Andreas Ejupi

ICT-based system to predict and prevent falls (iStoppFalls): study protocol for an international multicenter randomized controlled trial

BackgroundFalls are very common, especially in adults aged 65 years and older. Within the current international European Commission’s Seventh Framework Program (FP7) project ‘iStoppFalls’ an Information and Communication Technology (ICT) based system has been developed to regularly assess a person’s risk of falling in their own home and to deliver an individual and tailored home-based exercise and education program for fall prevention. The primary aims of iStoppFalls are to assess the feasibility and acceptability of the intervention program, and its effectiveness to improve balance, muscle strength and quality of life in older people.Methods/DesignThis international, multicenter study is designed as a single-blinded, two-group randomized controlled trial. A total of 160 community-dwelling older people aged 65 years and older will be recruited in Germany (n = 60), Spain (n = 40), and Australia (n = 60) between November 2013 and May 2014. Participants in the intervention group will conduct a 16-week exercise program using the iStoppFalls system through their television set at home. Participants are encouraged to exercise for a total duration of 180 minutes per week. The training program consists of a variety of balance and strength exercises in the form of video games using exergame technology. Educational material about a healthy lifestyle will be provided to each participant. Final reassessments will be conducted after 16 weeks. The assessments include physical and cognitive tests as well as questionnaires assessing health, fear of falling, quality of life and psychosocial determinants. Falls will be followed up for six months by monthly falls calendars.DiscussionWe hypothesize that the regular use of this newly developed ICT-based system for fall prevention at home is feasible for older people. By using the iStoppFalls sensor-based exercise program, older people are expected to improve in balance and strength outcomes. In addition, the exercise training may have a positive impact on quality of life by reducing the risk of falls. Taken together with expected cognitive improvements, the individual approach of the iStoppFalls program may provide an effective model for fall prevention in older people who prefer to exercise at home.Trial registrationAustralian New Zealand Clinical Trials Registry Trial ID: ACTRN12614000096651.International Standard Randomised Controlled Trial Number: ISRCTN15932647.

New methods for fall risk prediction.

Purpose of reviewAccidental falls are the leading cause of injury-related death and hospitalization in old age, with over one-third of the older adults experiencing at least one fall or more each year. Because of limited healthcare resources, regular objective fall risk assessments are not possible in the community on a large scale. New methods for fall prediction are necessary to identify and monitor those older people at high risk of falling who would benefit from participating in falls prevention programmes. Recent findingsTechnological advances have enabled less expensive ways to quantify physical fall risk in clinical practice and in the homes of older people. Recently, several studies have demonstrated that sensor-based fall risk assessments of postural sway, functional mobility, stepping and walking can discriminate between fallers and nonfallers. SummaryRecent research has used low-cost, portable and objective measuring instruments to assess fall risk in older people. Future use of these technologies holds promise for assessing fall risk accurately in an unobtrusive manner in clinical and daily life settings.

Kinect-Based Five-Times-Sit-to-Stand Test for Clinical and In-Home Assessment of Fall Risk in Older People

Background: Accidental falls remain an important problem in older people. The five-times-sit-to-stand (5STS) test is commonly used as a functional test to assess fall risk. Recent advances in sensor technologies hold great promise for more objective and accurate assessments. Objective: The aims of this study were: (1) to examine the feasibility of a low-cost and portable Kinect-based 5STS test to discriminate between fallers and nonfallers and (2) to investigate whether this test can be used for supervised clinical, supervised and unsupervised in-home fall risk assessments. Methods: A total of 94 community-dwelling older adults were assessed by the Kinect-based 5STS test in the laboratory and 20 participants were tested in their own homes. An algorithm was developed to automatically calculate timing- and speed-related measurements from the Kinect-based sensor data to discriminate between fallers and nonfallers. The associations of these measurements with standard clinical fall risk tests and the results of supervised and unsupervised in-home assessments were examined. Results: Fallers were significantly slower than nonfallers on Kinect-based measures. The mean velocity of the sit-to-stand transitions discriminated well between the fallers and nonfallers based on 12-month retrospective fall data. The Kinect-based measures collected in the laboratory correlated strongly with those collected in the supervised (r = 0.704-0.832) and unsupervised (r = 0.775-0.931) in-home assessments. Conclusion: In summary, we found that the Kinect-based 5STS test discriminated well between the fallers and nonfallers and was feasible to administer in clinical and supervised in-home settings. This test may be useful in clinical settings for identifying high-risk fallers for further intervention or for regular in-home assessments in the future.

