Interventions for preventing falls in older people often involve several components, multidisciplinary teams, and implementation in a variety of settings. We have developed a classification system (taxonomy) to describe interventions used to prevent falls in older people, with the aim of improving the design and reporting of clinical trials of fall-prevention interventions, and synthesis of evidence from these trials.

The European population is ageing, and there is a need for health solutions that keep older adults independent longer. With increasing access to mobile technology, such as smartphones and smartwatches, the development and use of mobile health applications is rapidly growing. To meet the societal challenge of changing demography, mobile health solutions are warranted that support older adults to stay healthy and active and that can prevent or delay functional decline. This paper reviews the literature on mobile technology, in particular wearable technology, such as smartphones, smartwatches, and wristbands, presenting new ideas on how this technology can be used to encourage an active lifestyle, and discusses the way forward in order further to advance development and practice in the field of mobile technology for active, healthy ageing.

Fall incidence differs considerably between studies and countries. Reasons may be differences between study samples or different assessment methods. The aim was to derive estimates of fall incidence from two population-based studies among older community-living people in Germany and compare retrospective and prospective falls data collection methods. Data were derived from the 2008–11 wave of the German health interview and examination survey for adults (DEGS1), and the Activity and Function of the Elderly in Ulm study (ActiFE-Ulm). Data collection took place in community facilities (DEGS1) or participants’ homes (ActiFE-Ulm). Participation rates were 42% (newly recruited) and 64% (panel component) in DEGS1 and 19.8% in ActiFE-Ulm. Self-report retrospective fall data covering the previous 12 month period in DEGS1 and ActiFE-Ulm were collected, but only ActiFE-Ulm used prospective 12 month fall calendars. The incidence of ‘any fall’ and ‘recurrent falls’ were calculated for both methods. Fall rates increased with age in men but not women. The ActiFE-Ulm prospectively assessed incidence (95% confidence interval) in women and men aged 65- < 90 years were 38.7 (36.9-40.5) and 29.7 (28.1-31.3) fallers/year and 13.7 (12.5-14.9) and 10.9 (9.9-12.0) recurrent fallers/year, respectively. Retrospective and prospective fall incidence in ActiFE-Ulm did not differ.The retrospectively assessed incidence of ‘any fall’ among persons 65- < 80 years were significantly lower in DEGS1 than ActiFE-Ulm (women: 25.7% (22.4-29.2) versus 37.4% (34.8-39.9); men: 16.3% (13.6-19.3) versus 28.9% (26.6-31.1). Retrospective incidence estimates of recurrent falls were similar in both studies for women (10.4% (8.3-12.9) versus 10.2% (8.5-11.8)) and men (6.1% (4.3-8.5) versus 8.4% (7.1-9.8)). Both studies were population-based, but retrospective self-reported fall incidence differed between studies. Study design influences retrospective reported fall incidence considerably. Costly collection of prospective data gives similar rates to the cheaper retrospective report method.

Background: The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. Methods/design: The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson’s Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. Discussion: The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility. Trial registration: ISRCTN12051706.

Real-world fall events objectively measured by body-worn sensors can improve the understanding of fall events in older people. However, these events are rare and hence challenging to capture. Therefore, the FARSEEING (FAll Repository for the design of Smart and sElf-adaptive Environments prolonging Independent livinG) consortium and associated partners started to build up a meta-database of real-world falls. Between January 2012 and December 2015 more than 300 real-world fall events have been recorded. This is currently the largest collection of real-world fall data recorded with inertial sensors. A signal processing and fall verification procedure has been developed and applied to the data. Since the end of 2015, 208 verified real-world fall events are available for analyses. The fall events have been recorded within several studies, with different methods, and in different populations. All sensor signals include at least accelerometer measurements and 58 % additionally include gyroscope and magnetometer measurements. The collection of data is ongoing and open to further partners contributing with fall signals. The FARSEEING consortium also aims to share the collected real-world falls data with other researchers on request. The FARSEEING meta-database will help to improve the understanding of falls and enable new approaches in fall risk assessment, fall prevention, and fall detection in both aging and disease.