Environmental enrichment through virtual reality as multisensory stimulation to mitigate the negative effects of prolonged bed rest

Front Aging Neurosci. 2023 Aug 22:15:1169683. doi: 10.3389/fnagi.2023.1169683. eCollection 2023.

Abstract

Prolonged bed rest causes a multitude of deleterious physiological changes in the human body that require interventions even during immobilization to prevent or minimize these negative effects. In addition to other interventions such as physical and nutritional therapy, non-physical interventions such as cognitive training, motor imagery, and action observation have demonstrated efficacy in mitigating or improving not only cognitive but also motor outcomes in bedridden patients. Recent technological advances have opened new opportunities to implement such non-physical interventions in semi- or fully-immersive environments to enable the development of bed rest countermeasures. Extended Reality (XR), which covers augmented reality (AR), mixed reality (MR), and virtual reality (VR), can enhance the training process by further engaging the kinesthetic, visual, and auditory senses. XR-based enriched environments offer a promising research avenue to investigate the effects of multisensory stimulation on motor rehabilitation and to counteract dysfunctional brain mechanisms that occur during prolonged bed rest. This review discussed the use of enriched environment applications in bedridden patients as a promising tool to improve patient rehabilitation outcomes and suggested their integration into existing treatment protocols to improve patient care. Finally, the neurobiological mechanisms associated with the positive cognitive and motor effects of an enriched environment are highlighted.

Keywords: bed rest; disuse; mechanical unloading; non-physical interventions; physical inactivity; virtual reality.

Publication types

  • Review

Grants and funding

This study was supported by the European Union’s Horizon 2020 Research and Innovation Program under grant agreement no. 952401 (TwinBrain – TWINning the BRAIN with machine learning for neuro-muscular efficiency). We also acknowledge financial support from the Slovenian Research Agency (research core funding no. P5-0381).