Combining physical and virtual worlds for motor-cognitive training interventions: Position paper with guidelines on technology classification in movement-related research

Abstract

Efficient movements require intact motor and cognitive function. There is a growing literature on motor-cognitive interventions to improve the overall quality of life of healthy or diseased older people. For such interventions, novel technological advances are crucial not only in terms of motivation but also to improve the user experience in a multi-stimuli world, usually offered as a mixture of real and virtual environments. This article provides a classification system for movement-related research dealing with motor-cognitive interventions performed in different extents of a virtual environment. The classification is divided into three categories: (a) type of digital device with the associated degree of immersiveness provided; (b) presence or absence of a human-computer interaction; and (c) activity engagement during training, defined by activity >1.5 Metabolic Equivalent of task. Since virtual reality (VR) often categorizes different technologies under the same term, we propose a taxonomy of digital devices ranging from computer monitors and projectors to head-mounted VR technology. All immersive technologies that have developed rapidly in recent years are grouped under the umbrella term Extended Reality (XR). These include augmented reality (AR), mixed reality (MR), and VR, as well as all technologies that have yet to be developed. This technology has potential not only for gaming and entertainment, but also for research, motor-cognitive training programs, rehabilitation, telemedicine, etc. This position paper provides definitions, recommendations, and guidelines for future movement-related interventions based on digital devices, human-computer interactions, and physical engagement to use terms more consistently and contribute to a clearer understanding of their implications.

Effects of digital-based interventions on muscular strength in adults: a systematic review, meta-analysis and meta-regression of randomized controlled trials with quality of evidence assessment

“…However, our results may not accurately reflect the capabilities of VR devices because no VR interventions (as defined in Šlosar et al. [ 30 ]) were performed on asymptomatic subjects, frail individuals, or stroke survivors. Therefore, future research on larger populations is warranted.…”

“…To summarize the effects relevant to different disorders, the included studies were further grouped based on participant health status and classified as asymptomatic population ( N = 918), stroke survivors ( N = 572), other neurological disorders ( N = 683), and frail population ( N = 173). Following the recent recommendations for classification of technologies in movement-related research [ 30 ], we included one VR-exergame, one VR-no-exergame, 45 PC-exergames, and nine PC-no-exergame studies. Nintendo Wii and Xbox Kinect are the primary digital devices used in PC exergame studies.…”

“…Using the classification of exergame vs. non-exergame interventions by Šlosar et al. [ 30 ], no differences were found between the two groups in any of the population categories, except for the frail population group, in which all interventions were classified as exergames. According to Šlosar et al.…”

“…According to Šlosar et al. [ 30 ], the main factors used to classify an intervention as an exergame are i) the presence of a digital device, ii) user-device interaction, and iii) energy expenditure set as higher than 1.5 MET. In our review, we found that energy expenditure was the main factor that determined study allocation.…”

Evidence-based yet still challenging! Research on physical activity in old age

“…This includes the use of novel devices, software, and wearable technologies, such as activity trackers (e.g., [50]), bodyworn sensors (e.g., [51]), mobile phone applications (e.g., [52]), tablets (e.g., [53]), virtual reality [54], exergames (e.g., [55]), global positioning system (GPS) devices, and map-based tools [56]). Gamification of exercise programs, for example, enables the targeting of both physical functions and cognitive ones, and allows users to perform their training programs in a location of their convenience, while being remotely monitored and/or supervised [57]. Although the large potential of such approaches has been acknowledged, there is a need for the filling of research gaps [58].…”