A Kinect and Inertial Sensor-Based System for the Self-Assessment of Fall Risk: A Home-Based Study in Older People

Falls remain an important problem in older people. There is strong evidence that falls can be prevented with appropriately designed intervention programs. To start a targeted fall prevention program, a first step is to identify those at high risk of falls. Sensor-based tests hold great promise for more frequent and accurate assessment of fall risk in clinical and home settings. The aims of this study were to (a) empirically examine the feasibility of the iStoppFalls (Information and communications technology–based System to Predict & Prevent Falls) assessment, a Kinect and inertial sensor-based test for regular and unsupervised fall risk assessments at home, (b) investigate the experience of older adults with this home-based self-assessment, and (c) make recommendations for future assessments. The iStoppFalls assessment system was installed into the homes of 62 community-living older people in Australia, Germany, and Spain for the duration of 4 months. Participants were asked to perform at least 1 assessment each month. The system use and the user experience were evaluated. To our knowledge, these are the first results on the long-term use of an unsupervised directed routine fall risk assessment system at private homes. In total, 241 assessments were independently performed by the participants. Most participants felt positive about their experience and could see themselves continuing with the assessment on a regular basis. Through the analysis the user motivation, the design and selection of appropriate tests, the user feedback, the reliability and usability of the applied technology, the frequency and duration of the assessment and the safety and support aspects were identified as important characteristics of a home-based self-assessment. The findings demonstrate the feasibility of a sensor-based self-assessment for fall risk but also highlight that further work is necessary. Future research should consider the necessary design requirements identified by this study.

The effect of sensor-based exercise at home on functional performance associated with fall risk in older people - a comparison of two exergame interventions

AbstractBackgroundThere is good evidence that balance challenging exercises can reduce falls in older people. However, older people often find it difficult to incorporate such programs in their daily life. Videogame technology has been proposed to promote enjoyable, balance-challenging exercise. As part of a larger analysis, we compared feasibility and efficacy of two exergame interventions: step-mat-training (SMT) and Microsoft-Kinect® (KIN) exergames.Methods148 community-dwelling people, aged 65+ years participated in two exergame studies in Sydney, Australia (KIN: n = 57, SMT: n = 91). Both interventions were delivered as unsupervised exercise programs in participants’ homes for 16 weeks. Assessment measures included overall physiological fall risk, muscle strength, finger-press reaction time, proprioception, vision, balance and executive functioning.ResultsFor participants allocated to the intervention arms, the median time played each week was 17 min (IQR 32) for KIN and 48 min (IQR 94) for SMT. Compared to the control group, SMT participants improved their fall risk score (p = 0.036), proprioception (p = 0.015), reaction time (p = 0.003), sit-to-stand performance (p = 0.011) and executive functioning (p = 0.001), while KIN participants improved their muscle strength (p = 0.032) and vision (p = 0.010), and showed a trend towards improved fall risk scores (p = 0.057).ConclusionsThe findings suggest that it is feasible for older people to conduct an unsupervised exercise program at home using exergames. Both interventions reduced fall risk and SMT additionally improved specific cognitive functions. However, further refinement of the systems is required to improve adherence and maximise the benefits of exergames to deliver fall prevention programs in older people’s homes.Trial registrationsACTRN12613000671763 (Step Mat Training RCT) ACTRN12614000096651 (MS Kinect RCT)

Choice stepping reaction time test using exergame technology for fall risk assessment in older people

