun: A Novel Platform to Deliver Effective Balance Training to Older Adults

BACKGROUND: Falls and fall-related injuries are symptomatic of an aging population. This study aimed to design, develop, and deliver a novel method of balance training, using an interactive game-based system to promote engagement, with the inclusion of older adults at both high and low risk of experiencing a fall.STUDY DESIGN: Eighty-two older adults (65 years of age and older) were recruited from sheltered accommodation and local activity groups. Forty volunteers were randomly selected and received 5 weeks of balance game training (5 males, 35 females; mean, 77.18 ± 6.59 years), whereas the remaining control participants recorded levels of physical activity (20 males, 22 females; mean, 76.62 ± 7.28 years). The effect of balance game training was measured on levels of functional balance and balance confidence in individuals with and without quantifiable balance impairments.RESULTS: Balance game training had a significant effect on levels of functional balance and balance confidence (P Peer reviewedFinal Published versio

StrokeCog Markov Model Projected Prevalent and Incident Cases of Stroke and Poststroke Cognitive Impairment to 2035 in Ireland

Background and Purpose: Cognitive impairment no dementia (CIND) and dementia are common stroke outcomes, with significant health and societal implications for aging populations. These outcomes are not included in current epidemiological models. We aimed to develop an epidemiological model to project incidence and prevalence of stroke, poststroke CIND and dementia, and life expectancy, in Ireland to 2035, informing policy and service planning. Methods: We developed a probabilistic Markov model (the StrokeCog model) applied to the Irish population aged 40 to 89 years to 2035. Data sources included official population and hospital-episode statistics, longitudinal cohort studies, and published estimates. Key assumptions were varied in sensitivity analysis. Results were externally validated against independent sources. The model tracks poststroke progression into health states characterized by no cognitive impairment, CIND, dementia, disability, stroke recurrence, and death. Results: We projected 69 051 people with prevalent stroke in Ireland in 2035 (22.0 per 1000 population [95% CI, 20.8-23.1]), with 25 274 (8.0 per 1000 population [95% CI, 7.1-9.0]) of those projected to have poststroke CIND, and 12 442 having poststroke dementia (4.0 per 1000 population [95% CI, 3.2-4.8]). We projected 8725 annual incident strokes in 2035 (2.8 per 1000 population [95% CI, 2.7-2.9]), with 3832 of these having CIND (1.2 per 1000 population [95% CI, 1.1-1.3]), and 1715 with dementia (0.5 per 1000 population [95% CI, 0.5-0.6]). Life expectancy for stroke survivors at age 50 was 23.4 years (95% CI, 22.3-24.5) for women and 20.7 (95% CI, 19.5-21.9) for men. Conclusions: This novel epidemiological model of stroke, poststroke CIND, and dementia draws on the best available evidence. Sensitivity analysis indicated that findings were robust to assumptions, and where there was uncertainty a conservative approach was taken. The StrokeCog model is a useful tool for service planning and cost-effectiveness analysis and is available for adaptation to other national contexts.Peer reviewe

European Stroke Organisation and European Academy of Neurology joint guidelines on post-stroke cognitive impairment.

The optimal management of post-stroke cognitive impairment remains controversial. These joint European Stroke Organisation (ESO) and European Academy of Neurology (EAN) guidelines provide evidence-based recommendations to assist clinicians in decision making around prevention, diagnosis, treatment and prognosis. These guidelines were developed according to ESO standard operating procedure and the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. The working group identified relevant clinical questions, performed systematic reviews and, where possible, meta-analyses of the literature, assessed the quality of the available evidence and made specific recommendations. Expert consensus statements were provided where insufficient evidence was available to provide recommendations based on the GRADE approach. There was limited randomised controlled trial evidence regarding single or multicomponent interventions to prevent post-stroke cognitive decline. Interventions to improve lifestyle and treat vascular risk factors may have many health benefits but a beneficial effect on cognition is not proven. We found no evidence around routine cognitive screening following stroke but recognise the importance of targeted cognitive assessment. We described the accuracy of various cognitive screening tests but found no clearly superior approach to testing. There was insufficient evidence to make a recommendation for use of cholinesterase inhibitors, memantine nootropics or cognitive rehabilitation. There was limited evidence on the use of prediction tools for post-stroke cognitive syndromes (cognitive impairment, dementia and delirium). The association between post-stroke cognitive impairment and most acute structural brain imaging features was unclear, although the presence of substantial white matter hyperintensities of presumed vascular origin on acute MRI brain may help predict cognitive outcomes. These guidelines have highlighted fundamental areas where robust evidence is lacking. Further, definitive randomised controlled trials are needed, and we suggest priority areas for future research

Does cognitive impairment impact adherence? A systematic review and meta-analysis of the association between cognitive impairment and medication non-adherence in stroke

<div><p>Background</p><p>While medication adherence is essential for the secondary prevention of stroke, it is often sub-optimal, and can be compromised by cognitive impairment. This study aimed to systematically review and meta-analyse the association between cognitive impairment and medication non-adherence in stroke.</p><p>Methods</p><p>A systematic literature search of longitudinal and cross-sectional studies of adults with any stroke type, which reported on the association between any measure of non-adherence and cognitive impairment, was carried out according to PRISMA guidelines. Odds ratios and 95% confidence intervals were the primary measure of effect. Risk of bias was assessed using the Cochrane Bias Methods Group's Tool to Assess Risk of Bias in Cohort Studies, with evidence quality assessed according to the GRADE approach. We conducted sensitivity analyses according to measure of cognitive impairment, measure of medication adherence, population, risk of bias and adjustment for covariates. The protocol was registered with PROSPERO.</p><p>Results</p><p>From 1,760 titles and abstracts, we identified 9 studies for inclusion. Measures of cognitive impairment varied from dementia diagnosis to standardised cognitive assessments. Medication adherence was assessed through self-report or administrative databases. The majority of studies were of medium risk of bias (n = 6); two studies had low risk of bias. Findings were mixed; when all studies were pooled, there was no evidence of an association between cognitive impairment and medication non-adherence post-stroke [OR (95% CI): 0.85 (0.66, 1.03)]. However, heterogeneity was substantial [<i>I</i>² = 90.9%, <i>p</i> < .001], and the overall evidence quality was low.</p><p>Conclusions</p><p>Few studies have explored associations between cognitive impairment and medication adherence post-stroke, with substantial heterogeneity in study populations, and definitions and assessments of non-adherence and cognitive impairment. Further research using clear, standardised and objective assessments is needed to clarify the association between cognitive impairment and medication non-adherence in stroke.</p></div

Funnel plot of included studies, stratified by adjustment for covariates.

<p>Funnel plot of included studies, stratified by adjustment for covariates.</p

Characteristics of included studies.

<p>Characteristics of included studies.</p

Flow chart of included studies.

<p>Flow chart of included studies.</p

GRADE quality of evidence.

<p>GRADE quality of evidence.</p

Forest plot of included studies.

<p>Forest plot of included studies.</p