Evaluation of the Frails' Fall Efficacy by Comparing Treatments (EFFECT) on reducing fall and fear of fall in moderately frail older adults: study protocol for a randomised control trial

<p>Abstract</p> <p>Background</p> <p>Falls are common in frail older adults and often result in injuries and hospitalisation. The Nintendo^®Wii™ is an easily available exercise modality in the community which has been shown to improve lower limb strength and balance. However, not much is known on the effectiveness of the Nintendo^®Wii™ to improve fall efficacy and reduce falls in a moderately frail older adult. Fall efficacy is the measure of fear of falling in performing various daily activities. Fear contributes to avoidance of activities and functional decline.</p> <p>Methods</p> <p>This randomised active-control trial is a comparison between the Nintendo WiiActive programme against standard gym-based rehabilitation of the older population. Eighty subjects aged above 60, fallers and non-fallers, will be recruited from the hospital outpatient clinic. The primary outcome measure is the Modified Falls Efficacy Scale and the secondary outcome measures are self-reported falls, quadriceps strength, walking agility, dynamic balance and quality of life assessments.</p> <p>Discussions</p> <p>The study is the first randomised control trial using the Nintendo Wii as a rehabilitation modality investigating a change in fall efficacy and self-reported falls. Longitudinally, the study will investigate if the interventions can successfully reduce falls and analyse the cost-effectiveness of the programme.</p> <p>Trial registration</p> <p>Australia and New Zealand Clinical Trials Register (ANZCTR): <a href="http://www.anzctr.org.au/ACTRN12610000576022.aspx">ACTRN12610000576022</a></p

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guessing. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as patient-specific biomarker trends. The submission system remains open via the website https://tadpole.grand-challenge.org, while code for submissions is being collated by TADPOLE SHARE: https://tadpole-share.github.io/. Our work suggests that current prediction algorithms are accurate for biomarkers related to clinical diagnosis and ventricle volume, opening up the possibility of cohort refinement in clinical trials for Alzheimer's disease