Don't lose sight of the importance of the individual in effective falls prevention interventions

Falls remain a major public health problem, despite strong growth in the research evidence of effective single and multifactorial interventions, particularly in the community setting. A number of aspects of falls prevention require individual tailoring, despite limitations being reported regarding some of these, including questions being raised regarding the role of falls risk screening and falls risk assessment. Being able to personalise an individual's specific risk and risk factors, increase their understanding of what interventions are likely to be effective, and exploring options of choice and preference, can all impact upon whether or not an individual undertakes and sustains participation in one or more recommendations, which will ultimately influence outcomes. On all of these fronts, the individual patient receiving appropriate and targeted interventions that are meaningful, feasible and that they are motivated to implement, remains central to effective translation of falls prevention research evidence into practice

Fall Classification by Machine Learning Using Mobile Phones

Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls–left and right lateral, forward trips, and backward slips–while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls

An interdisciplinary intervention to prevent falls in community-dwelling elderly persons: protocol of a cluster-randomized trial [PreFalls]

<p>Abstract</p> <p>Background</p> <p>Prevention of falls in the elderly is a public health target in many countries around the world. While a large number of trials have investigated the effectiveness of fall prevention programs, few focussed on interventions embedded in the general practice setting and its related network. In the Prevent Falls (PreFalls) trial we aim to investigate the effectiveness of a pre-tested multi-modal intervention compared to usual care in this setting.</p> <p>Methods/Design</p> <p>PreFalls is a controlled multicenter prospective study with cluster-randomized allocation of about 40 general practices to an experimental or a control group. We aim to include 382 community dwelling persons aged 65 and older with an increased risk of falling. All participating general practitioners are trained to systematically assess the risk of falls using a set of validated tests. Patients from intervention practices are invited to participate in a 16-weeks exercise program with focus on fall prevention delivered by specifically trained local physiotherapists. Patients from practices allocated to the control group receive usual care. Main outcome measure is the number of falls per individual in the first 12 months (analysis by negative binomial regression). Secondary outcomes include falls in the second year, the proportion of participants falling in the first and the second year, falls associated with injury, risk of falls, fear of falling, physical activity and quality of life.</p> <p>Discussion</p> <p>Reducing falls in the elderly remains a major challenge. We believe that with its strong focus on a both systematic and realistic fall prevention strategy adapted to primary care setting PreFalls will be a valuable addition to the scientific literature in the field.</p> <p>Trial registration</p> <p><a href="http://www.clinicaltrials.gov/ct2/show/NCT01032252">NCT01032252</a></p

Definitions and methods of measuring and reporting on injurious falls in randomised controlled fall prevention trials: a systematic review

<p>Abstract</p> <p>Background</p> <p>The standardisation of the assessment methodology and case definition represents a major precondition for the comparison of study results and the conduction of meta-analyses. International guidelines provide recommendations for the standardisation of falls methodology; however, injurious falls have not been targeted. The aim of the present article was to review systematically the range of case definitions and methods used to measure and report on injurious falls in randomised controlled trials (RCTs) on fall prevention.</p> <p>Methods</p> <p>An electronic literature search of selected comprehensive databases was performed to identify injurious falls definitions in published trials. Inclusion criteria were: RCTs on falls prevention published in English, study population ≥ 65 years, definition of injurious falls as a study endpoint by using the terms "injuries" and "falls".</p> <p>Results</p> <p>The search yielded 2089 articles, 2048 were excluded according to defined inclusion criteria. Forty-one articles were included. The systematic analysis of the methodology applied in RCTs disclosed substantial variations in the definition and methods used to measure and document injurious falls. The limited standardisation hampered comparability of study results. Our results also highlight that studies which used a similar, standardised definition of injurious falls showed comparable outcomes.</p> <p>Conclusions</p> <p>No standard for defining, measuring, and documenting injurious falls could be identified among published RCTs. A standardised injurious falls definition enhances the comparability of study results as demonstrated by a subgroup of RCTs used a similar definition. Recommendations for standardising the methodology are given in the present review.</p