Assessing the Feasibility, and Efficacy of Virtual Reality Navigational Training for Older Adults
<|reference_start|>Assessing the Feasibility, and Efficacy of Virtual Reality Navigational Training for Older Adults: Objective. Evaluate the feasibility of Virtual Reality (VR) wayfinding training with aging adults, and examine the impact of the training on wayfinding performance. Design. Design involved wayfinding tasks in a study with three groups: active VR training, passive video training, and no training, assigned randomly. The training featured 5 tasks in a digital version of a real building. Post-training assessments had 10 tasks in this building, half familiar from training and half new. The study was double-blinded, with each intervention lasting 10 minutes. Participants. A convenience sample of 49 participants; inclusion criteria: age >58, unfamiliar with the building; exclusion criteria: mobility or vision impairments, history of motion sickness, or medical implants. Outcomes. Time spent and Distance traveled on each wayfinding task with a fixed 10-min limit. Results. Participants in VR group reported moderate usability (63.82, SD=14.55) with respect to the training intervention and high Self Location (3.71, SD=0.94). There were no differences in task performance among the groups in the similar tasks. In the new tasks, compared to the control condition, Time spent on tasks was marginally significantly reduced in the VR group; Distance traveled to finish tasks was also reduced in the VR group, and marginally significantly reduced in the Video training group. No differences were found between VR and Video conditions. No adverse effects were reported during or post intervention. Conclusions. This study provides preliminary evidence that VR training can effectively improve wayfinding performance in older adults with no reported adverse effect.<|reference_end|>
@article{xu2024assessing,
    title={Assessing the Feasibility, and Efficacy of Virtual Reality Navigational
  Training for Older Adults},
    author={Tong Bill Xu, Armin Mostafavi, Walter R. Boot, Sara Czaja, Saleh
  Kalantari},
    journal={arXiv preprint arXiv:2407.12272},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12272},
    primaryClass={cs.HC}
}