A Fall Risk Evaluation and Feedback System for Older Adults: From a Technical to a Sociotechnical Need

A Fall Risk Evaluation and Feedback System for Older Adults: From a Technical to a Sociotechnical Need

Isa Jahnke (University of Missouri-Columbia, USA), Lorraine J. Phillips (University of Delaware, USA), Fatih Demir (Northern Illinois University, USA), Carmen Abbott (University of Missouri, USA) and Marjorie Skubic (University of Missouri, USA)
Copyright: © 2021 |Pages: 14
DOI: 10.4018/IJSKD.2021040107
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Abstract

Falls are widespread among older adults causing serious injuries and threatening their quality of life. An approach to estimate fall risk, and to prevent falls, is the Timed-Up-and-Go (TUG) test. The TUG test has established validity and reliability. However, as a clinical test, it is not accessible for personal use. To enhance its reach, the authors developed a prototype called Fall Risk Evaluation and Feedback System (FREFS). The prototype is a Kinect-based depth sensor system with interfaces that support older adults in completing the TUG test and receiving personalized test results. The personalized feedback feature is novel that existing prototypes do not include. This study's goal was to gain knowledge of the user experience of FREFS. This research applied methods of observation, interviews, and collected responses on the System Usability Scale (SUS). Results show participants perceived the system as usable, with SUS score of 84.3, but also revealed issues. First, users were unsure how to deal with the TUG tests results when the results showed high fall risk. Second, clearer instructions and reduction of information overload specifically for these age groups were needed. Third, a communication approach embedded into the system would be required (i.e., a link to a chatbot feature or a button to connect to a real person). Overall, the study demonstrated that such a prototype cannot be fully automated; it needs a sociotechnical system solution that includes human communication.
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Introduction

Falling is one of the major health threats for older adults. Every year, many seniors are confronted with the risk of falling (Stevens, 2005). In 2015, Fall occur in about 1 in 4 older adults and costs exceeded more than $50 billion in 2015 (CDC, 2017), and of those who fall, 37.5% suffer moderate to severe injuries that jeopardize independent living (CDC, 2015). Fall-related injuries represent a major burden, not only for individuals, but also for the health-care system as well as society as a whole.

It would be particularly advantageous for older adults to have easy access to fall risk evaluation services. Knowing their own potential risk of falling supports older adults to be able to take preventative measures. However, such fall risk evaluation is generally accomplished during a health care visit. Individual adults are not able to test themselves outside the clinical setting. One possible solution is to develop a technical system and to implement it in senior centers or pharmacies (similar to the current service of measuring blood pressure or diabetics).

One solution for such a system is to automate the Timed-Up-and-Go (TUG) test, a performance-based balance test used to assess fall risk in older adults (Vernon et al., 2015; Sprint, Cook, & Weeks, 2015; Lohmann, Luhmann, & Hein, 2012). The TUG test has been validated in clinical studies (Smith et al., 2016). The TUG test measures the times it takes for a person to stand up from a chair, walk 3 meters, turn, and return to sit in the chair, and was shown to be a sensitive and specific screening test for identifying older adults at increased risk of falling (Viccaro, Perera, & Studenski, 2011).

Hotrabhavananda et al. (2016) reported software to extract TUG time; it was validated in a laboratory setting. They used a Vicon marker-based motion-capture system software for the Microsoft Kinect X-Box 1 to automatically measure TUG time. Using this knowledge, we created a prototype of an automated system, the Fall Risk Evaluation & Feedback System (FREFS). It was designed for use by a layperson. The novel feature of this prototype is the personalized feedback system that existing prototypes do not provide (Gonçalves, Batista & Novo, 2014; Frenken, Vester, Brell, & Hein, 2011). This study presents the results of the user experience with the FREFS.

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