Edited by: Valeria Manera, University of Nice Sophia Antipolis, France
Reviewed by: Pascale Piolino, University Paris Descartes, France; Silvia Serino, I.R.C.C.S Istituto Auxologico Italiano, Italy
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
We provide a brief review and appraisal of recent and current virtual reality (VR) technology for Alzheimer’s disease (AD) applications. We categorize them according to their intended purpose (e.g., diagnosis, patient cognitive training, caregivers’ education, etc.), focus feature (e.g., spatial impairment, memory deficit, etc.), methodology employed (e.g., tasks, games, etc.), immersion level, and passive or active interaction. Critical assessment indicates that most of them do not yet take full advantage of virtual environments with high levels of immersion and interaction. Many still rely on conventional 2D graphic displays to create non-immersive or semi-immersive VR scenarios. Important improvements are needed to make VR a better and more versatile assessment and training tool for AD. The use of the latest display technologies available, such as emerging head-mounted displays and 3D smart TV technologies, together with realistic multi-sensorial interaction devices, and neuro-physiological feedback capacity, are some of the most beneficial improvements this mini-review suggests. Additionally, it would be desirable that such VR applications for AD be easily and affordably transferable to in-home and nursing home environments.
Recent brain plasticity theories and findings about the nervous system’s ability to reconstruct cellular synapses as a result of interaction with enriched environments, have spurred new research about memory rehabilitation. Consequently, non-invasive non-pharmacological cognitive rehabilitation (CR) interventions have gained increasing attention in recent years (Cotelli et al.,
Since the introduction of the use of computers for psychological testing over a quarter of a century ago (Riva,
Emerging VR applications today address the challenge of diagnosis and cognitive training of mild cognitive impairment (MCI) and dementia patients, concentrating on navigation and orientation, face recognition, cognitive functionality, and other instrumental activities of daily living (IADL) (Jekel et al.,
Slater et al. (
Based on the above considerations, three basic levels of system immersion may be defined: (1) non-immersive; (2) semi-immersive; and (3) fully-immersive. In a non-immersive system, the patient interacts with the VE using conventional graphic workstations (PC monitor, keyboard, and mouse) (Costello,
Immersion plays a crucial role on the subjective sense of “presence.” “Presence” refers to the experience of felling “being there,” that is, how well the VE truly represents a real-world situation, instead of being a simple video viewing experience. “Presence” is strongly related to immersion, since increasing the immersion level induces a higher intensity of the subjective sense of “presence” experienced by the patient (Slobounov et al.,
This mini-review does not pretend to be an exhaustive account of all existing applications of VR for AD, rather, it only aims to illustrate, through representative state of the art examples of the most significant types of VR applications, the advantages of using VR for developing a new class of tools in support of the diagnostic assessment of and cognitive training in AD.
The overall followed methodology may be divided into two phases: (1) literature search and selection of relevant work and (2) categorization of the selected works. The first phase consisted of an initial computer-based on-line non-systematic literature search, conducted in several high-profile databases, such as: PubMed, Web of Knowledge, IEEExplore, ScienceDirect, and Google Scholar. Only peer-reviewed journal articles in English were considered. Because of conciseness, and considering the relative novelty of the field, the search was limited to years from 2000 to the present. However, a few pre-2000 specific articles were included by reason of their outstanding relevance. VR technology studies and applications related to AD assessment and cognitive intervention were searched for using the following search terms, and combinations thereof: VR, VEs, virtual game, AD, cognitive impairment, CR, and cognitive training.
References cited by the initially retrieved articles became a secondary source for manual selection, and included whenever they contributed significant new information. In addition to the most relevant recent studies of VR applications that involve AD patients, some others aimed at healthy elderly people were included because of their comparative value. Articles dealing with MCI patients were also included since such impairment is often a transition from healthy aging to AD. We excluded those studies and applications in which the devices used to interact with the VE were not clearly described. A few articles whose full-text was not easily accessible were also excluded.
The second methodological phase consisted of categorizing, for later systematic study, the retrieved VR studies and application works, according to the predefined classification schematically portrayed in Figure
The potential usefulness and exceptional opportunities of VR systems as valuable ICT tools to assess and train patients in the early stages of AD has been already ascertained by several studies (Schultheis et al.,
The pursuit of future advances beyond current VR applications for AD state of the art will benefit from a broad awareness of recent and ongoing VR developments. Such understanding essentially consists of scrutinizing and comparing design and operation specificities that aim to fulfill the intended purpose of particular AD applications, such as the techniques of patient interaction. A useful starting point, particularly regarding design methodology type and immersion level, is Table
VR technology application and/or reference | Participants/users | Focus feature | Intended purpose | Interaction technique used |
|||||
---|---|---|---|---|---|---|---|---|---|
Methodology type |
Immersion type |
||||||||
Tasks | Games | IADL | Full | Semi | Non | ||||
Kalová et al. ( |
11 early-AD; 27 subjective problems with memory and concentration; 10 healthy controls | Sequential ordering of places, allothetic orientation, spatial navigation, non-verbal episodic memory | X | X | X | ||||
Burgess et al. ( |
1 early-AD with topographical disorientation; 4 healthy controls | Allocentric spatial memory. Topographical disorientation | X | X | |||||
Hort et al. ( |