Accidental falls remain an important problem in older people. Stepping is a common task to avoid a fall and requires good interplay between sensory functions, central processing and motor execution. Increased choice stepping reaction time has been associated with recurrent falls in older people. The aim of this study was to examine if a sensor-based Exergame Choice Stepping Reaction Time test can successfully discriminate older fallers from non-fallers. The stepping test was conducted in a cohort of 104 community-dwelling older people (mean age: 80.7 ± 7.0 years). Participants were asked to step laterally as quickly as possible after a light stimulus appeared on a TV screen. Spatial and temporal measurements of the lower and upper body were derived from a low-cost and portable 3D-depth sensor (i.e. Microsoft Kinect) and 3D-accelerometer. Fallers had a slower stepping reaction time (970 ± 228 ms vs. 858 ± 123 ms, P = 0.001) and a slower reaction of their upper body (719 ± 289 ms vs. 631 ± 166 ms, P = 0.052) compared to non-fallers. It took fallers significantly longer than non-fallers to recover their balance after initiating the step (2147 ± 800 ms vs. 1841 ± 591 ms, P = 0.029). This study demonstrated that a sensor-based, low-cost and easy to administer stepping test, with the potential to be used in clinical practice or regular unsupervised home assessments, was able to identify significant differences between performances by fallers and non-fallers.

The design of a purpose-built exergame for fall prediction and prevention for older people

BackgroundFalls in older people represent a major age-related health challenge facing our society. Novel methods for delivery of falls prevention programs are required to increase effectiveness and adherence to these programs while containing costs. The primary aim of the Information and Communications Technology-based System to Predict and Prevent Falls (iStoppFalls) project was to develop innovative home-based technologies for continuous monitoring and exercise-based prevention of falls in community-dwelling older people. The aim of this paper is to describe the components of the iStoppFalls system.MethodsThe system comprised of 1) a TV, 2) a PC, 3) the Microsoft Kinect, 4) a wearable sensor and 5) an assessment and training software as the main components.ResultsThe iStoppFalls system implements existing technologies to deliver a tailored home-based exercise and education program aimed at reducing fall risk in older people. A risk assessment tool was designed to identify fall risk factors. The content and progression rules of the iStoppFalls exergames were developed from evidence-based fall prevention interventions targeting muscle strength and balance in older people.ConclusionsThe iStoppFalls fall prevention program, used in conjunction with the multifactorial fall risk assessment tool, aims to provide a comprehensive and individualised, yet novel fall risk assessment and prevention program that is feasible for widespread use to prevent falls and fall-related injuries. This work provides a new approach to engage older people in home-based exercise programs to complement or provide a potentially motivational alternative to traditional exercise to reduce the risk of falling.

Comparison between clinical gait and daily-life gait assessments of fall risk in older people.

Falls are a leading cause of disability in older people. Here we investigate if daily‐life gait assessments are better than clinical gait assessments at discriminating between older people with and without a history of falls.

Wavelet-Based Sit-To-Stand Detection and Assessment of Fall Risk in Older People Using a Wearable Pendant Device

Goal: Wearable devices provide new ways to identify people who are at risk of falls and track long-term changes of mobility in daily life of older people. The aim of this study was to develop a wavelet-based algorithm to detect and assess quality of sit-to-stand movements with a wearable pendant device. Methods: The algorithm used wavelet transformations of the accelerometer and barometric air pressure sensor data. Detection accuracy was tested in 25 older people performing 30 min of typical daily activities. The ability to differentiate between people who are at risk of falls from people who are not at risk was investigated by assessing group differences of sensor-based sit-to-stand measurements in 34 fallers and 60 nonfallers (based on 12-month fall history) performing sit-to-stand movements as part of a laboratory study. Results: Sit-to-stand movements were detected with 93.1% sensitivity and a false positive rate of 2.9% during activities of daily living. In the laboratory study, fallers had significantly lower maximum acceleration, velocity, and power during the sit-to-stand movement compared to nonfallers. Conclusion: The new wavelet-based algorithm accurately detected sit-to-stand movements in older people and differed significantly between older fallers and nonfallers. Significance: Accurate detection and quantification of sit-to-stand movements may provide objective assessment and monitoring of fall risk during daily life in older people.