Efficient, objective measures of mild functional difficulties are lacking. Literature suggests that performance-based assessment is accurate and reliable. The Natural Action Test is one of the performance-based task able to evaluate cognitive functions using daily live activities. Despite their validity, objectivity, and potential for rich characterizations of functional difficulties, performance-based measures have not been widely adopted in clinics or research studies due to one major drawback - they generally require extraordinary effort to administer and score. Preliminary data from novel, non-immersive virtual reality, performance-based tasks based on the NAT has been obtained to address this gap by Giovannetti et al. (2018), but at this moment there is a lack of a full immersive Virtual Reality performance-based task.
A cross-over trial will test the Naturalistic Action Test and its immersive Virtual Reality performance-based task (VRAT) in three group of subjects, 12 younger, 12 older adults and 12 older adults with MCI.
Participants will complete a cognitive testing and an everyday task in two different conditions: a full immersive Virtual Environment; and with real objects (order counterbalanced). Automated performance measures will be obtained from the VRAT and human coders will score VRAT and NAT for errors and correlations.
Hypothesis of the study is that VRAT and NAT will be related in terms of specificity and their ability to assess cognitive functions and daily living activities. We expect also that older adults will make more errors than younger adults on the VRAT and NAT, with similar error patterns across measures.
Conclusion: The aim of the project is to firstly contribute to the development of a Virtual parallel form of the NAT, evaluating its feasibility within the context of MCI, as a valid and highly efficient performance-based measure of subtle functional difficulties with great potential for future clinical and research applications.
This analysis of literature dealing with Virtual Reality and older adults reveals that most VR applications for them do not offer today VEs with sufficient levels of immersion or interaction, but simpler non-immersive or semi-immersive VR scenarios. The present general tendency to develop personalized ICT-based healthcare applications (PMC, Personalized Medicine Coalition, 2014), together with the considerable recent advances in sensor, VR, and 3D technologies, highlights the urgency of continuing the improvement of existing early stage medical VR applications. The improvements should translate in better VR-based applications for older adults, with more immersive VEs, based on the latest innovative technologies available (e.g., novel HMDs, 3D smart televisions, etc.). The incorporation of emerging display and interactive technologies will enable innovative designs and implementations of more effective and versatile supportive VR applications for diagnosis and cognitive training of older adults with MCI.
We suggest that future developments of VR cognitive assessment and training applications for MCI should prioritize the specificity of the particular needs of this target sample and their symptoms evolution. VR platform designs must be able to incorporate emerging know-how and techniques, not only to better fulfill the intended specific purposes of VR applications for older adults with MCI, but also to equip those future applications with adequate capacity to supply assistive support to clinicians and caregivers, to significantly contribute to the improvement of the quality of life of MCI and their families. Advances in this field should also contemplate providing easy transfer of the applications in a simple and affordable way into in-home and nursing home environments.
Furthermore, a vital feature that could make an important impact on future VR applications for MCI usefulness is the capacity to timely gather and transmit relevant information (García-Betances et al., 2014). Such information should consist, not only of ongoing patient performance data, but also of pertinent psycho-physiological data, such as heart rate variability, respiration, ECG, EEG, etc., and any other multimodal information useful for affective (emotional) state recognition (García-Betances et al., 2015) and cognitive stress detection (McDuff et al., 2014). This data gathering feature should also include immediately accessible real-time feedback, to enable computer as well as specialist controlled intervention during the course of the session. Collected data would provide valuable up-to-date information about the patient's performance evolution, adherence to training and rehabilitation routines, as well as about synchronous physiological reactions of the patient. This feedback capacity could prove to be a valuable tool for implementing other future closed-loop applications, and also become a source of reliable data to build systematic and robust knowledge bases that are indispensable for advancing analytical research and future development.
In order progress beyond the state of the art the proposed project will implement the following devices:
Head Mounted Display for Virtual Reality: HTC brand, Vive Pro HMD model EYE;
HTC controllers;
PC desktop to connect Vive Pro and the HTC controllers;
Camcorder (Videocamera): Sony brand, HDR-PJ410 model;
Virtual Reality Enviroment developed through Unity Sofware.
Biofeedback Microsoft Band 2 or similar.
The acquisition of new knowledge about the feasibility and the validity of new full immersive systems for older adults with MCI, would open a new frontier of development for cognitive evaluation or training, tailored on targeted sample.