21 probable AD; 11 amnestic MCI single domain; 18 amnestic MCI multiple domain; 7 non-amnestic MCI; 8 subjective memory complaints; 26 healthy controls | Spatial memory. Spatial navigation: allocentric and egocentric navigation | X | X | |||||
Lange et al. ( |
30 mild dementia Alzheimer’s type; 30 healthy controls | Visuospatial and wayfinding orientation | X | X | |||||
Cushman et al. ( |
12 MCI; 14 early-AD; 35 young normal controls; 26 older normal controls | Navigational performance | X | X | |||||
Van Schaik et al. ( |
30 mild to moderate dementia | Evaluation of outdoor environments | X | X | |||||
Zakzanis et al. ( |
8 healthy young adults; 7 older adults with psychiatric or neurological disorders (2 with probable AD) | Spatial navigation. Spatial memory | X | X | |||||
Laczó et al. ( |
Amnestic and non-amnestic MCI | Spatial navigation. Hippocampal and non-hippocampal memory impairment | X | X | |||||
Optale et al. ( |
36 elderly with presence of memory deficits (Verbal Story Recall Test) | Improve memory functions | X | X | |||||
Weniger et al. ( |
29 amnestic MCI; 29 healthy controls | Egocentric and allocentric memory | X | X | |||||
Bellassen et al. ( |
16 mild AD; 11 frontotemporal lobar degeneration; 24 normal aging | Spatiotemporal navigation. Temporal order memory | X | X | |||||
Nedelska et al. ( |
23 amnestic MCI; 19 mild and moderate AD; 14 healthy controls | Allocentric spatial navigation | X | X | |||||
VREAD, Shamsuddin et al. ( |
31 healthy elderly and with MCI | Diagnosis of MCI. Cognitive performance. Topographical disorientation | X | X | X | ||||
Yeh et al. ( |
60 senile dementia; 30 healthy controls | Executive functions and memory | X | X | X | X | |||
Widmann et al. ( |
15 with AD; 31 healthy controls | Spatial and verbal memory | X | X | |||||
Plancher et al. ( |
15 amnesic MCI; 15 early to moderate AD; 21 healthy older adults | Episodic memory | X | X | |||||
VR-DOT, Tarnanas et al. ( |
2013: 65 amnestic MCI; 68 mild AD; 72 healthy controls. 2014: 134 with MCI; 75 healthy controls | Executive function. Prospective memory | X | X | |||||
VRAM, Lee et al. ( |
20 amnestic MCI; 20 mild AD; 20 normal controls | Spatial working memory | X | X | |||||
Allain et al. ( |
24 with AD; 31 healthy elderly controls | IADL functioning | X | X | X | ||||
Jebara et al. ( |
64 young adults; 64 elderly adults | Episodic memory | X | X | |||||
Hofmann et al. ( |
9 with AD; 9 with major depressive episode; 10 healthy controls | Psychomotor slowing, strategic and critical thinking, cognitive flexibility, problem solving, spatial orientation, delayed recall, long-term memory | X | X | X | ||||
Cognimat, Buss ( |
6–8 early-AD; 4 healthy controls | Train spatial orientation and working memory | X | X | X | ||||
PREVIRNEC, Tost et al. ( |
Patients with neuropsychological disorders | ADL training | X | X | |||||
eGaming, Bartolome et al. ( |
Patients with neurodegenerative disorders | Cognitive and memory functions | X | X | |||||
NeuroRacer, Anguera et al. ( |
Healthy young and older adults | Enhances cognitive control | X | X | |||||
BrightArm, Burdea et al. ( |
3 with dementia | Cognitive rehabilitation of advanced dementia | X | X | |||||
IVIRAGE, Chapoulie et al. ( |
13 healthy elderly adults | Reminiscence therapy | X | X | |||||
O’Connor et al. ( |
7 dementia caregivers | On-line support group | X | X |
We have identified three main types of planned goals among the reviewed recent and current VR systems. They may be described as: (1) assessment and diagnosis; (2) cognitive training or therapy; and (3) caregivers’ training. An additional purpose was proposed by Riva et al. (
Recent research has focused on certain specific aspects of AD cognitive impairment features that are generally deemed to be most relevant for VR diagnostic and training purposes. For the sake of the present mini-review, they may be roughly summarized as follows: (1) attention (Kalová et al.,
Some approaches look at more than one of the above mentioned aspects for better assessment. Examples of this combined focus are: Kalová et al. (
User interaction with VEs and scenarios might involve several methodological modalities. It could consist of playing serious games or performing different tasks or activities (e.g., IADL). Here, we refer to “tasks” and “activities” in reference to VR systems for AD, the term “task” specifically means a particular action that is intended, designed, and established to improve a specific cognitive function, while “activities” involve performing high-level sustained cognitive actions and processes such as: eating, bathing, dressing, shopping, etc. Furthermore, the term “game” refers to activities that are defined by rules and any type of user engagement. Most current VR systems for assessment and diagnosis of AD are based on performing tasks, such as navigation or memorization. Moreover, current VR systems for cognitive training concentrate on performing activities that are related to IADL, such as: cooking, driving, shopping, etc. It has been recently demonstrated that the use of a familiar image-based VE can stimulate recollections of autobiographical memory in healthy elderly subjects (Benoit et al.,
Recent progress in augmented reality (AR) indicates that this technology will probably become another useful ICT tool for AD. This form of mixed reality enhances a non-synthetic real environment by superimposing some synthetic elements into the users’ perception of that reality (Baus and Bouchard,
We have presented a glimpse at VR applications for diagnostic assessment and cognitive training in MCI and AD. Instead of presenting an exhaustive account of all VR applications currently available, this mini-review has focused on representative state of the art examples of the most significant types, which we have classified into specific categories to aid in their systematic scrutiny. This analysis reveals that most VR applications for AD 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,
We suggest that future developments of VR cognitive assessment and training applications for MCI and AD should prioritize the specificity of the particular needs of AD patients 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 AD, 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 AD patients 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 AD usefulness is the capacity to timely gather and transmit relevant information (García-Betances et al.,
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We would like to thank the consortia of Horizon2020 EU projects “MIAMI-MD” and “PD_Manager” for their valuable contributions to the systematic conceptualization of neuropsychological knowledge used in this study.