Are Virtual Rehabilitation Technologies Feasible Models to Scale an Evidence-Based Fall Prevention Program? A Pilot Study Using the Kinect Camera

Abstract

BackgroundFalls in older adults are a significant public health issue. Interventions have been developed and proven effective to reduce falls in older adults, but these programs typically last several months and can be resource intensive. Virtual rehabilitation technologies may offer a solution to bring these programs to scale. Off-the-shelf and custom exergames have demonstrated to be a feasible adjunct to rehabilitation with older adults. However, it is not known if older adults will be able or willing to use a virtual rehabilitation technology to participate in an evidence-based fall prevention program. To have the greatest impact, virtual rehabilitation technologies need to be acceptable to older adults from different backgrounds and level of fall risk. If these technologies prove to be a feasible option, they offer a new distribution channel to disseminate fall prevention programs.ObjectiveStand Tall (ST) is a virtual translation of the Otago Exercise Program (OEP), an evidence-based fall prevention program. Stand Tall was developed using the Virtual Exercise Rehabilitation Assistant (VERA) software, which uses a Kinect camera and a laptop to deliver physical therapy exercise programs. Our purpose in this pilot study was to explore if ST could be a feasible platform to deliver the OEP to older adults from a variety of fall risk levels, education backgrounds, and self-described level of computer expertise.MethodsAdults age 60 and over were recruited to participate in a one-time usability study. The study included orientation to the program, navigation to exercises, and completion of a series of strength and balance exercises. Quantitative analysis described participants and the user experience.ResultsA diverse group of individuals participated in the study. Twenty-one potential participants (14 women, 7 men) met the inclusion criteria. The mean age was 69.2 (± 5.8) years, 38% had a high school education, 24% had a graduate degree, and 66% classified as “at risk for falls”. Eighteen participants agreed they would like to use ST to help improve their balance, and 17 agreed or strongly agreed they would feel confident using the system in either the senior center or the home. Thirteen participants felt confident they could actually set up the system in their home. The mean System Usability Scale (SUS) score was 65.5 ± 21.2 with a range of 32.5 to 97.5. Ten participants scored ST as an above average usability experience compared to other technologies and 5 participants scored a less than optimal experience. Exploratory analysis revealed no significant relationships between user experience, education background, self-described computer experience, and fall risk.ConclusionsResults support the virtual delivery of the OEP by a Kinect camera and an avatar may be acceptable to older adults from a variety of backgrounds. Virtual technologies, like Stand Tall, could offer an efficient and effective approach to bring evidence-based fall prevention programs to scale to address the problem of falls and fall-related injuries....

Usability Issues of Clinical and Research Applications of Virtual Reality in Older People: A Systematic Review

“…All of the studies-except one that was principally devoted to the clinical efficacy of the training )were mainly designed for the usability, UX and feasibility of VR systems in aging Castilla et al, 2013;Corno et al, 2014;Epelde et al, 2014;Wüest et al, 2014;Im et al, 2015;Kiselev et al, 2015;Morán et al, 2015;Sáenz-de-Urturi et al, 2015;Shubert et al, 2015;Cook and Winkler, 2016;Brox et al, 2017;Desteghe et al, 2017;Trombetta et al, 2017; FIGURE 4 | Degree of virtual immersion; VR, virtual reality. Sessions lasted from nine to 90 min (mean time = 30 min ca.…”

“…), ranging from one to 36 sessions spread over the course of 1 single day to 4 years; indeed, usability along with effectiveness of training was tested for three , four Rebsamen et al, 2019;van Beek et al, 2019), six , eight , and 12 weeks Desteghe et al, 2017), and three and 4 years . Exergames (i.e., serious games used for balance and fall risk training) were used in most of the motor training Schwenk et al, 2014;Wüest et al, 2014;Kiselev et al, 2015;Sáenz-de-Urturi et al, 2015;Shubert et al, 2015;Brox et al, 2017;Trombetta et al, 2017;Money et al, 2019;Rebsamen et al, 2019;van Beek et al, 2019), physiotherapy exercise in others Im et al, 2015), cognitive-physical dual-task in Pedroli et al (2018), neuropsychological testing in three studies Corno et al, 2014;Plechatá et al, 2019;Valladares-Rodriguez et al, 2019), gesture therapy in Morán et al (2015) and Vanbellingen et al (2017), and psychosocial support/educational in four studies Castilla et al, 2013;Cook and Winkler, 2016;Desteghe et al, 2017).…”

“…If, on the one hand, the studies included collected data from the target population (e.g., Parkinson's disease patients tested usability for Parkinson's disease rehabilitation), several others assessed usability with healthy older people or mixed-pathologies patients (e.g., Wüest et al, 2014;Sáenz-de-Urturi et al, 2015;Shubert et al, 2015;Trombetta et al, 2017;Vanbellingen et al, 2017); in this sense, diagnostic criteria were not clear or endusers characteristic do not match potential technology barriers of end-users. Future research should use strict inclusion/exclusion criteria according to diagnostic criteria of the diseases or syndromes.…”

“…Importantly, some studies adopted technological theoretical frameworks adapted for older people, such as the senior UCD or the senior citizens' acceptance of information systems (SCAIS) Nikitina et al, 2018). However, several studies did not report a theoretical model to design their systems Castilla et al, 2013;Corno et al, 2014;Schwenk et al, 2014;Im et al, 2015;Shubert et al, 2015;Desteghe et al, 2017;Trombetta et al, 2017;Vanbellingen et al, 2017;Plechatá et al, 2019;van Beek et al, 2019). Concerning the assessment of usability of VR systems, a wide range of quantitative and qualitative methods have been used (see Table 1 for specific information and Figures 5-7 for models and methods overviews).…”

“…Concerning quantitative data, system usability scale (SUS) Sáenz-de-Urturi et al, 2015;Shubert et al, 2015;Vanbellingen et al, 2017;Nikitina et al, 2018;Pedroli et al, 2018;Money et al, 2019;Rebsamen et al, 2019;van Beek et al, 2019), TAM-based questionnaires Wüest et al, 2014;Morán et al, 2015;Cook and Winkler, 2016;Rebsamen et al, 2019;Valladares-Rodriguez et al, 2019), UX questionnaires Sáenzde-Urturi et al, 2015;Brox et al, 2017;Desteghe et al, 2017;Rebsamen et al, 2019), UCD-based questionnaire , flow of experience scale , other usability questionnaires Epelde et al, 2014;Trombetta et al, 2017;Plechatá et al, 2019;Valladares-Rodriguez et al, 2019), adherence or motivation to training questionnaires Desteghe et al, 2017;Vanbellingen et al, 2017;Nikitina et al, 2018;Rebsamen et al, 2019;van Beek et al, 2019) or with VR data Kiselev et al, 2015;Desteghe et al, 2017;Vanbellingen et al, 2017;Nikitina et al, 2018;Rebsamen et al, 2019;van Beek et al, 2019), cybersickness assessment Im et al, 2015;Plechatá et al, 2019), technology expertise Rebsamen et al, 2019;Valladares-Rodriguez et al, 2019), and video analysis were used. Regarding qual...…”