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Abstract
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.
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Andreas Ejupi
Andreas Ejupi ([email protected]) is a computer scientist with an interest in using information and communications technology for fall prevention; he is a PhD student in the Assistive Healthcare Information Technology Group of the Austrian Institute of Technology. Yves J. Gschwind ([email protected]) is a clinical scientist with an interest in exercise and drug interventions in older people and patients. He was a visiting research officer in the Falls and Balance Research Group of Neuroscience Research Australia. Trinidad Valenzuela ([email protected]) is an exercise physiologist with an interest in adherence to technology-based exercise programs in older adults; she is a PhD student in the Falls and Balance Research Group of Neuroscience Research Australia. Stephen R. Lord ([email protected]) is a leader researcher in his field with an interest in the identification of risk factors for falls in older people and the evaluation of fall prevention strategies; he is a Senior Principal Research Fellow and group leader of the Falls and Balance Research Group at Neuroscience Research Australia. Kim Delbaere ([email protected]) holds a PhD in rehabilitation sciences and physiotherapy with an interest in the identification of risk factors, from physiological to psychological and cognitive domains, and prediction of falls in older people; she is a Research Fellow in the Falls and Balance Research Group of Neuroscience Research Australia.