The prevention of injury associated with falls in older people is a public health target in many countries around the world. Although there is good evidence that interventions such as multifactorial fall prevention and individually prescribed exercise are effective in reducing falls, the effect on serious injury rates is unclear. Historically, trials have not been adequately powered to detect injury endpoints, and variations in case definition across trials have hindered meta-analysis. It is possible that fall-prevention strategies have limited effect on falls that result in injuries or are ineffective in populations who are at a higher risk of injury. Further research is required to determine whether fall-prevention interventions can reduce serious injuries. Prevention of Falls Network Europe (ProFaNE) is a collaborative project to reduce the burden of fall injury in older people through excellence in research and promotion of best practice (www.profane.eu.org). The European Commission funds the network, which links clinicians, members of the public, and researchers worldwide. The aims are to identify major gaps in knowledge in fall injury prevention and to facilitate the collaboration necessary for large-scale clinical research activity, including clinical trials, comparative research, and prospective meta-analysis. Work is being undertaken in a 4-year program. As a first step, the development of a common set of outcome definitions and measures for future trials or meta-analysis was considered.

The objectives were to identify fall-related psychological outcome measures and to undertake a systematic quality assessment of their key measurement properties. A Cochrane review of fall-prevention interventions in older adults was used to identify fall-related psychological measurements. PubMed, CINAHL, and PsycINFO were systematically searched to identify instruments not used in trials and papers reporting the methodological quality of relevant measures. Reference lists of articles were searched for additional literature, and researchers were contacted. Two reviewers undertook quality extraction relating to content, population, reliability, validity, responsiveness, practicality, and feasibility. Twenty-five relevant papers were identified. Twenty-three measures met the inclusion criteria: six single-item questions, Falls Efficacy Scale (FES), revised FES, modified FES, FES-UK, Activities-specific Balance and Confidence Scale (ABC), ABC-UK, Confidence in maintaining Balance Scale, Mobility Efficacy Scale, adapted FES, amended FES, Survey of Activities and Fear of Falling in the Elderly (SAFFE), University of Illinois at Chicago Fear of Falling Measure, Concern about Falling Scale, Falls Handicap Inventory, modified SAFFE, Consequences of Falling Scale, and Concern about the Consequences of Falling Scale. There is limited evidence about the measurement properties of single-item measures. Several multiitem measures obtained acceptable reliability and validity, but there is less evidence regarding responsiveness, practicality, and feasibility. Researchers should select measures based on the constructs they intend to study. Further research is needed to establish and compare the instruments' measurement properties.

the standard of reporting falls in published trials is poor and significantly impedes comparison between studies. The review has been used to inform an international consensus exercise to make recommendations for a core set of outcome measures for fall prevention trials.

Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean±std) 83.0%±30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0%±27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector.

Falls are a relevant economic burden to society. Efforts should be directed to economic evaluations of fall-prevention programmes aiming at reducing fall-related fractures, which contribute substantially to fall-related costs.